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Cong Wang

Publications and source records attributed to Cong Wang.

At least 217 records · Page 12Linked to original sources
arXiv2025

LiveVal: Time-aware Data Valuation via Adaptive Reference Points

Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient time-aware data valuation method with three key designs: 1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage. We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance.

cs.LG↗
arXiv2025

Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model

Recent advances in conditional diffusion models have shown promise for generating realistic TalkingFace videos, yet challenges persist in achieving consistent head movement, synchronized facial expressions, and accurate lip synchronization over extended generations. To address these, we introduce the \textbf{M}otion-priors \textbf{C}onditional \textbf{D}iffusion \textbf{M}odel (\textbf{MCDM}), which utilizes both archived and current clip motion priors to enhance motion prediction and ensure temporal consistency. The model consists of three key elements: (1) an archived-clip motion-prior that incorporates historical frames and a reference frame to preserve identity and context; (2) a present-clip motion-prior diffusion model that captures multimodal causality for accurate predictions of head movements, lip sync, and expressions; and (3) a memory-efficient temporal attention mechanism that mitigates error accumulation by dynamically storing and updating motion features. We also release the \textbf{TalkingFace-Wild} dataset, a multilingual collection of over 200 hours of footage across 10 languages. Experimental results demonstrate the effectiveness of MCDM in maintaining identity and motion continuity for long-term TalkingFace generation. Code, models, and datasets will be publicly available.

cs.CV↗
arXiv2025

JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation

Despite the implementation of safety alignment strategies, large language models (LLMs) remain vulnerable to jailbreak attacks, which undermine these safety guardrails and pose significant security threats. Some defenses have been proposed to detect or mitigate jailbreaks, but they are unable to withstand the test of time due to an insufficient understanding of jailbreak mechanisms. In this work, we investigate the mechanisms behind jailbreaks based on the Linear Representation Hypothesis (LRH), which states that neural networks encode high-level concepts as subspaces in their hidden representations. We define the toxic semantics in harmful and jailbreak prompts as toxic concepts and describe the semantics in jailbreak prompts that manipulate LLMs to comply with unsafe requests as jailbreak concepts. Through concept extraction and analysis, we reveal that LLMs can recognize the toxic concepts in both harmful and jailbreak prompts. However, unlike harmful prompts, jailbreak prompts activate the jailbreak concepts and alter the LLM output from rejection to compliance. Building on our analysis, we propose a comprehensive jailbreak defense framework, JBShield, consisting of two key components: jailbreak detection JBShield-D and mitigation JBShield-M. JBShield-D identifies jailbreak prompts by determining whether the input activates both toxic and jailbreak concepts. When a jailbreak prompt is detected, JBShield-M adjusts the hidden representations of the target LLM by enhancing the toxic concept and weakening the jailbreak concept, ensuring LLMs produce safe content. Extensive experiments demonstrate the superior performance of JBShield, achieving an average detection accuracy of 0.95 and reducing the average attack success rate of various jailbreak attacks to 2% from 61% across distinct LLMs.

cs.CR↗
arXiv2025

SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots

Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.

cs.RO↗
arXiv2025

High-Throughput Discovery of Kagome Materials in Transition Metal Oxide Monolayers

Kagome materials are known for hosting exotic quantum states, including quantum spin liquids, charge density waves, and unconventional superconductivity. The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level, which are more easily realized in the absence of interlayer interactions. However, this absence also destabilizes the monolayer forms of many bulk kagome materials, posing significant challenges to their discovery. In this work, we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a "1+3" design framework. Through high-throughput computational screening of 349 candidate materials, we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties. These materials were classified into three categories based on their lattice geometry, symmetry, band gaps, and magnetic configurations. Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels, with orbital contributions varying between oxygen 2p and transition metal d states. This study demonstrates the feasibility of the "1+3" strategy, offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.

cond-mat.mes-hall↗
arXiv2025

Two-dimensional Kagome Materials: Theoretical Insights, Experimental Realizations, and Electronic Structures

