arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 829 records · Page 46Linked to original sources

arXiv2020

SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images

The popularity of various social platforms has prompted more people to share their routine photos online. However, undesirable privacy leakages occur due to such online photo sharing behaviors. Advanced deep neural network (DNN) based object detectors can easily steal users' personal information exposed in shared photos. In this paper, we propose a novel adversarial example based privacy-preserving technique for social images against object detectors based privacy stealing. Specifically, we develop an Object Disappearance Algorithm to craft two kinds of adversarial social images. One can hide all objects in the social images from being detected by an object detector, and the other can make the customized sensitive objects be incorrectly classified by the object detector. The Object Disappearance Algorithm constructs perturbation on a clean social image. After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded. We use two metrics, privacy-preserving success rate and privacy leakage rate, to evaluate the effectiveness of the proposed method. Experimental results show that, the proposed method can effectively protect the privacy of social images. The privacy-preserving success rates of the proposed method on MS-COCO and PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the privacy leakage rates on these two datasets are as low as 0.57% and 0.07%, respectively. In addition, compared with existing image processing methods (low brightness, noise, blur, mosaic and JPEG compression), the proposed method can achieve much better performance in privacy protection and image visual quality maintenance.

cs.CV↗

arXiv2020

3D Invisible Cloak

In this paper, we propose a novel physical stealth attack against the person detectors in real world. The proposed method generates an adversarial patch, and prints it on real clothes to make a three dimensional (3D) invisible cloak. Anyone wearing the cloak can evade the detection of person detectors and achieve stealth. We consider the impacts of those 3D physical constraints (i.e., radian, wrinkle, occlusion, angle, etc.) on person stealth attacks, and propose 3D transformations to generate 3D invisible cloak. We launch the person stealth attacks in 3D physical space instead of 2D plane by printing the adversarial patches on real clothes under challenging and complex 3D physical scenarios. The conventional and 3D transformations are performed on the patch during its optimization process. Further, we study how to generate the optimal 3D invisible cloak. Specifically, we explore how to choose input images with specific shapes and colors to generate the optimal 3D invisible cloak. Besides, after successfully making the object detector misjudge the person as other objects, we explore how to make a person completely disappeared, i.e., the person will not be detected as any objects. Finally, we present a systematic evaluation framework to methodically evaluate the performance of the proposed attack in digital domain and physical world. Experimental results in various indoor and outdoor physical scenarios show that, the proposed person stealth attack method is robust and effective even under those complex and challenging physical conditions, such as the cloak is wrinkled, obscured, curved, and from different angles. The attack success rate in digital domain (Inria data set) is 86.56%, while the static and dynamic stealth attack performance in physical world is 100% and 77%, respectively, which are significantly better than existing works.

cs.CV↗

arXiv2020

Tunable discrete scale invariance in transition-metal pentatelluride flakes

Log-periodic quantum oscillations discovered in transition-metal pentatelluride give a clear demonstration of discrete scale invariance (DSI) in solid-state materials. The peculiar phenomenon is convincingly interpreted as the presence of two-body quasi-bound states in a Coulomb potential. However, the modifications of the Coulomb interactions in many-body systems having a Dirac-like spectrum are not fully understood. Here, we report the observation of tunable log-periodic oscillations and DSI in ZrTe5 and HfTe5 flakes. By reducing the flakes thickness, the characteristic scale factor is tuned to a much smaller value due to the reduction of the vacuum polarization effect. The decreasing of the scale factor demonstrates the many-body effect on the DSI, which has rarely been discussed hitherto. Furthermore, the cut-offs of oscillations are quantitatively explained by considering the Thomas-Fermi screening effect. Our work clarifies the many-body effect on DSI and paves a way to tune the DSI in quantum materials.

cond-mat.mtrl-sci↗

arXiv2020

Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach

Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks. BYOL works like a charm despite the fact that it discards the negative samples completely and there is no measure to prevent collapse in its training objective. In this paper, we suggest understanding BYOL from the view of our proposed interpretable self-supervised learning framework, Run Away From your Teacher (RAFT). RAFT optimizes two objectives at the same time: (i) aligning two views of the same data to similar representations and (ii) running away from the model's Mean Teacher (MT, the exponential moving average of the history models) instead of BYOL's running towards it. The second term of RAFT explicitly prevents the representation collapse and thus makes RAFT a more conceptually reliable framework. We provide basic benchmarks of RAFT on CIFAR10 to validate the effectiveness of our method. Furthermore, we prove that BYOL is equivalent to RAFT under certain conditions, providing solid reasoning for BYOL's counter-intuitive success.

