The following preprints are provided here to allow for a deeper view of our research work, as well as to
promote the rapid dissemination of research results. Please consider, on the other hand, that these preprints
can differ from their published version in ways that may not be entirely negligible, and for this reason we
recommend you to refer to the published version whenever they have to be used or cited.
top2022
Allante, L., Korfiati, A., Androutsos, L., Stojceski, F., Bompotas, A., Giannikos, I., Raftopoulos, C., Malavolta, M., Grasso, G., Mavroudi, S., Theofilatos, K., Piga, D., Deriu, M. (2022). Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Scientific Reports, Nature Publishing Group 12(1), 21735.
Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach
Authors: Allante, L. and Korfiati, A. and Androutsos, L. and Stojceski, F. and Bompotas, A. and Giannikos, I. and Raftopoulos, C. and Malavolta, M. and Grasso, G. and Mavroudi, S. and Theofilatos, K. and Piga, D. and Deriu, M.
Year: 2022
Abstract: The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.
Published in Scientific Reports, Nature Publishing Group 12(1), 21735.
Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach
@ARTICLE{piga2022a,
title = {Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach},
journal = {Scientific Reports, Nature Publishing Group},
volume = {12},
author = {Allante, L. and Korfiati, A. and Androutsos, L. and Stojceski, F. and Bompotas, A. and Giannikos, I. and Raftopoulos, C. and Malavolta, M. and Grasso, G. and Mavroudi, S. and Theofilatos, K. and Piga, D. and Deriu, M.},
number = {1},
pages = {21735},
year = {2022},
doi = {10.1038/s41598-022-25935-3},
url = {}
}
DownloadAntonucci, A., Mangili, F., Bonesana, C., Adorni, G. (2022). Intelligent tutoring systems by Bayesian nets with noisy gates. In 35.
Intelligent tutoring systems by Bayesian nets with noisy gates
Authors: Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.
Year: 2022
Abstract: Directed graphical models such as Bayesian nets are often
used to implement intelligent tutoring systems able
to interact in real-time with learners in a purely automatic
way. When coping with such models, keeping a
bound on the number of parameters might be important
for multiple reasons. First, as these models are typically
based on expert knowledge, a huge number of parameters
to elicit might discourage practitioners from adopting
them. Moreover, the number of model parameters
affects the complexity of the inferences, while a fast
computation of the queries is needed for real-time feedback.
We advocate logical gates with uncertainty for a
compact parametrization of the conditional probability
tables in the underlying Bayesian net used by tutoring
systems.We discuss the semantics of the model parameters
to elicit and the assumptions required to apply such
approach in this domain. We also derive a dedicated inference
scheme to speed up computations.
Published in The International FLAIRS Conference Proceedings 35.
Intelligent tutoring systems by Bayesian nets with noisy gates
@INPROCEEDINGS{antonucci2022a,
title = {Intelligent tutoring systems by {B}ayesian nets with noisy gates},
journal = {The International {FLAIRS} Conference Proceedings},
volume = {35},
author = {Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.},
year = {2022},
doi = {10.32473/flairs.v35i.130692},
url = {}
}
DownloadAzzimonti, L., Corani, G., Scutari, M. (2022). A bayesian hierarchical score for structure learning from related data sets. International Journal of Approximate Reasoning 142, pp. 248–265.
A bayesian hierarchical score for structure learning from related data sets
Authors: Azzimonti, L. and Corani, G. and Scutari, M.
Year: 2022
Abstract: Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they comprise different related, but not homogeneous, data sets collected in different ways. In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD). The proposed score is based on a hierarchical model that pools information across data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. We derive a closed-form expression for BHD using a variational approximation of the marginal likelihood, we study the associated computational cost and we evaluate its performance using simulated data. We find that, when data comprise multiple related data sets, BHD outperforms the Bayesian Dirichlet equivalent uniform (BDeu) score in terms of reconstruction accuracy as measured by the Structural Hamming distance, and that it is as accurate as BDeu when data are homogeneous. This improvement is particularly clear when either the number of variables in the network or the number of observations is large. Moreover, the estimated networks are sparser and therefore more interpretable than those obtained with BDeu thanks to a lower number of false positive arcs.
Published in International Journal of Approximate Reasoning 142, pp. 248–265.
A bayesian hierarchical score for structure learning from related data sets
@ARTICLE{azzimonti2022a,
title = {A bayesian hierarchical score for structure learning from related data sets},
journal = {International Journal of Approximate Reasoning},
volume = {142},
author = {Azzimonti, L. and Corani, G. and Scutari, M.},
pages = {248--265},
year = {2022},
doi = {10.1016/j.ijar.2021.11.013},
url = {}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2022). Quantum indistinguishability through exchangeability. International Journal of Approximate Reasoning 151, pp. 389–412.
Quantum indistinguishability through exchangeability
Authors: Benavoli, A. and Facchini, A. and Zaffalon, M.
Year: 2022
Abstract: Two particles are identical if all their intrinsic properties, such as spin and charge, are the same, meaning that no quantum experiment can distinguish them. In addition to the well known principles of quantum mechanics, understanding systems of identical particles requires a new postulate, the so called symmetrisation postulate. In this work, we show that the postulate corresponds to exchangeability assessments for sets of observables (gambles) in a quantum experiment, when quantum mechanics is seen as a normative and algorithmic theory guiding an agent to assess her subjective beliefs represented as (coherent) sets of gambles. Finally, we show how sets of exchangeable observables (gambles) may be updated after a measurement and discuss the issue of defining entanglement for indistinguishable particle systems.
Published in International Journal of Approximate Reasoning 151, pp. 389–412.
Quantum indistinguishability through exchangeability
@ARTICLE{benavoli2022a,
title = {Quantum indistinguishability through exchangeability},
journal = {International Journal of Approximate Reasoning},
volume = {151},
author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
pages = {389--412},
year = {2022},
doi = {10.1016/j.ijar.2022.10.003},
url = {}
}
DownloadBianchi, F., Piroddi, L., Bemporad, A., Halasz, G., Villani, M., Piga, D. (2022). Active preference-based optimization for human-in-the-loop feature selection. European Journal of Control 66, 100647.
Active preference-based optimization for human-in-the-loop feature selection
Authors: Bianchi, F. and Piroddi, L. and Bemporad, A. and Halasz, G. and Villani, M. and Piga, D.
Year: 2022
Abstract: In various classification problems characterized by a large number of features, feature selection (FS) is essential to guarantee generalization capabilities. The FS problem is often ill-posed due to significant correlations among features, which may lead to several different feature subsets with comparable scores in terms of classification performance. However, not all these subsets are equivalent from a domain-oriented point of view due to known relationships among features and their different acquisition costs in production to deploy the trained classifier. In this paper, we consider the potential benefits of including the domain expert’s preferences in the FS task, thus integrating both objective elements (e.g., classification accuracy) and subjective (often not quantifiable) considerations in the selection process. This goes in the direction of increasing the interpretability and the trustworthiness of the machine learning model, which is an often desired property in many application domains such as in medicine. The proposed method consists of an iterative procedure. At each iteration, the expert is asked to express a “human” preference on pairs of classifiers, each one trained from a different subset of features. The expressed preferences are used algorithmically to update a suitable surrogate function that mimics the latent subjective expert’s objective function, and then to propose a new classifier for testing and comparison. The proposed method has been tested on academic and experimental FS problems, and notably, on a COVID’19 patients record. The preliminary experimental results are promising, in that a parsimonious and accurate solution is obtained after a relatively short number of iterations.
Published in European Journal of Control 66, 100647.
Active preference-based optimization for human-in-the-loop feature selection
@ARTICLE{piga2022c,
title = {Active preference-based optimization for human-in-the-loop feature selection},
journal = {European Journal of Control},
volume = {66},
author = {Bianchi, F. and Piroddi, L. and Bemporad, A. and Halasz, G. and Villani, M. and Piga, D.},
pages = {100647},
year = {2022},
doi = {10.1016/j.ejcon.2022.100647},
url = {}
}
DownloadCasanova, A., Kohlas, J., Zaffalon, M. (2022). Information algebras in the theory of imprecise probabilities. International Journal of Approximate Reasoning 142, pp. 383–416.
Information algebras in the theory of imprecise probabilities
Authors: Casanova, A. and Kohlas, J. and Zaffalon, M.
Year: 2022
Abstract: In this paper we create a bridge between desirability and information algebras: we show how coherent sets of gambles, as well as coherent lower previsions, induce such structures. This allows us to enforce the view of such imprecise-probability objects as algebraic and logical structures; moreover, it enforces the interpretation of probability as information, and gives tools to manipulate them as such.
Published in International Journal of Approximate Reasoning 142, pp. 383–416.
Information algebras in the theory of imprecise probabilities
@ARTICLE{casanova2022a,
title = {Information algebras in the theory of imprecise probabilities},
journal = {International Journal of Approximate Reasoning},
volume = {142},
author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
pages = {383--416},
year = {2022},
doi = {10.1016/j.ijar.2021.12.017},
url = {}
}
DownloadCasanova, A., Kohlas, J., Zaffalon, M. (2022). Information algebras in the theory of imprecise probabilities, an extension. International Journal of Approximate Reasoning 150, pp. 311–336.
Information algebras in the theory of imprecise probabilities, an extension
Authors: Casanova, A. and Kohlas, J. and Zaffalon, M.
Year: 2022
Abstract: In recent works, we have shown how to construct an information algebra of coherent sets of gambles, considering firstly a particular model to represent questions, called the multivariate model, and then generalizing it. Here we further extend the construction made to the highest level of generality, setting up an associated information algebra of coherent lower previsions, analyzing the connection of both the information algebras constructed with an instance of set algebras and, finally, establishing and inspecting a version of the marginal problem in this framework.
Set algebras are particularly important information algebras since they are their prototypical structures.
They also represent the algebraic counterparts of classical propositional logic. As a consequence, this paper details as well how propositional logic is naturally embedded into the theory of imprecise probabilities.
Published in International Journal of Approximate Reasoning 150, pp. 311–336.
Information algebras in the theory of imprecise probabilities, an extension
@ARTICLE{casanova2022b,
title = {Information algebras in the theory of imprecise probabilities, an extension},
journal = {International Journal of Approximate Reasoning},
volume = {150},
author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
pages = {311--336},
year = {2022},
doi = {10.1016/j.ijar.2022.09.003},
url = {}
}
DownloadDelfanti, G., Cortesi, F., Perini, A., Antonini, G., Azzimonti, L., de Lalla, C., Garavaglia, C., Squadrito, M.L., Fedeli, M., Consonni, M., Sesana, S., Re, F., Shen, H., Dellabona, P., Casorati, G. (2022). TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells. Science Immunology 7(74), eabn6563.
TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells
Authors: Delfanti, G. and Cortesi, F. and Perini, A. and Antonini, G. and Azzimonti, L. and de Lalla, C. and Garavaglia, C. and Squadrito, M.L. and Fedeli, M. and Consonni, M. and Sesana, S. and Re, F. and Shen, H. and Dellabona, P. and Casorati, G.
Year: 2022
Abstract: Adoptive immunotherapy with T cells engineered with tumor-specific T cell receptors (TCRs) holds promise for cancer treatment. However, suppressive cues generated in the tumor microenvironment (TME) can hinder the efficacy of these therapies, prompting the search for strategies to overcome these detrimental conditions and improve cellular therapeutic approaches. CD1d-restricted invariant natural killer T (iNKT) cells actively participate in tumor immunosurveillance by restricting suppressive myeloid populations in the TME. Here, we showed that harnessing iNKT cells with a second TCR specific for a tumor-associated peptide generated bispecific effectors for CD1d- and major histocompatibility complex (MHC)–restricted antigens in vitro. Upon in vivo transfer, TCR-engineered iNKT (TCR-iNKT) cells showed the highest efficacy in restraining the progression of multiple tumors that expressed the cognate antigen compared with nontransduced iNKT cells or CD8+ T cells engineered with the same TCR. TCR-iNKT cells achieved robust cancer control by simultaneously modulating intratumoral suppressive myeloid populations and killing malignant cells. This dual antitumor function was further enhanced when the iNKT cell agonist α-galactosyl ceramide (α-GalCer) was administered as a therapeutic booster through a platform that ensured controlled delivery at the tumor site, named multistage vector (MSV). These preclinical results support the combination of tumor-redirected TCR-iNKT cells and local α-GalCer boosting as a potential therapy for patients with cancer. Redirecting iNKT cells against cancer cells by TCR engineering restrains immunosuppression and promotes strong antitumor responses. Adoptive cell therapy has had success in treating various blood cancers. However, in solid tumors, most of these therapies show little efficacy due to exhaustion and poor migration to tumors. Here, Delfanti et al. leveraged natural killer T cells as an adoptive cell therapy platform, specifically producing them to express a TCR specific for a tumor-associated antigen. In mouse models, they found that these TCR-engineered iNKT robustly delayed tumor growth, leading to rejection and prolonged antitumor effects in some mice. These cells migrated well into tumors, killed tumor cells directly, and helped to shift intratumorally myeloid cells to an antitumor phenotype. Boosting TCR-engineered iNKTs with α-GalCer improved antitumor immune responses. Thus, TCR-engineered iNKT cells might be a promising therapy for patients with cancer.
Published in Science Immunology 7(74), eabn6563.
TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells
@ARTICLE{azzimonti2022b,
title = {{TCR}-engineered {iNKT} cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells},
journal = {Science Immunology},
volume = {7},
author = {Delfanti, G. and Cortesi, F. and Perini, A. and Antonini, G. and Azzimonti, L. and de Lalla, C. and Garavaglia, C. and Squadrito, M.L. and Fedeli, M. and Consonni, M. and Sesana, S. and Re, F. and Shen, H. and Dellabona, P. and Casorati, G.},
number = {74},
pages = {eabn6563},
year = {2022},
doi = {10.1126/sciimmunol.abn6563},
url = {}
}
DownloadDelucchi, M., Spinner, G.R., Scutari, M., Bijlenga, P., Morel, S., Friedrich, C.M., Furrer, R., Hirsch, S. (2022). Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Computers in Biology and Medicine 147, 105740.
Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors
Authors: Delucchi, M. and Spinner, G.R. and Scutari, M. and Bijlenga, P. and Morel, S. and Friedrich, C.M. and Furrer, R. and Hirsch, S.
Year: 2022
Abstract: Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences.
From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods.
Correlation and regression analyses showed significant associations between IA rupture status and patient’s sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors.
This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.
Published in Computers in Biology and Medicine 147, 105740.
Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors
@ARTICLE{scutari22c,
title = {Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors},
journal = {Computers in Biology and Medicine},
volume = {147},
author = {Delucchi, M. and Spinner, G.R. and Scutari, M. and Bijlenga, P. and Morel, S. and Friedrich, C.M. and Furrer, R. and Hirsch, S.},
pages = {105740},
year = {2022},
doi = {10.1016/j.compbiomed.2022.105740},
url = {}
}
DownloadDržajić, D., Wiessner, M., Maradia, U., Piga, D. (2022). Virtual operators with self and transfer learning ability in EDM. Procedia CIRP 113, pp. 17–22.
Virtual operators with self and transfer learning ability in EDM
Authors: Držajić, D. and Wiessner, M. and Maradia, U. and Piga, D.
Year: 2022
Abstract: Increasing the manufacturing resource efficiency requires pushing the process limits by customizing the machining parameters to the job at hand. Such optimizations performed usually based on empirical methods and experience due to the sheer complexity of physical modeling. Current research presents a system comprised of advanced machine learning methods, where model-based optimization employed to simultaneously create a digital twin and optimize the process as a self-learning virtual operator using a case study with the EDM processes.
Published in Procedia CIRP 113, pp. 17–22.
Note: 21st CIRP CONFERENCE ON ELECTRO PHYSICAL AND CHEMICAL MACHINING, ISEM XXIJune, 14 to 17, 2022 in Zurich
Virtual operators with self and transfer learning ability in EDM
@ARTICLE{piga2022e,
title = {Virtual operators with self and transfer learning ability in {EDM}},
journal = {Procedia {CIRP}},
volume = {113},
author = {Držajić, D. and Wiessner, M. and Maradia, U. and Piga, D.},
pages = {17--22},
year = {2022},
doi = {10.1016/j.procir.2022.09.113},
url = {}
}
DownloadFormenti, A., Bucca, G., Shahid, A.A., Piga, D., Roveda, L. (2022). Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment. Complex Engineering Systems 2(3), 12.
Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment
Authors: Formenti, A. and Bucca, G. and Shahid, A.A. and Piga, D. and Roveda, L.
Year: 2022
Abstract: Hybrid impedance/admittance control aims to provide an adaptive behavior to the manipulator in order to interact with the surrounding environment. In fact, impedance control is suitable for stiff environments, while admittance control is suitable for soft environments/free motion. Hybrid impedance/admittance control, indeed, allows modulating the control actions to exploit the combination of such behaviors. While some work has addressed the proposed topic, there are still some open issues to be solved. In particular, the proposed contribution aims: (i) to satisfy the continuity of the interaction force in the switching from impedance to admittance control when a feedforward velocity term is present; and (ii) to adapt the switching parameters to improve the performance of the hybrid control framework to better exploit the properties of both impedance and admittance controllers. The proposed approach was compared in simulation with the standard hybrid impedance/admittance control in order to show the improved performance. A Franka EMIKA panda robot was used as a reference robotic platform to provide a realistic simulation.
Published in Complex Engineering Systems 2(3), 12.
Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment
@ARTICLE{Roveda2022f,
title = {Improved {Impedance/Admittance} switching controller for the interaction with a variable stiffness environment},
journal = {Complex Engineering Systems},
volume = {2},
author = {Formenti, A. and Bucca, G. and Shahid, A.A. and Piga, D. and Roveda, L.},
number = {3},
pages = {12},
year = {2022},
doi = {10.20517/ces.2022.16},
url = {}
}
DownloadHiel, S., Nicolaers, L., Ortega Vazquez, C., Mitrović, S., Baesens, B., De Weerdt, J. (2022). Evaluation of joint modeling techniques for node embedding and community detection on graphs. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
Evaluation of joint modeling techniques for node embedding and community detection on graphs
Authors: Hiel, S. and Nicolaers, L. and Ortega Vazquez, C. and Mitrović, S. and Baesens, B. and De Weerdt, J.
Year: 2022
Abstract: Novel joint techniques capture both the microscopic context and the mesoscopic structure of networks by leveraging two previously separated fields of research: node representation learning (NRL) and community detection (CD). However, several limitations exist in the literature. First, a comprehensive comparison between these joint NRL-CD techniques is nonexistent. Second, baseline techniques, datasets, evaluation metrics, and classification algorithms differ significantly between each method. Thirdly, the literature lacks a synchronized experimental approach, thus rendering comparison between these methods strenuous. To overcome these limitations, we present a unified experimental setup mutually comparing six joint NRL-CD techniques and comparing them with corresponding NRL/CD baselines in three different settings: non-overlapping and overlapping CD and node classification. Our results show that joint methods underperform on the node classification task but achieve relatively solid results for overlapping community detection. Our research contribution is two-fold: first, we show specific weaknesses of selected joint techniques in different tasks and data sets; and second, we suggest a more thorough experimental setup to benchmark joint techniques with simpler NRL and CD techniques.
Accepted in 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).
Evaluation of joint modeling techniques for node embedding and community detection on graphs
@INPROCEEDINGS{sandra2022a,
title = {Evaluation of joint modeling techniques for node embedding and community detection on graphs},
booktitle = {2022 {IEEE/ACM} International Conference on Advances in Social Networks Analysis and Mining ({ASONAM})},
author = {Hiel, S. and Nicolaers, L. and Ortega Vazquez, C. and Mitrović, S. and Baesens, B. and De Weerdt, J.},
year = {2022},
doi = {},
url = {}
}
DownloadKronauer, S., Mavkov, B., Mejari, M., Piga, D., Jaques, F., d'Amario, R., Di Campli, R., Nasciuti, A. (2022). Data-driven statistical analysis for discharge position prediction on Wire EDM. Procedia CIRP 113, pp. 143–148.
Data-driven statistical analysis for discharge position prediction on Wire EDM
Authors: Kronauer, S. and Mavkov, B. and Mejari, M. and Piga, D. and Jaques, F. and d'Amario, R. and Di Campli, R. and Nasciuti, A.
Year: 2022
Abstract: Wire-cut Electrical Discharge Machining (Wire EDM) is a machining technique widely used to cut high-precision punch tools and highly value added precision components. With increasing resource efficiency requirements and zero-defect manufacturing trend, pushing the limits of machining reliability, even at cutting speed next to the technical limits, is becoming imperative. Predicting the position of the sparks along the wire is thus needed to develop more efficient EDM processes, thanks to the suppression of discharges which are expected to happen in undesired positions. This will lead to a reduction of the number of machine stops caused by wire breaks. Motivated by this need, the paper presents a data-driven statistical analysis to get insight into the correlation between the discharge positions of two consecutive sparks, along with the relation between spark positions and discharge frequency. The underlying basis for this analysis was the possibility to obtain reliable real-time information about the position of sparks along the wire, a feature made available by a Discharge Location Tracker. Results on spark position correlation are presented for cutting experiments on a steel workpiece of 50 mm in plane parallel with different machining parameters using brass wire of diameter 0.25 mm.
Published in Procedia CIRP 113, pp. 143–148.
Note: 21st CIRP CONFERENCE ON ELECTRO PHYSICAL AND CHEMICAL MACHINING, ISEM XXIJune, 14 to 17, 2022 in Zurich
Data-driven statistical analysis for discharge position prediction on Wire EDM
@ARTICLE{mejari2022b,
title = {Data-driven statistical analysis for discharge position prediction on {W}ire {EDM}},
journal = {Procedia {CIRP}},
volume = {113},
author = {Kronauer, S. and Mavkov, B. and Mejari, M. and Piga, D. and Jaques, F. and d'Amario, R. and Di Campli, R. and Nasciuti, A.},
pages = {143--148},
year = {2022},
doi = {10.1016/j.procir.2022.09.122},
url = {}
}
DownloadLiew, B.X.W., de-la-Llave-Rincón, A.I., Scutari, M., Arias-Buría, J.L., Cook, C.E., Cleland, J., Fernández-de-las-Peñas, C. (2022). Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A Bayesian network analysis of a randomized clinical trial . Physical Therapy 102(4), pzac015.
Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A Bayesian network analysis of a randomized clinical trial
Authors: Liew, B.X.W. and de-la-Llave-Rincón, A.I. and Scutari, M. and Arias-Buría, J.L. and Cook, C.E. and Cleland, J. and Fernández-de-las-Peñas, C.
Year: 2022
Abstract: Objective: The purpose of this study was to develop a data-driven Bayesian network approach to understand the potential multivariate pathways of the effect of manual physical therapy in women with carpal tunnel syndrome (CTS).
Methods: Data from a randomized clinical trial (n = 104) were analyzed comparing manual therapy including desensitization maneuvers of the central nervous system versus surgery in women with CTS. All variables included in the original trial were included in a Bayesian network to explore its multivariate relationship. The model was used to quantify the direct and indirect pathways of the effect of physical therapy and surgery on short-term, mid-term, and long-term changes in the clinical variables of pain, related function, and symptom severity.
Results: Manual physical therapy improved function in women with CTS (between-groups difference: 0.09; 95% CI = 0.07 to 0.11). The Bayesian network showed that early improvements (at 1 month) in function and symptom severity led to long-term (at 12 months) changes in related disability both directly and via complex pathways involving baseline pain intensity and depression levels. Additionally, women with moderate CTS had 0.14-point (95% CI = 0.11 to 0.17 point) poorer function at 12 months than those with mild CTS and 0.12-point (95% CI = 0.09 to 0.15 point) poorer function at 12 months than those with severe CTS.
Conclusion: Current findings suggest that short-term benefits in function and symptom severity observed after manual therapy/surgery were associated with long-term improvements in function, but mechanisms driving these effects interact with depression levels and severity as assessed using electromyography. Nevertheless, it should be noted that between-group differences depending on severity determined using electromyography were small, and the clinical relevance is elusive. Further data-driven analyses involving a broad range of biopsychosocial variables are recommended to fully understand the pathways underpinning CTS treatment effects.
Impact: Short-term effects of physical manual therapy seem to be clinically relevant for obtaining long-term effects in women with CTS.
Published in Physical Therapy 102(4), pzac015.
Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A Bayesian network analysis of a randomized clinical trial
@ARTICLE{scutari22b,
title = {Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A {B}ayesian network analysis of a randomized clinical trial },
journal = {Physical Therapy},
volume = {102},
author = {Liew, B.X.W. and de-la-Llave-Rinc\'on, A.I. and Scutari, M. and Arias-Bur\'ia, J.L. and Cook, C.E. and Cleland, J. and Fern\'andez-de-las-Pe\~nas, C.},
number = {4},
pages = {pzac015},
year = {2022},
doi = {10.1093/ptj/pzac015},
url = {}
}
DownloadMalavolta, M., Pallante, L., Mavkov, B., Stojceski, F., Grasso, G., Korfiati, A., Mavroudi, S., Kalogeras, A., Alexakos, C., Martos, V., Daria, A., Giacomo, D.B.P.D., Theofilatos, K., Deriu, M. (2022). A survey on computational taste predictors. European Food Research and Technology 248(9), pp. 2215–2235.
A survey on computational taste predictors
Authors: Malavolta, M. and Pallante, L. and Mavkov, B. and Stojceski, F. and Grasso, G. and Korfiati, A. and Mavroudi, S. and Kalogeras, A. and Alexakos, C. and Martos, V. and Daria, A. and Giacomo, D.B.P.D. and Theofilatos, K. and Deriu, M.
Year: 2022
Abstract: Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.
Published in European Food Research and Technology 248(9), pp. 2215–2235.
A survey on computational taste predictors
@ARTICLE{piga2022d,
title = {A survey on computational taste predictors},
journal = {European Food Research and Technology},
volume = {248},
author = {Malavolta, M. and Pallante, L. and Mavkov, B. and Stojceski, F. and Grasso, G. and Korfiati, A. and Mavroudi, S. and Kalogeras, A. and Alexakos, C. and Martos, V. and Daria, A. and Giacomo, D.B.P.D. and Theofilatos, K. and Deriu, M.},
number = {9},
pages = {2215--2235},
year = {2022},
doi = {10.1007/s00217-022-04044-5},
url = {}
}
DownloadMalpetti, D., Bellisario, A., Macchiavello, C. (2022). Multipartite entanglement in qudit hypergraph states. Journal of Physics A: Mathematical and Theoretical 55(41), 415301.
Multipartite entanglement in qudit hypergraph states
Authors: Malpetti, D. and Bellisario, A. and Macchiavello, C.
Year: 2022
Abstract: We study entanglement properties of hypergraph states in arbitrary finite dimension. We compute multipartite entanglement of elementary qudit hypergraph states, namely those endowed with a single maximum-cardinality hyperedge. We show that, analogously to the qubit case, also for arbitrary dimension there exists a lower bound for multipartite entanglement of connected qudit hypergraph states; this is given by the multipartite entanglement of an equal-dimension elementary hypergraph state featuring the same number of qudits as the largest-cardinality hyperedge. We highlight interesting differences between prime and non-prime dimension in the entanglement features.
Published in IOP Publishing (Ed), Journal of Physics A: Mathematical and Theoretical 55(41), 415301.
Multipartite entanglement in qudit hypergraph states
@ARTICLE{malpetti2022b,
title = {Multipartite entanglement in qudit hypergraph states},
journal = {Journal of Physics A: Mathematical and Theoretical},
editor = {IOP Publishing},
volume = {55},
author = {Malpetti, D. and Bellisario, A. and Macchiavello, C.},
number = {41},
pages = {415301},
year = {2022},
doi = {10.1088/1751-8121/ac91b2},
url = {}
}
DownloadMangili, F., Adorni, G., Piatti, A., Bonesana, C., Antonucci, A. (2022). Modelling assessment rubrics through Bayesian networks: a pragmatic approach. Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM).
Modelling assessment rubrics through Bayesian networks: a pragmatic approach
Authors: Mangili, F. and Adorni, G. and Piatti, A. and Bonesana, C. and Antonucci, A.
Year: 2022
Abstract: Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems. An assessment rubric typically and effectively describes relevant competencies and competence levels. This paper presents an approach to deriving a learner model directly from an assessment rubric defining some (partial) ordering of competence levels. The model is based on Bayesian networks and exploits logical gates with uncertainty (often referred to as noisy gates) to reduce the number of parameters of the model, so to simplify their elicitation by experts and allow real-time inference in intelligent tutoring systems. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills. The simple elicitation of the model starting from the assessment rubric opens up the possibility of quickly automating the assessment of several tasks, making them more easily exploitable in the context of adaptive assessment tools and intelligent tutoring systems.
Published in Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), IEEE.
Modelling assessment rubrics through Bayesian networks: a pragmatic approach
@ARTICLE{mangili2022a,
title = {Modelling assessment rubrics through {B}ayesian networks: a pragmatic approach},
journal = {Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks ({SoftCOM})},
publisher = {IEEE},
author = {Mangili, F. and Adorni, G. and Piatti, A. and Bonesana, C. and Antonucci, A.},
year = {2022},
doi = {},
url = {}
}
DownloadMaroni, G., Pallante, L., Di Benedetto, G., Deriu, M.A., Piga, D., Grasso, G. (2022). Informed classification of sweeteners/bitterants compounds via explainable machine learning. Current Research in Food Science 5, pp. 2270–2280.
Informed classification of sweeteners/bitterants compounds via explainable machine learning
Authors: Maroni, G. and Pallante, L. and Di Benedetto, G. and Deriu, M.A. and Piga, D. and Grasso, G.
Year: 2022
Abstract: Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical
compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes
has been the subject of several machine learning studies for classification purposes. While previous studies have
provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching
the understanding of the molecular basis of bitter-sweet tastes. Towards these goals, our study focuses on the
development and testing of several machine learning strategies coupled with the novel SHapley Additive ex-
Planations (SHAP) for a rational sweetness/bitterness classification. This allows the identification of the chemical
descriptors of interest by allowing a more informed approach toward the rational design and screening of
sweeteners/bitterants. To support future research in this field, we make all datasets and machine learning models
publicly available and present an easy-to-use code for bitter-sweet taste prediction.
Published in Current Research in Food Science 5, pp. 2270–2280.
Informed classification of sweeteners/bitterants compounds via explainable machine learning
@ARTICLE{maroni2022a,
title = {Informed classification of sweeteners/bitterants compounds via explainable machine learning},
journal = {Current Research in Food Science},
volume = {5},
author = {Maroni, G. and Pallante, L. and Di Benedetto, G. and Deriu, M.A. and Piga, D. and Grasso, G.},
pages = {2270--2280},
year = {2022},
doi = {10.1016/j.crfs.2022.11.014},
url = {}
}
DownloadMejari, M., Mavkov, B., Forgione, M., Piga, D. (2022). Direct identification of continuous-time lpv state-space models via an integral architecture. Automatica 142, 110407.
Direct identification of continuous-time lpv state-space models via an integral architecture
Authors: Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.
Year: 2022
Abstract: In this paper, we present a block-structured architecture for direct identification of continuous-time Linear Parameter-Varying (LPV) state-space models. The proposed architecture consists of an LPV model followed by an integral block. This structure is used to approximate the continuous-time LPV system dynamics. The unknown LPV model matrices are estimated along with the state sequence by minimizing a properly constructed dual-objective criterion. A coordinate-descent algorithm is employed to optimize the desired objective, which alternates between computing the unknown LPV matrices and estimating the state sequence. Thanks to the linear parametric structure induced by the LPV model, the optimization variables within each coordinate-descent step can be updated analytically via ordinary least squares. Furthermore, in order to handle large-size datasets, we discuss how to perform optimization based on short-size subsequences. The effectiveness of the proposed methodology is demonstrated via an academic example and two case studies. The first case study consists of identifying an LPV model describing the behaviour of an electronic bandpass filter from benchmark experimental data. The second case study involves identification of the plasma safety factor from a tokamak plasma simulator.
Published in Automatica 142, 110407.
Direct identification of continuous-time lpv state-space models via an integral architecture
@ARTICLE{mejari2022c,
title = {Direct identification of continuous-time lpv state-space models via an integral architecture},
journal = {Automatica},
volume = {142},
author = {Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.},
pages = {110407},
year = {2022},
doi = {https://doi.org/10.1016/j.automatica.2022.110407},
url = {https://www.sciencedirect.com/science/article/pii/S0005109822002606}
}
DownloadMejari, M., Piga, D. (2022). Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling. ASME Letters in Dynamic Systems and Control 2(1).
Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling
Authors: Mejari, M. and Piga, D.
Year: 2022
Abstract: This article addresses maximum-a-posteriori (MAP) estimation of linear time-invariant state-space (LTI-SS) models. The joint posterior distribution of the model matrices and the unknown state sequence is approximated by using Rao-Blackwellized Monte-Carlo sampling algorithms. Specifically, the conditional distribution of the state sequence given the model parameters is derived analytically, while only the marginal posterior distribution of the model matrices is approximated using a Metropolis-Hastings Markov Chain Monte-Carlo sampler. From the joint distribution, MAP estimates of the unknown model matrices as well as the state sequence are computed. The performance of the proposed algorithm is demonstrated on a numerical example and on a real laboratory benchmark dataset of a hair dryer process.
Published in ASME Letters in Dynamic Systems and Control 2(1).
Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling
@ARTICLE{mejari2022a,
title = {Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling},
journal = {{ASME} Letters in Dynamic Systems and Control},
volume = {2},
author = {Mejari, M. and Piga, D.},
number = {1},
year = {2022},
doi = {10.1115/1.4051491},
url = {}
}
DownloadMitrović, S., Kanjirangat, V. (2022). Enhancing BERT performance with contextual valence shifters for panic detection in COVID-19 tweets. In The 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2022).
Enhancing BERT performance with contextual valence shifters for panic detection in COVID-19 tweets
Authors: Mitrović, S. and Kanjirangat, V.
Year: 2022
Abstract: Panic phenomenon is one of the main challenges in the current pandemic time. In this work, we aim to explore the approaches to detect the panic-related COVID-19 tweets. Aligned to this, we propose an unsupervised clustering approach considering negation cues as an extracted feature input to the pre-trained model. This task cannot be done by simply applying state-of-the-art transformer models, since we observed that they occasionally fail in handling negations. Hence, we propose to utilize features based on Contextual Valence Shifters (CVS) along with the pre-trained BERT embeddings. We evaluate and compare the approaches in an unsupervised setup, using standard clusteringmetrics on a large set of COVID-19 tweets. The obtained results show that CVS effectively facilitates negation handling (positive/negative tweet discrimination).
Accepted in The 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2022).
Enhancing BERT performance with contextual valence shifters for panic detection in COVID-19 tweets
@INPROCEEDINGS{mitrovic2022b,
title = {Enhancing {BERT} performance with contextual valence shifters for panic detection in {COVID}-19 tweets},
edition = {To appear},
booktitle = {The 6th International Conference on Natural Language Processing and Information Retrieval ({NLPIR} 2022)},
author = {Mitrović, S. and Kanjirangat, V.},
year = {2022},
doi = {},
url = {}
}
DownloadPaolillo, A., Nava, M., Piga, D., Giusti, A. (2022). Visual servoing with geometrically interpretable neural perception. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5300–5306.
Visual servoing with geometrically interpretable neural perception
Authors: Paolillo, A. and Nava, M. and Piga, D. and Giusti, A.
Year: 2022
Abstract: An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.
Published in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5300–5306.
Visual servoing with geometrically interpretable neural perception
@INPROCEEDINGS{piga2022b,
title = {Visual servoing with geometrically interpretable neural perception},
booktitle = {2022 {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
author = {Paolillo, A. and Nava, M. and Piga, D. and Giusti, A.},
pages = {5300--5306},
year = {2022},
doi = {10.1109/IROS47612.2022.9982163},
url = {}
}
DownloadPesenti, M., Gandolla, M., Folcio, C., Ouyang, S., Rovelli, L., Covarrubias, M., Roveda, L. (2022). Sensor-based task ergonomics feedback for a passive low-back exoskeleton. In Miesenberger, K., Kouroupetroglou, G., Mavrou, K., Manduchi, R., Covarrubias Rodriguez, M., Penáz, P. (Eds), Computers Helping People with Special Needs, Springer International Publishing, pp. 403–410.
Sensor-based task ergonomics feedback for a passive low-back exoskeleton
Authors: Pesenti, M. and Gandolla, M. and Folcio, C. and Ouyang, S. and Rovelli, L. and Covarrubias, M. and Roveda, L.
Year: 2022
Abstract: Low-back exoskeletons are a wide-spreading technology tackling low-back pain, the leading work-related musculoskeletal disorder in many work sectors. Currently, spring-based (i.e., passive) exoskeletons are the mostly adopted in the industry, being cheaper and generally less complex and more intuitive to use. We introduce a system of interconnected wireless sensing units to provide online ergonomics feedback to the wearer. We integrate the system into our passive low-back exoskeleton and evaluate its usability with healthy volunteers and potential end users. In this way, we provide the exoskeleton with a tool aimed both at monitoring the interaction of the system with the user, providing them with an ergonomics feedback during task execution. The sensor system can also be integrated with a custom-developed Unity3D application which can be used to interface with Augmented- or Virtual-Reality applications with higher potential for improved user feedback, ergonomics training, and offline ergonomics evaluation of the workplace. We believe that providing ergonomics feedback to exoskeleton users in the industrial sector could help further reduce the drastic impact of low-back pain and prevent its onset.
Published in Miesenberger, K., Kouroupetroglou, G., Mavrou, K., Manduchi, R., Covarrubias Rodriguez, M., Penáz, P. (Eds), Computers Helping People with Special Needs, Springer International Publishing, pp. 403–410.
Sensor-based task ergonomics feedback for a passive low-back exoskeleton
@INPROCEEDINGS{Roveda2022h,
title = {Sensor-based task ergonomics feedback for a passive low-back exoskeleton},
editor = {Miesenberger, K. and Kouroupetroglou, G. and Mavrou, K. and Manduchi, R. and Covarrubias Rodriguez, M. and Pen\'az, P.},
publisher = {Springer International Publishing},
booktitle = {Computers Helping People {w}ith Special Needs},
author = {Pesenti, M. and Gandolla, M. and Folcio, C. and Ouyang, S. and Rovelli, L. and Covarrubias, M. and Roveda, L.},
pages = {403--410},
year = {2022},
doi = {10.1007/978-3-031-08645-8_47},
url = {}
}
DownloadPesenti, M., Gandolla, M., Pedrocchi, A., Roveda, L. (2022). A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study. In 2022 International Conference on Rehabilitation Robotics (ICORR), pp. 1–6.
A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study
Authors: Pesenti, M. and Gandolla, M. and Pedrocchi, A. and Roveda, L.
Year: 2022
Abstract: Exoskeletons for the low-back have great potential as tools to both prevent low-back pain for healthy subjects and limit its impact for chronic patients. Here, we show a proof-of-concept evaluation of our low-back exoskeleton. Its peculiar feature is the backbone-tracking kinematic structure that allows tracking the motion of the human spine while bending the trunk. This mechanism is implemented with a rigid-yet-elongating structure that does not hinder nor constrain the motion of the wearer while providing assistance. In this work, we show the first prototype we manufactured. It is equipped with a traction spring to assist the wearer during trunk flexion/extension. Then, we report the results of a preliminary test with healthy subjects. We measured a reduction of the mean absolute value for some target muscles - including the erector spinae - when using the exoskeleton for payload manipulation tasks. This was achieved without affecting task performance, measured as task time and joints range of motion. We believe these preliminary results are encouraging, paving the way for a broader experimental campaign to evaluate our exoskeleton.
Published in 2022 International Conference on Rehabilitation Robotics (ICORR), pp. 1–6.
A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study
@INPROCEEDINGS{Roveda2022j,
title = {A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study},
booktitle = {2022 International Conference on Rehabilitation Robotics ({ICORR})},
author = {Pesenti, M. and Gandolla, M. and Pedrocchi, A. and Roveda, L.},
pages = {1--6},
year = {2022},
doi = {10.1109/ICORR55369.2022.9896514},
url = {}
}
DownloadPozzi, L., Gandolla, M., Pura, F., Maccarini, M., Pedrocchi, A., Braghin, F., Piga, D., Roveda, L. (2022). Grasping learning, optimization, and knowledge transfer in the robotics field. Scientific Reports 12(1), 4481.
Grasping learning, optimization, and knowledge transfer in the robotics field
Authors: Pozzi, L. and Gandolla, M. and Pura, F. and Maccarini, M. and Pedrocchi, A. and Braghin, F. and Piga, D. and Roveda, L.
Year: 2022
Abstract: Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable objects making use of a compliant, under-actuated, sensorless robotic hand. The main aim of the paper is, therefore, finding the best position and joint configuration for the mentioned robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. Due to the complex grasping dynamics, learning-from-simulations approaches (e.g., Reinforcement Learning) are not effective in the faced context. Thus, trial-and-error-based methodologies have to be exploited. In order to save resources, a samples-efficient approach has to be employed. Indeed, a Bayesian approach to address the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired knowledge to grasp (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of-freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., deformable) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%.
Published in Scientific Reports 12(1), 4481.
Grasping learning, optimization, and knowledge transfer in the robotics field
@ARTICLE{Roveda2022c,
title = {Grasping learning, optimization, and knowledge transfer in the robotics field},
journal = {Scientific Reports},
volume = {12},
author = {Pozzi, L. and Gandolla, M. and Pura, F. and Maccarini, M. and Pedrocchi, A. and Braghin, F. and Piga, D. and Roveda, L.},
number = {1},
pages = {4481},
year = {2022},
doi = {10.1038/s41598-022-08276-z},
url = {}
}
DownloadPozzi, L., Gandolla, M., Roveda, L. (2022). Pointing Gestures for Human-Robot Interaction in Service Robotics: a feasibility study. In Computers Helping People with Special Needs, Springer International Publishing, pp. 461–468.
Pointing Gestures for Human-Robot Interaction in Service Robotics: a feasibility study
Authors: Pozzi, L. and Gandolla, M. and Roveda, L.
Year: 2022
Abstract: Research in service robotics strives at having a positive impact on people’s quality of life by the introduction of robotic helpers for everyday activities. From this ambition arises the need of enabling natural communication between robots and ordinary people. For this reason, Human-Robot Interaction (HRI) is an extensively investigated topic, exceeding language-based exchange of information, to include all the relevant facets of communication. Each aspect of communication (e.g. hearing, sight, touch) comes with its own peculiar strengths and limits, thus they are often combined to improve robustness and naturalness. In this contribution, an HRI framework is presented, based on pointing gestures as the preferred interaction strategy. Pointing gestures are selected as they are an innate behavior to direct another attention, and thus could represent a natural way to require a service to a robot. To complement the visual information, the user could be prompted to give voice commands to resolve ambiguities and prevent the execution of unintended actions. The two layers (perceptive and semantic) architecture of the proposed HRI system is described. The perceptive layer is responsible for objects mapping, action detection, and assessment of the indicated direction. Moreover, it has to listen to uses’ voice commands. To avoid privacy issues and not burden the computational resources of the robot, the interaction would be triggered by a wake-word detection system. The semantic layer receives the information processed by the perceptive layer and determines which actions are available for the selected object. The decision is based on object’s characteristics, contextual information and user vocal feedbacks are exploited to resolve ambiguities. A pilot implementation of the semantic layer is detailed, and qualitative results are shown. The preliminary findings on the validity of the proposed system, as well as on the limitations of a purely vision-based approach, are discussed.
Published in Computers Helping People with Special Needs, Springer International Publishing, pp. 461–468.
Pointing Gestures for Human-Robot Interaction in Service Robotics: a feasibility study
@INPROCEEDINGS{Rovedag,
title = {Pointing {G}estures for {H}uman-{R}obot {I}nteraction in {S}ervice {R}obotics: a feasibility study},
publisher = {Springer International Publishing},
booktitle = {Computers Helping People {w}ith Special Needs},
author = {Pozzi, L. and Gandolla, M. and Roveda, L.},
pages = {461--468},
year = {2022},
doi = {10.1007/978-3-031-08645-8 54},
url = {}
}
DownloadRavasi, D., Mangili, F., Huber, D., Azzimonti, L., Engeler, L., Vermes, N., Del Rio, G., Guidi, V., Tonolla, M., Flacio, E. (2022). Risk-based mapping tools for surveillance and control of the invasive mosquito Aedes albopictus in Switzerland. International Journal of Environmental Research and Public Health 19(6), 3220.
Risk-based mapping tools for surveillance and control of the invasive mosquito Aedes albopictus in Switzerland
Authors: Ravasi, D. and Mangili, F. and Huber, D. and Azzimonti, L. and Engeler, L. and Vermes, N. and Del Rio, G. and Guidi, V. and Tonolla, M. and Flacio, E.
Year: 2022
Abstract: Background: In Switzerland, Aedes albopictus is well established in Ticino, south of the Alps, where surveillance and control are implemented. The mosquito has also been observed in Swiss cities north of the Alps. Decision-making tools are urgently needed by the local authorities in order to optimize surveillance and control. Methods: A regularized logistic regression was used to link the long-term dataset of Ae. albopictus occurrence in Ticino with socioenvironmental predictors. The probability of establishment of Ae. albopictus was extrapolated to Switzerland and more finely to the cities of Basel and Zurich. Results: The model performed well, with an AUC of 0.86. Ten socio-environmental predictors were selected as informative, including the road-based distance in minutes of travel by car from the nearest cell established in the previous year. The risk maps showed high suitability for Ae. albopictus establishment in the Central Plateau, the area of Basel, and the lower Rhone Valley in the Canton of Valais. Conclusions: The areas identified as suitable for Ae. albopictus establishment are consistent with the actual current findings of tiger mosquito. Our approach provides a useful tool to prompt authorities’ intervention in the areas where there is higher risk of introduction and establishment of Ae. albopictus.
Published in International Journal of Environmental Research and Public Health 19(6), 3220.
Risk-based mapping tools for surveillance and control of the invasive mosquito Aedes albopictus in Switzerland
@ARTICLE{mangili2022b,
title = {Risk-based mapping tools for surveillance and control of the invasive mosquito {A}edes albopictus in {S}witzerland},
journal = {International Journal of Environmental Research and Public Health},
volume = {19},
author = {Ravasi, D. and Mangili, F. and Huber, D. and Azzimonti, L. and Engeler, L. and Vermes, N. and Del Rio, G. and Guidi, V. and Tonolla, M. and Flacio, E.},
number = {6},
pages = {3220},
year = {2022},
doi = {10.3390/ijerph19063220},
url = {}
}
DownloadRavasi, D., Mangili, F., Huber, D., Cannata, M., Strigaro, D., Flacio, E. (2022). The effects of microclimatic winter conditions in urban areas on the risk of establishment for Aedes albopictus. Scientific Reports 12(1), pp. 1–14.
The effects of microclimatic winter conditions in urban areas on the risk of establishment for Aedes albopictus
Authors: Ravasi, D. and Mangili, F. and Huber, D. and Cannata, M. and Strigaro, D. and Flacio, E.
