Machine Learning Methods for Generating High Dimensional Discrete Datasets

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X\u27 that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons

Generating Synthetic Discrete Datasets with Machine Learning

The real data are not always available/accessible/sufficient or in many cases they are incomplete and lacking in semantic content necessary to the definition of optimization processes. In this paper we discuss about the synthetic data generation under two different perspectives. The core common idea is to analyze a limited set of real data to learn the main patterns that characterize them and exploit this knowledge to generate brand new data. The first perspective is constraint-based generation and consists in generating a synthetic dataset satisfying given support constraints on the real frequent patterns. The second one is based on probabilistic generative modeling and considers the synthetic generation as a sampling process from a parametric distribution learned on the real data, typically encoded as a neural network (e.g. Variational Autoencoders, Generative Adversarial Networks)

Probabilistic topic models for sequence data

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A Loosely-coupled Neural-symbolic approach to Compliance of Electric Panels

This paper presents an ongoing work on project MAP4ID “Multipurpose Analytics Platform 4 Industrial Data”, where one of the objectives is to propose suitable combinations of machine learning and Answer Set Programming (ASP) to cope with industrial problems. In particular, we focus on a specific use case of the project, where we combine deep learning and ASP to solve a problem of compliance to blueprints of electric panels. The use case data was provided by Elettrocablaggi srl, a SME leader in the market. Our proposed solution couples an object-recognition layer, implemented resorting to deep neural networks, that identifies components in an image of an electric panel, and sends this information to a logic program, that checks the compliance of the panel in the picture with the blueprint of the circuit

Nationwide consensus on the clinical management of treatment-resistant depression in Italy: a Delphi panel

Background: Treatment-resistant depression (TRD) is defined by the European Medicines Agency as a lack of clinically meaningful improvement after treatment, with at least two different antidepressants. Individual, familiar, and socio-economic burden of TRD is huge. Given the lack of clear guidelines, the large variability of TRD approaches across different countries and the availability of new medications to meet the need of effective and rapid acting therapeutic strategies, it is important to understand the consensus regarding the clinical characteristics and treatment pathways of patients with TRD in Italian routine clinical practice, particularly in view of the recent availability of esketamine nasal spray. Methods: A Delphi questionnaire with 17 statements (with a 7 points Likert scale for agreement) was administered via a customized web-based platform to Italian psychiatrists with at least 5 years of experience and specific expertise in the field of depression. In the second-round physicians were asked to answer the same statements considering the interquartile range of each question as an index of their colleagues' responses. Stata 16.1 software was used for the analyses. Results: Sixty panellists, representative of the Italian territory, answered the questionnaire at the first round. For 8/17 statements more than 75% of panellists reached agreement and a high consensus as they assigned similar scores; for 4 statements the panellists assigned similar scores but in the middle of the Likert scale showing a moderate agreement with the statement, while for 5 statements there was indecision in the agreement and low consensus with the statement. Conclusions: This Delphi Panel showed that there is a wide heterogeneity in Italy in the management of TRD patients, and a compelling need of standardised strategies and treatments specifically approved for TRD. A high level of consensus and agreement was obtained about the importance of adding lithium and/or antipsychotics as augmentation therapies and in the meantime about the need for long-term maintenance therapy. A high level of consensus and agreement was equally reached for the identification of esketamine nasal spray as the best option for TRD patients and for the possibility to administrate without difficulties esketamine in a community outpatient setting, highlighting the benefit of an appropriate educational support for patients