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Jing Zhang

Publications and source records attributed to Jing Zhang.

At least 865 records · Page 48Linked to original sources
arXiv2019

Anomaly detecting and ranking of the cloud computing platform by multi-view learning

Anomaly detecting as an important technical in cloud computing is applied to support smooth running of the cloud platform. Traditional detecting methods based on statistic, analysis, etc. lead to the high false-alarm rate due to non-adaptive and sensitive parameters setting. We presented an online model for anomaly detecting using machine learning theory. However, most existing methods based on machine learning linked all features from difference sub-systems into a long feature vector directly, which is difficult to both exploit the complement information between sub-systems and ignore multi-view features enhancing the classification performance. Aiming to this problem, the proposed method automatic fuses multi-view features and optimize the discriminative model to enhance the accuracy. This model takes advantage of extreme learning machine (ELM) to improve detection efficiency. ELM is the single hidden layer neural network, which is transforming iterative solution the output weights to solution of linear equations and avoiding the local optimal solution. Moreover, we rank anomies according to the relationship between samples and the classification boundary, and then assigning weights for ranked anomalies, retraining the classification model finally. Our method exploits the complement information between sub-systems sufficiently, and avoids the influence from imbalance dataset, therefore, deal with various challenges from the cloud computing platform. We deploy the privately cloud platform by Openstack, verifying the proposed model and comparing results to the state-of-the-art methods with better efficiency and simplicity.

cs.LG↗
arXiv2019

Gate tunable giant anisotropic resistance in ultra-thin GaTe

In crystals, the duplication of atoms often follows different periodicity along different directions. It thus gives rise to the so called anisotropy, which is usually even more pronounced in two dimensional (2D) materials due to the absence of $\textbf{z}$ dimension. Indeed, in the emerging 2D materials, electrical anisotropy has been one of the focuses in recent experimental efforts. However, key understandings of the in-plane anisotropic resistance in low-symmetry 2D materials, as well as demonstrations of model devices taking advantage of it, have proven difficult. Here, we show that, in few-layered semiconducting GaTe, electrical conductivity along $\textbf{x}$ and $\textbf{y}$ directions of the 2D crystal can be gate tuned from a ratio of less than one order to as large as 10$^{3}$. This effect is further demonstrated to yield an anisotropic memory resistor behaviour in ultra-thin GaTe, when equipped with an architecture of van der Waals floating gate. Our findings of gate tunable giant anisotropic resistance (GAR) effect pave the way for potential applications in nano-electronics such as multifunctional directional memories in the 2D limit.

cond-mat.mes-hall↗
arXiv2019

Topological phase and lattice structures in spin chain models

Finding new topological materials and understanding the physical essence of topology are crucial problems for researchers. We studied the topological property based on several proposed Su-Schrieffer-Heeger (SSH) related models. We show that the topologically non-trivial phase (TNP) in the SSH model can exist in other model conditions and can even be caused by anisotropy; its origin has a close relation with the even-bond dimerization. Further study shows that the TNP is not determined by the periodicity of the system, but the parity of the two kinds of bonds plays a significant role for the formation of the TNP. This finding reveals the topological invariant of the system and might supply potential extended topological materials for both theoretical and experimental researches.

cond-mat.str-el↗
arXiv2018

Sub-Doppler laser cooling of 23Na in gray molasses on the D2 line

We report on the efficient gray molasses cooling of sodium atoms using the $D_{2}$ optical transition at 589.1 nm. Thanks to the hyperfine split about 6$Γ$ between the $|F'=2\rangle$ and $|F'=3\rangle$ in the excited state 3$^{2}P_{3/2}$, this atomic transition is effective for the gray molasses cooling mechanism. Using this cooling technique, the atomic sample in $F = 2$ ground manifold is cooled from 700 $\upmu$K to 56 $\upmu$K in 3.5 ms. We observe that the loading efficiency into magnetic trap is increased due to the lower temperature and high phase space density of atomic cloud after gray molasses. This technique offers a promising route for the fast cooling of the sodium atoms in the $F=2$ state.

