arXiv subjects

Cong Wang

Publications and source records attributed to Cong Wang.

At least 271 records · Page 15Linked to original sources

arXiv2024

Multiple stochastic resonances and inverse stochastic resonances in asymmetric bistable system under the ultra-high frequency excitation

Ultra-high frequency linear frequency modulation (UHF-LFM) signal, as a kind of typical non-stationary signal, has been widely used in microwave radar and other fields, with advantages such as long transmission distance, strong anti-interference ability, and wide bandwidth. Utilizing optimal dynamics response has unique advantages in weak feature identification under strong background noise. We propose a new stochastic resonance method in an asymmetric bistable system with the time-varying parameter to handle this special non-stationary signal. Interestingly, the nonlinear response exhibits multiple stochastic resonances (MSR) and inverse stochastic resonances (ISR) under UHF-LFM signal excitation, and some resonance regions may deviate or collapse due to the influence of system asymmetry. In addition, we analyze the responses of each resonance region and the mechanism and evolution law of each resonance region in detail. Finally, we significantly expand the resonance region within the parameter range by optimizing the time scale, which verifies the effectiveness of the proposed time-varying scale method. The mechanism and evolution law of MSR and ISR will provide references for researchers in related fields.

nlin.AO↗

arXiv2024

Optimization of reactively sputtered Mn3GaN films based on resistivity measurements

Mn-based nitrides with antiperovskite structures have several properties that can be utilised for antiferromagnetic spintronics. Their magnetic properties depend on the structural quality, composition and doping of the cubic antiperovskite structure. Such nitride thin films are usually produced by reactive physical vapour deposition, where the deposition rate of N can only be controlled by the N2 gas flow. We show that the tuning of the N content can be optimised using low temperature resistivity measurements, which serve as an indicator of the degree of structural disorder. Several Mn3GaNx films were prepared by reactive magnetron sputtering under different N2 gas flows. Under optimised conditions, we obtain films that exhibit a metal-like temperature dependence, a vanishing logarithmic increase in resistivity towards zero, the highest resistivity ratio and a lattice contraction of 0.4 % along the growth direction when heated above that of the Néel temperature in agreement with the bulk samples.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating Distributed MoE Training and Inference with Lina

Scaling model parameters improves model quality at the price of high computation overhead. Sparsely activated models, usually in the form of Mixture of Experts (MoE) architecture, have sub-linear scaling of computation cost with model size, thus providing opportunities to train and serve a larger model at lower cost than their dense counterparts. However, distributed MoE training and inference is inefficient, mainly due to the interleaved all-to-all communication during model computation. This paper makes two main contributions. First, we systematically analyze all-to-all overhead in distributed MoE and present the main causes for it to be the bottleneck in training and inference, respectively. Second, we design and build Lina to address the all-to-all bottleneck head-on. Lina opportunistically prioritizes all-to-all over the concurrent allreduce whenever feasible using tensor partitioning, so all-to-all and training step time is improved. Lina further exploits the inherent pattern of expert selection to dynamically schedule resources during inference, so that the transfer size and bandwidth of all-to-all across devices are balanced amid the highly skewed expert popularity in practice. Experiments on an A100 GPU testbed show that Lina reduces the training step time by up to 1.73x and reduces the 95%ile inference time by an average of 1.63x over the state-of-the-art systems.

cs.DC↗

arXiv2024

Toward Evaluating Robustness of Reinforcement Learning with Adversarial Policy

Reinforcement learning agents are susceptible to evasion attacks during deployment. In single-agent environments, these attacks can occur through imperceptible perturbations injected into the inputs of the victim policy network. In multi-agent environments, an attacker can manipulate an adversarial opponent to influence the victim policy's observations indirectly. While adversarial policies offer a promising technique to craft such attacks, current methods are either sample-inefficient due to poor exploration strategies or require extra surrogate model training under the black-box assumption. To address these challenges, in this paper, we propose Intrinsically Motivated Adversarial Policy (IMAP) for efficient black-box adversarial policy learning in both single- and multi-agent environments. We formulate four types of adversarial intrinsic regularizers -- maximizing the adversarial state coverage, policy coverage, risk, or divergence -- to discover potential vulnerabilities of the victim policy in a principled way. We also present a novel bias-reduction method to balance the extrinsic objective and the adversarial intrinsic regularizers adaptively. Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and the bias-reduction method in enhancing black-box adversarial policy learning across a variety of environments. Our IMAP successfully evades two types of defense methods, adversarial training and robust regularizer, decreasing the performance of the state-of-the-art robust WocaR-PPO agents by 34\%-54\% across four single-agent tasks. IMAP also achieves a state-of-the-art attacking success rate of 83.91\% in the multi-agent game YouShallNotPass. Our code is available at \url{https://github.com/x-zheng16/IMAP}.