Year: 2022
Abstract: The tiger mosquito, Aedes albopictus, has adjusted well to urban environments by adopting artificial water containers as oviposition sites. Its spread in temperate regions is favoured by the deposition of cold-tolerant diapausing eggs that survive winter temperatures to a certain degree. The probability of establishment in new geographical areas is estimated using predictive models usually based on meteorological data measured at coarse resolution. Here, we investigated if we could obtain more precise and realistic risk scenarios for the spread of Ae. albopictus when considering the winter microclimatic conditions of catch basins, one of the major sites of oviposition and egg overwintering in temperate urban areas. We monitored winter microclimatic conditions of catch basins in four Swiss cities and developed a regression model to predict the average microclimatic temperatures of catch basins, based on available meteorological parameters, accounting for the observed differences between cities. We then used the microclimatic model to correct the predictions of our previously developed risk model for the prediction of Ae. albopictus establishment. Comparison of the predictive model’s results based on local climate data and microclimate data indicated that the risk of establishment for Ae. albopictus in temperate urban areas increases when microhabitat temperatures are considered.
Published in Scientific Reports 12(1), Nature Publishing Group, pp. 1–14.
The effects of microclimatic winter conditions in urban areas on the risk of establishment for Aedes albopictus
@ARTICLE{mangili2022c,
title = {The effects of microclimatic winter conditions in urban areas on the risk of establishment for {A}edes albopictus},
journal = {Scientific Reports},
publisher = {Nature Publishing Group},
volume = {12},
author = {Ravasi, D. and Mangili, F. and Huber, D. and Cannata, M. and Strigaro, D. and Flacio, E.},
number = {1},
pages = {1--14},
year = {2022},
doi = {10.1038/s41598-022-20436-9},
url = {}
}
DownloadRinaldi, A., Lazareth, H., Poindessous, V., Nemazanyy, I., Sampaio, J.L., Malpetti, D., Bignon, Y., Naesens, M., Rabant, M., Anglicheau, D., Cippà, P.E., Pallet, N. (2022). Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury. JCI Insight 7(18).
Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury
Authors: Rinaldi, A. and Lazareth, H. and Poindessous, V. and Nemazanyy, I. and Sampaio, J.L. and Malpetti, D. and Bignon, Y. and Naesens, M. and Rabant, M. and Anglicheau, D. and Cippà, P.E. and Pallet, N.
Year: 2022
Abstract: Energy metabolism failure in proximal tubule cells (PTCs) is a hallmark of chronic kidney injury. We combined transcriptomic, metabolomic, and lipidomic approaches in experimental models and patient cohorts to investigate the molecular basis of the progression to chronic kidney allograft injury initiated by ischemia/reperfusion injury (IRI). The urinary metabolome of kidney transplant recipients with chronic allograft injury and who experienced severe IRI was substantially enriched with long chain fatty acids (FAs). We identified a renal FA-related gene signature with low levels of carnitine palmitoyltransferase 2 (Cpt2) and acyl-CoA synthetase medium chain family member 5 (Acsm5) and high levels of acyl-CoA synthetase long chain family member 4 and 5 (Acsl4 and Acsl5) associated with IRI, transition to chronic injury, and established chronic kidney disease in mouse models and kidney transplant recipients. The findings were consistent with the presence of Cpt2–Acsl4+Acsl5+Acsm5– PTCs failing to recover from IRI as identified by single-nucleus RNA-Seq. In vitro experiments indicated that ER stress contributed to CPT2 repression, which, in turn, promoted lipids’ accumulation, drove profibrogenic epithelial phenotypic changes, and activated the unfolded protein response. ER stress through CPT2 inhibition and lipid accumulation engaged an auto-amplification loop leading to lipotoxicity and self-sustained cellular stress. Thus, IRI imprints a persistent FA metabolism disturbance in the proximal tubule, sustaining the progression to chronic kidney allograft injury.
Published in The American Society for Clinical Investigation (Ed), JCI Insight 7(18).
Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury
@ARTICLE{malpetti2022a,
title = {Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury},
journal = {{JCI} Insight},
editor = {The American Society for Clinical Investigation},
volume = {7},
author = {Rinaldi, A. and Lazareth, H. and Poindessous, V. and Nemazanyy, I. and Sampaio, J.L. and Malpetti, D. and Bignon, Y. and Naesens, M. and Rabant, M. and Anglicheau, D. and Cipp\`a, P.E. and Pallet, N.},
number = {18},
year = {2022},
doi = {10.1172/jci.insight.161783},
url = {}
}
DownloadRoveda, L., Bussolan, A., Braghin, F., Piga, D. (2022). Robot joint friction compensation learning enhanced by 6D virtual sensor. International Journal of Robust and Nonlinear Control 32(9), pp. 5741–5763.
Robot joint friction compensation learning enhanced by 6D virtual sensor
Authors: Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.
Year: 2022
Abstract: High-performance robot control is one of the most investigated topics in both research and industry. Being able to compensate for robot dynamics is indeed one major challenge. Joint friction is commonly the main issue, especially in sensorless (i.e., no availability of torque/force sensors) compliance-controlled robots for interaction application purposes. The presented paper aims to propose a methodology for sensorless Cartesian impedance controlled robots to learn local friction compensation controllers. Exploiting a 6D virtual sensor to quantify the joint friction effects, a Bayesian optimization (BO)-based algorithm is proposed to minimize the estimated external interaction in free-motion tasks (related to friction effects). The BO algorithm enhances the impedance control performance by tuning the model-based friction compensator parameters. To validate the proposed approach, experimental tests have been executed on a Franka EMIKA panda robot, highlighting the suitability of the proposed 6D virtual sensor and BO-based algorithm to minimize the estimated external interaction for joint friction compensation purposes.
Published in International Journal of Robust and Nonlinear Control 32(9), pp. 5741–5763.
Robot joint friction compensation learning enhanced by 6D virtual sensor
@ARTICLE{Roveda2022d,
title = {Robot joint friction compensation learning enhanced by {6D} virtual sensor},
journal = {International Journal of Robust and Nonlinear Control},
volume = {32},
author = {Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.},
number = {9},
pages = {5741--5763},
year = {2022},
doi = {10.1002/rnc.6108},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.6108}
}
DownloadRoveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Shahid, A.A., Bucca, G., Piga, D. (2022). Robot end-effector mounted camera pose optimization in object detection-based tasks. Journal of Intelligent & Robotic Systems 104(1), 16.
Robot end-effector mounted camera pose optimization in object detection-based tasks
Authors: Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Shahid, A.A. and Bucca, G. and Piga, D.
Year: 2022
Abstract: Robots equipped with the vision systems at the end-effector provide a powerful combination in industrial contexts, allowing to execute a wide range of manufacturing tasks, such as inspection applications. While many works are dedicated to machine vision algorithms, the optimization of the vision system pose is not properly addressed. Optimizing the sensor pose, in fact, can increase the object detection performance, avoiding occlusions and collisions in the real working scene. Therefore, the development of an approach capable of optimizing the pose of a vision system is the main objective of this paper. A complete pipeline for such optimization is proposed, composed of the following main components: working scene reconstruction, robot-environment collisions modeling, object detection, sensor pose optimization (exploiting Bayesian Optimization, a state of the art methodology), and collision-free robot motion planning. To validate the proposed approach, experimental tests have been executed considering two object detection-based tasks. A Franka EMIKA Panda robot equipped with an Intel{\textcopyright} RealSense D400 at its end-effector has been employed as a robotic platform. Achieved results show the high-fidelity reconstruction of the real working environment for an offline optimization (i.e., performed simulations), as well as the capabilities of the employed Bayesian Optimization-based approach to define the sensor pose. The proposed optimization methodology has been compared with the grid point approach, showing an improved performance for camera pose optimization purposes. An additional experiment has been performed in order to show the possibility to exploit a digital twin (if available) of the working scene instead of the environment reconstruction (to reduce the computational resources and to avoid measurements noise in the 3D reconstruction). Obtained results show the feasibility of the proposed pipeline employing such a digital twin.
Published in Journal of Intelligent & Robotic Systems 104(1), 16.
Robot end-effector mounted camera pose optimization in object detection-based tasks
@ARTICLE{Roveda2022e,
title = {Robot end-effector mounted camera pose optimization in object detection-based tasks},
journal = {Journal of Intelligent & Robotic Systems},
volume = {104},
author = {Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Shahid, A.A. and Bucca, G. and Piga, D.},
number = {1},
pages = {16},
year = {2022},
doi = {10.1007/s10846-021-01558-0},
url = {}
}
DownloadRoveda, L., Pesenti, M., Rossi, M., Covarrubias, M., Galluzzi, C., Combi, S., Pedrocchi, A., Braghin, F., Gandolla, M. (2022). User-centered back-support exoskeleton: design and prototyping. Procedia CIRP 107, pp. 522–527.
User-centered back-support exoskeleton: design and prototyping
Authors: Roveda, L. and Pesenti, M. and Rossi, M. and Covarrubias, M. and Galluzzi, C. and Combi, S. and Pedrocchi, A. and Braghin, F. and Gandolla, M.
Year: 2022
Abstract: Exhausting manual labor is still predominant in the industrial context. It typically consists in manipulating heavy parts or working in nonergonomic conditions. The resulting work-related musculoskeletal disorders are a major problem to tackle. The most-affected body section is the lumbar spine. Recently, exoskeletons have been identified as a possible non-invasive solution to reduce the impact of low-back pain. State-of-the-art prototypes have been optimized to: follow unconstrained human kinematics, (partially) relieve the load on assisted joints, and allow anthropometric adaptation. Yet, this technology still has limited adoption. Manufacturing optimization may address the following limitations: bulky/heavy resulting designs, complex assembly and maintenance, high manufacturing costs, long procedures for adaptation and wearing, and psychological effects (e.g., cognitive load and usability). In this contribution, the aforementioned issues are tackled improving a previous low-back exoskeleton prototype. In particular, kinematic analysis, Finite-Element-Method, and topological optimization have been combined to obtain a lightweight prototype, testing different materials (Nylon, carbon-fiber reinforced PC/ABS, etc.).We applied both Design for Assembly and Design for Manufacturability. The resulting exoskeleton prototype is described in the paper, ready for end-user field tests.
Published in Procedia CIRP 107, pp. 522–527.
User-centered back-support exoskeleton: design and prototyping
@ARTICLE{Roveda2022i,
title = {User-centered back-support exoskeleton: design and prototyping},
journal = {Procedia {CIRP}},
volume = {107},
author = {Roveda, L. and Pesenti, M. and Rossi, M. and Covarrubias, M. and Galluzzi, C. and Combi, S. and Pedrocchi, A. and Braghin, F. and Gandolla, M.},
pages = {522--527},
year = {2022},
doi = {10.1016/j.procir.2022.05.019},
url = {}
}
DownloadRoveda, L., Testa, A., Shahid, A.A., Braghin, F., Piga, D. (2022). Q-learning-based model predictive variable impedance control for physical human-robot collaboration. Artificial Intelligence, 103771.
Q-learning-based model predictive variable impedance control for physical human-robot collaboration
Authors: Roveda, L. and Testa, A. and Shahid, A.A. and Braghin, F. and Piga, D.
Year: 2022
Abstract: Physical human-robot collaboration is increasingly required in many contexts (such as industrial and rehabilitation applications). The robot needs to interact with the human to perform the target task while relieving the user from the workload. To do that, the robot should be able to recognize the human's intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field, where the operator guides the robot manually to manipulate heavy parts (e.g., while teaching a specific task). With this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in a physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement a decoupled compliant robot dynamics. The impedance control parameters (i.e., setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with the stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform. Each user was asked to interact with the robot along the controlled vertical z Cartesian direction. The proposed controller has been compared with a model-based reinforcement learning variable impedance controller (MBRLC) previously developed by some of the authors in order to evaluate the performance. As highlighted in the achieved results, the proposed controller is able to improve the pHRC performance. Additionally, two industrial tasks (a collaborative assembly and a collaborative deposition task) have been demonstrated to prove the applicability of the proposed solution in real industrial scenarios.
Published in Artificial Intelligence, 103771.
Q-learning-based model predictive variable impedance control for physical human-robot collaboration
@ARTICLE{Roveda2022a,
title = {Q-learning-based model predictive variable impedance control for physical human-robot collaboration},
journal = {Artificial Intelligence},
author = {Roveda, L. and Testa, A. and Shahid, A.A. and Braghin, F. and Piga, D.},
pages = {103771},
year = {2022},
doi = {10.1016/j.artint.2022.103771},
url = {}
}
DownloadScutari, M. (2022). Comments on: Hybrid Semiparametric Bayesian Networks. TEST 31, pp. 328–330.
Comments on: Hybrid Semiparametric Bayesian Networks
Authors: Scutari, M.
Year: 2022
Published in TEST 31, pp. 328–330.
Comments on: Hybrid Semiparametric Bayesian Networks
@ARTICLE{scutari22d,
title = {Comments on: {H}ybrid {S}emiparametric {B}ayesian {N}etworks},
journal = {{TEST}},
volume = {31},
author = {Scutari, M.},
pages = {328--330},
year = {2022},
doi = {10.1007/s11749-022-00818-x},
url = {}
}
DownloadScutari, M., Marquis, C., Azzimonti, L. (2022). Using mixed-effects models to learn bayesian networks from related data sets. In Salmerón, A., Rumı́, R. (Eds), Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186, JMLR.org, pp. 73–84.
Using mixed-effects models to learn bayesian networks from related data sets
Authors: Scutari, M. and Marquis, C. and Azzimonti, L.
Year: 2022
Abstract: We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are related but not homogeneous because they have been collected in different ways or from different populations. In a previous work, we proposed a closed-form Bayesian Hierarchical Dirichlet score for discrete data that pools information across related data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. In this paper, we provide an analogous solution for learning a Bayesian network from continuous data using mixed-effects models to pool information across the related data sets. We study its structural, parametric, predictive and classification accuracy and we show that it outperforms both conditional Gaussian Bayesian networks (that do not perform any pooling) and classical Gaussian Bayesian networks (that disregard the heterogeneous nature of the data). The improvement is marked for low sample sizes and for unbalanced data sets.
Published in Salmerón, A., Rumı́, R. (Eds), Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186, JMLR.org, pp. 73–84.
Using mixed-effects models to learn bayesian networks from related data sets
@INPROCEEDINGS{scutari2022a,
title = {Using mixed-effects models to learn bayesian networks from related data sets},
editor = {Salmer\'on, A. and Rumı́, R.},
publisher = {JMLR.org},
series = {PMLR},
volume = {186},
booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
author = {Scutari, M. and Marquis, C. and Azzimonti, L.},
pages = {73--84},
year = {2022},
doi = {},
url = {https://proceedings.mlr.press/v186/scutari22a.html}
}
DownloadScutari, M., Panero, F., Proissl, M. (2022). Achieving fairness with a simple ridge penalty. Statistics and Computing 32(5), 77.
Achieving fairness with a simple ridge penalty
Authors: Scutari, M. and Panero, F. and Proissl, M.
Year: 2022
Abstract: In this paper, we present a general framework for estimating regression models subject to a user-defined level of fairness. We enforce fairness as a model selection step in which we choose the value of a ridge penalty to control the effect of sensitive attributes. We then estimate the parameters of the model conditional on the chosen penalty value. Our proposal is mathematically simple, with a solution that is partly in closed form and produces estimates of the regression coefficients that are intuitive to interpret as a function of the level of fairness. Furthermore, it is easily extended to generalised linear models, kernelised regression models and other penalties, and it can accommodate multiple definitions of fairness. We compare our approach with the regression model from Komiyama et al. (in: Proceedings of machine learning research. 35th international conference on machine learning (ICML), vol 80, pp 2737–2746, 2018), which implements a provably optimal linear regression model and with the fair models from Zafar et al. (J Mach Learn Res 20:1–42, 2019). We evaluate these approaches empirically on six different data sets, and we find that our proposal provides better goodness of fit and better predictive accuracy for the same level of fairness. In addition, we highlight a source of bias in the original experimental evaluation in Komiyama et al. (in: Proceedings of machine learning research. 35th international conference on machine learning (ICML), vol 80, pp 2737–2746, 2018).
Published in Statistics and Computing 32(5), 77.
Achieving fairness with a simple ridge penalty
@ARTICLE{scutari22a,
title = {Achieving fairness with a simple ridge penalty},
journal = {Statistics and Computing},
volume = {32},
author = {Scutari, M. and Panero, F. and Proissl, M.},
number = {5},
pages = {77},
year = {2022},
doi = {10.1007/s11222-022-10143-w},
url = {}
}
DownloadShahid, A.A., Piga, D., Braghin, F., Roveda, L. (2022). Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning. Autonomous Robots 46(3), pp. 483–498.
Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
Authors: Shahid, A.A. and Piga, D. and Braghin, F. and Roveda, L.
Year: 2022
Abstract: This paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In order to accelerate the learning process, the fine-tuning procedure is proposed that demonstrates the continuous adaptation of on-policy RL to new environments, allowing the learned policy to adapt and execute the (partially) modified task. A dense reward function is designed for the task to enable an efficient learning of the agent. A grasping task involving a Franka Emika Panda manipulator is considered as the reference task to be learned. The learned control policy is demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations. The approach is finally tested on a real Franka Emika Panda robot, showing the possibility to transfer the learned behavior from simulation. Experimental results show 100% of successful grasping tasks, making the proposed approach applicable to real applications.
Published in Autonomous Robots 46(3), pp. 483–498.
Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
@ARTICLE{Roveda2022b,
title = {Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning},
journal = {Autonomous Robots},
volume = {46},
author = {Shahid, A.A. and Piga, D. and Braghin, F. and Roveda, L.},
number = {3},
pages = {483--498},
year = {2022},
doi = {10.1007/s10514-022-10034-z},
url = {}
}
DownloadSlavakis, K., Shetty, G.N., Cannelli, L., Scutari, G., Nakarmi, U., Ying, L. (2022). Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case. IEEE Transactions on Computational Imaging 8, pp. 133–147.
Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case
Authors: Slavakis, K. and Shetty, G.N. and Cannelli, L. and Scutari, G. and Nakarmi, U. and Ying, L.
Year: 2022
Abstract: This paper introduces a non-parametric approximation framework for imputation-by-regression on data with missing entries. The framework, coined kernel regression imputation in manifolds (KRIM), is built on the hypothesis that features, generated by the measured data, lie close to an unknown-to-the-user smooth manifold. A reproducing kernel Hilbert space (RKHS) forms the feature space where the smooth manifold is embedded in. Aiming at concise representations, KRIM identifies a small number of “landmark points” to define approximating “linear patches” that mimic tangent spaces to smooth manifolds. This geometric information is infused into the design through a novel bi-linear model which can be easily extended to accommodate multi-kernel contributions in the non-parametric approximations. To effect imputation-by-regression, a bi-linear inverse problem is solved by an iterative algorithm with guaranteed convergence to a stationary point of a non-convex loss function. To showcase KRIM’s modularity, the application of KRIM to dynamic magnetic resonance imaging (dMRI) is detailed, where reconstruction of images from severely under-sampled dMRI data is desired. Extensive numerical tests on synthetic and real dMRI data demonstrate the superior performance of KRIM over state-of-the-art approaches under several metrics and with a small computational footprint.
Published in IEEE Transactions on Computational Imaging 8, pp. 133–147.
Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case
@ARTICLE{cannelli2022a,
title = {Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-{MRI} case},
journal = {{IEEE} Transactions on Computational Imaging},
volume = {8},
author = {Slavakis, K. and Shetty, G.N. and Cannelli, L. and Scutari, G. and Nakarmi, U. and Ying, L.},
pages = {133--147},
year = {2022},
doi = {10.1109/TCI.2022.3148062},
url = {}
}
DownloadSzehr, O., Zarouf, R. (2022). On the asymptotic behavior of Jacobi polynomials with first varying parameter. Journal of Approximation Theory (277), 105702.
On the asymptotic behavior of Jacobi polynomials with first varying parameter
Authors: Szehr, O. and Zarouf, R.
Year: 2022
Abstract: We investigate the large behavior of Jacobi polynomials with varying parameters.
Published in Journal of Approximation Theory (277), 105702.
On the asymptotic behavior of Jacobi polynomials with first varying parameter
@ARTICLE{szehr2022a,
title = {On the asymptotic behavior of {J}acobi polynomials with first varying parameter},
journal = {Journal of Approximation Theory},
author = {Szehr, O. and Zarouf, R.},
number = {277},
pages = {105702},
year = {2022},
doi = {10.1016/j.jat.2022.105702},
url = {}
}
DownloadZaffalon, M., Antonucci, A., Cabañas, R., Huber, D., Azzimonti, D. (2022). Bounding counterfactuals under selection bias. In Salmerón, A., Rumí, R. (Eds), Proceedings of PGM 2022, PMLR 186, JMLR.org, pp. 289–300.
Bounding counterfactuals under selection bias
Authors: Zaffalon, M. and Antonucci, A. and Cabañas, R. and Huber, D. and Azzimonti, D.
Year: 2022
Abstract: Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.
Published in Salmerón, A., Rumí, R. (Eds), Proceedings of PGM 2022, PMLR 186, JMLR.org, pp. 289–300.
Bounding counterfactuals under selection bias
@INPROCEEDINGS{zaffalon2022a,
title = {Bounding counterfactuals under selection bias},
editor = {Salmerón, A. and Rumí, R.},
publisher = {JMLR.org},
series = {PMLR},
volume = {186},
booktitle = {Proceedings of {PGM} 2022},
author = {Zaffalon, M. and Antonucci, A. and Cabañas, R. and Huber, D. and Azzimonti, D.},
pages = {289--300},
year = {2022},
doi = {},
url = {https://proceedings.mlr.press/v186/zaffalon22a.html}
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Download top2021
Antonucci, A., Facchini, A., Mattei, L. (2021). Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption. In 4th Workshop on Tractable Probabilistic Modeling (TPM 2021 co-located with UAI 2021).
Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption
Authors: Antonucci, A. and Facchini, A. and Mattei, L.
Year: 2021
Abstract: Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical LearnSPN scheme to learn the structure of these models, we propose a new scheme based on a partial closed-world assumption: data implicitly provide the logical base of the circuit. Sum nodes are thus learned by recursively clustering batches in the initial data base, while the partitioning of the variables obeys a given input vtree. Preliminary experiments show that the proposed approach might properly fit training data, and generalize well to test data, provided that these remain consistent with the underlying logical base, that is a relaxation of the training data base.
Published in 4th Workshop on Tractable Probabilistic Modeling (TPM 2021 co-located with UAI 2021).
Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption
@INPROCEEDINGS{antonucci2021e,
title = {Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption},
booktitle = {4th Workshop on Tractable Probabilistic Modeling ({TPM} 2021 {c}o-{l}ocated {w}ith {UAI} 2021)},
author = {Antonucci, A. and Facchini, A. and Mattei, L.},
year = {2021},
doi = {10.48550/arXiv.2107.12130},
url = {}
}
DownloadAntonucci, A., Mangili, F., Bonesana, C., Adorni, G. (2021). A new score for adaptive tests in Bayesian and credal networks. In Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 399–412.
A new score for adaptive tests in Bayesian and credal networks
Authors: Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.
Year: 2021
Abstract: A test is adaptive when the sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, are used for adaptive tests as they allow to model the uncertainty about the questions and the skills in an explainable fashion, especially when coping with multiple skills. A better elicitation of the uncertainty in the question/skills relations can be achieved by interval probabilities. This turns the model into a credal network, thus increasing the inferential complexity of the queries required to select questions. This is especially the case for the information-theoretic quantities used as scores to drive the adaptive mechanism. We present an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain. This makes considerably simpler the evaluation in the credal case, without significantly affecting the quality of the adaptive process. Numerical tests on synthetic and real-world data are used to support this claim.
Published in Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 399–412.
A new score for adaptive tests in Bayesian and credal networks
@INPROCEEDINGS{antonucci2021c,
title = {A new score for adaptive tests in {B}ayesian and credal networks},
editor = {Vejnarov\'a, J. and Wilson, N.},
publisher = {Springer International Publishing},
address = {Cham},
booktitle = {Symbolic and Quantitative Approaches to Reasoning With Uncertainty},
author = {Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.},
pages = {399--412},
year = {2021},
doi = {10.1007/978-3-030-86772-0_29},
url = {}
}
DownloadAzzimonti, D., Rottondi, C., Giusti, A., Tornatore, M., Bianco, A. (2021). Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]. IEEE/OSA Journal of Optical Communications and Networking 13(1), pp. A56–A66.
Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]
Authors: Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.
Year: 2021
Abstract: Machine learning (ML) is currently being investigated as an emerging technique to automate quality of transmission (QoT) estimation during lightpath deployment procedures in optical networks. Even though the potential network-resource savings enabled by ML-based QoT estimation has been confirmed in several studies, some practical limitations hinder its adoption in operational network deployments. Among these, the lack of a comprehensive training dataset is recognized as a main limiting factor, especially in the early network deployment phase. In this study, we compare the performance of two ML methodologies explicitly designed to augment small-sized training datasets, namely, active learning (AL) and domain adaptation (DA), for the estimation of the signal-to-noise ratio (SNR) of an unestablished lightpath. This comparison also allows us to provide some guidelines for the adoption of these two techniques at different life stages of a newly deployed optical network infrastructure. Results show that both AL and DA permit us, starting from limited datasets, to reach a QoT estimation capability similar to that achieved by standard supervised learning approaches working on much larger datasets. More specifically, we observe that a few dozen additional samples acquired from selected probe lightpaths already provide significant performance improvement for AL, whereas a few hundred samples gathered from an external network topology are needed in the case of DA.
Published in IEEE/OSA Journal of Optical Communications and Networking 13(1), pp. A56–A66.
Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]
@ARTICLE{azzimontid2021,
title = {Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]},
journal = {{IEEE/OSA} Journal of Optical Communications and Networking},
volume = {13},
author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},
number = {1},
pages = {A56--A66},
year = {2021},
doi = {10.1364/JOCN.401918},
url = {}
}
DownloadBemporad, A., Piga, D. (2021). Global optimization based on active preference learning with radial basis functions. Machine Learning 110, pp. 417–448.
Global optimization based on active preference learning with radial basis functions
Authors: Bemporad, A. and Piga, D.
Year: 2021
Abstract: This paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as "this is better than that" between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.
Published in Machine Learning 110, Springer, pp. 417–448.
Global optimization based on active preference learning with radial basis functions
@ARTICLE{piga2021a,
title = {Global optimization based on active preference learning with radial basis functions},
journal = {Machine Learning},
publisher = {Springer},
volume = {110},
author = {Bemporad, A. and Piga, D.},
pages = {417--448},
year = {2021},
doi = {10.1007/s10994-020-05935-y},
url = {}
}
DownloadBenavoli, A., Azzimonti, D., Piga, D. (2021). Preferential bayesian optimisation with skew gaussian processes. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '21, Association for Computing Machinery, New York, NY, USA, pp. 1842–1850.
Preferential bayesian optimisation with skew gaussian processes
Authors: Benavoli, A. and Azzimonti, D. and Piga, D.
Year: 2021
Abstract: Preferential Bayesian optimisation (PBO) deals with optimisation problems where the objective function can only be accessed via preference judgments, such as "this is better than that" between two candidate solutions (like in A/B tests). The state-of-the-art approach to PBO uses a Gaussian process to model the preference function and a Bernoulli likelihood to model the observed pair-wise comparisons. Laplace's method is then employed to compute posterior inferences and, in particular, to build an appropriate acquisition function. In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation. We then derive an efficient method to compute the exact SkewGP posterior and use it as surrogate model for PBO employing standard acquisition functions (Upper-Credible-Bound, etc.). We illustrate the benefits of our exact PBO-SkewGP in a variety of experiments, by showing that it consistently outperforms PBO based on Laplace's approximation both in terms of convergence speed and computational time. We also show that our framework can be extended to deal with mixed preferential-categorical BO, where binary judgments (valid or non-valid) together with preference judgments are available.
Published in Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '21, Association for Computing Machinery, New York, NY, USA, pp. 1842–1850.
Preferential bayesian optimisation with skew gaussian processes
@INPROCEEDINGS{azzimontid2021b,
title = {Preferential bayesian optimisation with skew gaussian processes},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {GECCO '21},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
pages = {1842--1850},
year = {2021},
doi = {10.1145/3449726.3463128},
url = {}
}
DownloadBenavoli, A., Azzimonti, D., Piga, D. (2021). A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes. Machine Learning 110(11), pp. 3095–3133.
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes
Authors: Benavoli, A. and Azzimonti, D. and Piga, D.
Year: 2021
Abstract: Skew-Gaussian Processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution, we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning. In this paper, we generalize previous results and we prove that SkewGP is conjugate with both the normal and affine probit likelihood, and more in general, with their product. This allows us to (i) handle classification, preference, numeric and ordinal regression, and mixed problems in a unified framework; (ii) derive closed-form expression for the corresponding posterior distributions. We show empirically that the proposed framework based on SkewGP provides better performance than Gaussian processes in active learning and Bayesian (constrained) optimization. These two tasks are fundamental for design of experiments and in Data Science.
Published in Machine Learning 110(11), pp. 3095–3133.
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes
@ARTICLE{azzimontid2021c,
title = {A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes},
journal = {Machine Learning},
volume = {110},
author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
number = {11},
pages = {3095--3133},
year = {2021},
doi = {10.1007/s10994-021-06039-x},
url = {}
}
DownloadBenavoli, A., Corani, G. (2021). State Space approximation of Gaussian Processes for time-series forecasting. In Advanced Analytics and Learning on Temporal Data, Springer International Publishing, pp. 21–35.
State Space approximation of Gaussian Processes for time-series forecasting
Authors: Benavoli, A. and Corani, G.
Year: 2021
Abstract: Gaussian Processes (GPs), with a complex enough additive kernel, provide competitive results in time series forecasting compared to state-of-the-art approaches (arima, ETS) provided that: (i) during training the unnecessary components of the kernel are made irrelevant by automatic relevance determination; (ii) priors are assigned to each hyperparameter. However, GPs computational complexity grows cubically in time and quadratically in memory with the number of observations. The state space (SS) approximation of GPs allows to compute GPs based inferences with linear complexity. In this paper, we apply the SS representation to time series forecasting showing that SS models provide a performance comparable with that of full GP and better than state-of- the-art models (arima, ETS). Moreover, the SS representation allows us to derive new models by, for instance, combining ETS with kernels.
Published in Advanced Analytics and Learning on Temporal Data Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th ECML PKDD Workshop, AALTD 2021, Springer International Publishing, pp. 21–35.
State Space approximation of Gaussian Processes for time-series forecasting
@INPROCEEDINGS{corani2021b,
title = {State {S}pace approximation of {G}aussian {P}rocesses for time-series forecasting},
journal = {Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th {ECML} {PKDD} Workshop, {AALTD} 2021},
publisher = {Springer International Publishing},
booktitle = {Advanced Analytics and Learning on Temporal Data},
author = {Benavoli, A. and Corani, G.},
pages = {21--35},
year = {2021},
doi = {10.1007/978-3-030-91445-5_2},
url = {}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2021). The weirdness theorem and the origin of quantum paradoxes. Foundations of Physics 51(5), 95.
The weirdness theorem and the origin of quantum paradoxes
Authors: Benavoli, A., Facchini, A., Zaffalon, M.
Year: 2021
Abstract: We argue that there is a simple, unique, reason for all quantum paradoxes, and that such a reason is not uniquely related to quantum theory. It is rather a mathematical question that arises at the intersection of logic, probability, and computation. We give our ‘weirdness theorem’ that characterises the conditions under which the weirdness will show up. It shows that whenever logic has bounds due to the algorithmic nature of its tasks, then weirdness arises in the special form of negative probabilities or non-classical evaluation functionals. Weirdness is not logical inconsistency, however. It is only the expression of the clash between an unbounded and a bounded view of computation in logic. We discuss the implication of these results for quantum mechanics, arguing in particular that its interpretation should ultimately be computational rather than exclusively physical. We develop in addition a probabilistic theory in the real numbers that exhibits the phenomenon of entanglement, thus concretely showing that the latter is not specific to quantum mechanics.
Published in Foundations of Physics 51(5), 95.
The weirdness theorem and the origin of quantum paradoxes
@ARTICLE{benavoli2021b,
title = {The weirdness theorem and the origin of quantum paradoxes},
journal = {Foundations of Physics},
volume = {51},
author = {Benavoli, A., Facchini, A., Zaffalon, M.},
number = {5},
pages = {95},
year = {2021},
doi = {10.1007/s10701-021-00499-w},
url = {}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2021). Quantum indistinguishability through exchangeable desirable gambles. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 22–31.
Quantum indistinguishability through exchangeable desirable gambles
Authors: Benavoli, A., Facchini, A., Zaffalon, M.
Year: 2021
Abstract: Two particles are identical if all their intrinsic properties, such as spin and charge, are the same, meaning that no quantum experiment can distinguish them. In addition to the well known principles of quantum mechanics, understanding systems of identical particles requires a new postulate, the so called symmetrization postulate}, which is thus crucial to the understanding of the physical properties of almost all kinds of aggregates of matter. In this work, we show that the postulate corresponds to exchangeability assessments for sets of observables (gambles) in a quantum experiment, when quantum mechanics is seen as a normative and algorithmic theory guiding an agent to assess her subjective beliefs represented as (coherent) sets of gambles. Finally, we show how sets of exchangeable observables (gambles) may be updated after a measurement and discuss the issue of defining entanglement for indistinguishable particle systems.
Published in De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 22–31.
Quantum indistinguishability through exchangeable desirable gambles
@INPROCEEDINGS{benavoli2021a,
title = {Quantum indistinguishability through exchangeable desirable gambles},
editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
publisher = {JMLR.org},
series = {PMLR},
volume = {147},
booktitle = {{ISIPTA} 2021},
author = {Benavoli, A., Facchini, A., Zaffalon, M.},
pages = {22--31},
year = {2021},
doi = {},
url = {https://isipta21.sipta.org/papers.html}
}
DownloadBerjano, P., Langella, F., Ventriglia, L., Compagnone, D., Barletta, P., Huber, D., Mangili, F., Licandro, G., Galbusera, F., Cina, A., Bassani, T., Lamartina, C., Scaramuzzo, L., Bassani, R., Brayda-Bruno, M., Villafañe, J.H., Monti, L., Azzimonti, L. (2021). The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach. Journal of Personalized Medicine 11(12), 1377.
The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach
Authors: Berjano, P. and Langella, F. and Ventriglia, L. and Compagnone, D. and Barletta, P. and Huber, D. and Mangili, F. and Licandro, G. and Galbusera, F. and Cina, A. and Bassani, T. and Lamartina, C. and Scaramuzzo, L. and Bassani, R. and Brayda-Bruno, M. and Villafañe, J.H. and Monti, L. and Azzimonti, L.
Year: 2021
Abstract: The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.
Published in Journal of Personalized Medicine 11(12), 1377.
The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach
@ARTICLE{azzimonti2021b,
title = {The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach},
journal = {Journal of Personalized Medicine},
volume = {11},
author = {Berjano, P. and Langella, F. and Ventriglia, L. and Compagnone, D. and Barletta, P. and Huber, D. and Mangili, F. and Licandro, G. and Galbusera, F. and Cina, A. and Bassani, T. and Lamartina, C. and Scaramuzzo, L. and Bassani, R. and Brayda-Bruno, M. and Villafa\~ne, J.H. and Monti, L. and Azzimonti, L.},
number = {12},
pages = {1377},
year = {2021},
doi = {10.3390/jpm11121377},
url = {}
}
DownloadBianchi, F., Breschi, V., Piga, D., Piroddi, L. (2021). Model structure selection for switched narx system identification: a randomized approach. Automatica 125, 109415.
Model structure selection for switched narx system identification: a randomized approach
Authors: Bianchi, F. and Breschi, V. and Piga, D. and Piroddi, L.
Year: 2021
Abstract: The identification of switched systems is a challenging problem, which entails both combinatorial (sample-mode assignment) and continuous (parameter estimation) features. A general framework for this problem has been recently developed, which alternates between parameter estimation and sample-mode assignment, solving both tasks to global optimality under mild conditions. This article extends this framework to the nonlinear case, which further aggravates the combinatorial complexity of the identification problem, since a model structure selection task has to be addressed for each mode of the system. To solve this issue, we reformulate the learning problem in terms of the optimization of a probability distribution over the space of all possible model structures. Then, a randomized approach is employed to tune this distribution. The performance of the proposed approach on some benchmark examples is analyzed in detail.
Published in Automatica 125, 109415.
Model structure selection for switched narx system identification: a randomized approach
@ARTICLE{piga2021c,
title = {Model structure selection for switched narx system identification: a randomized approach},
journal = {Automatica},
volume = {125},
author = {Bianchi, F. and Breschi, V. and Piga, D. and Piroddi, L.},
pages = {109415},
year = {2021},
doi = {10.1016/j.automatica.2020.109415},
url = {}
}
DownloadBini, F., Pica, A., Azzimonti, L., Giusti, A., Ruinelli, L., Marinozzi, F., Trimboli, P. (2021). Artificial intelligence in thyroid field. A comprehensive review. Cancers 13(19), 4740.
Artificial intelligence in thyroid field. A comprehensive review
Authors: Bini, F. and Pica, A. and Azzimonti, L. and Giusti, A. and Ruinelli, L. and Marinozzi, F. and Trimboli, P.
Year: 2021
Abstract: Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.
Published in Cancers 13(19), 4740.
Artificial intelligence in thyroid field. A comprehensive review
@ARTICLE{azzimonti2021a,
title = {Artificial intelligence in thyroid field. A comprehensive review},
journal = {Cancers},
volume = {13},
author = {Bini, F. and Pica, A. and Azzimonti, L. and Giusti, A. and Ruinelli, L. and Marinozzi, F. and Trimboli, P.},
number = {19},
pages = {4740},
year = {2021},
doi = {10.3390/cancers13194740},
url = {https://www.mdpi.com/2072-6694/13/19/4740}
}
DownloadBodewes, T., Scutari, M. (2021). Learning bayesian networks from incomplete data with the node-averaged likelihood. International Journal of Approximate Reasoning 138, pp. 145–160.
Learning bayesian networks from incomplete data with the node-averaged likelihood
Authors: Bodewes, T. and Scutari, M.
Year: 2021
Abstract: Bayesian network (BN) structure learning from complete data has been extensively studied in the literature. However, fewer theoretical results are available for incomplete data, and most are related to the Expectation-Maximisation (EM) algorithm. Balov [1] proposed an alternative approach called Node-Average Likelihood (NAL) that is competitive with EM but computationally more efficient; and he proved its consistency and model identifiability for discrete BNs. In this paper, we give general sufficient conditions for the consistency of NAL; and we prove consistency and identifiability for conditional Gaussian BNs, which include discrete and Gaussian BNs as special cases. Furthermore, we confirm our results and the results in Balov [1] with an independent simulation study. Hence we show that NAL has a much wider applicability than originally implied in Balov [1], and that it is competitive with EM for conditional Gaussian BNs as well.
Published in International Journal of Approximate Reasoning 138, pp. 145–160.
Note: This is an extended version of the “Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data” PMLR paper.
Learning bayesian networks from incomplete data with the node-averaged likelihood
@ARTICLE{scutari21d,
title = {Learning bayesian networks from incomplete data with the node-averaged likelihood},
journal = {International Journal of Approximate Reasoning},
volume = {138},
author = {Bodewes, T. and Scutari, M.},
pages = {145--160},
year = {2021},
doi = {10.1016/j.ijar.2021.07.015},
url = {}
}
DownloadBonesana, C., Mangili, F., Antonucci, A. (2021). ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks. In AI4EDU: Artificial Intelligence for Education (@ Ijcai2021), Virtual Event.
ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks
Authors: Bonesana, C. and Mangili, F. and Antonucci, A.
Year: 2021
Abstract: We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. Adaptiveness is intended here as the dynamical choice of the question sequence on the basis of an evolving model of the skill level of the test taker. Bayesian networks offer a flexible and highly interpretable framework to describe such testing process, especially when coping with multiple skills. ADAPQUEST embeds dedicated elicitation strategies to simplify the elicitation of the questionnaire parameters. An application of this tool for the diagnosis of mental disorders is also discussed together with some implementation details.
Published in AI4EDU: Artificial Intelligence for Education (@ Ijcai2021), Virtual Event.
Note: IJCAI2021 Workshop
ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks
@INPROCEEDINGS{bonesana2021a,
title = {{ADAPQUEST}: a software for web-based adaptive questionnaires based on {B}ayesian networks},
address = {Virtual Event},
booktitle = {{AI4EDU}: Artificial Intelligence for Education (@ Ijcai2021)},
author = {Bonesana, C. and Mangili, F. and Antonucci, A.},
year = {2021},
doi = {10.48550/arXiv.2112.14476},
url = {}
}
DownloadBregoli, A., Scutari, M., Stella, F. (2021). A constraint-based algorithm for the structural learning of continuous-time bayesian networks. International Journal of Approximate Reasoning 138, pp. 105–122.
A constraint-based algorithm for the structural learning of continuous-time bayesian networks
Authors: Bregoli, A. and Scutari, M. and Stella, F.
Year: 2021
Abstract: Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first implementation of a constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Furthermore, we analyze and discuss the computational complexity of the best and worst cases for the proposed algorithm. Finally, we validate its performance using synthetic data, and we discuss its strengths and limitations comparing it with the score-based structure learning algorithm from Nodelman et al. [23]. We find the latter to be more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. Numerical experiments confirm that score-based and constraint-based algorithms are comparable in terms of computation time.
Published in International Journal of Approximate Reasoning 138, pp. 105–122.
Note: This is an extended version of the “Constraint-Based Learning for Continuous-Time Bayesian Networks” PMLR paper.
A constraint-based algorithm for the structural learning of continuous-time bayesian networks
@ARTICLE{scutari21e,
title = {A constraint-based algorithm for the structural learning of continuous-time bayesian networks},
journal = {International Journal of Approximate Reasoning},
volume = {138},
author = {Bregoli, A. and Scutari, M. and Stella, F.},
pages = {105--122},
year = {2021},
doi = {10.1016/j.ijar.2021.08.005},
url = {}
}
DownloadCabañas, R., Antonucci, A. (2021). CREPO: an open repository to benchmark credal network algorithms. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Eds), International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA-2021) 147, JMLR.org.
CREPO: an open repository to benchmark credal network algorithms
Authors: Cabañas, R. and Antonucci, A.
Year: 2021
Abstract: Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the (exact) task, a number of algorithms is available to approximate credal network inferences. In this paper we present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models. A Python tool is also delivered to load these data and interact with CREMA, thus making extremely easy to evaluate and compare existing and novel inference algorithms. To demonstrate such benchmarking scheme, we propose an approximate heuristic to be used inside variable elimination schemes to keep a bound on the maximum number of vertices generated during the combination step. A CREPO-based validation against approximate procedures based on linearization and exact techniques performed in CREMA is finally discussed.
Published in De Bock, J., Cano, A., Miranda, E., Moral, S. (Eds), International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA-2021) 147, JMLR.org.
CREPO: an open repository to benchmark credal network algorithms
@INPROCEEDINGS{cabanas2021b,
title = {{CREPO}: an open repository to benchmark credal network algorithms},
editor = {De Bock, J. and Cano, A. and Miranda, E. and Moral, S.},
publisher = {JMLR.org},
volume = {147},
booktitle = {International Symposium on Imprecise Probabilities: Theories and Applications ({ISIPTA}-2021)},
author = {Caba\~nas, R. and Antonucci, A.},
year = {2021},
doi = {},
url = {http://proceedings.mlr.press/v147/cabanas21a/cabanas21a.pdf}
}
DownloadCasanova, A., Benavoli, A., Zaffalon, M. (2021). Nonlinear desirability as a linear classification problem. In Proceedings of Machine Learning Research 147, pp. 617–71.
Nonlinear desirability as a linear classification problem
Authors: Casanova, A. and Benavoli, A. and Zaffalon, M.
Year: 2021
Abstract: The present paper proposes a generalization of linearity
axioms of coherence through a geometrical approach,
which leads to an alternative interpretation of
desirability as a classification problem. In particular,
we analyze different sets of rationality axioms and,
for each one of them, we show that proving that a
subject, who provides finite accept and reject statements,
respects these axioms, corresponds to solving
a binary classification task using, each time, a different
(usually nonlinear) family of classifiers. Moreover,
by borrowing ideas from machine learning, we show
the possibility to define a feature mapping allowing
us to reformulate the above nonlinear classification
problems as linear ones in a higher-dimensional space.
This allows us to interpret gambles directly as payoffs
vectors of monetary lotteries, as well as to reduce the task of proving the rationality of a subject to a linear
classification task.
Published in Proceedings of Machine Learning Research 147, pp. 617–71.
Nonlinear desirability as a linear classification problem
@INPROCEEDINGS{casanova2021c,
title = {Nonlinear desirability as a linear classification problem},
volume = {147},
booktitle = {Proceedings of Machine Learning Research},
author = {Casanova, A. and Benavoli, A. and Zaffalon, M.},
pages = {617--71},
year = {2021},
doi = {},
url = {https://proceedings.mlr.press/v147/casanova21a.html}
}
DownloadCasanova, A., Kohlas, J., Zaffalon, M. (2021). Algebras of sets and coherent sets of gambles. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Springer International Publishing, pp. 603–615.
Algebras of sets and coherent sets of gambles
Authors: Casanova, A. and Kohlas, J. and Zaffalon, M.
Year: 2021
Abstract: In a recent work we have shown how to construct an information algebra of coherent sets of gambles defined on general possibility spaces. Here we analyze the connection of such an algebra with the set algebra of sets of its atoms and the set algebra of subsets of the possibility space on which gambles are defined. Set algebras are particularly important information algebras since they are their prototypical structures. Furthermore, they are the algebraic counterparts of classical propositional logic. As a consequence, this paper also details how propositional logic is naturally embedded into the theory of imprecise probabilities.
Published in Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Springer International Publishing, pp. 603–615.
Algebras of sets and coherent sets of gambles
@INPROCEEDINGS{casanova2021d,
title = {Algebras of sets and coherent sets of gambles},
publisher = {Springer International Publishing},
booktitle = {Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},
author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
pages = {603--615},
year = {2021},
doi = {10.1007/978-3-030-86772-0_43},
url = {}
}
DownloadCasanova, A., Miranda, E., Zaffalon, M. (2021). Joint desirability foundations of social choice and opinion pooling. Annals of Mathematics and Artificial Intelligence 89(10–11), pp. 965–1011.
Joint desirability foundations of social choice and opinion pooling
Authors: Casanova, A. and Miranda, E. and Zaffalon, M.
Year: 2021
Abstract: We develop joint foundations for the fields of social choice and opinion pooling using coherent sets of desirable gambles, a general uncertainty model that allows to encompass both complete and incomplete preferences. This leads on the one hand to a new perspective of traditional results of social choice (in particular Arrow’s theorem as well as sufficient conditions for the existence of an oligarchy and democracy) and on the other hand to using the same framework to analyse opinion pooling. In particular, we argue that weak Pareto (unanimity) should be given the status of a rationality requirement and use this to discuss the aggregation of experts’ opinions based on probability and (state-independent) utility, showing some inherent limitation of this framework, with implications for statistics. The connection between our results and earlier work in the literature is also discussed.
Published in Annals of Mathematics and Artificial Intelligence 89(10–11), pp. 965–1011.
Joint desirability foundations of social choice and opinion pooling
@ARTICLE{casanova2021a,
title = {Joint desirability foundations of social choice and opinion pooling},
journal = {Annals of Mathematics and Artificial Intelligence},
volume = {89},
author = {Casanova, A. and Miranda, E. and Zaffalon, M.},
number = {10--11},
pages = {965--1011},
year = {2021},
doi = {10.1007/s10472-021-09733-7},
url = {}
}
DownloadCorani, G., Benavoli, A., Zaffalon, M. (2021). Time series forecasting with Gaussian Processes needs priors. In European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 103–117.
