Using Generic Summarization to Improve Music Information Retrieval Tasks

In order to satisfy processing time constraints, many MIR tasks process only a segment of the whole music signal. This practice may lead to decreasing performance, since the most important information for the tasks may not be in those processed segments. In this paper, we leverage generic summarization algorithms, previously applied to text and speech summarization, to summarize items in music datasets. These algorithms build summaries, that are both concise and diverse, by selecting appropriate segments from the input signal which makes them good candidates to summarize music as well. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the performance obtained using summarized datasets against the performances obtained using continuous segments (which is the traditional method used for addressing the previously mentioned time constraints) and full songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based Centrality model improve classification performance when compared to selected 30-second baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processin

On the Application of Generic Summarization Algorithms to Music

Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate this summarization's performance, we adopt an extrinsic approach: we compare a Fado Genre Classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on 2 different datasets. We show that Maximal Marginal Relevance (MMR), LexRank and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.Comment: 12 pages, 1 table; Submitted to IEEE Signal Processing Letter

Plasma membrane-specific interactome analysis reveals calpain 1 as a druggable modulator of rescued Phe508del-CFTR cell surface stability

Cystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding CF transmembrane conductance regulator (CFTR), a chloride channel normally expressed at the surface of epithelial cells. The most frequent mutation, resulting in Phe-508 deletion, causes CFTR misfolding and its premature degradation. Low temperature or pharmacological correctors can partly rescue the Phe508del-CFTR processing defect and enhance trafficking of this channel variant to the plasma membrane (PM). Nevertheless, the rescued channels have an increased endocytosis rate, being quickly removed from the PM by the peripheral protein quality-control pathway. We previously reported that rescued Phe508del-CFTR (rPhe508del) can be retained at the cell surface by stimulating signaling pathways that coax the adaptor molecule ezrin (EZR) to tether rPhe508del–Na+/H+-exchange regulatory factor-1 (NHERF1) complexes to the actin cytoskeleton, thereby averting the rapid internalization of this channel variant. However, the molecular basis for why rPhe508del fails to recruit active EZR to the PM remains elusive. Here, using a proteomics approach, we characterized and compared the core components of wt-CFTR– or rPhe508del–containing macromolecular complexes at the surface of human bronchial epithelial cells. We identified calpain 1 (CAPN1) as an exclusive rPhe508del interactor that prevents active EZR recruitment, impairs rPhe508del anchoring to actin, and reduces its stability in the PM. We show that either CAPN1 downregulation or its chemical inhibition dramatically improves the functional rescue of Phe508del-CFTR in airway cells. These observations suggest that CAPN1 constitutes an attractive target for pharmacological intervention, as part of CF combination therapies restoring Phe508del-CFTR function.This work was supported by a center grant UID/MULTI/04046/2019 to BioISI and project PTDC/BIA-CEL/28408/2017 and IF2012 to PM, both from FCT, Portugal. AMM was recipient of fellowship SFRH/BD/52490/2014 from BioSYS PhD programme PD65-2012, and PB of fellowship SFRH/BPD/94322/2013.N/

