arXiv subjects

Hong Zhao

Publications and source records attributed to Hong Zhao.

At least 73 records · Page 4Linked to original sources

arXiv2016

Modulating heat conduction by stretching or compressing

Recent studies have revealed that the symmetry of interparticle potential plays an important role in one-dimensional heat conduction problem. Here we demonstrate that by stretching or compressing the Fermi-Pasta-Ulam-\b{eta} lattice, one can control the symmetry of the potential, and thus manipulate the decaying behavior of the heat current autocorrelation function (HCAF). In fact, stretching or compressing induces a fast decaying stage (FDS) during which the HCAF decays faster than power-law manners or in a power law manner but faster than ~t -1. The time range as well as the decay amplitude of the HCAF over the FDS increase as the stretching or compressing ratio increase, or as the temperature decreases. As a consequence, the thermal conductivity calculated following the Green-Kubo formula shows a truncation-time independent window, implying a system-size independent conductivity. Stretching or compressing also changes the exponent of the power-law tail of the HCAF. The complicated heat conduction behavior induced by stretching or compressing can be connected to the change of the symmetry of the interparticle potential.

cond-mat.stat-mech↗

arXiv2016

General Vector Machine

The support vector machine (SVM) is an important class of learning machines for function approach, pattern recognition, and time-serious prediction, etc. It maps samples into the feature space by so-called support vectors of selected samples, and then feature vectors are separated by maximum margin hyperplane. The present paper presents the general vector machine (GVM) to replace the SVM. The support vectors are replaced by general project vectors selected from the usual vector space, and a Monte Carlo (MC) algorithm is developed to find the general vectors. The general project vectors improves the feature-extraction ability, and the MC algorithm can control the width of the separation margin of the hyperplane. By controlling the separation margin, we show that the maximum margin hyperplane can usually induce the overlearning, and the best learning machine is achieved with a proper separation margin. Applications in function approach, pattern recognition, and classification indicate that the developed method is very successful, particularly for small-set training problems. Additionally, our algorithm may induce some particular applications, such as for the transductive inference.

stat.ML↗

arXiv2015

Non-Gaussian normal diffusion induced by delocalization

The non-Gaussian normal diffusion, i.e., the probability distribution function (PDF) is non-Gaussian but the mean squared displacement (MSD) depends on time linearly, has been observed in particle motions. Here we show by numerical simulations that this phenomenon may manifest in energy diffusion along lattices at a non-zero, finite temperature. The model we study is one-dimensional disordered lattices with on-site potential. We find that the energy-density fluctuations are spatially localized if the nonlinear interaction is suppressed, but may relax with a non-Gaussian PDF and a linear time-dependent MSD when the nonlinear interaction is turned on. Our analysis suggests that the mechanism lies in the delocalization properties of the localized modes.

cond-mat.dis-nn↗

arXiv2015

Effective phonon treatment of asymmetric interparticle interaction potentials

We propose an effective phonon treatment in one dimensional momentum-conserved lattice system with asymmetric interparticle interaction potentials. Our strategy is to divide the potential into two segments by the zero-potential point, and then approximate them by piecewise harmonic potentials with effective force constants $\tilde{k}_L$ and $\tilde{k}_R$ respectively. The effective phonons can then be well described by $ω_c=\sqrt{2(\tilde{k}_L+\tilde{k}_R)}% |sin(\frac{1}{2}aq)|$. The numerical verifications show that this treatment works very well.

cond-mat.stat-mech↗

arXiv2015

Connection between heat diffusion and heat conduction in one-dimensional systems

Heat and energy are conceptually different, but often are assumed to be the same without justification. An effective method for investigating diffusion properties in equilibrium systems is discussed. With this method, we demonstrate that for one-dimensional systems, using the indices of particles as the space variable , which has been accepted as a convention, may lead to misleading conclusions. We then show that though in one-dimensional systems there is no general connection between energy diffusion and heat conduction, however, a general connection between heat diffusion and heat conduction may exist. Relaxation behavior of local energy current fluctuations and that of local heat current fluctuations are also studied. We find that they are significantly different, though the global energy current equals the globe heat current.

cond-mat.stat-mech↗

arXiv2015

Normal heat conduction in lattice models with asymmetry harmonic interparticle interactions

We study the thermal conduction behaviors of one-dimensional lattice models with asymmetry harmonic interparticle interactions in this paper. Normal thermal conductivity independent of the system size is observed when the lattice chains are long enough. Because only the harmonic interactions are involved, the result confirms without ambiguous interpretation that the asymmetry plays key role in resulting in the normal thermal conduction in one-dimensional momentum conserving lattices. Both equilibrium and nonequilibrium simulations are performed to support the conclusion.