Time series forecasting with Gaussian Processes needs priors
Authors: Corani, G. and Benavoli, A. and Zaffalon, M.
Year: 2021
Abstract: Automatic forecasting is the task of receiving a time series and returning a forecast for the next time steps without any human intervention. Gaussian Processes (GPs) are a powerful tool for modeling time series, but so far there are no competitive approaches for automatic forecasting based on GPs. We propose practical solutions to two problems: automatic selection of the optimal kernel and reliable estimation of the hyperparameters.
We propose a fixed composition of kernels, which contains the components needed to model most time series: linear trend, periodic patterns, and other flexible kernel for modeling the non-linear trend. Not all components are necessary to model each time series; during training the unnecessary components are automatically made irrelevant via automatic relevance determination (ARD). We moreover assign priors to the hyperparameters, in order to keep the inference within a plausible range; we design such priors through an empirical Bayes approach. We present results on many time series of different types; our GP model is more accurate than state-of-the-art time series models. Thanks to the priors, a single restart is enough the estimate the hyperparameters; hence the model is also fast to train.
Published in European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 103–117.
Time series forecasting with Gaussian Processes needs priors
@INPROCEEDINGS{corani2021a,
title = {Time series forecasting with {G}aussian {P}rocesses needs priors},
booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases},
author = {Corani, G. and Benavoli, A. and Zaffalon, M.},
pages = {103--117},
year = {2021},
doi = {10.1007/978-3-030-86514-6_7},
url = {}
}
DownloadForgione, M., Piga, D. (2021). dynoNet: a neural network architecture for learning dynamical systems. International Journal of Adaptive Control and Signal Processing 35, pp. 612–626.
dynoNet: a neural network architecture for learning dynamical systems
Authors: Forgione, M. and Piga, D.
Year: 2021
Abstract: This paper introduces a network architecture, called dynoNet, utilizing linear dynamical operators as elementary building blocks. Owing to the dynamical nature of these blocks, dynoNet networks are tailored for sequence modeling and system identification purposes. The backpropagation behavior of the linear dynamical operator with respect to both its parameters and its input sequence is defined. This enables end-toend training of structured networks containing linear dynamical operators and other differentiable units, exploiting existing deep learning software. Examples show the effectiveness of the proposed approach on well-known system identification benchmarks.
Published in International Journal of Adaptive Control and Signal Processing 35, pp. 612–626.
dynoNet: a neural network architecture for learning dynamical systems
@ARTICLE{forgione2021a,
title = {{dynoNet}: a neural network architecture for learning dynamical systems},
journal = {International Journal of Adaptive Control and Signal Processing},
volume = {35},
author = {Forgione, M. and Piga, D.},
pages = {612--626},
year = {2021},
doi = {10.1002/acs.3216},
url = {}
}
DownloadForgione, M., Piga, D. (2021). Continuous-time system identification with neural networks: model structures and fitting criteria. European Journal of Control 59, pp. 69–81.
Continuous-time system identification with neural networks: model structures and fitting criteria
Authors: Forgione, M. and Piga, D.
Year: 2021
Abstract: This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data.
Published in European Journal of Control 59, pp. 69–81.
Continuous-time system identification with neural networks: model structures and fitting criteria
@ARTICLE{forgione2021b,
title = {Continuous-time system identification with neural networks: model structures and fitting criteria},
journal = {European Journal of Control},
volume = {59},
author = {Forgione, M. and Piga, D.},
pages = {69--81},
year = {2021},
doi = {10.1016/j.ejcon.2021.01.008},
url = {}
}
DownloadDalla Gasperina, S., Roveda, L., Pedrocchi, A., Braghin, F., Gandolla, M. (2021). Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons. Frontiers in Robotics and AI 8.
Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons
Authors: Dalla Gasperina, S. and Roveda, L. and Pedrocchi, A. and Braghin, F. and Gandolla, M.
Year: 2021
Abstract: Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients’ status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on i) ”high-level” training modalities, ii) ”low-level” control strategies, and iii) ”hardware-level” implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.
Published in Frontiers in Robotics and AI 8.
Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons
@ARTICLE{Roveda2021e,
title = {Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons},
journal = {Frontiers in Robotics and {AI}},
volume = {8},
author = {Dalla Gasperina, S. and Roveda, L. and Pedrocchi, A. and Braghin, F. and Gandolla, M.},
year = {2021},
doi = {10.3389/frobt.2021.745018},
url = {}
}
DownloadGeluykens, J., Mitrovic, S., Vázquez, C.O., Laino, T., Vaucher, A.C., Weerdt, J.D. (2021). Neural machine translation for conditional generation of novel procedures. In 54th Hawaii International Conference on System Sciences, HICSS 2021, Kauai, Hawaii, Usa, January 5, 2021, ScholarSpace, pp. 1–10.
Neural machine translation for conditional generation of novel procedures
Authors: Geluykens, J. and Mitrovic, S. and Vázquez, C.O. and Laino, T. and Vaucher, A.C. and Weerdt, J.D.
Year: 2021
Abstract: Procedural knowledge is generally dispersed across many experts within or across organizations which might lead to inefficiencies and redundancy. Historically, computers have been well suited to store procedural knowledge but they have lacked the capability to produce natural language text. Nonetheless, recent advances in machine learning permit a higher linguistic coherence which benefits applications with longer text outputs such as procedures. This work closes the gap between human experts and computers by proposing a framework for automatic, computer generation of procedures based on neural machine translation and the BART model. Furthermore, we define two benchmark problems for procedure generation and establish a set of evaluation metrics that can be used as a reference in further work. We demonstrate the potential of this solution on the task of generating cooking recipes based on available ingredients. The evaluation results on the Recipe1M dataset showcase the method’s superiority over other, fairly novel, neural architectures.
Published in 54th Hawaii International Conference on System Sciences, HICSS 2021, Kauai, Hawaii, Usa, January 5, 2021, ScholarSpace, pp. 1–10.
Neural machine translation for conditional generation of novel procedures
@INPROCEEDINGS{mitrovic2021b,
title = {Neural machine translation for conditional generation of novel procedures},
publisher = {ScholarSpace},
booktitle = {54th Hawaii International Conference on System Sciences, {HICSS} 2021, Kauai, Hawaii, Usa, January 5, 2021},
author = {Geluykens, J. and Mitrovic, S. and V\'azquez, C.O. and Laino, T. and Vaucher, A.C. and Weerdt, J.D.},
pages = {1--10},
year = {2021},
doi = {},
url = {http://hdl.handle.net/10125/70744}
}
DownloadGómez-Olmedo, M., Cabañas, R., Cano, A., Moral, S., Retamero, O.P. (2021). Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models. International Journal of Intelligent Systems 36(11), pp. 6913–6943.
Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models
Authors: Gómez-Olmedo, M. and Cabañas, R. and Cano, A. and Moral, S. and Retamero, O.P.
Year: 2021
Abstract: When dealing with complex models (i.e., models with many variables, a high degree of dependency between variables, or many states per variable), the efficient representation of quantitative information in probabilistic graphical models (PGMs) is a challenging task. To address this problem, this study introduces several new structures, aptly named value-based potentials (VBPs), which are based exclusively on the values. VBPs leverage repeated values to reduce memory requirements. In the present paper, they are compared with some common structures, like standard tables or unidimensional arrays, and probability trees (PT). Like VBPs, PTs are designed to reduce the memory space, but this is achieved only if value repetitions correspond to context-specific independence patterns (i.e., repeated values are related to consecutive indices or configurations). VBPs are devised to overcome this limitation. The goal of this study is to analyze the properties of VBPs. We provide a theoretical analysis of VBPs and use them to encode the quantitative information of a set of well-known Bayesian networks, measuring the access time to their content and the computational time required to perform some inference tasks.
Published in International Journal of Intelligent Systems 36(11), pp. 6913–6943.
Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models
@ARTICLE{cabanas2021c,
title = {Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models},
journal = {International Journal of Intelligent Systems},
volume = {36},
author = {G\'omez-Olmedo, M. and Caba\~nas, R. and Cano, A. and Moral, S. and Retamero, O.P.},
number = {11},
pages = {6913--6943},
year = {2021},
doi = {10.1002/int.22573},
url = {}
}
DownloadGrasso, G., Gregorio, A.D., Mavkov, B., Piga, D., Labate, G.F.D., Danani, A., Deriu, M.A. (2021). Fragmented blind docking: a novel protein–ligand binding prediction protocol. Journal of Biomolecular Structure and Dynamics 40(24), pp. 13472–13481.
Fragmented blind docking: a novel protein–ligand binding prediction protocol
Authors: Grasso, G. and Gregorio, A.D. and Mavkov, B. and Piga, D. and Labate, G.F.D. and Danani, A. and Deriu, M.A.
Year: 2021
Abstract: AbstractIn the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein–ligand bound state. The FRAD protocol has been tested on 116 protein–ligand complexes of the Heat Shock Protein 90 – alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein–ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma
Published in Journal of Biomolecular Structure and Dynamics 40(24), Taylor & Francis, pp. 13472–13481.
Fragmented blind docking: a novel protein–ligand binding prediction protocol
@ARTICLE{piga2021f,
title = {Fragmented blind docking: a novel protein--ligand binding prediction protocol},
journal = {Journal of Biomolecular Structure and Dynamics},
publisher = {Taylor & Francis},
volume = {40},
author = {Grasso, G. and Gregorio, A.D. and Mavkov, B. and Piga, D. and Labate, G.F.D. and Danani, A. and Deriu, M.A.},
number = {24},
pages = {13472--13481},
year = {2021},
doi = {10.1080/07391102.2021.1988709},
url = {}
}
DownloadHalasz, G., Sperti, M., Villani, M., Michelucci, U., Agostoni, P., Biagi, A., Rossi, L., Botti, A., Mari, C., Maccarini, M., Pura, F., Roveda, L., Nardecchia, A., Mottola, E., Nolli, M., Salvioni, E., Mapelli, M., Deriu, M.A., Piga, D., Piepoli, M. (2021). A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score. Journal of Medical Internet Research 23(5), e29058.
A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score
Authors: Halasz, G. and Sperti, M. and Villani, M. and Michelucci, U. and Agostoni, P. and Biagi, A. and Rossi, L. and Botti, A. and Mari, C. and Maccarini, M. and Pura, F. and Roveda, L. and Nardecchia, A. and Mottola, E. and Nolli, M. and Salvioni, E. and Mapelli, M. and Deriu, M.A. and Piga, D. and Piepoli, M.
Year: 2021
Abstract: Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. Objective: We aimed to develop a machine learning–based score–-the Piacenza score–-for 30-day mortality prediction in patients with COVID-19 pneumonia. Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. Conclusions: Our findings demonstrated that a customizable machine learning–based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.
Published in Journal of Medical Internet Research 23(5), e29058.
A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score
@ARTICLE{piga2021g,
title = {A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score},
journal = {Journal of Medical Internet Research},
volume = {23},
author = {Halasz, G. and Sperti, M. and Villani, M. and Michelucci, U. and Agostoni, P. and Biagi, A. and Rossi, L. and Botti, A. and Mari, C. and Maccarini, M. and Pura, F. and Roveda, L. and Nardecchia, A. and Mottola, E. and Nolli, M. and Salvioni, E. and Mapelli, M. and Deriu, M.A. and Piga, D. and Piepoli, M.},
number = {5},
pages = {e29058},
year = {2021},
doi = {10.2196/29058},
url = {}
}
DownloadKania, L., Schürch, M., Azzimonti, D., Benavoli, A. (2021). Sparse information filter for fast gaussian process regression. In Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (Eds), Machine Learning and Knowledge Discovery in Databases. Research Track, Springer International Publishing, Cham, pp. 527–542.
Sparse information filter for fast gaussian process regression
Authors: Kania, L. and Schürch, M. and Azzimonti, D. and Benavoli, A.
Year: 2021
Abstract: Gaussian processes (GPs) are an important tool in machine learning and applied mathematics with applications ranging from Bayesian optimization to calibration of computer experiments. They constitute a powerful kernelized non-parametric method with well-calibrated uncertainty estimates, however, off-the-shelf GP inference procedures are limited to datasets with a few thousand data points because of their cubic computational complexity. For this reason, many sparse GPs techniques were developed over the past years. In this paper, we focus on GP regression tasks and propose a new algorithm to train variational sparse GP models. An analytical posterior update expression based on the Information Filter is derived for the variational sparse GP model. We benchmark our method on several real datasets with millions of data points against the state-of-the-art Stochastic Variational GP (SVGP) and sparse orthogonal variational inference for Gaussian Processes (SOLVEGP). Our method achieves comparable performances to SVGP and SOLVEGP while providing considerable speed-ups. Specifically, it is consistently four times faster than SVGP and on average 2.5 times faster than SOLVEGP.
Published in Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (Eds), Machine Learning and Knowledge Discovery in Databases. Research Track, Springer International Publishing, Cham, pp. 527–542.
Sparse information filter for fast gaussian process regression
@INPROCEEDINGS{schurch2021a,
title = {Sparse information filter for fast gaussian process regression},
editor = {Oliver, N. and P\'erez-Cruz, F. and Kramer, S. and Read, J. and Lozano, J.A.},
publisher = {Springer International Publishing},
address = {Cham},
booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track},
author = {Kania, L. and Sch\"urch, M. and Azzimonti, D. and Benavoli, A.},
pages = {527--542},
year = {2021},
doi = {10.1007/978-3-030-86523-8_32},
url = {}
}
DownloadKanjirangat, V., Rinaldi, F. (2021). Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information. Journal of Biomedical Informatics, 103893.
Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information
Authors: Kanjirangat, V. and Rinaldi, F.
Year: 2021
Abstract: Entity relation extraction plays an important role in the biomedical, healthcare, and clinical research areas. Recently, pre-trained models based on transformer architectures and their variants have shown remarkable performances in various natural language processing tasks. Most of these variants were based on slight modifications in the architectural components, representation schemes and augmenting data using distant supervision methods. In distantly supervised methods, one of the main challenges is pruning out noisy samples. A similar situation can arise when the training samples are not directly available but need to be constructed from the given dataset. The BioCreative V Chemical Disease Relation (CDR) task provides a dataset that does not explicitly offer mention-level gold annotations and hence replicates the above scenario. Selecting the representative sentences from the given abstract or document text that could convey a potential entity relationship becomes essential. Most of the existing methods in literature propose to either consider the entire text or all the sentences which contain the entity mentions. This could be a computationally expensive and time consuming approach. This paper presents a novel approach to handle such scenarios, specifically in biomedical relation extraction. We propose utilizing the Shortest Dependency Path (SDP) features for constructing data samples by pruning out noisy information and selecting the most representative samples for model learning. We also utilize triplet information in model learning using the biomedical variant of BERT, viz., BioBERT. The problem is represented as a sentence pair classification task using the sentence and the entity-relation pair as input. We analyze the approach on both intra-sentential and inter-sentential relations in the CDR dataset. The proposed approach that utilizes the SDP and triplet features presents promising results, specifically on the inter-sentential relation extraction task.
Published in Journal of Biomedical Informatics, Elsevier, 103893.
Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information
@ARTICLE{vani2021b,
title = {Enhancing {B}iomedical {R}elation {E}xtraction with {T}ransformer {M}odels using {S}hortest {D}ependency {P}ath {F}eatures and {T}riplet {I}nformation},
journal = {Journal of Biomedical Informatics},
publisher = {Elsevier},
author = {Kanjirangat, V. and Rinaldi, F.},
pages = {103893},
year = {2021},
doi = {10.1016/j.jbi.2021.103893},
url = {}
}
DownloadKohlas, J., Casanova, A., Zaffalon, M. (2021). Information algebras of coherent sets of gambles in general possibility spaces. In Proceedings of Machine Learning Research 147, pp. 191–200.
Information algebras of coherent sets of gambles in general possibility spaces
Authors: Kohlas, J. and Casanova, A. and Zaffalon, M.
Year: 2021
Abstract: In this paper, we show that coherent sets of gambles
can be embedded into the algebraic structure of information algebra. This leads firstly, to a new perspective
of the algebraic and logical structure of desirability
and secondly, it connects desirability, hence imprecise probabilities, to other formalism in computer science sharing the same underlying structure. Both the
domain-free and the labeled view of the information
algebra of coherent sets of gambles are presented, considering general possibility spaces.
Published in Proceedings of Machine Learning Research 147, pp. 191–200.
Information algebras of coherent sets of gambles in general possibility spaces
@INPROCEEDINGS{casanova2021b,
title = {Information algebras of coherent sets of gambles in general possibility spaces},
volume = {147},
booktitle = {Proceedings of Machine Learning Research},
author = {Kohlas, J. and Casanova, A. and Zaffalon, M.},
pages = {191--200},
year = {2021},
doi = {},
url = {http://proceedings.mlr.press/v147/kohlas21a/kohlas21a.pdf}
}
DownloadLiew, B.X.W., Ford, J.J., Scutari, M., Hahne, A.J. (2021). How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data. PLoS ONE 16, pp. 1–16.
How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data
Authors: Liew, B.X.W. and Ford, J.J. and Scutari, M. and Hahne, A.J.
Year: 2021
Abstract: Purpose.
Individualised physiotherapy is an effective treatment for low back pain. We sought to determine how this treatment works by using randomised controlled trial data to develop a Bayesian Network model.
Methods.
300 randomised controlled trial participants (153 male, 147 female, mean age 44.1) with low back pain (of duration 6–26 weeks) received either individualised physiotherapy or advice. Variables with potential to explain how individualised physiotherapy works were included in a multivariate Bayesian Network model. Modelling incorporated the intervention period (0–10 weeks after study commencement–“early” changes) and the follow-up period (10–52 weeks after study commencement–“late” changes). Sequences of variables in the Bayesian Network showed the most common direct and indirect recovery pathways followed by participants with low back pain receiving individualised physiotherapy versus advice.
Results.
Individualised physiotherapy directly reduced early disability in people with low back pain. Individualised physiotherapy exerted indirect effects on pain intensity, recovery expectations, sleep, fear, anxiety, and depression via its ability to facilitate early improvement in disability. Early improvement in disability, led to an early reduction in depression both directly and via more complex pathways involving fear, recovery expectations, anxiety, and pain intensity. Individualised physiotherapy had its greatest influence on early change variables (during the intervention period).
Conclusion.
Individualised physiotherapy for low back pain appears to work predominately by facilitating an early reduction in disability, which in turn leads to improvements in other biopsychosocial outcomes. The current study cannot rule out that unmeasured mechanisms (such as tissue healing or reduced inflammation) may mediate the relationship between individualised physiotherapy treatment and improvement in disability. Further data-driven analyses involving a broad range of plausible biopsychosocial variables are recommended to fully understand how treatments work for people with low back pain.
Published in PLoS ONE 16, pp. 1–16.
How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data
@ARTICLE{scutari21c,
title = {How does individualised physiotherapy work for people with low back pain? A {B}ayesian {N}etwork analysis using randomised controlled trial data},
journal = {{PLoS} {ONE}},
volume = {16},
author = {Liew, B.X.W. and Ford, J.J. and Scutari, M. and Hahne, A.J.},
pages = {1--16},
year = {2021},
doi = {10.1371/journal.pone.0258515},
url = {}
}
DownloadLiew, B.X.W., Peolsson, A., Falla, D., Cleland, J.A., Scutari, M., Kierkegaard, M., r A Dedering, (2021). Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy. European Journal of Pain 25(5), pp. 1162–1172.
Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy
Authors: Liew, B.X.W. and Peolsson, A. and Falla, D. and Cleland, J.A. and Scutari, M. and Kierkegaard, M. and r A Dedering,
Year: 2021
Abstract: Background
The mechanisms of action that facilitate improved outcomes after conservative rehabilitation are unclear in individuals with cervical radiculopathy (CR). This study aims to determine the pathways of recovery of disability with different exercise programs in individuals with CR.
Methods
We analysed a dataset of 144 individuals with CR undergoing conservative rehabilitation. Eleven variables collected at baseline, 3, 6 and 12 months follow-up were used to build a Bayesian Network (BN) model: treatment group (neck-specific vs. general exercises), age, sex, self-efficacy, catastrophizing, kinesiophobia, anxiety, neck–arm pain intensity, headache pain intensity and disability. The model was used to quantify the contribution of different mediating pathways on the outcome of disability at 12th months.
Results
All modelled variables were conditionally independent from treatment groups. A one-point increase in anxiety at 3rd month was associated with a 2.45-point increase in 12th month disability (p <.001). A one-point increase in head pain at 3rd month was associated with a 0.08-point increase in 12th month disability (p <.001). Approximately 83% of the effect of anxiety on disability was attributable to self-efficacy. Approximately 88% of the effect of head pain on disability was attributable to neck–arm pain.
Conclusions
No psychological or pain-related variables mediated the different treatment programs with respect to the outcome of disability. Thus, the specific characteristics investigated in this study did not explain the differences in mechanisms of effect between neck-specific training and prescribed physical activity. The present study provides candidate modifiable mediators that could be the target of future intervention trials.
Significance
Psychological and pain characteristics did not differentially explain the mechanism of effect that two exercise regimes had on disability in individuals with cervical radiculopathy. In addition, we found that improvements in self-efficacy was approximately five times more important than that of neck–arm pain intensity in mediating the anxiety-disability relationship. A mechanistic understanding of recovery provides candidate modifiable mediators that could be the target of future intervention trials.
Published in European Journal of Pain 25(5), pp. 1162–1172.
Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy
@ARTICLE{scutari21b,
title = {Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy},
journal = {European Journal of Pain},
volume = {25},
author = {Liew, B.X.W. and Peolsson, A. and Falla, D. and Cleland, J.A. and Scutari, M. and Kierkegaard, M. and r A Dedering, },
number = {5},
pages = {1162--1172},
year = {2021},
doi = {10.1002/ejp.1741},
url = {}
}
DownloadLlerena, J.V., Mauá, D.D., Antonucci, A. (2021). Cautious classification with data missing not at random using generative random forests. In Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 284–298.
Cautious classification with data missing not at random using generative random forests
Authors: Llerena, J.V. and Mauá, D.D. and Antonucci, A.
Year: 2021
Abstract: Missing data present a challenge for most machine learning approaches. When a generative probabilistic model of the data is available, an effective approach is to marginalize missing values out. Probabilistic circuits are expressive generative models that allow for efficient exact inference. However, data is often missing not at random, and marginalization can lead to overconfident and wrong conclusions. In this work, we develop an efficient algorithm for assessing the robustness of classifications made by probabilistic circuits to imputations of the non-ignorable portion of missing data at prediction time. We show that our algorithm is exact when the model satisfies certain constraints, which is the case for the recent proposed Generative Random Forests, that equip Random Forest Classifiers with a full probabilistic model of the data. We also show how to extend our approach to handle non-ignorable missing data at training time.
Published in Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 284–298.
Cautious classification with data missing not at random using generative random forests
@INPROCEEDINGS{antonucci2021b,
title = {Cautious classification with data missing not at random using generative random forests},
editor = {Vejnarov\'a, J. and Wilson, N.},
publisher = {Springer International Publishing},
address = {Cham},
booktitle = {Symbolic and Quantitative Approaches to Reasoning With Uncertainty},
author = {Llerena, J.V. and Mau\'a, D.D. and Antonucci, A.},
pages = {284--298},
year = {2021},
doi = {10.1007/978-3-030-86772-0_21},
url = {}
}
DownloadMaggioni, B., Marescotti, E., Zanchettin, M., Piga, D., Roveda, L. (2021). Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives. In .
Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives
Authors: Maggioni, B. and Marescotti, E. and Zanchettin, M. and Piga, D. and Roveda, L.
Year: 2021
Abstract: Many industrial tasks, such as welding and sealing, require not only a precise path reference, but also an advanced velocity planning in order to achieve the target quality for the final products. In this paper, a novel approach is proposed to perform robotic trajectory planning. The developed algorithm exploits Fuzzy Logic (FL) to relate the path features (such as curves or sharp edges) to the proper execution velocity. Such a computed velocity reference is then used as an input for Dynamical Movement Primitives (DMP), providing the reference signals to the robot controller. The main improved methodology features are: path-based velocity planning, extension of DMP to variable velocity reference and smoothing of the velocity reference including robot velocity/acceleration limits. The algorithm can be implemented in a collaborative framework, defining a compliant controller embedded into the DMP for online trajectory planning.
Published in ARCI 2021.
Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives
@INPROCEEDINGS{Roveda2021g,
title = {Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives},
journal = {{ARCI} 2021},
author = {Maggioni, B. and Marescotti, E. and Zanchettin, M. and Piga, D. and Roveda, L.},
year = {2021},
doi = {},
url = {}
}
DownloadPedrero-Martin, Y., Falla, D., Martinez-Calderon, J., Liew, B.X.W., Scutari, M., Luque-Suarez, A. (2021). Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders. European Spine Journal 20(6), pp. 1689–1698.
Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders
Authors: Pedrero-Martin, Y. and Falla, D. and Martinez-Calderon, J. and Liew, B.X.W. and Scutari, M. and Luque-Suarez, A.
Year: 2021
Abstract: Purpose
To evaluate whether a set of pre-accident demographic, accident-related, post-accident treatment and psychosocial factors assessed in people with acute/subacute whiplash-associated disorders (WAD) mediate the association between pain intensity and: (1) pain interference and (2) expectations of recovery, using Bayesian networks (BNs) analyses. This study also explored the potential mediating pathways (if any) between different psychosocial factors.
Methods
This was a cross-sectional study conducted on a sample of 173 participants with acute/subacute WAD. Pain intensity, pain interference, pessimism, expectations of recovery, pain catastrophizing, and self-efficacy beliefs were assessed. BN analyses were conducted to analyse the mediating effects of psychological factors on the association between pain intensity and pain-related outcomes.
Results
The results revealed that self-efficacy beliefs partially mediated the association between pain intensity and pain interference. Kinesiophobia partially mediated the association between self-efficacy and pain catastrophizing. Psychological factors did not mediate the association between pain intensity and expectations of recovery.
Conclusion
These results indicate that individuals with acute/subacute WAD may present with lesser pain interference associated with a determined pain intensity value when they show greater self-efficacy beliefs. As the cross-sectional nature of this study limits firm conclusions on the causal impact, researchers are encouraged to investigate the role that patient’s self-efficacy beliefs play in the transition to chronic WAD via longitudinal study designs.
Published in European Spine Journal 20(6), pp. 1689–1698.
Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders
@ARTICLE{scutari21a,
title = {Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders},
journal = {European Spine Journal},
volume = {20},
author = {Pedrero-Martin, Y. and Falla, D. and Martinez-Calderon, J. and Liew, B.X.W. and Scutari, M. and Luque-Suarez, A.},
number = {6},
pages = {1689--1698},
year = {2021},
doi = {10.1007/s00586-021-06731-5},
url = {}
}
DownloadMasegosa, A.R., Cabañas, R., Langseth, H., Nielsen, T.D., Salmerón, A. (2021). Probabilistic models with deep neural networks. Entropy 23(1), 117.
Probabilistic models with deep neural networks
Authors: Masegosa, A.R. and Cabañas, R. and Langseth, H. and Nielsen, T.D. and Salmerón, A.
Year: 2021
Abstract: Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.
Published in Entropy 23(1), 117.
Probabilistic models with deep neural networks
@ARTICLE{cabanas2021a,
title = {Probabilistic models with deep neural networks},
journal = {Entropy},
volume = {23},
author = {Masegosa, A.R. and Caba\~nas, R. and Langseth, H. and Nielsen, T.D. and Salmer\'on, A.},
number = {1},
pages = {117},
year = {2021},
doi = {10.3390/e23010117},
url = {}
}
DownloadMejari, M., Mavkov, B., Forgione, M., Piga, D. (2021). An integral architecture for identification of continuous-time state-space lpv models. In 4th IFAC Workshop on Linear Parameter-Varying Systems LPVS 2021 54(8), Milan, Italy, pp. 7–12.
An integral architecture for identification of continuous-time state-space lpv models
Authors: Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.
Year: 2021
Abstract: This paper presents an integral architecture for direct identifi cation of continuous-time linear parameter-varying (LPV) state-space models. The main building block of the
proposed architecture consist of an LPV model followed by an integral block, which is used to approximate the continuous-time state map of an LPV representation. The unknown LPV model matrices are estimated along with the state sequence by minimizing a properly constructed dual-objective criterion. A coordinate descent algorithm is employed to optimize the desired objective, which alternates between computing the unknown LPV matrices and estimating the state sequence. Thanks to the linear parametric structure induced by the LPV models, the unknown parameters within each coordinate descent step can be computed analytically via ordinary least squares. The effectiveness of the proposed methodology is assessed via a numerical example.
Published in 4th IFAC Workshop on Linear Parameter-Varying Systems LPVS 2021 IFAC-PapersOnLine 54(8), Milan, Italy, pp. 7–12.
An integral architecture for identification of continuous-time state-space lpv models
@INPROCEEDINGS{mejari2021a,
title = {An integral architecture for identification of continuous-time state-space lpv models},
journal = {{IFAC}-{PapersOnLine}},
address = {Milan, Italy},
volume = {54},
booktitle = {4th {IFAC} Workshop on Linear Parameter-Varying Systems {LPVS} 2021},
author = {Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.},
number = {8},
pages = {7--12},
year = {2021},
doi = {10.1016/j.ifacol.2021.08.573},
url = {}
}
DownloadMellace, S., Kanjirangat, V., Antonucci, A. (2021). Relation clustering in narrative knowledge graphs. In AI4Narratives Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 20).
Relation clustering in narrative knowledge graphs
Authors: Mellace, S. and Kanjirangat, V. and Antonucci, A.
Year: 2021
Abstract: When coping with literary texts such as novels or short stories, the extraction of structured information in the form of a knowledge graph might be hindered by the huge number of possible relations between the entities corresponding to the characters in the novel and the consequent hurdles in gathering supervised information about them. Such issue is addressed here as an unsupervised task empowered by transformers: relational sentences in the original text are embedded (with SBERT) and clustered in order to merge together semantically similar relations. All the sentences in the same cluster are finally summarized (with BART) and a descriptive label extracted from the summary. Preliminary tests show that such clustering might successfully detect similar relations, and provide a valuable preprocessing for semi-supervised approaches.
Published in AI4Narratives Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 20).
Relation clustering in narrative knowledge graphs
@INPROCEEDINGS{vani2021a,
title = {Relation clustering in narrative knowledge graphs},
booktitle = {{AI4Narratives} Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence ({IJCAI}-{PRICAI} 20)},
author = {Mellace, S. and Kanjirangat, V. and Antonucci, A.},
year = {2021},
doi = {},
url = {http://ceur-ws.org/Vol-2794/paper5.pdf}
}
DownloadMichelucci, U., Sperti, M., Piga, D., Venturini, F., Deriu, M.A. (2021). A model-agnostic algorithm for bayes error determination in binary classification. Algorithms 14(11), 301.
A model-agnostic algorithm for bayes error determination in binary classification
Authors: Michelucci, U. and Sperti, M. and Piga, D. and Venturini, F. and Deriu, M.A.
Year: 2021
Abstract: This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.
Published in Algorithms 14(11), 301.
A model-agnostic algorithm for bayes error determination in binary classification
@ARTICLE{piga2021d,
title = {A model-agnostic algorithm for bayes error determination in binary classification},
journal = {Algorithms},
volume = {14},
author = {Michelucci, U. and Sperti, M. and Piga, D. and Venturini, F. and Deriu, M.A.},
number = {11},
pages = {301},
year = {2021},
doi = {10.3390/a14110301},
url = {}
}
DownloadAwal, M.R., Cao, R., Lee, R.K., Mitrovic, S. (2021). Angrybert: joint learning target and emotion for hate speech detection. In Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R.K., Reddy, P.K., Srivastava, J., Chakraborty, T. (Eds), Advances in Knowledge Discovery and Data Mining - 25th Pacific-asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I, Lecture Notes in Computer Science 12712, Springer, pp. 701–713.
Angrybert: joint learning target and emotion for hate speech detection
Authors: Awal, M.R. and Cao, R. and Lee, R.K. and Mitrovic, S.
Year: 2021
Abstract: Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment results show that AngryBERT outperforms state-of-the-art single-task-learning and multitask learning baselines. We conduct ablation studies and case studies to empirically examine the strengths and characteristics of our AngryBERT model and show that the secondary tasks are able to improve hate speech detection.
Published in Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R.K., Reddy, P.K., Srivastava, J., Chakraborty, T. (Eds), Advances in Knowledge Discovery and Data Mining - 25th Pacific-asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I, Lecture Notes in Computer Science 12712, Springer, pp. 701–713.
Angrybert: joint learning target and emotion for hate speech detection
@INPROCEEDINGS{mitrovic2021a,
title = {Angrybert: joint learning target and emotion for hate speech detection},
editor = {Karlapalem, K. and Cheng, H. and Ramakrishnan, N. and Agrawal, R.K. and Reddy, P.K. and Srivastava, J. and Chakraborty, T.},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {12712},
booktitle = {Advances in Knowledge Discovery and Data Mining - 25th Pacific-{a}sia Conference, {PAKDD} 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I},
author = {Awal, M.R. and Cao, R. and Lee, R.K. and Mitrovic, S.},
pages = {701--713},
year = {2021},
doi = {10.1007/978-3-030-75762-5_55},
url = {}
}
DownloadPiga, D., Forgione, M., Mejari, M. (2021). Deep learning with transfer functions: New applications in system identification. In Proceedings of the 19th IFAC Symposium System Identification: learning models for decision and control 54(7), pp. 415–420.
Deep learning with transfer functions: New applications in system identification
Authors: Piga, D. and Forgione, M. and Mejari, M.
Year: 2021
Abstract: This paper presents a linear dynamical operator described in terms of a rational transfer function, endowed with a well-defined and efficient back-propagation behavior for automatic derivatives computation. The operator enables end-to-end training of structured networks containing linear transfer functions and other differentiable units by exploiting standard deep learning software. Two relevant applications of the operator in system identification are presented. The first one consists in the integration of prediction error methods in deep learning. The dynamical operator is included as the last layer of a neural network in order to obtain the optimal one-step-ahead prediction error. The second one considers identification of general block-oriented models from quantized data. These block-oriented models are constructed by combining linear dynamical operators with static nonlinearities described as standard feed-forward neural networks. A custom loss function corresponding to the log-likelihood of quantized output observations is defined. For gradient-based optimization, the derivatives of the log-likelihood are computed by applying the back-propagation algorithm through the whole network. Two system identification benchmarks are used to show the effectiveness of the proposed methodologies.
Published in Proceedings of the 19th IFAC Symposium System Identification: learning models for decision and control IFAC-PapersOnLine 54(7), pp. 415–420.
Deep learning with transfer functions: New applications in system identification
@INPROCEEDINGS{forgione2021c,
title = {Deep learning with transfer functions: {N}ew applications in system identification},
journal = {{IFAC}-{PapersOnLine}},
volume = {54},
booktitle = {Proceedings of the 19th {IFAC} Symposium System Identification: {l}earning {m}odels for {d}ecision and {c}ontrol},
author = {Piga, D. and Forgione, M. and Mejari, M.},
number = {7},
pages = {415--420},
year = {2021},
doi = {10.1016/j.ifacol.2021.08.395},
url = {}
}
DownloadRopero, R.F., Flores, M.J., Cabañas, R., Rumí, R. (2021). A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management. In Proceedings of the 15th Uai Conference on Bayesian Modeling Applications Workshop.
A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management
Authors: Ropero, R.F. and Flores, M.J. and Cabañas, R. and Rumí, R.
Year: 2021
Abstract: Bayesian networks are extensively used in different research areas, environmental modelling in particular, because of their advantages. This has encouraged the development of several tools and software. In this paper, a comparison between Elvira software and AMIDST toolbox is made using data from a flood risk modelling example. Even when Elvira model presents better results, it is computationally inefficient in large datasets, which makes necessary to explore new and more powerful tools, like AMIDST, for environmental modelling tasks.
Published in Proceedings of the 15th Uai Conference on Bayesian Modeling Applications Workshop.
A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management
@INPROCEEDINGS{cabanas2021d,
title = {A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management},
booktitle = {Proceedings of the 15th Uai Conference on Bayesian Modeling Applications Workshop},
author = {Ropero, R.F. and Flores, M.J. and Caba\~nas, R. and Rum\'i, R.},
year = {2021},
doi = {},
url = {}
}
DownloadRoveda, L., Maggioni, B., Marescotti, E., Shahid, A., Zanchettin, M., Bemporad, A., Piga, D. (2021). Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks. IEEE Robotics and Automation Letters 6(4), pp. 6632–6639.
Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks
Authors: Roveda, L. and Maggioni, B. and Marescotti, E. and Shahid, A. and Zanchettin, M. and Bemporad, A. and Piga, D.
Year: 2021
Abstract: Production plants are being re-designed to implement human-centered solutions. Especially considering high added-value operations, robots are required to optimize their behavior to achieve a task quality at least comparable to the one obtained by the skilled operators. A manual programming and tuning of the manipulator is not an efficient solution, requiring to adopt towards automated strategies. Adding external sensors (e.g., cameras) increases the robotic cell complexity and it doesn't solve the issue since it is usually difficult to build explicit reward functions measuring the robot performance, while it is easier for the user to define a qualitative comparison between two experiments. According to these needs, in this letter, the recently-developed preferences-based optimization approach GLISp is employed and adapted to tune the novel developed path-based velocity planner. The implemented solution defines an intuitive human-centered procedure, capable of transferring (through pairwise preferences between experiments) the task knowledge from the operator to the manipulator. A Franka EMIKA panda robot has been employed as a test platform to perform a robotic sealing task (i.e., material deposition task), validating the proposed methodology. The proposed approach has been compared with a programming by demonstration approach, and with the manual tuning of the path-based velocity planner. Achieved results demonstrate the improved deposition quality obtained with the proposed optimized path-based velocity planner methodology in a limited number of experimental trials (20).
Published in IEEE Robotics and Automation Letters 6(4), pp. 6632–6639.
Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks
@ARTICLE{Roveda2021c,
title = {Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks},
journal = {{IEEE} Robotics and Automation Letters},
volume = {6},
author = {Roveda, L. and Maggioni, B. and Marescotti, E. and Shahid, A. and Zanchettin, M. and Bemporad, A. and Piga, D.},
number = {4},
pages = {6632--6639},
year = {2021},
doi = {10.1109/LRA.2021.3094479},
url = {}
}
DownloadRoveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Bucca, G., Piga, D. (2021). Enhancing object detection performance through sensor pose Definition with bayesian optimization. In , pp. 699–703.
Enhancing object detection performance through sensor pose Definition with bayesian optimization
Authors: Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Bucca, G. and Piga, D.
Year: 2021
Abstract: Robots equipped with vision systems at the end-effector provide a powerful combination in industrial contexts. While much attention is dedicated to machine vision algorithms, the optimization of the vision system pose is not properly addressed (to increase object detection performance). A complete pipeline for such optimization is proposed. To this aim, Bayesian Optimization is employed. A Franka EMIKA Panda robot has been used as a robotic platform, equipped at its end-effector with an Intel© RealSense D400. Achieved results show the high-fidelity reconstruction of the real working environment for the offline optimization (i.e., performed simulations), together with capabilities of the proposed Bayesian Optimization-based approach to defining the sensor pose in a limited number of experimental trials (50 maximum iterations has been considered).
Published in Metrology2021, pp. 699–703.
Enhancing object detection performance through sensor pose Definition with bayesian optimization
@INPROCEEDINGS{Roveda2021h,
title = {Enhancing object detection performance through sensor pose {D}efinition with bayesian optimization},
journal = {Metrology2021},
author = {Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Bucca, G. and Piga, D.},
pages = {699--703},
year = {2021},
doi = {10.1109/METROIND4.0IOT51437.2021.9488517},
url = {}
}
DownloadRoveda, L., Piga, D. (2021). Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks. Autonomous Robots 45(3), pp. 371–388.
Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks
Authors: Roveda, L. and Piga, D.
Year: 2021
Abstract: Industrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.
Published in Autonomous Robots 45(3), pp. 371–388.
Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks
@ARTICLE{Roveda2021a,
title = {Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks},
journal = {Autonomous Robots},
volume = {45},
author = {Roveda, L. and Piga, D.},
number = {3},
pages = {371--388},
year = {2021},
doi = {10.1007/s10514-021-09970-z},
url = {}
}
DownloadRoveda, L., Riva, D., Bucca, G., Piga, D. (2021). External joint torques estimation for a position-controlled manipulator employing an extended kalman filter. In , pp. 101–107.
External joint torques estimation for a position-controlled manipulator employing an extended kalman filter
Authors: Roveda, L. and Riva, D. and Bucca, G. and Piga, D.
Year: 2021
Abstract: Industrial robots are required to interact with the surrounding environment to perform a given task (e.g., an assembly task). However, standard industrial robots are commonly position-controlled. Therefore, there is the need to implement an outer compliance controller to guarantee a safe interaction. Such compliance controllers require force/torque measurements to close the loop, and most of the industrial manipulators available on the market do not have embedded force/torque sensor(s), requiring additional efforts (i.e., additional costs and implementation resources) for such integration in the robotic setup. To provide a standard industrial sensorless position-controlled robot with the capabilities to execute an interaction task, the proposed paper defines an external joint torques observer for the implementation of an outer sensorless compliance controller. More in detail, exploiting the resulting position-controlled robot dynamics, an Extended Kalman Filter (EKF) is proposed to estimate the external joint torques. Exploiting such an estimation, an outer impedance controller can be designed, providing a position/velocity reference to the inner position controller. The described approach has been validated with experiments on a Franka EMIKA panda robot. A human operator interacts with the controlled robot, applying external wrenches (i.e., both Cartesian forces and torques). The resulting external joint torques are estimated making use of the proposed EKF, comparing the achieved results with the signals provided by the joint torque sensors of the Franka EMIKA panda robot (measurements used as a baseline for validation purposes). Experimental results show the capabilities of the proposed control framework in estimating the applied external joint torques while implementing a position control-based compliance controller.
Published in UbiquitousRobots2021, pp. 101–107.
External joint torques estimation for a position-controlled manipulator employing an extended kalman filter
@INPROCEEDINGS{Roveda2021i,
title = {External joint torques estimation for a position-controlled manipulator employing an extended kalman filter},
journal = {{UbiquitousRobots2021}},
author = {Roveda, L. and Riva, D. and Bucca, G. and Piga, D.},
pages = {101--107},
year = {2021},
doi = {10.1109/UR52253.2021.9494674},
url = {}
}
DownloadRoveda, L., Riva, D., Bucca, G., Piga, D. (2021). Sensorless optimal switching Impact/Force controller. IEEE Access 9, pp. 158167–158184.
Sensorless optimal switching Impact/Force controller
Authors: Roveda, L. and Riva, D. and Bucca, G. and Piga, D.
Year: 2021
Abstract: Intelligent interaction control is required in many fields of application, in which different operative situations
have to be faced with different controllers. Being able to switch between optimized controllers is, indeed, of
extreme importance to maximize the task performance in the different operative conditions (i.e., free-space
motion and contact), especially when considering sensorless robots. To deal with the proposed context,
a sensorless optimal switching impact/force (OSIF) controller is proposed. The low-level robot control
is composed of an inner joint position controller, fed by an outer Cartesian impedance controller with a
reference position. The estimation of the external wrench is implemented by means of an Extended Kalman
Filter (EKF). The high-level controller (feeding the Cartesian impedance controller with the setpoint) is
composed of an optimized impact controller (LQR-based controller), an optimized force controller (SDRE-
based controller), and a continuous switching mechanism (Fuzzy Logic-based). In addition, the output of
the switching mechanism is used to adapt the Cartesian impedance control parameters (i.e., stiffness and
damping parameters). Experimental tests have been performed on a Franka EMIKA panda robot to validate
the proposed controller. Obtained results show the capabilities of the OSIF controller, being able to detect
task phase transitions while satisfying the target performance.
Published in IEEE Access 9, pp. 158167–158184.
Sensorless optimal switching Impact/Force controller
@ARTICLE{Roveda2021f,
title = {Sensorless optimal switching {Impact/Force} controller},
journal = {{IEEE} Access},
volume = {9},
author = {Roveda, L. and Riva, D. and Bucca, G. and Piga, D.},
pages = {158167--158184},
year = {2021},
doi = {10.1109/ACCESS.2021.3131390},
url = {}
}
DownloadRoveda, L., Shahid, A., Iannacci, N., Piga, D. (2021). Sensorless optimal interaction control exploiting environment stiffness estimation. IEEE Transactions on Control System Technology 30(1), pp. 218–233.
Sensorless optimal interaction control exploiting environment stiffness estimation
Authors: Roveda, L. and Shahid, A. and Iannacci, N. and Piga, D.
Year: 2021
Abstract: Industrial robots are increasingly used to perform tasks that require an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot (in terms of dynamic characteristics), demanding the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop, making it, if possible, to adapt the robot behavior to the specific environment. However, most of the industrial manipulators do not have embedded force/torque sensor(s), which entails additional effort in terms of costs and implementation for their integration in the robotic setup. To extend the use of sensorless compliant controllers to force control, a robot-environment interaction dynamics model-based methodology is presented in this article. Relying on the sensorless Cartesian impedance control, an extended Kalman filter (EKF) is proposed to estimate the stiffness of an interaction environment. Exploiting the provided estimation, the robot–environment coupled dynamic modeling is used to design an optimal LQR interaction controller to close the force loop. The control gains can be analytically computed by solving the related Riccati equation, as such gains are a function of the impedance control and environment parameters. In addition, the interaction force can be estimated to close the force loop, making the sensorless robot able to perform the target interaction task. The described approach has been validated with experiments by analyzing two scenarios: a probing task and a closing of a plastic (i.e., compliant) box with a snap-fit closure mechanism. The performance of the proposed control framework has been evaluated, highlighting the capabilities of the EKF and the optimal LQR interaction controller. Finally, the proposed control schema is enhanced by the adaptation of the EKF for the estimation of the external wrench. Two additional experiments are provided to show the improvements on the control schema (a polishing-like task and an assembly task). The Franka EMIKA panda robot has been used as the reference robotic platform for experimental validation.
Published in IEEE Transactions on Control System Technology 30(1), pp. 218–233.
Sensorless optimal interaction control exploiting environment stiffness estimation
@ARTICLE{Roveda2021b,
title = {Sensorless optimal interaction control exploiting environment stiffness estimation},
journal = {{IEEE} Transactions on Control System Technology},
volume = {30},
author = {Roveda, L. and Shahid, A. and Iannacci, N. and Piga, D.},
number = {1},
pages = {218--233},
year = {2021},
doi = {10.1109/TCST.2021.3061091},
url = {}
}
DownloadSelvi, D., Piga, D., Battistelli, G., Bemporad, A. (2021). Optimal direct data-driven control with stability guarantees. European Journal of Control 59, pp. 175–187.
Optimal direct data-driven control with stability guarantees
Authors: Selvi, D. and Piga, D. and Battistelli, G. and Bemporad, A.
Year: 2021
Abstract: For model-free optimal control design, this paper proposes an approach based on optimizing the reference model that is used in direct data-driven controller synthesis. Optimality is defined with respect to suitable cost functions reflecting desired performance and control objectives. We rely on the well-known Virtual Reference Feedback Tuning technique and on a direct control design approach that ensures stability of the resulting closed-loop system. The proposed design method leads to a non-convex optimization problem with a small number of variables that can be easily solved by a global optimizer, such as by particle swarm optimization. The effectiveness of the proposed solution is illustrated in simulation examples.
