Quality dimension in equity factor investing

This paper investigates and define the Quality dimension in Equity Factor Investing, as it lacks a common definition within the economic literature. Several accounting-based factors are studied in different geographical areas, such as Accruals, Profitability, Leverage, Pay-out, Growth, and Investment. The text aims to study and recognize which of these factors lead to the true composition of the Quality dimension through a portfolio (Long/Short) implementation. In addition, the thesis recreates a systematic investment strategy that aims to optimize the Sharpe ratio of the Quality portfolio, to enhance the performance, and integration, of the Factors in a true portfolio scenario

The unbearable (technical) unreliability of automated facial emotion recognition

Emotion recognition, and in particular acial emotion recognition (FER), is among the most controversial applications of machine learning, not least because of its ethical implications for human subjects. In this article, we address the controversial conjecture that machines can read emotions from our facial expressions by asking whether this task can be performed reliably. This means, rather than considering the potential harms or scientific soundness of facial emotion recognition systems, focusing on the reliability of the ground truths used to develop emotion recognition systems, assessing how well different human observers agree on the emotions they detect in subjects' faces. Additionally, we discuss the extent to which sharing context can help observers agree on the emotions they perceive on subjects' faces. Briefly, we demonstrate that when large and heterogeneous samples of observers are involved, the task of emotion detection from static images crumbles into inconsistency. We thus reveal that any endeavour to understand human behaviour from large sets of labelled patterns is over-ambitious, even if it were technically feasible. We conclude that we cannot speak of actual accuracy for facial emotion recognition systems for any practical purposes

Innate heuristics and fast learning support escape route selection in mice

When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to threatening stimuli in ∼250 milliseconds and, in simple environments, use spatial memory to quickly escape to shelter. Natural habitats, however, often offer multiple routes to safety that animals must identify and choose from. This is challenging because although rodents can learn to navigate complex mazes, learning the value of different routes through trial and error during escape could be deadly. Here, we investigated how mice learn to choose between different escape routes. Using environments with paths to shelter of varying length and geometry, we find that mice prefer options that minimize path distance and angle relative to the shelter. This strategy is already present during the first threat encounter and after only ∼10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic assigns survival value to each path after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modeling shows that model-based reinforcement learning agents replicate the observed behavior in environments where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. The results further suggest that integrating prior knowledge acquired through evolution with knowledge learned from experience supports adaptation to changing environments and minimizes the need for trial and error when the errors are costly

The role of the periaqueductal gray in escape behavior

Escape behavior is a defensive action deployed by animals in response to imminent threats. In mammalian species, a variety of different brain circuits are known to participate in this crucial survival behavior. One of these circuits is the periaqueductal gray, a midbrain structure that can command a variety of instinctive behaviors. Recent experiments using modern systems neuroscience techniques have begun to elucidate the specific role of the periaqueductal gray in controlling escape. These have shown that periaqueductal gray neurons are crucial units for gating and commanding the initiation of escape, specifically activated in situations of imminent, escapable threat. In addition, it is becoming clear that the periaqueductal gray integrates brain-wide information that can modulate escape initiation to generate flexible defensive behaviors

The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering

This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming

Painting the black box white: experimental findings from applying XAI to an ECG reading setting

The shift from symbolic AI systems to black-box, sub-symbolic, and statistical ones has motivated a rapid increase in the interest toward explainable AI (XAI), i.e. approaches to make black-box AI systems explainable to human decision makers with the aim of making these systems more acceptable and more usable tools and supports. However, we make the point that, rather than always making black boxes transparent, these approaches are at risk of \emph{painting the black boxes white}, thus failing to provide a level of transparency that would increase the system's usability and comprehensibility; or, even, at risk of generating new errors, in what we termed the \emph{white-box paradox}. To address these usability-related issues, in this work we focus on the cognitive dimension of users' perception of explanations and XAI systems. To this aim, we designed and conducted a questionnaire-based experiment by which we involved 44 cardiology residents and specialists in an AI-supported ECG reading task. In doing so, we investigated different research questions concerning the relationship between users' characteristics (e.g. expertise) and their perception of AI and XAI systems, including their trust, the perceived explanations' quality and their tendency to defer the decision process to automation (i.e. technology dominance), as well as the mutual relationships among these different dimensions. Our findings provide a contribution to the evaluation of AI-based support systems from a Human-AI interaction-oriented perspective and lay the ground for further investigation of XAI and its effects on decision making and user experience.Comment: 15 pages, 7 figure

Utilização de dados setorizados do uso do solo e da infra-estrutura urbana

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.Este estudo tem como foco central a analise das implicações referentes à utilização de dados setorizados do uso do solo e da infra-estrutura urbana, tendo como unidade geográfica de análise a Praia dos Ingleses, na Ilha de Santa Catarina. É de interesse, também, a identificação das informações contidas em bancos de dados desenvolvidos por empresas públicas com seus órgãos específicos (empresas de energia elétrica, telecomunicações, prefeituras, abastecimento de água e saneamento e outras) formando um sistema gerenciador integrado para que se possa realizar um planejamento territorial urbano e regional que possibilite a análise e acompanhamento do crescimento da ocupação espacial. No desenvolvimento do trabalho foram analisados referenciais teóricos e/ou empíricos de questões referentes à Organização do Espaço Urbano e Regional, Tecnologia de Sistemas de Informações Geográficas (SIG), Geografia, que conduzem ações voltadas para o entendimento da problemática urbana bem como compreender o interesse da sociedade em monitorar a evolução da infra-estrutura urbana. Concluindo-se, enfatiza-se que os objetivos estabelecidos no inicio dos trabalhos de pesquisa, todos foram concretizados pelo estudo desenvolvido. E, apesar das dificuldades, pelo fato dos dados serem escassos, utilizando-se os bancos de dados existentes na CELESC e na Secretaria de Finanças do Município de Florianópolis, foi possível efetuar os cruzamentos necessários associados ao aplicativo/ferramenta que foi desenvolvido, em Vision. Assim, realizou-se um trabalho baseado em Bancos de Dados, existentes nessas empresas, sendo que estes Bancos de Dados foram escolhidos por serem os mais completos e atualizados e os que nos oportunizaram acesso