arXiv subjects

Jiale Chen

Publications and source records attributed to Jiale Chen.

At least 55 records · Page 3Linked to original sources

arXiv2025

Self-Supervised Contrastive Learning for Multi-Label Images

Self-supervised learning (SSL) has demonstrated its effectiveness in learning representations through comparison methods that align with human intuition. However, mainstream SSL methods heavily rely on high body datasets with single label, such as ImageNet, resulting in intolerable pre-training overhead. Besides, more general multi-label images are frequently overlooked in SSL, despite their potential for richer semantic information and broader applicability in downstream scenarios. Therefore, we tailor the mainstream SSL approach to guarantee excellent representation learning capabilities using fewer multi-label images. Firstly, we propose a block-wise augmentation module aimed at extracting additional potential positive view pairs from multi-label images. Subsequently, an image-aware contrastive loss is devised to establish connections between these views, thereby facilitating the extraction of semantically consistent representations. Comprehensive linear fine-tuning and transfer learning validate the competitiveness of our approach despite challenging sample quality and quantity.

cs.CV↗

arXiv2025

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

One approach to reducing the massive costs of large language models (LLMs) is the use of quantized or sparse representations for training or deployment. While post-training compression methods are very popular, the question of obtaining even more accurate compressed models by directly training over such representations, i.e., Quantization-Aware Training (QAT), is still open: for example, a recent study (arXiv:2411.04330) put the "optimal" bit-width at which models can be trained using QAT, while staying accuracy-competitive with standard FP16/BF16 precision, at 8-bits weights and activations. We advance this state-of-the-art via a new method called QuEST, for which we demonstrate optimality at 4-bits and stable convergence as low as 1-bit weights and activations. QuEST achieves this by improving two key aspects of QAT methods: (1) accurate and fast quantization of the (continuous) distributions of weights and activations via Hadamard normalization and MSE-optimal fitting; (2) a new trust gradient estimator based on the idea of explicitly minimizing the error between the noisy gradient computed over quantized states and the "true" (but unknown) full-precision gradient. Experiments on Llama-type architectures show that QuEST induces stable scaling laws across the entire range of hardware-supported precisions, and can be extended to sparse representations. We provide GPU kernel support showing that models produced by QuEST can be executed efficiently. Our code is available at https://github.com/IST-DASLab/QuEST.

cs.LG↗

arXiv2025

Ferromagnetic Two-dimensional Electron Gases with Magnetic Doping and Proximity Effects

The advent of magnetic two-dimensional electron gases (2DEGs) at oxide interfaces has provided new opportunities in the field of spintronics. The enhancement of magnetism in 2DEGs at oxide interfaces continues to be a significant challenge, as exemplified by the relatively weak magnetism observed in the classical LaAlO3/SrTiO3 interface. Here, we present ferromagnetic (FM) 2DEGs at the interface fabricated between the FM insulator EuTiO3 (ETO) and the strong spin-orbit coupled (SOC) perovskite insulator KTaO3 (KTO). With the combined effects of magnetic atom doping and magnetic proximity from ETO films, the coercive field of 2DEGs can be significantly enhanced. Magnetoresistance (MR) curve with a high coercive field of 1000 Oe has been observed, in conjunction with a temperature-dependent unambiguous hysteresis loop in the anomalous Hall effect (AHE). Furthermore, within the 2DEGs, we have identified a synergetic interplay between magnetic scattering and the weak antilocalization (WAL) effect on transport. This study provides fresh insights into the formation of FM 2DEGs at ETO/KTO interfaces, and introduce an innovative pathway for creating high-performance magnetic 2DEGs at oxide interfaces.

cond-mat.mtrl-sci↗

arXiv2025

Absolutely summing Carleson embeddings on weighted Fock spaces with $A_{\infty}$-type weights