Published in European Journal of Control 59, pp. 175–187.
Optimal direct data-driven control with stability guarantees
@ARTICLE{piga2021b,
title = {Optimal direct data-driven control with stability guarantees},
journal = {European Journal of Control},
volume = {59},
author = {Selvi, D. and Piga, D. and Battistelli, G. and Bemporad, A.},
pages = {175--187},
year = {2021},
doi = {10.1016/j.ejcon.2020.09.005},
url = {}
}
DownloadShahid, A., Sesin, J., Pecioski, D., Braghin, F., Piga, D., Roveda, L. (2021). Decentralized multi-agent control of a manipulator in continuous task learning. MDPI Applied Science 11(21), 10227.
Decentralized multi-agent control of a manipulator in continuous task learning
Authors: Shahid, A. and Sesin, J. and Pecioski, D. and Braghin, F. and Piga, D. and Roveda, L.
Year: 2021
Abstract: Many real-world tasks require multiple agents to work together. When talking about multiple agents in robotics, it is usually referenced to multiple manipulators in collaboration to solve a given task, where each one is controlled by a single agent. However, due to the increasing development of modular and re-configurable robots, it is also important to investigate the possibility of implementing multi-agent controllers that learn how to manage the manipulator’s degrees of freedom (DoF) in separated clusters for the execution of a given application (e.g., being able to face faults or, partially, new kinematics configurations). Within this context, this paper focuses on the decentralization of the robot control action learning and (re)execution considering a generic multi-DoF manipulator. Indeed, the proposed framework employs a multi-agent paradigm and investigates how such a framework impacts the control action learning process. Multiple variations of the multi-agent framework have been proposed and tested in this research, comparing the achieved performance w.r.t. a centralized (i.e., single-agent) control action learning framework, previously proposed by some of the authors. As a case study, a manipulation task (i.e., grasping and lifting) of an unknown object (to the robot controller) has been considered for validation, employing a Franka EMIKA panda robot. The MuJoCo environment has been employed to implement and test the proposed multi-agent framework. The achieved results show that the proposed decentralized approach is capable of accelerating the learning process at the beginning with respect to the single-agent framework while also reducing the computational effort. In fact, when decentralizing the controller, it is shown that the number of variables involved in the action space can be efficiently separated into several groups and several agents. This simplifies the original complex problem into multiple ones, efficiently improving the task learning process.
Published in MDPI Applied Science 11(21), 10227.
Decentralized multi-agent control of a manipulator in continuous task learning
@ARTICLE{Roveda2021d,
title = {Decentralized multi-agent control of a manipulator in continuous task learning},
journal = {{MDPI} Applied Science},
volume = {11},
author = {Shahid, A. and Sesin, J. and Pecioski, D. and Braghin, F. and Piga, D. and Roveda, L.},
number = {21},
pages = {10227},
year = {2021},
doi = {10.3390/app112110227},
url = {}
}
DownloadSommer, M., Rüegsegger, M., Szehr, O., Del Rio, G. (2021). Deep self-optimizing artificial intelligence for tactical analysis, training and optimization. In , NATO.
Deep self-optimizing artificial intelligence for tactical analysis, training and optimization
Authors: Sommer, M. and Rüegsegger, M. and Szehr, O. and Del Rio, G.
Year: 2021
Abstract: The increasing complexity of modern multi-domain conflicts has made their tactical and strategic understanding and the identification of appropriate courses of action challenging endeavours. Modelling and simulation as part of concept development and experimentation (CD&E) provide new insight at higher speed and lower cost than what physical manoeuvres can achieve. Amongst other, human-machine teaming through computer games is a powerful means of simulating defence scenarios at various abstraction levels. However, conventional human-machine interaction is time-consuming and restricted to pre-designed scenarios, e.g., in terms of pre-programmed conditional computer actions. If one side of the game could be taken by Artificial Intelligence (AI), this would increase the diversity of explored courses of actions and lead to a more robust and comprehensive analysis. If the AI plays both sides, this allows employing the Data Farming methodology and creating and analysing a database of a large number of investigated scenarios. To achieve a high degree of variability and generalization capability of the investigated scenarios, we employ combined Reinforcement Learning and search algorithms, which have demonstrated super-human performance in various complex planning problems. Such AI systems avoid the reliance on training data, human experience and predictions by learning tactics and strategy through exploration and self-optimization. In this contribution, we present the benefits and challenges of applying a Neural-Network-based Monte Carlo Tree Search algorithm for strategic planning and training in air-defence scenarios and virtual war-gaming with systems that are available currently or potentially in the future to the Swiss Armed Forces.
Published in MSG-Symposium, NATO.
Deep self-optimizing artificial intelligence for tactical analysis, training and optimization
@INPROCEEDINGS{szehr2021b,
title = {Deep self-optimizing artificial intelligence for tactical analysis, training and optimization},
journal = {{MSG}-Symposium},
publisher = {NATO},
author = {Sommer, M. and R\"uegsegger, M. and Szehr, O. and Del Rio, G.},
year = {2021},
doi = {},
url = {}
}
DownloadSzehr, O., Zarouf, R. (2021). Explicit counterexamples to Schäffer's conjecture. Journal de Mathématiques Pures et Appliquées 146, pp. 1–30.
Explicit counterexamples to Schäffer's conjecture
Authors: Szehr, O. and Zarouf, R.
Year: 2021
Abstract: In this article we propose a new and entirely constructive approach to Schäffer's conjecture.
For full abstract please read the preprint.
Published in Journal de Mathématiques Pures et Appliquées 146, pp. 1–30.
Explicit counterexamples to Schäffer's conjecture
@ARTICLE{szehr2021a,
title = {Explicit counterexamples to {S}ch\"affer's conjecture},
journal = {Journal {d}e Math\'ematiques Pures {e}t Appliqu\'ees},
volume = {146},
author = {Szehr, O. and Zarouf, R.},
pages = {1--30},
year = {2021},
doi = {10.1016/j.matpur.2020.10.006},
url = {}
}
DownloadTermine, A., Antonucci, A., Primiero, G., Facchini, A. (2021). Logic and model checking by imprecise probabilistic interpreted systems. In Rosenfeld, A., Talmon, N. (Eds), Multi-Agent Systems. EUMAS 2021. Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 211–227.
Logic and model checking by imprecise probabilistic interpreted systems
Authors: Termine, A. and Antonucci, A. and Primiero, G. and Facchini, A.
Year: 2021
Abstract: Stochastic multi-agent systems raise the necessity to extend probabilistic model checking to the epistemic domain. Results in this direction have been achieved by epistemic extensions of Probabilistic Computation Tree Logic and related Probabilistic Interpreted Systems. The latter, however, suffer of an important limitation: they require the probabilities governing the system’s behaviour to be fully specified. A promising way to overcome this limitation is represented by imprecise probabilities. In this paper we introduce imprecise probabilistic inter- preted systems and present a related logical language and model-checking procedures based on recent advances in the study of imprecise Markov processes.
Published in Rosenfeld, A., Talmon, N. (Eds), Multi-Agent Systems. EUMAS 2021. Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 211–227.
Logic and model checking by imprecise probabilistic interpreted systems
@INPROCEEDINGS{antonucci2021d,
title = {Logic and model checking by imprecise probabilistic interpreted systems},
editor = {Rosenfeld, A. and Talmon, N.},
publisher = {Springer International Publishing},
address = {Cham},
booktitle = {Multi-Agent Systems. {EUMAS} 2021. Lecture Notes in Computer Science},
author = {Termine, A. and Antonucci, A. and Primiero, G. and Facchini, A.},
pages = {211--227},
year = {2021},
doi = {10.1007/978-3-030-82254-5_13},
url = {}
}
DownloadTermine, A., Antonucci, A., Facchini, A., Primiero, G. (2021). Robust model checking with imprecise markov reward models. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, Proceedings of Machine Learning Research 147, JMLR.org, pp. 299–309.
Robust model checking with imprecise markov reward models
Authors: Termine, A. and Antonucci, A. and Facchini, A. and Primiero, G.
Year: 2021
Abstract: In recent years probabilistic model checking has be- come an important area of research because of the diffusion of computational systems of stochastic nature. Despite its great success, standard probabilistic model checking suffers the limitation of requiring a sharp specification of the probabilities governing the model behaviour. The theory of imprecise probabilities offers a natural approach to overcome such limitation by a sensitivity analysis with respect to the values of these parameters. However, only extensions based on discrete-time imprecise Markov chains have been con- sidered so far for such a robust approach to model checking. We present a further extension based on imprecise Markov reward models. In particular, we derive efficient algorithms to compute lower and upper bounds of the expected cumulative reward and proba- bilistic bounded rewards based on existing results for imprecise Markov chains. These ideas are tested on a real case study involving the spend-down costs of geriatric medicine departments.
Published in De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, Proceedings of Machine Learning Research 147, JMLR.org, pp. 299–309.
Robust model checking with imprecise markov reward models
@INPROCEEDINGS{antonucci2021a,
title = {Robust model checking with imprecise markov reward models},
editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
publisher = {JMLR.org},
series = {Proceedings of Machine Learning Research},
volume = {147},
booktitle = {{ISIPTA} 2021},
author = {Termine, A. and Antonucci, A. and Facchini, A. and Primiero, G.},
pages = {299--309},
year = {2021},
doi = {},
url = {https://proceedings.mlr.press/v147/termine21a.html}
}
DownloadZaffalon, M., Antonucci, A., Cabañas, R. (2021). Causal expectation-maximisation. In WHY-21 @ NeurIPS 2021.
Causal expectation-maximisation
Authors: Zaffalon, M. and Antonucci, A. and Cabañas, R.
Year: 2021
Abstract: Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation. But they often contain latent variables that limit their application to special settings. This appears to be a consequence of the fact, proven in this paper, that causal inference is NP-hard even in models characterised by polytree-shaped graphs. To deal with such a hardness, we introduce the causal EM algorithm. Its primary aim is to reconstruct the uncertainty about the latent variables from data about categorical manifest variables. Counterfactual inference is then addressed via standard algorithms for Bayesian networks. The result is a general method to approximately compute counterfactuals, be they identifiable or not (in which case we deliver bounds). We show empirically, as well as by deriving credible intervals, that the approximation we provide becomes accurate in a fair number of EM runs. These results lead us finally to argue that there appears to be an unnoticed limitation to the trending idea that counterfactual bounds can often be computed without knowledge of the structural equations.
Published in WHY-21 @ NeurIPS 2021.
Causal expectation-maximisation
@INPROCEEDINGS{zaffalon2021c,
title = {Causal expectation-maximisation},
booktitle = {{WHY}-21 @ {NeurIPS} 2021},
author = {Zaffalon, M. and Antonucci, A. and Cabañas, R.},
year = {2021},
doi = {},
url = {https://why21.causalai.net/papers/WHY21_52.pdf}
}
DownloadZaffalon, M., Miranda, E. (2021). Desirability foundations of robust rational decision making. Synthese 198(27), pp. S6529–S6570.
Desirability foundations of robust rational decision making
Authors: Zaffalon, M. and Miranda, E.
Year: 2021
Abstract: Recent work has formally linked the traditional axiomatisation of incomplete preferences à la Anscombe-Aumann with the theory of desirability developed in the context of imprecise probability, by showing in particular that they are the very same theory. The equivalence has been established under the constraint that the set of possible prizes is finite. In this paper, we relax such a constraint, thus de facto creating one of the most general theories of rationality and decision making available today. We provide the theory with a sound interpretation and with basic notions, and results, for the separation of beliefs and values, and for the case of complete preferences. Moreover, we discuss the role of conglomerability for the presented theory, arguing that it should be a rationality requirement under very broad conditions.
Published in Synthese 198(27), Springer, pp. S6529–S6570.
Note: published online in 2018.
Desirability foundations of robust rational decision making
@ARTICLE{zaffalon2019a,
title = {Desirability foundations of robust rational decision making},
journal = {Synthese},
publisher = {Springer},
volume = {198},
author = {Zaffalon, M. and Miranda, E.},
number = {27},
pages = {S6529--S6570},
year = {2021},
doi = {10.1007/s11229-018-02010-x},
url = {}
}
DownloadZaffalon, M., Miranda, E. (2021). The sure thing. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 342–351.
The sure thing
Authors: Zaffalon, M., Miranda, E.
Year: 2021
Abstract: If we prefer action a to b both under an event and under its complement, then we should just prefer a to b. This is Savage's sure-thing principle. In spite of its intuitive- and simple-looking nature, for which it gets almost immediate acceptance, the sure thing is not a logical principle. So where does it get its support from? In fact, the sure thing may actually fail. This is related to a variety of deep and foundational concepts in causality, decision theory, and probability, as well as to Simpsons’ paradox and Blyth’s game. In this paper we try to systematically clarify such a network of relations. Then we propose a general desirability theory for nonlinear utility scales. We use that to show that the sure thing is primitive to many of the previous concepts: In non-causal settings, the sure thing follows from considerations of temporal coherence and coincides with conglomerability; it can be understood as a rationality axiom to enable well-behaved conditioning in logic. In causal settings, it can be derived using only coherence and a causal independence condition.
Published in De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 342–351.
The sure thing
@INPROCEEDINGS{zaffalon2021a,
title = {The sure thing},
editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
publisher = {JMLR.org},
series = {PMLR},
volume = {147},
booktitle = {{ISIPTA} 2021},
author = {Zaffalon, M., Miranda, E.},
pages = {342--351},
year = {2021},
doi = {},
url = {https://www.sipta.org/isipta21/pmlr/zaffalon21.pdf}
}
DownloadZhu, M., Bemporad, A., Piga, D. (2021). Preference-based MPC calibration. In 2021 European Control Conference (ECC), Napoli, Italy, pp. 638–645.
Preference-based MPC calibration
Authors: Zhu, M. and Bemporad, A. and Piga, D.
Year: 2021
Abstract: Automating the calibration of the parameters of a control policy by means of global optimization requires quantifying a closed-loop performance function. As this can be impractical in many situations, in this paper we suggest a semiautomated calibration approach that requires instead a human calibrator to express a preference on whether a certain control policy is “better” than another one, therefore eliminating the need of an explicit performance index. In particular, we focus our attention on semi-automated calibration of Model Predictive Controllers (MPCs), for which we attempt computing the set of best calibration parameters by employing the recently-developed active preference-based optimization algorithm GLISp. Based on the preferences expressed by the human operator, GLISp learns a surrogate of the underlying closed-loop performance index that the calibrator (unconsciously) uses and proposes, iteratively, a new set of calibration parameters to him or her for testing and for comparison against previous experimental results. The resulting semi-automated calibration procedure is tested on two case studies, showing the capabilities of the approach in achieving near-optimal performance within a limited number of experiments.
Published in 2021 European Control Conference (ECC), Napoli, Italy, pp. 638–645.
Preference-based MPC calibration
@INPROCEEDINGS{piga2021e,
title = {Preference-based {MPC} calibration},
address = {Napoli, Italy},
booktitle = {2021 European Control Conference ({ECC})},
author = {Zhu, M. and Bemporad, A. and Piga, D.},
pages = {638--645},
year = {2021},
doi = {10.23919/ECC54610.2021.9654900},
url = {}
}
Download top2020
Akata, Z., Ballier, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L.C., van Harmelen, F., van Hoof, H., van Riemsdijk, B., van Wynsberghe, A., Verbrugge, R., Berheij, B., Vossen, P., Welling, M. (2020). A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence. IEEE Computer 53, pp. 18–28.
A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence
Authors: Akata, Z. and Ballier, D. and de Rijke, M. and Dignum, F. and Dignum, V. and Eiben, G. and Fokkens, A. and Grossi, D. and Hindriks, K. and Hoos, H. and Hung, H. and Jonker, C. and Monz, C. and Neerincx, M. and Oliehoek, F. and Prakken, H. and Schlobach, S. and van der Gaag, L.C. and van Harmelen, F. and van Hoof, H. and van Riemsdijk, B. and van Wynsberghe, A. and Verbrugge, R. and Berheij, B. and Vossen, P. and Welling, M.
Year: 2020
Abstract: We define Hybrid Intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them, and achieve goals that were unreachable by either humans or machines alone. We identify Hybrid Intelligence as an important new research focus for the field of Artificial Intelligence, and we set a research agenda for Hybrid Intelligence by formulating four challenges: how do we develop AI systems that work in synergy with humans (Collaborative HI); how can these systems learn from and adapt to humans and their environment (Adaptive HI); how do we ensure that they behave ethically and responsibly (Responsible HI); and how can AI systems and humans share and explain their awareness, goals and strategies (Explainable HI). For each of these four challenges, we survey the state of the art and define a research agenda.
Published in IEEE Computer 53, pp. 18–28.
A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence
@ARTICLE{Linda2020b,
title = {A research agenda for hybrid intelligence: {A}ugmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence},
journal = {{IEEE} Computer},
volume = {53},
author = {Akata, Z. and Ballier, D. and de Rijke, M. and Dignum, F. and Dignum, V. and Eiben, G. and Fokkens, A. and Grossi, D. and Hindriks, K. and Hoos, H. and Hung, H. and Jonker, C. and Monz, C. and Neerincx, M. and Oliehoek, F. and Prakken, H. and Schlobach, S. and van der Gaag, L.C. and van Harmelen, F. and van Hoof, H. and van Riemsdijk, B. and van Wynsberghe, A. and Verbrugge, R. and Berheij, B. and Vossen, P. and Welling, M.},
pages = {18--28},
year = {2020},
doi = {10.1109/MC.2020.2996587},
url = {}
}
DownloadAntonucci, A., Tiotto, T. (2020). Approximate MMAP by marginal search. In Brawner, K.W., Barták, R., Bell, E. (Eds), Proceedings of the Thirty-third International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), AAAI Press, North Miami Beach, Florida, USA, pp. 181–184.
Approximate MMAP by marginal search
Authors: Antonucci, A. and Tiotto, T.
Year: 2020
Abstract: We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference computations. Given an input evidence, the marginals mass functions of the variables to be explained are computed. Marginal information gain is used to decide the variables to be explained first, and their most probable marginal states are consequently moved to the evidence. The sequential iteration of this procedure leads to a MMAP explanation and the minimum information gain obtained during the process can be regarded as a confidence measure for the explanation. Preliminary experiments show that the proposed confidence measure is properly detecting instances for which the algorithm is accurate and, for sufficiently high confidence levels, the algorithm gives the exact solution or an approximation whose Hamming distance from the exact one is small.
Published in Brawner, K.W., Barták, R., Bell, E. (Eds), Proceedings of the Thirty-third International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), AAAI Press, North Miami Beach, Florida, USA, pp. 181–184.
Approximate MMAP by marginal search
@INPROCEEDINGS{antonucci2020a,
title = {Approximate {MMAP} by marginal search},
editor = {Brawner, K.W. and Bart\'ak, R. and Bell, E.},
publisher = {AAAI Press},
address = {North Miami Beach, Florida, USA},
booktitle = {Proceedings of the Thirty-{t}hird International Florida Artificial Intelligence Research Society Conference ({FLAIRS}-33)},
author = {Antonucci, A. and Tiotto, T.},
pages = {181--184},
year = {2020},
doi = {},
url = {}
}
DownloadAzzimonti, L., Corani, G., Scutari, M. (2020). Structure learning from related data sets with a hierarchical Bayesian score. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, PMLR, pp. 5–16.
Structure learning from related data sets with a hierarchical Bayesian score
Authors: Azzimonti, L. and Corani, G. and Scutari, M.
Year: 2020
Abstract: Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they comprise different related, but not homogeneous, data sets collected in different ways. In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD). The proposed score is based on a hierarchical model that pools information across data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. We derive a closed-form expression for BHD using a variational approximation of the marginal likelihood and we study its performance using simulated data. We find that, when data comprise multiple related data sets, BHD outperforms the Bayesian Dirichlet equivalent uniform (BDeu) score in terms of reconstruction accuracy as measured by the Structural Hamming distance, and that it is as accurate as BDeu when data are homogeneous. Moreover, the estimated networks are sparser and therefore more interpretable than those obtained with BDeu, thanks to a lower number of false positive arcs.
Published in Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, PMLR, pp. 5–16.
Structure learning from related data sets with a hierarchical Bayesian score
@INPROCEEDINGS{azzimonti2020a,
title = {Structure learning from related data sets with a hierarchical {B}ayesian score},
publisher = {PMLR},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Azzimonti, L. and Corani, G. and Scutari, M.},
pages = {5--16},
year = {2020},
doi = {},
url = {http://proceedings.mlr.press/v138/azzimonti20a.html}
}
DownloadAzzimonti, D., Rottondi, C., Giusti, A., Tornatore, M., Bianco, A. (2020). Active vs transfer learning approaches for qot estimation with small training datasets. In Optical Fiber Communication Conference (OFC 2020), Optical Society of America, M4E.1.
Active vs transfer learning approaches for qot estimation with small training datasets
Authors: Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.
Year: 2020
Abstract: We compare the level of accuracy achieved by active learning and domain adaptation approaches for quality of transmission estimation of an unestablished lightpath, in presence of small-sized training datasets.
Published in Optical Fiber Communication Conference (OFC 2020), Optical Society of America, M4E.1.
Active vs transfer learning approaches for qot estimation with small training datasets
@INPROCEEDINGS{azzimontid2020b,
title = {Active vs transfer learning approaches for qot estimation with small training datasets},
publisher = {Optical Society of America},
booktitle = {Optical Fiber Communication Conference ({OFC} 2020)},
author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},
pages = {M4E.1},
year = {2020},
doi = {10.1364/OFC.2020.M4E.1},
url = {}
}
DownloadAzzimonti, D., Rottondi, C., Tornatore, M. (2020). Reducing probes for quality of transmission estimation in optical networks with active learning. J. Opt. Commun. Netw. 12(1), pp. A38–A48.
Reducing probes for quality of transmission estimation in optical networks with active learning
Authors: Azzimonti, D. and Rottondi, C. and Tornatore, M.
Year: 2020
Abstract: Estimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or “samples,” required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use active learning to reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples.
Published in J. Opt. Commun. Netw. 12(1), OSA, pp. A38–A48.
Reducing probes for quality of transmission estimation in optical networks with active learning
@ARTICLE{azzimontid2020a,
title = {Reducing probes for quality of transmission estimation in optical networks with active learning},
journal = {J. Opt. Commun. Netw.},
publisher = {OSA},
volume = {12},
author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},
number = {1},
pages = {A38--A48},
year = {2020},
doi = {10.1364/JOCN.12.000A38},
url = {}
}
DownloadBenavoli, A., Azzimonti, D., Piga, D. (2020). Skew gaussian processes for classification. Machine Learning 109(9), pp. 1877–1902.
Skew gaussian processes for classification
Authors: Benavoli, A. and Azzimonti, D. and Piga, D.
Year: 2020
Abstract: Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that the mean and the median coincide, while the mean and median in an asymmetric (skewed) distribution can be different numbers. In this paper, we propose skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions. A SkewGP extends the multivariate unified skew-normal distribution over finite dimensional vectors to a stochastic processes. The SkewGP class of distributions includes GPs and, therefore, SkewGPs inherit all good properties of GPs and increase their flexibility by allowing asymmetry in the probabilistic model. By exploiting the fact that SkewGP and probit likelihood are conjugate model, we derive closed form expressions for the marginal likelihood and predictive distribution of this new nonparametric classifier. We verify empirically that the proposed SkewGP classifier provides a better performance than a GP classifier based on either Laplace’s method or expectation propagation.
Published in Machine Learning 109(9), pp. 1877–1902.
Skew gaussian processes for classification
@ARTICLE{azzimontid2020c,
title = {Skew gaussian processes for classification},
journal = {Machine Learning},
volume = {109},
author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
number = {9},
pages = {1877--1902},
year = {2020},
doi = {10.1007/s10994-020-05906-3},
url = {}
}
DownloadBodewes, T., Scutari, M. (2020). Identifiability and consistency of bayesian network structure learning from incomplete data. Proceedings of Machine Learning Research (PGM 2020) 138, pp. 29–40.
Identifiability and consistency of bayesian network structure learning from incomplete data
Authors: Bodewes, T. and Scutari, M.
Year: 2020
Abstract: Bayesian network (BN) structure learning from complete data has been extensively studied in the literature. However, fewer theoretical results are available for incomplete data, and most are based on the use of the Expectation-Maximisation (EM) algorithm. Balov (2013) proposed an alternative approach called Node-Average Likelihood (NAL) that is competitive with EM but computationally more efficient; and proved its consistency and model identifiability for discrete BNs. In this paper, we give general sufficient conditions for the consistency of NAL; and we prove consistency and identifiability for conditional Gaussian BNs, which include discrete and Gaussian BNs as special cases. Hence NAL has a wider applicability than originally stated in Balov (2013).
Published in Proceedings of Machine Learning Research (PGM 2020) 138, pp. 29–40.
Identifiability and consistency of bayesian network structure learning from incomplete data
@ARTICLE{scutari20c,
title = {Identifiability and consistency of bayesian network structure learning from incomplete data},
journal = {Proceedings of Machine Learning Research ({PGM} 2020)},
volume = {138},
author = {Bodewes, T. and Scutari, M.},
pages = {29--40},
year = {2020},
doi = {},
url = {https://proceedings.mlr.press/v138/bodewes20a.html}
}
DownloadBregoli, A., Scutari, M., Stella, F. (2020). Constraint-based learning for continuous-time bayesian networks. Proceedings of Machine Learning Research (PGM 2020) 138, pp. 41–52.
Constraint-based learning for continuous-time bayesian networks
Authors: Bregoli, A. and Scutari, M. and Stella, F.
Year: 2020
Abstract: Dynamic Bayesian networks have been well explored in the literature as discrete-time models;
however, their continuous-time extensions have seen comparatively little attention. In this paper, we
propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian
networks. We discuss the different statistical tests and the underlying hypotheses used by our
proposal to establish conditional independence. Finally, we validate its performance using synthetic
data, and discuss its strengths and limitations. We find that score-based is more accurate in learning
networks with binary variables, while our constraint-based approach is more accurate with variables
assuming more than two values. However, more experiments are needed for confirmation.
Published in Proceedings of Machine Learning Research (PGM 2020) 138, pp. 41–52.
Note: Best Student Paper award.
Constraint-based learning for continuous-time bayesian networks
@ARTICLE{scutari20g,
title = {Constraint-based learning for continuous-time bayesian networks},
journal = {Proceedings of Machine Learning Research ({PGM} 2020)},
volume = {138},
author = {Bregoli, A. and Scutari, M. and Stella, F.},
pages = {41--52},
year = {2020},
doi = {},
url = {}
}
DownloadBreschi, V., Mejari, M. (2020). Shrinkage strategies for structure selection and identification of piecewise affine models. In 2020 59th Ieee Conference on Decision and Control (cdc), pp. 1626–1631.
Shrinkage strategies for structure selection and identification of piecewise affine models
Authors: Breschi, V. and Mejari, M.
Year: 2020
Published in 2020 59th Ieee Conference on Decision and Control (cdc), pp. 1626–1631.
Shrinkage strategies for structure selection and identification of piecewise affine models
@INPROCEEDINGS{mejari2020d,
title = {Shrinkage strategies for structure selection and identification of piecewise affine models},
booktitle = {2020 59th Ieee Conference on Decision and Control ({c}dc)},
author = {Breschi, V. and Mejari, M.},
pages = {1626--1631},
year = {2020},
doi = {10.1109/CDC42340.2020.9303927},
url = {}
}
DownloadBriganti, G., Scutari, M., Linkowski, P. (2020). A machine learning approach to relationships among alexithymia components. Psychiatria Danubina 32(Suppl. 1), pp. 180–187.
A machine learning approach to relationships among alexithymia components
Authors: Briganti, G. and Scutari, M. and Linkowski, P.
Year: 2020
Abstract: Background: The aim of this paper is to explore the network structures of alexithymia components and compare results with relevant prior literature.
Subjects and methods: In a large sample of university students, undirected and directed network structures of items from the Bermond Vorst Alexithymia Questionnaire form B are estimated with state-of-the-art network analysis and structure learning tools. Centrality estimates are used to address the topic of item redundancy and select relevant alexithymia components to study.
Results: Alexithymia components present positive as well as negative connections; poor fantasy and emotional insight are identified as central items in the network.
Conclusions: The undirected network structure of alexithymia components reports new features with respect to prior literature, and the directed network structures offers new insight on the construct.
Published in Psychiatria Danubina 32(Suppl. 1), pp. 180–187.
A machine learning approach to relationships among alexithymia components
@ARTICLE{scutari20e,
title = {A machine learning approach to relationships among alexithymia components},
journal = {Psychiatria Danubina},
volume = {32},
author = {Briganti, G. and Scutari, M. and Linkowski, P.},
number = {Suppl. 1},
pages = {180--187},
year = {2020},
doi = {},
url = {}
}
DownloadCabañas, R., Antonucci, A., Huber, D., Zaffalon, M. (2020). Credici: a java library for causal inference by credal networks. In Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.
Credici: a java library for causal inference by credal networks
Authors: Cabañas, R. and Antonucci, A. and Huber, D. and Zaffalon, M.
Year: 2020
Abstract: We present CREDICI, a Java open-source tool for causal inference based on credal networks. Credal networks are an extension of Bayesian networks where local probability mass functions are only constrained to belong to given, so-called \emphcredal, sets. CREDICI is based on the recent work of Zaffalon et al. (2020), where an equivalence between Pearl's structural causal models and credal networks has been derived. This allows to reduce a counterfactual query in a causal model to a standard query in a credal network, even in the case of unidentifiable causal effects. The necessary transformations and data structures are implemented in CREDICI, while inferences are eventually computed by CREMA (Huber et al., 2020), a twin library for general credal network inference. Here we discuss the main implementation challenges and possible outlooks.
Published in Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.
Credici: a java library for causal inference by credal networks
@INPROCEEDINGS{cabanas2020a,
title = {Credici: a java library for causal inference by credal networks},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
author = {Caba\~nas, R. and Antonucci, A. and Huber, D. and Zaffalon, M.},
year = {2020},
doi = {},
url = {}
}
DownloadCabañas, R., Cózar, J., Salmerón, A., Masegosa, A.R. (2020). Probabilistic graphical models with neural networks in inferpy. In Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 601–604.
Probabilistic graphical models with neural networks in inferpy
Authors: Cabañas, R. and Cózar, J. and Salmerón, A. and Masegosa, A.R.
Year: 2020
Abstract: InferPy is an open-source Python package for variational inference in probabilistic models containing neural networks. Other similar libraries are often difficult for non-expert users. InferPy provides a much more compact and simple way to code such models, at the expense of slightly reducing expressibility and flexibility. The main objective of this package is to permit its use without having a strong theoretical background or thorough knowledge of the deep learning frameworks.
Published in Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 601–604.
Probabilistic graphical models with neural networks in inferpy
@INPROCEEDINGS{cabanas2020b,
title = {Probabilistic graphical models with neural networks in inferpy},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
author = {Caba\~nas, R. and C\'ozar, J. and Salmer\'on, A. and Masegosa, A.R.},
pages = {601--604},
year = {2020},
doi = {},
url = {https://proceedings.mlr.press/v138/cabanas20b}
}
DownloadCannelli, L., Facchinei, F., Scutari, G., Kungurtsev, V. (2020). Asynchronous optimization over graphs: linear convergence under error bound conditions. IEEE Transactions on Automatic Control 66(10), pp. 4604–4619.
Asynchronous optimization over graphs: linear convergence under error bound conditions
Authors: Cannelli, L. and Facchinei, F. and Scutari, G. and Kungurtsev, V.
Year: 2020
Abstract: We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is known only by the associated agent and depends on the variables of that agent and those of a few others. This partitioned setting arises in several applications of practical interest. We propose what is, to the best of our knowledge, the first distributed, asynchronous algorithm with rate guarantees for this class of problems. When the objective function is nonconvex, the algorithm provably converges to a stationary solution at a sublinear rate whereas linear rate is achieved under the renowned Luo-Tseng error bound condition (which is less stringent than strong convexity). Numerical results on matrix completion and LASSO problems show the effectiveness of our method.
Published in IEEE Transactions on Automatic Control 66(10), pp. 4604–4619.
Asynchronous optimization over graphs: linear convergence under error bound conditions
@ARTICLE{cannelli2020a,
title = {Asynchronous optimization over graphs: linear convergence under error bound conditions},
journal = {{IEEE} Transactions on Automatic Control},
volume = {66},
author = {Cannelli, L. and Facchinei, F. and Scutari, G. and Kungurtsev, V.},
number = {10},
pages = {4604--4619},
year = {2020},
doi = {10.1109/TAC.2020.3033490},
url = {}
}
DownloadCasanova, A., Miranda, E., Zaffalon, M. (2020). Social pooling of beliefs and values with desirability. Proceedings of the 33rd International Flairs Conference (FLAIRS-33).
Social pooling of beliefs and values with desirability
Authors: Casanova, A. and Miranda, E. and Zaffalon, M.
Year: 2020
Abstract: The problem of aggregating beliefs and values of rational
subjects is treated with the formalism of sets of desirable
gambles. This leads on the one hand to a new perspective
of traditional results of social choice (in particular Arrow’s
theorem as well as sufficient conditions for the existence of
an oligarchy and democracy) and on the other hand to use the
same framework to create connections with opinion pooling.
In particular, we show that weak Pareto can be derived as a
coherence requirement and discuss the aggregation of state
independent beliefs.
Introduction
Published in Proceedings of the 33rd International Flairs Conference (FLAIRS-33).
Social pooling of beliefs and values with desirability
@ARTICLE{casanova2020a,
title = {Social pooling of beliefs and values with desirability},
journal = {Proceedings of the 33rd International Flairs Conference ({FLAIRS}-33)},
author = {Casanova, A. and Miranda, E. and Zaffalon, M.},
year = {2020},
doi = {},
url = {https://www.flairs-33.info/}
}
DownloadColetti, G., van der Gaag, L.C., Petturiti, D., Vantaggi, B. (2020). Detecting correlation between extreme probability events. International Journal of General Systems 49(1), pp. 64–87.
Detecting correlation between extreme probability events
Authors: Coletti, G. and van der Gaag, L.C. and Petturiti, D. and Vantaggi, B.
Year: 2020
Abstract: As classical definitions of correlation give rise to counterintuitive statements when extreme probability events are involved, we introduce enhanced notions of positive and negative correlation in the general framework of coherent conditional probability. These notions allow to handle extreme probability events in a principled way by accommodating the different levels of strength of the zero probabilities involved (namely, zero layers). Since the detection of correlations by means of zero layers is computationally challenging, we provide a full characterization relying on only conditional probability values.
Keywords: Conditional probability, Extreme probability event, Coherence, Correlation
Published in International Journal of General Systems 49(1), pp. 64–87.
Detecting correlation between extreme probability events
@ARTICLE{Linda2019e,
title = {Detecting correlation between extreme probability events},
journal = {International Journal of General Systems },
volume = {49},
author = {Coletti, G. and van der Gaag, L.C. and Petturiti, D. and Vantaggi, B.},
number = {1},
pages = {64--87},
year = {2020},
doi = {10.1080/03081079.2019.1692005},
url = {}
}
DownloadCorani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2020). Probabilistic reconciliation of hierarchical forecast via Bayes’ rule. In Hutter, Frank, Kersting, Kristian, Lijffijt, Jefrey, Valera, Isabel (Eds), Joint European Conference on Machine Learning and Knowledge Discovery in Database (ECML- PKDD), Springer International Publishing, pp. 211–226.
Probabilistic reconciliation of hierarchical forecast via Bayes’ rule
Authors: Corani, G. and Azzimonti, D. and Augusto, J.P.S.C. and Zaffalon, M.
Year: 2020
Abstract: We present a novel approach for reconciling hierarchical forecasts, based on Bayes’ rule. We define a prior distribution for the bottom time series of the hierarchy, based on the bottom base forecasts. Then we update their distribution via Bayes’ rule, based on the base forecasts
for the upper time series. Under the Gaussian assumption, we derive the updating in closed-form. We derive two algorithms, which differ as for the assumed independencies. We discuss their relation with the MinT reconciliation algorithm and with the Kalman filter, and we compare them experimentally.
Published in Hutter, Frank, Kersting, Kristian, Lijffijt, Jefrey, Valera, Isabel (Eds), Joint European Conference on Machine Learning and Knowledge Discovery in Database (ECML- PKDD), Springer International Publishing, pp. 211–226.
Probabilistic reconciliation of hierarchical forecast via Bayes’ rule
@INPROCEEDINGS{corani2020a,
title = {Probabilistic reconciliation of hierarchical forecast via {B}ayes’ rule},
editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel},
publisher = {Springer International Publishing},
booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Database ({ECML}- {PKDD})},
author = {Corani, G. and Azzimonti, D. and Augusto, J.P.S.C. and Zaffalon, M.},
pages = {211--226},
year = {2020},
doi = {10.1007/978-3-030-67664-3_13},
url = {}
}
DownloadCózar, J., Cabañas, R., Salmerón, A., Masegosa, A.R. (2020). Inferpy: probabilistic modeling with deep neural networks made easy. Neurocomputing 415, pp. 408–410.
Inferpy: probabilistic modeling with deep neural networks made easy
Authors: Cózar, J. and Cabañas, R. and Salmerón, A. and Masegosa, A.R.
Year: 2020
Abstract: InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this package allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way. InferPy is built on top of Tensorflow Probability and Keras.
Published in Neurocomputing 415, pp. 408–410.
Inferpy: probabilistic modeling with deep neural networks made easy
@ARTICLE{cabanas2020c,
title = {Inferpy: probabilistic modeling with deep neural networks made easy},
journal = {Neurocomputing},
volume = {415},
author = {C\'ozar, J. and Caba\~nas, R. and Salmer\'on, A. and Masegosa, A.R.},
pages = {408--410},
year = {2020},
doi = {10.1016/j.neucom.2020.07.117},
url = {}
}
DownloadFisher, H., Gittoes, M., Evans, L., Bitchell, L., Mullen, R., Scutari, M. (2020). An interdisciplinary examination of stress and injury occurrence in athletes. Frontiers in Sports and Active Living 2, 595619.
An interdisciplinary examination of stress and injury occurrence in athletes
Authors: Fisher, H. and Gittoes, M. and Evans, L. and Bitchell, L. and Mullen, R. and Scutari, M.
Year: 2020
Abstract: This paper adopts a novel, interdisciplinary approach to explore the relationship between stress-related psychosocial factors, physiological markers and occurrence of injury in athletes using a repeated measures prospective design. At four data collection time-points, across 1-year of a total 2-year data collection period, athletes completed measures of major life events, the reinforcement sensitivity theory personality questionnaire, muscle stiffness, heart rate variability and postural stability, and reported any injuries they had sustained since the last data collection. Two Bayesian networks were used to examine the relationships between variables and model the changes between data collection points in the study. Findings revealed muscle stiffness to have the strongest relationship with injury occurrence, with high levels of stiffness increasing the probability of sustaining an injury. Negative life events did not increase the probability of injury occurrence at any single time-point; however, when examining changes between time points, increases in negative life events did increase the probability of injury. In addition, the combination of increases in negative life events and muscle stiffness resulted in the greatest probability of sustaining an injury. Findings demonstrated the importance of both an interdisciplinary approach and a repeated measures design to furthering our understanding of the relationship between stress-related markers and injury occurrence.
Published in Frontiers in Sports and Active Living 2, 595619.
An interdisciplinary examination of stress and injury occurrence in athletes
@ARTICLE{scutari20f,
title = {An interdisciplinary examination of stress and injury occurrence in athletes},
journal = {Frontiers in Sports and Active Living},
volume = {2},
author = {Fisher, H. and Gittoes, M. and Evans, L. and Bitchell, L. and Mullen, R. and Scutari, M.},
pages = {595619},
year = {2020},
doi = {10.3389/fspor.2020.595619},
url = {}
}
DownloadForgione, M., Piga, D. (2020). Model structures and fitting criteria for system identification with neural networks. In Proceedings of the 14th IEEE International Conference Application of Information and Communication Technologies (AICT 20).
Model structures and fitting criteria for system identification with neural networks
Authors: Forgione, M. and Piga, D.
Year: 2020
Abstract: This paper focuses on the identification of dynamical systems with tailor-made model structures, where neural networks are used to approximate uncertain components and domain knowledge is retained, if avail-able. These model structures are fitted to measured data using different criteria including a computationally efficient approach minimizing a regularized multi-step ahead simulation error. In this approach, the neural network parameters are estimated along with the initial conditions used to simulate the output signal in small-size subsequences. A regularization term is included in the fitting cost in order to enforce these initial conditions to be consistent with the estimated system dynamics. Pitfalls and limitations of naive one-step prediction and simulation error minimization are also discussed.
Published in Proceedings of the 14th IEEE International Conference Application of Information and Communication Technologies (AICT 20).
Model structures and fitting criteria for system identification with neural networks
@INPROCEEDINGS{forgione2020b,
title = {Model structures and fitting criteria for system identification with neural networks},
booktitle = {Proceedings of the 14th {IEEE} International Conference Application of Information and Communication Technologies ({AICT} 20)},
author = {Forgione, M. and Piga, D.},
year = {2020},
doi = {},
url = {}
}
DownloadForgione, M., Piga, D., Bemporad, A. (2020). Efficient Calibration of Embedded MPC. In Proceedings of the 21st IFAC World Congress (IFAC 20) 53(2), pp. 5189–5194.
Efficient Calibration of Embedded MPC
Authors: Forgione, M. and Piga, D. and Bemporad, A.
Year: 2020
Abstract: Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challenge for the practitioner applying MPC is the need of tuning a large number of parameters such as prediction and control horizons, weight matrices of the MPC cost function, and observer gains, according to different trade-offs. The MPC design task is even more involved when the control law has to be deployed to an embedded hardware unit endowed with limited computational resources. In this case, real-time system requirements limit the complexity of the applicable MPC configuration, engendering additional design tradeoffs and requiring to tune further parameters, such as the sampling time and the tolerances used in the on-line numerical solver. To take into account closed-loop performance and real-time requirements, in this paper we tackle the em-bedded MPC design problem using a global, data-driven, optimization approach We showcase the potential of this approach by tuning an MPC controller on two hardware platforms characterized by largely different computational capabilities
Published in Proceedings of the 21st IFAC World Congress (IFAC 20) IFAC-PapersOnLine 53(2), pp. 5189–5194.
Note: 21st IFAC World Congress
Efficient Calibration of Embedded MPC
@INPROCEEDINGS{forgione2020a,
title = {Efficient {C}alibration of {E}mbedded {MPC}},
journal = {{IFAC}-{PapersOnLine}},
volume = {53},
booktitle = {Proceedings of the 21st {IFAC} World Congress ({IFAC} 20)},
author = {Forgione, M. and Piga, D. and Bemporad, A.},
number = {2},
pages = {5189--5194},
year = {2020},
doi = {10.1016/j.ifacol.2020.12.1188},
url = {}
}
Downloadvan der Gaag, L.C., Bolt, J.H. (2020). Poset representations for sets of elementary triplets. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 521–532.
Poset representations for sets of elementary triplets
Authors: van der Gaag, L.C. and Bolt, J.H.
Year: 2020
Abstract: Semi-graphoid independence relations, composed of independence triplets, are typically exponentially large in the number of variables involved. For compact representation of such a relation, just a subset of its triplets, called a basis, are listed explicitly, while its other triplets remain implicit through a set of derivation rules. Two types of basis were defined for this purpose, which are the dominant-triplet basis and the elementary-triplet basis, of which the latter is commonly assumed to be significantly larger in size in general. In this paper we introduce the elementary po-triplet as a compact representation of multiple elementary triplets, by using separating posets. By exploiting this new representation, the size of an elementary-triplet basis can be reduced considerably. For computing the elementary closure of a starting set of po-triplets, we present an elegant algorithm that operates on the least and largest elements of the separating posets involved.
Published in Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 521–532.
Poset representations for sets of elementary triplets
@INPROCEEDINGS{Linda2020c,
title = {Poset representations for sets of elementary triplets},
publisher = {JMLR.org},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {van der Gaag, L.C. and Bolt, J.H.},
pages = {521--532},
year = {2020},
doi = {},
url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}
}
Downloadvan der Gaag, L.C., Renooij, S., Facchini, A. (2020). Building causal interaction models by recursive unfolding. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 509–520.
Building causal interaction models by recursive unfolding
Authors: van der Gaag, L.C. and Renooij, S. and Facchini, A.
Year: 2020
Abstract: Causal interaction models, such as the well-known noisy-or and leaky noisy-or models, have become quite popular as a means to parameterize conditional probability tables for Bayesian networks. In this paper we focus on the engineering of subnetworks to represent such models and present a novel technique called recursive unfolding for this purpose. This technique allows inserting, removing and merging cause variables in an interaction model at will, without affecting the underlying represented information. We detail the technique, with the recursion invariants involved, and illustrate its practical use for Bayesian-network engineering by means of a small example.
Published in Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 509–520.
Building causal interaction models by recursive unfolding
@INPROCEEDINGS{Linda2020a,
title = {Building causal interaction models by recursive unfolding},
publisher = {JMLR.org},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {van der Gaag, L.C. and Renooij, S. and Facchini, A.},
pages = {509--520},
year = {2020},
doi = {},
url = {https://proceedings.mlr.press/v138/van-der-gaag20a.html}
}
DownloadGeh, R., Mauá, D.D., Antonucci, A. (2020). Learning probabilistic sentential decision diagrams by sampling. In Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning (KMILE 2020), SBC, Porto Alegre, RS, Brasil, pp. 129–136.
Learning probabilistic sentential decision diagrams by sampling
Authors: Geh, R. and Mauá, D.D. and Antonucci, A.
Year: 2020
Abstract: Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit’s structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit’s graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains.
Published in Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning (KMILE 2020), SBC, Porto Alegre, RS, Brasil, pp. 129–136.
Learning probabilistic sentential decision diagrams by sampling
@INPROCEEDINGS{antonucci2020c,
title = {Learning probabilistic sentential decision diagrams by sampling},
publisher = {SBC},
address = {Porto Alegre, RS, Brasil},
booktitle = {Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning ({KMILE} 2020)},
author = {Geh, R. and Mau\'a, D.D. and Antonucci, A.},
pages = {129--136},
year = {2020},
doi = {10.5753/kdmile.2020.11968},
url = {}
}
DownloadHuber, D., Cabañas, R., Antonucci, A., Zaffalon, M. (2020). CREMA: a Java library for credal network inference. In Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 613–616.
CREMA: a Java library for credal network inference
Authors: Huber, D. and Cabañas, R. and Antonucci, A. and Zaffalon, M.
Year: 2020
Abstract: We present CREMA (Credal Models Algorithms), a Java library for inference in credal networks. These models are analogous to Bayesian networks, but their local parameters are only constrained to vary in, so-called credal, sets. Inference in credal networks is intended as the computation of the bounds of a query with respect to those local variations. For credal networks the task is harder than in Bayesian networks, being NPPP-hard in general models. Yet, scalable approximate algorithms have been shown to provide good accuracies on large or dense models, while exact techniques can be designed to process small or sparse models. CREMA embeds these algorithms and also offers an API to build and query credal networks together with a specification format. This makes CREMA, whose features are discussed and described by a simple example, the most advanced tool for credal network modelling and inference developed so far.
Published in Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 613–616.
