arXiv subjects

Jian Yang

Publications and source records attributed to Jian Yang.

At least 775 records · Page 43Linked to original sources

arXiv2015

Link prediction based on path entropy

Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream link predictors.

physics.soc-ph↗

arXiv2015

Inventory Control Involving Unknown Demand of Discrete Nonperishable Items - Analysis of a Newsvendor-based Policy

Inventory control with unknown demand distribution is considered, with emphasis placed on the case involving discrete nonperishable items. We focus on an adaptive policy which in every period uses, as much as possible, the optimal newsvendor ordering quantity for the empirical distribution learned up to that period. The policy is assessed using the regret criterion, which measures the price paid for ambiguity on demand distribution over $T$ periods. When there are guarantees on the latter's separation from the critical newsvendor parameter $β=b/(h+b)$, a constant upper bound on regret can be found. Without any prior information on the demand distribution, we show that the regret does not grow faster than the rate $T^{1/2+ε}$ for any $ε>0$. In view of a known lower bound, this is almost the best one could hope for. Simulation studies involving this along with other policies are also conducted.

stat.ML↗

arXiv2015

Information transport in multiplex networks

In this paper, we study information transport in multiplex networks comprised of two coupled subnetworks. The upper subnetwork, called the logical layer, employs the shortest paths protocol to determine the logical paths for packets transmission, while the lower subnetwork acts as the physical layer, in which packets are delivered by the biased random walk mechanism characterized with a parameter $α$. Through simulation, we obtain the optimal $α$ corresponding to the maximum network lifetime and the maximum number of the arrival packets. Assortative coupling is better than the random coupling and the disassortative coupling, since it achieves much better transmission performances. Generally, the more homogeneous the lower subnetwork, the better the transmission performances are, which is opposite for the upper subnetwork. Finally, we propose an attack centrality for nodes based on the topological information of both subnetworks, and further investigate the transmission performances under targeted attacks. Our work helps to understand the spreading and robustness issues of multiplex networks and provides some clues about the designing of more efficient and robust routing architectures in communication systems.

physics.soc-ph↗

arXiv2015

Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning

Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the stochastic behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.

math.NA↗

arXiv2015

Traffic-driven SIR epidemic model on networks

We propose a novel SIR epidemic model which is driven by the transmission of infection packets in networks. Specifically, infected nodes generate and deliver infection packets causing the spread of the epidemic, while recovered nodes block the delivery of infection packets, and this inhibits the epidemic spreading. The efficient routing protocol governed by a control parameter $α$ is used in the packet transmission. We obtain the maximum instantaneous population of infected nodes, the maximum population of ever infected nodes, as well as the corresponding optimal $α$ through simulation. We find that generally more balanced load distribution leads to more intense and wide spread of an epidemic in networks. Increasing either average node degree or homogeneity of degree distribution will facilitate epidemic spreading. When packet generation rate $ρ$ is small, increasing $ρ$ favors epidemic spreading. However, when $ρ$ is large enough, traffic congestion appears which inhibits epidemic spreading.

physics.soc-ph↗

arXiv2015

Entangling Color-Different Photons via Time-Resolved Measurement and Active Feed-Forward

Entangling independent photons is not only of fundamental interest but also of crucial importance for quantum information science. Two-photon interference is a major method to entangle independent identical photons. If two photons are color-different, perfect two-photon coalescence cannot happen anymore, which makes the entangling of color-different photons difficult to realize. In this letter by exploring and developing time-resolved measurement and active feed-forward, we have entangled two independent photons of different colors for the first time. We find that entanglement with a varying form can be identified for different two-photon temporal modes through time-resolved measurement. By using active feed-forward we are able to convert the varying entanglement into uniform. Adopting these measures, we have successfully entangled two photons with a frequency separation of 16 times larger than their linewidths. In addition to its fundamental interest, our work also provides an approach to solve the frequency mismatch problem for future quantum networks.

quant-ph↗

arXiv2015

Sparse Deep Stacking Network for Image Classification

Sparse coding can learn good robust representation to noise and model more higher-order representation for image classification. However, the inference algorithm is computationally expensive even though the supervised signals are used to learn compact and discriminative dictionaries in sparse coding techniques. Luckily, a simplified neural network module (SNNM) has been proposed to directly learn the discriminative dictionaries for avoiding the expensive inference. But the SNNM module ignores the sparse representations. Therefore, we propose a sparse SNNM module by adding the mixed-norm regularization (l1/l2 norm). The sparse SNNM modules are further stacked to build a sparse deep stacking network (S-DSN). In the experiments, we evaluate S-DSN with four databases, including Extended YaleB, AR, 15 scene and Caltech101. Experimental results show that our model outperforms related classification methods with only a linear classifier. It is worth noting that we reach 98.8% recognition accuracy on 15 scene.

cs.CV↗

arXiv2014

Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories

Map matching of the GPS trajectory serves the purpose of recovering the original route on a road network from a sequence of noisy GPS observations. It is a fundamental technique to many Location Based Services. However, map matching of a low sampling rate on urban road network is still a challenging task. In this paper, the characteristics of Conditional Random Fields with regard to inducing many contextual features and feature selection are explored for the map matching of the GPS trajectories at a low sampling rate. Experiments on a taxi trajectory dataset show that our method may achieve competitive results along with the success of reducing model complexity for computation-limited applications.

stat.ML↗

arXiv2014

Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network

Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditional Random Fields (CRFs) for map matching. Our preliminary results are obtained from real-world taxi GPS trajectories.

