arXiv subjects

Zhao Wang

Publications and source records attributed to Zhao Wang.

At least 163 records · Page 9Linked to original sources

arXiv2022

Experimental test of Tsirelson's bound with a single photonic qubit

For many protocols, quantum strategies have advantages compared with their classical counter-partners, and these advantages have attracted many interests and applications. One of the famous examples is the Clauser-Horne-Shimony-Holt (CHSH) game, which recasts Bell's theorem~\cite{2} into the framework of a game. In the CHSH game, two space-like separated players, Alice and Bob are each assigned a classical bit $a$ and $b$ respectively. Then they return bits $x$ and $y$ according to some pre-agreed strategies. They will win the game when $x\oplus y= a\cdot b$. In the game, if the players use the classical strategies, the optimal success probability $w(\text{CHSH})=0.75$.However, if they add some quantum resources, the success probability will increase and up to maximal value $cos^2(π/8)$, which is know as the Tsirelson's bound. Moreover, Popescu and Rohrlich noted that the perfect success probability $1$ can also be achieved in a more general theory without violating the no-signaling assumption

quant-ph↗

arXiv2021

Self-attention based anchor proposal for skeleton-based action recognition

Skeleton sequences are widely used for action recognition task due to its lightweight and compact characteristics. Recent graph convolutional network (GCN) approaches have achieved great success for skeleton-based action recognition since its grateful modeling ability of non-Euclidean data. GCN is able to utilize the short-range joint dependencies while lack to directly model the distant joints relations that are vital to distinguishing various actions. Thus, many GCN approaches try to employ hierarchical mechanism to aggregate wider-range neighborhood information. We propose a novel self-attention based skeleton-anchor proposal (SAP) module to comprehensively model the internal relations of a human body for motion feature learning. The proposed SAP module aims to explore inherent relationship within human body using a triplet representation via encoding high order angle information rather than the fixed pair-wise bone connection used in the existing hierarchical GCN approaches. A Self-attention based anchor selection method is designed in the proposed SAP module for extracting the root point of encoding angular information. By coupling proposed SAP module with popular spatial-temporal graph neural networks, e.g. MSG3D, it achieves new state-of-the-art accuracy on challenging benchmark datasets. Further ablation study have shown the effectiveness of our proposed SAP module, which is able to obviously improve the performance of many popular skeleton-based action recognition methods.

cs.CV↗

arXiv2021

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search. Prior conceptual graph construction approaches typically extract high-frequent, coarse-grained, and time-invariant concepts from formal texts. In real applications, however, it is necessary to extract less-frequent, fine-grained, and time-varying conceptual knowledge and build taxonomy in an evolving manner. In this paper, we introduce an approach to implementing and deploying the conceptual graph at Alibaba. Specifically, We propose a framework called AliCG which is capable of a) extracting fine-grained concepts by a novel bootstrapping with alignment consensus approach, b) mining long-tail concepts with a novel low-resource phrase mining approach, c) updating the graph dynamically via a concept distribution estimation method based on implicit and explicit user behaviors. We have deployed the framework at Alibaba UC Browser. Extensive offline evaluation as well as online A/B testing demonstrate the efficacy of our approach.

cs.AI↗

arXiv2021

End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization

The visual signal compression is a long-standing problem. Fueled by the recent advances of deep learning, exciting progress has been made. Despite better compression performance, existing end-to-end compression algorithms are still designed towards better signal quality in terms of rate-distortion optimization. In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision. We propose an inverted bottleneck structure for the encoder of the end-to-end compression towards machine vision, which specifically accounts for efficient representation of the semantic information. Moreover, we quest the capability of optimization by incorporating the analytics accuracy into the optimization process, and the optimality is further explored with generalized rate-accuracy optimization in an iterative manner. We use object detection as a showcase for end-to-end compression towards machine vision, and extensive experiments show that the proposed scheme achieves significant BD-rate savings in terms of analysis performance. Moreover, the promise of the scheme is also demonstrated with strong generalization capability towards other machine vision tasks, due to the enabling of signal-level reconstruction.

cs.CV↗

arXiv2021

Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons

Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random forest algorithm can be utilized to explore the physical correlation between emission features. Astronomical correlations between infrared bands are analyzed as examples of demonstration by finding the common molecular fragments responsible for different bands, which improves the current understanding of the long-observed correlations. We propose a way to quantify the band correlation by measuring the similarity of the feature importance arrays of different bands, via which a correlation map is obtained for emissions in the out-of-plane bending region. Moreover, a comparison between the predictions using different combinations of descriptors underscores the strong prediction power of the extended-connectivity molecular fingerprint, and shows that the combinations of multiple descriptors of other types in general lead to improved predictivity.