In this paper, we investigate the $r$-summing Carleson embeddings on weighted Fock spaces $F^p_{α,w}$. By using duality arguments, translating techniques and block diagonal operator skills, we completely characterize the $r$-summability of the natural embeddings $I_d:F^p_{α,w}\to L^p_α(μ)$ for any $r\geq1$ and $p>1$, where $w$ is a weight on the complex plane $\mathbb{C}$ that satisfies an $A_p$-type condition. As applications, we establish some results on the $r$-summability of differentiation and integration operators, Volterra-type operators and composition operators. Especially, we completely characterize the boundedness of Volterra-type operators and composition operators on vector-valued Fock spaces for all $1<p<\infty$, which were left open before for the case $1<p<2$.

math.FA↗

arXiv2025

Weighted theory of Toeplitz operators on the Fock spaces

We study the weighted compactness and boundedness of Toeplitz operators on the Fock spaces. Fix $α>0$. Let $T_φ$ be the Toeplitz operator on the Fock space $F^2_α$ over $\mathbb{C}^n$ with symbol $φ\in L^{\infty}$. For $1<p<\infty$ and any finite sum $T$ of finite products of Toeplitz operators $T_φ$'s, we show that $T$ is compact on the weighted Fock space $F^p_{α,w}$ if and only if its Berezin transform vanishes at infinity, where $w$ is a restricted $A_p$-weight on $\mathbb{C}^n$. Concerning boundedness, for $1\leq p<\infty$, we characterize the $r$-doubling weights $w$ such that $T_φ$ is bounded on the weighted spaces $L^p_{α,w}$ via a $φ$-adapted $A_p$-type condition. Our method also establishes a two weight inequality for the Fock projections in the case of $r$-doubling weights. Moreover, we characterize the corresponding weighted compactness of Bergman--Toeplitz operators, which answers a question raised by Stockdale and Wagner [Math. Z. 305 (2023), no. 1, Paper No. 10].

math.FA↗

arXiv2025

UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms

Open-vocabulary, task-oriented grasping of specific functional parts, particularly with dual arms, remains a key challenge, as current Vision-Language Models (VLMs), while enhancing task understanding, often struggle with precise grasp generation within defined constraints and effective dual-arm coordination. We innovatively propose UniDiffGrasp, a unified framework integrating VLM reasoning with guided part diffusion to address these limitations. UniDiffGrasp leverages a VLM to interpret user input and identify semantic targets (object, part(s), mode), which are then grounded via open-vocabulary segmentation. Critically, the identified parts directly provide geometric constraints for a Constrained Grasp Diffusion Field (CGDF) using its Part-Guided Diffusion, enabling efficient, high-quality 6-DoF grasps without retraining. For dual-arm tasks, UniDiffGrasp defines distinct target regions, applies part-guided diffusion per arm, and selects stable cooperative grasps. Through extensive real-world deployment, UniDiffGrasp achieves grasp success rates of 0.876 in single-arm and 0.767 in dual-arm scenarios, significantly surpassing existing state-of-the-art methods, demonstrating its capability to enable precise and coordinated open-vocabulary grasping in complex real-world scenarios.

cs.RO↗

arXiv2025

Chaotic diffusion in multi-scale turbulence

This study investigates chaotic diffusion in multi-scale turbulence driven by nonlinear wave-particle resonance coupling. Turbulent waves with distinct characteristic wavelengths across scales coherently interact with charged particles when their phase velocities match the particles' velocities. A multi-wavenumber mapping framework is developed to model chaotic transport under multi-scale turbulence. By analytically deriving velocity correlation functions, we quantify the diffusion coefficient under conditions of cross-scale wave intensity parity. A critical analysis reveals that chaotic dynamics at smaller scales prove insufficient to completely erase phase-space correlations established by large-scale turbulent components. The largest-scale turbulence components dominate deviations from quasi-linear (QL) theory predictions, establishing a scale-dependent hierarchy in chaotic transport. Mere reduction of inter-wave phase velocity spacing for small-scale components cannot recover QL diffusion at finite wave amplitudes in multi-scale turbulence. Incorporating a larger-scale component into a small-scale-driven strong chaotic system can induce non-QL diffusion. Specifically, for two-scale turbulence, the QL approximation systematically underestimates transport. Increasing the number of smaller-scale components with strong overlap parameters drives convergence toward the QL approximation. This framework provides a methodology for analyzing resonance-driven turbulence in laboratory and astrophysical plasmas.