cond-mat.stat-mech↗

arXiv2015

Key role of asymmetric interactions in low-dimensional heat transport

We study the heat current autocorrelation function (HCAF) in one-dimensional, momentum-conserving lattices. In particular, we explore if there is any relation between the decaying characteristics of the HCAF and asymmetric interparticle interactions. The Lennard-Jones model is intensively investigated in view of its significance to applications. It is found that in wide ranges of parameters, the HCAF decays faster than power-law manners, and in some cases it decays even exponentially. Following the Green-Kubo formula, the fast decay of HCAF implies the convergence of heat conductivity, which is also corroborated by numerical simulations. In addition, with a comparison to the Fermi-Pasta-Ulam-$β$ model of symmetric interaction, the HCAF of the Fermi-Pasta-Ulam-$α$-$β$ model of asymmetric interaction is also investigated. Our study suggests that, in certain ranges of parameters, the decaying behavior of the HCAF is correlated to the asymmetry degree of interaction.

cond-mat.stat-mech↗

arXiv2015

On Extraction of Chemical Potentials of Quarks from Particle Transverse Momentum Spectra in High Energy Collisions

We present two methods to extract the chemical potentials of quarks in high energy collisions. The first method is based on the ratios of negatively/positively charged particles, and the temperatures extracted from the transverse momentum spectra of related hadrons are needed. The second method is based on the chemical potentials of some particles, and we also need the transverse momentum spectra of related hadrons. To extract the quark chemical potentials, we would like to propose experimental collaborations to measure simultaneously not only the transverse momentum spectra of anti-p, p, K^-, K^+, pion^-, and pion^+, but also those of D^-, D^+, B^-, and B^+ (even those of Delta^++, Delta^-, and Omega^-) in high energy nuclear collisions

hep-ph↗

arXiv2014

Audio Splicing Detection and Localization Using Environmental Signature

Audio splicing is one of the most common manipulation techniques in the area of audio forensics. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are proposed as the environmental signature. Specifically, the spliced audio segments are detected according to the magnitude correlation between the query frames and reference frames via a statically optimal threshold. The detection accuracy is further refined by comparing the adjacent frames. The effectiveness of the proposed method is tested on two data sets. One is generated from TIMIT database, and the other one is made in four acoustic environments using a commercial grade microphones. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments with near perfect accuracy. Comparison results illustrate that the identification accuracy of the proposed scheme is higher than the previous schemes. In addition, experimental results also show that the proposed scheme is robust to MP3 compression attack, which is also superior to the previous works.

cs.CR↗

arXiv2014

Mass transportation of thermally driven nanotube nanomotors with defects

Thermally driven nanotube nanomotors provide linear mass transportation controlled by a temperature gradient. However, the underlying mechanism is still unclear where the mass transportation velocity in experiment is much lower than that resulting from simulations. Considering that defects are common in fabricated nanotubes, we use molecular dynamics simulations to show that the mass transportation would be considerably impeded by the potential barriers or wells induced by the defects, which provides a possible picture to understand the relative low value at microscopic level. The optimal structure and the factors which would affect the performance are discussed. The result indicates considering defects is helpful in designing nanotube nanomotor and other new nanomotor-based devices.

cond-mat.mes-hall↗

arXiv2014

Finite-size effects on current correlation functions

We study why the calculation of current correlation functions (CCFs) still suffers from finite size effects even when the periodic boundary condition is taken. Two important one dimensional, momentum conserving systems are investigated as examples. Intriguingly, it is found that the state of a system recurs in the sense of microcanonical ensemble average, and such recurrence may result in oscillations in CCFs. Meanwhile, we find that the sound mode collisions induce an extra time decay in a current so that its correlation function decays faster (slower) in a smaller (larger) system. Based on these two unveiled mechanisms, a procedure for correctly evaluating the decay rate of a CCF is proposed, with which our analysis suggests that the global energy CCF decays as $\sim t^{-\frac{2}{3}}$ in the diatomic hard-core gas model and in a manner close to $\sim t^{-\frac{1}{2}}$ in the Fermi-Pasta-Ulam-$β$ model.

cond-mat.stat-mech↗

arXiv2014

Fragmentation of Spin-orbit Coupled Spinor Bose-Einstein Condensates

The fragmentation of spin-orbit coupled spin-1 Bose gas with a weak interaction in external harmonic trap is explored by both exact diagonalization and mean-field theory. This fragmentation tendency, which originates from the total angular momentum conservation, is affected obviously by the spin-orbit coupling strength and the spin-dependent interaction. Strong spin-orbit interaction raises the inverse participation ratio, which describes the number of significantly occupied single-particle states. As the spin-dependent interaction changes from anti-ferromagnetic to ferromagnetic, the peak values in the inverse participation ratio become lower. Without the confinement of the appointed total angular momentum, the condensate chooses a zero or finite total angular momentum ground state, which is determined by both the interaction and the spin-orbit coupling strength.

