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Haibo Chen

Publications and source records attributed to Haibo Chen.

139 records · Page 8Linked to original sources

A class of non-weight modules over the Virasoro algebra

For any triple $(μ,λ,α)$ of complex numbers and an $\mathfrak a$-module ${V}$, a class of non-weight modules $\mathcal{M}\big(V,μ,Ω(λ,α)\big)$ over the Virasoro algebra $\mathcal L$ is constructed in this paper. We prove if $V$ is a nontrivial simple $\mathfrak a$-module satisfying: for any $v\in V$ there exists $r\in\Z_+$ such that $L_{r+i}v=0$ for all $i\geq1$, then $\mathcal{M}\big(V,μ,Ω(λ,α)\big)$ is simple if and only if $μ\neq1, λ\neq0,α\neq0,$. We also give the necessary and sufficient conditions for two such simple $\mathcal L$-modules being isomorphic. Finally, we prove that these simple $\mathcal L$-modules $\mathcal{M}\big(V,μ,Ω(λ,α)\big)$ are new by showing they are not isomorphic to any other known simple non-weight module provided that $V$ is not a highest weight $\mathfrak a$-module with highest weight nonzero.

math.RT↗

Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory

Fast, byte-addressable non-volatile memory (NVM) embraces both near-DRAM latency and disk-like persistence, which has generated considerable interests to revolutionize system software stack and programming models. However, it is less understood how NVM can be combined with managed runtime like Java virtual machine (JVM) to ease persistence management. This paper proposes Espresso, a holistic extension to Java and its runtime, to enable Java programmers to exploit NVM for persistence management with high performance. Espresso first provides a general persistent heap design called Persistent Java Heap (PJH) to manage persistent data as normal Java objects. The heap is then strengthened with a recoverable mechanism to provide crash consistency for heap metadata. It then provides a new abstraction called Persistent Java Object (PJO) to provide an easy-to-use but safe persistent programming model for programmers to persist application data. The evaluation confirms that Espresso significantly outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being compatible to existing data structures in Java programs.

cs.PL↗

Two classes of non-weight modules over the twisted Heisenberg-Virasoro algebra

In the present paper, we construct two classes of non-weight modules $Ω(λ,α,β)\otimes\mathrm{Ind}(M)$ and $\mathcal{M}\big(V,Ω(λ,α,β)\big)$ over the twisted Heisenberg-Virasoro algebra, which are both associated with the modules $Ω(λ,α,β)$. We present the necessary and sufficient conditions under which modules in these two classes are irreducible and isomorphic, and also show that the irreducible modules in these two classes are new. Finally, we construct non-weight modules $\mathrm{Ind}_{\underline y,λ}(\C_{RS})$ and $\mathrm{Ind}_{\underline z,λ}(\C_{PQ})$ over the twisted Heisenberg-Virasoro algebra and then apply the established results to give irreducible conditions for $\mathrm{Ind}_{\underline y,λ}(\C_{RS})$ and $\mathrm{Ind}_{\underline z,λ}(\C_{PQ})$.

math.RT↗

Nonstationary POT modelling of air pollution concentrations: Statistical analysis of the traffic and meteorological impact

Predicting the occurrence, level and duration of high air pollution concentrations exceeding a given critical level enables researchers to study the health impact of road traffic on local air quality and to inform public policy action. Precise estimates of the probabilities of occurrence and level of extreme concentrations are formidable due to the combination of complex physical and chemical processes involved. This underpins the need for developing sophisticated extreme value models, in particular allowing for non-stationarity of environmental time series. In this paper, extremes of nitrogen oxide (NO), nitrogen dioxide (NO$_2$) and ozone (O$_3$) concentrations are investigated using two models. Model I is based on an extended peaks-over-threshold (POT) approach developed by A. C. Davison and R. L. Smith, whereby the parameters of the underlying generalized Pareto distribution (GPD) are treated as functions of covariates (i.e., traffic and meteorological factors). The new Model II resolves the lack of threshold stability in the Davison--Smith model by constructing a special functional form for the GPD parameters. For each of the models, the effects of trafic and meteorological factors on the frequency and size of extreme values are estimated using Markov chain Monte Carlo methods. Finally, appropriate goodness-of-fit tests and model selection criteria confirm that Model II significantly outperforms Model I in estimation and forecasting of extremes.

stat.AP↗

A family of new simple modules over the Schrödinger-Virasoro algebra

In this article, a large class of simple modules over the Schrödinger-Virasoro algebra $\mathcal{G}$ are constructed, which include highest weight modules and Whittaker modules. These modules are determined by the simple modules over the finite-dimensional quotient algebras of some subalgebras. Moreover, we show that all simple modules of $\mathcal{G}$ with locally finite actions of elements in a certain positive part belong to this class of simple modules. Similarly, a large class of simple modules over the $W$-algebra $W(2,2)$ are constructed.