CREMA: a Java library for credal network inference
@INPROCEEDINGS{huber2020a,
title = {{CREMA}: a {J}ava library for credal network inference},
editor = {Jaeger, M. and Nielsen, T.D.},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},
pages = {613--616},
year = {2020},
doi = {},
url = {https://pgm2020.cs.aau.dk}
}
DownloadKanjirangat, V., Mellace, S., Antonucci, A. (2020). Temporal embeddings and transformer models for narrative text understanding. In Third International Workshop on Narrative Extraction from Texts (Text2Story 20), 42nd European Conference on Information Retrieval (ECIR 20), ceur.
Temporal embeddings and transformer models for narrative text understanding
Authors: Kanjirangat, V. and Mellace, S. and Antonucci, A.
Year: 2020
Abstract: We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. An empirical analysis of the corresponding character trajectories shows that such approaches are effective in depicting dynamic evolution. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters. The empirical validation shows that such events (e.g., two characters belonging to the same family) might be spotted with good accuracy, even when using automatically annotated data. This provides a deeper understanding of narrative plots based on the identification of key facts. Standard clustering techniques are finally used for character de-aliasing, a necessary pre-processing step for both approaches. Overall, deep learning models appear to be suitable for narrative text understanding, while also providing a challenging and unexploited benchmark for general natural language understanding.
Published in Third International Workshop on Narrative Extraction from Texts (Text2Story 20), 42nd European Conference on Information Retrieval (ECIR 20), ceur.
Temporal embeddings and transformer models for narrative text understanding
@INPROCEEDINGS{vani2020b,
title = {Temporal embeddings and transformer models for narrative text understanding},
publisher = {ceur},
booktitle = {Third International Workshop on Narrative Extraction {f}rom Texts ({Text2Story} 20), 42nd European Conference on Information Retrieval ({ECIR} 20)},
author = {Kanjirangat, V. and Mellace, S. and Antonucci, A.},
year = {2020},
doi = {},
url = {http://ceur-ws.org/Vol-2593/paper9.pdf}
}
DownloadKanjirangat, V., Mitrovic, S., Antonucci, A., Rinaldi, F. (2020). SST-BERT at SemEval-2020 task 1: semantic shift tracing by clustering in BERT-based embedding spaces. In SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection.To appear In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain, pp. 214–221.
SST-BERT at SemEval-2020 task 1: semantic shift tracing by clustering in BERT-based embedding spaces
Authors: Kanjirangat, V. and Mitrovic, S. and Antonucci, A. and Rinaldi, F.
Year: 2020
Abstract: Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences. The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines.
Published in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection.To appear In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain, pp. 214–221.
SST-BERT at SemEval-2020 task 1: semantic shift tracing by clustering in BERT-based embedding spaces
@INPROCEEDINGS{vani2020semeval,
title = {{SST}-{BERT} at {SemEval}-2020 task 1: semantic shift tracing by clustering in {BERT}-based embedding spaces},
booktitle = {{SemEval}-2020 Task 1: Unsupervised Lexical Semantic Change Detection.To {a}ppear In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain},
author = {Kanjirangat, V. and Mitrovic, S. and Antonucci, A. and Rinaldi, F.},
pages = {214--221},
year = {2020},
doi = {10.18653/v1/2020.semeval-1.26},
url = {}
}
DownloadKern, H., Corani, G., Huber, D., Vermes, N., Zaffalon, M., Varini, M., Wenzel, C., Fringer, A. (2020). Impact on place of death in cancer patients: a causal exploration in southern switzerland. BMC Palliative Care 19, 160.
Impact on place of death in cancer patients: a causal exploration in southern switzerland
Authors: Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M. and Varini, M. and Wenzel, C. and Fringer, A.
Year: 2020
Abstract:
Background
Most terminally ill cancer patients prefer to die at home, but a majority die in institutional settings. Research questions about this discrepancy have not been fully answered. This study applies artificial intelligence and machine learning techniques to explore the complex network of factors and the cause-effect relationships affecting the place of death, with the ultimate aim of developing policies favouring home-based end-of-life care.
Methods
A data mining algorithm and a causal probabilistic model for data analysis were developed with information derived from expert knowledge that was merged with data from 116 deceased cancer patients in southern Switzerland. This data set was obtained via a retrospective clinical chart review.
Results
Dependencies of disease and treatment-related decisions demonstrate an influence on the place of death of 13%. Anticancer treatment in advanced disease prevents or delays communication about the end of life between oncologists, patients and families. Unknown preferences for the place of death represent a great barrier to a home death. A further barrier is the limited availability of family caregivers for terminal home care. The family’s preference for the last place of care has a high impact on the place of death of 51%, while the influence of the patient’s preference is low, at 14%. Approximately one-third of family systems can be empowered by health care professionals to provide home care through open end-of-life communication and good symptom management. Such intervention has an influence on the place of death of 17%. If families express a convincing preference for home care, the involvement of a specialist palliative home care service can increase the probability of home deaths by 24%.
Conclusion
Concerning death at home, open communication about death and dying is essential. Furthermore, for the patient preference for home care to be respected, the family’s decision for the last place of care seems to be key. The early initiation of family-centred palliative care and the provision of specialist palliative home care for patients who wish to die at home are suggested.
Published in BMC Palliative Care 19, Research Square, 160.
Impact on place of death in cancer patients: a causal exploration in southern switzerland
@ARTICLE{Kern2020,
title = {Impact on place of death in cancer patients: a causal exploration in southern switzerland},
journal = {{BMC} Palliative Care},
publisher = {Research Square},
volume = {19},
author = {Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M. and Varini, M. and Wenzel, C. and Fringer, A.},
pages = {160},
year = {2020},
doi = {10.21203/rs.3.rs-29758/v3},
url = {}
}
DownloadLaurain, V., Tóth, R., Piga, D., Darwish, M.A.H. (2020). Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification. Automatica 115, 108914.
Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification
Authors: Laurain, V. and Tóth, R. and Piga, D. and Darwish, M.A.H.
Year: 2020
Abstract: Reproducing kernel Hilbert spaces, Elastic net, Support vector machines, Gaussian processes, Non-parametric estimation, Linear parameter-varying systems, Model order selection",
abstract = "Function estimation using the Reproducing Kernel Hilbert Space (RKHS) framework is a powerful tool for identification of a general class of nonlinear dynamical systems without requiring much a priori information on model orders and nonlinearities involved. However, the high degrees-of-freedom (DOFs) of RKHS estimators has its price, as in case of large scale function estimation problems, they often require a serious amount of data samples to explore the search space adequately for providing high-performance model estimates. In cases where nonlinear dynamic relations can be expressed as a sum of functions, the literature proposes solutions to this issue by enforcing sparsity for adequate restriction of the DOFs of the estimator, resulting in parsimonious model estimates. Unfortunately, all existing solutions are based on greedy approaches, leading to optimization schemes which cannot guarantee convergence to the global optimum. In this paper, we propose an L1-regularized non-parametric RKHS estimator which is the solution of a quadratic optimization problem. Effectiveness of the scheme is demonstrated on the non-parametric identification problem of LPV-IO models where the method solves simultaneously (i) the model order selection problem (in terms of number of input–output lags and input delay in the model structure) and (ii) determining the unknown functional dependency of the model coefficients on the scheduling variable directly from data. The paper also provides an extensive simulation study to illustrate effectiveness of the proposed scheme.
Published in Automatica 115, 108914.
Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification
@ARTICLE{piga2020b,
title = {Sparse {RKHS} estimation via globally convex optimization and its application in {LPV}-{IO} identification},
journal = {Automatica},
volume = {115},
author = {Laurain, V. and T\'oth, R. and Piga, D. and Darwish, M.A.H.},
pages = {108914},
year = {2020},
doi = {10.1016/j.automatica.2020.108914},
url = {}
}
DownloadLiew, B.X.W., Peolsson, A., Scutari, M., Löfgren, H., Wibault, J., r A Dedering, , Öberg, B., Zsigmond, P., Falla, D. (2020). Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks. European Journal of Pain 24(5), pp. 909–920.
Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks
Authors: Liew, B.X.W. and Peolsson, A. and Scutari, M. and Löfgren, H. and Wibault, J. and r A Dedering, and Öberg, B. and Zsigmond, P. and Falla, D.
Year: 2020
Abstract: Background
Rehabilitation approaches should be based on an understanding of the mechanisms underpinning functional recovery. Yet, the mediators that drive an improvement in post-surgical pain-related disability in individuals with cervical radiculopathy (CR) are unknown. The aim of the present study is to use Bayesian networks (BN) to learn the probabilistic relationships between physical and psychological factors, and pain-related disability in CR.
Methods
We analysed a prospective cohort dataset of 201 post-surgical individuals with CR. In all, 15 variables were used to build a BN model: age, sex, neck muscle endurance, neck range of motion, neck proprioception, hand grip strength, self-efficacy, catastrophizing, depression, somatic perception, arm pain intensity, neck pain intensity and disability.
Results
A one point increase in a change of self-efficacy at 6 months was associated with a 0.09 point decrease in a change in disability at 12 months (t = −64.09, p < .001). Two pathways led to a change in disability: a direct path leading from a change in self-efficacy at 6 months to disability, and an indirect path which was mediated by neck and arm pain intensity changes at 6 and 12 months.
Conclusions
This is the first study to apply BN modelling to understand the mechanisms of recovery in post-surgical individuals with CR. Improvements in pain-related disability was directly and indirectly driven by changes in self-efficacy levels. The present study provides potentially modifiable mediators that could be the target of future intervention trials. BN models could increase the precision of treatment and outcome assessment of individuals with CR.
Significance
Using Bayesian Network modelling, we found that changes in self-efficacy levels at 6-month post-surgery directly and indirectly influenced the change in disability in individuals with CR. A mechanistic understanding of recovery provides potentially modifiable mediators that could be the target of future intervention trials.
Published in European Journal of Pain 24(5), pp. 909–920.
Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks
@ARTICLE{scutari20a,
title = {Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks},
journal = {European Journal of Pain},
volume = {24},
author = {Liew, B.X.W. and Peolsson, A. and Scutari, M. and L\"ofgren, H. and Wibault, J. and r A Dedering, and \"Oberg, B. and Zsigmond, P. and Falla, D.},
number = {5},
pages = {909--920},
year = {2020},
doi = {10.1002/ejp.1537},
url = {}
}
DownloadMangili, F., Broggini, D., Antonucci, A. (2020). Conversational recommender system by Bayesian methods. In Davis, Jesse, Tabia, Karim (Eds), Proceedings of the Fourteenth International Conference on Scalable Uncertainty Management (SUM 2020), Lecture Notes in Artificial Intelligence 12322, Springer, Cham, pp. 200–213.
Conversational recommender system by Bayesian methods
Authors: Mangili, F. and Broggini, D. and Antonucci, A.
Year: 2020
Abstract: We present a Bayesian approach to conversational recommender systems. After any interaction with the user, a probability mass function over the items is updated by the system. The conversational feature corresponds to a sequential discovery of the user preferences based on questions. Information-theoretic criteria are used to optimally shape the interactions and decide when the conversation ends. Most probable items are consequently recommended. Dedicated elicitation techniques for the prior probabilities of the parameters modelling the interactions are derived from basic structural judgements based on logical compatibility and symmetry assumptions. Such prior knowledge is combined with data for better item discrimination. Our Bayesian approach is validated against matrix factorization techniques for cold-start recommendations based on metadata using the popular benchmark data set MovieLens. Results show that the proposed approach allows to considerably reduce the number of interactions while maintaining good ranking performance.
Published in Davis, Jesse, Tabia, Karim (Eds), Proceedings of the Fourteenth International Conference on Scalable Uncertainty Management (SUM 2020), Lecture Notes in Artificial Intelligence 12322, Springer, Cham, pp. 200–213.
Conversational recommender system by Bayesian methods
@INPROCEEDINGS{mangili2020b,
title = {Conversational recommender system by {B}ayesian methods},
editor = {Davis, Jesse and Tabia, Karim},
publisher = {Springer, Cham},
series = {Lecture Notes in Artificial Intelligence},
volume = {12322},
booktitle = {Proceedings of the Fourteenth International Conference on Scalable Uncertainty Management ({SUM} 2020)},
author = {Mangili, F. and Broggini, D. and Antonucci, A.},
pages = {200--213},
year = {2020},
doi = {10.1007/978-3-030-58449-8_14},
url = {}
}
DownloadMangili, F., Broggini, D., Antonucci, A., Alberti, M., Cimasoni, L. (2020). A Bayesian approach to conversational recommendation systems. AAAI 2020 Workshop on Interactive and Conversational Recommendation Systems (WICRS-20).
A Bayesian approach to conversational recommendation systems
Authors: Mangili, F. and Broggini, D. and Antonucci, A. and Alberti, M. and Cimasoni, L.
Year: 2020
Abstract: We present a conversational recommendation system based on a Bayesian approach. A probability mass function over the items is updated after any interaction with the user, with information-theoretic criteria optimally shaping the interaction and deciding when the conversation should be terminated and the most probable item consequently recommended. Dedicated elicitation techniques for the prior probabilities of the parameters modeling the interactions are derived from basic structural judgements. Such prior information can be combined with historical data to discriminate items with different recommendation histories. A case study based on the application of this approach to stagend.com, an online platform for booking entertainers, is finally discussed together with an empirical analysis showing the advantages in terms of recommendation quality and efficiency.
Published in AAAI 2020 Workshop on Interactive and Conversational Recommendation Systems (WICRS-20).
Note: Accepted for oral presentation
A Bayesian approach to conversational recommendation systems
@ARTICLE{mangili2020a,
title = {A {B}ayesian approach to conversational recommendation systems},
journal = {{AAAI} 2020 Workshop on Interactive and Conversational Recommendation Systems ({WICRS}-20)},
author = {Mangili, F. and Broggini, D. and Antonucci, A. and Alberti, M. and Cimasoni, L.},
year = {2020},
doi = {},
url = {https://sites.google.com/view/wicrs2020}
}
DownloadMattei, L., Antonucci, A., Mauá, D.D., Facchini, A., Llerena, J.V. (2020). Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning 125, pp. 26–48.
Tractable inference in credal sentential decision diagrams
Authors: Mattei, L. and Antonucci, A. and Mauá, D.D. and Facchini, A. and Llerena, J.V.
Year: 2020
Abstract: Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of joint probability mass functions defined over sets of Boolean variables, that are also consistent with the logical constraints defined by the circuit. The probabilities in such a model are usually “learned” from a set of observations. This leads to overconfident and prior-dependent inferences when data are scarce, unreliable or conflicting. In this work, we develop the credal sentential decision diagrams, a generalisation of their probabilistic counterpart that allows for replacing the local probabilities with (so-called credal) sets of mass functions. These models induce a joint credal set over the set of Boolean variables, that sharply assigns probability zero to states inconsistent with the logical constraints. Three inference algorithms are derived for these models. These allow to compute: (i) the lower and upper probabilities of an observation for an arbitrary number of variables; (ii) the lower and upper conditional probabilities for the state of a single variable given an observation; (iii) whether or not all the probabilistic sentential decision diagrams compatible with the credal specification have the same most probable explanation of a given set of variables given an observation of the other variables. These inferences are tractable, as all the three algorithms, based on bottom-up traversal with local linear programming tasks on the disjunctive gates, can be solved in polynomial time with respect to the circuit size. The first algorithm is always exact, while the remaining two might induce a conservative (outer) approximation in the case of multiply connected circuits. A semantics for this approximation together with an auxiliary algorithm able to decide whether or not the result is exact is also provided together with a brute-force characterization of the exact inference in these cases. For a first empirical validation, we consider a simple application based on noisy seven-segment display images. The credal models are observed to properly distinguish between easy and hard-to-detect instances and outperform other generative models not able to cope with logical constraints.
Published in International Journal of Approximate Reasoning 125, pp. 26–48.
Tractable inference in credal sentential decision diagrams
@ARTICLE{antonucci2020d,
title = {Tractable inference in credal sentential decision diagrams},
journal = {International Journal of Approximate Reasoning},
volume = {125},
author = {Mattei, L. and Antonucci, A. and Mau\'a, D.D. and Facchini, A. and Llerena, J.V.},
pages = {26--48},
year = {2020},
doi = {10.1016/j.ijar.2020.06.005},
url = {}
}
DownloadMauà, D.D., Ribeiro, H., Katague, G., Antonucci, A. (2020). Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. In Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 293–304.
Two reformulation approaches to maximum-a-posteriori inference in sum-product networks
Authors: Mauà, D.D. and Ribeiro, H. and Katague, G. and Antonucci, A.
Year: 2020
Abstract: There exists a dichotomy between classical probabilistic graphical models, such as Bayesian net- works (BNs), and modern tractable models, such as sum-product networks (SPNs). The former generally have intractable inference, but provide a high level of interpretability, while the latter admit a wide range of tractable inference routines, but are typically harder to interpret. Due to this dichotomy, tools to convert between BNs and SPNs are desirable. While one direction – compiling BNs into SPNs – is well discussed in Darwiche’s seminal work on arithmetic circuit compilation, the converse direction – decompiling SPNs into BNs – has received surprisingly little attention. In this paper, we fill this gap by proposing SPN2BN, an algorithm that decompiles an SPN into a BN. SPN2BN has several salient features when compared to the only other two works decompiling SPNs. Most significantly, the BNs returned by SPN2BN are minimal independence-maps that are more parsimonious with respect to the introduction of latent variables. Secondly, the output BN produced by SPN2BN can be precisely characterized with respect to a compiled BN. More specifically, a certain set of directed edges will be added to the input BN, giving what we will call the moral-closure. Lastly, it is established that our compilation-decompilation process is idempotent. This has practical significance as it limits the size of the decompiled SPN.
Published in Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 293–304.
Two reformulation approaches to maximum-a-posteriori inference in sum-product networks
@INPROCEEDINGS{antonucci2020b,
title = {Two reformulation approaches to maximum-a-posteriori inference in sum-product networks},
editor = {Jaeger, M. and Nielsen, T.D.},
publisher = {PMLR},
address = {Aalborg, Denmark},
series = {Proceedings of Machine Learning Research},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Mau\`a, D.D. and Ribeiro, H. and Katague, G. and Antonucci, A.},
pages = {293--304},
year = {2020},
doi = {},
url = {https://proceedings.mlr.press/v138/maua20a.html}
}
DownloadMejari, M., Breschi, V., Naik, V.V., Piga, D. (2020). A bias-correction approach for the identification of piecewise affine output-error models. In 21st IFAC World Congress (IFAC 2020) 53(2), Berlin, Germany, pp. 1096–1101.
A bias-correction approach for the identification of piecewise affine output-error models
Authors: Mejari, M. and Breschi, V. and Naik, V.V. and Piga, D.
Year: 2020
Abstract: The paper presents an algorithm for the identification of PieceWise Affine Output-Error (PWA-OE) models, which involves the estimation of the parameters defining affine submodels as well as a partition of the regressor space. For the estimation of affine submodel parameters, a bias-correction scheme is presented to correct the bias in the least-squares estimates which is caused by the output-error noise structure. The obtained bias-corrected estimates are proven to be consistent under suitable assumptions. The bias-correction method is then combined with a recursive estimation algorithm for clustering the regressors.
These clusters are used to compute a partition of the regressor space by employing linear multi-category discrimination. The effectiveness of the proposed methodology is demonstrated via a simulation case study.
Published in 21st IFAC World Congress (IFAC 2020) IFAC-PapersOnLine 53(2), Berlin, Germany, pp. 1096–1101.
A bias-correction approach for the identification of piecewise affine output-error models
@INPROCEEDINGS{mejari2020a,
title = {A bias-correction approach for the identification of piecewise affine output-error models},
journal = {{IFAC}-{PapersOnLine}},
address = {Berlin, Germany},
volume = {53},
booktitle = {21st {IFAC} World Congress ({IFAC} 2020)},
author = {Mejari, M. and Breschi, V. and Naik, V.V. and Piga, D.},
number = {2},
pages = {1096--1101},
year = {2020},
doi = {10.1016/j.ifacol.2020.12.1307},
url = {https://www.ifac2020.org}
}
DownloadMejari, M., Breschi, V., Piga, D. (2020). Recursive bias-correction method for identification of piecewise affine output-error models. IEEE Control Systems Letters 4, pp. 970–975.
Recursive bias-correction method for identification of piecewise affine output-error models
Authors: Mejari, M. and Breschi, V. and Piga, D.
Year: 2020
Abstract: Learning PieceWise Affine Output-Error (PWA-OE) models from data requires to estimate a finite set of affine output-error sub-models as well as a partition of the regressors space over which the sub-models are defined. For an output-error type noise structure, the algorithms based on ordinary least squares (LS) fail to compute a consistent estimate of the sub-model parameters. On the other hand, the prediction error methods (PEMs) provide a consistent parameter estimate, however, they require to solve a non-convex optimization problem for which the numerical algorithms may get trapped in a local minimum, leading to inaccurate estimates. In this paper, we propose a recursive bias-correction scheme for identifying PWA-OE models, retaining the computational efficiency of the standard LS algorithms while providing a consistent estimate of the sub-model parameters, under suitable assumptions. The proposed approach allows one to recursively update the estimates of the sub-models parameters and to cluster the regressors. Linear multi-category techniques are then employed to estimate a partition of the regressor space based on the estimated clusters. The performance of the proposed algorithm is demonstrated via an academic example.
Published in IEEE Control Systems Letters 4, pp. 970–975.
Recursive bias-correction method for identification of piecewise affine output-error models
@ARTICLE{mejari2020b,
title = {Recursive bias-correction method for identification of piecewise affine output-error models},
journal = {{IEEE} Control Systems Letters},
volume = {4},
author = {Mejari, M. and Breschi, V. and Piga, D.},
pages = {970--975},
year = {2020},
doi = {10.1109/LCSYS.2020.2998282},
url = {}
}
DownloadMejari, M., Naik, V.V., Piga, D., Bemporad, A. (2020). Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming. International Journal of Robust and Nonlinear Control 30(15), pp. 5802–5819.
Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming
Authors: Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.
Year: 2020
Abstract: Summary This article presents a two-stage algorithm for piecewise affine (PWA) regression. In the first stage, a moving horizon strategy is employed to simultaneously estimate the model parameters and to classify the training data by solving a small-size mixed-integer quadratic programming problem. In the second stage, linear multicategory separation methods are used to partition the regressor space. The framework of PWA regression is adapted to the identification of PWA AutoRegressive with eXogenous input (PWARX) models as well as linear parameter-varying (LPV) models. The performance of the proposed algorithm is demonstrated on an academic example and on two benchmark experimental case studies. The first experimental example concerns modeling the placement process in a pick-and-place machine, while the second one consists in the identification of an LPV model describing the input-output relationship of an electronic bandpass filter with time-varying resonant frequency.
Published in International Journal of Robust and Nonlinear Control 30(15), pp. 5802–5819.
Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming
@ARTICLE{mejari2020c,
title = {Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming},
journal = {International Journal of Robust and Nonlinear Control},
volume = {30},
author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
number = {15},
pages = {5802--5819},
year = {2020},
doi = {https://doi.org/10.1002/rnc.5198},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.5198}
}
DownloadMiranda, E., Zaffalon, M. (2020). Compatibility, desirability, and the running intersection property. Artificial Intelligence 283, 103724.
Compatibility, desirability, and the running intersection property
Authors: Miranda, E., Zaffalon, M.
Year: 2020
Abstract: Compatibility is the problem of checking whether some given probabilistic assessments have a common joint probabilistic model. When the assessments are unconditional, the problem is well established in the literature and finds a solution through the running intersection property (RIP). This is not the case of conditional assessments. In this paper, we study the compatibility problem in a very general setting: any possibility space, unrestricted domains, imprecise (and possibly degenerate) probabilities. We extend the unconditional case to our setting, thus generalising most of previous results in the literature. The conditional case turns out to be fundamentally different from the unconditional one. For such a case, we prove that the problem can still be solved in general by RIP but in a more involved way: by constructing a junction tree and propagating information over it. Still, RIP does not allow us to optimally take advantage of sparsity: in fact, conditional compatibility can be simplified further by joining junction trees with coherence graphs.
Published in Artificial Intelligence 283, 103724.
Compatibility, desirability, and the running intersection property
@ARTICLE{zaffalon2020a,
title = {Compatibility, desirability, and the running intersection property},
journal = {Artificial Intelligence},
volume = {283},
author = {Miranda, E., Zaffalon, M.},
pages = {103724},
year = {2020},
doi = {10.1016/j.artint.2020.103274},
url = {}
}
DownloadPiga, D., Bemporad, A., Benavoli, A. (2020). Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models. Automatica 117, 109002.
Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models
Authors: Piga, D. and Bemporad, A. and Benavoli, A.
Year: 2020
Abstract: This paper addresses batch (offline) and recursive (online) Bayesian inference of Piecewise Affine (PWA) regression models. By exploiting the particular structure of PWA models, efficient Rao-Blackwellized Monte Carlo sampling algorithms are developed to approximate the joint posterior distribution of the model parameters. Only the marginal posterior of the parameters used to describe the regressor-space partition is approximated, either in a batch mode using a Metropolis–Hastings Markov-Chain Monte Carlo (MCMC) sampler, or sequentially using particle filters, while the conditional distribution of the other model parameters is computed analytically. Probability distributions for the predicted outputs given new test inputs are derived and modifications of the proposed approaches to address maximum-a-posteriori estimate are discussed. The performance of the proposed algorithms is shown via a numerical example and through a benchmark case study on data-driven modelling of the electronic component placement process in a pick-and-place machine.
Published in Automatica 117, 109002.
Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models
@ARTICLE{piga2020a,
title = {Rao-{B}lackwellized sampling for batch and recursive {B}ayesian inference of piecewise affine models},
journal = {Automatica},
volume = {117},
author = {Piga, D. and Bemporad, A. and Benavoli, A.},
pages = {109002},
year = {2020},
doi = {10.1016/j.automatica.2020.109002},
url = {}
}
DownloadPiga, D., Breschi, V., Bemporad, A. (2020). Estimation of jump box–jenkins models. Automatica 120, 109126.
Estimation of jump box–jenkins models
Authors: Piga, D. and Breschi, V. and Bemporad, A.
Year: 2020
Abstract: Jump Box–Jenkins (BJ) models are a collection of a finite set of linear dynamical submodels in BJ form that switch over time, according to a Markov chain. This paper addresses the problem of maximum-a-posteriori estimation of jump BJ models from a given training input/output dataset. The proposed solution method estimates the coefficients of the BJ submodels, the state transition probabilities of the Markov chain regulating the switching of operating modes, and the corresponding mode sequence hidden in the dataset. In particular, the posterior distribution of all the unknown variables characterizing the jump BJ model is derived and then maximized using a coordinate ascent algorithm. The resulting estimation algorithm alternates between Gauss–Newton optimization of the coefficients of the BJ submodels, a method derived based on an instance of prediction error methods tailored to BJ models with switching coefficients, and approximated dynamic programming for optimization of the sequence of active modes. The quality of the proposed estimation approach is evaluated on a numerical example based on synthetic data and in a case study related to segmentation of honeybee dances.
Published in Automatica 120, 109126.
Estimation of jump box–jenkins models
@ARTICLE{piga2020c,
title = {Estimation of jump box--jenkins models},
journal = {Automatica},
volume = {120},
author = {Piga, D. and Breschi, V. and Bemporad, A.},
pages = {109126},
year = {2020},
doi = {10.1016/j.automatica.2020.109126},
url = {http://www.sciencedirect.com/science/article/pii/S0005109820303241}
}
DownloadRoveda, L., Bussolan, A., Braghin, F., Piga, D. (2020). 6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter. MDPI Machines 8(4), 67.
6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter
Authors: Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.
Year: 2020
Abstract: Industrial robots are commonly used to perform interaction tasks (such as assemblies or
polishing), requiring the robot to be in contact with the surrounding environment. Such environments
are (partially) unknown to the robot controller. Therefore, there is the need to implement interaction
controllers capable of suitably reacting to the established contacts. Although standard force controllers
require force/torque measurements to close the loop, most of the industrial manipulators do not have
installed force/torque sensor(s). In addition, the integration of external sensors results in additional
costs and implementation effort, not affordable in many contexts/applications. To extend the use
of compliant controllers to sensorless interaction control, a model-based methodology is presented
in this paper for the online estimation of the interaction wrench, implementing a 6D virtual sensor.
Relying on sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is proposed
for the interaction wrench estimation. The described approach has been validated in simulations,
taking into account four different scenarios. In addition, experimental validation has been performed
employing a Franka EMIKA panda robot. A human–robot interaction scenario and an assembly task
have been considered to show the capabilities of the developed EKF, which is able to perform the
estimation with high bandwidth, achieving convergence with limited errors.
Published in MDPI Machines 8(4), 67.
6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter
@ARTICLE{Roveda2020b,
title = {{6D} virtual sensor for wrench estimation in robotized interaction tasks exploiting extended {K}alman filter},
journal = {{MDPI} Machines},
volume = {8},
author = {Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.},
number = {4},
pages = {67},
year = {2020},
doi = {10.3390/machines8040067},
url = {}
}
DownloadRoveda, L., Castaman, N., Franceschi, P., Ghidoni, S., Pedrocchi, N. (2020). A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks. In IEEE International Conference on Robotics and Automation (ICRA) 2020, pp. 6819–6825.
A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks
Authors: Roveda, L. and Castaman, N. and Franceschi, P. and Ghidoni, S. and Pedrocchi, N.
Year: 2020
Abstract: Within the Industry 4.0 context, industrial robots need to show increasing autonomy. The manipulator has to be able to react to uncertainties/changes in the working environment, displaying a robust behavior. In this paper, a control framework is proposed to perform industrial interaction tasks in uncertain working scenes. The proposed methodology relies
on two components: i) a 6D pose estimation algorithm aiming
to recognize large and featureless parts; ii) a variable damping impedance controller (inner loop) enhanced by an adaptive saturation PI (outer loop) for high accuracy force control (i.e., zero steady-state force error and force overshoots avoidance). The proposed methodology allows to be robust w.r.t. task uncertainties (i.e., positioning errors and interaction dynamics). The proposed approach has been evaluated in an assembly task of a side-wall panel to be installed inside the aircraft cabin. As a test platform, the KUKA iiwa 14 R820 has been used together with the Microsoft Kinect 2.0 as RGB-D sensor. Experiments show the reliability in the 6D pose estimation and the high-performance in the force-tracking task, avoiding force overshoots while achieving the tracking of the reference force.
Published in IEEE International Conference on Robotics and Automation (ICRA) 2020, pp. 6819–6825.
A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks
@INPROCEEDINGS{Roveda2020f,
title = {A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks},
booktitle = {{IEEE} International Conference on Robotics and Automation ({ICRA}) 2020},
author = {Roveda, L. and Castaman, N. and Franceschi, P. and Ghidoni, S. and Pedrocchi, N.},
pages = {6819--6825},
year = {2020},
doi = {10.1109/ICRA40945.2020.9197141},
url = {}
}
DownloadRoveda, L., Forgione, M., Piga, D. (2020). Robot control parameters auto-tuning in trajectory tracking applications. Control Engineering Practice 101, 104488.
Robot control parameters auto-tuning in trajectory tracking applications
Authors: Roveda, L. and Forgione, M. and Piga, D.
Year: 2020
Abstract: Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.
Published in Control Engineering Practice 101, 104488.
Robot control parameters auto-tuning in trajectory tracking applications
@ARTICLE{roveda2020a,
title = {Robot control parameters auto-tuning in trajectory tracking applications},
journal = {Control Engineering Practice},
volume = {101},
author = {Roveda, L. and Forgione, M. and Piga, D.},
pages = {104488},
year = {2020},
doi = {10.1016/j.conengprac.2020.104488},
url = {}
}
DownloadRoveda, L., Forgione, M., Piga, D. (2020). One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications. In Ubiquitous Robots 2020, pp. 105–112.
One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications
Authors: Roveda, L. and Forgione, M. and Piga, D.
Year: 2020
Abstract: Autonomy is increasingly demanded by industrial
manipulators. Robots have to be capable to regulate their
behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., robot dynamics compensation) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in 125 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved.
Published in Ubiquitous Robots 2020, pp. 105–112.
One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications
@INPROCEEDINGS{Roveda2020h,
title = {One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications},
booktitle = {Ubiquitous Robots 2020},
author = {Roveda, L. and Forgione, M. and Piga, D.},
pages = {105--112},
year = {2020},
doi = {10.1109/UR49135.2020.9144761},
url = {}
}
DownloadRoveda, L., Magni, M., Cantoni, M., Piga, D., Bucca, G. (2020). Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization. Robotics and Autonomous Systems 136, 103711.
Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization
Authors: Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.
Year: 2020
Abstract: Robots are increasingly exploited in production plants. Within the Industry 4.0 paradigm, the robot
complements the human’s capabilities, learning new tasks and adapting itself to compensate for
uncertainties. With this aim, the presented paper focuses on the investigation of machine learning
techniques to make a sensorless robot able to learn and optimize an industrial assembly task.
Relying on sensorless Cartesian impedance control, two main contributions are defined: (1) a task-
trajectory learning algorithm based on a few human’s demonstrations (exploiting Hidden Markov
Model approach), and (2) an autonomous optimization procedure of the task execution (exploiting
Bayesian Optimization). To validate the proposed methodology, an assembly task has been selected as
a reference application. The task consists of mounting a gear into its square-section shaft on a fixed
base to simulate the assembly of a gearbox. A Franka EMIKA Panda manipulator has been used as a
test platform, implementing the proposed methodology. The experiments, carried out on a population
of 15 subjects, show the effectiveness of the proposed strategy, making the robot able to learn and
optimize its behavior to accomplish the assembly task, even in the presence of task uncertainties.
Published in Robotics and Autonomous Systems 136, 103711.
Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization
@ARTICLE{Roveda2020j,
title = {Human--robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via {B}ayesian optimization},
journal = {Robotics and Autonomous Systems},
volume = {136},
author = {Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.},
pages = {103711},
year = {2020},
doi = {10.1016/j.robot.2020.103711},
url = {}
}
DownloadRoveda, L., Magni, M., Cantoni, M., Piga, D., Bucca, G. (2020). Assembly task learning and optimization through Human’s demonstration and machine learning. In IEEE International Conference on Systems, Man, and Cybernetics, pp. 1852–1859.
Assembly task learning and optimization through Human’s demonstration and machine learning
Authors: Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.
Year: 2020
Abstract: Robots are increasingly exploited in production plants, with the need to learn and to adapt themselves to new tasks. This paper focuses on the investigation of machine learning techniques to make a sensorless robot able to learn and optimize an industrial assembly task. Relying on sensorless Cartesian impedance control, a task-trajectory learning algorithm exploiting a limited number of human’s demonstrations (based on Hidden Markov Model), and an autonomous optimization procedure (based on Bayesian Optimization) are proposed to learn and optimize the assembly task. To validate the proposed methodology, an assembly task of a gear into its square-section shaft has been considered. A Franka EMIKA Panda manipulator has been used as a test platform. The experiments show the effectiveness of the proposed strategy, making the robot able to learn and optimize its behaviour to accomplish the assembly task, even in the presence of uncertainties.
Published in IEEE International Conference on Systems, Man, and Cybernetics, pp. 1852–1859.
Assembly task learning and optimization through Human’s demonstration and machine learning
@INPROCEEDINGS{Roveda2020k,
title = {Assembly task learning and optimization through {H}uman’s demonstration and machine learning},
booktitle = {{IEEE} International Conference on Systems, Man, and Cybernetics},
author = {Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.},
pages = {1852--1859},
year = {2020},
doi = {10.1109/SMC42975.2020.9282911},
url = {}
}
DownloadRoveda, L., Maskani, J., Franceschi, P., Arash, A., Braghin, F., Molinari Tosatti, L., Pedrocchi, N. (2020). Model-based reinforcement learning variable impedance control for human-robot collaboration. Journal of Intelligent & Robotic Systems 100(2), pp. 417–433.
Model-based reinforcement learning variable impedance control for human-robot collaboration
Authors: Roveda, L. and Maskani, J. and Franceschi, P. and Arash, A. and Braghin, F. and Molinari Tosatti, L. and Pedrocchi, N.
Year: 2020
Abstract: Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots
enhance productivity and flexibility while reducing human’s fatigue and the risk of injuries, exploiting advanced control
methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot
interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable
impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved human-robot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system.
Published in Journal of Intelligent & Robotic Systems 100(2), Springer, pp. 417–433.
Model-based reinforcement learning variable impedance control for human-robot collaboration
@ARTICLE{Roveda2020c,
title = {Model-based reinforcement learning variable impedance control for human-robot collaboration},
journal = {Journal of Intelligent & Robotic Systems},
publisher = {Springer},
volume = {100},
author = {Roveda, L. and Maskani, J. and Franceschi, P. and Arash, A. and Braghin, F. and Molinari Tosatti, L. and Pedrocchi, N.},
number = {2},
pages = {417--433},
year = {2020},
doi = {10.1007/s10846- 020-01183-3},
url = {}
}
DownloadRoveda, L., Piga, D. (2020). Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks. International Journal of Intelligent Robotics and Applications 4(4), pp. 507–519.
Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks
Authors: Roveda, L. and Piga, D.
Year: 2020
Abstract: Industrial robots are increasingly used in highly flexible interaction tasks, where the intrinsic variability makes difficult to pre-program the manipulator for all the different scenarios. In such applications, interaction environments are commonly (partially) unknown to the robot, requiring the implemented controllers to take in charge for the stability of the interaction. While standard controllers are sensor-based, there is a growing need to make sensorless robots (i.e., most of the commercial robots are not equipped with force/torque sensors) able to sense the environment, properly reacting to the established interaction. This paper proposes a new methodology to sensorless force control manipulators. On the basis of sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is designed to estimate the interaction exchanged between the robot and the environment. Such an estimation is then used in order to close a robust high-performance force loop, designed exploiting a variable impedance control and a State Dependent Riccati Equation (SDRE) force controller. The described approach has been validated in simulations. A Franka EMIKA panda robot has been considered as a test platform. A probing task involving different materials (i.e., with different stiffness properties) has been considered to show the capabilities of the developed EKF (able to converge with limited errors) and controller (preserving stability and avoiding overshoots). The proposed controller has been compared with an LQR controller to show its improved performance.
Published in International Journal of Intelligent Robotics and Applications 4(4), Springer, pp. 507–519.
Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks
@ARTICLE{Roveda2020d,
title = {Robust state dependent {R}iccati equation variable impedance control for robotic force-tracking tasks},
journal = {International Journal of Intelligent Robotics and Applications},
publisher = {Springer},
volume = {4},
author = {Roveda, L. and Piga, D.},
number = {4},
pages = {507--519},
year = {2020},
doi = {10.1007/s41315-020-00153-0},
url = {}
}
DownloadRoveda, L., Piga, D. (2020). Interaction force computation exploiting environment stiffness estimation for sensorless robot applications. In IEEE Metrology for Industry 4.0 and IoT 2020, pp. 360–363.
Interaction force computation exploiting environment stiffness estimation for sensorless robot applications
Authors: Roveda, L. and Piga, D.
Year: 2020
Abstract: Industrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment.
However, standard controllers require force/torque measurements to close the loop. Most of the industrial manipulators do not have embedded force/torque sensor(s), requiring additional efforts (i.e., additional costs and implementation resources) for such integration in the robotic setup. To extend the use of compliant controllers to sensorless force control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is proposed to estimate the interaction environment stiffness. Exploiting such estimation, the interaction force can be computed, e.g., to close the force loop, making the sensorless robot able to perform the target task (e.g., probing task, assembly task). The described approach has been validated with experiments. A Franka EMIKA panda robot has been used as a test platform. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKF. The computed interaction force (on the basis of the estimated environment stiffness) has been compared with the Franka EMIKA panda robot force measurements to prove the effectiveness of the proposed approach.
Published in IEEE Metrology for Industry 4.0 and IoT 2020, pp. 360–363.
Interaction force computation exploiting environment stiffness estimation for sensorless robot applications
@INPROCEEDINGS{Roveda2020g,
title = {Interaction force computation exploiting environment stiffness estimation for sensorless robot applications},
booktitle = {{IEEE} Metrology for Industry 4.0 and {IoT} 2020},
author = {Roveda, L. and Piga, D.},
pages = {360--363},
year = {2020},
doi = {10.1109/MetroInd4.0IoT48571.2020.9138189},
url = {}
}
DownloadRoveda, L., Savani, L., Arlati, S., Dinon, T., Legnani, G., Molinari Tosatti, L. (2020). Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics. International Journal of Industrial Ergonomics 79, 102991.
Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics
Authors: Roveda, L. and Savani, L. and Arlati, S. and Dinon, T. and Legnani, G. and Molinari Tosatti, L.
Year: 2020
Abstract: non-ergonomic conditions and to manipulate heavy parts. As a result, work-related musculoskeletal disorders are a major problem to tackle in workplace. In particular, back is one of the most affected regions. To solve such issue, many efforts have been made in the design and control of exoskeleton devices, relieving the human from the task load. Besides upper limbs and lower limbs exoskeletons, back-support exoskeletons have been also investigated, proposing both passive and active solutions. While passive solutions cannot empower the human’s capabilities, common active devices are rigid, without the possibility to track the human’s spine kinematics while executing the task. The here proposed paper describes a methodology to design an active back-support exoskeleton with backbone-based kinematics. On the basis of the (easily implementable) scissor hinge mechanism, a one-degree of freedom device has been designed. In particular, the resulting device allows tracking the motion of a reference vertebra, i.e., the vertebrae in the correspondence of the connection between the scissor hinge mechanism and the back of the operator. Therefore, the proposed device is capable to adapt to the human posture, guaranteeing the support while relieving the person from the task load. In addition, the proposed mechanism can be easily optimized and realized for different subjects, involving a subject-based design procedure, making possible to adapt its kinematics to track the spine motion of the specific user. A prototype of the proposed device has been 3D-printed to show the achieved kinematics. Preliminary tests for discomfort evaluation show the potential of the proposed methodology, foreseeing extensive subjects-based optimization, realization and testing of the device.
Published in International Journal of Industrial Ergonomics 79, Elsevier, 102991.
Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics
@ARTICLE{Roveda2020e,
title = {Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics},
journal = {International Journal of Industrial Ergonomics},
publisher = {Elsevier},
volume = {79},
author = {Roveda, L. and Savani, L. and Arlati, S. and Dinon, T. and Legnani, G. and Molinari Tosatti, L.},
pages = {102991},
year = {2020},
doi = {10.1016/j.ergon.2020.102991},
url = {}
}
DownloadRuggieri, A., Stranieri, F., Stella, F., Scutari, M. (2020). Hard and soft em in bayesian network learning from incomplete data. Algorithms 13(12), 329.
Hard and soft em in bayesian network learning from incomplete data
Authors: Ruggieri, A. and Stranieri, F. and Stella, F. and Scutari, M.
Year: 2020
Abstract: Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) using belief propagation. Similarly, the Structural Expectation-Maximisation algorithm (Structural EM) learns the network structure of the BN from those sufficient statistics using algorithms designed for complete data. However, practical implementations of parameter and structure learning often impute missing data (“hard EM”) to compute sufficient statistics instead of using belief propagation, for both ease of implementation and computational speed. In this paper, we investigate the question: what is the impact of using imputation instead of belief propagation on the quality of the resulting BNs? From a simulation study using synthetic data and reference BNs, we find that it is possible to recommend one approach over the other in several scenarios based on the characteristics of the data. We then use this information to build a simple decision tree to guide practitioners in choosing the EM algorithm best suited to their problem.
Published in Algorithms 13(12), 329.
Hard and soft em in bayesian network learning from incomplete data
@ARTICLE{scutari20h,
title = {Hard and soft em in bayesian network learning from incomplete data},
journal = {Algorithms},
volume = {13},
author = {Ruggieri, A. and Stranieri, F. and Stella, F. and Scutari, M.},
number = {12},
pages = {329},
year = {2020},
doi = {10.3390/a13120329},
url = {}
}
DownloadSchürch, M., Azzimonti, D., Benavoli, A., Zaffalon, M. (2020). Recursive estimation for sparse gaussian process regression. Automatica 120, 109127.
Recursive estimation for sparse gaussian process regression
Authors: Schürch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.
Year: 2020
Abstract: Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples.
Published in Automatica 120, Elsevier, 109127.
Recursive estimation for sparse gaussian process regression
@ARTICLE{schurch2020a,
title = {Recursive estimation for sparse gaussian process regression},
journal = {Automatica},
publisher = {Elsevier},
volume = {120},
author = {Sch\"urch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.},
pages = {109127},
year = {2020},
doi = {10.1016/j.automatica.2020.109127},
url = {}
}
DownloadShahid, A.A., Roveda, L., Piga, D., Braghin, F. (2020). Learning continuous control actions for robotic grasping with reinforcement learning. In IEEE International Conference on Systems, Man, and Cybernetics, pp. 4066–4072.
Learning continuous control actions for robotic grasping with reinforcement learning
Authors: Shahid, A.A. and Roveda, L. and Piga, D. and Braghin, F.
Year: 2020
Abstract: Robots are nowadays increasingly required to deal with (partially) unknown tasks and situations. The robot has, therefore, to adapt its behavior to the specific working conditions. Classical control methods in robotics require manually programming all actions of a robot. While very effective in fixed conditions, such model-based approaches cannot handle variations, demanding tedious tuning of parameters for every new task. Reinforcement learning (RL) holds the promise of autonomously learning new control policies through trial-and-error. However, RL approaches are prone to learning with high samples, particularly for continuous control problems. In this paper, a learning-based method is presented that leverages simulation data to learn an object manipulation task through RL. The control policy is parameterized by a neural network and learned using modern Proximal Policy Optimization (PPO) algorithm. A dense reward function has been designed for the task to enable efficient learning of an agent. The proposed approach is trained entirely in simulation (exploiting the MuJoCo environment) from scratch without any demonstrations of the task. A grasping task involving a Franka Emika Panda manipulator has been considered as the reference task to be learned. The task requires the robot to reach the part, grasp it, and lift it off the contact surface. The proposed approach has been demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations, having the robot able to learn and re-execute the target task.
Published in IEEE International Conference on Systems, Man, and Cybernetics, pp. 4066–4072.
Learning continuous control actions for robotic grasping with reinforcement learning
@INPROCEEDINGS{Roveda2020l,
title = {Learning continuous control actions for robotic grasping with reinforcement learning},
booktitle = {{IEEE} International Conference on Systems, Man, and Cybernetics},
author = {Shahid, A.A. and Roveda, L. and Piga, D. and Braghin, F.},
pages = {4066--4072},
year = {2020},
doi = {10.1109/SMC42975.2020.9282951},
url = {}
}
DownloadSheldrake, T.E., Caricchi, L., Scutari, M. (2020). Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs. Frontiers in Earth Sciences 8(127), pp. 1–14.
Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs
Authors: Sheldrake, T.E. and Caricchi, L. and Scutari, M.
