Unsupervised reinforcement learning via state entropy maximization

Reinforcement Learning (RL) provides a powerful framework to address sequential decision-making problems in which the transition dynamics is unknown or too complex to be represented. The RL approach is based on speculating what is the best decision to make given sample estimates obtained from previous interactions, a recipe that led to several breakthroughs in various domains, ranging from game playing to robotics. Despite their success, current RL methods hardly generalize from one task to another, and achieving the kind of generalization obtained through unsupervised pre-training in non-sequential problems seems unthinkable. Unsupervised RL has recently emerged as a way to improve generalization of RL methods. Just as its non-sequential counterpart, the unsupervised RL framework comprises two phases: An unsupervised pre-training phase, in which the agent interacts with the environment without external feedback, and a supervised fine-tuning phase, in which the agent aims to efficiently solve a task in the same environment by exploiting the knowledge acquired during pre-training. In this thesis, we study unsupervised RL via state entropy maximization, in which the agent makes use of the unsupervised interactions to pre-train a policy that maximizes the entropy of its induced state distribution. First, we provide a theoretical characterization of the learning problem by considering a convex RL formulation that subsumes state entropy maximization. Our analysis shows that maximizing the state entropy in finite trials is inherently harder than RL. Then, we study the state entropy maximization problem from an optimization perspective. Especially, we show that the primal formulation of the corresponding optimization problem can be (approximately) addressed through tractable linear programs. Finally, we provide the first practical methodologies for state entropy maximization in complex domains, both when the pre-training takes place in a single environment as well as multiple environments.

The transformative potential of community engagement in higher education: an analysis of service-learning effects on students, communities, and institutions.

Service-learning in higher education is gaining attention as a reliable tool to support students’ learning and fulfil the mission of higher education institutions (HEIs). This dissertation addresses existing gaps in the literature by examining the effects and perspectives of service-learning in HEIs through three studies. The first study compares the effects of a voluntary semester-long service-learning course with traditional courses. A survey completed by 110 students before and after the lectures found no significant group differences in the psychosocial variables under inspection. Nevertheless, service-learning students showed higher scores concerning the quality of participation. Factors such as students’ perception of competence, duration of service-learning, and self-reported measures may have influenced the results. The second study explores the under-researched perspective of community partners in higher education and European settings. Twelve semi-structured interviews were conducted with community partners from various community organisations across Europe. The results highlight positive effects on community members and organisations, intrinsic motivations, organisational empowerment, different forms of reciprocity, the co-educational role of community partners, and the significant role of a sense of community and belonging. The third study focuses on faculty perspectives on service-learning in the European context. Twenty-two semi-structured interviews were conducted in 14 European countries. The findings confirm the transformative impact of service-learning on the community, students, teachers, and HEIs, emphasising the importance of motivation and institutionalisation processes in sustaining engaged scholarship. The study also identifies the relevance of the community experience, sense of community, and community responsibility with the service-learning experience; relatedness is proposed as the fifth pillar of service-learning. Overall, this dissertation provides new insights into the effects and perspectives of service-learning in higher education. It integrates the 4Rs model with the addition of relatedness, guiding the theoretical and practical implications of the findings. The dissertation also suggests limitations and areas for further research.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

A deep learning model via thermodynamics and condensed matter physics

Deep Learning architectures give brilliant results in a large variety of fields, but a comprehensive theoretical description of their inner functioning is still lacking. In this work, we try to understand the behavior of neural networks by modelling in the frameworks of Thermodynamics and Condensed Matter Physics. We approach neural networks as in a real laboratory and we measure the frequency spectrum and the entropy of the weights of the trained model. The stochasticity of the training occupies a central role in the dynamics of the weights and makes it difficult to assimilate neural networks to simple physical systems. However, the analogy with Thermodynamics and the introduction of a well defined temperature leads us to an interesting result: if we eliminate from a CNN the "hottest" filters, the performance of the model remains the same, whereas, if we eliminate the "coldest" ones, the performance gets drastically worst. This result could be exploited in the realization of a training loop which eliminates the filters that do not contribute to loss reduction. In this way, the computational cost of the training will be lightened and more importantly this would be done by following a physical model. In any case, beside important practical applications, our analysis proves that a new and improved modeling of Deep Learning systems can pave the way to new and more efficient algorithms.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

Comment On 'practical Advice For Avoidance Of Pain Associated With Insertion Of Intrauterine Contraceptives': Authors' Response.