cs.LG↗

arXiv2020

Higgs boson pair production at N$^3$LO QCD

Understanding the Higgs potential by measuring its self-interactions is fundamental in answering several big questions, such as electroweak symmetry breaking, electroweak baryogenesis, electroweak phase transition, and electroweak vacuum stability. The most promising way to probe the Higgs potential is to detect Higgs boson pair final state at high-energy colliders. In this talk, we report a recent perturbative calculation for the di-Higgs gluon-fusion process by taking into account N$^3$LO QCD radiative corrections in the approximation of infinite top quark mass limit. Finite top quark mass effects are also incorporated with several approximate schemes, which are known to be crucial in phenomenological applications. We show a very good asymptotic perturbative convergence at $\mathcal{O}(α_s^5)$, and demonstrate that the remaining scale uncertainty is only at percent level.

hep-ph↗

arXiv2020

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of the blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, named motion-based adversarial blur attack (ABBA). To this end, we first formulate the kernel-prediction-based attack where an input image is convolved with kernels in a pixel-wise way, and the misclassification capability is achieved by tuning the kernel weights. To generate visually more natural and plausible examples, we further propose the saliency-regularized adversarial kernel prediction, where the salient region serves as a moving object, and the predicted kernel is regularized to achieve naturally visual effects. Besides, the attack is further enhanced by adaptively tuning the translations of object and background. A comprehensive evaluation on the NeurIPS'17 adversarial competition dataset demonstrates the effectiveness of ABBA by considering various kernel sizes, translations, and regions. The in-depth study further confirms that our method shows more effective penetrating capability to the state-of-the-art GAN-based deblurring mechanisms compared with other blurring methods. We release the code to https://github.com/tsingqguo/ABBA.

cs.CV↗

arXiv2020

Full Counting Statistics of Phonon Transport in Disordered Systems

The coherent potential approximation (CPA) within full counting statistics (FCS) formalism is shown to be a suitable method to investigate average electric conductance, shot noise as well as higher order cumulants in disordered systems. We develop a similar FCS-CPA formalism for phonon transport through disordered systems. As a byproduct, we derive relations among coefficients of different phonon current cumulants. We apply the FCS-CPA method to investigate phonon transport properties of graphene systems in the presence of disorders. For binary disorders as well as Anderson disorders, we calculate up to the $8$-th phonon transmission moments and demonstrate that the numerical results of the FCS-CPA method agree very well with that of the brute force method. The benchmark shows that the FCS-CPA method achieves $20$ times more speedup ratio. Collective features of phonon current cumulants are also revealed.

cond-mat.mes-hall↗

arXiv2020

Exponential ergodicity for SDEs and McKean-Vlasov processes with Lévy noise

We study stochastic differential equations (SDEs) of McKean-Vlasov type with distribution dependent drifts and driven by pure jump Lévy processes. We prove a uniform in time propagation of chaos result, providing quantitative bounds on convergence rate of interacting particle systems with Lévy noise to the corresponding McKean-Vlasov SDE. By applying techniques that combine couplings, appropriately constructed $L^1$-Wasserstein distances and Lyapunov functions, we show exponential convergence of solutions of such SDEs to their stationary distributions. Our methods allow us to obtain results that are novel even for a broad class of Lévy-driven SDEs with distribution independent coefficients.

math.PR↗

arXiv2020

Soliton dynamics in microresonators with XPM induced negative thermo-optic effect

Optical frequency comb generation in microresonators has attracted significant attention over the past decade, as it offers the promising potential for chip-scale optical frequency synthesis, optical clocks and precise optical spectroscopy. However, accessing temporal dissipative Kerr soliton (DKSs) is known to be severely hampered by thermal effects. Furthermore, due to the degeneracy of soliton existence range with respect to soliton number, deterministically accessing single soliton state is another challenge. Here, we demonstrate stable and deterministic single soliton generation in AlN-on-sapphire platform via auxiliary laser pumping scheme without the requirement of fast control of the pump power and detuning. Moreover, we reveal the underlying physics of soliton switching in a dual-pumped microcomb, which is fully described by the Lugiato - Lefever equation. The switching process is attributed to cross-phase modulation (XPM) induced degeneracy lifting of the soliton existence range, corresponding to an effective negative thermo-optic effect.

