Self-supervised Information Retrieval basato su deep metric learning

La tesi ha lo scopo di ricercare, esaminare ed implementare un sistema di Machine Learning, un Recommendation Systems per precisione, che permetta la racommandazione di documenti di natura giuridica, i quali sono già stati analizzati e categorizzati appropriatamente, in maniera ottimale, il cui scopo sarebbe quello di accompagnare un sistema già implementato di Information Retrieval, istanziato sopra una web application, che permette di ricercare i documenti giuridici appena menzionati.

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

Remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) fish-bone settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - K-means: k=0.68; k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). Results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.

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

Universidade Estadual de Campinas. Faculdade de Educação Física

Inclusão, necessidades especiais e Educação Fisica : considerações sobre a ação pedagogica no ambiente escolar

Universidade Estadual de Campinas . Faculdade de Educação Física

A prática de exercícios físicos em pacientes com transtornos alimentares

CONTEXTO: Diferentes estudos discutem a relação da prática excessiva de exercícios físicos com transtornos alimentares como estratégia para perda de peso. OBJETIVO: Revisar a literatura sobre a prática de exercícios físicos em pacientes com transtornos alimentares, discutindo definições, critérios diagnósticos e propostas terapêuticas. MÉTODOS: Levantamento bibliográfico foi realizado por meio de MedLine, LiLacs e Cochrane Library, com os termos "transtornos alimentares", "anorexia", "bulimia", "exercício físico excessivo", "atividade física", "exercício obrigatório", "exercício compulsivo" e "exercício excessivo". RESULTADOS: Dos 80 artigos encontrados, foram selecionados 12 que incluíam a investigação de um padrão de atividade física considerado excessivo em indivíduos acima dos 18 anos e uso de algum instrumento de avaliação para essa finalidade. A prática de exercícios físicos em pacientes com transtornos do comportamento alimentar é revisada. CONCLUSÃO: Não há consenso sobre critérios diagnósticos e instrumentos para considerar o exercício físico como inadequado ou excessivo e seu uso como recurso para perder peso. Por outro lado, a prática de exercícios físicos durante o tratamento de pacientes com transtornos alimentares pode ser benéfica desde que orientada e supervisionada.

Diferenças na adesão ao tratamento da tuberculose em relação ao sexo no distrito de saúde da Freguesia do Ó/Brasilândia - São Paulo

Os motivos para as diferenças epidemiológicas e para a adesão ao tratamento da tuberculose em relação a homens e mulheres são desconhecidos. Este trabalho tem como objetivo verificar diferenças na adesão ao tratamento da tuberculose em relação ao sexo; identificar aspectos facilitadores e dificultadores para a adesão ao tratamento da tuberculose em relação ao sexo; analisar as crenças consideradas importantes para a adesão ao tratamento da tuberculose. Foi utilizado o referencial teórico do Modelo de Crenças em Saúde de Rosenstock e a técnica da Análise de Conteúdos de Bardin. Foram realizadas 28 entrevistas semiestruturadas com homens e mulheres em tratamento supervisionado de tuberculose do Distrito de Saúde da Freguesia do Ó/Brasilândia. Os resultados mostraram que o perfil daqueles que falharam na terapêutica da tuberculose em relação ao sexo foi: mulher - solteira e separada, com atividade remunerada não comprovada, nível de escolaridade entre fundamental I completo e ensino médio completo; homem - casado, com atividade remunerada comprovada, nível de escolaridade entre ensino fundamental II completo e ensino médio completo. Os aspectos facilitadores encontrados para a boa adesão residem no bom atendimento dos profissionais de saúde e na percepção, por parte do paciente, da sua melhora de saúde. As crenças para a boa adesão ao tratamento no sexo masculino e feminino foram: bom atendimento do serviço de saúde e bom tratamento (em relação aos medicamentos).

Gastroenterology training in Latin America

Latin America is characterized by ethnic, geographical, cultural, and economic diversity; therefore, training in gastroenterology in the region must be considered in this context. The continent's medical education is characterized by a lack of standards and the volume of research continues to be relatively small. There is a multiplicity of events in general gastroenterology and in sub-disciplines, both at regional and local levels, which ensure that many colleagues have access to information. Medical education programs must be based on a clinical vision and be considered in close contact with the patients. The programs should be properly supervised, appropriately defined, and evaluated on a regular basis. The disparity between the patients' needs, the scarce resources available, and the pressures exerted by the health systems on doctors are frequent cited by those complaining of poor professionalism. Teaching development can play a critical role in ensuring the quality of teaching and learning in universities. Continuing professional development programs activities must be planned on the basis of the doctors' needs, with clearly defined objectives and using proper learning methodologies designed for adults. They must be evaluated and accredited by a competent body, so that they may become the basis of a professional regulatory system. The specialty has made progress in the last decades, offering doctors various possibilities for professional development. The world gastroenterology organization has contributed to the speciality through three distinctive, but closely inter-related, programs: Training Centers, Train-the-Trainers, and Global Guidelines, in which Latin America is deeply involved. (C) 2011 Baishideng. All rights reserved.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

New concept for lifting in onshore oil wells

This paper presents the development of a prototype of a tubular linear induction motor applied to onshore oil exploitation, named MAT AE OS (which is the Portuguese acronym for Tubular Asynchronous Motor for Onshore Oil Exploitation). The function of this motor is to directly drive the sucker-rod pump installed in the down hole of the oil well. Considering the drawbacks and operational costs of the conventional oil extraction method, which is based on the walking beam and rod, string system, the developed prototype is intended to become a feasible alternative from both technical and economic points of view. At the present time, the MAT AE OS prototype is installed in a test bench at the Applied Electromagnetism Laboratory at the Escola Politecnica da Universidade de Sao Paulo. The complete testing system is controlled and supervised by special software, enabling good flexibility in operation, data acquisition, and performance analysis. The test results indicate that the motor develops a constant lift force along the pumping cycle, as shown by the measured dynamometric charts. Also, the evaluated electromechanical performance seems to be superior to that obtained by the traditional method. The system utilizing the MAT AE OS prototype allows the complete elimination of the rod string sets required by the conventional equipment, indicating that the new system may advantageously replace the surface mechanical components presently utilized.

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcriptional responses of host peripheral blood cells to tuberculosis infection

Host responses following exposure to Mycobacterium tuberculosis (TB) are complex and can significantly affect clinical outcome. These responses, which are largely mediated by complex immune mechanisms involving peripheral blood cells (PBCs) such as T-lymphocytes, NK cells and monocyte-derived macrophages, have not been fully characterized. We hypothesize that different clinical outcome following TB exposure will be uniquely reflected in host gene expression profiles, and expression profiling of PBCs can be used to discriminate between different TB infectious outcomes. In this study, microarray analysis was performed on PBCs from three TB groups (BCG-vaccinated, latent TB infection, and active TB infection) and a control healthy group. Supervised learning algorithms were used to identify signature genomic responses that differentiate among group samples. Gene Set Enrichment Analysis was used to determine sets of genes that were co-regulated. Multivariate permutation analysis (p < 0.01) gave 645 genes differentially expressed among the four groups, with both distinct and common patterns of gene expression observed for each group. A 127-probeset, representing 77 known genes, capable of accurately classifying samples into their respective groups was identified. In addition, 13 insulin-sensitive genes were found to be differentially regulated in all three TB infected groups, underscoring the functional association between insulin signaling pathway and TB infection. Published by Elsevier Ltd.