A graph-based Markov decision process framework applied to the optimization of strategies for integrated management of diseases
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We consider a networked control system, where each subsystem evolves as a Markov decision process with some extra inputs from other systems. Each subsystem is coupled to its neighbors via communication links over which the signals are delayed, but are otherwise transmitted noise-free. A centralized controller receives delayed state information from each subsystem. The control action applied to each subsystem takes effect after a certain delay rather than immediately. We give an explicit bound on the finite history of measurement and control that is required for the optimal control of such networked Markov decision processes. We also show that these bounds depend only on the underlying graph structure as well as the associated delays. Thus, the partially observed Markov decision process associated with a networked Markov decision process can be converted into an information state Markov decision process, whose state does not grow with time.
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The algorithm GDBSCAN only needs two parameters.It can discover clusters of any shape.But it is very sensitive to the parameter Eps.This paper puts forward a kind of method to make sure the Eps,which is based on datagrid.The method can achieve better result on the case of data distributing unevenly and shapes of clusters reaching each other.
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We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where humans still significantly outperform machine learning algorithms due to their unique capabilities. We have implemented a system which achieves very promising performance in terms of recognition accuracy. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore we have decided to accelerate selected parts of the system using OpenCL. In particular, we seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and 256 synapses, respectively. We have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.
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This paper analyzed the DataGrid Web Server Control. DataGrid is one of the most popular control of ASP .NET which is used to render data to a Web page in tabular form. This paper provides two types of typical usage of DataGrid.
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First, two methods to realize the bidirectional sorts including the ascendant sort order and the descendant sort order in the DataGrid through storing the sort expression and the sort direction by means of attributes and viewstate are summarized in order that the users browse and look up more conveniently; Second, the method to choose a row by click any cell in the DataGrid is given in order that the users could operate more conveniently; Third, the methods to update or delete the selected record row in the DataGrid from Web forms controls are given to ensure data validation.
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Desirable properties of the infinite histories of a finite-state Markov decision process are specified in terms of a finite number of events represented as /spl omega/-regular sets. An infinite history of the process produces a reward which depends on the properties it satisfies. The authors investigate the existence of optimal policies and provide algorithms for the construction of such policies.
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The nationally-recognized Susquehanna
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