Saving fun for a boring future

We discuss how experiences that fill a future waiting period, such as focusing on fun or boring future activities, affect intertemporal choices. We propose that savoring, the positive utility derived from anticipating future pleasant outcomes, is more likely to have an impact on intertemporal choices when the future seems boring than when it seems fun. We provide empirical evidence that people who foresee a busy future full of boring activities are more likely to prefer to delay rewards than people who foresee a future not so busy with boring activities

Does the Use of Facebook Lead to Purchases?

The ability of social media to attract large numbers of people around the world also makes these websites a platform of interest for advertisers. While these sites were hesitant at first to ‘sell out’ to massive amounts of advertising, advertising has produced for them a major revenue stream. However, an issue is whether the use of social media leads people to purchase. This paper will analyse the results of a survey of 169 Facebook users to determine the predictors for a purchase based on information from Facebook. The findings indicate that Facebook engagement, seeking friends, seeking information and gender are the main predictors of purchase

Relational Orientation versus Firm Orientation: Want versus Should

This paper provides insights into employee decision making when there is a conflict between doing what is best for the firm (firm orientation) and doing what is best for one s interpersonal relationship with an external stakeholder representative (relational orientation). We apply construal level theory (Liberman and Trope, 1998; Trope and Liberman, 2003) to propose a framework that explains the effects of psychological distance dimensions on an employee's choice to act either in the best interests of their interpersonal relationships (what they want to do), or their firm (what they should do)

Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service

In recent years, there has been an increased interest in the application of Natural Language Processing (NLP) to legal documents. The use of convolutional and recurrent neural networks along with word embedding techniques have presented promising results when applied to textual classification problems, such as sentiment analysis and topic segmentation of documents. This paper proposes the use of NLP techniques for textual classification, with the purpose of categorizing the descriptions of the services provided by the Public Prosecutor's Office of the State of Paran\'a to the population in one of the areas of law covered by the institution. Our main goal is to automate the process of assigning petitions to their respective areas of law, with a consequent reduction in costs and time associated with such process while allowing the allocation of human resources to more complex tasks. In this paper, we compare different approaches to word representations in the aforementioned task: including document-term matrices and a few different word embeddings. With regards to the classification models, we evaluated three different families: linear models, boosted trees and neural networks. The best results were obtained with a combination of Word2Vec trained on a domain-specific corpus and a Recurrent Neural Network (RNN) architecture (more specifically, LSTM), leading to an accuracy of 90\% and F1-Score of 85\% in the classification of eighteen categories (law areas)

Revisiting gap locations in amino acid sequence alignments and a proposal for a method to improve them by introducing solvent accessibility

In comparative modeling, the quality of amino acid sequence alignment still constitutes a major bottleneck in the generation of high quality models of protein three-dimensional (3D) structures. Substantial efforts have been made to improve alignment quality by revising the substitution matrix, introducing multiple sequences, replacing dynamic programming with hidden Markov models, and incorporating 3D structure information. Improvements in the gap penalty have not been a major focus, however, following the development of the affine gap penalty and of the secondary structure dependent gap penalty. We revisited the correlation between protein 3D structure and gap location in a large protein 3D structure data set, and found that the frequency of gap locations approximated to an exponential function of the solvent accessibility of the inserted residues. The nonlinearity of the gap frequency as a function of accessibility corresponded well to the relationship between residue mutation pattern and residue accessibility. By introducing this relationship into the gap penalty calculation for pairwise alignment between template and target amino acid sequences, we were able to obtain a sequence alignment much closer to the structural alignment. The quality of the alignments was substantially improved on a pair of sequences with identity in the “twilight zone” between 20 and 40%. The relocation of gaps by our new method made a significant improvement in comparative modeling, exemplified here by the Bacillus subtilis yitF protein. The method was implemented in a computer program, ALAdeGAP (ALignment with Accessibility dependent GAp Penalty), which is available at http://cib.cf.ocha.ac.jp/target_protein/. Proteins 2011; © 2011 Wiley-Liss, Inc

Aplicação de técnicas de classificação textual na predição de áreas de atuação do Ministério Público