In recent years, kagome materials have attracted significant attention due to their rich emergent phenomena arising from the quantum interplay of geometry, topology, spin, and correlations. However, in the search for kagome materials, it has been found that bulk compounds with electronic properties related to the kagome lattice are relatively scarce, primarily due to the hybridization of kagome layers with adjacent layers. Therefore, researchers have shown increasing interest in the discovery and construction of two-dimensional (2D) kagome materials, aiming to achieve clean kagome bands near the Fermi level in monolayer or few-layer systems. Substantial advancements have already been made in this area. In this review, we summarize the current progress in the construction and development of 2D kagome materials. We begin by introducing the geometric and electronic structures of the kagome lattice model and its variants, followed by discussions on the experimental realizations and electronic structure characterizations of 2D kagome materials. Finally, we provide an outlook on the future developments of 2D kagome materials.

cond-mat.mtrl-sci↗
arXiv2025

CALM: Curiosity-Driven Auditing for Large Language Models

Auditing Large Language Models (LLMs) is a crucial and challenging task. In this study, we focus on auditing black-box LLMs without access to their parameters, only to the provided service. We treat this type of auditing as a black-box optimization problem where the goal is to automatically uncover input-output pairs of the target LLMs that exhibit illegal, immoral, or unsafe behaviors. For instance, we may seek a non-toxic input that the target LLM responds to with a toxic output or an input that induces the hallucinative response from the target LLM containing politically sensitive individuals. This black-box optimization is challenging due to the scarcity of feasible points, the discrete nature of the prompt space, and the large search space. To address these challenges, we propose Curiosity-Driven Auditing for Large Language Models (CALM), which uses intrinsically motivated reinforcement learning to finetune an LLM as the auditor agent to uncover potential harmful and biased input-output pairs of the target LLM. CALM successfully identifies derogatory completions involving celebrities and uncovers inputs that elicit specific names under the black-box setting. This work offers a promising direction for auditing black-box LLMs. Our code is available at https://github.com/x-zheng16/CALM.git.

cs.AI↗
arXiv2024

A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space

Open-set object detection (OSOD) is highly desirable for robotic manipulation in unstructured environments. However, existing OSOD methods often fail to meet the requirements of robotic applications due to their high computational burden and complex deployment. To address this issue, this paper proposes a light-weight framework called Decoupled OSOD (DOSOD), which is a practical and highly efficient solution to support real-time OSOD tasks in robotic systems. Specifically, DOSOD builds upon the YOLO-World pipeline by integrating a vision-language model (VLM) with a detector. A Multilayer Perceptron (MLP) adaptor is developed to transform text embeddings extracted by the VLM into a joint space, within which the detector learns the region representations of class-agnostic proposals. Cross-modality features are directly aligned in the joint space, avoiding the complex feature interactions and thereby improving computational efficiency. DOSOD operates like a traditional closed-set detector during the testing phase, effectively bridging the gap between closed-set and open-set detection. Compared to the baseline YOLO-World, the proposed DOSOD significantly enhances real-time performance while maintaining comparable accuracy. The slight DOSOD-S model achieves a Fixed AP of $26.7\%$, compared to $26.2\%$ for YOLO-World-v1-S and $22.7\%$ for YOLO-World-v2-S, using similar backbones on the LVIS minival dataset. Meanwhile, the FPS of DOSOD-S is $57.1\%$ higher than YOLO-World-v1-S and $29.6\%$ higher than YOLO-World-v2-S. Meanwhile, we demonstrate that the DOSOD model facilitates the deployment of edge devices. The codes and models are publicly available at https://github.com/D-Robotics-AI-Lab/DOSOD.

cs.CV↗
arXiv2024

Taming Imbalance and Complexity in WAN Traffic Engineering

The rapid expansion of global cloud infrastructures, coupled with the growing volume and complexity of network traffic, has fueled active research into scalable and resilient Traffic Engineering (TE) solutions for Wide Area Networks (WANs). Despite recent advancements, achieving an optimal balance between solution quality and computational complexity remains a significant challenge, especially for larger WAN topologies under dynamic traffic demands and stringent resource constraints. This paper presents empirical evidence of a critical shortcoming in existing TE solutions: their oversight inadequately accounting for traffic demand heterogeneities and link utilization imbalances. We identify key factors contributing to these issues, including traffic distribution, solver selection, resiliency, and resource overprovisioning. To address these gaps, we propose a holistic solution featuring new performance metrics and a novel resilient TE algorithm. The proposed metrics, critical link set and network criticality, provide a more comprehensive assessment of resilient TE solutions, while the tunnel-based TE algorithm dynamically adapts to changing traffic demands. Through extensive simulations on diverse WAN topologies, we demonstrate that this holistic solution significantly improves network performance, achieving a superior balance across key objectives. This work represents a significant advancement in the development of resilient and scalable TE solutions for WANs.

