Keyword spotting (KWS) refers to detection of a limited number of given keywords in speech utterances. In this paper, we evaluate a robust keyword spotting system based on hidden markov models for speaker independent ...
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Diffusion tensor imaging (DTI) has been widely used for nondestructive characterization of microstructures of myocardium or brain connectivity. It requires repeated acquisition with different diffusion gradients. The ...
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Diffusion tensor imaging (DTI) has been widely used for nondestructive characterization of microstructures of myocardium or brain connectivity. It requires repeated acquisition with different diffusion gradients. The long acquisition time greatly limits the clinical application of DTI. In this paper, a novel method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images in direct estimation of the diffusion tensor from highly undersampled k-space data. Experimental results demonstrate that the proposed method is able to estimate the diffusion tensors more accurately than the existing method when a high net reduction factor is used.
Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary...
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The High-Level-Trigger (HLT) cluster of the ALICE experiment is a computer cluster with about 200 nodes and 20 infrastructure machines. In its current state, the cluster consists of nearly 10 different configurations ...
The High-Level-Trigger (HLT) cluster of the ALICE experiment is a computer cluster with about 200 nodes and 20 infrastructure machines. In its current state, the cluster consists of nearly 10 different configurations of nodes in terms of installed hardware, software and network structure. In such a heterogeneous environment with a distributed application, information about the actual configuration of the nodes is needed to automatically distribute and adjust the application accordingly. An inventory database provides a unified interface to such information. To be useful, the data in the inventory has to be up to date, complete and consistent. Manual maintenance of such databases is error-prone and data tends to become outdated. The inventory module of the ALICE HLT cluster overcomes these drawbacks by automatically updating the actual state periodically and, in contrast to existing solutions, it allows the definition of a target state for each node. A target state can simply be a fully operational state, i.e. a state without malfunctions, or a dedicated configuration of the node. The target state is then compared to the actual state to detect deviations and malfunctions which could induce severe problems when running the application. The inventory module of the ALICE HLT cluster has been integrated into the monitoring and management framework SysMES in order to use existing functionality like transactionality and monitoring infrastructure. Additionally, SysMES allows to solve detected problems automatically via its rule-system. To describe the heterogeneous environment with all its specifics, like custom hardware, the inventory module uses an object-oriented model which is based on the Common Information Model. The inventory module provides an automatically updated actual state of the cluster, detects discrepancies between the actual and the target state and is able to solve detected problems automatically. This contribution presents the current implementation state
A fast and efficient algorithm is presented to label the connected components for binary image, especially for very huge images or any image larger than the available memory. The cascading style scheme compresses the ...
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This paper presents the prior PAC-Bayes bound and explores its capabilities as a tool to provide tight predictions of SVMs' generalization. The computation of the bound involves estimating a prior of the distribut...
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This paper presents the prior PAC-Bayes bound and explores its capabilities as a tool to provide tight predictions of SVMs' generalization. The computation of the bound involves estimating a prior of the distribution of classifiers from the available data, and then manipulating this prior in the usual PAC-Bayes generalization bound. We explore two alternatives: to learn the prior from a separate data set, or to consider an expectation prior that does not need this separate data set. The prior PAC-Bayes bound motivates two SVM-like classification algorithms, prior SVM and ν-prior SVM, whose regularization term pushes towards the minimization of the prior PAC-Bayes bound. The experimental work illustrates that the new bounds can be significantly tighter than the original PAC-Bayes bound when applied to SVMs, and among them the combination of the prior PAC-Bayes bound and the prior SVM algorithm gives the tightest bound.
In this paper, we propose a higher order safe ambients calculus. In this setting, we propose an extended labelled transition system and an extended labelled bisimulation for this calculus. We also give the reduction b...
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Criterion, standard and policy of Korean language have played a very important role as Autonomy regulations in many aspects, especially input standards of computer using Korean language. So it is very necessary to inv...
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The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape,...
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A network code assisted HARQ system considering uplink (UL) transmission with multiple mobile stations (MSs) cooperating with a relay-station (RS) is proposed. The RS is assumed to successfully monitor and decode the ...
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