physics.plasm-ph↗

arXiv2025

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning

We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient modeling of long-term dynamics without sacrificing accuracy or interpretability. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources. Consequently, S-MNN can drop-in replace the original MNN in applications, providing a practical and efficient tool for integrating mechanistic bottlenecks into neural network models of complex dynamical systems. Source code is available at https://github.com/IST-DASLab/ScalableMNN.

cs.LG↗

arXiv2025

Deep Robust Reversible Watermarking

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction in lossy channels. Existing RRW methods, mostly non-deep learning-based, face complex designs, high computational costs, and poor robustness, limiting their practical use. This paper proposes Deep Robust Reversible Watermarking (DRRW), a deep learning-based RRW scheme. DRRW uses an Integer Invertible Watermark Network (iIWN) to map integer data distributions invertibly, addressing conventional RRW limitations. Unlike traditional RRW, which needs distortion-specific designs, DRRW employs an encoder-noise layer-decoder framework for adaptive robustness via end-to-end training. In inference, cover image and watermark map to an overflowed stego image and latent variables, compressed by arithmetic coding into a bitstream embedded via reversible data hiding for lossless recovery. We introduce an overflow penalty loss to reduce pixel overflow, shortening the auxiliary bitstream while enhancing robustness and stego image quality. An adaptive weight adjustment strategy avoids manual watermark loss weighting, improving training stability and performance. Experiments show DRRW outperforms state-of-the-art RRW methods, boosting robustness and cutting embedding, extraction, and recovery complexities by 55.14$\times$, 5.95$\times$, and 3.57$\times$, respectively. The auxiliary bitstream shrinks by 43.86$\times$, with reversible embedding succeeding on 16,762 PASCAL VOC 2012 images, advancing practical RRW. DRRW exceeds irreversible robust watermarking in robustness and quality while maintaining reversibility.

cs.CV↗

arXiv2025

Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms

Efficient path planning for unmanned aerial vehicles (UAVs) is crucial in remote sensing and information collection. As task scales expand, the cooperative deployment of multiple UAVs significantly improves information collection efficiency. However, collaborative communication and decision-making for multiple UAVs remain major challenges in path planning, especially in noisy environments. To efficiently accomplish complex information collection tasks in 3D space and address robust communication issues, we propose a multi-agent reinforcement learning (MARL) framework for UAV path planning based on the Counterfactual Multi-Agent Policy Gradients (COMA) algorithm. The framework incorporates attention mechanism-based UAV communication protocol and training-deployment system, significantly improving communication robustness and individual decision-making capabilities in noisy conditions. Experiments conducted on both synthetic and real-world datasets demonstrate that our method outperforms existing algorithms in terms of path planning efficiency and robustness, especially in noisy environments, achieving a 78\% improvement in entropy reduction.

cs.MA↗

arXiv2025

Strain-engineering quantum anomalous Hall effect in janus MnBi2SexTe4-x monolayers

Exploring intrinsic magnetic topological insulators (TIs) for next-generation spintronic devices is still challenging in recent years. Here, we present a theoretical investigation on the electronic, magnetic and topological properties of monolayer (ML) Janus MnBi2TexSe4-x, derived from two trivial magnetic semiconductors ML MnBi2Se4 and MnBi2Te4. Our band structure analysis reveals that two out of the eight Janus structures exhibit band inversion induced by spin-orbit coupling. These structures are confirmed to have nonzero integer Chern numbers, indicating their topological nature. Moreover, the topological state is robust under moderate biaxial strains. Interestingly, applying compressive strain results in a high Chern number of 2 and enhances their magnetic stability at elevated temperatures. Our findings offer an effective strategy to engineer magnetic TI states within the ML MnBi2Te4 family.