Forecasting volatility using GARCH models

Dissertação de mestrado em FinançasEsta dissertação tem como ponto central a previsão da volatilidade usando vários modelos GARCH (General autoregressive conditional heteroeskedasticity) de modo a testar qual tem a melhor capacidade de previsão. O foco desta dissertação é o estudo do mercado dos EUA.Os dados usados para este estudo são cotações do NASDAQ-100, de 1986 até 2016. Neste estudo são considerados três períodos de estimação para os modelos GARCH: 500 dias, 1000 dias e 2000 dias de modo a minimizar a possível presença de mudanças na estrutura dos dados. Regressões lineares (Mincer-Zarnowitz) foram efectuadas de forma a avaliar a performance individual de cada modelo GARCH. Depois disso, de forma a detectar qual o melhor modelo para prever a volatilidade, o teste de SPA de Hansen and Lunde (2005) foi utilizado. Os resultados são conclusivos de que os modelos são semelhantes no que toca à previsão da volatilidade condicional do dia seguinte, com a possível excepção do modelo IGARCH. O modelo GJR não apresenta resultados satisfatórios quando a janela de estimação utilizada na estimação dos modelos é de 1000 dias.The purpose of these research is to forecast volatility using different GARCH (General autoregressive conditional heteroeskedasticity) models in order to test which model has best forecasting ability. The focus of this research is the US market. The data is composed by NASDAQ-100 quotations from 1986 to 2016. The study considers three estimation periods for the GARCH family models: 500 days, 1000 days and 2000 days in order to minimize structure changes that might be present in the data. A series of Mincer-Zarnowitz regressions were completed in order to assess the performance of each GARCH model. Afterwards, the SPA test from Hansen and Lunde (2005) is used in order to detect which is the best model. The empirical results show that the GARCH models produce similar results in what comes to forecasting next day conditional volatility, with the possible exception of the IGARCH model. There is also reason to believe that the GJR model does not provide good estimations of volatility when the rolling window used in the estimation of the models is 1000 days

Forecasting indexes volatilities by using machine learning techniques, econometric and randomized models: A study on the forecasting capacity prediction of each model on the first days of the Ukraine’s Conflict

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementPredicting the volatility of returns for a stock index is an attractive and defying task in the field of Machine Learning (ML). The comparison of Machine Learning models, and their resulting predictions, with several Time Series algorithms and Monte Carlo simulations, could provide valuable insight regarding the advantage of using more recent Machine Learning methods to predict stock index volatility. In this article, a study is presented on the various models’ ability to predict for five worldwide Indexes, the returns and therefore, their volatilities, at the beginning of the Ukraine’s conflict. By applying and comparing the performance of different algorithms, this study aims to investigate if recent ML models could lead to enhanced predictive capabilities, when in comparison to more established and frequently used statical methods and/or random models. Therefore, as mentioned above, this study will be based on five indexes, namely the Euronext 100 (Europe), the National Stock Exchange India (India), the São Paulo Stock Exchange (South America), the NASDAQ (North America) and the Hang Seng Index (Hong Kong), and the data source will be the financial information, explained in detail in section 3, from January 1st 2015 until the March 4th 2022. The study and forecasting of volatility are of high value, since Pension/Investment funds, as well as other stakeholders in Financial Markets, recognize that the risk should be minimized to the maximum level, and be within the standards that Pension/Fund members agreed upon. With this being said, the main focus of this project will not be to try to obtain the most accurate model to predict the daily volatility, but to compare how different models said volatility and if their predictions fall very far from one another. The main finding of the study was that multivariable models had performed better than univariable and randomized models. Also, models that include data with different levels of frequency (daily, monthly, quarterly) have a better forecasting capacity

Internationalization of Papo D’anjo to Mexico

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsPapo d’Anjo wanted to expand its international operations to new markets and so this was the purpose of my work in the field of SME competitiveness: Internationalization strategy. With my analysis I reached the Mexican market as the best market for Papo d’Anjo to expand its operations, for example due to its attractiveness in sales of luxury goods, as it is within the top ten markets in the world. After that I pointed that the company should start with a corner in El Palacio de Hierro and then expand its operations, in channels, where Papo d’Anjo is used to perform its activity. Finally I think that my analysis is constructed in order to help this Portuguese young company to build a strong brand in the Mexican market and have success in its internationalization

Best option for Alpha-Jet fleet replacement

This work is titled “Best option for Alpha-Jet fleet replacement” and it is part of requirements for the award of a Master in Management from NOVA SBE. The objective of this work is to do a complete and detailed analysis through a capital budgeting decision for the replacement of the Portuguese Air Force Alpha-Jet fleet. It starts with an introduction to the current fleet and its history and then the possible scenarios are given. Afterwards, the scenarios conditions and incremental values are calculated and a project decision will take place. In the end, conclusions and a decision are presented