physics.optics↗

arXiv2020

Integrated Gallium Nitride Nonlinear Photonics

Gallium nitride (GaN) as a wide-band gap material has been widely used in solid-state lighting. Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications. Despite its intriguing optical proprieties, nonlinear applications of GaN have rarely been studied due to the relatively high optical loss of GaN waveguides (2 dB/cm). In this letter, we report GaNOI microresonator with intrinsic quality factor over 2 million, corresponding to an optical loss of 0.26 dB/cm. Parametric oscillation threshold power as low as 8.8 mW is demonstrated, and the experimentally extracted nonlinear index of GaN at telecom wavelengths is estimated to be n2 = 1.2*10 -18 m2W-1, which is comparable with silicon. Single soliton generation in GaN is implemented by an auxiliary laser pumping scheme, so as to mitigate the high thermorefractive effect in GaN. The large intrinsic nonlinear refractive index, together with its broadband transparency window and high refractive index contrast, make GaNOI a most balanced platform for chip-scale nonlinear applications.

physics.optics↗

arXiv2020

Turán Problems for Vertex-disjoint Cliques in Multi-partite Hypergraphs

For two $s$-uniform hypergraphs $H$ and $F$, the Turán number $ex_s(H,F)$ is the maximum number of edges in an $F$-free subgraph of $H$. Let $s, r, k, n_1, \ldots, n_r$ be integers satisfying $2\leq s\leq r$ and $n_1\leq n_2\leq \cdots\leq n_r$. De Silva, Heysse and Young determined $ex_2(K_{n_1, \ldots, n_r}, kK_2)$ and De Silva, Heysse, Kapilow, Schenfisch and Young determined $ex_2(K_{n_1, \ldots, n_r},kK_r)$. In this paper, as a generalization of these results, we consider three Turán-type problems for $k$ disjoint cliques in $r$-partite $s$-uniform hypergraphs. First, we consider a multi-partite version of the Erdős matching conjecture and determine $ex_s(K_{n_1, \ldots, n_r}^{(s)},kK_s^{(s)})$ for $n_1\geq s^3k^2+sr$. Then, using a probabilistic argument, we determine $ex_s(K_{n_1, \ldots, n_r}^{(s)},kK_r^{(s)})$ for all $n_1\geq k$. Recently, Alon and Shikhelman determined asymptotically, for all $F$, the generalized Turán number $ex_2(K_n,K_s,F)$, which is the maximum number of copies of $K_s$ in an $F$-free graph on $n$ vertices. Here we determine $ex_2(K_{n_1, \ldots, n_r}, K_s, kK_r)$ with $n_1\geq k$ and $n_3=\cdots=n_r$. Utilizing a result on rainbow matchings due to Glebov, Sudakov and Szabó, we determine $ex_2(K_{n_1, \ldots, n_r}, K_s, kK_r)$ for all $n_1, \ldots, n_r$ with $n_4\geq r^r(k-1)k^{2r-2}$.

math.CO↗

arXiv2020

The Maximum Number of Cliques in Hypergraphs without Large Matchings

Let $[n]$ denote the set $\{1, 2, \ldots, n\}$ and $\mathcal{F}^{(r)}_{n,k,a}$ be an $r$-uniform hypergraph on the vertex set $[n]$ with edge set consisting of all the $r$-element subsets of $[n]$ that contains at least $a$ vertices in $[ak+a-1]$. For $n\geq 2rk$, Frankl proved that $\mathcal{F}^{(r)}_{n,k,1}$ maximizes the number of edges in $r$-uniform hypergraphs on $n$ vertices with the matching number at most $k$. Huang, Loh and Sudakov considered a multicolored version of the Erdős matching conjecture, and provided a sufficient condition on the number of edges for a multicolored hypergraph to contain a rainbow matching of size $k$. In this paper, we show that $\mathcal{F}^{(r)}_{n,k,a}$ maximizes the number of $s$-cliques in $r$-uniform hypergraphs on $n$ vertices with the matching number at most $k$ for sufficiently large $n$, where $a=\lfloor \frac{s-r}{k} \rfloor+1$. We also obtain a condition on the number of $s$-clques for a multicolored $r$-uniform hypergraph to contain a rainbow matching of size $k$, which reduces to the condition of Huang, Loh and Sudakov when $s=r$.

math.CO↗

arXiv2020

Quenched invariance principle for long range random walks in balanced random environments

We establish via a probabilistic approach the quenched invariance principle for a class of long range random walks in independent (but not necessarily identically distributed) balanced random environments, with the transition probability from $x$ to $y$ on average being comparable to $|x-y|^{-(d+α)}$ with $α\in (0,2]$. We use the martingale property to estimate exit time from balls and establish tightness of the scaled processes, and apply the uniqueness of the martingale problem to identify the limiting process. When $α\in (0,1)$, our approach works even for non-balanced cases. When $α=2$, under a diffusive with the logarithmic perturbation scaling, we show that the limit of scaled processes is a Brownian motion.

