Sense of Community in contexts of multi culture

The theme “Belongingness” has a central role in the current debate on Community Psychology and in daily life. To try to understand the consequences of these changes, the research focuses on the concept of Sense of Community. In fact, Sense of Community has always been a central tool (Sarason, 1974; MacMillan & Chavis, 1986) to study communities (McMillan, 2011; Nowell & Boyd, 2011) and for a long time has represented positive values and something to aspire to. However, current debates indicate that Sense of Community is an outmoded concept and underline the problematic issue of “promotion of Sense of Community” in contexts of multi culture. The aim of the present research is to analyze Sense of Community in context of multi culture, as we consider that it can still be a fundamental tool to study and understand communities. In particular we are interested in understanding the role of Multiple Sense of Community (Brodsky, 2009) on Identity and Wellbeing (and its dimensions). We focused on a specific context, the Station Zone in Reggio Emilia, that is characterized by high levels of cultural diversity and different social problems (Giovannini & Vezzali, 2011). The research is developed and divided into two parts. The first part consists of an exploratory qualitative study that analyzes meanings of community among leaders of different ethnic groups living in the Station Zone. The second part consists of a “General Model” study and four parallel studies to analyze Multiple Sense of Community in different ethnic groups (Albanians, Moroccans, Chinese and Italians. The results indicate the different role of Multiple SOC in the relation between Identity and Wellbeing, in particular the relevance of Relational SOC and its different implications. Moreover, the factor “culture” represents an significant element in order to consider differences among ethnic groups.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.