arXiv subjects

Tao Zhang

Publications and source records attributed to Tao Zhang.

At least 541 records · Page 30Linked to original sources

arXiv2021

SWFC-ART: A Cost-effective Approach for Fixed-Size-Candidate-Set Adaptive Random Testing through Small World Graphs

Adaptive random testing (ART) improves the failure-detection effectiveness of random testing by leveraging properties of the clustering of failure-causing inputs of most faulty programs: ART uses a sampling mechanism that evenly spreads test cases within a software's input domain. The widely-used Fixed-Sized-Candidate-Set ART (FSCS-ART) sampling strategy faces a quadratic time cost, which worsens as the dimensionality of the software input domain increases. In this paper, we propose an approach based on small world graphs that can enhance the computational efficiency of FSCS-ART: SWFC-ART. To efficiently perform nearest neighbor queries for candidate test cases, SWFC-ART incrementally constructs a hierarchical navigable small world graph for previously executed, non-failure-causing test cases. Moreover, SWFC-ART has shown consistency in programs with high dimensional input domains. Our simulation and empirical studies show that SWFC-ART reduces the computational overhead of FSCS-ART from quadratic to log-linear order while maintaining the failure-detection effectiveness of FSCS-ART, and remaining consistent in high dimensional input domains. We recommend using SWFC-ART in practical software testing scenarios, where real-life programs often have high dimensional input domains and low failure rates.

cs.SE↗

arXiv2021

On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment

This paper studies dynamic mechanism design in a quasilinear Markovian environment and analyzes a direct mechanism model of a principal-agent framework in which the agent is allowed to exit at any period. We consider that the agent's private information, referred to as state, evolves over time. The agent makes decisions of whether to stop or continue and what to report at each period. The principal, on the other hand, chooses decision rules consisting of an allocation rule and a set of payment rules to maximize her ex-ante expected payoff. In order to influence the agent's stopping decision, one of the terminal payment rules is posted-price, i.e., it depends only on the realized stopping time of the agent. We define the incentive compatibility in this dynamic environment in terms of Bellman equations, which is then simplified by establishing a one-shot deviation principle. Given the optimality of the stopping rule, a sufficient condition for incentive compatibility is obtained by constructing the state-dependent payment rules in terms of a set of functions parameterized by the allocation rule. A necessary condition is derived from envelope theorem, which explicitly formulates the state-dependent payment rules in terms of allocation rules. A class of monotone environment is considered to characterize the optimal stopping by a threshold rule. The posted-price payment rules are then pinned down in terms of the allocation rule and the threshold function up to a constant. The incentive compatibility constraints restrict the design of the posted-price payment rule by a regular condition.

math.DS↗

arXiv2021

Search for Light Dark Matter-Electron Scatterings in the PandaX-II Experiment

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$.

hep-ex↗

arXiv2021

FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution

Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale. However, residual networks suffer from representational redundancy by introducing identity paths that impede the full exploitation of model capacity. Besides, blindly enlarging network scale can cause more problems in model training, even with residual learning. In this paper, a novel fully channel-concatenated network (FC$^2$N) is presented to make further mining of representational capacity of deep models, in which all interlayer skips are implemented by a simple and straightforward operation, i.e., weighted channel concatenation (WCC), followed by a 1$\times$1 conv layer. Based on the WCC, the model can achieve the joint attention mechanism of linear and nonlinear features in the network, and presents better performance than other state-of-the-art SR models with fewer model parameters. To our best knowledge, FC$^2$N is the first CNN model that does not use residual learning and reaches network depth over 400 layers. Moreover, it shows excellent performance in both largescale and lightweight implementations, which illustrates the full exploitation of the representational capacity of the model.

