Showing 1–20 of 447 results
Peng Chen, Ji Li, Lesley A. Ward, Lixin Yan
We obtain weak-type $(p, p)$ endpoint bounds for Bochner-Riesz means for the Hermite operator $H=-Δ+|x|^2$ in ${\mathbb R}^n, n\geq 2$ and for other related operators for $1\leq p\leq 2n/(n+2)$, extending earlier results of Thangavelu and of Karadzhov.
Peng Chen, Jianzhong Liu, Hao Chen
Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to approaches for solving individual path constraints. However, they have difficulty solving path constraints that involve deeply nested conditional statements, which are common in image and video decoders, network packet analyzers, and checksum tools. We propose an approach for addressing this problem. First, we identify all the control flow-dependent conditional statements of the target conditional statement. Next, we select the data flow-dependent conditional statements. Finally, we use three strategies to find an input that satisfies all conditional statements simultaneously. We implemented this approach in a tool called Matryoshka and compared its effectiveness on 13 open source programs against other state-of-the-art fuzzers. Matryoshka found significantly more unique crashes than AFL, QSYM, and Angora. We manually classified those crashes into 41 unique new bugs, and obtained 12 CVEs. Our evaluation also uncovered the key technique contributing to Matryoshka's impressive performance: it collects only the nesting constraints that may cause the target conditional statements unreachable, which greatly simplifies the constraints that it has to solve.
Peng Chen, Xuan Thinh Duong, Danqing He, Sanghyuk Lee, Lixin Yan
Let $H = -Δ+ |x|^2$ be the Hermite operator in ${\mathbb R}^n$. In this paper we study almost everywhere convergence of the Bochner-Riesz means associated with $H$ which is defined by $S_R^λ(H)f(x) = \sum\limits_{k=0}^{\infty} \big(1-{2k+n\over R^2}\big)_+^λ P_k f(x).$ Here $P_k f$ is the $k$-th Hermite spectral projection operator. For $2\le p<\infty$, we prove that $$ \lim\limits_{R\to \infty} S_R^λ(H) f=f \ \ \ \text{a.e.} $$ for all $f\in L^p(\mathbb R^n)$ provided that $λ> λ(p)/2$ and $λ(p)=\max\big\{ n\big({1/2}-{1/p}\big)-{1/ 2}, \, 0\big\}.$ Conversely, we also show the convergence generally fails if $λ< λ(p)/2$ in the sense that there is an $f\in L^p(\mathbb R^n)$ for $2n/(n-1)\le p$ such that the convergence fails. This is in surprising contrast with a.e. convergence of the classical Bochner-Riesz means for the Laplacian. For $n\geq 2$ and $p\ge 2$ our result tells that the critical summability index for a.e. convergence for $S_R^λ(H)$ is as small as only the \emph{half} of the critical index for a.e. convergence of the classical Bochner-Riesz means. When $n = 1$, we show a.e. convergence holds for $f\in L^p({\mathbb R})$ with $ p\geq 2$ whenever $λ>0$. Compared with the classical result due to Askey and Wainger who showed the optimal $L^p$ convergence for $S_R^λ(H)$ on ${\mathbb R}$ we only need smaller summability index for a.e. convergence.
Chen Peng, Long Zhang, Zhong-Yi Lu
A higher-order (HO) symmetry-protected topological (SPT) state can be realized in a plaquette-modulated square lattice antiferromagnet, which hosts a spin-$1/2$ degenerate mode on each corner of the lattice with open boundaries. In this work, we show with the field-theoretic analysis and quantum Monte Carlo simulations that the plaquette modulation can drive a direct topological quantum phase transition from the HOSPT to a trivial disordered phase across the deconfined quantum critical point (DQCP) between the antiferromagnetic (AF) order and the valence bond solid (VBS) order, thus the DQCP is a multicritical point bridging both the AF-VBS transition and the topological transition of the HOSPT phase. Our work thus reveals the ubiquitous duality between topological transitions of SPT phases and DQCPs.
