905 resultados para Reinforcement Learning,resource-constrained devices,iOS devices,on-device machine learning


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In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

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Accompanying the call for increased evidence-based policy the developed world is implementing more longitudinal panel studies which periodically gather information about the same people over a number of years. Panel studies distinguish between transitory and persistent states (e.g. poverty, unemployment) and facilitate causal explanations of relationships between variables. However, they are complex and costly. A growing number of developing countries are now implementing or considering starting panel studies. The objectives of this paper are to identify challenges that arise in panel studies, and to give examples of how these have been addressed in resource-constrained environments. The main issues considered are: the development of a conceptual framework which links macro and micro contexts; sampling the cohort in a cost-effective way; tracking individuals; ethics and data management and analysis. Panel studies require long term funding, a stable institution and an acceptance that there will be limited value for money in terms of results from early stages, with greater benefits accumulating in the study's mature years. Copyright © 2003 John Wiley & Sons, Ltd.

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The disruption of the human immunolobulin E–high affinity receptor I (IgE–FcεRI) protein–protein interaction (PPI) is a validated strategy for the development of anti asthma therapeutics. Here, we describe the synthesis of an array of conformationally constrained cyclic peptides based on an epitope of the A–B loop within the Cε3 domain of IgE. The peptides contain various tolan (i.e., 1,2-biarylethyne) amino acids and their fully and partially hydrogenated congeners as conformational constraints. Modest antagonist activity (IC50 660 μM) is displayed by the peptide containing a 2,2′-tolan, which is the one predicted by molecular modeling to best mimic the conformation of the native A–B loop epitope in IgE.

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The strategic integration of the human resource (HR) function is regarded as crucial in the literature on (strategic) human resource management ((S)HRM). Evidence on the contextual or structural influences on this integration is, however, limited. The structural implications of unionism are particularly intriguing given the evolution of study of the employment relationship. Pluralism is typically seen as antithetical to SHRM, and unions as an impediment to the strategic integration of HR functions, but there are also suggestions in the literature that unionism might facilitate the strategic integration of HR. This paper deploys large-scale international survey evidence to examine the organization-level influence of unionism on this strategic integration, allowing for other established and plausible influences. The analysis reveals that exceptionally, where the organization-level role of unions is particularly contested, unionism does impede the strategic integration of HR. However, it is the predominance of the facilitation of the strategic integration of HR by unionism which is most remarkable.

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The strategic integration of the human resource (HR) function is regarded as crucial in the literature on (strategic) human resource management ((S)HRM). Evidence on the contextual or structural influences on this integration is, however, limited. The structural implications of unionism are particularly intriguing given the evolution of study of the employment relationship. Pluralism is typically seen as antithetical to SHRM, and unions as an impediment to the strategic integration of HR functions, but there are also suggestions in the literature that unionism might facilitate the strategic integration of HR. This paper deploys large-scale international survey evidence to examine the organization-level influence of unionism on this strategic integration, allowing for other established and plausible influences. The analysis reveals that exceptionally, where the organization-level role of unions is particularly contested, unionism does impede the strategic integration of HR. However, it is the predominance of the facilitation of the strategic integration of HR by unionism which is most remarkable.

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The objective of reservoir engineering is to manage fields of oil production in order to maximize the production of hydrocarbons according to economic and physical restrictions. The deciding of a production strategy is a complex activity involving several variables in the process. Thus, a smart system, which assists in the optimization of the options for developing of the field, is very useful in day-to-day of reservoir engineers. This paper proposes the development of an intelligent system to aid decision making, regarding the optimization of strategies of production in oil fields. The intelligence of this system will be implemented through the use of the technique of reinforcement learning, which is presented as a powerful tool in problems of multi-stage decision. The proposed system will allow the specialist to obtain, in time, a great alternative (or near-optimal) for the development of an oil field known

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Neste trabalho é proposto um novo algoritmo online para o resolver o Problema dos k-Servos (PKS). O desempenho desta solução é comparado com o de outros algoritmos existentes na literatura, a saber, os algoritmos Harmonic e Work Function, que mostraram ser competitivos, tornando-os parâmetros de comparação significativos. Um algoritmo que apresente desempenho eficiente em relação aos mesmos tende a ser competitivo também, devendo, obviamente, se provar o referido fato. Tal prova, entretanto, foge aos objetivos do presente trabalho. O algoritmo apresentado para a solução do PKS é baseado em técnicas de aprendizagem por reforço. Para tanto, o problema foi modelado como um processo de decisão em múltiplas etapas, ao qual é aplicado o algoritmo Q-Learning, um dos métodos de solução mais populares para o estabelecimento de políticas ótimas neste tipo de problema de decisão. Entretanto, deve-se observar que a dimensão da estrutura de armazenamento utilizada pela aprendizagem por reforço para se obter a política ótima cresce em função do número de estados e de ações, que por sua vez é proporcional ao número n de nós e k de servos. Ao se analisar esse crescimento (matematicamente, ) percebe-se que o mesmo ocorre de maneira exponencial, limitando a aplicação do método a problemas de menor porte, onde o número de nós e de servos é reduzido. Este problema, denominado maldição da dimensionalidade, foi introduzido por Belmann e implica na impossibilidade de execução de um algoritmo para certas instâncias de um problema pelo esgotamento de recursos computacionais para obtenção de sua saída. De modo a evitar que a solução proposta, baseada exclusivamente na aprendizagem por reforço, seja restrita a aplicações de menor porte, propõe-se uma solução alternativa para problemas mais realistas, que envolvam um número maior de nós e de servos. Esta solução alternativa é hierarquizada e utiliza dois métodos de solução do PKS: a aprendizagem por reforço, aplicada a um número reduzido de nós obtidos a partir de um processo de agregação, e um método guloso, aplicado aos subconjuntos de nós resultantes do processo de agregação, onde o critério de escolha do agendamento dos servos é baseado na menor distância ao local de demanda