cs.LG↗

arXiv2024

LMEraser: Large Model Unlearning through Adaptive Prompt Tuning

To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser. Existing unlearning research suffers from entangled training data and complex model architectures, incurring extremely high computational costs for large models. LMEraser takes a divide-and-conquer strategy with a prompt tuning architecture to isolate data influence. The training dataset is partitioned into public and private datasets. Public data are used to train the backbone of the model. Private data are adaptively clustered based on their diversity, and each cluster is used to optimize a prompt separately. This adaptive prompt tuning mechanism reduces unlearning costs and maintains model performance. Experiments demonstrate that LMEraser achieves a $100$-fold reduction in unlearning costs without compromising accuracy compared to prior work. Our code is available at: \url{https://github.com/lmeraser/lmeraser}.

cs.LG↗

arXiv2024

Site-ordering/disordering-induced magnetic textures in a vdW ferromagnet by competing global and broken inversion-symmetry

Fe5GeTe2 single crystals can be divided into nonquenched (NQ) and quench-cooled (QC) phases with different magnetic properties. A comprehensive understanding of the magnetic property variations in the NQ and QC phases is imperative for guiding Fe5GeTe2 towards spintronics applications; however, it remains elusive. Here, we report a real-space study on the structural and magnetic properties of these two magnetic phases using cryogenic magnetic force microscopy and scanning tunneling microscopy. The thermal history introduces disorder and order to the Fe(1) sites, resulting in the NQ and QC phases exhibiting global and broken inversion symmetry, respectively. The observed magnetic domain transitions (branching to labyrinthine) in the spin reorientation process and the distinct 3D spin textures stabilized by magnetic dipolar interaction observed in field-dependent studies allow the NQ phase to exhibit a more resilient global magnetic state. In contrast, the QC phase exhibits enhanced magnetic anisotropy, resulting in a higher TC. Meanwhile, the Dzyaloshinskii-Moriya interaction (DMI) introduced by the broken inversion symmetry causes the QC phase to exhibit a localized magnetic state: no domain transformation occurs during spin reorientation, and irregular domain states are observed in field-related studies. Our work provides an important reference for understanding the complex magnetic properties in Fe5GeTe2.

cond-mat.mtrl-sci↗

arXiv2024

SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency

This work presents an effective depth-consistency self-prompt Transformer for image dehazing. It is motivated by an observation that the estimated depths of an image with haze residuals and its clear counterpart vary. Enforcing the depth consistency of dehazed images with clear ones, therefore, is essential for dehazing. For this purpose, we develop a prompt based on the features of depth differences between the hazy input images and corresponding clear counterparts that can guide dehazing models for better restoration. Specifically, we first apply deep features extracted from the input images to the depth difference features for generating the prompt that contains the haze residual information in the input. Then we propose a prompt embedding module that is designed to perceive the haze residuals, by linearly adding the prompt to the deep features. Further, we develop an effective prompt attention module to pay more attention to haze residuals for better removal. By incorporating the prompt, prompt embedding, and prompt attention into an encoder-decoder network based on VQGAN, we can achieve better perception quality. As the depths of clear images are not available at inference, and the dehazed images with one-time feed-forward execution may still contain a portion of haze residuals, we propose a new continuous self-prompt inference that can iteratively correct the dehazing model towards better haze-free image generation. Extensive experiments show that our method performs favorably against the state-of-the-art approaches on both synthetic and real-world datasets in terms of perception metrics including NIQE, PI, and PIQE.

cs.CV↗

arXiv2024

General-order open-shell coupled-cluster method with partial spin adaptation I: formulations

A general-order open-shell coupled-cluster method based on spatial orbitals is formulated. The method is an extension of the partial-spin adaptation (PSA) scheme from Janssen and Schaefer (Theor. Chim. Acta, 79, 1-42, 1991). By increasing the order of excitation operator and spin adaptation, the full configuration interaction (CI) limit is expected to be achieved. In the meanwhile, the advantages of spin-free approach in closed-shell systems: fourth-order truncation of the Baker-Campbell-Hausdorff (BCH) expansion and only $HT$-type connected terms are conserved.

physics.chem-ph↗

arXiv2024

How Powerful Potential of Attention on Image Restoration?

