The large forward-backward asymmetry of top quark pair production measured by hadron colliders sheds light on new physics signals beyond the Standard Model. In the warped extra dimension model with an additional SU(3)...
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The large forward-backward asymmetry of top quark pair production measured by hadron colliders sheds light on new physics signals beyond the Standard Model. In the warped extra dimension model with an additional SU(3) group in the strong sector, we compare the cross section and forward-backward asymmetry of top quark pair production with recent data obtained by CDF and D0. Our numerical analysis shows that the parameter cq≥0.5, ct∼−0.6, tan ϕ≥20, and the first excitation of an axial gluon with a mass of about 5–6 TeV can accommodate this large anomaly without violating other experimental constraints. We show that a large ratio of strong couplings gD/gS sets a strong limit on the parameter space of this model.
We examine a supersymmetric explanation for the anomalously high forward-backward asymmetry in top pair production measured by CDF and D0. We suppose that it is due to the t-channel exchange of a right-handed sbottom ...
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We examine a supersymmetric explanation for the anomalously high forward-backward asymmetry in top pair production measured by CDF and D0. We suppose that it is due to the t-channel exchange of a right-handed sbottom which couples to dR and tR, as is present in the R-parity violating minimal supersymmetric standard model. We show that all Tevatron and LHC experiments’ tt¯ constraints may be respected for a sbottom mass between 300 and 1200 GeV, and a large Yukawa coupling >2.2, yielding AFB up to 0.18. The non-Standard Model contribution to the LHC charge asymmetry parameter is ΔACy=0.017–0.045, small enough to be consistent with current measurements but nonzero and positive, allowing for LHC confirmation in the future within 20 fb−1. A small additional contribution to the LHC tt¯ production cross section is also predicted, allowing a further test. We estimate that 10 fb−1 of LHC luminosity would be sufficient to rule out the proposal to 95% confidence level, if the measurements of the tt¯ cross section turn out to be centered on the Standard Model prediction.
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.
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