astro-ph.GA↗

arXiv2021

Gallai-Ramsey numbers involving a rainbow $4$-path

Given two non-empty graphs $G,H$ and a positive integer $k$, the Gallai-Ramsey number $\operatorname{gr}_k(G:H)$ is defined as the minimum integer $N$ such that for all $n\geq N$, every $k$-edge-coloring of $K_n$ contains either a rainbow colored copy of $G$ or a monochromatic copy of $H$. In this paper, we got some exact values or bounds for $\operatorname{gr}_k(P_5:H) \ (k\geq 3)$ if $H$ is a general graph or a star with extra independent edges or a pineapple.

math.CO↗

arXiv2021

Enhancing Model Robustness and Fairness with Causality: A Regularization Approach

Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.

cs.LG↗

arXiv2021

Improvements in Micro-CT Method for Characterizing X-ray Monocapillary Optics

Accurate characterization of the inner surface of X-ray monocapillary optics (XMCO) is of great significance in X-ray optics research. Compared with other characterization methods, the micro computed tomography (micro-CT) method has its unique advantages but also has some disadvantages, such as a long scanning time, long image reconstruction time, and inconvenient scanning process. In this paper, sparse sampling was proposed to shorten the scanning time, GPU acceleration technology was used to improve the speed of image reconstruction, and a simple geometric calibration algorithm was proposed to avoid the calibration phantom and simplify the scanning process. These methodologies will popularize the use of the micro-CT method in XMCO characterization.

physics.ins-det↗

arXiv2021

Nordhaus-Guddum type results for the Steiner Gutman index of graphs

Building upon the notion of Gutman index $\operatorname{SGut}(G)$, Mao and Das recently introduced the Steiner Gutman index by incorporating Steiner distance for a connected graph $G$. The \emph{Steiner Gutman $k$-index} $\operatorname{SGut}_k(G)$ of $G$ is defined by $\operatorname{SGut}_k(G)$ $=\sum_{S\subseteq V(G), \ |S|=k}\left(\prod_{v\in S}deg_G(v)\right) d_G(S)$, in which $d_G(S)$ is the Steiner distance of $S$ and $deg_G(v)$ is the degree of $v$ in $G$. In this paper, we derive new sharp upper and lower bounds on $\operatorname{SGut}_k$, and then investigate the Nordhaus-Gaddum-type results for the parameter $\operatorname{SGut}_k$. We obtain sharp upper and lower bounds of $\operatorname{SGut}_k(G)+\operatorname{SGut}_k(\overline{G})$ and $\operatorname{SGut}_k(G)\cdot \operatorname{SGut}_k(\overline{G})$ for a connected graph $G$ of order $n$, $m$ edges and maximum degree $Δ$, minimum degree $δ$.

math.CO↗

arXiv2021

Gallai-Ramsey numbers of odd cycles

Given two graphs $G$ and $H$ and a positive integer $k$, the $k$-color Gallai-Ramsey number, denoted by $gr_{k}(G : H)$, is the minimum integer $N$ such that for all $n \geq N$, every $k$-coloring of the edges of $K_{n}$ contains either a rainbow copy of $G$ or a monochromatic copy of $H$. We prove that $gr_{k} (K_{3} : C_{2\ell + 1}) = \ell \cdot 2^{k} + 1$ for all $k \geq 1$ and $\ell \geq 3$.

math.CO↗

arXiv2021

Low-Temperature Friction of Suspended Graphene: Negative friction?

Using molecular dynamics simulations, we probe a suspended graphene layer by a diamond-like-carbon tip at various temperatures. The force acting on the tip in the sliding direction is measured to be negative at liquid-helium temperature. This negative force is found to be associated with a spontaneous lateral oscillation of the suspended graphene in favor of a low interface potential corrugation. Our hypothesis is that, at low temperature, this oscillation induces an important hidden contribution to the friction force in the lateral direction. This functions as a particular energy dissipation mechanism at nanoscale.

cond-mat.mes-hall↗

arXiv2021

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

By leveraging deep learning based technologies, the data-driven based approaches have reached great success with the rapid increase of data generated of Industrial Indernet of Things(IIot). However, security and privacy concerns are obstacles for data providers in many sensitive data-driven industrial scenarios, such as healthcare and auto-driving. Many Federated Learning(FL) approaches have been proposed with DNNs for IIoT applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topogy based decentralized federated learning(RDFL) scheme for Deep Generative Models(DGMs), where DGMs is a promising solution for solving the aforementioned data usability issues. Compare with existing IIoT FL works, our RDFL schemes provides communication efficiency and maintain training performance to boost DGMs in target IIoT tasks. A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization. In addition, InterPlanetary File System(IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstate the superiority of RDFL with either independent and identically distributed(IID) datasets or non-independent and identically distributed(Non-IID) datasets.