Ru Xu, Peng Chen, Jing Zhou, Yimeng Li, Yuyin Li, Tinggang Zhu, Kai Cheng, Dunjun Chen, Zili Xie, Jiandong Ye, Bin Liu, Xiangqian Xiu, Ping Han, Yi Shi, Rong Zhang, Youdou Zheng
GaN-based lateral Schottky diodes (SBDs) have attracted great attention for high-power applications due to its combined high electron mobility and large critical breakdown field. However, the breakdown voltage (BV) of the SBDs are far from exploiting the material advantages of GaN at present, limiting the desire to use GaN for ultra-high voltage (UHV) applications. Then, a golden question is whether the excellent properties of GaN-based materials can be practically used in the UHV field? Here we demonstrate UHV AlGaN/GaN SBDs on sapphire with a BV of 10.6 kV, a specific on-resistance of 25.8 mΩ.cm2, yielding a power figure of merit of more than 3.8 GW/cm2. These devices are designed with single channel and 85-μm anode-to-cathode spacing, without other additional electric field management, demonstrating its great potential for the UHV application in power electronics.
Peng Chen, Johannes O. Royset
Computational approaches to PDE-constrained optimization under uncertainty may involve finite-dimensional approximations of control and state spaces, sample average approximations of measures of risk and reliability, smooth approximations of nonsmooth functions, penalty approximations of constraints as well as many other kinds of inaccuracies. In this paper, we analyze the performance of controls obtained by an approximation-based algorithm and in the process develop estimates of optimality gaps for general optimization problems defined on metric spaces. Under mild assumptions, we establish that limiting controls have arbitrarily small optimality gaps provided that the inaccuracies in the various approximations vanish. We carry out the analysis for a broad class of problems with multiple expectation, risk, and reliability functions involving PDE solutions and appearing in objective as well as constraint expressions. In particular, we address problems with buffered failure probability constraints approximated via an augmented Lagrangian. We demonstrate the framework on an elliptic PDE with a random coefficient field and a distributed control function.
Zhimin Chen, Peng Chen, Ziyu Guo, Yudong Zhang, Xianbin Wang
With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle's direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram'{e}r-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://github.com/chenpengseu/PassiveDOA-ISAC-RIS.git.
Philip Sperl, Ching-Yu Kao, Peng Chen, Konstantin Böttinger
In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even showed super-human capabilities in a broad range of domains. This led people to trust in DNNs' classifications and resulting actions even in security-sensitive environments like autonomous driving. Despite their impressive achievements, DNNs are known to be vulnerable to adversarial examples. Such inputs contain small perturbations to intentionally fool the attacked model. In this paper, we present a novel end-to-end framework to detect such attacks during classification without influencing the target model's performance. Inspired by recent research in neuron-coverage guided testing we show that dense layers of DNNs carry security-sensitive information. With a secondary DNN we analyze the activation patterns of the dense layers during classification runtime, which enables effective and real-time detection of adversarial examples. Our prototype implementation successfully detects adversarial examples in image, natural language, and audio processing. Thereby, we cover a variety of target DNNs, including Long Short Term Memory (LSTM) architectures. In addition, to effectively defend against state-of-the-art attacks, our approach generalizes between different sets of adversarial examples. Thus, our method most likely enables us to detect even future, yet unknown attacks. Finally, during white-box adaptive attacks, we show our method cannot be easily bypassed.
Peng Chen, Tong Jia, Pengfei Wu, Jianjun Wu, Dongyue Chen
Most existing person re-identification (ReID) methods have good feature representations to distinguish pedestrians with deep convolutional neural network (CNN) and metric learning methods. However, these works concentrate on the similarity between encoder output and ground-truth, ignoring the correlation between input and encoder output, which affects the performance of identifying different pedestrians. To address this limitation, We design a Deep InfoMax (DIM) network to maximize the mutual information (MI) between the input image and encoder output, which doesn't need any auxiliary labels. To evaluate the effectiveness of the DIM network, we propose end-to-end Global-DIM and Local-DIM models. Additionally, the DIM network provides a new solution for cross-dataset unsupervised ReID issue as it needs no extra labels. The experiments prove the superiority of MI theory on the ReID issue, which achieves the state-of-the-art results.
