Feature subset selection, as a special case of the general subset selection problem, attracted a lot of research attention due to the growing importance of data-mining applications. However, since finding the optimal ...
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ISBN:
(纸本)9781479903573
Feature subset selection, as a special case of the general subset selection problem, attracted a lot of research attention due to the growing importance of data-mining applications. However, since finding the optimal subset is an NP-hard problem, very different heuristic search methods have been suggested to approximate it. Here we propose a new second-order cone programming based search strategy to efficiently solve the feature subset selection for large-scale problems. Experimentally, it is shown that its performance is almost always better than the greedy search methods especially when the features are strongly dependent.
Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially co...
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The thesis also puts forward a fast handoff method based on RSSI and LQI, which makes improvements on preset parameters, subnet selection, decision standards and reparation of switching failure. The new method which c...
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Cooperative software engineering typically involves many actors and resources that cooperate in a complex distributed and heterogeneous world. In the DIPS (Distributed Integrated Process Services) project, a 3D model ...
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Cooperative software engineering typically involves many actors and resources that cooperate in a complex distributed and heterogeneous world. In the DIPS (Distributed Integrated Process Services) project, a 3D model is used for the definition, enactment and tracing of software development processes, which expresses both the structure and evolution of such processes. This paper discusses how an optimal architecture was evaluated to implement the process model in a process support framework. Process-specific and general requirements are identified, and expected usage patterns of a DIPS-based environment are analyzed. A set of potential architecture variants is proposed, and implications of the requirements and usage patterns on the variants are discussed qualitatively. An evaluation of the architecture alternatives leads to the design of a hybrid DIPS architecture based on distributed heterogeneous objects. The prototype DIPS implementation is briefly outlined.
Camera calibration using patterns is widely used in computer vision and industry. The accuracy of calibration depends on the accuracy of the pattern. A high accuracy pattern is usually difficult to manufacture in labs...
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Modern simulation applications that carry out large scale iterative processes, such as Monte-Carlo simulations, or manipulate large data structures, tend to be extremely time-consuming due to the shortage of computati...
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ISBN:
(纸本)9781934142301
Modern simulation applications that carry out large scale iterative processes, such as Monte-Carlo simulations, or manipulate large data structures, tend to be extremely time-consuming due to the shortage of computational resources or the inherent nature of the simulation itself. Typical simulations can take up to several days to complete on conventional systems, while high-end supercomputers can be cost-prohibitive. Therefore, the need for effective parallelization of software execution and resource management is more imperative. The goal of this paper is to present a fully-distributed platform that enables software simulations to be executed within user-acceptable time periods, by predicting the resource requirements of each simulation. In this context, the platform analyzes files that contain historical data about past executions of the particular simulation. Processor and memory utilization, overall execution time, level of parallelization and distributed execution are some of the information collected and used by an efficient training algorithm, in order to determine the optimal amount of resources to be allocated in a particular simulation. The training algorithm applies several machine-learning techniques such as multi-linear regression in order to efficiently predict the resource vector that will satisfy the user requirements.
Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method ...
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Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.
VANETs are gaining significant prominence from both academia and industry in recent years. In this paper, we introduce an Efficient RSU-aided Group-based Hierarchical Privacy Enhancement Protocol for VANETs. The proto...
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VANET has some prominent features such as channel-opening, large-scale, fast, dynamic-changing, so there is some misbehavior vehicles. Due to the large number of vehicles, even if there is only 1% percent of misbehavi...
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