cond-mat.quant-gas↗
arXiv2018

Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named "Click-Through Rate Prediction" (\textit{CTR}) and "Conversion Rate Prediction" (\textit{CVR}) are included, where \textit{CVR} module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multi-Level Deep Cascade Trees (\textit{ldcTree}), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (\textit{GBDT}) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding \textit{GBDT} as the input feature representation for next level \textit{GBDT}, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level \textit{GBDT} corresponds to the combination of certain traversals in the preceding \textit{GBDT}. The deep cascade structure and the combination rule enable the proposed \textit{ldcTree} to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble \textit{ldcTree} (\textit{E-ldcTree}) to encourage the model's diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on \textit{EldcTree} (\textit{F-EldcTree}) for taking adequate use of weak and strong correlation features identified by pre-trained \textit{GBDT} models. Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods.

cs.LG↗
arXiv2018

Controllable coupled-resonator-induced transparency in a dual-recycled Michelson interferometer

We theoretically and experimentally study the effect of the coupled-resonator-induced transparency in the Michelson interferometer with the dual-recycled configuration, which is equivalent to the adjustable coupled resonator. The coupling strength, corresponding to the splitting of the reflection spectrum and associated with the width of the coupled-resonator-induced transparent window,can be controlled by adjusting the arm lengths, i.e., the relative phase of the interference arms on the 50/50 beam splitter. Thus, this tunability of the coupling strength can be very fast, and the absorptive and dispersive properties can be effectively controlled. This work provides a new system to coherently control and storage optical field.

physics.optics↗
arXiv2018

Background Subtraction using Compressed Low-resolution Images

Image processing and recognition are an important part of the modern society, with applications in fields such as advanced artificial intelligence, smart assistants, and security surveillance. The essential first step involved in almost all the visual tasks is background subtraction with a static camera. Ensuring that this critical step is performed in the most efficient manner would therefore improve all aspects related to objects recognition and tracking, behavior comprehension, etc.. Although background subtraction method has been applied for many years, its application suffers from real-time requirement. In this letter, we present a novel approach in implementing the background subtraction. The proposed method uses compressed, low-resolution grayscale image for the background subtraction. These low-resolution grayscale images were found to preserve the salient information very well. To verify the feasibility of our methodology, two prevalent methods, ViBe and GMM, are used in the experiment. The results of the proposed methodology confirm the effectiveness of our approach.

cs.CV↗
arXiv2018

Marrying Tracking with ELM: A Metric Constraint Guided Multiple Feature Fusion Method

Object Tracking is one important problem in computer vision and surveillance system. The existing models mainly exploit the single-view feature (i.e. color, texture, shape) to solve the problem, failing to describe the objects comprehensively. In this paper, we solve the problem from multi-view perspective by leveraging multi-view complementary and latent information, so as to be robust to the partial occlusion and background clutter especially when the objects are similar to the target, meanwhile addressing tracking drift. However, one big problem is that multi-view fusion strategy can inevitably result tracking into non-efficiency. To this end, we propose to marry ELM (Extreme learning machine) to multi-view fusion to train the global hidden output weight, to effectively exploit the local information from each view. Following this principle, we propose a novel method to obtain the optimal sample as the target object, which avoids tracking drift resulting from noisy samples. Our method is evaluated over 12 challenge image sequences challenged with different attributes including illumination, occlusion, deformation, etc., which demonstrates better performance than several state-of-the-art methods in terms of effectiveness and robustness.

cs.CV↗
arXiv2018

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.

cs.CV↗
arXiv2018

Enhancing squeezing and nonclassicality of light in atom-optomechanical systems

Quadrature squeezing of light is investigated in a hybrid atom-optomechanical system comprising a cloud of two-level atoms and a movable mirror mediated by a single-mode cavity field. When the system is at high temperatures with quadrature fluctuations of light much above the standard quantum limit (SQL), excitation counting on the collective atomic state can effectively reduce the light noise close to the SQL. When the system is at low temperatures, considerable squeezing of light below the SQL is found at steady state. The squeezing is enhanced by simply increasing the atom-light coupling strength with the laser power optimized close to the unstable regime, and further noise reduction is achieved by decreasing various losses in the system. The presence of atoms and excitation counting on the atoms lessen the limitation of thermal noise, and the squeezing can be achieved at environment temperature of the order K. The nonclassicality of the light, embodied by the negative distributions of the Wigner function, is also studied by making non-Gaussian measurements on the atoms. It is shown that with feasible parameters excitation counting on the atoms is effective in inducing strongly optical nonclassicality.