Peng Chen, Xin Du, Zhihui Lu, Hongfeng Chai
Vertical federated learning (VFL) is a cloud-edge collaboration paradigm that enables edge nodes, comprising resource-constrained Internet of Things (IoT) devices, to cooperatively train artificial intelligence (AI) models while retaining their data locally. This paradigm facilitates improved privacy and security for edges and IoT devices, making VFL an essential component of Artificial Intelligence of Things (AIoT) systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing the universal trigger and model parameters of VFL sub-problems. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information not accessible within the split VFL architecture. In contrast, our approach does not necessitate any additional data to execute the attack. On the LendingClub and Zhongyuan datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100\% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance to revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure AIoT.
Chen Peng, Xianzhong Long, Yun Li
Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer from the problem of false negative samples. Inspired by the mean shift for self-supervised learning, we propose a new simple framework, namely Multiple Sample Views and Queues (MSVQ). We jointly construct three soft labels on-the-fly by utilizing two complementary and symmetric approaches: multiple augmented positive views and two momentum encoders that generate various semantic features for negative samples. Two teacher networks perform similarity relationship calculations with negative samples and then transfer this knowledge to the student network. Let the student network mimic the similarity relationships between the samples, thus giving the student network a more flexible ability to identify false negative samples in the dataset. The classification results on four benchmark image datasets demonstrate the high effectiveness and efficiency of our approach compared to some classical methods. Source code and pretrained models are available \href{https://github.com/pc-cp/MSVQ}{here}.
Peng Chen, Xuan Thinh Duong, Ji Li, Liang Song, Lixin Yan
Let $X$ be a metric space with doubling measure, and $L$ be a nonnegative self-adjoint operator on $L^2(X)$ whose heat kernel satisfies the Gaussian upper bound. Let $f$ be in the space $ {\rm BMO}_L(X)$ associated with the operator $L$ and we define its distance from the subspace $L^{\infty}(X)$ under the $ {\rm BMO}_L(X)$ norm as follows: $$ {\rm dist} (f, L^{\infty}):= \inf_{g\in L^{\infty}} \|f -g\|_{{\rm BMO}_L(X)}. $$ In this paper we prove that ${\rm dist} (f, L^{\infty})$ is equivalent to the infimum of the constant $\varepsilon$ in the John-Nirenberg inequality for the space ${\rm BMO}_L(X)$: $$ \sup_B { μ\big(\{ x\in B: |f(x)-e^{-{r_B^2}L}f(x)|>λ\}\big) \over μ(B)} \leq e^{-λ/\varepsilon}\ \ \ \ {\rm for\ large\ } λ. $$ This extends the well-known result of Garnett and Jones \cite{GJ1} for the classical ${\rm BMO}$ space (introduced by John and Nirenberg). As an application, we show that a ${\rm BMO}_L(X)$ function with compact support can be decomposed as the summation of an $L^\infty$-function and the integral of the heat kernel (associated with $L$) against a finite Carleson measure on $X\times[0,\infty)$. The key new technique is a geometric construction involving the semigroup $e^{-tL}$. We also resort to several fundamental tools including the stopping time argument and the random dyadic lattice.
Peng Chen, Jin-Yu Zou, Bang-Gui Liu
The electronic, magnetic, and topological properties of CoBr2 monolayer are studied in the frame-work of the density-functional theory (DFT) combined with tight-binding (TB) modeling in terms of Wannier basis. Our DFT investigation and Monte Carlo simulation show that there exists intrinsic two-dimensional ferromagnetism in the CoBr2 monolayer thanks to large out-of-plane magnetocrystalline anisotropic energy. Our further study shows that the spin-orbits coupling makes it become a topologically nontrivial insulator with quantum anomalous Hall effect and topological Chern number C=4, and its edge states can be manipulated by changing the width of its nanoribbons and applying strains. The CoBr2 monolayer can be exfoliated from the layered CoBr2 bulk material because its exfoliation energy is between those of graphene and MoS2 monolayer and it is dynamically stable. These results make us believe that the CoBr2 monolayer can make a promising spintronic material for future high-performance devices.