eess.IV↗

arXiv2021

Low Pass Filter for Anti-aliasing in Temporal Action Localization

In temporal action localization methods, temporal downsampling operations are widely used to extract proposal features, but they often lead to the aliasing problem, due to lacking consideration of sampling rates. This paper aims to verify the existence of aliasing in TAL methods and investigate utilizing low pass filters to solve this problem by inhibiting the high-frequency band. However, the high-frequency band usually contains large amounts of specific information, which is important for model inference. Therefore, it is necessary to make a tradeoff between anti-aliasing and reserving high-frequency information. To acquire optimal performance, this paper learns different cutoff frequencies for different instances dynamically. This design can be plugged into most existing temporal modeling programs requiring only one additional cutoff frequency parameter. Integrating low pass filters to the downsampling operations significantly improves the detection performance and achieves comparable results on THUMOS'14, ActivityNet~1.3, and Charades datasets. Experiments demonstrate that anti-aliasing with low pass filters in TAL is advantageous and efficient.

cs.CV↗

arXiv2021

Bootstrap inference for quantile-based modal regression

In this paper, we develop uniform inference methods for the conditional mode based on quantile regression. Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator. Building on high-dimensional Gaussian approximation techniques, we establish the validity of simultaneous confidence rectangles constructed from the two bootstrap methods for the conditional mode. We also extend the preceding analysis to the case where the dimension of the covariate vector is increasing with the sample size. Finally, we conduct simulation experiments and a real data analysis using U.S. wage data to demonstrate the finite sample performance of our inference method.

math.ST↗

arXiv2021

Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack

Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown deep diagnostic models may not be robust in the inference process and may pose severe security concerns in clinical practice. Among all the factors that make the model not robust, the most serious one is adversarial examples. The so-called "adversarial example" is a well-designed perturbation that is not easily perceived by humans but results in a false output of deep diagnostic models with high confidence. In this paper, we evaluate the robustness of deep diagnostic models by adversarial attack. Specifically, we have performed two types of adversarial attacks to three deep diagnostic models in both single-label and multi-label classification tasks, and found that these models are not reliable when attacked by adversarial example. We have further explored how adversarial examples attack the models, by analyzing their quantitative classification results, intermediate features, discriminability of features and correlation of estimated labels for both original/clean images and those adversarial ones. We have also designed two new defense methods to handle adversarial examples in deep diagnostic models, i.e., Multi-Perturbations Adversarial Training (MPAdvT) and Misclassification-Aware Adversarial Training (MAAdvT). The experimental results have shown that the use of defense methods can significantly improve the robustness of deep diagnostic models against adversarial attacks.

cs.CV↗

arXiv2021

Universal central extensions of Hom-Lie antialgebras

We develop a theory of universal central extensions for Hom-Lie antialgebra. It is proved that a Hom-Lie antialgebra admits a universal central extension if and only if it is perfect. Moreover, we show that the kernel of the universal central extension is equal to the second homology group with trivial coefficients.

math.RA↗

arXiv2021

GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS

Object detection is widely used on embedded devices. With the wide availability of CNN (Convolutional Neural Networks) accelerator chips, the object detection applications are expected to run with low power consumption, and high inference speed. In addition, the CPU load is expected to be as low as possible for a CNN accelerator chip working as a co-processor with a host CPU. In this paper, we optimize the object detection model on the CNN accelerator chip by minimizing the CPU load. The resulting model is called GnetDet. The experimental result shows that the GnetDet model running on a 224mW chip achieves the speed of 106FPS with excellent accuracy.

cs.AR↗

arXiv2021

Crossed modules for Hom-Lie antialgebras

In this paper, we introduced the concept of crossed module for Hom-Lie antialgebras. It is proved that the category of crossed modules for Hom-Lie antialgebras and the category of $Cat^1$-Hom-Lie antialgebras are equivalent to each other. The relationship between the crossed module extension of Hom-Lie antialgebras and the third cohomology group are investigated.

math.RA↗

arXiv2021

Determination of responses of liquid xenon to low energy electron and nuclear recoils using the PandaX-II detector

