In heavy-ion (A-A) collisions, the correlations among the particles produced across a wide range in rapidity probe the early stages of the reaction. The analyses of forward-backward multiplicity correlations in these ...
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In heavy-ion (A-A) collisions, the correlations among the particles produced across a wide range in rapidity probe the early stages of the reaction. The analyses of forward-backward multiplicity correlations in these collisions are complicated by several effects, which are absent or minimized in hadron-hadron collisions. This includes effects, such as the centrality selection in the A-A collisions, that interfere with the measurement of the dynamical correlations. A method that takes into account the fluctuations in centrality selection has been utilized to determine the forward-backward correlation strength bcorr in A-A collisions. This method has been validated by using the hijing event generator in the case of Au-Au collisions at sNN=200 GeV and Pb-Pb collisions at sNN=2.76 TeV. It is shown that the effect of impact parameter fluctuations is to be considered properly in order to obtain meaningful results.
Our paper presents a new approach for the recognition of highlights in soccer video. Our contribution consists of the combination of Bayesian theorem inferences and Hidden Markov Models (HMMs). We build HMMs to calcul...
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ISBN:
(纸本)9781424437566
Our paper presents a new approach for the recognition of highlights in soccer video. Our contribution consists of the combination of Bayesian theorem inferences and Hidden Markov Models (HMMs). We build HMMs to calculate probabilities that a test video segment belongs to highlight and non highlight classes. Then, we apply the Bayesian theorem on the two previous probabilities. Our system has achieved an accuracy of 95.6% which is a good result of highlights detection in comparison with other methods.
A Bayesian approach is suggested for inferring stationary autoregressive models allowing for possible structural changes (known as breaks) in both the mean and the error variance of economic series occurring at unknow...
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A Bayesian approach is suggested for inferring stationary autoregressive models allowing for possible structural changes (known as breaks) in both the mean and the error variance of economic series occurring at unknown times. Efficient Bayesian inference for the unknown number and positions of the structural breaks is performed by using filtering recursions similar to those of the forward-backward algorithm. A Bayesian approach to unit root testing is also proposed, based on the comparison of stationary autoregressive models with multiple breaks to their counterpart unit root models. In the Bayesian setting, the unknown initial conditions are treated as random variables, which is particularly appropriate in unit root testing. Simulation experiments are conducted with the aim to assess the performance of the suggested inferential procedure, as well as to investigate if the Bayesian model comparison approach can distinguish unit root models from stationary autoregressive models with multiple structural breaks in the parameters. The proposed method is applied to key economic series with the aim to investigate whether they are subject to shifts in the mean and/or the error variance. The latter has recently received an economic policy interest as improved monetary policies have also as a target to reduce the volatility of economic series. (c) 2017 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets. One of the key limitations of distributed SGD is the need to regularly c...
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ISBN:
(纸本)9781479981311
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets. One of the key limitations of distributed SGD is the need to regularly communicate the gradients between different computation nodes. To reduce this communication bottleneck, recent work has considered a one-bit variant of SGD, where only the sign of each gradient element is used in optimization. In this paper, we extend this idea by proposing a stochastic variant of the proximal-gradient method that also uses one-bit per update element. We prove the theoretical convergence of the method for non-convex optimization under a set of explicit assumptions. Our results indicate that the compressed method can match the convergence rate of the uncompressed one, making the proposed method potentially appealing for distributed processing of large datasets.
To improve the accuracy of automatic speech recognition, a two-pass decoding strategy is widely adopted. The first-pass model generates compact word lattices, which are utilized by the second-pass model to perform res...
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ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066315
To improve the accuracy of automatic speech recognition, a two-pass decoding strategy is widely adopted. The first-pass model generates compact word lattices, which are utilized by the second-pass model to perform rescoring. Currently, the most popular rescoring methods are N-best rescoring and lattice rescoring with long short-term memory language models (LSTMLMs). However, these methods encounter the problem of limited search space or inconsistency between training and evaluation. In this paper, we address these problems with an end-to-end model for accurately extracting the best hypothesis from the word lattice. Our model is composed of a bidirectional LatticeLSTM encoder followed by an attentional LSTM decoder. The model takes word lattice as input and generates the single best hypothesis from the given lattice space. When combined with an LSTMLM, the proposed model yields 9.7% and 7.5% relative WER reduction compared to N -best rescoring methods and lattice rescoring methods within the same amount of decoding time.
Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting struc...
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Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward-backward algorithm. Existing methods of testing for Structural breaks are also Used for comparison. We investigate the presence of structural breaks in several hedge fund indices: our results are consistent with market events and episodes that Caused Substantial volatility in hedge fund returns during the last decade. (C) 2008 Elsevier B.V. All rights reserved.
PSI-BLAST remains one of the popular tools for searching remote homologs in sequence databases. We recently demonstrated that hybrid alignment can function as the alignment core for PSI-BLAST without loss of sensitivi...
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ISBN:
(纸本)0769524761
PSI-BLAST remains one of the popular tools for searching remote homologs in sequence databases. We recently demonstrated that hybrid alignment can function as the alignment core for PSI-BLAST without loss of sensitivity. Here, we start to exploit the benefits of hybrid alignment. We show that incorporating information about the suboptimal alignments, otherwise ignored in PSI-BLAST already improves the sensitivity of our enhanced version of PSI-BLAST More interestingly, we find a set of sequences on which our tool disagrees with the classification given by SCOP Careful examination points to a possible misclassification in SCOP Cross-referencing with two other methods of protein structure classification, CATH and DALI, supports this view, indicating that the enriched information from suboptimal alignments is valuable for detecting more weakly homologous sequences.
This paper presents a novel load-side maximum power point tracker using a multiple step difference algorithm. This technique maximizes the power into any given load using a current-mode, load-side controller under var...
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ISBN:
(纸本)9781467378949
This paper presents a novel load-side maximum power point tracker using a multiple step difference algorithm. This technique maximizes the power into any given load using a current-mode, load-side controller under various insolation levels. MATLAB/Simulink was used for simulation studies using a normalized, heuristic, photovoltaic model while an off-the-shelf, four-switch buck-boost converter was employed along with a controllable, indoor, built-in-house, solar simulator for experimental validations. The proposed method guarantees maximum power tracking under various weather conditions and operates at unity power factor on a self-synchronized basis.
Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long...
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ISBN:
(数字)9781728155494
ISBN:
(纸本)9781728155494
Diffusion Magnetic Resonance Imaging is a state-of-the-art technique that can provide accurate identification of complex neuronal fiber configurations in the human brain. Typical acquisition times are however too long for the clinical application. We propose a method to recover the fiber orientation distribution (FOD) at high spatio-angular resolution via practical kq-space under-sampling patterns that enable both acceleration and super-resolution. The inverse problem for FOD reconstruction is regularized by a structured sparsity prior promoting simultaneously voxelwise sparsity and spatial smoothness of fiber orientations. A convex minimization problem is formulated and solved via a forward-backward algorithm. Real data analysis suggest that high spatio-angular resolution FOD mapping can be achieved from severe kq-space acceleration.
Racetrack memory (RM) has attracted much attention. In RM, insertion and deletion (ID) errors occur as a result of an unstable reading process and are called position errors. In this paper, we first define a probabili...
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Racetrack memory (RM) has attracted much attention. In RM, insertion and deletion (ID) errors occur as a result of an unstable reading process and are called position errors. In this paper, we first define a probabilistic channel model of ID errors in RM with multiple read-heads (RHs). Then, we propose a joint iterative decoding algorithm for spatially coupled low-density parity-check (SC-LDPC) codes over such a channel. We investigate the asymptotic behaviors of SC-LDPC codes under the proposed decoding algorithm using density evolution (DE). With DE, we reveal the relationship between the number of RHs and achievable information rates, along with the iterative decoding thresholds. The results show that increasing the number of RHs provides higher decoding performances, although the proposed decoding algorithm requires each codeword bit to be read only once regardless of the number of RHs. Moreover, we show the performance improvement produced by adjusting the order of the SC-LDPC codeword bits in RM.
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