Peng Chen, Chang-Yuan Yao, Gui-Jun Ding
The neutrino mass matrix has remnant CP symmetry expressed in terms of the lepton mixing matrix, and vice versa the remnant CP transformations allow us to reconstruct the mixing matrix. We study the scenario that all the four remnant CP transformations are preserved by the neutrino mass matrix. The most general parameterization of remnant CP transformations is presented. The lepton mixing matrix is completely fixed by the remnant CP, and its explicit form is derived. The necessary and sufficient condition for conserved Dirac CP violating phase is found. If the Klein four flavor symmetry generated by the postulated remnant CP transformations arises from a finite flavor symmetry group, the phenomenologically viable lepton flavor mixing would be the trimaximal pattern, both Dirac CP phase $δ_{CP}$ and Majorana phase $α_{31}$ are either $0$ or $π$ while another Majorana phase $α_{21}$ is a rational multiple of $π$. These general results are confirmed to be true in the case that the finite flavor symmetry group is $Δ(6n^2)$.
Peng Chen, Gui-Jun Ding, Stephen F. King
We discuss flavour dependent leptogenesis in the framework of lepton flavour models based on discrete flavour and CP symmetries applied to the type-I seesaw model. Working in the flavour basis, we analyse the case of two general residual CP symmetries in the neutrino sector, which corresponds to all possible semi-direct models based on a preserved $Z_2$ in the neutrino sector, together with a CP symmetry, which constrains the PMNS matrix up to a single free parameter which may be fixed by the reactor angle. We systematically study and classify this case for all possible residual CP symmetries, and show that the $R$-matrix is tightly constrained up to a single free parameter, with only certain forms being consistent with successful leptogenesis, leading to possible connections between leptogenesis and PMNS parameters. The formalism is completely general in the sense that the two residual CP symmetries could result from any high energy discrete flavour theory which respects any CP symmetry. As a simple example, we apply the formalism to a high energy $S_4$ flavour symmetry with a generalized CP symmetry, broken to two residual CP symmetries in the neutrino sector, recovering familiar results for PMNS predictions, together with new results for flavour dependent leptogenesis.
Peng Chen, Xue-Jing Zhang, Bang-Gui Liu
Structural, electronic, ferroelectric, and optical properties of two-dimensional (2D) BiN monolayer material with phosphorene-like structure are studied in terms of the density functional theory and modern Berry phase ferroelectric method. Both phonon spectra and molecular dynamics simulations indicate that the BiN monolayer is a room-temperature stable 2D ferroelectric with polarization as large as 580 pC/m. Further studies show that the polarization in the BiN monolayer can be easily switched from [100] to [010] direction over the bridging saddle phase by applying a tensile [010] stress of 2.54 N/m or compressive [100] stress of -1.18 N/m. This phase transition makes its lattice constants vary in a large range compared to other non-ferroelectric 2D materials. Moreover, through applying uniaxial tensile stress parallel to the polarization, one can fix the polarization and change the semiconductor energy gap from direct to indirect one. The optical properties feature a very strong anisotropy in reflectivity below the photon energy of 4 eV. All these significant ferroelectric, electronic, and optical properties make us believe that the 2D BiN monolayer can be used to make stretchable electronic devices and optical applications.