We report a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or $^{220}$Rn source, and with $^{241}$Am-Be neutron source, within an energy range from $1-25$ keV (ER) and $4-80$ keV (NR), under the two drift fields of 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can well describe the calibration data. The systematic uncertainties of the fitted models are obtained via statistical comparison against the data.

physics.ins-det↗

arXiv2021

Generative Partial Visual-Tactile Fused Object Clustering

Visual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering performance. However, the missing data (i.e., partial data) issues always happen due to occlusion and noises during the data collecting process. This issue is not well solved by most existing partial multi-view clustering methods for the heterogeneous modality challenge. Naively employing these methods would inevitably induce a negative effect and further hurt the performance. To solve the mentioned challenges, we propose a Generative Partial Visual-Tactile Fused (i.e., GPVTF) framework for object clustering. More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces. A conditional cross-modal clustering generative adversarial network is then developed to synthesize one modality conditioning on the other modality, which can compensate missing samples and align the visual and tactile modalities naturally by adversarial learning. To the end, two pseudo-label based KL-divergence losses are employed to update the corresponding modality-specific encoders. Extensive comparative experiments on three public visual-tactile datasets prove the effectiveness of our method.

cs.RO↗

arXiv2021

On the Equilibrium Elicitation of Markov Games Through Information Design

This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (designer) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient perfect Bayesian Markov Nash equilibria (O-PBME). A new framework for information design is proposed based on an approach of maximizing the optimal slack variables. Finally, we formulate the designer's goal selection problem and characterize it in terms of information design by establishing a relationship between the O-PBME and the Bayesian Markov correlated equilibria, in which we build upon the revelation principle in classic information design in economics. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.

cs.MA↗

arXiv2021

Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems

This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) with no need of re-training. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions.

cs.NE↗

arXiv2021

Improving the Certified Robustness of Neural Networks via Consistency Regularization

A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker. However, most of these provable defense methods treat all examples equally during training process, which ignore the inconsistent constraint of certified robustness between correctly classified (natural) and misclassified examples. In this paper, we explore this inconsistency caused by misclassified examples and add a novel consistency regularization term to make better use of the misclassified examples. Specifically, we identified that the certified robustness of network can be significantly improved if the constraint of certified robustness on misclassified examples and correctly classified examples is consistent. Motivated by this discovery, we design a new defense regularization term called Misclassification Aware Adversarial Regularization (MAAR), which constrains the output probability distributions of all examples in the certified region of the misclassified example. Experimental results show that our proposed MAAR achieves the best certified robustness and comparable accuracy on CIFAR-10 and MNIST datasets in comparison with several state-of-the-art methods.

cs.LG↗

arXiv2021

Correlated Data in Differential Privacy: Definition and Analysis

Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real-world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: 1) using parameters to describe data correlation in differential privacy, 2) using models to describe data correlation in differential privacy, and 3) describing data correlation based on the framework of Pufferfish. Firstly, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.

cs.CR↗

arXiv2021

Internal Calibration of the PandaX-II Detector with Radon Gaseous Sources

We have developed a low-energy electron recoil (ER) calibration method with $^{220}$Rn for the PandaX-II detector. $^{220}$Rn, emanated from natural thorium compounds, was fed into the detector through the xenon purification system. From 2017 to 2019, we performed three dedicated calibration campaigns with different radon sources. We studied the detector response to $α$, $β$, and $γ$ particles with focus on low energy ER events. During the runs in 2017 and 2018, the amount of radioactivity of $^{222}$Rn were on the order of 1\% of that of $^{220}$Rn and thorium particulate contamination was negligible, especially in 2018. We also measured the background contribution from $^{214}$Pb for the first time in PandaX-II with the help from a $^{222}$Rn injection. Calibration strategy with $^{220}$Rn and $^{222}$Rn will be implemented in the upcoming PandaX-4T experiment and can be useful for other xenon-based detectors as well.

physics.ins-det↗

arXiv2021

Results of Dark Matter Search using the Full PandaX-II Exposure

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

astro-ph.CO↗