math.PR↗

arXiv2020

Breaking Rayleigh's Criterion via Discernibility in High-Dimensional Light-Field Space with Snapshot Ghost Imaging

By encoding the high-dimensional light-field imaging information into a detectable two-dimensional speckle plane, ghost imaging camera via sparsity constraints (GISC camera) can directly catch the high-dimensional light-field imaging information with only one snapshot. This makes it worth to revisit the spatial resolution limit of this optical imaging system. In this paper we show both theoretically and experimentally that, while the resolution in high-dimensional light-field space is still limited by diffraction, the statistical spatial resolution of GISC camera can be greatly improved comparing to classical Rayleigh's criterion by utilizing the discernibility in high-dimensional light-field space. The interaction between imaging resolution, degrees of freedom of light field, and degrees of freedom of objects in high-dimensional light-field space is also demonstrated.

physics.optics↗

arXiv2020

Heat kernel upper bounds for symmetric Markov semigroups

It is well known that Nash-type inequalities for symmetric Dirichlet forms are equivalent to on-diagonal heat kernel upper bounds for the associated symmetric Markov semigroups. In this paper, we show that both imply (and hence are equivalent to) off-diagonal heat kernel upper bounds under some mild assumptions. Our approach is based on a new generalized Davies' method. Our results extend that of \cite{CKS} for Nash-type inequalities with power order considerably and also extend that of \cite{Gri} for second order differential operators on a complete non-compact manifold.

math.PR↗

arXiv2020

Capping layer influence and isotropic in-plane upper critical field of the superconductivity at the FeSe/SrTiO3 interface

Understanding the superconductivity at the interface of FeSe/SrTiO3 is a problem of great contemporary interest due to the significant increase in critical temperature (Tc) compared to that of bulk FeSe, as well as the possibility of an unconventional pairing mechanism and topological superconductivity. We report a study of the influence of a capping layer on superconductivity in thin films of FeSe grown on SrTiO3 using molecular beam epitaxy. We used in vacuo four-probe electrical resistance measurements and ex situ magneto-transport measurements to examine the effect of three capping layers that provide distinctly different charge transfer into FeSe: compound FeTe, non-metallic Te, and metallic Zr. Our results show that FeTe provides an optimal cap that barely influences the inherent Tc found in pristine FeSe/SrTiO3, while the transfer of holes from a non-metallic Te cap completely suppresses superconductivity and leads to insulating behavior. Finally, we used ex situ magnetoresistance measurements in FeTe-capped FeSe films to extract the angular dependence of the in-plane upper critical magnetic field. Our observations reveal an almost isotropic in-plane upper critical field, providing insight into the symmetry and pairing mechanism of high temperature superconductivity in FeSe.

cond-mat.supr-con↗

arXiv2020

ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities

As a novel method eliminating chromatic aberration on objects, computational color constancy has becoming a fundamental prerequisite for many computer vision applications. Among algorithms performing this task, the learning-based ones have achieved great success in recent years. However, they fail to fully consider the spatial information of images, leaving plenty of room for improvement of the accuracy of illuminant estimation. In this paper, by exploiting the spatial information of images, we propose a color constancy algorithm called Attention Dense Color Constancy (ADCC) using convolutional neural network (CNN). Specifically, based on the 2D log-chrominance histograms of the input images as well as their specially augmented ones, ADCC estimates the illuminant with a self-attention DenseNet. The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation. Simulations and experiments on benchmark datasets demonstrate that the proposed algorithm is effective for illuminant estimation compared to the state-of-the-art methods. Thus, ADCC offers great potential in promoting applications of smart cities, such as smart camera, where color is an important factor for distinguishing objects.

cs.CV↗

arXiv2020

Anomalous linear magnetoresistance in high quality crystalline lead thin films

Intriguing novel phenomena in lead films inspire new understanding of quantum physics, such as quantum size effect and quantum phase transitions etc. The improvement of the sample quality makes it even more promising to explore the intrinsic properties in two-dimensional system. In this paper, we show that the crystalline interfacial striped incommensurate layer can increase the quality of the lead films and significantly enhance the magnitude of magnetoresistance. By performing systematic transport measurement, a predominant anomalous linear magnetoresistance is revealed, and the widely used Parish-Littlewood model and Abrikosov's explanation fail to describe the observation. Instead, we propose a new model of linear magnetoresistance based on linear band structure, which shows a good agreement with the experimental results. Our studies reveal a novel origin of linear magnetoresistance which may also be helpful to understand the linear magnetoresistance in other materials with linear dispersion of electronic structure.

cond-mat.mes-hall↗