ObeOne: uma abordagem mHealth para pacientes obesos

Obesity is one of the most concerning diseases around the globe. It has been significantly growing, in the last decades, and across different age groups, with a considerable impact on the prevalence of a diverse set of health conditions, such as type 2 diabetes, heart disease, respiratory problems, sleep deprivation, and cancer. In the psychological component, conditions such as anxiety and depression are factors that can increase people’s weight, due to some of the medicines that need to be prescribed, and the fact that people can turn food into emotional comfort. Also, these conditions can lead to social integration problems, for example, the discrimination of obese people. Therefore, tackling obesity is one of the most important health goals worldwide. The widespread availability of mobile technologies has motivated the proposal of several systems supporting users in a multitude of everyday situations, including those pertaining health. Mobile health – mHealth – approaches have also been widely proposed to tackle different aspects concerning obesity, such as nutrition and physical exercise. However, the adherence to these tools is often poor, mostly resulting from a rapid decrease in motivation, and their impact on patient prognosis lacks more objective evidence, e.g., regarding which features might be more helpful and how these impact patients over long periods of time. In order to provide the grounds for a long-term research effort in proposing solutions to support obesity patients, addressing some of these open questions, this work adopts an iterative user-centered design and development approach, to understand the needs and motivations of obese patients and materializes the gathered information in a set of Personas and context scenarios for the different stakeholders. Then, it identifies a set of requirements that should be considered to enable this sustained user-centred effort. As a result, and working as a proof-of-concept of the devised requirements, ObeOne, an mHealth approach for the obesity context is presented. Its distinctive mark relies on its multiplatform, modular approach and in adopting a multidimensional view over the obesity context by explicitly considering the need to provide, in the same tool, support for multiple aspects, such as the nutritional, mental, educational and physical dimensions.A obesidade é uma das doenças mais preocupantes no mundo. Tem crescido significativamente nas últimas décadas e em diferentes faixas etárias. Isto tem um impacto considerável na prevalência de um conjunto diversificado de condições de saúde, como diabetes tipo 2, doenças cardíacas, problemas respiratórios, privação de sono e cancro. Na componente psicológica, condições como a ansiedade e depressão são fatores que podem aumentar o peso das pessoas, devido a alguns dos medicamentos que precisam de ser prescritos, e pelo facto de que as pessoas podem transformar comida em conforto emocional. Além disso, estas condições podem levar a problemas de integração social devido, por exemplo, à discriminação de pessoas com obesidade. Portanto, combater a obesidade é um dos objetivos mais importantes da saúde no mundo. A vasta oferta de tecnologias móveis tem motivado a proposta de vários sistemas de suporte aos utilizadores num conjunto de acontecimentos quotidianos, incluindo também tecnologias relacionadas com a saúde. As abordagens de Mobile Health - mHealth - também têm sido vastamente propostas para abordar diferentes aspectos relacionados com a obesidade, como nutrição e exercícios físico. No entanto, a adesão a estas ferramentas é frequentemente pobre, resultando principalmente num rápido decréscimo de motivação, e seu impacto no prognóstico do paciente carece de evidências mais objetivas, por exemplo, sobre quais funcionalidades podem ser mais úteis e como elas afetam os pacientes por longos períodos de tempo. De forma a providenciar bases para um esforço de pesquisa a longo termo na proposta de soluções para apoiar pacientes com obesidade, abordando algumas dessas questões em aberto, este trabalho adota uma abordagem iterativa de design e desenvolvimento centrada no utilizador, para perceber as necessidades e motivações de pacientes obesos, e materializa as informações reunidas num conjunto de Personas e cenários de contexto para as diferentes partes interessadas. Seguidamente, identifica também um conjunto de requisitos que devem ser considerados de forma a permitir este esforço sustentado centrado no utilizador. Como resultado, e trabalhando como uma prova de conceito dos requisitos, é apresentada para o contexto de obesidade uma abordagem mHealth, a ObeOne. A sua marca distintiva assenta na sua abordagem modular, multiplataforma e na adoção de uma visão multidimensional para o contexto da obesidade, considerando explicitamente a necessidade de providenciar, na mesma ferramenta, suporte para múltiplos aspectos, como as dimensões nutricionais, mentais, educacionais e físicas.Mestrado em Engenharia de Computadores e Telemátic