arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 757 records · Page 42Linked to original sources

arXiv2022

Can the graviton have a large mass near black holes?

The mass of the graviton, if nonzero, is usually considered to be very small, e.g. of the Hubble scale, from several observational constraints. In this paper, we propose a gravity model where the graviton mass is very small in the usual weak gravity environments, below all the current graviton mass bounds, but becomes much larger in the strong gravity regime such as a black hole's vicinity. For black holes in this model, significant deviations from general relativity emerge very close to the black hole horizon and alter the black hole quasi-normal modes, which can be extracted from the ringdown waveform of black hole binary mergers. Also, the enhancement of the graviton mass near the horizon can result in echoes in the late time ringdown, which can be verified in the upcoming gravitational wave observations of higher sensitivity.

gr-qc↗

arXiv2022

An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8

We extend the halo-based group finder developed by \citet[][]{Yang2005a} to use data {\it simultaneously} with either photometric or spectroscopic redshifts. A mock galaxy redshift survey constructed from a high-resolution N-body simulation is used to evaluate the performance of this extended group finder. For galaxies with magnitude ${\rm z\le 21}$ and redshift $0<z\le 1.0$ in the DESI legacy imaging surveys (the Legacy Surveys), our group finder successfully identifies more than 60\% of the members in about $90\%$ of halos with mass $\ga 10^{12.5}\msunh$. Detected groups with mass $\ga 10^{12.0}\msunh$ have a purity (the fraction of true groups) greater than 90\%. The halo mass assigned to each group has an uncertainty of about 0.2 dex at the high mass end $\ga 10^{13.5}\msunh$ and 0.40 dex at the low mass end. Groups with more than 10 members have a redshift accuracy of $\sim 0.008$. We apply this group finder to the Legacy Surveys DR8 and find 5.2 Million groups with at least 3 members. About 387,000 of these groups have at least 10 members. The resulting catalog containing 3D coordinates, richness, halo masses, and total group luminosities, is made publicly available.

astro-ph.GA↗

arXiv2022

Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting

Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models. However, when the source data is unavailable, it is challenging to just erase the watermark without jeopardizing the model performance. In this context, we introduce Attention Distraction (AD), a novel source data-free watermark removal attack, to make the model selectively forget the embedded watermarks by customizing continual learning. In particular, AD first anchors the model's attention on the main task using some unlabeled data. Then, through continual learning, a small number of \textit{lures} (randomly selected natural images) that are assigned a new label distract the model's attention away from the watermarks. Experimental results from different datasets and networks corroborate that AD can thoroughly remove the watermark with a small resource budget without compromising the model's performance on the main task, which outperforms the state-of-the-art works.

cs.CV↗

arXiv2022

A class of twisted generalized Reed-Solomon codes

Let $\mathbb{F}_q$ be a finite field of size $q$ and $\mathbb{F}_q^*$ the set of non-zero elements of $\mathbb{F}_q$. In this paper, we study a class of twisted generalized Reed-Solomon code $C_\ell(D, k, η, \vec{v})\subset \mathbb{F}_q^n$ generated by the following matrix \[ \left(\begin{array}{cccc} v_{1} & v_{2} & \cdots & v_{n} \\ v_{1} α_{1} & v_{2} α_{2} & \cdots & v_{n} α_{n} \\ \vdots & \vdots & \ddots & \vdots \\ v_{1} α_{1}^{\ell-1} & v_{2} α_{2}^{\ell-1} & \cdots & v_{n} α_{n}^{\ell-1} \\ v_{1} α_{1}^{\ell+1} & v_{2} α_{2}^{\ell+1} & \cdots & v_{n} α_{n}^{\ell+1} \\ \vdots & \vdots & \ddots & \vdots \\ v_{1} α_{1}^{k-1} & v_{2} α_{2}^{k-1} & \cdots & v_{n} α_{n}^{k-1} \\ v_{1}\left(α_{1}^{\ell}+ηα_{1}^{q-{2}}\right) & v_{2}\left(α_{2}^{\ell}+ ηα_{2}^{q-2}\right) &\cdots & v_{n}\left(α_{n}^{\ell}+ηα_{n}^{q-2}\right) \end{array}\right) \] where $0\leq \ell\leq k-1,$ the evaluation set $D=\{α_{1},α_{2},\cdots, α_{n}\}\subseteq \mathbb{F}_q^*$, scaling vector $\vec{v}=(v_1,v_2,\cdots,v_n)\in (\mathbb{F}_q^*)^n$ and $η\in\mathbb{F}_q^*$. The minimum distance and dual code of $C_\ell(D, k, η, \vec{v})$ will be determined. For the special case $\ell=k-1,$ a sufficient and necessary condition for $C_{k-1}(D, k, η, \vec{v})$ to be self-dual will be given. We will also show that the code is MDS or near-MDS. Moreover, a complete classification when the code is near-MDS or MDS will be presented.