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The state has changed over time in order to meet a society with increasingly stringent demands. Techniques of private means begin to be employed in an attempt to overcome the dysfunctions entrenched bureaucracy, making the machine faster. By federal law by the People Management Skills was established as a reference for the administration of Human Resources of the public sector in an attempt to develop professionally servers, based mainly on the three pillars of the model: the knowledge, skills and attitudes. This thesis aims at understanding, in the view of employees, the perceived impacts on the organizational changes occurring in the Department of Administration and Human Resources of the State of Rio Grande do Norte in order to implement a People Management Skills-based. It is a simple case study, characterized by the research during a certain period of time, collecting data in a real environment of an organization, in this case SEARH/RN. The procedures used in collecting data were the literature review, documental research and field research. We used a qualitative approach with exploratory and descriptive approach. Every reform was implemented in the institution and reported from there analyzed the impacts observed by the servers. As a result we observed a considerable advance in institutional activities, mainly relating to physical structure / organizational and human resource policies, with minor advances on labor policies, in much the result of the guiding focus of the reform on SEARH/RN. The impacts in total were more positive than negative and direct paths to improvement in public organizations. Making a general analysis of the modernization program implemented in SEARH/RN, we can conclude that there was a distinct change in all dimensions studied, mostly pointing out positive aspects, and contrary to the opinion of some authors, who claim to be very difficult to implement reforms in public organizations, since they are highly institutionalized environments. What was found was a big organization, with gaps and weaknesses, but with a much larger number of hits and recognition from institutional actors

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In this work the use of coconut fiber (coir) and bamboo shafts as reinforcement of soil-cement was studied, in order to obtain an alternative material to make stakes for fences in rural properties. The main objective was to study the effect of the addition of reinforcement to the soil-cement matrix. The effect of humidity on the mechanical properties was also analyzed. The soil-cement mortar was composed by a mixture, in equal parts, of soil and river sand, 14% in weight of cement and 10 % in weight of water. As reinforcement, different combinations of (a) coconut fiber with 15 mm mean length (0,3 %, 0,6 % and 1,2 % in weight) and (b) bamboo shafts, also in crescent quantities (2, 4 and 8 shafts per specimen) were used. For each combination 6 specimens were made and these were submitted to three point flexural test after 28 days of cure. In order to evaluate the effect of humidity, 1 specimen from each of the coconut fiber reinforced combination was immersed in water 24 hours prior to flexural test. The results of the tests carried out indicated that the addition of the reinforcement affected negatively the mechanical resistance and, on the other hand, increased the tenacity and the ductility of the material.

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Two methods for calculating inner products of Schur functions in terms of outer products and plethysms are given and they are easy to implement on a machine. One of these is derived from a recent analysis of the SO(8) proton-neutron pairing model of atomic nuclei. The two methods allow for generation of inner products for the Schur functions of degree up to 20 and even beyond.

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The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

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O presente estudo investigou se a manutenção, ou não, do comportamento de seguir regras discrepantes das contingências de reforço programadas em situação experimental depende mais da história experimental do ouvinte ou da sua história pré-experimental, inferida das respostas destes a um questionário sobre inflexibilidade. Dezesseis estudantes universitários selecionados previamente com base em suas respostas a um questionário sobre inflexibilidade, foram expostos a um procedimento de escolha segundo o modelo. Em cada tentativa, um estímulo modelo e três de comparação eram apresentados ao participante, que deveria apontar para os três de comparação, em uma determinada seqüência. Os participantes foram atribuídos a duas condições e cada condição continha quatro fases. As condições diferiram somente quanto ao esquema de reforço utilizado. Na Condição 1 o esquema de reforço era contínuo (CRF) e na Condição 2 era de razão fixa (FR4). Nas duas condições a Fase 1 era iniciada com a apresentação de instruções mínimas e uma seqüência de respostas era estabelecida por reforço diferencial; a Fase 2 era iniciada com a apresentação de uma regra discrepante; a Fase 3 era iniciada com a apresentação de uma regra correspondente e a Fase 4 com a reapresentação da regra discrepante. Oito participantes (quatro classificados de flexíveis e quatro classificados de inflexíveis) foram expostos à Condição 1 (CRF) e oito participantes (quatro classificados de flexíveis e quatro classificados de inflexíveis) foram expostos à Condição 2 (FR4). Os resultados mostraram que independente da classificação, os oito participantes da Condição 1 abandonaram o seguimento da regra discrepante das contingências, indicando que o controle exercido pela história experimental construída, impediu a observação dos efeitos de variáveis pré-experimentais sobre o comportamento de seguir regras discrepantes dos participantes. Já os resultados da Condição 2 mostraram que os quatro participantes classificados de flexíveis abandonaram o seguimento da regra discrepante e os quatro participantes classificados de inflexíveis mantiveram o seguimento da regra discrepante das contingências, indicando que sob estas condições o controle por diferentes histórias pré-experimentais, prevaleceu. Comparativamente os resultados das duas condições permitem concluir que a manutenção do comportamento de seguir regras discrepantes não depende somente da história experimental ou da história pré-experimental do ouvinte, mas sim da combinação de um número de condições favoráveis ou desfavoráveis a manutenção do comportamento de seguir regra discrepante.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.