Year: 2020
Abstract: Linking tectonic setting to eruptive activity in volcanic arcs provides a framework to understand processes that control the production, accumulation and eruption of magma on Earth. We use the Holocene eruptive records of 162 volcanoes, which are selected based on an assessment of recording biases, to calculate the probability of recording large eruptions (between Magnitudes 4 and 7). We quantify regional variability in the sizes of volcanic eruptions and compare it with subduction parameters influencing the generation, transport and storage of magma. Given the tectonic setting of a subduction zone is multidimensional (e.g., age, speed, obliquity of the subducting plate) we use a graphical model to explore the strength of probabilistic relationships between tectonic and volcanic variables. The variable that exhibits the strongest probabilistic relationship with eruption size is convergence obliquity, with larger eruptions favored in settings where convergence is normal. Normal convergence favors the storage and accumulation of larger volumes of magma, whereas oblique convergence favors the transport and eruption of smaller volumes of magma. In low-obliquity arcs where magma storage is promoted, the subduction of older slabs results in higher mantle productivity, which thermally favors the accumulation of eruptible magma and larger eruptions on average. However, the highest mantle productivity also results in more frequent magma injection and pressurization of crustal reservoirs. Consequently, arcs with moderate slab ages and low obliquity produce the highest proportion of larger eruptions. In high-obliquity arcs mantle productivity does not dominantly control eruption sizes. Instead, thinner crust facilitates frequent transport of magma to the surface, resulting in smaller eruptions. For the largest eruptions on Earth (e.g., Magnitude 8), however, accumulation of eruptible magma will be dominantly controlled by thermomechanical modification of the crust and not the frequency of magma intrusion. Despite the importance of convergence obliquity, our results show that variability in the sizes of volcanic eruptions is controlled by complex relationships with other parameters including slab age and crustal thickness. By using a graphical model, we have been able to explore complex volcano-tectonic relationships. We suggest a similar approach could be extremely valuable for exploring other complex multidimensional datasets within the Earth Sciences.
Published in Frontiers in Earth Sciences 8(127), pp. 1–14.
Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs
@ARTICLE{scutari20b,
title = {Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs},
journal = {Frontiers in Earth Sciences},
volume = {8},
author = {Sheldrake, T.E. and Caricchi, L. and Scutari, M.},
number = {127},
pages = {1--14},
year = {2020},
doi = {10.3389/feart.2020.00127},
url = {}
}
DownloadSorgini, F., Airò Farulla, G., Lukic, N., Danilov, I., Roveda, L., Milivojevic, M., Babu Pulikottil, T., Carrozza, M.C., Prinetto, P., Tolio, T., Oddo, C.M., Petrovic, P., Bojovic, B. (2020). Tactile sensing with gesture-controlled collaborative robot. In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT), pp. 364–368.
Tactile sensing with gesture-controlled collaborative robot
Authors: Sorgini, F. and Airò Farulla, G. and Lukic, N. and Danilov, I. and Roveda, L. and Milivojevic, M. and Babu Pulikottil, T. and Carrozza, M.C. and Prinetto, P. and Tolio, T. and Oddo, C.M. and Petrovic, P. and Bojovic, B.
Year: 2020
Abstract: Sensors and human machine interfaces for
collaborative robotics will allow smooth interaction in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to
sequentially press six buttons, illuminated according to a
random sequence, and comparing the tasks executed without
and with tactile feedback. The results demonstrated the
reliability of the hand tracking strategy developed for
controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.
Published in 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT), pp. 364–368.
Tactile sensing with gesture-controlled collaborative robot
@INPROCEEDINGS{Roveda2020i,
title = {Tactile sensing with gesture-controlled collaborative robot},
booktitle = {2020 {IEEE} International Workshop on Metrology for Industry 4.0 & {IoT} ({MetroInd4}.0 & {IoT})},
author = {Sorgini, F. and Air\`o Farulla, G. and Lukic, N. and Danilov, I. and Roveda, L. and Milivojevic, M. and Babu Pulikottil, T. and Carrozza, M.C. and Prinetto, P. and Tolio, T. and Oddo, C.M. and Petrovic, P. and Bojovic, B.},
pages = {364--368},
year = {2020},
doi = {10.1109/MetroInd4.0IoT48571.2020.9138183},
url = {}
}
DownloadSzehr, O., Zarouf, R. (2020). Interpolation without commutants. Journal of Operator Theory 84(1), pp. 239–256.
Interpolation without commutants
Authors: Szehr, O. and Zarouf, R.
Year: 2020
Abstract: We introduce a "dual-space approach" to mixed Nevanlinna-Pick/Carathéodory-Schur interpolation in Banach spaces X of holomorphic functions on the disk. Our approach can be viewed as complementary to the well-known commutant lifting approach of D. Sarason and B. Nagy-C.Foiaş. We compute the norm of the minimal interpolant in X by a version of the Hahn-Banach theorem, which we use to extend functionals defined on a subspace of kernels without increasing their norm. This Functional extensions lemma plays a similar role as Sarason's Commutant lifting theorem but it only involves the predual of X and no Hilbert space structure is needed. As an example, we present the respective Pick-type interpolation theorems for Beurling-Sobolev spaces.
Published in Journal of Operator Theory 84(1), pp. 239–256.
Interpolation without commutants
@ARTICLE{szehr2020aa,
title = {Interpolation without commutants},
journal = {Journal of Operator Theory},
volume = {84},
author = {Szehr, O. and Zarouf, R.},
number = {1},
pages = {239--256},
year = {2020},
doi = {10.7900/jot.2019may21.2264},
url = {}
}
DownloadVolpetti, C., Kanjirangat, V., Antonucci, A. (2020). Temporal word embeddings for narrative understanding. In 12th International Conference on Machine Learning and Computing (ICMLC 2020) (5), ACM, pp. 68–72.
Temporal word embeddings for narrative understanding
Authors: Volpetti, C. and Kanjirangat, V. and Antonucci, A.
Year: 2020
Abstract: We propose temporal word embeddings as a suitable tool to study the evolution of characters and their sentiments across the plot of a narrative text. The dynamic evolution of instances within a narrative text is a challenging task, where complex behavioral evolutions and other characteristics specific to the narrative text
need to be inferred and interpreted. While starting from an existing approach to the learning of these models, we propose an alternative initialization procedure which seems to be especially suited for the case of narrative text. As a validation benchmark, we use the Harry Potter series of books as a challenging case study for such character
trait evolution. A benchmark data set based on temporal word
analogies related to the characters in the plot of the series is considered. The results are promising, and the empirical validation seems to support the working ideas behind this proposal.
Published in 12th International Conference on Machine Learning and Computing (ICMLC 2020) (5), ACM, pp. 68–72.
Temporal word embeddings for narrative understanding
@INPROCEEDINGS{supsi2020a,
title = {Temporal word embeddings for narrative understanding},
publisher = {ACM},
booktitle = {12th International Conference on Machine Learning and Computing ({ICMLC} 2020)},
author = {Volpetti, C. and Kanjirangat, V. and Antonucci, A.},
number = {5},
pages = {68--72},
year = {2020},
doi = {10.1145/3383972.3383988},
url = {}
}
DownloadYoo, J., Kang, U., Scanagatta, M., Corani, G., Zaffalon, M. (2020). Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference. In Proceedings of International Conference on Web Search and Data Mining (WSDM '20) (9), pp. 708–16.
Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference
Authors: Yoo, J. and Kang, U. and Scanagatta, M. and Corani, G. and Zaffalon, M.
Year: 2020
Abstract: How can we run graphical inference on large graphs efficiently and accurately? Many real-world networks are modeled as graphical models, and graphical inference is fundamental to understand the properties of those networks. In this work, we propose a novel approach for fast and accurate inference, which first samples a small subgraph and then runs inference over the subgraph instead of the given graph. This is done by the bounded treewidth (BTW) sampling, our novel algorithm that generates a subgraph with guar- anteed bounded treewidth while retaining as many edges as pos- sible. We first analyze the properties of BTW theoretically. Then, we evaluate our approach on node classification and compare it with the baseline which is to run loopy belief propagation (LBP) on the original graph. Our approach can be coupled with various inference algorithms: it shows higher accuracy up to 13.7% with the junction tree algorithm, and allows faster inference up to 23.8 times with LBP. We further compare BTW with previous graph sampling algorithms and show that it gives the best accuracy.
Published in Proceedings of International Conference on Web Search and Data Mining (WSDM '20) (9), pp. 708–16.
Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference
@INPROCEEDINGS{corani2019e,
title = {Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference},
booktitle = {Proceedings of International Conference on Web Search and Data Mining ({WSDM} '20)},
author = {Yoo, J. and Kang, U. and Scanagatta, M. and Corani, G. and Zaffalon, M.},
number = {9},
pages = {708--16},
year = {2020},
doi = {10.1145/3336191.3371815},
url = {}
}
DownloadZaffalon, M., Antonucci, A., Cabañas, R. (2020). Structural causal models are (solvable by) credal networks. In Jaeger, M., Nielsen, T. D. (Ed), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), PMLR 138, JMLR.org, pp. 581–592.
Structural causal models are (solvable by) credal networks
Authors: Zaffalon, M. and Antonucci, A. and Cabañas, R.
Year: 2020
Abstract: A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model into a credal network. Causal inferences, such as interventions and counterfactuals, can consequently be obtained by standard algorithms for the updating of credal nets. These natively return sharp values in the identifiable case, while intervals corresponding to the exact bounds are produced for unidentifiable queries. A characterization of the causal models that allow the map above to be compactly derived is given, along with a discussion about the scalability for general models. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks and hence to systematically compute causal inferences. A number of demonstrative examples is presented to clarify our methodology. Extensive experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
Published in Jaeger, M., Nielsen, T. D. (Ed), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), PMLR 138, JMLR.org, pp. 581–592.
Structural causal models are (solvable by) credal networks
@INPROCEEDINGS{zaffalon2020b,
title = {Structural causal models are (solvable by) credal networks},
editor = {Jaeger, M., Nielsen, T. D. },
publisher = {JMLR.org},
series = {PMLR},
volume = {138},
booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
author = {Zaffalon, M. and Antonucci, A. and Cabañas, R.},
pages = {581--592},
year = {2020},
doi = {},
url = {http://proceedings.mlr.press/v138/zaffalon20a/zaffalon20a.pdf}
}
Download top2019
Antonucci, A. (2019). Reliable discretisation of deterministic equations in Bayesian networks. In Proceedings of the 32nd International Flairs Conference (FLAIRS-32), AAAI Press.
Reliable discretisation of deterministic equations in Bayesian networks
Authors: Antonucci, A.
Year: 2019
Abstract: We focus on the problem of modeling deterministic equations over continuous variables in discrete Bayesian networks. This is typically achieved by a discretisation of both input and output variables and a degenerate quantification of the corre- sponding conditional probability tables. This approach, based on classical probabilities, cannot properly model the information loss induced by the discretisation. We show that a reli- able modeling of such epistemic uncertainty can be instead achieved by credal sets, i.e., convex sets of probability mass functions. This transforms the original Bayesian network in a credal network, possibly returning interval-valued inferences, that are robust with respect to the information loss induced by the discretisation. Algorithmic strategies for an optimal choice of the discretisation bins are also discussed.
Published in Proceedings of the 32nd International Flairs Conference (FLAIRS-32), AAAI Press.
Note: Accepted for pubblication.
Reliable discretisation of deterministic equations in Bayesian networks
@INPROCEEDINGS{supsi2019c,
title = {Reliable discretisation of deterministic equations in {B}ayesian networks},
publisher = {AAAI Press},
booktitle = {Proceedings of the 32nd International Flairs Conference ({FLAIRS}-32)},
author = {Antonucci, A.},
year = {2019},
doi = {},
url = {}
}
DownloadAntonucci, A., Facchini, A., Mattei, L. (2019). Credal sentential decision diagrams. In Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19) 103, PMLR, pp. 14–22.
Credal sentential decision diagrams
Authors: Antonucci, A. and Facchini, A. and Mattei, L.
Year: 2019
Abstract: Probabilistic sentential decision diagrams are logical circuits annotated by probability mass functions on the disjunctive gates. This allows for a compact representation of joint mass functions consistent with logical constraints. We propose a credal generalisation of the probabilistic quantification of these models, that allows to replace the local probabilities with (credal) sets of mass functions specified by linear constraints. This induces a joint credal set, that sharply assigns probability zero to states inconsistent with the constraints. These models can support cautious estimates of the local parameters when only small amounts of training data are available. Algorithmic strategies to compute lower and upper bounds of marginal and conditional queries are provided. The task can be achieved in lin- ear time with respect to the diagram size for marginal queries. The same can be done for conditional queries if the topology of the circuit is singly connected.
Published in Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19) 103, PMLR, pp. 14–22.
Note: Accepted for pubblication.
Credal sentential decision diagrams
@INPROCEEDINGS{supsi2019b,
title = {Credal sentential decision diagrams},
publisher = {PMLR},
volume = {103},
booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
author = {Antonucci, A. and Facchini, A. and Mattei, L.},
pages = {14--22},
year = {2019},
doi = {},
url = {https://proceedings.mlr.press/v103/antonucci19a.html}
}
DownloadArnone, E., Azzimonti, L., Nobile, F., Sangalli, L.M. (2019). Modeling spatially dependent functional data via regression with differential regularization. Journal of Multivariate Analysis 170, pp. 275–295.
Modeling spatially dependent functional data via regression with differential regularization
Authors: Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.
Year: 2019
Abstract: We propose a method for modeling spatially dependent functional data, based on regression with differential regularization. The regularizing term enables to include problem-specific information about the spatio-temporal variation of the phenomenon under study, formalized in terms of a time-dependent partial differential equation. The method is implemented using a discretization based on finite elements in space and finite differences in time. This non-tensor product basis allows to handle efficiently data distributed over complex domains and where the shape of the domain influences the phenomenon’s behavior. Moreover, the method can comply with specific conditions at the boundary of the domain of interest. Simulation studies compare the proposed model to available techniques for spatio-temporal data. The method is also illustrated via an application to the study of blood-flow velocity field in a carotid artery affected by atherosclerosis, starting from echo-color doppler and magnetic resonance imaging data.
Published in Journal of Multivariate Analysis 170, pp. 275–295.
Note: Special Issue on Functional Data Analysis and Related Topics
Modeling spatially dependent functional data via regression with differential regularization
@ARTICLE{azzimonti2018a,
title = {Modeling spatially dependent functional data via regression with differential regularization},
journal = {Journal of Multivariate Analysis},
volume = {170},
author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},
pages = {275--295},
year = {2019},
doi = {10.1016/j.jmva.2018.09.006},
url = {}
}
DownloadAzzimonti, L., Corani, G., Zaffalon, M. (2019). Hierarchical estimation of parameters in Bayesian networks. Computational Statistics and Data Analysis 137, pp. 67–91.
Hierarchical estimation of parameters in Bayesian networks
Authors: Azzimonti, L. and Corani, G. and Zaffalon, M.
Year: 2019
Abstract: A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyper-prior in the Multinomial-Dirichlet model, traditionally used for conditional distribution estimation in Bayesian networks. The resulting hierarchical model jointly estimates different conditional distributions belonging to the same conditional probability table, thus borrowing statistical strength from each other. An analytical study of the dependence structure a priori induced by the hierarchical model is performed and an ad hoc variational algorithm for fast and accurate inference is derived. The proposed hierarchical model yields a major performance improvement in classification with Bayesian networks compared to traditional models. The proposed variational algorithm reduces by two orders of magnitude the computational time, with the same accuracy in parameter estimation, compared to traditional MCMC methods.
Moreover, motivated by a real case study, the hierarchical model is applied to the estimation of Bayesian networks parameters by borrowing strength from related domains.
Published in Computational Statistics and Data Analysis 137, pp. 67–91.
Hierarchical estimation of parameters in Bayesian networks
@ARTICLE{azzimonti2019a,
title = {Hierarchical estimation of parameters in {B}ayesian networks},
journal = {Computational Statistics and Data Analysis},
volume = {137},
author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},
pages = {67--91},
year = {2019},
doi = {10.1016/j.csda.2019.02.004},
url = {}
}
DownloadAzzimonti, D., Ginsbourger, D., Chevalier, C., Bect, J., Richet, Y. (2019). Adaptive design of experiments for conservative estimation of excursion sets. Technometrics 63(1), pp. 13–26.
Adaptive design of experiments for conservative estimation of excursion sets
Authors: Azzimonti, D. and Ginsbourger, D. and Chevalier, C. and Bect, J. and Richet, Y.
Year: 2019
Abstract: We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i.e. the set of points where the function takes values above or below some prescribed threshold. The objective function is emulated with a Gaussian Process (GP) model based on an initial design of experiments enriched with evaluation results at (batch-) sequentially determined input points. The GP model provides conservative estimates for the excursion set, which control false positives while minimizing false negatives. We introduce adaptive strategies that sequentially select new evaluations of the function by reducing the uncertainty on conservative estimates. Following the Stepwise Uncertainty Reduction approach we obtain new evaluations by minimizing adapted criteria. Tractable formulae for the conservative criteria are derived, which allow more convenient optimization. The method is benchmarked on random functions generated under the model assumptions in different scenarios of noise and batch size. We then apply it to a reliability engineering test case. Overall, the proposed strategy of minimizing false negatives in conservative estimation achieves competitive performance both in terms of model-based and model-free indicators.
Published in Technometrics 63(1), Taylor & Francis, pp. 13–26.
Adaptive design of experiments for conservative estimation of excursion sets
@ARTICLE{azzimontid2019c,
title = {Adaptive design of experiments for conservative estimation of excursion sets},
journal = {Technometrics},
publisher = {Taylor & Francis},
volume = {63},
author = {Azzimonti, D. and Ginsbourger, D. and Chevalier, C. and Bect, J. and Richet, Y.},
number = {1},
pages = {13--26},
year = {2019},
doi = {10.1080/00401706.2019.1693427},
url = {}
}
DownloadAzzimonti, D., Ginsbourger, D., Rohmer, J., Idier, D. (2019). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. Technometrics 61(4), pp. 474–493.
Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding
Authors: Azzimonti, D. and Ginsbourger, D. and Rohmer, J. and Idier, D.
Year: 2019
Abstract: We consider the problem of describing excursion sets of a real-valued function f, i.e. the set of inputs where f is above a fixed threshold. Such regions are hard to visualize if the input space dimension, d, is higher than 2. For a given projection matrix from the input space to a lower dimensional (usually 1, 2) subspace, we introduce profile sup (inf) functions that associate to each point in the projection’s image the sup (inf) of the function constrained over the pre-image of this point by the considered projection. Plots of profile extrema functions convey a simple, although intrinsically partial, visualization of the set. We consider expensive to evaluate functions where only a very limited number of evaluations, n, is available, e.g. n<100d, and we surrogate f with a posterior quantity of a Gaussian process (GP) model. We first compute profile extrema functions for the posterior mean given n evaluations of f. We quantify the uncertainty on such estimates by studying the distribution of GP profile extrema with posterior quasi-realizations obtained from an approximating process. We control such approximation with a bound inherited from the Borell-TIS inequality. The technique is applied to analytical functions (d = 2, 3) and to a 5-dimensional coastal flooding test case for a site located on the Atlantic French coast. Here f is a numerical model returning the area of flooded surface in the coastal region given some offshore conditions. Profile extrema functions allowed us to better understand which offshore conditions impact large flooding events.
Published in Technometrics 61(4), Taylor & Francis, pp. 474–493.
Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding
@ARTICLE{azzimontid2019a,
title = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},
journal = {Technometrics},
publisher = {Taylor & Francis},
volume = {61},
author = {Azzimonti, D. and Ginsbourger, D. and Rohmer, J. and Idier, D.},
number = {4},
pages = {474--493},
year = {2019},
doi = {10.1080/00401706.2018.1562987},
url = {}
}
DownloadAzzimonti, D., Rottondi, C., Tornatore, M. (2019). Using active learning to decrease probes for QoT estimation in optical networks. In , Optical Society of America, Th1H.1.
Using active learning to decrease probes for QoT estimation in optical networks
Authors: Azzimonti, D. and Rottondi, C. and Tornatore, M.
Year: 2019
Abstract: We use active learning to reduce the number of probes needed for machine learning-based QoT estimation. When building an estimation model based on Gaussian processes, only QoT instances that minimize estimation uncertainty are iteratively requested.
Published in Optical Fiber Communication Conference (OFC) 2019, Optical Society of America, Th1H.1.
Using active learning to decrease probes for QoT estimation in optical networks
@INPROCEEDINGS{azzimontid2019b,
title = {Using active learning to decrease probes for {QoT} estimation in optical networks},
journal = {Optical Fiber Communication Conference ({OFC}) 2019},
publisher = {Optical Society of America},
author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},
pages = {Th1H.1},
year = {2019},
doi = {10.1364/OFC.2019.Th1H.1},
url = {}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2019). Bernstein's socks, polynomial-time provable coherence and entanglement. In De Bock, J., de Campos, C., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), ISIPTA '19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications, PMLR 103, JMLR.org, pp. 23–31.
Bernstein's socks, polynomial-time provable coherence and entanglement
Authors: Benavoli, A. and Facchini, A. and Zaffalon, M.
Year: 2019
Abstract: We recently introduced a bounded rationality approach for the theory of desirable gambles. It is based on the unique requirement that being nonnegative for a gamble has to be defined so that it can be provable in polynomial time. In this paper we continue to investigate properties of this class of models. In particular we verify that the space of Bernstein polynomials in which nonnegativity is specified by the Krivine-Vasilescu certificate is yet another instance of this theory. As a consequence, we show how it is possible to construct in it a thought experiment uncovering entanglement with classical (hence non quantum) coins.
Published in De Bock, J., de Campos, C., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), ISIPTA '19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications, PMLR 103, JMLR.org, pp. 23–31.
Bernstein's socks, polynomial-time provable coherence and entanglement
@INPROCEEDINGS{zaffalon2019b,
title = {Bernstein's socks, polynomial-time provable coherence and entanglement},
editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
publisher = {JMLR.org},
series = {PMLR},
volume = {103},
booktitle = {{ISIPTA };'19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications},
author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
pages = {23--31},
year = {2019},
doi = {},
url = {https://proceedings.mlr.press/v103/benavoli19a.html}
}
DownloadBenavoli, A., Facchini, A., Piga, D., Zaffalon, M. (2019). Sum-of-squares for bounded rationality. International Journal of Approximate Reasoning 105, pp. 130–152.
Sum-of-squares for bounded rationality
Authors: Benavoli, A., Facchini, A., Piga, D., Zaffalon, M.
Year: 2019
Abstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the experiment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in ℝn, the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to desire all nonnegative gambles, we only require her to desire gambles for which she can efficiently determine the nonnegativity (in particular sum-of-squares polynomials). We refer to this new criterion as bounded rationality.
Published in International Journal of Approximate Reasoning 105, pp. 130–152.
Sum-of-squares for bounded rationality
@ARTICLE{benavoli2019a,
title = {Sum-of-squares for bounded rationality},
journal = {International Journal of Approximate Reasoning},
volume = {105},
author = {Benavoli, A., Facchini, A., Piga, D., Zaffalon, M.},
pages = {130--152},
year = {2019},
doi = {10.1016/j.ijar.2018.11.012},
url = {}
}
DownloadBolt, J.H., van der Gaag, L.C. (2019). On minimum elementary-triplet bases for independence relations. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 32–37.
On minimum elementary-triplet bases for independence relations
Authors: Bolt, J.H. and van der Gaag, L.C.
Year: 2019
Abstract: A semi-graphoid independence relation is a set of independence statements, called triplets, and is typically exponentially large in the number of variables involved. For concise representation of such a relation, a subset of its triplets is listed in a so-called basis; its other triplets are defined implicitly through a set of axioms. An elementary-triplet basis for this purpose consists of all elementary triplets of a relation. Such a basis however, may include redundant information. In this paper we provide two lower bounds on the size of an elementary-triplet basis in general and an upper bound on the size of a minimum elementary-triplet basis. We further specify the construction of an elementary-triplet basis of minimum size for restricted relations.
Keywords: Independence relations, Axioms of independence, Elementary triplets, Basis representation.
Published in De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 32–37.
On minimum elementary-triplet bases for independence relations
@INPROCEEDINGS{Linda2019c,
title = {On minimum elementary-triplet bases for independence relations},
editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
publisher = {JMLR.org},
series = {PMLR},
volume = {103},
booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
author = {Bolt, J.H. and van der Gaag, L.C.},
pages = {32--37},
year = {2019},
doi = {},
url = {https://proceedings.mlr.press/v103/bolt19a.html}
}
DownloadBreschi, V., Piga, D., Bemporad, A. (2019). Online end-use energy disaggregation via jump linear models. Control Engineering Practice 89, pp. 30–42.
Online end-use energy disaggregation via jump linear models
Authors: Breschi, V. and Piga, D. and Bemporad, A.
Year: 2019
Abstract: This paper presents two iterative algorithms for non-intrusive appliance load monitoring, which aims to decompose the aggregate power consumption only measured at the household level into the contributions of the individual electric appliances. The approaches are based on modelling the total power consumption as a combination of jump linear sub-models, each of them describing the behaviour of the individual appliance. Dynamic-programming and multi-model Kalman filtering techniques are used to reconstruct the power consumptions at the single-appliance level from the aggregate power in an iterative way.
Published in Control Engineering Practice 89, pp. 30–42.
Online end-use energy disaggregation via jump linear models
@ARTICLE{piga2019b,
title = {Online end-use energy disaggregation via jump linear models},
journal = {Control Engineering Practice},
volume = {89},
author = {Breschi, V. and Piga, D. and Bemporad, A.},
pages = {30--42},
year = {2019},
doi = {10.1016/j.conengprac.2019.05.011},
url = {}
}
DownloadBucher, D., Mangili, F., Cellina, F., Bonesana, C., Jonietz, D., Raubal, M. (2019). From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behaviour and Society 14, pp. 43–56.
From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors
Authors: Bucher, D. and Mangili, F. and Cellina, F. and Bonesana, C. and Jonietz, D. and Raubal, M.
Year: 2019
Abstract: Nowadays, most people carry around a powerful smartphone which is well suited to constantly monitor the location and sometimes even the activity of its user. This makes tracking prevalent and leads to a large number of projects concerned with trajectory data. One area of particular interest is transport and mobility, where data is important for urban planning and smart city-related activities, but can also be used to provide individual users with feedback and suggestions for personal behavior change. As part of a large-scale study based in Switzerland, we use activity tracking data to provide people with eco-feedback on their own mobility patterns and stimulate them to adopt more energy-efficient mobility choices. In this paper we explore the opportunities offered by smartphone based activity tracking, propose a general framework to exploit location data to foster more sustainable mobility behavior, describe the technical solutions chosen and discuss a range of outcomes in terms of user perception and sustainability potential. The presented approach extracts mobility patterns from users’ trajectories, computes credible alternative transport options, and presents the results in a concise and clear way. The resulting eco-feedback helps people to understand their mobility choices, discover the most non-ecological parts of their travel behavior, and explore feasible alternatives.
Published in Travel Behaviour and Society 14, pp. 43–56.
From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors
@ARTICLE{mangili2019a,
title = { From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors},
journal = {Travel Behaviour and Society},
volume = {14},
author = {Bucher, D. and Mangili, F. and Cellina, F. and Bonesana, C. and Jonietz, D. and Raubal, M.},
pages = {43--56},
year = {2019},
doi = {10.1016/j.tbs.2018.09.005},
url = {}
}
DownloadCarollo, V., Piga, D., Borri, C., Paggi, M. (2019). Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics. Engineering Fracture Mechanics 205, pp. 439–454.
Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics
Authors: Carollo, V. and Piga, D. and Borri, C. and Paggi, M.
Year: 2019
Abstract: Silver-plated copper busbars are screen printed onto silicon solar cells and have the key role to collect the electric current produced by the solar cell. Busbars of two adjacent solar cells are then connected by a soldered ribbon made of the same material. Due to mechanical and thermal loads, such a ribbon is subject to axial deformation that, often, causes plasticity and, in some cases, its breakage due to crack growth. A procedure based on the gradient-descent method and particle swarm optimization is herein proposed for the identification of elasto-plastic and nonlinear (cohesive zone model, CZM) fracture mechanics parameters of silver-plated copper busbars. The proposed method requires the experimental determination of the force-displacement curves from uniaxial tensile tests on busbar samples with and without initial notches. The inspection of in situ SEM images during the tests allows also the estimation of the crack opening, which is found to be an important local quantity to assess the reliability of different CZMs in simulating a crack growth process consistent with the real one.
Published in Engineering Fracture Mechanics 205, pp. 439–454.
Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics
@ARTICLE{piga2019d,
title = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},
journal = {Engineering Fracture Mechanics},
volume = {205},
author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},
pages = {439--454},
year = {2019},
doi = {10.1016/j.engfracmech.2018.11.014},
url = {}
}
DownloadCellina, F., Bucher, D., Mangili, F., Simão, J.V., Rudel, R., Raubal, M. (2019). A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt. Sustainability 11(9), 2674.
A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt
Authors: Cellina, F. and Bucher, D. and Mangili, F. and Simão, J.V. and Rudel, R. and Raubal, M.
Year: 2019
Abstract: The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, public transportation and soft mobility would be able to effectively satisfy many travel needs. However, they tend to be neglected, due to a deep-rooted car dependency. How can we encourage people to make sustainable mobility choices, reducing car use and the related CO 2 emissions and energy consumption? Taking advantage of the wide availability of smartphone devices, we designed GoEco!, a smartphone application exploiting automatic mobility tracking, eco-feedback, social comparison and gamification elements to persuade individual modal change. We tested the effectiveness of GoEco! in two regions of Switzerland (Cantons Ticino and Zurich), in a large-scale, one year long randomized controlled trial. Notwithstanding a large drop-out rate experienced throughout the experiment, GoEco! was observed to produce a statistically significant impact (a decrease in CO 2 emissions and energy consumption per kilometer) for systematic routes in highly car-dependent urban areas, such as the Canton Ticino. In Zurich, instead, where high quality public transport is already available, no statistically significant effects were found. In this paper we present the GoEco! experiment and discuss its results and the lessons learnt, highlighting practical difficulties in performing randomized controlled trials in the field of mobility and providing recommendations for future research.
Published in mdpi (Ed), Sustainability 11(9), 2674.
Note: Special Issue Environmental and Behavioral Consequences of Interventions for Sustainable Travel
A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt
@ARTICLE{mangili2019b,
title = {A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt},
journal = {Sustainability},
editor = {mdpi},
volume = {11},
author = {Cellina, F. and Bucher, D. and Mangili, F. and Sim\~ao, J.V. and Rudel, R. and Raubal, M.},
number = {9},
pages = {2674},
year = {2019},
doi = {10.3390/su11092674},
url = {}
}
DownloadColic, N., Rinaldi, F. (2019). Improving spaCy dependency annotation and PoS tagging web service using independent NER services. Genomics Inform 17(2), e21.
Improving spaCy dependency annotation and PoS tagging web service using independent NER services
Authors: Colic, N. and Rinaldi, F.
Year: 2019
Abstract: Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that including named entity annotations can improve the speed and quality of dependency parses. As part of BLAH5, we built a web service delivering improved dependency parses by taking into account named entity annotations obtained by third party services. Our evaluation shows improved results and better speed.
Published in Genomics Inform 17(2), e21.
Improving spaCy dependency annotation and PoS tagging web service using independent NER services
@ARTICLE{rinaldi2019c,
title = {Improving {spaCy} dependency annotation and {PoS} tagging web service using independent {NER} services},
journal = {Genomics Inform},
volume = {17},
author = {Colic, N. and Rinaldi, F.},
number = {2},
pages = {e21},
year = {2019},
doi = {10.5808/GI.2019.17.2.e21},
url = {}
}
DownloadCorreia, A.H.C., de Campos, C.P., van der Gaag, L.C. (2019). An experimental study of prior dependence in Bayesian network structure learning. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 78–81.
An experimental study of prior dependence in Bayesian network structure learning
Authors: Correia, A.H.C. and de Campos, C.P. and van der Gaag, L.C.
Year: 2019
Abstract: The Bayesian Dirichlet equivalent uniform (BDeu) function is a popular score to evaluate the goodness of a Bayesian network structure given complete categorical data. Despite its interesting properties, such as likelihood equivalence, it does require a prior expressed via a user-defined parameter known as Equivalent Sample Size (ESS), which significantly affects the final structure. We study conditions to obtain prior independence in BDeu-based structure learning. We show in experiments that the amount of data needed to render the learning robust to different ESS values is prohibitively large, even in big data times.
Keywords: Robustness, Bayesian Networks, Structure Learning, BDeu
Published in De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 78–81.
An experimental study of prior dependence in Bayesian network structure learning
@INPROCEEDINGS{Linda2910d,
title = {An experimental study of prior dependence in {B}ayesian network structure learning},
editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
publisher = {JMLR.org},
series = {PMLR},
volume = {103},
booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
author = {Correia, A.H.C. and de Campos, C.P. and van der Gaag, L.C.},
pages = {78--81},
year = {2019},
doi = {},
url = {https://proceedings.mlr.press/v103/correia19a.html}
}
DownloadEllendorff, T., Furrer, L., Colic, N., Aepli, N., Rinaldi, F. (2019). Approaching SMM4H with merged models and multi-task learning. In Proceedings of the Fourth Social Media Mining for Health Applications (#smm4h) Workshop & Shared Task, Association for Computational Linguistics, pp. 58–61.
Approaching SMM4H with merged models and multi-task learning
Authors: Ellendorff, T. and Furrer, L. and Colic, N. and Aepli, N. and Rinaldi, F.
Year: 2019
Abstract: We describe our submissions to the 4th edi- tion of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generaliz- able identification of personal health expe- rience mentions (Task 4). For our submis- sions, we exploited ensembles based on a pre- trained language representation with a neu- ral transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific pre- processing steps.
Published in Proceedings of the Fourth Social Media Mining for Health Applications (#smm4h) Workshop & Shared Task, Association for Computational Linguistics, pp. 58–61.
Approaching SMM4H with merged models and multi-task learning
@INPROCEEDINGS{rinaldi2019g,
title = {Approaching {SMM4H} with merged models and multi-task learning},
publisher = {Association for Computational Linguistics},
booktitle = {Proceedings of the Fourth Social Media Mining for Health Applications (\#smm4h) Workshop & Shared Task},
author = {Ellendorff, T. and Furrer, L. and Colic, N. and Aepli, N. and Rinaldi, F.},
pages = {58--61},
year = {2019},
doi = {10.18653/v1/W19-3208},
url = {https://www.aclweb.org/anthology/W19-3208}
}
DownloadFurrer, L., Cornelius, J., Rinaldi, F. (2019). UZH@CRAFT-ST: a sequence-labeling approach to concept recognition. In Proceedings of the 5th Workshop on Bionlp Open Shared Tasks, Association for Computational Linguistics, pp. 185–195.
UZH@CRAFT-ST: a sequence-labeling approach to concept recognition
Authors: Furrer, L. and Cornelius, J. and Rinaldi, F.
Year: 2019
Abstract: As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.
Published in Proceedings of the 5th Workshop on Bionlp Open Shared Tasks, Association for Computational Linguistics, pp. 185–195.
UZH@CRAFT-ST: a sequence-labeling approach to concept recognition
@INPROCEEDINGS{rinaldi2019h,
title = {{UZH@CRAFT}-{ST}: a sequence-labeling approach to concept recognition},
publisher = {Association for Computational Linguistics},
booktitle = {Proceedings of the 5th Workshop on Bionlp Open Shared Tasks},
author = {Furrer, L. and Cornelius, J. and Rinaldi, F.},
pages = {185--195},
year = {2019},
doi = {10.18653/v1/D19-5726},
url = {}
}
DownloadFurrer, L., Jancso, A., Colic, N., Rinaldi, F. (2019). Oger++: hybrid multi-type entity recognition. Journal of Cheminformatics 11(1), 7.
Oger++: hybrid multi-type entity recognition
Authors: Furrer, L. and Jancso, A. and Colic, N. and Rinaldi, F.
Year: 2019
Abstract: Background: We present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step. Results: We evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7% F1 for named entity recognition and concept recognition, respectively. Conclusions: Combining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining.
Published in Journal of Cheminformatics 11(1), BioMed Central, 7.
Oger++: hybrid multi-type entity recognition
@ARTICLE{rinaldi2019d,
title = {Oger++: hybrid multi-type entity recognition},
journal = {Journal of Cheminformatics},
publisher = {BioMed Central},
volume = {11},
author = {Furrer, L. and Jancso, A. and Colic, N. and Rinaldi, F.},
number = {1},
pages = {7},
year = {2019},
doi = {10.1186/s13321-018-0326-3},
url = {https://doi.org/10.5167/uzh-162875}
}
DownloadKanjirangat, V., Antonucci, A. (2019). NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning. Proceedings of the Text2StoryIR'19 Workshop, Cologne, Germany, 14-April-2019, pp. 29–37.
NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning
Authors: Kanjirangat, V. and Antonucci, A.
Year: 2019
Abstract: A machine learning approach to the creation of visual summaries for narrative text is presented. Standard natural language processing tools for named entities recognition are used together with a clustering algorithm to detect the characters of the novel and their aliases. The most relevant ones and their relations are evaluated on the basis of a simple statistical analysis. These characters are visually depicted as nodes of an undirected graph whose edges describe relations with other characters. Specialized sentiment analysis techniques based on sentence embedding decide the colours of characters/nodes and their relations/edges. Additional information about the characters (e.g., gender) and their relations (e.g., siblings or partnerships) are returned by binary classifiers and visually depicted in the graph. For those specialized tasks, small amounts of manually annotated data are sufficient to achieve good accuracy. Compared to analogous tools, the machine learning approach we present allows for a richer representation of texts of this kind. A case study to demonstrate this approach for a series of books is also reported.
Published in A. Jorge, R. Campos, A. Jatowt, S. Bhatia (Ed), Proceedings of the Text2StoryIR'19 Workshop, Cologne, Germany, 14-April-2019, ceur, pp. 29–37.
NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning
@ARTICLE{vani2019a,
title = {{NOVEL2GRAPH}: {V}isual summaries of narrative text enhanced by machine learning},
journal = {Proceedings of the {Text2StoryIR'19} Workshop, Cologne, Germany, 14-April-2019},
editor = {A. Jorge, R. Campos, A. Jatowt, S. Bhatia},
publisher = {ceur},
author = {Kanjirangat, V. and Antonucci, A.},
pages = {29--37},
year = {2019},
doi = {},
url = {http://ceur-ws.org/Vol-2342/paper4.pdf}
}
DownloadKanjirangat, V., Oita, M., Oezdemir-Zaech, F. (2019). Semantically corroborating neural attention for biomedical question answering. In Machine Learning and Knowledge Discovery in Databases, Springer, Lecture Notes in Computer Science, pp. 670–685.
Semantically corroborating neural attention for biomedical question answering
Authors: Kanjirangat, V. and Oita, M. and Oezdemir-Zaech, F.
Year: 2019
Abstract: Biomedical question answering is a great challenge in NLP
due to complex scientific vocabulary and lack of massive annotated corpora, but, at the same time, is full of potential in optimizing in critical ways the biomedical practices. This paper describes the work carried out as a part of the BioASQ challenge (Task-7B Phase-B), and targets an integral step in the question answering process: extractive answer selection.This deals with the identification of the exact answer (words, phrases or sentences) from given article snippets that are related to the question at hand. We address this problem in the context of factoid and summarization question types, using a variety of deep learning and semantic methods, including various architectures (e.g, Dynamic Memory Networks and Bidirectional Attention Flow), transfer learning, biomedical
named entity recognition and corroboration of semantic evidence. On the top of candidate answer selection module, answer prediction to yes/no question types is also addressed by incorporating a sentiment analysis approach. The evaluation with respect to Rouge, MRR and F1 scores, in
relation to the type of question answering task being considered, exhibits the potential of this hybrid method in extracting the correct answer to a question. In addition, the proposed corroborating semantics module can be added on top of the typical QA pipeline to gain a measured 5% improvement in identifying the exact answer with respect to the gold standard.
Published in Machine Learning and Knowledge Discovery in Databases, Springer, Lecture Notes in Computer Science, pp. 670–685.
Note: BioASQ: Large-Scale Biomedical Semantic Indexing and Question Answering: Workshop of ECML/PKDD 2019
Semantically corroborating neural attention for biomedical question answering
@INPROCEEDINGS{supsi2019d,
title = {Semantically corroborating neural attention for biomedical question answering},
publisher = {Springer, Lecture Notes in Computer Science},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
author = {Kanjirangat, V. and Oita, M. and Oezdemir-Zaech, F.},
pages = {670--685},
year = {2019},
doi = {10.1007/978-3-030-43887-6_60},
url = {}
}
DownloadKim, J.D., Cohen, K.B., Collier, N., Lu, Z., Rinaldi, F. (2019). Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining. Genomics Inform 17(2), e12.
Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining
Authors: Kim, J.D. and Cohen, K.B. and Collier, N. and Lu, Z. and Rinaldi, F.
Year: 2019
Abstract: BLAH is organized annually by the Database Center for Life Science (DBCLS), Research Organization of Information and Systems (ROIS). The goal of the BLAH series is to enhance the interoperability of resources for biomedical text annotation and mining, which we believe is a key for the next breakthrough of biomedical text mining. This special issue delivers seven application notes and two mini reviews, under the theme, “biomedical text mining.” They are outcomes from the 5th Biomedical Linked Annotation Hackathon (BLAH5), which was held from 12th through 15th February 2019 in Kashiwa, Japan.
Published in Genomics Inform 17(2), e12.
Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining
@ARTICLE{rinaldi2019b,
title = {Introduction to {BLAH5} special issue: recent progress on interoperability of biomedical text mining},
journal = {Genomics Inform},
volume = {17},
author = {Kim, J.D. and Cohen, K.B. and Collier, N. and Lu, Z. and Rinaldi, F.},
number = {2},
pages = {e12},
year = {2019},
doi = {10.5808/GI.2019.17.2.e12},
url = {}
}
DownloadMattei, L., Soares, D.L., Antonucci, A., Mauà, D.D., Facchini, A. (2019). Exploring the space of probabilistic sentential decision diagrams. In Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning.
Exploring the space of probabilistic sentential decision diagrams
Authors: Mattei, L. and Soares, D.L. and Antonucci, A. and Mauà, D.D. and Facchini, A.
Year: 2019
Abstract: Probabilistic sentential decision diagrams (PS- DDs) are annotated circuits providing a possibly compact specification of joint probability mass functions consistent with a formula over a set of propositional variables. PSDD inference is tractable in the sense that marginal queries can be achieved in linear time with respect to the circuit size by traversal algorithms. Unlike other probabilistic graphical models such as Bayesian networks, the problem of learning the structure for PSDDs received relatively little attention. We discuss some preliminary ideas related to the development of pure likelihood-score-based search methods for the learning of PSDD structures fit- ting a formula and a data set of consistent obser- vations. A sampling algorithm for these models is also provided.
Published in Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning.
Note: Accepted for pubblication.
Exploring the space of probabilistic sentential decision diagrams
@INPROCEEDINGS{supsi2019a,
title = {Exploring the space of probabilistic sentential decision diagrams},
booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning},
author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},
year = {2019},
doi = {},
url = {https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view}
}
DownloadMauri, A., Lettori, J., Fusi, G., Fausti, D., Mor, M., Braghin, F., Legnani, G., Roveda, L. (2019). Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks. MDPI Robotics.
Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks
Authors: Mauri, A. and Lettori, J. and Fusi, G. and Fausti, D. and Mor, M. and Braghin, F. and Legnani, G. and Roveda, L.
Year: 2019
Abstract: Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. Moreover, state-of-the-art empowering controllers do not allow for online assistance regulation and do not embed advanced safety rules. In the presented work, an industrial exoskeleton with high payload ratio for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is described, exploiting compliant actuation at the shoulder joint to increase safety in human-robot cooperation. A hierarchic model-based controller with embedded safety rules is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer safety-based fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the inner optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.
Published in MDPI Robotics.
Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks
@ARTICLE{Roveda2019b,
title = {Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks},
journal = {{MDPI} Robotics},
author = {Mauri, A. and Lettori, J. and Fusi, G. and Fausti, D. and Mor, M. and Braghin, F. and Legnani, G. and Roveda, L.},
year = {2019},
doi = {10.3390/robotics8030065},
url = {}
}
DownloadMejari, M., Petreczky, M. (2019). Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.. IFAC-PapersOnLine 52(28), pp. 13–19.
Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.
Authors: Mejari, M. and Petreczky, M.
Year: 2019
Abstract: In this paper, we present a realization and an identification algorithm for stochastic Linear Parameter-Varying State-Space Affine (LPV-SSA) representations. The proposed realization algorithm combines the deterministic LPV input output to LPV state-space realization scheme based on correlation analysis with a stochastic covariance realization algorithm. Based on this realization algorithm, a computationally efficient and statistically consistent identification algorithm is proposed to estimate the LPV model matrices, which are computed from the empirical covariance matrices of outputs, inputs and scheduling signal observations. The effectiveness of the proposed algorithm is shown via a numerical case study.
Published in IFAC-PapersOnLine 52(28), pp. 13–19.
Note: 3rd IFAC Workshop on Linear Parameter Varying Systems LPVS 2019
Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.
@ARTICLE{mejari2019a,
title = {Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.},
journal = {{IFAC}-{PapersOnLine}},
volume = {52},
author = {Mejari, M. and Petreczky, M.},
number = {28},
pages = {13--19},
year = {2019},
doi = {https://doi.org/10.1016/j.ifacol.2019.12.340},
url = {https://www.sciencedirect.com/science/article/pii/S2405896319322402}
}
DownloadMejari, M., Petreczky, M. (2019). Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach. In 2019 Ieee 58th Conference on Decision and Control (cdc), pp. 3805–3810.
Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach
Authors: Mejari, M. and Petreczky, M.
Year: 2019
Published in 2019 Ieee 58th Conference on Decision and Control (cdc), pp. 3805–3810.
Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach
@INPROCEEDINGS{mejari2019b,
title = {Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach},
booktitle = {2019 Ieee 58th Conference on Decision and Control ({c}dc)},
author = {Mejari, M. and Petreczky, M.},
pages = {3805--3810},
year = {2019},
doi = {10.1109/CDC40024.2019.9030164},
url = {}
}
DownloadMejari, M., Piga, D., Toth, R., Bemporad, A. (2019). Kernelized identification of linear parameter-varying models with linear fractional representation. In 2019 European Control Conference (ecc), Naples, Italy.
Kernelized identification of linear parameter-varying models with linear fractional representation
Authors: Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.
Year: 2019
Abstract: The article presents a method for the identification of Linear Parameter-Varying (LPV) models in a Linear Fractional Representation (LFR), which corresponds to a Linear Time-Invariant (LTI) model connected to a scheduling variable dependency via a feedback path. A two-stage identification approach is proposed. In the first stage, Kernelized Canonical Correlation Analysis (KCCA) is formulated to estimate the state sequence of the underlying LPV model. In the second stage, a non-linear least squares cost function is minimized by employing a coordinate descent algorithm to estimate latent variables characterizing the LFR and the unknown model
matrices of the LTI block by using the state estimates obtained at the first stage. Here, it is assumed that the structure of the scheduling variable dependent block in the feedback path is fixed. For a special case of affine dependence of the model on the feedback block, it is shown that the optimization problem in the second stage reduces to ordinary least-squares followed by a singular value decomposition.
Published in 2019 European Control Conference (ecc), Naples, Italy.
Kernelized identification of linear parameter-varying models with linear fractional representation
@INPROCEEDINGS{piga2019e,
title = {Kernelized identification of linear parameter-varying models with linear fractional representation},
address = {Naples, Italy},
booktitle = {2019 European Control Conference ({e}cc)},
author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},
year = {2019},
doi = {10.23919/ECC.2019.8796150},
url = {}
}
DownloadMiranda, E., Zaffalon, M. (2019). Compatibility, coherence and the RIP. In Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Ed), Uncertainty Modelling in Data Science, Advances in Intelligent Systems and Computing 832, Springer, pp. 166–174.
