Graphical Models for Multivariate Time-Series

Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. In particular, lots of interests have been devoted to graphical models for temporal data, or dynamical graphical models, to understand the relation of variables evolving in time. While powerful in modelling complex systems, such models suffer from computational issues both in terms of convergence rates and memory requirements, and may fail to detect temporal patterns in case the information on the system is partial. This thesis comprises two main contributions in the context of dynamical graphical models, tackling these two aspects: the need of reliable and fast optimisation methods and an increasing modelling power, which are able to retrieve the model in practical applications. The first contribution consists in a forward-backward splitting (FBS) procedure for Gaussian graphical modelling of multivariate time-series which relies on recent theoretical studies ensuring global convergence under mild assumptions. Indeed, such FBS-based implementation achieves, with fast convergence rates, optimal results with respect to ground truth and standard methods for dynamical network inference. The second main contribution focuses on the problem of latent factors, that influence the system while hidden or unobservable. This thesis proposes the novel latent variable time-varying graphical lasso method, which is able to take into account both temporal dynamics in the data and latent factors influencing the system. This is fundamental for the practical use of graphical models, where the information on the data is partial. Indeed, extensive validation of the method on both synthetic and real applications shows the effectiveness of considering latent factors to deal with incomplete information

Exporting under financial constraints: margins, switching dynamics and prices

Using data on cross border transactions together with an informative measure of financing constraints this paper provides new evidence that limited access to external capital narrows the scale of foreign sales, the exporters? product scope and the number of trade partners. It shows that constrained firms have a reduced probability of adding and a higher probability of dropping products and destinations. Further it documents that constrained firms sell their products at higher prices as compared to unconstrained firms. All the results are robust to specific control for unobserved heterogeneity, self-selection into export and potential endogeneity of the financial constraints proxyfinancial constraints, margins of export, export prices

Annotating digital libraries and electronic editions in a collaborative and semantic perspective

The distinction between digital libraries and electronic editions is becom-ing more and more subtle. The practice of annotation represents a point of conver-gence of two only apparently separated worlds. The aim of this paper is to present a model of collaborative semantic annotation of texts (SemLib project), suggesting a system that find in Semantic Web and Linked Data the solution technologies for en-abling structured semantic annotation, also in the field of electronic editions in Digi-tal Humanities domain. The main purpose of SemLib is to develop an application so to make easy for developers the integration of annotation software in digital librar-ies, which are different both for technical implementations and managed contents, and provide to users, indifferently from their cultural backgrounds, a simple system which could be used as a front-end. We present, for this purpose, a final example of semantic annotation in a specific context: a digital edition of a literary text and the issues that an annotation task involves

Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make recommendations. Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. Using this simulator we develop and train a modified Deep Q-Network, the action head DQN (AH-DQN), in a manner that addresses the challenges imposed by the large state and action space of our RL formulation. The resulting policy is capable of making recommendations from large and dynamic sets of candidate items with the expectation of maximizing consumption metrics. We analyze and evaluate agents offline via simulations that use environment models trained on both public and proprietary streaming datasets. We show how these agents lead to better user-satisfaction metrics compared to baseline methods during online A/B tests. Finally, we demonstrate that performance assessments produced from our simulator are strongly correlated with observed online metric results.Comment: 10 pages. KDD 2

Multi-task multiple kernel learning reveals relevant frequency bands for critical areas localization in focal epilepsy

The localization of epileptic zone in pharmacoresistant focal epileptic patients is a daunting task, typically performed by medical experts through visual inspection over highly sampled neural recordings. For a finer localization of the epileptogenic areas and a deeper understanding of the pathology both the identification of pathogenical biomarkers and the automatic characterization of epileptic signals are desirable. In this work we present a data integration learning method based on multi-level representation of stereo-electroencephalography recordings and multiple kernel learning. To the best of our knowledge, this is the first attempt to tackle both aspects simultaneously, as our approach is devised to classify critical vs. non-critical recordings while detecting the most discriminative frequency bands. The learning pipeline is applied to a data set of 18 patients for a total of 2347 neural recordings analyzed by medical experts. Without any prior knowledge assumption, the data-driven method reveals the most discriminative frequency bands for the localization of epileptic areas in the high-frequency spectrum (>=80 Hz) while showing high performance metric scores (mean balanced accuracy of 0.89 +- 0.03). The promising results may represent a starting point for the automatic search of clinical biomarkers of epileptogenicity