cond-mat.quant-gas↗

arXiv2013

Rough matroids based on coverings

The introduction of covering-based rough sets has made a substantial contribution to the classical rough sets. However, many vital problems in rough sets, including attribution reduction, are NP-hard and therefore the algorithms for solving them are usually greedy. Matroid, as a generalization of linear independence in vector spaces, it has a variety of applications in many fields such as algorithm design and combinatorial optimization. An excellent introduction to the topic of rough matroids is due to Zhu and Wang. On the basis of their work, we study the rough matroids based on coverings in this paper. First, we investigate some properties of the definable sets with respect to a covering. Specifically, it is interesting that the set of all definable sets with respect to a covering, equipped with the binary relation of inclusion $\subseteq$, constructs a lattice. Second, we propose the rough matroids based on coverings, which are a generalization of the rough matroids based on relations. Finally, some properties of rough matroids based on coverings are explored. Moreover, an equivalent formulation of rough matroids based on coverings is presented. These interesting and important results exhibit many potential connections between rough sets and matroids.

cs.AI↗

arXiv2013

Why asymmetric interparticle interaction can result in convergent heat conductivity

We show that the asymmetric inter-particle interactions may induce rapid decay of heat current autocorrelation in one-dimensional momentum conserving lattices. When the asymmetry degree and the temperature are appropriate, the decay is sufficient rapid for resulting a convergence conductivity practically. To understand the underlying mechanism, we further studied the relaxation behavior of the hydrodynamic modes. It is shown that for lattice with symmetric potential, the heat mode relaxs in the superdiffusive manner, while in the case of asymmetric potential, the heat mode may relax in the normal manner.

cond-mat.stat-mech↗

arXiv2013

Test-cost-sensitive attribute reduction of data with normal distribution measurement errors

The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision making based on attribute values of objects, we shall select an attribute subset with appropriate measurement error to minimize the total test cost. Recently, error-range-based covering rough set with uniform distribution error was proposed to investigate this issue. However, the measurement errors satisfy normal distribution instead of uniform distribution which is rather simple for most applications. In this paper, we introduce normal distribution measurement errors to covering-based rough set model, and deal with test-cost-sensitive attribute reduction problem in this new model. The major contributions of this paper are four-fold. First, we build a new data model based on normal distribution measurement errors. With the new data model, the error range is an ellipse in a two-dimension space. Second, the covering-based rough set with normal distribution measurement errors is constructed through the "3-sigma" rule. Third, the test-cost-sensitive attribute reduction problem is redefined on this covering-based rough set. Fourth, a heuristic algorithm is proposed to deal with this problem. The algorithm is tested on ten UCI (University of California - Irvine) datasets. The experimental results show that the algorithm is more effective and efficient than the existing one. This study is a step toward realistic applications of cost-sensitive learning.

cs.AI↗

arXiv2013

Minimal cost feature selection of data with normal distribution measurement errors

Minimal cost feature selection is devoted to obtain a trade-off between test costs and misclassification costs. This issue has been addressed recently on nominal data. In this paper, we consider numerical data with measurement errors and study minimal cost feature selection in this model. First, we build a data model with normal distribution measurement errors. Second, the neighborhood of each data item is constructed through the confidence interval. Comparing with discretized intervals, neighborhoods are more reasonable to maintain the information of data. Third, we define a new minimal total cost feature selection problem through considering the trade-off between test costs and misclassification costs. Fourth, we proposed a backtracking algorithm with three effective pruning techniques to deal with this problem. The algorithm is tested on four UCI data sets. Experimental results indicate that the pruning techniques are effective, and the algorithm is efficient for data sets with nearly one thousand objects.

cs.AI↗

arXiv2013

Cost-Sensitive Feature Selection of Data with Errors

In data mining applications, feature selection is an essential process since it reduces a model's complexity. The cost of obtaining the feature values must be taken into consideration in many domains. In this paper, we study the cost-sensitive feature selection problem on numerical data with measurement errors, test costs and misclassification costs. The major contributions of this paper are four-fold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. Second, a covering-based rough set with measurement errors is constructed. Given a confidence interval, the neighborhood is an ellipse in a two-dimension space, or an ellipsoidal in a three-dimension space, etc. Third, a new cost-sensitive feature selection problem is defined on this covering-based rough set. Fourth, both backtracking and heuristic algorithms are proposed to deal with this new problem. The algorithms are tested on six UCI (University of California - Irvine) data sets. Experimental results show that (1) the pruning techniques of the backtracking algorithm help reducing the number of operations significantly, and (2) the heuristic algorithm usually obtains optimal results. This study is a step toward realistic applications of cost-sensitive learning.

cs.LG↗

arXiv2013

Diffusion of heat, energy, momentum, and mass in one-dimensional systems

We study diffusion processes of local fluctuations of heat, energy, momentum, and mass in three paradigmatic one-dimensional systems. For each system, diffusion processes of four physical quantities are simulated and the cross correlations between them are investigated. We find that, in all three systems, diffusion processes of energy and mass can be perfectly expressed as a linear combination of those of heat and momentum, suggesting that diffusion processes of heat and momentum may represent the heat mode and the sound mode in the hydrodynamic theory. In addition, the dynamic structure factor, which describes the diffusion behavior of local mass density fluctuations, is in general insufficient for probing diffusion processes of other quantities because in some cases there is no correlation between them. We also find that the diffusion behavior of heat can be qualitatively different from that of energy, and, as a result, previous studies trying to relate heat conduction to energy diffusion should be revisited.

cond-mat.stat-mech↗