Parallel implementation of Expectation-Maximisation algorithm for the training of Gaussian Mixture Models

Most machine learning algorithms need to handle large data sets. This feature often leads to limitations on processing time and memory. The Expectation-Maximization (EM) is one of such algorithms, which is used to train one of the most commonly used parametric statistical models, the Gaussian Mixture Models (GMM). All steps of the algorithm are potentially parallelizable once they iterate over the entire data set. In this study, we propose a parallel implementation of EM for training GMM using CUDA. Experiments are performed with a UCI dataset and results show a speedup of 7 if compared to the sequential version. We have also carried out modifications to the code in order to provide better access to global memory and shared memory usage. We have achieved up to 56.4% of achieved occupancy, regardless the number of Gaussians considered in the set of experiments

RECONHECIMENTO DE PADRÕES BIOMÉDICOS UTILIZANDO MÁQUINAS DE APRENDIZADO PROFUNDO

The brain-computer interface is one of the emerging fields of human-computer interaction due to its broad spectrum of applications, especially those that deal with human cognition. In this work, electroencephalography (EEG) is used as base data for classifying the state of the eyes (open or closed) by applying Long Short Term Memory (LSTM) networks and variants. For benchmarking purposes, the EEG data set with the eye state record was used, available in the Machine Learning repository at UCI. The results obtained indicated that the model is applicable to the classification of the data and that its performance is good compared to the more expensive models computationally.A interface cérebro-computador é um dos campos emergentes da interação homem-computador devido ao seu amplo espectro de aplicações, especialmente as que lidam com a cognição humana. Neste trabalho, a eletroencefalografia (EEG) é usada como dado base para classificar o estado dos olhos (abertos ou fechados) aplicando redes Long Short Term Memory (LSTM) e variantes. Para fins de benchmarking, foi utilizado o conjunto de dados de EEG com registro do estado do olho, disponível no repositório de Aprendizado de Máquina da UCI. Os resultados obtidos indicaram que o modelo é aplicável para a classificação dos dados e que seu desempenho é bom comparado aos modelos mais caros computacionalmente

RECONHECIMENTO DE ESTADOS DOS OLHOS UTILIZANDO MÁQUINAS DE APRENDIZADO PROFUNDO A PARTIR DE ONDAS CEREBRAIS

The brain-computer interface is one of the emerging fields of human-computer interaction due to its broad spectrum of applications, especially those that deal with human cognition. In this work, electroencephalography (EEG) is used as base data for classifying the state of the eyes (open or closed) by applying Long Short-Term Memory (LSTM) networks and variants. For benchmarking purposes, the EEG data set with the eye state record was used, available in the Machine Learning repository at UCI. The results obtained indicated that the LSTM and GRU bidirectional cells models are applicable to the classification of the data, presenting an accuracy greater than 95%, and that its performance is good compared to the more expensive models computationally.A interface cérebro-computador é um dos campos emergentes da interação homem-computador devido ao seu amplo espectro de aplicações, especialmente as que lidam com a cognição humana. Neste trabalho, a eletroencefalografia (EEG) é usada como dado base para classificar o estado dos olhos (abertos ou fechados) aplicando redes Long Short-Term Memory (LSTM) e variantes. Para fins de benchmarking, foi utilizado o conjunto de dados de EEG com registro do estado do olho, disponível no repositório de Aprendizado de Máquina da UCI. Os resultados obtidos indicaram que os modelos bidirecionais das células LSTM e GRU são aplicáveis na classificação dos dados, apresentando acurácia superior a 95%, e que seu desempenho é bom comparado aos modelos mais caros computacionalmente

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin

Activated Carbon, Carbon Nanofibers and Carbon-Covered Alumina as Support for W2C in Stearic Acid Hydrodeoxygenation

Carbon materials play a crucial role in sorbents and heterogeneous catalysis and are widely used as catalyst support for several reactions. This paper reports on an investigation of tungsten carbide (W2C) catalyst on three types of carbon support, namely activated carbon (AC), carbon nanofibers (CNF) and carbon-covered alumina (CCA). We evaluated their activity and selectivity in stearic acid hydrodeoxygenation at 350 °C and 30 bar H2. Although all three W2C catalysts displayed similar intrinsic catalytic activities, the support did influence product distribution. At low conversions (50%), W2C/CCA presented the highest C18-unsaturated/C18-saturated ratio in product distribution, which appears to be linked to W2C/CCA having the highest ratio of acid/metallic sites

Modelos e Abordagens de Projeto para o Desenvolvimento de Tecnologias Assistivas

DOI: http://dx.doi.org/10.13071/regec.2317-5087.2014.3.1.5936.107-121. É cada vez maior a conscientização da importância e benefício da inclusão e participação na sociedade de pessoas portadoras de necessidades especiais. A eliminação de barreiras que habilitem a participação plena na sociedade dos portadores de necessidades especiais requer o desenvolvimento de novos sistemas de tecnologia assistiva e o aperfeiçoamento dos mecanismos de informação e acesso às tecnologias existentes. Para atingir esse objetivo, há a necessidade de um diálogo efetivo entre os diversos atores, isto é, usuários, comunidade médica e de serviços sociais e profissionais da área da engenharia. Para dar suporte a esse diálogo, faz-se necessário que terminologias comuns, conceitos e definições sejam agrupados em um modelo unificado e comum. Este artigo apresenta modelos em desenvolvimento e o ponto de interseção com os métodos tradicionais de projeto

Modelos e Abordagens de Projeto para o Desenvolvimento de Tecnologias Assistivas

É cada vez maior a conscientização da importância e benefício da inclusão e participação na sociedade de pessoas portadoras de necessidades especiais. A eliminação de barreiras que habilitem a participação plena na sociedade dos portadores de necessidades especiais requer o desenvolvimento de novos sistemas de tecnologia assistiva e o aperfeiçoamento dos mecanismos de informação e acesso às tecnologias existentes. Para atingir esse objetivo, há a necessidade de um diálogo efetivo entre os diversos atores, isto é, usuários, comunidade médica e de serviços sociais e profissionais da área da engenharia. Para dar suporte a esse diálogo, faz-se necessário que terminologias comuns, conceitos e definições sejam agrupados em um modelo unificado e comum. Este artigo apresenta modelos em desenvolvimento e o ponto de interseção com os métodos tradicionais de projeto

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

Background: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. Methods: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. Results: SVR24 rates were 46.1 % (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1,2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced ≥1 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with ≥1 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not ≥5. Conclusions: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginter-feron alfa-2a/ribavirin