Orientador: Prof. Luiz Eduardo Soares OliveiraMonografia (especialização) - Universidade Federal do Paraná, Setor de Ciências Exatas, Curso de Especialização em Data Science & Big Data.Inclui referênciasResumo: Observa-se nos últimos anos um crescimento no volume de pesquisas relativas a Processamento de Linguagem Natural (PLN). A utilização de redes neurais convolucionais e recorrentes em conjunto com técnicas de vetorização de palavras vem apresentando resultados promissores quando aplicadas a problemas de classificação textual, como análise de sentimentos e segmentação de documentos em tópicos. Neste artigo propõe-se o uso de técnicas de PLN na categorização de textos curtos, com o objetivo de classificar as descrições dos atendimentos realizados pelo Ministério Público do Paraná à população em uma das áreas de atuação da instituição. Buscou-se elaborar um modelo capaz de automatizar a rotulação dos atendimentos, reduzindo o tempo gasto com a seleção do atributo e a validação do cadastro, possibilitando a alocação de funcionários em demandas mais complexas. Foram utilizados métodos de extração de características textuais a partir de matrizes termo-documento e representações vetoriais. Na etapa classificatória foram apresentadas as performances obtidas por diferentes classificadores, dentre eles modelos lineares e ensembles, bem como algumas arquiteturas de redes neurais. Ao final, observou-se que o melhor resultado foi obtido através da representação vetorial de palavras com Wang2Vec associada à rede neural recorrente GRU, atingindo uma acurácia de 93% e F1-Score de 87,4% na classificação de doze categoriasAbstract: In recent years, there has been an increase in the volume of research related to Natural Language Processing (NLP). The use of convolutional and recurrent neural networks together with word embedding techniques has presented promising results when applied to textual classification problems, such as sentiment analysis and topic segmentation of documents. This paper proposes the use of NLP techniques for categorization of short texts, with the purpose of classifying the descriptions of the services performed by the Public Prosecutor of Paraná to the population in one of the institution’s areas of activity. It was intended to elaborate a model capable of automating the labeling of the attendances, reducing the time spent selecting the attribute and validating the register, allowing the allocation of employees in more complex demands. Methods of feature extraction from texts were compared by using document-term matrices and vector representations. In the classificatory stage were presented the performances obtained by different classifiers, among them linear models and ensembles, as well as some neural networks architectures. At the end, it was observed that the best result was obtained through vector representation of words with Wang2Vec associated with the GRU recurrent neural network, reaching an accuracy of 93% and F1-Score of 87.4% in the classification of twelve categories

Development of serum dexamethasone radioimmunoassay to corroborate Cushing's syndrome diagnosis

INTRODUCTION: The 1 mg dexamethasone suppression test (DxST) is widely used to screen Cushing's syndrome (CS) due to its high diagnostic accuracy. CS is an endocrine-metabolic disorder caused by hypercorticism, which is characterized by the absence of cortisol suppression in DxST. OBJECTIVE: To develop a radioimmunoassay (RIA) for the measurement of serum dexamethasone (Dx) to complement DxST. METHODS: Three rabbits were inoculated with dexamethasone-21-hemisuccinate-BSA in order to choose the best antibody. Serum Dx RIA was performed according to RE 899/2003 (Agência Nacional de Vigilância Sanitária [ANVISA]) regulations. Serum samples from 96 volunteers from Universidade Federal de São Paulo (UNIFESP) and Santa Casa de Misericórdia de São Paulo were analyzed, 67 of which were submitted to DxST and 29 were not. There were 12 patients with CS. RESULTS: The Dx antibody chosen showed good specificity. Intra- and interassay CV were < 20% with 93.8% accuracy and the lowest detection limit was 19.5 ng/dl. Serum Dx concentration was similar among both volunteers and CS patients (absence of cortisol suppression): 205 to 703 ng/dl and 174 to 661 ng/dl (95% CI), respectively. Values were undetectable among those that were not submitted to the test. Discussion: The anti-Dx antibody shows high specificity and reliability to quantify serum Dx in DxST. The Dx RIA presented reproducibility and reliability in the determination of serum Dx levels during DxST. CONCLUSION: The current RIA for serum Dx is accurate and reliable, which permits to establish a reference value range to substantiate DxST interpretation.INTRODUÇÃO: O teste de supressão com 1 mg de dexametasona (TSDx) é amplamente empregado no rastreamento da síndrome de Cushing (SC) dada sua elevada acurácia diagnóstica. A SC é um distúrbio endocrinometabólico resultante do hipercortisolismo crônico com característica de ausência de supressão do cortisol no TSDx. OBJETIVO: Desenvolver um radioimunoensaio (RIE) para a dosagem de dexametasona (Dx) no soro para complementar o TSDx. MÉTODOS: Foram imunizados três coelhos com o conjugado dexametasona-21-hemissuccinato-BSA para escolha do melhor anticorpo e o RIE foi desenvolvido de acordo com as recomendações da RE 899/2003 da Agência Nacional de Vigilância Sanitária (ANVISA). Analisamos 96 voluntários, sendo 67 submetidos ao TSDx e 29 não, e 12 pacientes com SC, estudados na Universidade Federal de São Paulo (UNIFESP) e na Santa Casa de Misericórdia de São Paulo. RESULTADOS: O anticorpo contra Dx selecionado mostrou boa especificidade, coeficiente de variação (CV%) intra e interensaio < 20%, exatidão de 93,8% e dose mínima detectada de 19,5 ng/dl. A concentração de Dx no soro foi semelhante nos voluntários e pacientes com SC (ausência/supressão do cortisol): 205 a 703 ng/dl e 174 a 661 ng/dl (intervalo de confiança [IC] 95%), respectivamente; os valores foram indetectáveis naqueles que não se submeteram ao teste. DISCUSSÃO: O anticorpo empregado apresenta boa afinidade e especificidade para quantificar a Dx no soro. O RIE mostrou reprodutibilidade e eficiência na determinação dos níveis séricos de Dx durante o TSDx. CONCLUSÃO: O presente RIE para a dosagem de Dx no soro é acurado e confiável, permitindo estabelecer uma faixa de referência de valores para subsidiar a interpretação do TSDx.Universidade Federal de São Paulo (UNIFESP)UNIFESPSciEL