cs.IT↗

arXiv2022

Who is next: rising star prediction via diffusion of user interest in social networks

Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to best-sellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.

cs.SI↗

arXiv2022

Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images

Exploiting light field data makes it possible to obtain dense and accurate depth map. However, synthetic scenes with limited disparity range cannot contain the diversity of real scenes. By training in synthetic data, current learning-based methods do not perform well in real scenes. In this paper, we propose a self-supervised learning framework for light field depth estimation. Different from the existing end-to-end training methods using disparity label per pixel, our approach implements network training by estimating EPI disparity shift after refocusing, which extends the disparity range of epipolar lines. To reduce the sensitivity of EPI to noise, we propose a new input mode called EPI-Stack, which stacks EPIs in the view dimension. This method is less sensitive to noise scenes than traditional input mode and improves the efficiency of estimation. Compared with other state-of-the-art methods, the proposed method can also obtain higher quality results in real-world scenarios, especially in the complex occlusion and depth discontinuity.

cs.CV↗

arXiv2022

Symplectic non-convexity of toric domains

We investigate the convexity up to symplectomorphism (called symplectic convexity) of star-shaped toric domains in $\mathbb R^4$. In particular, based on the criterion from Chaidez-Edtmair via Ruelle invariant and systolic ratio of the boundary of star-shaped toric domains, we provide elementary operations on domains that can kill the symplectic convexity. These operations only result in $C^0$-small perturbations in terms of domains' volume. Moreover, one of the operations is a systematic way to produce examples of dynamically convex but not symplectically convex toric domains. Finally, we are able to provide concrete bounds for the constants that appear in Chaidez-Edtmair's criterion.

math.SG↗

arXiv2022

Hamiltonian perturbations in contact Floer homology

We study the contact Floer homology ${\rm HF}_*(W, h)$ introduced by Merry-Uljarević, which associates a Floer-type homology theory to a Liouville domain $W$ and a contact Hamiltonian $h$ on its boundary. The main results investigate the behavior of ${\rm HF}_*(W, h)$ under the perturbations of the input contact Hamiltonian $h$. In particular, we provide sufficient conditions that guarantee ${\rm HF}_*(W, h)$ to be invariant under the perturbations. This can be regarded as a contact geometry analogue of the continuation and bifurcation maps along the Hamiltonian perturbations of Hamiltonian Floer homology in symplectic geometry. As an application, we give an algebraic proof of a rigidity result concerning the positive loops of contactomorphisms for a wide class of contact manifolds.

math.SG↗

arXiv2022

Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack

DeepFakes are raising significant social concerns. Although various DeepFake detectors have been developed as forensic countermeasures, these detectors are still vulnerable to attacks. Recently, a few attacks, principally adversarial attacks, have succeeded in cloaking DeepFake images to evade detection. However, these attacks have typical detector-specific designs, which require prior knowledge about the detector, leading to poor transferability. Moreover, these attacks only consider simple security scenarios. Less is known about how effective they are in high-level scenarios where either the detectors or the attacker's knowledge varies. In this paper, we solve the above challenges with presenting a novel detector-agnostic trace removal attack for DeepFake anti-forensics. Instead of investigating the detector side, our attack looks into the original DeepFake creation pipeline, attempting to remove all detectable natural DeepFake traces to render the fake images more "authentic". To implement this attack, first, we perform a DeepFake trace discovery, identifying three discernible traces. Then a trace removal network (TR-Net) is proposed based on an adversarial learning framework involving one generator and multiple discriminators. Each discriminator is responsible for one individual trace representation to avoid cross-trace interference. These discriminators are arranged in parallel, which prompts the generator to remove various traces simultaneously. To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies. The experimental results show that the proposed attack can significantly compromise the detection accuracy of six state-of-the-art DeepFake detectors while causing only a negligible loss in visual quality to the original DeepFake samples.