Peng Chen, Bing Wang, Hau-San Wong, De-Shuang Huang
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Peng Chen, Zili Xie, Xiangqian Xiu, Dunjun Chen, Bin Liu, Hong Zhao, Yi Shi, Rong Zhang, Youdou Zheng
III-V nitride semiconductors, represented by GaN, have attracted significant research attention. Driven by the growing interest in smart micro-displays, there is a strong desire to achieve enhanced light output from even smaller light-emitting diode (LED) chips. However, the most perplexing phenomenon and the most significant challenge in the study of emission properties under high-injection conditions in GaN has always been efficiency droop for decades, where LEDs exhibit a substantial loss in efficiency at high driving currents. In this paper, we present our study on the intrinsic emission properties of high-quality GaN material based on the density of states and the principles of momentum conservation. Our theoretical calculations reveal a momentum distribution mismatch between the non-equilibrium excess electrons and holes, which becomes more significant as the carrier concentration increases. Our excitation-dependent photoluminescence measurements conducted at 6 K exhibited a clear droop for all exciton recombinations, but droop-free for phonon-assisted recombination due to phonons compensating for the momentum mismatch. These findings indicate that the momentum distribution mismatch between the non-equilibrium excess electrons and holes is one of the intrinsic causes of the efficiency droop, which originates from the intrinsic band properties of GaN. These results suggest that proper active region design aimed at reducing this mismatch will contribute to the development of ultra-highly efficient lighting devices in the future.
Yuyin Li, Jing Zhou, Ziwen Yan, Xianfei Zhang, Zili Xie, Xiangqian Xiu, Dunjun Chen, Bin Liu, Hong Zhao, Yi Shi, Rong Zhang, Youdou Zheng, Peng Chen
We fabricated polygonal nanoholes in the top p-GaN layer of the InGaN/GaN light-emitting diode, followed by the deposition of Au/Al metal thin film within the nanoholes to create metal microcavities, thereby constructing the surface plasmon structure. The findings indicate that with increased current injection, the light output of the LEDs rose by 46%, accompanied by a shift of the gain peak position towards the plasmon resonance energy. The maximum enhancement factor increases to 2.38 as the coupling distance decreases from 60 nm to 30 nm. Interestingly, time-resolved photoluminescence data showed that the spontaneous emission decay time lengthened due to the plasmon coupling, suggesting the presence of a new plasmon coupling mechanism. Finite-Difference Time-Domain simulation results show that the electric field is localized at certain locations around the metal microcavity, generating a new type of shape-sensitive plasmon, named Cavity Plasmon here. This intense localization leads to a longer lifetime and enhances the recombination efficiency of excitons. We discuss several unique properties of the cavity plasmon generated by the polygonal metal microcavity with several specific angular shapes. The results demonstrate that the cavity plasmon generated by the polygonal metal microcavity is a highly promising technique for enhancing the light emission performance of of relevant semiconductor optoelectronic devices.
Peng Chen, Xiaobao Wei, Ming Lu, Hui Chen, Feng Tian
Real-time speech-driven 3D facial animation has been attractive in academia and industry. Traditional methods mainly focus on learning a deterministic mapping from speech to animation. Recent approaches start to consider the nondeterministic fact of speech-driven 3D face animation and employ the diffusion model for the task. Existing diffusion-based methods can improve the diversity of facial animation. However, personalized speaking styles conveying accurate lip language is still lacking, besides, efficiency and compactness still need to be improved. In this work, we propose DiffusionTalker to address the above limitations via personalizer-guided distillation. In terms of personalization, we introduce a contrastive personalizer that learns identity and emotion embeddings to capture speaking styles from audio. We further propose a personalizer enhancer during distillation to enhance the influence of embeddings on facial animation. For efficiency, we use iterative distillation to reduce the steps required for animation generation and achieve more than 8x speedup in inference. To achieve compactness, we distill the large teacher model into a smaller student model, reducing our model's storage by 86.4\% while minimizing performance loss. After distillation, users can derive their identity and emotion embeddings from audio to quickly create personalized animations that reflect specific speaking styles. Extensive experiments are conducted to demonstrate that our method outperforms state-of-the-art methods. The code will be released at: https://github.com/ChenVoid/DiffusionTalker.