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Carotenoids Are Effective Inhibitors Of In Vitro Hemolysis Of Human Erythrocytes, As Determined By A Practical And Optimized Cellular Antioxidant Assay.

β-Carotene, zeaxanthin, lutein, β-cryptoxanthin, and lycopene are liposoluble pigments widely distributed in vegetables and fruits and, after ingestion, these compounds are usually detected in human blood plasma. In this study, we evaluated their potential to inhibit hemolysis of human erythrocytes, as mediated by the toxicity of peroxyl radicals (ROO•). Thus, 2,2'-azobis (2-methylpropionamidine) dihydrochloride (AAPH) was used as ROO• generator and the hemolysis assay was carried out in experimental conditions optimized by response surface methodology, and successfully adapted to microplate assay. The optimized conditions were verified at 30 × 10(6) cells/mL, 17 mM of AAPH for 3 h, at which 48 ± 5% of hemolysis was achieved in freshly isolated erythrocytes. Among the tested carotenoids, lycopene (IC(50) = 0.24 ± 0.05 μM) was the most efficient to prevent the hemolysis, followed by β-carotene (0.32 ± 0.02 μM), lutein (0.38 ± 0.02 μM), and zeaxanthin (0.43 ± 0.02 μM). These carotenoids were at least 5 times more effective than quercetin, trolox, and ascorbic acid (positive controls). β-Cryptoxanthin did not present any erythroprotective effect, but rather induced a hemolytic effect at the highest tested concentration (3 μM). These results suggest that selected carotenoids may have potential to act as important erythroprotective agents by preventing ROO•-induced toxicity in human erythrocytes.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Resonancias discursivas y cortesía en prácticas de lecto-escritura

In this paper we present a study of reading comprehension based on a contrastive argumentative-discursive approach. We examine the relationship between linguistic materiality and discursive processes, observing the connection between reading in a foreign language, writing production and textual memories in the mother tongue. In addition to an interest in practical language teaching and learning processes (in this case of Spanish and Portuguese), we investigate the question of politeness and the theoretical relationship between subjectivity, language, and textuality. The latter, being understood as the result of discourse regularities, is unique for each and every production, yet is also conditioned by plural discursive memories resulting from contradictory social relationships in a specific historical context (Foucault, 1986; Pêcheux, 1990). In the experiment presented here, we follow some of the procedures of the methodology applied in the European Galatea Project developed for the study of reading strategies in the inter-comprehension between Romance languages (Dabène, 1996). We use the procedure of simulation and the subjective projection of participants as well as the notion of discursive resonance in the analysis. The results, having to do with directness and indirectness in speech and the question of politeness in two typologically close languages, lead to the conclusion that the concept of politeness goes beyond a pragmatic strategy used to avoid conflicts to be approached as a marker of cultural identity constitution. The relevance of discursive awareness and its theoretical and practical consequences are then emphasized.

Flicker perimetry in healthy subjects: influence of age and gender, learning effect and short-term fluctuation

PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

A questão da licenciatura em Educação Fisica : a transição a pratica profissional

Universidade Estadual de Campinas. Faculdade de Educação Física

Pedagogia do esporte : pressupostos para uma teoria da avaliação da aprendizagem = Sport pedagogy: presuppositions for a learning assessment theory

Universidade Estadual de Campinas . Faculdade de Educação Física

O esporte paralímpico na formação do profissional em educação física : percepção de professores e acadêmicos = The paralympic sport in physical education professional forming: perceptions of teachers and students

Universidade Estadual de Campinas . Faculdade de Educação Física