arXiv subjects

Yue Hu

Publications and source records attributed to Yue Hu.

317 records · Page 18Linked to original sources

arXiv2016

$D \rightarrow a_1, f_1$ transition form factors and semileptonic decays via 3-point QCD sum rules

By using the 3-point QCD sum rules, we calculate the transition form factors of $D$ decays into the spin triplet axial vector mesons $a_1(1260)$, $f_1(1285) $, $f_1(1420)$. In the calculations, we consider the quark contents of each meson in detail. In view of the fact that the isospin of $a_1(1260)$ is one, we calculate the $D^+ \rightarrow a_1^0 (1260)$ and $D^0 \rightarrow a_1^- (1260)$ transition form factors separately. In the case of $ f_1(1285), f_1(1420)$, the mixing between light flavor $SU(3)$ singlet and octet is taken into account. Based on the form factors obtained here, we give predictions for the branching ratios of relevant semileptonic decays, which can be tested in the future experiments.

hep-ph↗

arXiv2015

Mining and Analyzing the Future Works in Scientific Articles

Future works in scientific articles are valuable for researchers and they can guide researchers to new research directions or ideas. In this paper, we mine the future works in scientific articles in order to 1) provide an insight for future work analysis and 2) facilitate researchers to search and browse future works in a research area. First, we study the problem of future work extraction and propose a regular expression based method to address the problem. Second, we define four different categories for the future works by observing the data and investigate the multi-class future work classification problem. Third, we apply the extraction method and the classification model to a paper dataset in the computer science field and conduct a further analysis of the future works. Finally, we design a prototype system to search and demonstrate the future works mined from the scientific papers. Our evaluation results show that our extraction method can get high precision and recall values and our classification model can also get good results and it outperforms several baseline models. Further analysis of the future work sentences also indicates interesting results.

cs.DL↗

arXiv2015

Local-Aggregate Modeling for Big-Data via Distributed Optimization: Applications to Neuroimaging

Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-subject neuroimaging data based on each subject's brain imaging scans. This is an ultra-high-dimensional problem that consists of a matrix of covariates (brain locations by time points) for each subject; few methods currently exist to fit supervised models directly to this tensor data. We propose a novel modeling and algorithmic strategy to apply generalized linear models (GLMs) to this massive tensor data in which one set of variables is associated with locations. Our method begins by fitting GLMs to each location separately, and then builds an ensemble by blending information across locations through regularization with what we term an aggregating penalty. Our so called, Local-Aggregate Model, can be fit in a completely distributed manner over the locations using an Alternating Direction Method of Multipliers (ADMM) strategy, and thus greatly reduces the computational burden. Furthermore, we propose to select the appropriate model through a novel sequence of faster algorithmic solutions that is similar to regularization paths. We will demonstrate both the computational and predictive modeling advantages of our methods via simulations and an EEG classification problem.

stat.ME↗

arXiv2015

Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo

Recent decades have witnessed online social media being a big-data window for quantificationally testifying conventional social theories and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden "ambivalent users" are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights or at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and thus serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift of words adopted in ambivalent tweets is more evident than usual and exhibits a clear "negative to positive" pattern. The above observations, though being promiscuous seemingly, actually point to the self regulation of negative mood in Weibo, which could find its base from the emotion management theories in sociology but makes an interesting extension to the online environment. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.

cs.SI↗

arXiv2015

ADMM Algorithmic Regularization Paths for Sparse Statistical Machine Learning

Optimization approaches based on operator splitting are becoming popular for solving sparsity regularized statistical machine learning models. While many have proposed fast algorithms to solve these problems for a single regularization parameter, conspicuously less attention has been given to computing regularization paths, or solving the optimization problems over the full range of regularization parameters to obtain a sequence of sparse models. In this chapter, we aim to quickly approximate the sequence of sparse models associated with regularization paths for the purposes of statistical model selection by using the building blocks from a classical operator splitting method, the Alternating Direction Method of Multipliers (ADMM). We begin by proposing an ADMM algorithm that uses warm-starts to quickly compute the regularization path. Then, by employing approximations along this warm-starting ADMM algorithm, we propose a novel concept that we term the ADMM Algorithmic Regularization Path. Our method can quickly outline the sequence of sparse models associated with the regularization path in computational time that is often less than that of using the ADMM algorithm to solve the problem at a single regularization parameter. We demonstrate the applicability and substantial computational savings of our approach through three popular examples, sparse linear regression, reduced-rank multi-task learning, and convex clustering.

stat.ME↗

arXiv2014

On an infinite series for $(1+1/x)^x$

The aim of this paper is to construct a new expansion of $(1+1/x)^x$ related to Carleman's inequality. Our results extend some results of Yang [Approximations for constant e and their applications J. Math. Anal. Appl. 262 (2001) 651-659].

math.CA↗

arXiv2014

Analysis of Personalized Information Service System for Digital Libraries

Along with the rapid development of digital library, digital library of the third generation, taking the main characteristics of the personalized service, has already become the mainstream today. This article first analyzes the concept and characteristics of digital library, then elaborates the inevitability of personalized service as well as the necessity of its development and based on the Propose of establishing interested knowledge-base, enumerates several ways of personalized service, at last according to individualized service, this paper proposes the model of "learningoriented individual digital library" and prospects the trends of digital library.

cs.DL↗

arXiv2014

On the coefficients of an expansion of $(1+1/x)^x$ related to Carleman's inequality

In this note, we present new properties for a sequence arising in some refinements of Carleman's inequality. Our results extend some results of Yang [Approximations for constant e and their applications J. Math. Anal. Appl. 262 (2001) 651-659] and Alzer and Berg [some classes of completely monotonic functions Ann. Acad. Sci. Fennicae 27(2002) 445-460].

math.CA↗

arXiv1996

An Effective Potential for Composite Operators

We study the effective potential for composite operators. Introducing a source coupled to the composite operator, we define the effective potential by a Legendre transformation. We find that in three or fewer dimensions, one can use the conventionally defined renormalized operator to couple to the source. However, in four dimensions, the effective potential for the conventional renormalized composite operator is divergent. We overcome this difficulty by adding additional counterterms to the operator and adjusting these order by order in perturbation theory. These counterterms are found to be non-polynomial. We find that, because of the extra counterterms, the composite effective potential is gauge dependent. We display this gauge-dependence explicitly at two-loop order.

hep-ph↗

arXiv1994

Bubble Nucleation Rate In Fourth-Order Gravity

We calculate the bubble nucleation rate in a fourth-order gravity theory whose action contains an $R^2$ term, under the thin-wall approximation, by two different methods. First the bounce solution is found for the Euclidean version of the original action. Next, we use a conformal transformation to transform the theory into general relativity with an additional scalar field and then proceed to find the bounce solution. The same results are obtained from both calculations.

hep-ph↗

arXiv1994

Dynamical Solutions to the Horizon and Flatness Problems

We discuss in some detail the requirements on an early-Universe model that solves the horizon and flatness problems during the epoch of classical cosmology ($t\ge t_i\gg 10^{-43}\sec$). We show that a dynamical resolution of the horizon problem requires superluminal expansion (or very close to it) and that a truly satisfactory resolution of the flatness problem requires entropy production. This implies that a proposed class of adiabatic models in which the Planck mass varies by many orders of magnitude cannot fully resolve the flatness problem. Furthermore, we show that, subject to minimal assumptions, such models cannot solve the horizon problem either. Because superluminal expansion and entropy production are the two generic features of inflationary models, our results suggest that inflation, or something very similar, may be the only dynamical solution to the horizon and flatness problems.

astro-ph↗