cs.NI↗
arXiv2024

Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via Infrared Side Channel

Virtual Reality (VR) technologies are increasingly employed in numerous applications across various areas. Therefore, it is essential to ensure the security of interactions between users and VR devices. In this paper, we disclose a new side-channel leakage in the constellation tracking system of mainstream VR platforms, where the infrared (IR) signals emitted from the VR controllers for controller-headset interactions can be maliciously exploited to reconstruct unconstrained input keystrokes on the virtual keyboard non-intrusively. We propose a novel keystroke inference attack named VRecKey to demonstrate the feasibility and practicality of this novel infrared side channel. Specifically, VRecKey leverages a customized 2D IR sensor array to intercept ambient IR signals emitted from VR controllers and subsequently infers (i) character-level key presses on the virtual keyboard and (ii) word-level keystrokes along with their typing trajectories. We extensively evaluate the effectiveness of VRecKey with two commercial VR devices, and the results indicate that it can achieve over 94.2% and 90.5% top-3 accuracy in inferring character-level and word-level keystrokes with varying lengths, respectively. In addition, empirical results show that VRecKey is resilient to several practical impact factors and presents effectiveness in various real-world scenarios, which provides a complementary and orthogonal attack surface for the exploration of keystroke inference attacks in VR platforms.

cs.CR↗
arXiv2024

Interlayer coupling rotatable magnetic easy-axis in MnSe2 mono- and bi-layers

Interlayer coupling plays a critical role in tuning the electronic structures and magnetic ground states of two-dimensional materials, influenced by the number of layers, interlayer distances, and stacking order. However, its effect on the orientation of the magnetic easy axis remains underexplored. In this study, we demonstrate that interlayer coupling can significantly alter the magnetic easy-axis orientation, as shown by the magnetic easy-axis of monolayer 1T-MnSe2 tilting 67° from the z-axis, while aligning with the z-axis in the bilayer. This change results from variations in orbital occupations near the Fermi level, particularly involving nonmetallic Se atoms. Contrary to the traditional focus on magnetic metal atoms, our findings reveal that Se orbitals play a key role in influencing the easy-axis orientation and topological Chern numbers. Furthermore, we validated our conclusions by changing stacking orders, introducing charge doping, applying in-plane biaxial strains, and substituting non-metallic atoms. Our results highlight the pivotal role of interlayer coupling in tuning the magnetic properties of layered materials, with important implications for spintronic applications.

cond-mat.mtrl-sci↗
arXiv2024

The existence of ground state solutions for critical Hénon equations in $\mathbb{R}^N$

In this paper we confirm that $2^*(γ)=\frac{2(N+γ)}{N-2}$ with $γ>0$ is exactly the critical exponent for the embedding from $H_r^1(\mathbb{R}^N)$ into $L^q(\mathbb{R}^N;|x|^γ)$($N\geqslant 3$) (see \cite{2007SWW-1,2007SWW-2}) and name it as the upper Hénon-Sobolev critical exponent. Based on this fact we study the ground state solutions of critical Hénon equations in $\mathbb{R}^N$ via the Nehari manifold methods and the great idea of Brezis-Nirenberg in \cite{1983BN}. We establish the existence of the positive radial ground state solutions for the problem with one single upper Hénon-Sobolev critical exponent. We also deal with the existence of the nonnegative radial ground state solutions for the problems with multiple critical exponents, including Hardy-Sobolev critical exponents or Sobolev critical exponents or the upper Hénon-Sobolev critical exponents.