Transformers have demonstrated their effectiveness in image restoration tasks. Existing Transformer architectures typically comprise two essential components: multi-head self-attention and feed-forward network (FFN). The former captures long-range pixel dependencies, while the latter enables the model to learn complex patterns and relationships in the data. Previous studies have demonstrated that FFNs are key-value memories \cite{geva2020transformer}, which are vital in modern Transformer architectures. In this paper, we conduct an empirical study to explore the potential of attention mechanisms without using FFN and provide novel structures to demonstrate that removing FFN is flexible for image restoration. Specifically, we propose Continuous Scaling Attention (\textbf{CSAttn}), a method that computes attention continuously in three stages without using FFN. To achieve competitive performance, we propose a series of key components within the attention. Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance. We apply our \textbf{CSAttn} to several image restoration tasks and show that our model can outperform CNN-based and Transformer-based image restoration approaches.

cs.CV↗

arXiv2024

Machine Unlearning: Solutions and Challenges

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of the solutions in machine unlearning. We categorize existing solutions into exact unlearning approaches that remove data influence thoroughly and approximate unlearning approaches that efficiently minimize data influence. By comprehensively reviewing solutions, we identify and discuss their strengths and limitations. Furthermore, we propose future directions to advance machine unlearning and establish it as an essential capability for trustworthy and adaptive machine learning models. This paper provides researchers with a roadmap of open problems, encouraging impactful contributions to address real-world needs for selective data removal.

cs.LG↗

arXiv2024

The long-term evolution of the GW170817 remnant

GW170817 represents the first observed binary neutron star merger event by humanity. The observation of GW170817 has identified the correlation between Kilonova, gravitational wave and short GRB. The shocks from GW170817 have the capacity to inject significant thermal and kinetic energies into the interstellar medium and evolve for over a million years. In this letter, we adopt the special relativity fluid dynamics equations to simulate the evolution of the GW170817 remnant over a span of one million years. Our simulations yield the evolution profiles of the velocity, density, mass, radius, luminosity, and energies of the remnant. We estimate that the GW170817 remnant will reach the average maximum luminosity $ 2.56\times 10^{39}$ erg s$^{-1}$at approximately $3.96\times 10^4$ yr. At the end of the cooling stage, the contaminated radius and mass are $48.35$ pc and $2.25\times 10^4 M_{\odot}$, respectively.

astro-ph.HE↗

arXiv2024

High-Entropy Enhanced Negative Thermal Expansion Perfomance in Antiperovkites

The negative thermal expansion (NTE) materials, which can act as thermal-expansion compensators to counteract the positive thermal expansion, have great applications merit in precision engineering. However, the exploration of NTE behavior with a wide temperature range has reached its upper ceiling through traditional doping strategies due to composition limitations. The unique sluggish characteristic in phase transition and extended optimization space in recent high entropy systems has great potential to broaden the temperature range in electronic transitions-induced NTE materials. Mn-based anti-perovskites offer an ideal platform for the exploration of high entropy NTE material due to their abundant element selection and controllable NTE performance. In this paper, the high entropy strategy is first introduced to broaden the NTE temperature range by relaxing the abrupt phase transition in Mn-based anti-perovskite nitride. We propose an empirical screening method to synthesize the high-entropy anti-perovskite (HEAP). it is found that magnetic phase separation from anti-ferromagnetic CII to paramagnetic CI surviving in an ultra-wide temperature range of 5K<=T<=350K (Delta_T=345K), revealing a unique sluggish characteristic. Consequently, a remarkable NTE behavior (up to Delta_T=235K, 5K<=T<=240K) with a coefficient of thermal expansion of -4.7x10-6/K, has been obtained in HEAP. It is worth noting that the temperature range is two/three times wider than that of low-entropy systems. The sluggish characteristic has been further experimentally proved to come from disturbed phase transition dynamics due to distortion in atomic spacing and chemical environmental fluctuation observed by the spherical aberration-corrected electron microscope. Our demonstration provides a unique paradigm for broadening the temperature range of NTE materials induced by phase transition through entropy engineering.

cond-mat.mtrl-sci↗

arXiv2024

ED2: Environment Dynamics Decomposition World Models for Continuous Control

Model-based reinforcement learning (MBRL) achieves significant sample efficiency in practice in comparison to model-free RL, but its performance is often limited by the existence of model prediction error. To reduce the model error, standard MBRL approaches train a single well-designed network to fit the entire environment dynamics, but this wastes rich information on multiple sub-dynamics which can be modeled separately, allowing us to construct the world model more accurately. In this paper, we propose the Environment Dynamics Decomposition (ED2), a novel world model construction framework that models the environment in a decomposing manner. ED2 contains two key components: sub-dynamics discovery (SD2) and dynamics decomposition prediction (D2P). SD2 discovers the sub-dynamics in an environment automatically and then D2P constructs the decomposed world model following the sub-dynamics. ED2 can be easily combined with existing MBRL algorithms and empirical results show that ED2 significantly reduces the model error, increases the sample efficiency, and achieves higher asymptotic performance when combined with the state-of-the-art MBRL algorithms on various continuous control tasks. Our code is open source and available at https://github.com/ED2-source-code/ED2.

