913 resultados para Discrete Time Branching Processes
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Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.
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This paper addresses an uplink power control dynamic game where we assume that each user battery represents the system state that changes with time following a discrete-time version of a differential game. To overcome the complexity of the analysis of a dynamic game approach we focus on the concept of Dynamic Potential Games showing that the game can be solved as an equivalent Multivariate Optimum Control Problem. The solution of this problem is quite interesting because different users split the activity in time, avoiding higher interferences and providing a long term fairness.
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São escassos os estudos que analisam o contínuo temporal dos estados de ânimo ao longo de um período competitivo esportivo. Embora os estados de ânimo pareçam estáveis ao longo do tempo, diferentes estímulos e contextos presentes modificam a intensidade e a valência desses estados. Além disso, há fenômenos psicológicos como decaimento, em que traços de informação perdem sua ativação devido, principalmente, à passagem do tempo e a expectativa, que é a espera pela ocorrência de um evento em um determinado tempo. O objetivo desse estudo foi examinar as alterações dos estados de ânimo em jovens atletas de futebol, separados por posição e função, que ocorreram num período competitivo, em função do decurso temporal. Assim, processos como decaimento dos estados de ânimo e a influência da expectativa pela ocorrência jogo foram analisados, bem como a influência do contexto nas variações dos estados de ânimo dos atletas. Participaram deste estudo 18 jovens atletas (média de 15,4 anos ± 0,266) de um clube de futebol que estava disputando um campeonato estadual. Para o acesso aos estados de ânimo, foi utilizada a versão reduzida da Lista de Estados de Ânimo Presentes (LEAP), juntamente com um formulário de instruções de preenchimento, aplicada minutos antes de alguns treinamentos e jogos. Foram calculados os valores de presença de cada Fator da LEAP em cada evento para cada participante. Os dados foram coletados em três tipos de Eventos: antes do último treino antecedente ao jogo (Treino-Pré), antes do jogo (Pré-jogo) e antes do primeiro treino subsequente ao jogo (Treino-Pós). Os 18 jogadores foram divididos em dois grupos: Ações Defensivas (AD) e Ações Ofensivas (AO). Foram encontrados padrões de alteração dos estados de ânimo, representados pelos Fatores II (Fadiga), VII (Interesse) e XII (Serenidade) da LEAP, em função do decurso temporal, permitindo a análise dos processos de decaimento desses estados de ânimo e a influência da expectativa nessas alterações. Também foi encontrado que alguns estados de ânimo diferiram seus padrões de alteração de acordo com um intervalo temporal (Fatores IV Limerência/Empatia e; VII Interesse), bem como tiveram valores de presença diferentes na comparação entre esses intervalos. Além disso, os Fatores III (Esperança), V (Fisiológico) e XI (Receptividade) apresentaram padrões de alteração em função do decurso temporal em diferentes intervalos temporais. Variáveis contextuais, como o resultado das partidas e a competição esportiva em si, também foram influentes nessas alterações. Fadiga, esperança, empatia, estados ligados à propriocepção, interesse, receptividade e serenidade foram os estados de ânimo presentes durante todo o estudo. Ressalta-se a importância de incluir a temporalidade como variável influente nos modelos de variação de processos neurobiológicos, sobretudo nas investigações acerca de aspectos subjetivos como os estados de ânimo.
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Ukraine is a difficult partner for the West, and recently, it has been perceived as an enfant terrible because of the Kolchuga affair. Western and Polish press describe Ukraine as an authoritarian regime plagued by an internal crisis or even threatened by destabilisation. This picture is certainly one-sided and oversimplified, and it fails to accurately present the reality of Ukraine. Apart from experiencing definitely adverse political developments, Ukraine has also been successful in some ways in terms of its internal and foreign policies and the economy. At the same time, certain processes take place among the Ukrainian elites, that might be difficult to capture in social surveys, but may become the starting point for the future modernisation and democratisation of the country. To realise that there are some favourable aspects to the changes occurring in Ukraine is not to recognise Ukraine as a country heading to democratisation and a market economy in the Western sense. Still, it is worthwhile to understand that the positive developments, even if isolated, do occur in Ukraine and that they could be strengthened by adequate policies of the Ukrainian authorities and the Western states.
