A neural-symbolic system for temporal reasoning with application to model verification and learning

The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies. An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour. In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role

Equity research - Under Armour, INC.

Mestrado Bolonha em FinançasThe objective of this report is to study and provide an investment recommendation for Under Armour, Inc. This report is based on public information available until 1st of August 2021 and it was developed according to the CFA Institute recommendation for the CFA Research Challenge. Under Armour, Inc. is an American company engaged in the development, marketing and distribution of branded sports performance and casual apparel, footwear and accessories for men, women and youth. During the course of Equity Research I had the opportunity to study this industry, which helped better understand its ins and outs. Under Armour’s valuation was calculated using a Weighted Average Cost of Capital (WACC) Method through Free Cash Flow to the Firm (FCFF) for the industry, complemented with the Adjusted Present Value (APV) and a Relative Multiples Valuation. At the end, the recommendation of this report is to sell the common share, with a price target of $9.62 per share, in the end of 2021, representing a potential downside of 49.11$ 9.62, no final do ano de 2021, representando uma queda potencial de 49.11%, face ao preço da ação à data da elaboração deste relatório. Esta recomendação é baseada num nível de risco elevado, visto a empresa atuar num mercado bastante saturado, com uma margem operacional muito ténue, onde a oferta é extremamente alta, e onde o seu poder de branding e marketing não são tão fortes como alguns dos seus concorrentes.info:eu-repo/semantics/publishedVersio

The absent players : the impact of firms with no trading activity : estudo exploratório

O nosso estudo têm por objetivo analisar as diferenças que existem entre os níveis de transação em empresas que se encontram cotadas no mercado de capitais e perceber o motivo pelo qual permanecem cotadas, com recurso a uma nova medida que analisa a percentagem de dias em que não existe qualquer retorno, denominada por Zero-Return Metric. Utilizando uma base de dados de 2.502 empresas para o período entre 2004 a 2014 do principal índice acionista do Reino Unido, é apresentado uma mediana de 58,7% de dias em que não existe qualquer retorno. Partimos do pressuposto que as empresas pretendem aceder a esta estrutura de financiamento devido às vantagens que apresenta, contudo nem todas as empresas conseguem captar de igual forma os seus benefícios. Com base nisto, é possível observar diferenças significativas na visibilidade, nas necessidades de capital, no grau de concentração da estrutura acionista e na liquidez entre as empresas, o que pode explicar algumas diferenças no nível de transação. Como consequência, a taxa de sobrevivência deste tipo de empresas é bastante inferior, sendo um importante indicador do seu grau de desempenho. O nosso objetivo passa por apresentar um estudo compreensivo sobre este efeito, lançando as bases para um estudo mais aprofundado nesta área.Our study aims to analyze the differences between the transactions levels in companies that are listed on the capital market, and realize why they remain listed, using a new measure that analyses the proportion of zero-return days (thereafter referred to as the zero-return metric). Using a Database with 2.502 firms for the period 2004-2014 of the main UK stock market index, a median with 58,7% of days without a return is represented. We assume that the companies want to access this financing structure due to the advantages they offer, however not all companies are able to capture the same way its benefits. On this basis, it is possible to observe significant differences in visibility, capital needs and growth, in the degree of shareholders structure and liquidity, which may explain some differences in transaction level. As a result, the survival rates for those companies is much lower, being an important indicator of their level of performance. Our objective is to present a comprehensive descriptive study of this effect, laying the foundations to a more in depth study

Learning and Representing Temporal Knowledge in Recurrent Networks

The effective integration of knowledge representation, reasoning, and learning in a robust computational model is one of the key challenges of computer science and artificial intelligence. In particular, temporal knowledge and models have been fundamental in describing the behavior of computational systems. However, knowledge acquisition of correct descriptions of a system's desired behavior is a complex task. In this paper, we present a novel neural-computation model capable of representing and learning temporal knowledge in recurrent networks. The model works in an integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models given a set of desirable system properties, and effective learning from examples, which in turn can lead to temporal knowledge extraction from the corresponding trained networks. The model is sound from a theoretical standpoint, but it has also been tested on a case study in the area of model verification and adaptation. The results contained in this paper indicate that model verification and learning can be integrated within the neural computation paradigm, contributing to the development of predictive temporal knowledge-based systems and offering interpretable results that allow system researchers and engineers to improve their models and specifications. The model has been implemented and is available as part of a neural-symbolic computational toolkit

A neural-symbolic system for temporal reasoning with application to model verification and learning

The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies. An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour. In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Estudo da umidade relativa do ar em Brasília : uma aplicação de cadeias de ordem variável

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2018.Neste trabalho, verificamos o bom desempenho do Algoritmo BIC proposta por Csiszar e Talata (2005) quando utilizamos amostras contaminadas, desta maneira viabilizando a aplicação em modelos de umidade relativa do ar considerados nesse trabalho. Para a aplicação do Cadeias de Ordem Variável, modelamos dados de umidade relativa do ar de Brasília considerando o período de 1962 à 2018 e tentamos prever a possibilidade do próximo dia ter a umidade adequada pra saúde, estar em estado de atenção, estado de alerta ou estado de emergência. Em um segundo momento, dividimos a série histórica em dois períodos verificamos se houveram mudanças na umidade relativa do ar em Brasília devido ao processo de urbanização ao longo do tempo. Todas as simulações e estimativas foram realizadas através do ambiente RStudio Versão 3.5.0