cs.LG↗

arXiv2024

Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection

Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training. This is a practical yet challenging task as it requires the model to address domain shift without incorporating target domain data into training. In this paper, we propose a novel phrase grounding-based style transfer (PGST) approach for the task. Specifically, we first define textual prompts to describe potential objects for each unseen target domain. Then, we leverage the grounded language-image pre-training (GLIP) model to learn the style of these target domains and achieve style transfer from the source to the target domain. The style-transferred source visual features are semantically rich and could be close to imaginary counterparts in the target domain. Finally, we employ these style-transferred visual features to fine-tune GLIP. By introducing imaginary counterparts, the detector could be effectively generalized to unseen target domains using only a single source domain for training. Extensive experimental results on five diverse weather driving benchmarks demonstrate our proposed approach achieves state-of-the-art performance, even surpassing some domain adaptive methods that incorporate target domain images into the training process.The source codes and pre-trained models will be made available.

cs.CV↗

arXiv2024

Employing Iterative Feature Selection in Fuzzy Rule-Based Binary Classification

The feature selection in a traditional binary classification algorithm is always used in the stage of dataset preprocessing, which makes the obtained features not necessarily the best ones for the classification algorithm, thus affecting the classification performance. For a traditional rule-based binary classification algorithm, classification rules are usually deterministic, which results in the fuzzy information contained in the rules being ignored. To do so, this paper employs iterative feature selection in fuzzy rule-based binary classification. The proposed algorithm combines feature selection based on fuzzy correlation family with rule mining based on biclustering. It first conducts biclustering on the dataset after feature selection. Then it conducts feature selection again for the biclusters according to the feedback of biclusters evaluation. In this way, an iterative feature selection framework is build. During the iteration process, it stops until the obtained bicluster meets the requirements. In addition, the rule membership function is introduced to extract vectorized fuzzy rules from the bicluster and construct weak classifiers. The weak classifiers with good classification performance are selected by Adaptive Boosting and the strong classifier is constructed by "weighted average". Finally, we perform the proposed algorithm on different datasets and compare it with other peers. Experimental results show that it achieves good classification performance and outperforms its peers.

cs.LG↗

arXiv2024

Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par and Alpaka, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats. We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.

physics.acc-ph↗

arXiv2024

Two Necessary and Sufficient Conditions to the Solvability of the Exterior Dirichlet Problem for the Monge-Ampère Equation

The present paper provides two necessary and sufficient conditions for the existence of solutions to the exterior Dirichlet problem of the Monge-Ampère equation with prescribed asymptotic behavior at infinity. By an adapted smooth approximation argument, we prove that the problem is solvable if and only if the boundary value is semi-convex with respect to the inner boundary, which is our first proposed new concept. Along the lines of Perron's method for Laplace equation, we obtain the threshold for solvability in the asymptotic behavior at infinity of the solution, and remove the $C^2$ regularity assumptions on the boundary value and on the inner boundary which are required in the proofs of the corresponding existence theorems in the recent literatures.

math.AP↗

arXiv2024

Hijacking Attacks against Neural Networks by Analyzing Training Data

Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended outputs by introducing (small) perturbations to the inputs. Backdoor attacks, despite the high success rates, often require a strong assumption, which is not always easy to achieve in reality. Adversarial example attacks, which put relatively weaker assumptions on attackers, often demand high computational resources, yet do not always yield satisfactory success rates when attacking mainstream black-box models in the real world. These limitations motivate the following research question: can model hijacking be achieved more simply, with a higher attack success rate and more reasonable assumptions? In this paper, we propose CleanSheet, a new model hijacking attack that obtains the high performance of backdoor attacks without requiring the adversary to tamper with the model training process. CleanSheet exploits vulnerabilities in DNNs stemming from the training data. Specifically, our key idea is to treat part of the clean training data of the target model as "poisoned data," and capture the characteristics of these data that are more sensitive to the model (typically called robust features) to construct "triggers." These triggers can be added to any input example to mislead the target model, similar to backdoor attacks. We validate the effectiveness of CleanSheet through extensive experiments on 5 datasets, 79 normally trained models, 68 pruned models, and 39 defensive models. Results show that CleanSheet exhibits performance comparable to state-of-the-art backdoor attacks, achieving an average attack success rate (ASR) of 97.5% on CIFAR-100 and 92.4% on GTSRB, respectively. Furthermore, CleanSheet consistently maintains a high ASR, when confronted with various mainstream backdoor defenses.

cs.CR↗