cs.CV↗

arXiv2022

Invariant Information Bottleneck for Domain Generalization

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers and the original optimization objective could fail when pseudo-invariant features and geometric skews exist. Inspired by IRM, in this paper we propose a novel formulation for domain generalization, dubbed invariant information bottleneck (IIB). IIB aims at minimizing invariant risks for nonlinear classifiers and simultaneously mitigating the impact of pseudo-invariant features and geometric skews. Specifically, we first present a novel formulation for invariant causal prediction via mutual information. Then we adopt the variational formulation of the mutual information to develop a tractable loss function for nonlinear classifiers. To overcome the failure modes of IRM, we propose to minimize the mutual information between the inputs and the corresponding representations. IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM. Furthermore, experiments on DomainBed show that IIB outperforms $13$ baselines by $0.9\%$ on average across $7$ real datasets.

cs.LG↗

arXiv2022

How Neural Architectures Affect Deep Learning for Communication Networks?

In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures inherited from applications such as computer vision, which suffer from poor generalization, scalability, and lack of interpretability. To tackle these issues, domain knowledge has been integrated into the neural architecture design, which achieves near-optimal performance in large-scale networks and generalizes well under different system settings. This paper endeavors to theoretically validate the importance and effects of neural architectures when applying deep learning to design communication networks. We prove that by exploiting permutation invariance, a common property in communication networks, graph neural networks (GNNs) converge faster and generalize better than fully connected multi-layer perceptrons (MLPs), especially when the number of nodes (e.g., users, base stations, or antennas) is large. Specifically, we prove that under common assumptions, for a communication network with $n$ nodes, GNNs converge $O(n \log n)$ times faster and their generalization error is $O(n)$ times lower, compared with MLPs.

cs.IT↗

arXiv2022

Communication-Efficient Federated Distillation with Active Data Sampling

Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning from distributed data. Most previous works are based on federated average (FedAvg), which, however, faces several critical issues, including a high communication overhead and the difficulty in dealing with heterogeneous model architectures. Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients. However, so far there is no unified algorithmic framework or theoretical analysis for FD-based methods. In this paper, we first present a generic meta-algorithm for FD and investigate the influence of key parameters through empirical experiments. Then, we verify the empirical observations theoretically. Based on the empirical results and theory, we propose a communication-efficient FD algorithm with active data sampling to improve the model performance and reduce the communication overhead. Empirical simulations on benchmark datasets will demonstrate that our proposed algorithm effectively and significantly reduces the communication overhead while achieving a satisfactory performance.

cs.LG↗

arXiv2022

Enhanced clamshell swimming with asymmetric beating at low Reynolds number

A single flexible filament can be actuated to escape from the scallop theorem and generate net propulsion at low Reynolds number. In this work, we study the dynamics of a simple boundary-driven multi-filament swimmer, a two-arm clamshell actuated at the hinged point, using a nonlocal slender body approximation with full hydrodynamic interactions. We first consider an elastic clamshell consisted of flexible filaments with intrinsic curvature, and then build segmental models consisted of rigid segments connected by different mechanical joints with different forms of response torques. The simplicity of the system allows us to fully explore the effect of various parameters on the swimming performance. Optimal included angles and elastoviscous numbers are identified. The segmental models capture the characteristic dynamics of the elastic clamshell. We further demonstrate how the swimming performance can be significantly enhanced by the asymmetric beating patterns induced by biased torques.

physics.flu-dyn↗

arXiv2022

Berry-phase switch in electrostatically-confined topological surface states

Here we visualize the trapping of topological surface states in the circular n-p junctions on the top surface of the 7-quintuple-layer three dimensional (3D) topological insulator (TI) Sb2Te3 epitaxial films. As shown by spatially- and field-dependent tunneling spectra, these trapped resonances show field-induced splittings between the degenerate time-reversal-symmetric states at zero magnetic field. These behaviors are attributed unambiguously to Berry-phase switch by comparing the experimental data with both numerical and semi-classical simulations. The successful electrostatic trapping of topological surface states in epitaxial films and the observation of Berry-phase switch provide a rich platform of exploiting new ideas for TI-based quantum devices.