stat.ML↗

arXiv2014

Epidemic spreading driven by biased random walks

Random walk is one of the basic mechanisms found in many network applications. We study the epidemic spreading dynamics driven by biased random walks on complex networks. In our epidemic model, each time infected nodes constantly spread some infected packets by biased random walks to their neighbor nodes causing the infection of the susceptible nodes that receive the packets. An infected node get recovered from infection with a fixed probability. Simulation and analytical results on model and real-world networks show that the epidemic spreading becomes intense and wide with the increase of delivery capacity of infected nodes, average node degree, homogeneity of node degree distribution. Furthermore, there are corresponding optimal parameters such that the infected nodes have instantaneously the largest population, and the epidemic spreading process covers the largest part of a network.

physics.soc-ph↗

arXiv2014

Unsupervised Pretraining Encourages Moderate-Sparseness

It is well known that direct training of deep neural networks will generally lead to poor results. A major progress in recent years is the invention of various pretraining methods to initialize network parameters and it was shown that such methods lead to good prediction performance. However, the reason for the success of pretraining has not been fully understood, although it was argued that regularization and better optimization play certain roles. This paper provides another explanation for the effectiveness of pretraining, where we show pretraining leads to a sparseness of hidden unit activation in the resulting neural networks. The main reason is that the pretraining models can be interpreted as an adaptive sparse coding. Compared to deep neural network with sigmoid function, our experimental results on MNIST and Birdsong further support this sparseness observation.

cs.LG↗

arXiv2014

Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes

Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients. Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion. We experiment using four popular face image databases, the Extended Yale B database, the AR database, the Multi-PIE and the FRGC database. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression based face recognition methods.

cs.CV↗

arXiv2013

Asymptotic Behavior of Solutions for Competitive Models with Free Boundaries

In this paper, we study a competitive model involving two species. When the competition is strong enough, the two species are separated by a free boundary. If the initial data has a positive bound at infinity. We prove that the solution will converge, as $t\rightarrow \infty$, to the traveling wave solution and the free boundary will move to infinity with a constant speed.

math.AP↗

arXiv2013

Study of n-γ discrimination in low energy range (above 40 keVee) by charge comparison method with a BC501A liquid scintillation detector

A VME-based experiment system for n-γ discrimination using the charge comparison method was established. A data acquisition program for controlling the programmable modules and processing data online via VME64X bus was developed through the use of LabVIEW. The two-dimensional (2D) scatter plots of the charge in the slow component vs. the total charge of recorded pulses from Am-Be and Cf neutron sources were presented. The 2D scatter plots of the energy vs. the ratio of the charge in the slow component to the total charge of the pulses were presented at the meantime. The quality of n-γ discrimination was checked by the figure-of-merit, and the results showed good performance of n-γ discrimination at low energy range. Neutrons and γ-rays were separated above 50 keVee (electron-equivalent energy). The quality of n-γ discrimination have been improved compared with others' results at 5 energies (150, 250, 350, 450, 550 keVee).

physics.ins-det↗

arXiv2013

Rapid and reliable thickness identification of two-dimensional nanosheets using optical microscopy

The physical and electronic properties of ultrathin two-dimensional (2D) layered nanomaterials are highly related to their thickness. Therefore, the rapid and accurate identification of single- and few- to multi-layer nanosheets is essential to their fundamental study and practical applications. Here, a universal optical method has been developed for simple, rapid and reliable identification of single- to quindecuple-layer (1L-15L) 2D nanosheets, including graphene, MoS2, WSe2 and TaS2, on Si substrates coated with 90 nm or 300 nm SiO2. The optical contrast differences between the substrates and 2D nanosheets with different layer numbers were collected and tabulated, serving as a standard reference, from which the layer number of a given nanosheet can be readily and reliably determined without using complex calculation nor expensive instrument. Our general optical identification method will facilitate the thickness-dependent study of various 2D nanomaterials, and expedite their research toward practical applications.

cond-mat.mtrl-sci↗

arXiv2013

Connection dynamics of a gauge theory of gravity coupled with matter

We study the coupling of the gravitational action, which is a linear combination of the Hilbert-Palatini term and the quadratic torsion term, to the action of Dirac fermions. The system possesses local Poincare invariance and hence belongs to Poincare gauge theory with matter. The complete Hamiltonian analysis of the theory is carried out without gauge fixing but under certain ansatz on the coupling parameters, which leads to a consistent connection dynamics with second-class constraints and torsion. After performing a partial gauge fixing, all second-class constraints can be solved, and a SU(2)-connection dynamical formalism of the theory can be obtained. Hence, the techniques of loop quantum gravity can be employed to quantize this Poincare gauge theory with non-zero torsion. Moreover, the Barbero-Immirzi parameter in loop quantum gravity acquires its physical meaning as the coupling parameter between the Hilbert-Palatini term and the quadratic torsion term in this gauge theory of gravity.

gr-qc↗

arXiv2013

Traveling Wave Solutions of Competitive Models with Free Boundaries

We study two systems of reaction diffusion equations with monostable or bistable type of nonlinearity and with free boundaries. These systems are used as multi-species competitive model. For two-species models, we prove the existence of a traveling wave solution which consists of two semi-waves intersecting at the free boundary. For three-species models, we also prove the existence of a traveling wave solution which, however, consists of two semi-waves and one compactly supported wave in between, each intersecting with its neighbor at the free boundary.

math.AP↗

arXiv2013

Zero-point attracting projection algorithm for sequential compressive sensing

Sequential Compressive Sensing, which may be widely used in sensing devices, is a popular topic of recent research. This paper proposes an online recovery algorithm for sparse approximation of sequential compressive sensing. Several techniques including warm start, fast iteration, and variable step size are adopted in the proposed algorithm to improve its online performance. Finally, numerical simulations demonstrate its better performance than the relative art.

cs.IT↗