Compatibility, coherence and the RIP
Authors: Miranda, E. and Zaffalon, M.
Year: 2019
Abstract: We generalise the classical result on the compatibility of marginal, possible non-disjoint, assessments in terms of the running intersection property to the imprecise case, where our beliefs are modelled in terms of sets of desirable gambles. We consider the case where we have unconditional and conditional assessments, and show that the problem can be simplified via a tree decomposition.
Published in Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Ed), Uncertainty Modelling in Data Science, Advances in Intelligent Systems and Computing 832, Springer, pp. 166–174.
Note: SMPS 2018: Proceedings of the 9th international conference on Soft Methods in Probability and Statistics
Compatibility, coherence and the RIP
@INCOLLECTION{zaffalon2018a,
title = {Compatibility, coherence and the {RIP}},
editor = {Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O.},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
volume = {832},
booktitle = {Uncertainty Modelling in Data Science},
author = {Miranda, E. and Zaffalon, M.},
pages = {166--174},
year = {2019},
doi = {10.1007/978-3-319-97547-4_22},
url = {}
}
DownloadMusumeci, F., Rottondi, C.E.M., Corani, G., Shahkarami, S., Cugini, F., Tornatore, M. (2019). A tutorial on machine learning for failure management in optical networks. Journal of Lightwave Technology 37(16), pp. 4125–4139.
A tutorial on machine learning for failure management in optical networks
Authors: Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.
Year: 2019
Abstract: Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.
Published in Journal of Lightwave Technology 37(16), pp. 4125–4139.
A tutorial on machine learning for failure management in optical networks
@ARTICLE{corani2019b,
title = {A tutorial on machine learning for failure management in optical networks},
journal = {Journal of Lightwave Technology},
volume = {37},
author = {Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.},
number = {16},
pages = {4125--4139},
year = {2019},
doi = {10.1109/JLT.2019.2922586},
url = {}
}
DownloadOita, M. (2019). Reverse engineering creativity into interpretable neural networks. In Future of Information and Communications, Lecture Notes in Networks and Systems 70, pp. 235–247.
Reverse engineering creativity into interpretable neural networks
Authors: Oita, M.
Year: 2019
Abstract: In the field of AI the ultimate goal is to achieve generic intelligence, also called True AI, but which depends on the successful enablement of imagination and creativity in artificial agents. To address this problem, this paper presents a novel deep learning framework for creativity, called INNGenuity. Pursuing an interdisciplinary implementation of creativity conditions, INNGenuity aims at the resolution of the various flaws of current AI learning architectures, which stem from the opacity of their models. Inspired by the neuroanatomy of the brain during creative cognition, the proposed framework's hybrid architecture blends both symbolic and connectionist AI, inline with Minsky's ”society of mind”. At its core, semantic gates are designed to facilitate an input/output flow of semantic structures and enable the usage of aligning mechanisms between neural activation clusters and semantic graphs. Having as goal alignment maximization, such a system would enable interpretability through the creation of labeled patterns of computation, and propose unaligned but relevant computation patterns as novel and useful, therefore creative.
Published in Future of Information and Communications, Lecture Notes in Networks and Systems 70, pp. 235–247.
Reverse engineering creativity into interpretable neural networks
@INPROCEEDINGS{oita2019innGenuity,
title = {Reverse engineering creativity into interpretable neural networks},
series = {Lecture Notes in Networks and Systems},
volume = {70},
booktitle = {Future of Information and Communications},
author = {Oita, M.},
pages = {235--247},
year = {2019},
doi = {10.1007/978-3-030-12385-7_19},
url = {}
}
DownloadOita, M. (2019). Incremental alignment of metaphoric language model for poetry composition. In Intelligent Computing, Springer, "Advances in Intelligent Systems and Computing", pp. 834–845.
Incremental alignment of metaphoric language model for poetry composition
Authors: Oita, M.
Year: 2019
Abstract: The ability to automatically generate meaningful text with respect to a topic is an important AI mission. In particular, the automatic generation of content which is deemed creative is a great challenge. In this paper, poetry generation is approached through the lenses of a new architectural design for creativity that leverages semantics for the creation of variance and the preservation of the content coherency throughout the generation process. The fully implemented system is made available on github.
Published in Intelligent Computing, Springer, "Advances in Intelligent Systems and Computing", pp. 834–845.
Incremental alignment of metaphoric language model for poetry composition
@INPROCEEDINGS{oita2019poetryComposition,
title = {Incremental alignment of metaphoric language model for poetry composition},
publisher = {Springer, "Advances in Intelligent Systems and Computing"},
booktitle = {Intelligent Computing},
author = {Oita, M.},
pages = {834--845},
year = {2019},
doi = {10.1007/978-3-030-22871-2_59},
url = {}
}
DownloadPiga, D. (2019). Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators. International Journal of Control 93(10), pp. 2378–2391.
Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators
Authors: Piga, D.
Year: 2019
Abstract: Direct identification of continuous-time dynamical models from sampled data is now a mature discipline, which is known to have many advantages with respect to indirect approaches based on the identification of discretised models. This paper faces the problem of continuous-time identification of linear time-invariant systems through finite-horizon numerical integration and least-square estimation. The bias in the least-squares estimator due to the noise corrupting the signal observations is quantified, and the benefits of numerical integration in the attenuation of this bias are discussed. An extension of the approach which combines numerical integration, least-squares estimation and particle swarm optimisation is proposed for the identification of nonlinear systems and nonlinear-in-the-parameter models, and then applied to the estimation of the torque-displacement characteristic of a commercial variable stiffness actuator driving a one-degree-of-freedom pendulum.
Published in International Journal of Control 93(10), Taylor & Francis, pp. 2378–2391.
Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators
@ARTICLE{piga2019c,
title = {Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators},
journal = {International Journal of Control},
publisher = {Taylor & Francis},
volume = {93},
author = {Piga, D.},
number = {10},
pages = {2378--2391},
year = {2019},
doi = {10.1080/00207179.2018.1557348},
url = {}
}
DownloadPiga, D., Benavoli, A. (2019). Semialgebraic outer approximations for set-valued nonlinear filtering. In 2019 European Control Conference (ECC), Naples, Italy.
Semialgebraic outer approximations for set-valued nonlinear filtering
Authors: Piga, D. and Benavoli, A.
Year: 2019
Abstract: This paper addresses the set-valued filtering problem for discrete time-varying dynamical systems, whose process and measurement equations are polynomial functions of the system state. According to a set-membership framework, the process and measurement noises, as well as the initial state, are assumed to belong to bounded uncertainty regions, which are supposed to be generic semialgebraic sets described by polynomial inequalities. A sequential algorithm, based on sum-of-squares (SOS) representation of positive polynomials is proposed to compute a semialgebraic set described by an a-priori fixed number of polynomial constraints which is guaranteed to contain the true state of the system with certainty.
Published in 2019 European Control Conference (ECC), Naples, Italy.
Semialgebraic outer approximations for set-valued nonlinear filtering
@INPROCEEDINGS{piga2019f,
title = {Semialgebraic outer approximations for set-valued nonlinear filtering},
address = {Naples, Italy},
booktitle = {2019 European Control Conference ({ECC})},
author = {Piga, D. and Benavoli, A.},
year = {2019},
doi = {10.23919/ECC.2019.8795731},
url = {}
}
DownloadPiga, D., Forgione, M., Formentin, S., Bemporad, A. (2019). Performance-oriented model learning for data-driven MPC design. IEEE Control Systems Letters 3(3), pp. 577–582.
Performance-oriented model learning for data-driven MPC design
Authors: Piga, D. and Forgione, M. and Formentin, S. and Bemporad, A.
Year: 2019
Abstract: Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs. However, in MPC closed-loop performance is pushed to the limits only if the plant under control is accurately modeled; otherwise, robust architectures need to be employed, at the price of reduced performance due to worst-case conservative assumptions. In this paper, instead of adapting the controller to handle uncertainty, we adapt the learning procedure so that the prediction model is selected to provide the best closed-loop performance. More specifically, we apply for the first time the above “identification for control” rationale to hierarchical MPC using data-driven methods and Bayesian optimization.
Published in IEEE Control Systems Letters 3(3), pp. 577–582.
Performance-oriented model learning for data-driven MPC design
@ARTICLE{piga2019a,
title = {Performance-oriented model learning for data-driven {MPC} design},
journal = {{IEEE} Control Systems Letters},
volume = {3},
author = {Piga, D. and Forgione, M. and Formentin, S. and Bemporad, A.},
number = {3},
pages = {577--582},
year = {2019},
doi = {10.1109/LCSYS.2019.2913347},
url = {}
}
DownloadRenooij, S., van der Gaag, L.C. (2019). The hidden elegance of causal interaction models. In Ben Amor, N., Quost, B., Theobald, M (Eds), 13th International Conference on Scalable Uncertainty Management (SUM '19), Lecture Notes in Artificial Intelligence 11940, Springer, pp. 38–51.
The hidden elegance of causal interaction models
Authors: Renooij, S. and van der Gaag, L.C.
Year: 2019
Abstract: Causal interaction models such as the noisy-OR model, are used in Bayesian networks to simplify probability acquisition for variables with large numbers of modelled causes. These models essentially prescribe how to complete an exponentially large probability table from a linear number of parameters. Yet, typically the full probability tables are required for inference with Bayesian networks in which such interaction models are used, although inference algorithms tailored to specific types of network exist that can directly exploit the decomposition properties of the interaction models. In this paper we revisit these decomposition properties in view of general inference algorithms and demonstrate that they allow an alternative representation of causal interaction models that is quite concise, even with large numbers of causes involved. In addition to forestalling the need of tailored algorithms, our alternative representation brings engineering benefits beyond those widely recognised.
Keywords: Bayesian networks, causal interaction models, maintenance robustness
Published in Ben Amor, N., Quost, B., Theobald, M (Eds), 13th International Conference on Scalable Uncertainty Management (SUM '19), Lecture Notes in Artificial Intelligence 11940, Springer, pp. 38–51.
The hidden elegance of causal interaction models
@INPROCEEDINGS{linda2019a,
title = {The hidden elegance of causal interaction models},
editor = {Ben Amor, N. and Quost, B. and Theobald, M},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
volume = {11940},
booktitle = {13th International Conference on Scalable Uncertainty Management ({SUM} '19)},
author = {Renooij, S. and van der Gaag, L.C.},
pages = {38--51},
year = {2019},
doi = {10.1007/978-3-030-35514-2_4},
url = {}
}
DownloadRenooij, S., van der Gaag, L.C., Leray, Ph. (2019). On intercausal interactions in probabilistic relational models. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '19), Proceedings of Machine Learning Research 103, pp. 327–329.
On intercausal interactions in probabilistic relational models
Authors: Renooij, S. and van der Gaag, L.C. and Leray, Ph.
Year: 2019
Abstract: Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian network is composed by replicating parts of the PRM. The interactions between the objects that are thereby induced, are not always obvious from the PRM. We demonstrate in this paper that the replicative structure of the ground network in fact constrains the space of possible probability distributions and thereby the possibly patterns of intercausal interaction.
Keywords:
PRM instances, qualitative constraints on probability distributions, intercausal interaction.
Published in De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '19), Proceedings of Machine Learning Research 103, pp. 327–329.
On intercausal interactions in probabilistic relational models
@INPROCEEDINGS{Linda2019b,
title = {On intercausal interactions in probabilistic relational models},
editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
series = {Proceedings of Machine Learning Research},
volume = {103},
booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications ({ISIPTA} '19)},
author = {Renooij, S. and van der Gaag, L.C. and Leray, Ph.},
pages = {327--329},
year = {2019},
doi = {},
url = {https://proceedings.mlr.press/v103/renooij19a.html}
}
DownloadRodriguez-Esteban, R., Vishnyakova, D., Rinaldi, F. (2019). Revisiting the decay of scientific email addresses. bioRxiv.
Revisiting the decay of scientific email addresses
Authors: Rodriguez-Esteban, R. and Vishnyakova, D. and Rinaldi, F.
Year: 2019
Abstract: Email is the primary means of communication for scientists. However, scientific authors change email address over time. Using a new method, we have calculated that approximately 18% of all authorstextquoteright contact email addresses in MEDLINE are invalid. While an unfortunate number, it is, however, lower than previously estimated. To mitigate this problem, institutions should provide email forwarding and scientific authors should use more stable email addresses. In fact, a steadily growing share already use free private email addresses: 32% of all new addresses in MEDLINE in 2018 were of this kind.
Published in bioRxiv, Cold Spring Harbor Laboratory.
Revisiting the decay of scientific email addresses
@ARTICLE{rinaldi2019j,
title = {Revisiting the decay of scientific email addresses},
journal = {{bioRxiv}},
publisher = {Cold Spring Harbor Laboratory},
author = {Rodriguez-Esteban, R. and Vishnyakova, D. and Rinaldi, F.},
year = {2019},
doi = {10.1101/633255},
url = {https://www.biorxiv.org/content/early/2019/05/12/633255}
}
DownloadRoveda, L., Haghshenas, S., Caimmi, M., Pedrocchi, N., Molinari Tosatti, L. (2019). Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules. Frontiers in Robotics and AI 6, 75.
Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules
Authors: Roveda, L. and Haghshenas, S. and Caimmi, M. and Pedrocchi, N. and Molinari Tosatti, L.
Year: 2019
Abstract: Human-robot cooperation is increasingly demanded in industrial applications. Many tasks require the robot to enhance the capabilities of humans. In this scenario, safety also plays an important role in avoiding any accident involving humans, robots, and the environment. With this aim, the paper proposes a cooperative fuzzy-impedance control with embedded safety rules to assist human operators in heavy industrial applications while manipulating unknown weight parts. The proposed methodology is composed by four main components: (i) an inner Cartesian impedance controller (to achieve the compliant robot behavior), (ii) an outer fuzzy controller (to provide the assistance to the human operator), (iii) embedded safety rules (to limit force/velocity during the human-robot interaction enhancing safety), and (iv) a neural network approach (to optimize the control parameters for the human-robot collaboration on the basis of the target indexes of assistance performance defined for this purpose). The main achieved result refers to the capability of the controller to deal with uncertain payloads while assisting and empowering the human operator, both embedding in the controller safety features at force and velocity levels and minimizing the proposed performance indexes. The effectiveness of the proposed approach is verified with a KUKA iiwa 14 R820 manipulator in an experimental procedure where human subjects evaluate the robot performance in a collaborative lifting task of a 10 kg part.
Published in Frontiers in Robotics and AI 6, 75.
Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules
@ARTICLE{Roveda2019a,
title = {Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules},
journal = {Frontiers in Robotics and {AI}},
volume = {6},
author = {Roveda, L. and Haghshenas, S. and Caimmi, M. and Pedrocchi, N. and Molinari Tosatti, L.},
pages = {75},
year = {2019},
doi = {10.3389/frobt.2019.00075},
url = {}
}
DownloadSalani, M., Corbellini, G., Corani, G. (2019). Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects. Computers & Operations Research 108, pp. 112–120.
Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects
Authors: Salani, M. and Corbellini, G. and Corani, G.
Year: 2019
Abstract: We consider the Photovoltaic Installation Design Problem (PIDP). In this problem, photovoltaic modules must be connected in “strings” and wired to a set of electronic components. The aim is to minimize installation costs and maximize power production, which is affected by “mismatch losses” caused by non-uniform irradiation (shading) and is also directly related to design decisions. We relate the problem to the known class of location routing problems and we design a route-first/cluster-second heuristic. We propose an efficient machine learning approach to evaluate Photovoltaic (PV) string performances accounting for mismatch losses. We prove that our approach is effective in real-world instances provided by our industrial partner.
Published in Computers & Operations Research 108, pp. 112–120.
Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects
@ARTICLE{corani2019a,
title = {Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects},
journal = {Computers & Operations Research},
volume = {108},
author = {Salani, M. and Corbellini, G. and Corani, G.},
pages = {112--120},
year = {2019},
doi = {10.1016/j.cor.2019.04.009},
url = {}
}
DownloadSechidis, K., Azzimonti, L., Pocock, A., Corani, G., Weatherall, J., Brown, G. (2019). Efficient feature selection using shrinkage estimators. Machine Learning 108(8), pp. 1261–1286.
Efficient feature selection using shrinkage estimators
Authors: Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.
Year: 2019
Abstract: Information theoretic feature selection methods quantify the importance of each feature by estimating mutual information terms to capture: the relevancy, the redundancy and the complementarity. These terms are commonly estimated by maximum likelihood, while an under-explored area of research is how to use shrinkage methods instead. Our work suggests a novel shrinkage method for data-efficient estimation of information theoretic terms. The small sample behaviour makes it particularly suitable for estimation of discrete distributions with large number of categories (bins). Using our novel estimators we derive a framework for generating feature selection criteria that capture any high-order feature interaction for redundancy and complementarity. We perform a thorough empirical study across datasets from diverse sources and using various evaluation measures. Our first finding is that our shrinkage based methods achieve better results, while they keep the same computational cost as the simple maximum likelihood based methods. Furthermore, under our framework we derive efficient novel high-order criteria that outperform state-of-the-art methods in various tasks.
Published in Machine Learning 108(8), pp. 1261–1286.
Efficient feature selection using shrinkage estimators
@ARTICLE{azzimonti2019b,
title = {Efficient feature selection using shrinkage estimators},
journal = {Machine Learning},
volume = {108},
author = {Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.},
number = {8},
pages = {1261--1286},
year = {2019},
doi = {10.1007/s10994-019-05795-1},
url = {}
}
DownloadSheikhalishahi, S., Miotto, R., Dudley, J.T., Lavelli, A., Rinaldi, F., Osmani, V. (2019). Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform 7(2), e12239.
Natural language processing of clinical notes on chronic diseases: systematic review
Authors: Sheikhalishahi, S. and Miotto, R. and Dudley, J.T. and Lavelli, A. and Rinaldi, F. and Osmani, V.
Year: 2019
Abstract: Background: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset. Objective: The goal of the research was to provide a comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes,” “natural language processing,” and “chronic disease” and their variations as keywords to maximize coverage of the articles. Results: Of the 2652 articles considered, 106 met the inclusion criteria. Review of the included papers resulted in identification of 43 chronic diseases, which were then further classified into 10 disease categories using the International Classification of Diseases, 10th Revision. The majority of studies focused on diseases of the circulatory system (n=38) while endocrine and metabolic diseases were fewest (n=14). This was due to the structure of clinical records related to metabolic diseases, which typically contain much more structured data, compared with medical records for diseases of the circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches; however, deep learning methods remain emergent (n=3). Consequently, the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from the free text or integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions: Efforts are still required to improve (1) progression of clinical NLP methods from extraction toward understanding; (2) recognition of relations among entities rather than entities in isolation; (3) temporal extraction to understand past, current, and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.
Published in JMIR Med Inform 7(2), e12239.
Natural language processing of clinical notes on chronic diseases: systematic review
@ARTICLE{rinaldi2019f,
title = {Natural language processing of clinical notes on chronic diseases: systematic review},
journal = {{JMIR} Med Inform},
volume = {7},
author = {Sheikhalishahi, S. and Miotto, R. and Dudley, J.T. and Lavelli, A. and Rinaldi, F. and Osmani, V.},
number = {2},
pages = {e12239},
year = {2019},
doi = {10.2196/12239},
url = {http://medinform.jmir.org/2019/2/e12239/}
}
DownloadVishnyakova, D., Rodriguez-Esteban, R., Rinaldi, F. (2019). A new approach and gold standard toward author disambiguation in MEDLINE. J Am Med Inform Assoc 26(10), pp. 1037–1045.
A new approach and gold standard toward author disambiguation in MEDLINE
Authors: Vishnyakova, D. and Rodriguez-Esteban, R. and Rinaldi, F.
Year: 2019
Abstract: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation. Following a new corpus design method, publication pairs randomly picked from MEDLINE were evaluated by both crowdsourcing and expert curators. Because the latter showed higher accuracy than crowdsourcing, expert curators were tasked to create a full corpus. The corpus was then used to explore new features that could improve state-of-the-art author disambiguation algorithms that would not have been discoverable with previously existing gold standards. We created a gold standard based on 1900 publication pairs that shows close similarity to MEDLINE in terms of chronological distribution and information completeness. A machine-learning algorithm that includes new features related to the ethnic origin of authors showed significant improvements over the current state of the art and demonstrates the necessity of realistic gold standards to further develop effective author disambiguation algorithms. An unbiased gold standard can give a more accurate picture of the status of author disambiguation research and help in the discovery of new features for machine learning. The principles and methods shown here can be applied to other reference databases beyond MEDLINE.
Published in J Am Med Inform Assoc 26(10), pp. 1037–1045.
A new approach and gold standard toward author disambiguation in MEDLINE
@ARTICLE{rinaldi2019a,
title = {A new approach and gold standard toward author disambiguation in {MEDLINE}},
journal = {J Am Med Inform Assoc},
volume = {26},
author = {Vishnyakova, D. and Rodriguez-Esteban, R. and Rinaldi, F.},
number = {10},
pages = {1037--1045},
year = {2019},
doi = {10.1093/jamia/ocz028},
url = {}
}
Download top2018
Antonucci, A., Facchini, A. (2018). A credal extension of independent choice logic. In Proceedings of the 12th International Conference on Scalable Uncertainty Management (SUM 2018), pp. 35–49.
A credal extension of independent choice logic
Authors: Antonucci, A. and Facchini, A.
Year: 2018
Abstract: We propose an extension of Poole’s independent choice logic based on a relaxation of the underlying independence assumptions. A credal semantics involving multiple joint probability mass functions over the possible worlds is adopted. This represents a conservative approach to probabilistic logic program- ming achieved by considering all the mass functions consistent with the prob- abilistic facts. This allows to model tasks for which independence among some probabilistic choices cannot be assumed, and a specific dependence model cannot be assessed. Preliminary tests on an object ranking application show that, despite the loose underlying assumptions, informative inferences can be extracted.
Published in Proceedings of the 12th International Conference on Scalable Uncertainty Management (SUM 2018), pp. 35–49.
A credal extension of independent choice logic
@INPROCEEDINGS{antonucci2018c,
title = {A credal extension of independent choice logic},
booktitle = {Proceedings of the 12th International Conference on Scalable Uncertainty Management ({SUM} 2018)},
author = {Antonucci, A. and Facchini, A.},
pages = {35--49},
year = {2018},
doi = {10.1007/978-3-030-00461-3_3},
url = {https://arxiv.org/abs/1806.08298}
}
DownloadAntonucci, A., Facchini, A. (2018). Set-valued probabilistic sentential decision diagrams. In Proceedings of the 5th Workshop on Probabilistic Logic Programming, pp. 3–8.
Set-valued probabilistic sentential decision diagrams
Authors: Antonucci, A. and Facchini, A.
Year: 2018
Abstract: Probabilistic sentential decision diagrams are a class of arithmetic circuits locally annotated by probability mass functions. This al- lows for a compact representation of joint mass functions in the presence of logical constraints. Here we propose a set-valued generalisation of the probabilistic quantification in these models, that allows to replace the sharp specification of the local probabilities with linear constraints over them. This induces a (convex) set of joint probability mass functions, all consistent with the assigned logical constraints. These models should be adopted for a cautious learning of the local parameters if only small amount of data are available. Algorithmic strategies to compute the lower and upper bounds of marginal and conditional queries with respect to these sets of joint mass functions are sketched. The task can be achieved in linear time with respect to the diagram size for marginal queries and, if the diagram is singly connected, the same can be done for conditional queries.
Published in Proceedings of the 5th Workshop on Probabilistic Logic Programming, pp. 3–8.
Set-valued probabilistic sentential decision diagrams
@INPROCEEDINGS{antonucci2018d,
title = {Set-valued probabilistic sentential decision diagrams},
booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},
author = {Antonucci, A. and Facchini, A.},
pages = {3--8},
year = {2018},
doi = {},
url = {https://ceur-ws.org/Vol-2219/}
}
DownloadBemporad, A., Breschi, V., Piga, D., Boyd, S. (2018). Fitting jump models. Automatica 96, pp. 11–21.
Fitting jump models
Authors: Bemporad, A. and Breschi, V. and Piga, D. and Boyd, S.
Year: 2018
Abstract: We describe a new framework for fitting jump models to a sequence of data. The key idea is to alternate between minimizing a loss function to fit multiple model parameters, and minimizing a discrete loss function to determine which set of model parameters is active at each data point. The framework is quite general and encompasses popular classes of models, such as hidden Markov models and piecewise affine models. The shape of the chosen loss functions to minimize determines the shape of the resulting jump model.
Published in Automatica 96, pp. 11–21.
Fitting jump models
@ARTICLE{piga2018e,
title = {Fitting jump models},
journal = {Automatica},
volume = {96},
author = {Bemporad, A. and Breschi, V. and Piga, D. and Boyd, S.},
pages = {11--21},
year = {2018},
doi = {10.1016/j.automatica.2018.06.022},
url = {}
}
DownloadBreschi, V., Bemporad, A., Piga, D., Boyd, S. (2018). Prediction error methods in learning jump ARMAX models. In 2018 IEEE Conference on Decision and Control (cdc), pp. 2247–2252.
Prediction error methods in learning jump ARMAX models
Authors: Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.
Year: 2018
Abstract: Jump models describe systems that change their dynamics over time. Identifying jump models amounts both to learn the behavior of the system at each operating mode and to reconstruct the active mode sequence from data. This paper focuses on the identification of jump autoregressive moving-average models with exogenous inputs (JARMAX), combining prediction error methods with a coordinate descent algorithm for fitting jump models. The resulting identification algorithm alternates between minimizing the sum of prediction errors with respect to the parameters of the ARMAX models, and minimizing a discrete loss function with respect to the sequence of active modes.
Published in 2018 IEEE Conference on Decision and Control (cdc), pp. 2247–2252.
Prediction error methods in learning jump ARMAX models
@INPROCEEDINGS{piga2018i,
title = {Prediction error methods in learning jump {ARMAX} models},
booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},
author = {Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.},
pages = {2247--2252},
year = {2018},
doi = {10.1109/CDC.2018.8619819},
url = {}
}
DownloadBreschi, V., Piga, D., Bemporad, A. (2018). Kalman filtering for energy disaggregation. In Proc. of the 1st IFAC Workshop on Integrated Assessment Modelling for Environmental Systems 51(5), pp. 108–113.
Kalman filtering for energy disaggregation
Authors: Breschi, V. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: Providing the users information on the energy consumed in the household at the appliance level is of major importance for increasing their awareness of their consumption behavior. In this paper, we propose a technique based on Kalman filters to estimate the devices' consumption patterns from aggregate readings, i.e., to solve the so called disaggregation problem. The method is suited for on-line disaggregation and the proposed results show that it is robust against modelling errors and unmodelled appliances.
Published in Proc. of the 1st IFAC Workshop on Integrated Assessment Modelling for Environmental Systems IFAC-PapersOnLine 51(5), pp. 108–113.
Kalman filtering for energy disaggregation
@INPROCEEDINGS{piga2018b,
title = {Kalman filtering for energy disaggregation},
journal = {{IFAC}-{PapersOnLine}},
volume = {51},
booktitle = {Proc. {o}f the 1st {IFAC} Workshop on Integrated Assessment Modelling for Environmental Systems},
author = {Breschi, V. and Piga, D. and Bemporad, A.},
number = {5},
pages = {108--113},
year = {2018},
doi = {10.1016/j.ifacol.2018.06.219},
url = {}
}
DownloadBreschi, V., Piga, D., Bemporad, A. (2018). Jump model learning and filtering for energy end-use disaggregation. In Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 275–280.
Jump model learning and filtering for energy end-use disaggregation
Authors: Breschi, V. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: Energy disaggregation aims at reconstructing the power consumed by each electric appliance available in a household from the aggregate power readings collected by a single-point smart meter. With the ultimate goal of fully automatizing this procedure, we first estimate a set of jump models, each of them describing the consumption behaviour of each electric appliance. By representing the total power consumed at the household level as the sum of the outputs of the estimated jump models, a filtering algorithm, based on dynamic programming, is then employed to reconstruct, in an iterative way, the power consumption at an individual appliance level.
Published in Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 275–280.
Jump model learning and filtering for energy end-use disaggregation
@INPROCEEDINGS{piga2018f,
title = {Jump model learning and filtering for energy end-use disaggregation},
volume = {51},
booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},
author = {Breschi, V. and Piga, D. and Bemporad, A.},
number = {15},
pages = {275--280},
year = {2018},
doi = {10.1016/j.ifacol.2018.09.147},
url = {}
}
Downloadde Campos, C.P., Scanagatta, M., Corani, G., Zaffalon, M. (2018). Entropy-based pruning for learning Bayesian networks using BIC. Artificial Intelligence 260, pp. 42–50.
Entropy-based pruning for learning Bayesian networks using BIC
Authors: de Campos, C.P. and Scanagatta, M. and Corani, G. and Zaffalon, M.
Year: 2018
Abstract: For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score. We target the task of constructing the collection of candidate parent sets when the score of choice is the Bayesian Information Criterion (BIC). We provide new non-trivial results that can be used to prune the search space of candidate parent sets of each node. We analyze how these new results relate to previous ideas in the literature both theoretically and empirically. We show in experiments with UCI data sets that gains can be significant. Since the new pruning rules are easy to implement and have low computational costs, they can be promptly integrated into all state-of-the-art methods for structure learning of Bayesian networks.
Published in Artificial Intelligence 260, pp. 42–50.
Entropy-based pruning for learning Bayesian networks using BIC
@ARTICLE{deCampos2018a,
title = {Entropy-based pruning for learning {B}ayesian networks using {BIC}},
journal = {Artificial Intelligence},
volume = {260},
author = {de Campos, C.P. and Scanagatta, M. and Corani, G. and Zaffalon, M.},
pages = {42--50},
year = {2018},
doi = {10.1016/j.artint.2018.04.002},
url = {}
}
DownloadGiusti, A., Huber, D., Gambardella, L.M. (2018). Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures. In Proc. of AAAI Symposium On Educational Advances In Artificial Intelligence 32(1).
Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures
Authors: Giusti, A. and Huber, D. and Gambardella, L.M.
Year: 2018
Abstract: We present an interactive guided activity to introduce supervised learning by training a deep neural network (treated as a black box) to recognize "rock paper scissors" hand gestures from unconstrained images. The audience is actively involved in acquiring a varied and representative dataset, on which the rest of the activity is based. Covered concepts include the training/evaluation split, classifier evaluation, baseline accuracy, overfitting, generalization, data augmentation.
Published in Proc. of AAAI Symposium On Educational Advances In Artificial Intelligence Proceedings of the AAAI Conference on Artificial Intelligence 32(1).
Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures
@INPROCEEDINGS{huber2018a,
title = {Introducing {M}achine {L}earning {C}oncepts by {T}raining a {N}eural {N}etwork to {R}ecognize {H}and {G}estures},
journal = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
volume = {32},
booktitle = {Proc. {o}f {AAAI} Symposium On Educational Advances In Artificial Intelligence},
author = {Giusti, A. and Huber, D. and Gambardella, L.M.},
number = {1},
year = {2018},
doi = {10.1609/aaai.v32i1.11400},
url = {}
}
DownloadKern, H., Corani, G., Huber, D., Vermes, N., Zaffalon, M. (2018). What interplay of factors influences the place of death in cancer patients? an innovative probabilistic approach sheds light on a well-known question. Journal of Pain and Symptom Management 56(6), e25.
What interplay of factors influences the place of death in cancer patients? an innovative probabilistic approach sheds light on a well-known question
Authors: Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M.
Year: 2018
Abstract: Most terminally ill cancer patients would prefer to die at home although a majority die in institutional settings. Questions about this discrepancy are not yet fully answered. This study applies machine learning techniques to explore the complicated network of factors and the cause-effect relationships affecting the place of death with the ultimate aim of developing policies.
Published in Journal of Pain and Symptom Management Journal of Pain and Symptom Management 56(6), Elsevier, e25.
What interplay of factors influences the place of death in cancer patients? an innovative probabilistic approach sheds light on a well-known question
@ARTICLE{kern2018a,
title = {What interplay of factors influences the place of death in cancer patients? {a}n innovative probabilistic approach sheds light on a well-known question},
journal = {Journal of Pain and Symptom Management},
publisher = {Elsevier},
volume = {56},
booktitle = {Journal of Pain and Symptom Management},
author = {Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M.},
number = {6},
pages = {e25},
year = {2018},
doi = {10.1016/j.jpainsymman.2018.10.016},
url = {}
}
DownloadMarchetti, S., Antonucci, A. (2018). Reliable uncertain evidence modeling in Bayesian networks by credal networks. In Proceedings of the 31st International Flairs Conference (FLAIRS-31), AAAI Press, pp. 513–518.
Reliable uncertain evidence modeling in Bayesian networks by credal networks
Authors: Marchetti, S. and Antonucci, A.
Year: 2018
Abstract: A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.
Published in Proceedings of the 31st International Flairs Conference (FLAIRS-31), AAAI Press, pp. 513–518.
Reliable uncertain evidence modeling in Bayesian networks by credal networks
@INPROCEEDINGS{antonucci2018a,
title = {Reliable uncertain evidence modeling in {B}ayesian networks by credal networks},
publisher = {AAAI Press},
booktitle = {Proceedings of the 31st International Flairs Conference ({FLAIRS}-31)},
author = {Marchetti, S. and Antonucci, A.},
pages = {513--518},
year = {2018},
doi = {},
url = {}
}
DownloadMarchetti, S., Antonucci, A. (2018). Imaginary kinematics. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, AUAI Press, pp. 104–113.
Imaginary kinematics
Authors: Marchetti, S. and Antonucci, A.
Year: 2018
Abstract: We introduce a novel class of adjustment rules for a collection of beliefs. This is an extension of Lewis’ imaging to absorb probabilistic evidence in generalized settings. Unlike standard tools for belief revision, our proposal may be used when information is inconsistent with an agent’s belief base. We show that the functionals we introduce are based on the imaginary counterpart of probability kinematics for standard belief revision, and prove that, under certain conditions, all standard postulates for belief revision are satisfied.
Published in Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, AUAI Press, pp. 104–113.
Imaginary kinematics
@INPROCEEDINGS{antonucci2018b,
title = {Imaginary kinematics},
publisher = {AUAI Press},
booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence},
author = {Marchetti, S. and Antonucci, A.},
pages = {104--113},
year = {2018},
doi = {},
url = {https://www.auai.org/uai2018/accepted.php#top}
}
DownloadMejari, M., Naik, V.V., Piga, D., Bemporad, A. (2018). Regularized moving-horizon PWA regression for LPV system identification. In Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 1092–1097.
Regularized moving-horizon PWA regression for LPV system identification
Authors: Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: This paper addresses the identification of Linear Parameter-Varying (LPV) models through regularized moving-horizon PieceWise Affine (PWA) regression. Specifically, the scheduling-variable space is partitioned into polyhedral regions, where each region is assigned to a PWA function describing the local affine dependence of the LPV model coefficients on the scheduling variable. The regression approach consists of two stages. In the first stage, the data samples are processed iteratively, and a Mixed-Integer Quadratic Programming (MIQP) problem is solved to cluster the scheduling variable observations and simultaneously fit the model parameters to the training data, within a relatively short moving-horizon window of the past. At the second stage, the polyhedral partition of the scheduling-variable space is computed by separating the estimated clusters through linear multi-category discrimination.
Published in Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 1092–1097.
Note: 18th IFAC Symposium on System Identification SYSID 2018
Regularized moving-horizon PWA regression for LPV system identification
@INPROCEEDINGS{piga2018d,
title = {Regularized moving-horizon {PWA} regression for {LPV} system identification},
volume = {51},
booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},
author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
number = {15},
pages = {1092--1097},
year = {2018},
doi = {10.1016/j.ifacol.2018.09.048},
url = {}
}
DownloadMejari, M., Naik, V.V., Piga, D., Bemporad, A. (2018). Energy disaggregation using piecewise affine regression and binary quadratic programming. In 2018 IEEE Conference on Decision and Control (cdc), pp. 3116–3121.
Energy disaggregation using piecewise affine regression and binary quadratic programming
Authors: Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: In this paper we consider the problem of energy disaggregation, commonly referred in the literature as “non-intrusive load monitoring”. The problem is to estimate the end-use power consumption profiles of individual household appliance using only aggregated power measurements. We propose a two-stage supervised approach. At the first stage, dynamical models of individual appliances are estimated using disaggregated training data gathered over a short intrusive period. The consumption profiles of individual appliances are described by PieceWise Affine AutoRegressive (PWA-AR) models with multiple operating modes, which are estimated via a moving horizon PWA regression algorithm. Once the model of each appliance is identified, a binary quadratic programming problem is solved at the second stage to determine the set of active appliances which contribute to the instantaneous aggregated power, along with their operating modes. A benchmark dataset is used to assess the performance of the presented disaggregation approach.
Published in 2018 IEEE Conference on Decision and Control (cdc), pp. 3116–3121.
Energy disaggregation using piecewise affine regression and binary quadratic programming
@INPROCEEDINGS{piga2018h,
title = {Energy disaggregation using piecewise affine regression and binary quadratic programming},
booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},
author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
pages = {3116--3121},
year = {2018},
doi = {10.1109/CDC.2018.8619175},
url = {}
}
DownloadMejari, M., Piga, D., Bemporad, A. (2018). A bias-correction method for closed-loop identification of linear parameter-varying systems. Automatica 87, pp. 128–141.
A bias-correction method for closed-loop identification of linear parameter-varying systems
Authors: Mejari, M. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: Due to safety constraints and unstable open-loop dynamics, system identification of many real-world processes often requiresgathering data from closed-loop experiments. In this paper, we present a bias-correction scheme for closed-loop identification of Linear Parameter-Varying Input–Output (LPV-IO) models, which aims at correcting the bias caused by the correlation between the input signal exciting the process and output noise. The proposed identification algorithm provides a consistent estimate of the open-loop model parameters when both the output signal and the scheduling variable are corrupted by measurement noise. The effectiveness of the proposed methodology is tested in two simulation case studies.
Published in Automatica 87, pp. 128–141.
A bias-correction method for closed-loop identification of linear parameter-varying systems
@ARTICLE{piga2018c,
title = {A bias-correction method for closed-loop identification of linear parameter-varying systems},
journal = {Automatica},
volume = {87},
author = {Mejari, M. and Piga, D. and Bemporad, A.},
pages = {128--141},
year = {2018},
doi = {10.1016/j.automatica.2017.09.014},
url = {}
}
DownloadPiga, D., Formentin, S., Bemporad, A. (2018). Direct data-driven control of constrained systems. IEEE Transactions on Control Systems Technology 26(4), pp. 1422–1429.
Direct data-driven control of constrained systems
Authors: Piga, D. and Formentin, S. and Bemporad, A.
Year: 2018
Abstract: In model-based control design, one often has to describe the plant by a linear model. Deriving such a model poses issues of parameterization, estimation, and validation of the model before designing the controller. In this paper, a direct data-driven control method is proposed for designing controllers that can handle constraints without deriving a model of the plant and directly from data. A hierarchical control architecture is used, in which an inner linear time-invariant for linear parameter-varying controller is first designed to match a simple and a priori specified closed-loop model. Then, an outer model predictive controller is synthesized to handle input/output constraints and to enhance the performance of the inner loop. The effectiveness of the approach is illustrated by means of a simulation and an experimental example. Practical implementation issues are also discussed.
Published in IEEE Transactions on Control Systems Technology 26(4), pp. 1422–1429.
Direct data-driven control of constrained systems
@ARTICLE{piga2018a,
title = {Direct data-driven control of constrained systems},
journal = {{IEEE} Transactions on Control Systems Technology},
volume = {26},
author = {Piga, D. and Formentin, S. and Bemporad, A.},
number = {4},
pages = {1422--1429},
year = {2018},
doi = {10.1109/TCST.2017.2702118},
url = {}
}
DownloadScanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M. (2018). Approximate structure learning for large Bayesian networks. Machine Learning 107(8-10), pp. 1209–1227.
Approximate structure learning for large Bayesian networks
Authors: Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.
Year: 2018
Abstract: We present approximate structure learning algorithms for Bayesian networks. We discuss on the two main phases of the task: the preparation of the cache of the scores and structure optimization, both with bounded and unbounded treewidth. We improve on state-of-the-art methods that rely on an ordering-based search by sampling more effectively the space of the orders. This allows for a remarkable improvement in learning Bayesian networks from thousands of variables.
We also present a thorough study of the accuracy and the running time of inference, comparing bounded-treewidth and unbounded-treewidth models.
Published in Machine Learning 107(8-10), Springer, pp. 1209–1227.
Approximate structure learning for large Bayesian networks
@ARTICLE{scanagatta2018b,
title = {Approximate structure learning for large {B}ayesian networks},
journal = {Machine Learning},
publisher = {Springer},
volume = {107},
author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},
number = {8-10},
pages = {1209--1227},
year = {2018},
doi = {10.1007/s10994-018-5701-9},
url = {}
}
DownloadScanagatta, M., Corani, G., Zaffalon, M., Yoo, J., Kang, U. (2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. International Journal of Approximate Reasoning 95, pp. 152–166.
Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets
Authors: Scanagatta, M. and Corani, G. and Zaffalon, M. and Yoo, J. and Kang, U.
Year: 2018
Abstract: Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through
extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem
of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt
the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the
resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about
one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable.
Published in International Journal of Approximate Reasoning 95, pp. 152–166.
Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets
@ARTICLE{scanagatta2018a,
title = {Efficient learning of bounded-treewidth {B}ayesian networks from complete and incomplete data sets},
journal = {International Journal of Approximate Reasoning},
volume = {95},
author = {Scanagatta, M. and Corani, G. and Zaffalon, M. and Yoo, J. and Kang, U.},
pages = {152--166},
year = {2018},
doi = {10.1016/j.ijar.2018.02.004},
url = {}
}
DownloadSelvi, D., Piga, D., Bemporad, A. (2018). Towards direct data-driven model-free design of optimal controllers. In 2018 European Control Conference (ecc), pp. 2836–2841.
Towards direct data-driven model-free design of optimal controllers
Authors: Selvi, D. and Piga, D. and Bemporad, A.
Year: 2018
Abstract: The most critical step in modern direct data-driven control design approaches, such as virtual reference feedback tuning and non-iterative correlation-based tuning, is the choice of an adequate closed-loop reference model. Indeed, the chosen reference model should reflect the desired closed-loop performance but also be reproducible by the underlying unknown process when in closed loop with the synthesized controller. In this paper, we propose a novel approach to compute, directly from data, an “optimal” reference model along with the corresponding controller. The performance index used to define the optimality of the reference model measures the tracking error and the actuator efforts (as it is typical in performance-driven controllers such as linear-quadratic Gaussian control and model predictive control), along with a term penalizing the expected mismatch between the reference model and the actual closed-loop system. The performance index depends on the variables used to parametrize the reference model and the controller, which are optimized through a suitable combination of particle swarm optimization and virtual reference feedback tuning.
Published in 2018 European Control Conference (ecc), pp. 2836–2841.
Towards direct data-driven model-free design of optimal controllers
@INPROCEEDINGS{piga2018g,
title = {Towards direct data-driven model-free design of optimal controllers},
booktitle = {2018 European Control Conference ({e}cc)},
author = {Selvi, D. and Piga, D. and Bemporad, A.},
pages = {2836--2841},
year = {2018},
doi = {10.23919/ECC.2018.8550184},
url = {}
}
Download top2017
Arnone, E., Azzimonti, L., Nobile, F., Sangalli, L.M. (2017). A time-dependent PDE regularization to model functional data defined over spatio-temporal domains. In Aneiros G., Bongiorno E.G., Cao R., Vieu P. (Ed), Functional Statistics and Related Fields, Springer International Publishing, pp. 41–44.
A time-dependent PDE regularization to model functional data defined over spatio-temporal domains
Authors: Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.
Year: 2017
Abstract: We propose a method for the analysis of functional data defined over spatio-temporal domains when prior knowledge on the phenomenon under study is available. The model is based on regression with Partial Differential Equations (PDE) penalization. The PDE formalizes the information on the phenomenon and models the regularity of the field in space and time.
Published in Aneiros G., Bongiorno E.G., Cao R., Vieu P. (Ed), Functional Statistics and Related Fields, Springer International Publishing, pp. 41–44.
A time-dependent PDE regularization to model functional data defined over spatio-temporal domains
@INBOOK{azzimonti2017b,
title = {A time-dependent {PDE} regularization to model functional data defined over spatio-temporal domains},
editor = {Aneiros G., Bongiorno E.G., Cao R., Vieu P. },
publisher = {Springer International Publishing},
booktitle = {Functional Statistics and Related Fields},
author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},
pages = {41--44},
year = {2017},
doi = {10.1007/978-3-319-55846-2_6},
url = {}
}
DownloadAzzimonti, L., Corani, G., Zaffalon, M. (2017). Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables. In Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (Ed), 2017 IEEE 17th International Conference on Data Mining (ICDM), pp. 739–744.
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables
Authors: Azzimonti, L. and Corani, G. and Zaffalon, M.
Year: 2017
Abstract: We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We then apply this method to the estimation of parameters in a Bayesian network. Given the structure of the network, the proposed approach better estimates the joint distribution and significantly improves the classification performance with respect to traditional approaches.
Published in Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (Ed), 2017 IEEE 17th International Conference on Data Mining (ICDM), pp. 739–744.
Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables
@INPROCEEDINGS{azzimonti2017c,
title = {Hierarchical {M}ultinomial-{D}irichlet model for the estimation of conditional probability tables},
editor = {Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X.},
booktitle = {2017 {IEEE} 17th International Conference on Data Mining ({ICDM})},
author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},
pages = {739--744},
year = {2017},
doi = {10.1109/ICDM.2017.85},
url = {}
}
DownloadBalleri, A., Farina, A., Benavoli, A. (2017). Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems. IET Radar, Sonar & Navigation 11(7), pp. 1132–139.
Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems
Authors: Balleri, A. and Farina, A. and Benavoli, A.
Year: 2017
Abstract: We present an algorithm that allows an interceptor aircraft equipped with an airborne radar to meet another air target (the intercepted) by developing a guidance law and automatically adapting and optimising the transmitted waveform on a pulse to pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target. The transmitted waveform is automatically selected based on its ambiguity function and accuracy properties along the approaching path. For each pulse, the interceptor predicts its position and velocity with respect to the target, takes a measurement of range and radial velocity and, with the Kalman filter, refines the relative range and range rate estimates. These are fed into a Linear Quadratic Gaussian (LQG) controller that ensures the interceptor reaches the target automatically and successfully with minimum error and with the minimum guidance energy consumption.
Published in IET Radar, Sonar & Navigation 11(7), Institution of Engineering and Technology, pp. 1132–139.
Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems
@ARTICLE{benavoli2017a,
title = {Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems},
journal = {{IET} Radar, Sonar & Navigation},
publisher = {Institution of Engineering and Technology},
volume = {11},
author = {Balleri, A. and Farina, A. and Benavoli, A.},
number = {7},
pages = {1132--139},
year = {2017},
doi = {10.1049/iet-rsn.2016.0547},
url = {}
}
DownloadBenavoli, A., Corani, G., Demsar, J., Zaffalon, M. (2017). Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. Journal of Machine Learning Research 18(77), pp. 1–36.
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
Authors: Benavoli, A. and Corani, G. and Demsar, J. and Zaffalon, M.