quant-ph↗
arXiv2018

Robust Tracking via Weighted Online Extreme Learning Machine

The tracking method based on the extreme learning machine (ELM) is efficient and effective. ELM randomly generates input weights and biases in the hidden layer, and then calculates and computes the output weights by reducing the iterative solution to the problem of linear equations. Therefore, ELM offers the satisfying classification performance and fast training time than other discriminative models in tracking. However, the original ELM method often suffers from the problem of the imbalanced classification distribution, which is caused by few target objects, leading to under-fitting and more background samples leading to over-fitting. Worse still, it reduces the robustness of tracking under special conditions including occlusion, illumination, etc. To address above problems, in this paper, we present a robust tracking algorithm. First, we introduce the local weight matrix that is the dynamic creation from the data distribution at the current frame in the original ELM so as to balance between the empirical and structure risk, and fully learn the target object to enhance the classification performance. Second, we improve it to the incremental learning method ensuring tracking real-time and efficient. Finally, the forgetting factor is used to strengthen the robustness for changing of the classification distribution with time. Meanwhile, we propose a novel optimized method to obtain the optimal sample as the target object, which avoids tracking drift resulting from noisy samples. Therefore, our tracking method can fully learn both of the target object and background information to enhance the tracking performance, and it is evaluated in 20 challenge image sequences with different attributes including illumination, occlusion, deformation, etc., which achieves better performance than several state-of-the-art methods in terms of effectiveness and robustness.

cs.LG↗
arXiv2018

Dissipative solitary wave at the interface of a binary complex plasma

The propagation of a dissipative solitary wave across an interface is studied in a binary complex plasma. The experiments were performed under microgravity conditions in the PK-3 Plus Laboratory on board the International Space Station using microparticles with diameters of 1.55 micrometre and 2.55 micrometre immersed in a low-temperature plasma. The solitary wave was excited at the edge of a particle-free region and propagated from the sub-cloud of small particles into that of big particles. The interfacial effect was observed by measuring the deceleration of particles in the wave crest. The results are compared with a Langevin dynamics simulation, where the waves were excited by a gentle push on the edge of the sub-cloud of small particles. Reflection of the wave at the interface is induced by increasing the strength of the push. By tuning the ion drag force exerted on big particles in the simulation, the effective width of the interface is adjusted. We show that the strength of reflection increases with narrower interfaces.

physics.plasm-ph↗
arXiv2018

Experimental observation of one-dimensional superradiance lattices in ultracold atoms

We measure the superradiant emission in a one-dimensional (1D) superradiance lattice (SL) in ultracold atoms. Resonantly excited to a superradiant state, the atoms are further coupled to other collectively excited states, which form a 1D SL. The directional emission of one of the superradiant excited states in the 1D SL is measured. The emission spectra depend on the band structure, which can be controlled by the frequency and intensity of the coupling laser fields. This work provides a platform for investigating the collective Lamb shift of resonantly excited superradiant states in Bose-Einstein condensates and paves the way for realizing higher dimensional superradiance lattices.

cond-mat.quant-gas↗
arXiv2018

The Obstacle Problem for Quasilinear Stochastic PDEs with Neumann boundary condition

We prove the existence and uniqueness of solution of the obstacle problem for quasilinear stochastic partial differential equations (OSPDEs for short) with Neumann boundary condition. Our method is based on the analytical technics coming from parabolic potential theory. The solution is expressed as a pair $(u,ν)$ where $u$ is a predictable continuous process which takes values in a proper Sobolev space and $ν$ is a random regular measure satisfying minimal Skohorod condition.

math.PR↗