math.RT↗

Deep Learning At Scale and At Ease

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort especially when the model is large and complex. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model based on the common layer abstraction of deep learning models. Good scalability is achieved through flexible distributed training architecture and specific optimization techniques. SINGA runs on GPUs as well as on CPUs, and we show that it outperforms many other state-of-the-art deep learning systems. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.

cs.LG↗

Loop Schrödinger-Virasoro Lie conformal algebra

In this paper, we introduce two kinds of Lie conformal algebras associated with the loop Schrödinger-Virasoro Lie algebra and the extended loop Schrödinger-Virasoro Lie algebra, respectively. The conformal derivations, the second cohomology groups of these two conformal algebras are completely determined. And nontrivial free conformal modules of rank one and Z-graded free intermediate series modules over these two conformal algebras are also classified in the present paper

math.QA↗

The Scale-free Network of Passwords : Visualization and Estimation of Empirical Passwords

In this paper, we present a novel vision of large scale of empirical password sets available and improve the understanding of passwords by revealing their interconnections and considering the security on a level of the whole password set instead of one single password level. Through the visualization of Yahoo, Phpbb, 12306, etc. we, for the first time, show what the spatial structure of empirical password sets are like and take the community and clustering patterns of the passwords into account to shed lights on the definition of popularity of a password based on their frequency and degree separately. Furthermore, we propose a model of statistical guessing attack from the perspective of the data's topological space, which provide an explanation of the "cracking curve". We also give a lower bound of the minimum size of the dictionary needed to compromise arbitrary ratio of any given password set by proving that it is equivalent to the minimum dominating set problem, which is a NP-complete problem. Hence the minimal dictionary problem is also NP-complete.

cs.CR↗

Lie Bialgebra Structures on Generalized Heisenberg-Virasoro Algebra

In this paper, Lie bialgebra structures on generalized Heisenberg-Virasoro algebra $\mathfrak{L}$ are considered. Also, $H^1({\mathfrak{L}} ,\mathfrak{L}\bigotimes\mathfrak{L})$ is given explicitly. Moreover, it is proved that all Lie bialgebra structure on centerless generalized Heisenberg-Virasoro algebra $\bar{\mathfrak{L}}$ are coboundary triangular.

math.QA↗

Quantization on Generalized Heisenberg-Virasoro Algebra

In a recent paper by the authors, Lie bialgebra structures on generalized Heisenberg- Virasoro algebra L are considered. In this paper, the explicit formula of the quantization on generalized Heisenberg-Virasoro algebra is presented.

math.QA↗

Repulsion and attraction in high Tc superconductors

The influence of repulsion and attraction in high-Tc superconductors to the gap functions is studied. A systematic method is proposed to compute the gap functions using the irreducible representations of the point group. It is found that a pure s-wave superconductivity exists only at very low temperatures, and attractive potentials on the near shells significantly expand the gap functions and increase significantly the critical temperature of superconductivity. A strong on-site repulsion drives the $A_{1g}$ gap into a $B_{1g}$ gap. It is expected that superconductivity with the $A_{1g}$ symmetry reaches a high critical temperature due to the cooperation of the on-site and the next-nearest neighbor attractions.

cond-mat.supr-con↗

Multiple change-point Poisson model for threshold exceedances of air pollution concentrations

A Bayesian multiple change-point model is proposed to analyse violations of air quality standards by pollutants such as nitrogen oxides (NO2 and NO) and carbon monoxide (CO). The model is built on the assumption that the occurrence of threshold exceedances may be described by a non-homogeneous Poisson process with a step rate function. Unlike earlier approaches, our model is not restricted by a predetermined number of change-points, nor does it involve any covariates. Possible short-range correlations in the exceedance data (e.g., due to chemical and meteorological factors) are removed via declusterisation. The unknown rate function is estimated using a reversible jump MCMC sampling algorithm adapted from Green (1995), which allows for transitions between parameter subspaces of varying dimension. This technique is applied to the 17-year (1993-2009) daily NO2, NO and CO concentration data in the City of Leeds, UK. The results are validated by running the MCMC estimator on simulated data replicated via a posterior estimate of the rate function. The findings are interpreted and discussed in relation to some known traffic control actions. The proposed methodology may be useful in the air quality management context by providing quantitative objective means to measure the efficacy of pollution control programmes.

stat.AP↗