cs.LG↗

arXiv2021

Multi-Density Attention Network for Loop Filtering in Video Compression

Video compression is a basic requirement for consumer and professional video applications alike. Video coding standards such as H.264/AVC and H.265/HEVC are widely deployed in the market to enable efficient use of bandwidth and storage for many video applications. To reduce the coding artifacts and improve the compression efficiency, neural network based loop filtering of the reconstructed video has been developed in the literature. However, loop filtering is a challenging task due to the variation in video content and sampling densities. In this paper, we propose a on-line scaling based multi-density attention network for loop filtering in video compression. The core of our approach lies in several aspects: (a) parallel multi-resolution convolution streams for extracting multi-density features, (b) single attention branch to learn the sample correlations and generate mask maps, (c) a channel-mutual attention procedure to fuse the data from multiple branches, (d) on-line scaling technique to further optimize the output results of network according to the actual signal. The proposed multi-density attention network learns rich features from multiple sampling densities and performs robustly on video content of different resolutions. Moreover, the online scaling process enhances the signal adaptability of the off-line pre-trained model. Experimental results show that 10.18% bit-rate reduction at the same video quality can be achieved over the latest Versatile Video Coding (VVC) standard. The objective performance of the proposed algorithm outperforms the state-of-the-art methods and the subjective quality improvement is obvious in terms of detail preservation and artifact alleviation.

eess.IV↗

arXiv2021

Minimization of ion micromotion with artificial neural network

Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work,but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation. Here we demonstrate that systematic machine learning based on artificial neural networks can quickly and efficiently find optimal voltage settings for the electrodes using rf-photon correlation technique, consequently minimizing the micromotion to the minimum. Our approach achieves a very high level of control for the ion micromotion, and can be extended to other configurations of Paul trap.

physics.atom-ph↗

arXiv2021

A Cross Channel Context Model for Latents in Deep Image Compression

This paper presents a cross channel context model for latents in deep image compression. Generally, deep image compression is based on an autoencoder framework, which transforms the original image to latents at the encoder and recovers the reconstructed image from the quantized latents at the decoder. The transform is usually combined with an entropy model, which estimates the probability distribution of the quantized latents for arithmetic coding. Currently, joint autoregressive and hierarchical prior entropy models are widely adopted to capture both the global contexts from the hyper latents and the local contexts from the quantized latent elements. For the local contexts, the widely adopted 2D mask convolution can only capture the spatial context. However, we observe that there are strong correlations between different channels in the latents. To utilize the cross channel correlations, we propose to divide the latents into several groups according to channel index and code the groups one by one, where previously coded groups are utilized to provide cross channel context for the current group. The proposed cross channel context model is combined with the joint autoregressive and hierarchical prior entropy model. Experimental results show that, using PSNR as the distortion metric, the combined model achieves BD-rate reductions of 6.30% and 6.31% over the baseline entropy model, and 2.50% and 2.20% over the latest video coding standard Versatile Video Coding (VVC) for the Kodak and CVPR CLIC2020 professional dataset, respectively. In addition, when optimized for the MS-SSIM metric, our approach generates visually more pleasant reconstructed images.

eess.IV↗

arXiv2021

Homogeneous Linear Ion Crystal in a Hybrid Potential

We investigate the properties of a linear ion crystal in a combination of quadratic and quartic potentials. Both the discrete and the continuous model are employed to explore the homogeneity of a linear ion crystal by controlling the proportional parameter between the quadratic and quartic components. It is found that a uniform ion distribution in such a hybrid potential can be made larger than that in a purely quadratic or quartic potential. The zigzag transition is also investigated in the hybrid potential. Its critical condition and phase diagram are determined numerically, which agrees well with previous theoretical and experimental results. This paves the way for experimental investigation of phase transition in a large linear coulomb crystal.

physics.atom-ph↗

arXiv2020

Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals

Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data, it can quickly degrade when the test distribution changes. For example, it has been shown that classifiers perform poorly when humans make minor modifications to change the label of an example. One solution to increase model reliability and generalizability is to identify causal associations between features and classes. In this paper, we propose to train a robust text classifier by augmenting the training data with automatically generated counterfactual data. We first identify likely causal features using a statistical matching approach. Next, we generate counterfactual samples for the original training data by substituting causal features with their antonyms and then assigning opposite labels to the counterfactual samples. Finally, we combine the original data and counterfactual data to train a robust classifier. Experiments on two classification tasks show that a traditional classifier trained on the original data does very poorly on human-generated counterfactual samples (e.g., 10%-37% drop in accuracy). However, the classifier trained on the combined data is more robust and performs well on both the original test data and the counterfactual test data (e.g., 12%-25% increase in accuracy compared with the traditional classifier). Detailed analysis shows that the robust classifier makes meaningful and trustworthy predictions by emphasizing causal features and de-emphasizing non-causal features.

cs.LG↗

arXiv2020

Low Mach number limit of full compressible Navier-Stokes equations with revised Maxwell law

In this paper, we study the low Mach number limit of the full compressible Navier-Stokes equations with revised Maxwell law. By applying the uniform estimation of the error system, we prove that the solutions of the full compressible Navier-Stokes equations with time relaxation converge to that of the incompressible Navier-Stokes equations as the Mach number tends to zero. Moreover, the convergence rates are also obtained.

math.AP↗