Year: 2017
Abstract: The machine learning community adopted the use of null hypothesis significance testing (NHST) in order to ensure the statistical validity of results. Many scientific fields however realized the shortcomings of frequentist reasoning and in the most radical cases even banned its use in publications. We should do the same: just as we have embraced the Bayesian paradigm in the development of new machine learning methods, so we should also use it in the analysis of our own results. We argue for abandonment of NHST by exposing its fallacies and, more importantly, offer better - more sound and useful - alternatives for it.
Published in Journal of Machine Learning Research 18(77), pp. 1–36.
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
@ARTICLE{benavoli2016e,
title = {Time for a change: a tutorial for comparing multiple classifiers through {B}ayesian analysis},
journal = {Journal of Machine Learning Research},
volume = {18},
author = {Benavoli, A. and Corani, G. and Demsar, J. and Zaffalon, M.},
number = {77},
pages = {1--36},
year = {2017},
doi = {},
url = {http://jmlr.org/papers/v18/16-305.html}
}
DownloadBenavoli, A., Facchini, A., Vicente-Perez, J., Zaffalon, M. (2017). A polarity theory for sets of desirable gambles. In Proc. ISIPTA '17 Int. Symposium on Imprecise Probability: Theories and Applications 62, PMLR, pp. 1–12.
A polarity theory for sets of desirable gambles
Authors: Benavoli, A. and Facchini, A. and Vicente-Perez, J. and Zaffalon, M.
Year: 2017
Abstract: Coherent sets of almost desirable gambles and credal sets are known to be equivalent models. That is, there exists a bijection between the two collections of sets preserving the usual operations, e.g. conditioning. Such a correspondence is based on the polarity theory for closed convex cones. Learning from this simple observation, in this paper we introduce a new (lexicographic) polarity theory for general convex cones and then we apply it in order to establish an analogous correspondence between coherent sets of desirable gambles and convex sets of lexicographic probabilities.
Published in Proc. ISIPTA '17 Int. Symposium on Imprecise Probability: Theories and Applications 62, PMLR, pp. 1–12.
A polarity theory for sets of desirable gambles
@INPROCEEDINGS{Benavoli2017c,
title = {A polarity theory for sets of desirable gambles},
publisher = {PMLR},
volume = {62},
booktitle = {Proc. {ISIPTA} '17 Int. Symposium on Imprecise Probability: Theories and Applications},
author = {Benavoli, A. and Facchini, A. and Vicente-Perez, J. and Zaffalon, M.},
pages = {1--12},
year = {2017},
doi = {},
url = {http://proceedings.mlr.press/v62/benavoli17b/benavoli17b.pdf}
}
DownloadBenavoli, A., Facchini, A., Piga, D., Zaffalon, M. (2017). SOS for bounded rationality. In Proceedings of Machine Learning Research 62, PMLR, pp. 25–36.
SOS for bounded rationality
Authors: Benavoli, A. and Facchini, A. and Piga, D. and Zaffalon, M.
Year: 2017
Abstract: In the gambling foundation of probability theory, rationality requires that a subject should always (never) find desirable all nonnegative (negative) gambles, because no matter the result of the exper- iment the subject never (always) decreases her money. Evaluating the nonnegativity of a gamble in infinite spaces is a difficult task. In fact, even if we restrict the gambles to be polynomials in R n , the problem of determining nonnegativity is NP-hard. The aim of this paper is to develop a computable theory of desirable gambles. Instead of requiring the subject to accept all nonnegative gambles, we only require her to accept gambles for which she can efficiently determine the nonnegativity (in particular SOS polynomials). We call this new criterion bounded rationality.
Published in Proceedings of Machine Learning Research 62, PMLR, pp. 25–36.
Note: ISIPTA '17 Int. Symposium on Imprecise Probability: Theories and Applications
SOS for bounded rationality
@INPROCEEDINGS{Benavoli2017b,
title = {{SOS} for bounded rationality},
publisher = {PMLR},
volume = {62},
booktitle = {Proceedings of Machine Learning Research},
author = {Benavoli, A. and Facchini, A. and Piga, D. and Zaffalon, M.},
pages = {25--36},
year = {2017},
doi = {},
url = {http://proceedings.mlr.press/v62/benavoli17a/benavoli17a.pdf}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2017). Bayes + Hilbert = Quantum Mechanics. In Proceedings of the 14th Interational Conference on Quantum Physics and Logic (qpl 2017), Nijmegen, the Netherlands, 3-7 July.
Bayes + Hilbert = Quantum Mechanics
Authors: Benavoli, A. and Facchini, A. and Zaffalon, M.
Year: 2017
Published in Proceedings of the 14th Interational Conference on Quantum Physics and Logic (qpl 2017), Nijmegen, the Netherlands, 3-7 July.
Bayes + Hilbert = Quantum Mechanics
@INPROCEEDINGS{Benavoli2017m,
title = {Bayes + {H}ilbert = {Q}uantum {M}echanics},
booktitle = {Proceedings of the 14th Interational Conference on Quantum Physics and Logic ({q}pl 2017), Nijmegen, the Netherlands, 3-7 July},
author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
year = {2017},
doi = {},
url = {http://qpl.science.ru.nl/papers/QPL_2017_paper_4.pdf}
}
DownloadBucher, D., Mangili, F., Bonesana, C., Jonietz, D., Cellina, F., Raubal, M. (2017). Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. In 33(1), pp. 1–2.
Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior
Authors: Bucher, D. and Mangili, F. and Bonesana, C. and Jonietz, D. and Cellina, F. and Raubal, M.
Year: 2017
Abstract: Nowadays, most people own a smartphone which
is well suited to constantly record the movement of its user.
One use of the gathered mobility data is to provide users
with feedback and suggestions for personal behavior change.
Such eco-feedback on mobility patterns may stimulate users
to adopt more energy-efficient mobility choices. In this paper,
we present a methodology to extract mobility patterns from
users’ trajectories, compute alternative transport options, and
aggregate and present them in an intuitive way. The resulting
eco-feedback helps people understand their mobility choices
and explore sustainable alternatives.
Published in Computer Science - Research and Development 33(1), pp. 1–2.
Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior
@INPROCEEDINGS{mangili2017c,
title = {Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior},
journal = {Computer Science - Research and Development},
volume = {33},
author = {Bucher, D. and Mangili, F. and Bonesana, C. and Jonietz, D. and Cellina, F. and Raubal, M.},
number = {1},
pages = {1--2},
year = {2017},
doi = {10.1007/s00450-017-0375-2},
url = {}
}
DownloadCorani, G., Benavoli, A., Demšar, J., Mangili, F., Zaffalon, M. (2017). Statistical comparison of classifiers through Bayesian hierarchical modelling. Machine Learning 106(11), pp. 1817–1837.
Statistical comparison of classifiers through Bayesian hierarchical modelling
Authors: Corani, G. and Benavoli, A. and Demšar, J. and Mangili, F. and Zaffalon, M.
Year: 2017
Abstract: Usually one compares the accuracy of two competing classifiers using null hypothesis significance tests (nhst).
Yet the nhst tests suffer from important shortcomings, which
can be overcome by switching to Bayesian hypothesis testing.
We propose a Bayesian hierarchical model that jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. The model estimates more accurately the difference between classifiers on the individual data sets than the traditional approach of averaging, independently on each data set, the cross-validation results.
It does so by jointly analyzing the results obtained on all data sets, and applying shrinkage to the estimates.
The model eventually returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different.
Published in Machine Learning 106(11), pp. 1817–1837.
Statistical comparison of classifiers through Bayesian hierarchical modelling
@ARTICLE{corani2017a,
title = {Statistical comparison of classifiers through {B}ayesian hierarchical modelling},
journal = {Machine Learning},
volume = {106},
author = {Corani, G. and Benavoli, A. and Demšar, J. and Mangili, F. and Zaffalon, M.},
number = {11},
pages = {1817--1837},
year = {2017},
doi = {10.1007/s10994-017-5641-9},
url = {}
}
DownloadCruder, C., Falla, D., Mangili, F., Azzimonti, L., Araújo, L., Williamon, A., Barbero, M. (2017). Profiling the location and extent of musicians' pain using digital pain drawings. PAIN Practice 18(1), pp. 53–66.
Profiling the location and extent of musicians' pain using digital pain drawings
Authors: Cruder, C. and Falla, D. and Mangili, F. and Azzimonti, L. and Araújo, L. and Williamon, A. and Barbero, M.
Year: 2017
Abstract: BACKGROUND AND AIMS:
According to the existing literature, musicians are at risk to experience a range of musculoskeletal painful conditions. Recently, digital technology has been developed to investigate pain location and pain extent. The aim of this study was to describe pain location and pain extent in musicians using a digital method for pain drawing analysis. Additionally, the association between pain drawing (PD) variables and clinical features in musicians with pain were explored.
MATERIALS AND METHODS:
One hundred fifty-eight musicians (90 women and 68 men; age 22.4±3.6 years) were recruited from Swiss and UK conservatoires. Participants were asked to complete a survey including both background musical information and clinical features, the Quick Dash (QD) questionnaire and the digital PDs.
RESULTS:
Of the 158 participants, 126 musicians (79.7%) reported having pain, with higher prevalence in the areas of the neck and shoulders, the lower back and the right arm. The mean of pain extent was 3.1% ±6.5. The mean of QD was larger for musicians showing the presence of pain than for those without pain. Additionally, the results indicated a positive correlation between QD score and pain extent, and there were significant correlations between age and pain intensity, as well as between pain extent and pain intensity.
CONCLUSIONS:
The high prevalence of pain among musicians has been confirmed using a digital PD. In addition, positive correlations between pain extent and upper limb disability has been demonstrated. Our findings highlight the need for effective prevention and treatment strategies for musicians. This article is protected by copyright. All rights reserved.
Published in PAIN Practice 18(1), Wiley, pp. 53–66.
Profiling the location and extent of musicians' pain using digital pain drawings
@ARTICLE{mangili2017a,
title = {Profiling the location and extent of musicians' pain using digital pain drawings},
journal = {{PAIN} Practice},
publisher = {Wiley},
volume = {18},
author = {Cruder, C. and Falla, D. and Mangili, F. and Azzimonti, L. and Ara\'ujo, L. and Williamon, A. and Barbero, M.},
number = {1},
pages = {53--66},
year = {2017},
doi = {10.1111/papr.12581},
url = {}
}
DownloadGorini, F., Azzimonti, L., Delfanti, G., Scarfò, L., Scielzo, C., Bertilaccio, M.T., Ranghetti, P., Gulino, A., Doglioni, C., Napoli, A.D., Capri, M., Franceschi, C., Calligaris-Cappio, F., Ghia, P., Bellone, M., Dellabona, P., Casorati, G., de Lalla, C. (2017). Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis. Blood 129(26), pp. 3440–3451.
Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis
Authors: Gorini, F. and Azzimonti, L. and Delfanti, G. and Scarfò, L. and Scielzo, C. and Bertilaccio, M.T. and Ranghetti, P. and Gulino, A. and Doglioni, C. and Napoli, A.D. and Capri, M. and Franceschi, C. and Calligaris-Cappio, F. and Ghia, P. and Bellone, M. and Dellabona, P. and Casorati, G. and de Lalla, C.
Year: 2017
Abstract: Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5+ B lymphocytes in blood, bone marrow, and lymphoid organs. CD1d-restricted invariant natural killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease in the Eμ-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and that iNKT cells critically delay disease onset but become functionally impaired upon disease progression. In patients, disease progression correlates with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative or low CD1d expression on CLL cells and normal iNKT cells, suggesting indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing nurse-like cells, a relevant proleukemia macrophage population. Multivariable analysis identified iNKT cell frequency as an independent predictor of disease progression. Together, these results support the contribution of iNKT cells to CLL immune surveillance and highlight iNKT cell frequency as a prognostic marker for disease progression.
Published in Blood 129(26), pp. 3440–3451.
Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis
@ARTICLE{azzimonti2017a,
title = {Invariant {NKT} cells contribute to {C}hronic {L}ymphocytic {L}eukemia surveillance and prognosis},
journal = {Blood},
volume = {129},
author = {Gorini, F. and Azzimonti, L. and Delfanti, G. and Scarf\`o, L. and Scielzo, C. and Bertilaccio, M.T. and Ranghetti, P. and Gulino, A. and Doglioni, C. and Napoli, A.D. and Capri, M. and Franceschi, C. and Calligaris-Cappio, F. and Ghia, P. and Bellone, M. and Dellabona, P. and Casorati, G. and de Lalla, C.},
number = {26},
pages = {3440--3451},
year = {2017},
doi = {10.1182/blood-2016-11-751065},
url = {}
}
DownloadMangili, F., Bonesana, C., Antonucci, A. (2017). Reliable knowledge-based adaptive tests by credal networks. In Antonucci, A., Cholvy, L., Papini, O. (Eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017, Lecture Notes in Computer Science 10369, Springer, Cham, pp. 282–291.
Reliable knowledge-based adaptive tests by credal networks
Authors: Mangili, F. and Bonesana, C. and Antonucci, A.
Year: 2017
Abstract: An adaptive test is a computer-based testing technique which adjusts the sequence of questions on the basis of the estimated ability level of the test taker. We suggest the use of credal networks, a generalization of Bayesian networks based on sets of probability mass functions, to implement adaptive tests exploiting the knowledge of the test developer instead of training on databases of answers. Compared to Bayesian networks, these models might offer higher expressiveness and hence a more reliable modeling of the qualitative expert knowledge. The counterpart is a less straightforward identification of the information-theoretic measure controlling the question-selection and the test-stopping criteria. We elaborate on these issues and propose a sound and computationally feasible procedure. Validation against a Bayesian-network approach on a benchmark about German language proficiency assessments suggests that credal networks can be reliable in assessing the student level and effective in reducing the number of questions required to do it.
Published in Antonucci, A., Cholvy, L., Papini, O. (Eds), Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017, Lecture Notes in Computer Science 10369, Springer, Cham, pp. 282–291.
Reliable knowledge-based adaptive tests by credal networks
@INPROCEEDINGS{mangili2017b,
title = {Reliable knowledge-based adaptive tests by credal networks},
editor = {Antonucci, A. and Cholvy, L. and Papini, O.},
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
volume = {10369},
booktitle = {Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty. {ECSQARU} 2017 },
author = {Mangili, F. and Bonesana, C. and Antonucci, A.},
pages = {282--291},
year = {2017},
doi = {10.1007/978-3-319-61581-3_26},
url = {}
}
DownloadPiga, D., Benavoli, A. (2017). A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices. IEEE Transactions on Automatic Control PP(99).
A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices
Authors: Piga, D. and Benavoli, A.
Year: 2017
Abstract: In control theory, we are often interested in robust D-stability analysis, which aims at verifying if all the eigenvalues of an uncertain matrix lie in a given region D. Although many algorithms have been developed to provide conditions for an uncertain matrix to be robustly D-stable, the problem of computing the probability of an uncertain matrix to be D-stable is still unexplored. The goal of this paper is to fill this gap in two directions. First, the only constraint on the stability region D that we impose is that its complement is a semialgebraic set. This comprises many important cases in robust control theory. Second, the D-stability analysis problem is formulated in a probabilistic framework, by assuming that only few probabilistic information is available on the uncertain parameters, such as support and some moments. We will show how to compute the minimum probability that the matrix is D-stable by using convex relaxations based on the theory of moments.
Published in IEEE Transactions on Automatic Control PP(99).
A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices
@ARTICLE{piga2017a,
title = {A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices},
journal = {{IEEE} Transactions on Automatic Control},
volume = {PP},
author = {Piga, D. and Benavoli, A.},
number = {99},
year = {2017},
doi = {10.1109/TAC.2017.2699281},
url = {}
}
DownloadScanagatta, M., Corani, G., Zaffalon, M. (2017). Improved local search in Bayesian networks structure learning. In Antti Hyttinen, Joe Suzuki, Brandon Malone (Eds), Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), Proceedings of Machine Learning Research 73, PMLR, pp. 45–56.
Improved local search in Bayesian networks structure learning
Authors: Scanagatta, M. and Corani, G. and Zaffalon, M.
Year: 2017
Abstract: We present a novel approach for score-based structure learning of Bayesian network, which couples an existing ordering-based algorithm for structure optimization with a novel operator for exploring the neighborhood of a given order in the space of the orderings. Our approach achieves state-of-the-art performances in data sets containing thousands of variables.
Published in Antti Hyttinen, Joe Suzuki, Brandon Malone (Eds), Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), Proceedings of Machine Learning Research 73, PMLR, pp. 45–56.
Improved local search in Bayesian networks structure learning
@INPROCEEDINGS{scanagatta2017,
title = {Improved local search in {B}ayesian networks structure learning},
editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
volume = {73},
booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks ({AMBN})},
author = {Scanagatta, M. and Corani, G. and Zaffalon, M.},
pages = {45--56},
year = {2017},
doi = {},
url = {https://proceedings.mlr.press/v73/scanagatta17a.html}
}
DownloadSoullard, Y., Antonucci, A., Destercke, S. (2017). Technical gestures recognition by set-valued hidden Markov models with prior knowledge. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), Soft Methods for Data Science, Advances in Intelligent Systems and Computing 456, Springer, pp. 455–462.
Technical gestures recognition by set-valued hidden Markov models with prior knowledge
Authors: Soullard, Y. and Antonucci, A. and Destercke, S.
Year: 2017
Abstract: Hidden Markov models are popular tools for gesture recognition. Once the generative processes of gestures have been identified, an observation sequence is usually classified as the gesture having the highest likelihood, thus ignoring possible prior information. In this paper, we consider two potential improvements of such methods: the inclusion of prior information, and the possibility of considering convex sets of probabilities (in the likelihoods and the prior) to infer imprecise, but more reliable, predictions when information is insufficient. We apply the proposed approach to technical gestures, typically characterized by severe class imbalance. By modelling such imbalances as a prior information, we achieve more accurate results, while the imprecise quantification is shown to produce more reliable estimates.
Published in Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), Soft Methods for Data Science, Advances in Intelligent Systems and Computing 456, Springer, pp. 455–462.
Note: SMPS 2016: Proceedings of the 8th international conference on Soft Methods in Probability and Statistics
Technical gestures recognition by set-valued hidden Markov models with prior knowledge
@INCOLLECTION{antonucci2016a,
title = {Technical gestures recognition by set-valued hidden {M}arkov models with prior knowledge},
editor = {Ferraro, M. B. and Giordani, P. and Vantaggi, B. and Gagolewski, M. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
volume = {456},
booktitle = {Soft Methods for Data Science},
author = {Soullard, Y. and Antonucci, A. and Destercke, S.},
pages = {455--462},
year = {2017},
doi = {10.1007/978-3-319-42972-4_56},
url = {}
}
DownloadMiranda, E., Zaffalon, M. (2017). Full conglomerability, continuity and marginal extension. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), Soft Methods for Data Science, Advances in Intelligent Systems and Computing 456, Springer, pp. 355–362.
Full conglomerability, continuity and marginal extension
Authors: Miranda, E., Zaffalon, M.
Year: 2017
Abstract: We investigate fully conglomerable coherent lower previsions in the sense of Walley, and some particular cases of interest: envelopes of fully conglomerable linear previsions, envelopes of countably additive linear previsions and fully disintegrable linear previsions. We study the connections with continuity and countable super-additivity, and show that full conglomerability can be characterised in terms of a supremum of marginal extension models.
Published in Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), Soft Methods for Data Science, Advances in Intelligent Systems and Computing 456, Springer, pp. 355–362.
Note: SMPS 2016: Proceedings of the 8th international conference on Soft Methods in Probability and Statistics
Full conglomerability, continuity and marginal extension
@INCOLLECTION{zaffalon2017a,
title = {Full conglomerability, continuity and marginal extension},
editor = {Ferraro, M. B. and Giordani, P. and Vantaggi, B. and Gagolewski, M. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
volume = {456},
booktitle = {Soft Methods for Data Science},
author = {Miranda, E., Zaffalon, M.},
pages = {355--362},
year = {2017},
doi = {10.1007/978-3-319-42972-4},
url = {}
}
DownloadMiranda, E., Zaffalon, M. (2017). Full conglomerability. Journal of Statistical Theory and Practice 11(4), pp. 634–669.
Full conglomerability
Authors: Miranda, E., Zaffalon, M.
Year: 2017
Abstract: We do a thorough mathematical study of the notion of full conglomerability, that is, conglomerability with respect to all the partitions of an infinite possibility space, in the sense considered by Peter Walley (1991). We consider both the cases of precise and imprecise probability (sets of probabilities). We establish relations between conglomerability and countable additivity, continuity, super-additivity and marginal extension. Moreover, we discuss the special case where a model is conglomerable with respect to a subset of all the partitions, and try to sort out the different notions of conglomerability present in the literature. We conclude that countable additivity, which is routinely used to impose full conglomerability in the precise case, appears to be the most well-behaved way to do so in the imprecise case as well by taking envelopes of countably additive probabilities. Moreover, we characterise these envelopes by means of a number of necessary and sufficient conditions.
Published in Journal of Statistical Theory and Practice 11(4), pp. 634–669.
Full conglomerability
@ARTICLE{zaffalon2017b,
title = {Full conglomerability},
journal = {Journal of Statistical Theory and Practice},
volume = {11},
author = {Miranda, E., Zaffalon, M.},
number = {4},
pages = {634--669},
year = {2017},
doi = {10.1080/15598608.2017.1295890},
url = {}
}
DownloadZaffalon, M., Miranda, E. (2017). Axiomatising incomplete preferences through sets of desirable gambles. Journal of Artificial Intelligence Research 60, pp. 1057–1126.
Axiomatising incomplete preferences through sets of desirable gambles
Authors: Zaffalon, M. and Miranda, E.
Year: 2017
Abstract: We establish the equivalence of two very general theories: the first is the decision-theoretic formalisation of incomplete preferences based on the mixture independence axiom; the second is the theory of coherent sets of desirable gambles (bounded variables) developed in the context of imprecise probability and extended here to vector-valued gambles. Such an equivalence allows us to analyse the theory of incomplete preferences from the point of view of desirability. Among other things, this leads us to uncover an unexpected and clarifying relation: that the notion of `state independence'---the traditional assumption that we can have separate models for beliefs (probabilities) and values (utilities)---coincides with that of `strong independence' in imprecise probability; this connection leads us also to propose much weaker, and arguably more realistic, notions of state independence. Then we simplify the treatment of complete beliefs and values by putting them on a more equal footing. We study the role of the Archimedean condition---which allows us to actually talk of expected utility---, identify some weaknesses and propose alternatives that solve these. More generally speaking, we show that desirability is a valuable alternative foundation to preferences for decision theory that streamlines and unifies a number of concepts while preserving great generality. In addition, the mentioned equivalence shows for the first time how to extend the theory of desirability to imprecise non-linear utility, thus enabling us to formulate one of the most powerful self-consistent theories of reasoning and decision-making available today.
Published in Journal of Artificial Intelligence Research 60, pp. 1057–1126.
Axiomatising incomplete preferences through sets of desirable gambles
@ARTICLE{zaffalon2017c,
title = {Axiomatising incomplete preferences through sets of desirable gambles},
journal = {Journal of Artificial Intelligence Research},
volume = {60},
author = {Zaffalon, M. and Miranda, E.},
pages = {1057--1126},
year = {2017},
doi = {10.1613/jair.5230},
url = {}
}
Download top2016
Antonucci, A., Corani, G. (2016). The multilabel naive credal classifier. International Journal of Approximate Reasoning 83, pp. 320–336.
The multilabel naive credal classifier
Authors: Antonucci, A. and Corani, G.
Year: 2016
Abstract: A credal classifier for multilabel data is presented. This is obtained as an extension of Zaffalon's naive credal classifier to the case of non-exclusive class labels. The dependence relations among the labels are shaped with a tree topology. The classifier, based on a polynomial-time algorithm to compute whether or not a class label is optimal, returns a compact description of the set of optimal sequences of labels. Extensive experiments on real multilabel data show that the classifier gives more robust predictions than its Bayesian counterpart. In practice, when multiple sequences are returned in output, the Bayesian model is more likely to be inaccurate, while the sequences returned by the credal classifier are more likely to include the correct one.
Published in International Journal of Approximate Reasoning 83, pp. 320–336.
The multilabel naive credal classifier
@ARTICLE{antonucci2016c,
title = {The multilabel naive credal classifier},
journal = {International Journal of Approximate Reasoning},
volume = {83},
author = {Antonucci, A. and Corani, G.},
pages = {320--336},
year = {2016},
doi = {10.1016/j.ijar.2016.10.006},
url = {}
}
DownloadBenavoli, A., de Campos, C.P. (2016). Bayesian dependence tests for continuous, binary and mixed continuous-binary variables. Entropy 18(9), pp. 1–24.
Bayesian dependence tests for continuous, binary and mixed continuous-binary variables
Authors: Benavoli, A. and de Campos, C.P.
Year: 2016
Abstract: Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests.
Published in Entropy 18(9), Multidisciplinary Digital Publishing Institute, pp. 1–24.
Bayesian dependence tests for continuous, binary and mixed continuous-binary variables
@ARTICLE{benavoli2016g,
title = {Bayesian dependence tests for continuous, binary and mixed continuous-binary variables},
journal = {Entropy},
publisher = {Multidisciplinary Digital Publishing Institute},
volume = {18},
author = {Benavoli, A. and de Campos, C.P.},
number = {9},
pages = {1--24},
year = {2016},
doi = {10.3390/e18090326},
url = {http://www.mdpi.com/1099-4300/18/9/326}
}
DownloadBenavoli, A., Corani, G., Mangili, F. (2016). Should we really use post-hoc tests based on mean-ranks?. Journal of Machine Learning Research 17(5), pp. 1–10.
Should we really use post-hoc tests based on mean-ranks?
Authors: Benavoli, A. and Corani, G. and Mangili, F.
Year: 2016
Abstract: The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. The multiple comparisons are usually performed using the mean-ranks test. The aim of this technical note is to discuss the inconsistencies of the mean-ranks post-hoc test with the goal of discouraging its use in machine learning as well as in medicine, psychology, etc.. We show that the outcome of the mean-ranks test depends on the pool of algorithms originally included in the experiment. In other words, the outcome of the comparison between algorithms A and B depends also on the performance of the other algorithms included in the original experiment. This can lead to paradoxical situations. For instance the difference between A and B could be declared significant if the pool comprises algorithms C, D, E and not significant if the pool comprises algorithms F, G, H. To overcome these issues, we suggest instead to perform the multiple comparison using a test whose outcome only depends on the two algorithms being compared, such as the sign-test or the Wilcoxon signed-rank test.
Published in Journal of Machine Learning Research 17(5), pp. 1–10.
Should we really use post-hoc tests based on mean-ranks?
@ARTICLE{benavoli2015c,
title = {Should we really use post-hoc tests based on mean-ranks?},
journal = {Journal of Machine Learning Research},
volume = {17},
author = {Benavoli, A. and Corani, G. and Mangili, F.},
number = {5},
pages = {1--10},
year = {2016},
doi = {},
url = {http://jmlr.org/papers/volume17/benavoli16a/benavoli16a.pdf}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2016). Quantum mechanics: the Bayesian theory generalized to the space of hermitian matrices. Phys. Rev. A 94, 042106.
Quantum mechanics: the Bayesian theory generalized to the space of hermitian matrices
Authors: Benavoli, A. and Facchini, A. and Zaffalon, M.
Year: 2016
Abstract: We consider the problem of gambling on a quantum experiment and enforce rational behavior by a few rules. These rules yield, in the classical case, the Bayesian theory of probability via duality theorems. In our quantum setting, they yield the Bayesian theory generalized to the space of Hermitian matrices. This very theory is quantum mechanics: in fact, we derive all its four postulates from the generalized Bayesian theory. This implies that quantum mechanics is self-consistent. It also leads us to reinterpret the main operations in quantum mechanics as probability rules: Bayes' rule (measurement), marginalization (partial tracing), independence (tensor product). To say it with a slogan, we obtain that quantum mechanics is the Bayesian theory in the complex numbers.
Published in Phys. Rev. A 94, American Physical Society, 042106.
Quantum mechanics: the Bayesian theory generalized to the space of hermitian matrices
@ARTICLE{benavoli2016d,
title = {Quantum mechanics: the {B}ayesian theory generalized to the space of hermitian matrices},
journal = {Phys. Rev. A},
publisher = {American Physical Society},
volume = {94},
author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
pages = {042106},
year = {2016},
doi = {10.1103/PhysRevA.94.042106},
url = {http://arxiv.org/abs/1605.08177}
}
DownloadBenavoli, A., Facchini, A., Zaffalon, M. (2016). Quantum rational preferences and desirability. In Proceedings of the 1st International Workshop on “imperfect Decision Makers: Admitting Real-world Rationality”, Nips 2016 58, pp. 87–96.
Quantum rational preferences and desirability
Authors: Benavoli, A. and Facchini, A. and Zaffalon, M.
Year: 2016
Abstract: We develop a theory of quantum rational decision making in the tradition of Anscombe and Aumann's axiomatisation of preferences on horse lotteries. It is essentially the Bayesian decision theory generalised to the space of Hermitian matrices. Among other things, this leads us to give a representation theorem showing that quantum complete rational preferences are obtained by means of expected utility considerations.
Published in Proceedings of the 1st International Workshop on “imperfect Decision Makers: Admitting Real-world Rationality”, Nips 2016 ArXiv e-prints 1610.06764 58, pp. 87–96.
Quantum rational preferences and desirability
@INPROCEEDINGS{benavoli2016h,
title = {Quantum rational preferences and desirability},
journal = {{ArXiv} {e}-{p}rints 1610.06764},
volume = {58},
booktitle = {Proceedings of the 1st International Workshop on ``imperfect Decision Makers: Admitting Real-{w}orld Rationality'', Nips 2016},
author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
pages = {87--96},
year = {2016},
doi = {},
url = {https://proceedings.mlr.press/v58/benavoli17a.html}
}
DownloadBenavoli, A., Piga, D. (2016). A probabilistic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding. Automatica 70, pp. 158–172.
A probabilistic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding
Authors: Benavoli, A. and Piga, D.
Year: 2016
Abstract: Set-membership estimation is usually formulated in the context of set-valued calculus and no probabilistic calculations are necessary. In this paper, we show that set-membership estimation can be equivalently formulated in the probabilistic setting by employing sets of probability measures. Inference in set-membership estimation is thus carried out by computing expectations with respect to the updated set of probability measures P as in the probabilistic case. In particular, it is shown that inference can be performed by solving a particular semi-infinite linear programming problem, which is a special case of the truncated moment problem in which only the zero-th order moment is known (i.e., the support). By writing the dual of the above semi- infinite linear programming problem, it is shown that, if the nonlinearities in the measurement and process equations are polynomial and if the bounding sets for initial state, process and measurement noises are described by polynomial inequalities, then an approximation of this semi-infinite linear programming problem can efficiently be obtained by using the theory of sum- of-squares polynomial optimization. We then derive a smart greedy procedure to compute a polytopic outer-approximation of the true membership-set, by computing the minimum-volume polytope that outer-bounds the set that includes all the means computed with respect to P.
Published in Automatica 70, pp. 158–172.
A probabilistic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding
@ARTICLE{benavoli2016a,
title = {A probabilistic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding},
journal = {Automatica},
volume = {70},
author = {Benavoli, A. and Piga, D.},
pages = {158--172},
year = {2016},
doi = {10.1016/j.automatica.2016.03.021},
url = {}
}
DownloadBucher, D., Cellina, F., Mangili, F., Raubal, M., Rudel, R., Rizzoli, A.E., Elabed, O. (2016). Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App. In Proceedings of the 2016 conference ICT for Sustainability, Advances in Computer Science Research, pp. 89–98.
Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App
Authors: Bucher, D. and Cellina, F. and Mangili, F. and Raubal, M. and Rudel, R. and Rizzoli, A.E. and Elabed, O.
Year: 2016
Abstract: The large interest in analyzing one’s own fitness led
to the development of more and more powerful smartphone applications. Most are capable of tracking a user’s position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one
month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.
Published in Proceedings of the 2016 conference ICT for Sustainability, Advances in Computer Science Research, pp. 89–98.
Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App
@INPROCEEDINGS{mangili2016c,
title = {Exploiting fitness apps for sustainable mobility - challenges deploying the {GoEco}! App},
series = {Advances in Computer Science Research},
booktitle = {Proceedings of the 2016 {c}onference {ICT} for Sustainability},
author = {Bucher, D. and Cellina, F. and Mangili, F. and Raubal, M. and Rudel, R. and Rizzoli, A.E. and Elabed, O.},
pages = {89--98},
year = {2016},
doi = {doi:10.2991/ict4s-16.2016.11},
url = {}
}
DownloadCabañas, R., Antonucci, A., Cano, A., Gómez-Olmedo, M. (2016). Evaluating interval-valued influence diagrams. International Journal of Approximate Reasoning 80, pp. 393–411.
Evaluating interval-valued influence diagrams
Authors: Cabañas, R. and Antonucci, A. and Cano, A. and Gómez-Olmedo, M.
Year: 2016
Abstract: Influence diagrams are probabilistic graphical models used to represent and solve sequential decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. This might be an issue with real models, whose parameters are typically obtained from expert judgements or partially reliable data. We consider an interval-valued quantification of the parameters to gain realism in the modeling and evaluate the sensitivity of the inferences with respect to perturbations in the sharp values of the parameters. An extension of the classical influence diagrams formalism to support such interval-valued potentials is presented. The variable elimination and arc reversal inference algorithms are generalized to cope with these models. At the price of an outer approximation, the extension keeps the same complexity as with sharp values. Numerical experiments show improved performances with respect to previous methods. As a natural application, we propose these models for practical sensitivity analysis in traditional influence diagrams. The maximum perturbation level on single or multiple parameters preserving the optimal strategy can be computed. This allows the identification of the parameters deserving a more careful elicitation.
Published in International Journal of Approximate Reasoning 80, pp. 393–411.
Evaluating interval-valued influence diagrams
@ARTICLE{antonucci2016b,
title = {Evaluating interval-valued influence diagrams},
journal = {International Journal of Approximate Reasoning},
volume = {80},
author = {Caba\~nas, R. and Antonucci, A. and Cano, A. and G\'omez-Olmedo, M.},
pages = {393--411},
year = {2016},
doi = {10.1016/j.ijar.2016.05.004},
url = {}
}
Downloadde Campos, C.P., Benavoli, A. (2016). Joint analysis of multiple algorithms and performance measures. New Generation Computing, pp. 1–18.
Joint analysis of multiple algorithms and performance measures
Authors: de Campos, C.P. and Benavoli, A.
Year: 2016
Abstract: There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers are used to show a practical application of our tests.
Published in New Generation Computing, pp. 1–18.
Joint analysis of multiple algorithms and performance measures
@ARTICLE{deCampos2016,
title = {Joint analysis of multiple algorithms and performance measures},
journal = {New Generation Computing},
author = {de Campos, C.P. and Benavoli, A.},
pages = {1--18},
year = {2016},
doi = {10.1007/s00354-016-0005-8},
url = {http://people.idsia.ch/~alessio/decampos-benavoli-ngc2016.pdf}
}
Downloadde Campos, C.P., Corani, G., Scanagatta, M., Cuccu, M., Zaffalon, M. (2016). Learning extended tree augmented naive structures. International Journal of Approximate Reasoning. 68, pp. 153–163.
Learning extended tree augmented naive structures
Authors: de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.
Year: 2016
Abstract: This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments show that we obtain models with better prediction accuracy than Naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.
Published in International Journal of Approximate Reasoning. 68, pp. 153–163.
Learning extended tree augmented naive structures
@ARTICLE{decampos2015a,
title = {Learning extended tree augmented naive structures},
journal = {International Journal of Approximate Reasoning.},
volume = {68},
author = {de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.},
pages = {153--163},
year = {2016},
doi = {10.1016/j.ijar.2015.04.006},
url = {}
}
DownloadCorani, G., Scanagatta, M. (2016). Air pollution prediction via multi-label classification. Environmental Modelling & Software 80, pp. 259–264.
Air pollution prediction via multi-label classification
Authors: Corani, G. and Scanagatta, M.
Year: 2016
Abstract: A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classifier for each class variable being predicted; yet this approach overlooks the dependencies among the class variables. By appropriately modeling such dependencies one can improve the accuracy of the forecasts. We address this problem by designing a multi-label classifier, which simultaneously predict multiple air pollution variables. To this end we design a multi-label classifier based on Bayesian networks and learn its structure through structural learning. We present experiments in three different case studies regarding the prediction of PM2.5 and ozone. The multi-label classifier outperforms the independent approach, allowing to take better decisions.
Published in Environmental Modelling & Software 80, pp. 259–264.
Air pollution prediction via multi-label classification
@ARTICLE{corani2016a,
title = {Air pollution prediction via multi-label classification},
journal = {Environmental Modelling & Software},
volume = {80},
author = {Corani, G. and Scanagatta, M.},
pages = {259--264},
year = {2016},
doi = {10.1016/j.envsoft.2016.02.030},
url = {}
}
DownloadFu, S. (2016). Hierarchical Bayesian LASSO for a negative binomial regression. Journal of Statistical Computation and Simulation.
Hierarchical Bayesian LASSO for a negative binomial regression
Authors: Fu, S.
Year: 2016
Abstract: Numerous researches have been carried out to explain the relationship between the count data y and numbers of covariates x through a generalized linear model (GLM). This paper proposes a hierarchical Bayesian least absolute shrinkage and selection operator (LASSO) solution using six different prior models to the negative binomial regression. Latent variables Z have been introduced to simplify the GLM to a standard linear regression model. The proposed models regard two conjugate zero-mean Normal priors for the regression parameters and three independent priors for the variance: the Exponential, Inverse-Gamma and Scaled Inverse- distributions. Different types of priors result in different amounts of shrinkage. A Metropolis–Hastings-within-Gibbs algorithm is used to compute the posterior distribution of the parameters of interest through a data augmentation process. Based on the posterior samples, an original double likelihood ratio test statistic have been proposed to choose the most relevant covariates and shrink the insignificant coefficients to zero. Numerical experiments on a real-life data set prove that Bayesian LASSO methods achieved significantly better predictive accuracy and robustness than the classical maximum likelihood estimation and the standard Bayesian inference.
Published in Journal of Statistical Computation and Simulation.
Hierarchical Bayesian LASSO for a negative binomial regression
@ARTICLE{shuaiFu2015a,
title = {Hierarchical {B}ayesian {LASSO} for a negative binomial regression},
journal = {Journal of Statistical Computation and Simulation},
author = {Fu, S.},
year = {2016},
doi = {10.1080/00949655.2015.1106541},
url = {}
}
DownloadGuerciotti, B., Vergara, C., Azzimonti, L., Forzenigo, L., Buora, A., Biondetti, P., Domanin, M. (2016). Computational study of the fluid-dynamics in carotids before and after endarterectomy. Journal of Biomechanics 49(1), pp. 26–38.
Computational study of the fluid-dynamics in carotids before and after endarterectomy
Authors: Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.
Year: 2016
Abstract: In this work, we provide a computational study of the effects of carotid endarterectomy (CEA) on the fluid-dynamics at internal carotid bifurcations. We perform numerical simulations in real geometries of the same patients before and after CEA, using patient-specific boundary data obtained by Echo-Color Doppler measurements. We analyze four patients with a primary closure and other four where a patch was used to close arteriotomies. The results show that (i) CEA is able to restore physiological fluid-dynamic conditions; (ii) among the post-operative cases, the presence of patch leads to local hemodynamic conditions which might imply a higher risk of restenosis in comparison with the cases without patch.
Published in Elsevier (Ed), Journal of Biomechanics 49(1), pp. 26–38.
Computational study of the fluid-dynamics in carotids before and after endarterectomy
@ARTICLE{azzimonti2016a,
title = {Computational study of the fluid-dynamics in carotids before and after endarterectomy},
journal = {Journal of Biomechanics},
editor = {Elsevier},
volume = {49},
author = {Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.},
number = {1},
pages = {26--38},
year = {2016},
doi = {10.1016/j.jbiomech.2015.11.009},
url = {}
}
DownloadMangili, F. (2016). A prior near-ignorance Gaussian process model for nonparametric regression. International Journal of Approximate Reasoning 78, pp. 153–171.
A prior near-ignorance Gaussian process model for nonparametric regression
Authors: Mangili, F.
Year: 2016
Abstract: Abstract This paper proposes a prior near-ignorance model for regression based on a set of Gaussian Processes (GP). GPs are natural prior distributions for Bayesian regression. They offer a great modeling flexibility and have found widespread application in many regression problems. However, a GP requires the prior elicitation of its mean function, which represents our prior belief about the shape of the regression function, and of the covariance between any two function values. In the absence of prior information, it may be difficult to fully specify these infinite dimensional parameters. In this work, by modeling the prior mean of the GP as a linear combination of a set of basis functions and assuming as prior for the combination coefficients a set of conjugate distributions obtained as limits of truncate exponential priors, we have been able to model prior ignorance about the mean of the GP. The resulting model satisfies translation invariance, learning and, under some constraints, convergence, which are desirable properties for a prior near-ignorance model. Moreover, it is shown in this paper how this model can be extended to allow for a weaker specification of the GP covariance between function values, by letting each basis function to vary in a set of functions. Application to hypothesis testing has shown how the use of this model induces the capability of automatically detecting when a reliable decision cannot be made based on the available data.
Published in International Journal of Approximate Reasoning 78, pp. 153–171.
A prior near-ignorance Gaussian process model for nonparametric regression
@ARTICLE{mangili2016b,
title = {A prior near-ignorance {G}aussian process model for nonparametric regression},
journal = {International Journal of Approximate Reasoning },
volume = {78},
author = {Mangili, F.},
pages = {153--171},
year = {2016},
doi = {10.1016/j.ijar.2016.07.005},
url = {}
}
DownloadMangili, F., Bonesana, C., Antonucci, A., Zaffalon, M., Rubegni, E., Addimando, L. (2016). Adaptive testing by Bayesian networks with application to language assessment. In Micarelli, Alessandro, Stamper, John, Panourgia, Kitty (Eds), Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings, Lecture Notes in Computer Science, pp. 471–472.
Adaptive testing by Bayesian networks with application to language assessment
Authors: Mangili, F. and Bonesana, C. and Antonucci, A. and Zaffalon, M. and Rubegni, E. and Addimando, L.
Year: 2016
Abstract: We present a general procedure for computerized adaptive testing based on probabilistic graphical models, and show on a real-world benchmark how this procedure can increase the internal consistency of the test and reduce the number of questions without affecting accuracy.
Published in Micarelli, Alessandro, Stamper, John, Panourgia, Kitty (Eds), Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings, Lecture Notes in Computer Science, pp. 471–472.
Adaptive testing by Bayesian networks with application to language assessment
@INPROCEEDINGS{mangili2016a,
title = {Adaptive testing by {B}ayesian networks with application to language assessment},
editor = {Micarelli, Alessandro and Stamper, John and Panourgia, Kitty},
series = {Lecture Notes in Computer Science},
booktitle = {Intelligent Tutoring Systems: 13th International Conference, {ITS} 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings},
author = {Mangili, F. and Bonesana, C. and Antonucci, A. and Zaffalon, M. and Rubegni, E. and Addimando, L.},
pages = {471--472},
year = {2016},
doi = {},
url = {http://link.springer.com/content/pdf/bbm%3A978-3-319-39583-8%2F1.pdf}
}
DownloadMauá, D.D., Antonucci, A., de Campos, C.P. (2016). Hidden Markov models with set-valued parameters. Neurocomputing 180, pp. 94–107.
Hidden Markov models with set-valued parameters
Authors: Mauá, D.D. and Antonucci, A. and de Campos, C.P.
Year: 2016
Abstract: Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the speci- fication of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability dis- tributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient infer- ence algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filter- ing and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.
Published in Neurocomputing 180, pp. 94–107.
Hidden Markov models with set-valued parameters
@ARTICLE{antonucci2015c,
title = {Hidden {M}arkov models with set-valued parameters},
journal = {Neurocomputing},
volume = {180},
author = {Mau\'a, D.D. and Antonucci, A. and de Campos, C.P.},
pages = {94--107},
year = {2016},
doi = {doi:10.1016/j.neucom.2015.08.095},
url = {}
}
DownloadMiranda, E., Zaffalon, M. (2016). Conformity and independence with coherent lower previsions. International Journal of Approximate Reasoning 78, pp. 125–137.
Conformity and independence with coherent lower previsions
Authors: Miranda, E. and Zaffalon, M.
Year: 2016
Abstract: We define the conformity of marginal and conditional models with a joint model within Walley's theory of coherent lower previsions. Loosely speaking, conformity means that the joint can reproduce the marginal and conditional models we started from. By studying conformity with and without additional assumptions of epistemic irrelevance and independence, we establish connections with a number of prominent models in Walley's theory: the marginal extension, the irrelevant natural extension, the independent natural extension and the strong product.
Published in International Journal of Approximate Reasoning 78, pp. 125–137.
Conformity and independence with coherent lower previsions
@ARTICLE{zaffalon2016c,
title = {Conformity and independence with coherent lower previsions},
journal = {International Journal of Approximate Reasoning},
volume = {78},
author = {Miranda, E. and Zaffalon, M.},
pages = {125--137},
year = {2016},
doi = {10.1016/j.ijar.2016.07.004},
url = {}
}
DownloadRancoita, P.M.V., Zaffalon, M., Zucca, E., Bertoni, F., de Campos, C.P. (2016). Bayesian network data imputation with application to survival tree analysis. Computational Statistics and Data Analysis 93, pp. 373–387.
Bayesian network data imputation with application to survival tree analysis
Authors: Rancoita, P.M.V. and Zaffalon, M. and Zucca, E. and Bertoni, F. and de Campos, C.P.
Year: 2016
Abstract: Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation-maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).
Published in Computational Statistics and Data Analysis 93, pp. 373–387.
Bayesian network data imputation with application to survival tree analysis
@ARTICLE{zaffalon2015b,
title = {Bayesian network data imputation with application to survival tree analysis},
journal = {Computational Statistics and Data Analysis},
volume = {93},
author = {Rancoita, P.M.V. and Zaffalon, M. and Zucca, E. and Bertoni, F. and de Campos, C.P.},
pages = {373--387},
year = {2016},
doi = {10.1016/j.csda.2014.12.008},
url = {}
}
DownloadScanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M. (2016). Learning treewidth-bounded Bayesian networks with thousands of variables. In Daniel D. Lee, Masashi Sugiyama, Ulrike V. Luxburg, Isabelle Guyon, Roman Garnett (Eds), NIPS 2016: Advances in Neural Information Processing Systems 29 29.
Learning treewidth-bounded Bayesian networks with thousands of variables
Authors: Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.
Year: 2016
Abstract: We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.
Published in Daniel D. Lee, Masashi Sugiyama, Ulrike V. Luxburg, Isabelle Guyon, Roman Garnett (Eds), NIPS 2016: Advances in Neural Information Processing Systems 29 NIPS 29.
Learning treewidth-bounded Bayesian networks with thousands of variables
@INPROCEEDINGS{scanagatta2016a,
title = {Learning treewidth-bounded {B}ayesian networks with thousands of variables},
journal = {{NIPS}},
editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike V. Luxburg and Isabelle Guyon and Roman Garnett},
volume = {29},
booktitle = {{NIPS} 2016: Advances in Neural Information Processing Systems 29},
author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},
year = {2016},
doi = {},
url = {http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables}
}
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