cond-mat.mtrl-sci↗

arXiv2025

Stable Matching with Interviews

In several two-sided markets, including labor and dating, agents typically have limited information about their preferences prior to mutual interactions. This issue can result in matching frictions, as arising in the labor market for medical residencies, where high application rates are followed by a large number of interviews. Yet, the extensive literature on two-sided matching primarily focuses on models where agents know their preferences, leaving the interactions necessary for preference discovery largely overlooked. This paper studies this problem using an algorithmic approach, extending Gale-Shapley's deferred acceptance to this context. Two algorithms are proposed. The first is an adaptive algorithm that expands upon Gale-Shapley's deferred acceptance by incorporating interviews between applicants and positions. Similar to deferred acceptance, one side sequentially proposes to the other. However, the order of proposals is carefully chosen to ensure an interim stable matching is found. Furthermore, with high probability, the number of interviews conducted by each applicant or position is limited to $O(\log^2 n)$. In many seasonal markets, interactions occur more simultaneously, consisting of an initial interview phase followed by a clearing stage. We present a non-adaptive algorithm for generating a single stage set of in tiered random markets. The algorithm finds an interim stable matching in such markets while assigning no more than $O(\log^3 n)$ interviews to each applicant or position.

cs.DS↗

arXiv2024

Entropy Regularization and Faster Decremental Matching in General Graphs

We provide an algorithm that maintains, against an adaptive adversary, a $(1-\varepsilon)$-approximate maximum matching in $n$-node $m$-edge general (not necessarily bipartite) undirected graph undergoing edge deletions with high probability with (amortized) $O(\mathrm{poly}(\varepsilon^{-1}, \log n))$ time per update. We also obtain the same update time for maintaining a fractional approximate weighted matching (and hence an approximation to the value of the maximum weight matching) and an integral approximate weighted matching in dense graphs. Our unweighted result improves upon the prior state-of-the-art which includes a $\mathrm{poly}(\log{n}) \cdot 2^{O(1/\varepsilon^2)}$ update time [Assadi-Bernstein-Dudeja 2022] and an $O(\sqrt{m} \varepsilon^{-2})$ update time [Gupta-Peng 2013], and our weighted result improves upon the $O(\sqrt{m}\varepsilon^{-O(1/\varepsilon)}\log{n})$ update time due to [Gupta-Peng 2013]. To obtain our results, we generalize a recent optimization approach to dynamic algorithms from [Jambulapati-Jin-Sidford-Tian 2022]. We show that repeatedly solving entropy-regularized optimization problems yields a lazy updating scheme for fractional decremental problems with a near-optimal number of updates. To apply this framework we develop optimization methods compatible with it and new dynamic rounding algorithms for the matching polytope.

cs.DS↗

arXiv2024

SIESEF-FusionNet: Spatial Inter-correlation Enhancement and Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic Segmentation

The ambiguity at the boundaries of different semantic classes in point cloud semantic segmentation often leads to incorrect decisions in intelligent perception systems, such as autonomous driving. Hence, accurate delineation of the boundaries is crucial for improving safety in autonomous driving. A novel spatial inter-correlation enhancement and spatially-embedded feature fusion network (SIESEF-FusionNet) is proposed in this paper, enhancing spatial inter-correlation by combining inverse distance weighting and angular compensation to extract more beneficial spatial information without causing redundancy. Meanwhile, a new spatial adaptive pooling module is also designed, embedding enhanced spatial information into semantic features for strengthening the context-awareness of semantic features. Experimental results demonstrate that 83.7% mIoU and 97.8% OA are achieved by SIESEF-FusionNet on the Toronto3D dataset, with performance superior to other baseline methods. A value of 61.1% mIoU is reached on the semanticKITTI dataset, where a marked improvement in segmentation performance is observed. In addition, the effectiveness and plug-and-play capability of the proposed modules are further verified through ablation studies.