cond-mat.mes-hall↗

arXiv2022

Video is All You Need: Attacking PPG-based Biometric Authentication

Unobservable physiological signals enhance biometric authentication systems. Photoplethysmography (PPG) signals are convenient owning to its ease of measurement and are usually well protected against remote adversaries in authentication. Any leaked PPG signals help adversaries compromise the biometric authentication systems, and the advent of remote PPG (rPPG) enables adversaries to acquire PPG signals through restoration. While potentially dangerous, rPPG-based attacks are overlooked because existing methods require the victim's PPG signals. This paper proposes a novel spoofing attack approach that uses the waveforms of rPPG signals extracted from video clips to fool the PPG-based biometric authentication. We develop a new PPG restoration model that does not require leaked PPG signals for adversarial attacks. Test results on state-of-art PPG-based biometric authentication show that the signals recovered through rPPG pose a severe threat to PPG-based biometric authentication.

cs.CR↗

arXiv2022

Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification

The surrogate model-based uncertainty quantification method has drawn much attention in many engineering fields. Polynomial chaos expansion (PCE) and deep learning (DL) are powerful methods for building a surrogate model. However, PCE needs to increase the expansion order to improve the accuracy of the surrogate model, which causes more labeled data to solve the expansion coefficients, and DL also requires a lot of labeled data to train the deep neural network (DNN). First of all, this paper proposes the adaptive arbitrary polynomial chaos (aPC) and proves two properties about the adaptive expansion coefficients. Based on the adaptive aPC, a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method is proposed to reduce the training data cost and improve the surrogate model accuracy. For one hand, the Deep aPCE method uses two properties of the adaptive aPC to assist in training the DNN based on only a small amount of labeled data and many unlabeled data, significantly reducing the training data cost. On the other hand, the Deep aPCE method adopts the DNN to fine-tune the adaptive expansion coefficients dynamically, improving the Deep aPCE model accuracy with lower expansion order. Besides, the Deep aPCE method can directly construct accurate surrogate models of the high dimensional stochastic systems without complex dimension-reduction and model decomposition operations. Five numerical examples and an actual engineering problem are used to verify the effectiveness of the Deep aPCE method.

cs.LG↗

arXiv2022

Optimal quaternary $(r,δ)$-Locally Recoverable Codes: Their Structures and Complete Classification

Aiming to recover the data from several concurrent node failures, linear $r$-LRC codes with locality $r$ were extended into $(r, δ)$-LRC codes with locality $(r, δ)$ which can enable the local recovery of a failed node in case of more than one node failure. Optimal LRC codes are those whose parameters achieve the generalized Singleton bound with equality. In the present paper, we are interested in studying optimal LRC codes over small fields and, more precisely, over $\mathbb{F}_4$. We shall adopt an approach by investigating optimal quaternary $(r,δ)$-LRC codes through their parity-check matrices. Our study includes determining the structural properties of optimal $(r,δ)$-LRC codes, their constructions, and their complete classification over $\F_4$ by browsing all possible parameters. We emphasize that the precise structure of optimal quaternary $(r,δ)$-LRC codes and their classification are obtained via the parity-check matrix approach use proofs-techniques different from those used recently for optimal binary and ternary $(r,δ)$-LRC codes obtained by Hao et al. in [IEEE Trans. Inf. Theory, 2020, 66(12): 7465-7474].

cs.IT↗

arXiv2022

Various Activities above Sunspot Light Bridges in IRIS Observations: Classification and Comparison

Light bridges (LBs) are among the most striking sub-structures in sunspots, where various activities have been revealed by recent high-resolution observations from the Interface Region Imaging Spectrograph (IRIS). According to the variety of physical properties, we classified these activities into four distinct categories: transient brightening (TB), intermittent jet (IJ), type-I light wall (LW-I), and type-II light wall (LW-II). In IRIS 1400/1330 Å observations, TBs are characterized by abrupt emission enhancements, and IJs appear as collimated plasma ejections with a width of 1-2 Mm at some LB sites. Most observed TBs are associated with IJs and show superpositions of some chromosphere absorption lines on enhanced and broadened wings of C II and Si IV lines, which could be driven by intermittent magnetic reconnection in the lower atmosphere. LW-I and LW-II are wall-shaped structures with bright fronts above the whole LB. An LW-I has a continuous oscillating front with a typical height of several Mm and an almost stationary period of 4-5 minutes. On the contrary, an LW-II has a indented front with a height of over 10 Mm, which has no stable period and is accompanied by recurrent TBs in the entire LB. These results support that LW-IIs are driven by frequent reconnection occurring along the whole LB due to large-scale magnetic flux emergence or intrusion, rather than the leakage of waves producing LW-Is. Our observations reveal a highly dynamical scenario of activities above LBs driven by different basic physical processes, including magneto-convection, magnetic reconnection, and wave leakage.

astro-ph.SR↗