Frequency of positive antiphospholipid antibodies in pregnant women with SARS-CoV-2 infection and impact on pregnancy outcome: A single-center prospective study on 151 pregnancies

Background: At the beginning of the SARS-CoV-2 pandemic, there was a lack of information about the infection’s impact on pregnancy and capability to induce de novo autoantibodies. It soon became clear that thrombosis was a manifestation of COVID-19, therefore the possible contribution of de novo antiphospholipid antibodies (aPL) raised research interest. We aimed at screening SARS-CoV-2 positive pregnant patients for aPL. Methods: The study included consecutive pregnant women who were hospitalized in our Obstetric Department between March 2020 and July 2021 for either a symptomatic SARS-CoV-2 infection or for other reasons (obstetric complications, labour, delivery) and found positive at the admission nasopharyngeal swab. All these women underwent the search for aPL by means of Lupus Anticoagulant (LA), IgG/IgM anti-cardiolipin (aCL), IgG/IgM anti-beta2glycoprotein I (aB2GPI). Data about comorbidities, obstetric and neonatal complications were collected. Results: 151 women were included. Sixteen (11%) were positive for aPL, mostly at low titre. Pneumonia was diagnosed in 20 women (5 with positive aPL) and 5 required ICU admission (2 with positive aPL). Obstetric complications occurred in 10/16 (63%) aPL positive and in 36/135 (27%) negative patients. The occurrence of HELLP syndrome and preeclampsia was significantly associated with positive aPL (p=0,004). One case of maternal thrombosis occurred in an aPL negative woman. aPL positivity was checked after at least 12 weeks in 7/16 women (44%): 3 had become negative; 2 were still positive (1 IgG aB2GPI + IgG aCL; 1 IgM aB2GPI); 1 remained positive for IgG aCL but became negative for aB2GPI; 1 became negative for LA but displayed a new positivity for IgG aCL at high titre. Conclusions: The frequency of positive aPL in pregnant women with SARS- CoV-2 infection was low in our cohort and similar to the one described in the general obstetric population. aPL mostly presented as single positive, low titre, transient antibodies. The rate of obstetric complications was higher in aPL positive women as compared to negative ones, particularly hypertensive disorders. Causality cannot be excluded; however, other risk factors, including a full-blown picture of COVID-19, may have elicited the pathogenic potential of aPL and contributed themselves to the development of complications

Reliability assessment of the 2018 classification case definitions of peri-implant health, peri-implant mucositis, and peri-implantitis

Background: The purpose of this study was to evaluate the reliability and accuracy in the assignment of the case definitions of peri-implant health and diseases according to the 2018 Classification of Periodontal and Peri-implant Diseases and Conditions. MethodsTen undergraduate students, 10 general dentists, and 10 experts in implant dentistry participated in this study. All examiners were provided with clinical and radiographic documentation of 25 dental implants. Eleven out the 25 cases were also accompanied by baseline readings. Examiners were asked to define all cases using the 2018 classification case definitions. Reliability among examiners was evaluated using the Fleiss kappa statistic. Accuracy was estimated using percentage of complete agreement and quadratic weighted kappa for pairwise comparisons between each rater and a gold standard diagnosis. ResultsThe Fleiss kappa was 0.50 (95% CI: 0.48 to 0.51) and the mean quadratic weighted kappa value was 0.544. Complete agreement with the gold standard diagnosis was achieved in 59.8% of the cases. Expertise in implantology affected accuracy positively (p < 0.001) while the absence of baseline readings affected it negatively (p < 0.001). ConclusionBoth reliability and accuracy in assigning case definitions to dental implants according to the 2018 classification were mostly moderate. Some difficulties arose in the presence of specific challenging scenarios