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The authors study the timing of leniency applications using a novel application of multi-spell discrete-time survival analysis for a sample of cartels prosecuted by the European Commission between 1996 and 2014. The start of a Commission investigation does not affect the rate by which conspirators apply for leniency in the market investigated, but increases the rate of application in separate markets in which a conspirator in the investigated market also engaged in collusion. The revision of the Commission’s leniency programme in 2002 increased the rate of pre-investigation applications. Our results shed light on enforcement efforts against cartels and other forms of
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In this study, the Mean Transit Time and Mixing Model Analysis methods are combined to unravel the runoff generation process of the San Francisco River basin (73.5 km**2) situated on the Amazonian side of the Cordillera Real in the southernmost Andes of Ecuador. The montane basin is covered with cloud forest, sub-páramo, pasture and ferns. Nested sampling was applied for the collection of streamwater samples and discharge measurements in the main tributaries and outlet of the basin, and for the collection of soil and rock water samples. Weekly to biweekly water grab samples were taken at all stations in the period April 2007-November 2008. Hydrometric data, Mean Transit Time and Mixing Model Analysis allowed preliminary evaluation of the processes controlling the runoff in the San Francisco River basin. Results suggest that flow during dry conditions mainly consists of lateral flow through the C-horizon and cracks in the top weathered bedrock layer, and that all subcatchments have an important contribution of this deep water to runoff, no matter whether pristine or deforested. During normal to low precipitation intensities, when antecedent soil moisture conditions favour water infiltration, vertical flow paths to deeper soil horizons with subsequent lateral subsurface flow contribute most to streamflow. Under wet conditions in forested catchments, streamflow is controlled by near surface lateral flow through the organic horizon. Exceptionally, saturation excess overland flow occurs. By absence of the litter layer in pasture, streamflow under wet conditions originates from the A horizon, and overland flow.
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Thesis (Master's)--University of Washington, 2016-06
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Thesis (Ph.D.)--University of Washington, 2016-06
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Thesis (Master's)--University of Washington, 2016-06
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In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.
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The Australian lungfish, Neoceratodus forsteri, has a dentition consisting of enamel, mantle dentine and bone, enclosing circumdenteonal, core and interdenteonal dentines. Branching processes from cells that produce interdenteonal dentine leave the cell surface at different angles, with collagen fibrils aligned parallel to the long axis of each process. In the interdenteonal dentine, crystals of calcium hydroxyapatite, form within fibrils of collagen, and grow within a matrix of non-collagenous protein. Crystals are aligned parallel to the cell process, as are the original collagen fibrils. Because the processes are angled to the cell surface, the crystals within the core or interdenteonal dentine are arranged in bundles set at angles to each other. Apatite crystals in circumdenteonal dentine are finer and denser than those of the interdenteonal dentine, and form outside the fibrils of collagen. In mature circumdenteonal dentine the crystals of circumdenteonal dentine form a dense tangled mass, linked to interdenteonal dentine by isolated crystals. The functional lungfish tooth plate contains prisms of large apatite crystals in the interdenteonal dentine and masses of fine tangled crystals around each denteon. This confers mechanical strength on a structure with little enamel that is subjected to heavy wear. (c) 2006 Elsevier Ltd. All rights reserved.
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Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.
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A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The algorithm resembles back-propagation in that an error function is minimized using a gradient-based method, but the optimization is carried out in the hidden part of state space either instead of, or in addition to weight space. Computational results are presented for some simple dynamical training problems, one of which requires response to a signal 100 time steps in the past.
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A simple method for training the dynamical behavior of a neural network is derived. It is applicable to any training problem in discrete-time networks with arbitrary feedback. The method resembles back-propagation in that it is a least-squares, gradient-based optimization method, but the optimization is carried out in the hidden part of state space instead of weight space. A straightforward adaptation of this method to feedforward networks offers an alternative to training by conventional back-propagation. Computational results are presented for simple dynamical training problems, with varied success. The failures appear to arise when the method converges to a chaotic attractor. A patch-up for this problem is proposed. The patch-up involves a technique for implementing inequality constraints which may be of interest in its own right.