math.AP↗
arXiv2024

Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models

Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance. Then, the second stage establishes a dense correspondence between the source and target images using the global features from the previous stage, and an inpainting conditional diffusion model is proposed to further align and enhance the contextual features, generating a coarse-grained person image. In the third stage, we propose a refining conditional diffusion model to utilize the coarsely generated image from the previous stage as a condition, achieving texture restoration and enhancing fine-detail consistency. The three-stage PCDMs work progressively to generate the final high-quality and high-fidelity synthesized image. Both qualitative and quantitative results demonstrate the consistency and photorealism of our proposed PCDMs under challenging scenarios.The code and model will be available at https://github.com/tencent-ailab/PCDMs.

cs.CV↗
arXiv2024

On the double critical Maxwell equations

In this paper, we focus on (no)existence and asymptotic behavior of solutions for the double critical Maxwell equation involving with the Hardy, Hardy-Sobolev, Sobolev critical exponents. The existence and noexistence of solutions completely depend on the power exponents and coefficients of equation. On one hand, based on the concentration-compactness ideas, applying the Nehari manifold and the mountain pass theorem, we prove the existence of the ground state solutions for the critical Maxwell equation for three different scenarios. On the other hand, for the case $λ<0$ and $0\leq s_2<s_1<2$, which is a type open problem raised by Li and Lin. Draw support from a changed version of Caffarelli-Kohn-Nirenberg inequality, we find that there exists a constant $λ^*$ which is a negative number having explicit expression, such that the problem has no nontrivial solution as the coefficient $λ<λ^*$. Moreover, there exists a constant $λ^*<λ^{**}<0$ such that, as $λ^{**}<λ<0$, the equation has a nontrivial solution using truncation methods. Furthermore, we establish the asymptotic behavior of solutions of equation as coefficient converges to zero for the all cases above.

math.AP↗
arXiv2024

Layered semiconducting electrides in p-block metal oxides

In conventional electrides, excess electrons are localized in crystal voids to serve as anions. Most of these electrides are metallic and the metal cations are primarily from the s-block, d-block, or rare-earth elements. Here, we report a class of p-block metal-based electrides found in bilayer SnO and PbO, which are semiconducting and feature electride states in both the valence band (VB) and conduction band (CB), as referred to 2D "bipolar" electrides. These bilayers are hybrid electrides where excess electrons are localized in the interlayer region and hybridize with the orbitals of Sn atoms in the VB, exhibiting strong covalent-like interactions with neighboring metal atoms. Compared to previously studied hybrid electrides, the higher electronegativity of Sn and Pb enhances these covalent-like interactions, leading to largely enhanced semiconducting bandgap of up to 2.5 eV. Moreover, the CBM primarily arises from the overlap between metal states and interstitial charges, denoting a potential electride and forming a free-electron-like (FEL) state with small effective mass. This state offers high carrier mobilities for both electron and hole in bilayer SnO, suggesting its potential as a promising p-type semiconductor material.

cond-mat.mtrl-sci↗
arXiv2024

FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.

cs.LG↗
arXiv2024

Ultra-High-Definition Image Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution

Ultra-High-Definition (UHD) image restoration has acquired remarkable attention due to its practical demand. In this paper, we construct UHD snow and rain benchmarks, named UHD-Snow and UHD-Rain, to remedy the deficiency in this field. The UHD-Snow/UHD-Rain is established by simulating the physics process of rain/snow into consideration and each benchmark contains 3200 degraded/clear image pairs of 4K resolution. Furthermore, we propose an effective UHD image restoration solution by considering gradient and normal priors in model design thanks to these priors' spatial and detail contributions. Specifically, our method contains two branches: (a) feature fusion and reconstruction branch in high-resolution space and (b) prior feature interaction branch in low-resolution space. The former learns high-resolution features and fuses prior-guided low-resolution features to reconstruct clear images, while the latter utilizes normal and gradient priors to mine useful spatial features and detail features to guide high-resolution recovery better. To better utilize these priors, we introduce single prior feature interaction and dual prior feature interaction, where the former respectively fuses normal and gradient priors with high-resolution features to enhance prior ones, while the latter calculates the similarity between enhanced prior ones and further exploits dual guided filtering to boost the feature interaction of dual priors. We conduct experiments on both new and existing public datasets and demonstrate the state-of-the-art performance of our method on UHD image low-light enhancement, dehazing, deblurring, desonwing, and deraining. The source codes and benchmarks are available at \url{https://github.com/wlydlut/UHDDIP}.

cs.CV↗