cs.CV↗

arXiv2024

Enhancing Recommendation with Denoising Auxiliary Task

The historical interaction sequences of users plays a crucial role in training recommender systems that can accurately predict user preferences. However, due to the arbitrariness of user behavior, the presence of noise in these sequences poses a challenge to predicting their next actions in recommender systems. To address this issue, our motivation is based on the observation that training noisy sequences and clean sequences (sequences without noise) with equal weights can impact the performance of the model. We propose a novel self-supervised Auxiliary Task Joint Training (ATJT) method aimed at more accurately reweighting noisy sequences in recommender systems. Specifically, we strategically select subsets from users' original sequences and perform random replacements to generate artificially replaced noisy sequences. Subsequently, we perform joint training on these artificially replaced noisy sequences and the original sequences. Through effective reweighting, we incorporate the training results of the noise recognition model into the recommender model. We evaluate our method on three datasets using a consistent base model. Experimental results demonstrate the effectiveness of introducing self-supervised auxiliary task to enhance the base model's performance.

cs.IR↗

arXiv2024

The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical applications. Existing depth estimation systems, however, suffer inevitably from real-world corruptions and perturbations and are struggled to provide reliable depth predictions under such cases. In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation. This challenge was developed based on the newly established KITTI-C and NYUDepth2-C benchmarks. We hosted two stand-alone tracks, with an emphasis on robust self-supervised and robust fully-supervised depth estimation, respectively. Out of more than two hundred participants, nine unique and top-performing solutions have appeared, with novel designs ranging from the following aspects: spatial- and frequency-domain augmentations, masked image modeling, image restoration and super-resolution, adversarial training, diffusion-based noise suppression, vision-language pre-training, learned model ensembling, and hierarchical feature enhancement. Extensive experimental analyses along with insightful observations are drawn to better understand the rationale behind each design. We hope this challenge could lay a solid foundation for future research on robust and reliable depth estimation and beyond. The datasets, competition toolkit, workshop recordings, and source code from the winning teams are publicly available on the challenge website.

cs.CV↗

arXiv2024

First-principles study of electronic and magnetic properties of Fe atoms on Cu2N/Cu(100)

First-principles calculations were conducted to investigate the structural, electronic and magnetic properties of single Fe atoms and Fe dimers on Cu2N/Cu(100). Upon adsorption of an Fe atom onto Cu2N/Cu(100), robust Fe-N bonds form, resulting in the incorporation of both single Fe atoms and Fe dimers within the surface Cu2N layer. The partial occupancy of Fe-3d orbitals lead to large spin moments on the Fe atoms. Interestingly, both single Fe atoms and Fe dimers exhibit in-plane magnetic anisotropy, with the magnetic anisotropy energy (MAE) of an Fe dimer exceeding twice that of a single Fe atom. This magnetic anisotropy can be attributed to the predominant contribution of the component along the x direction of the spin-orbital coupling Hamiltonian. Additionally, the formation of Fe-Cu dimers may further boost the magnetic anisotropy, as the energy levels of the Fe-3d orbitals are remarkably influenced by the presence of Cu atoms. Our study manifests the significance of uncovering the origin of magnetic anisotropy in engineering the magnetic properties of magnetic nanostructures.

cond-mat.mtrl-sci↗

arXiv2024

A class of Berezin-type operators on weighted Fock spaces with $A_{\infty}$-type weights

Let $0<α,β,t<\infty$ and $μ$ be a positive Borel measure on $\mathbb{C}^n$. We consider the Berezin-type operator $S^{t,α,β}_μ$ defined by $$S^{t,α,β}_μf(z):=\left(\int_{\mathbb{C}^n}e^{-\fracβ{2}|z-u|^2}|f(u)|^te^{-\frac{αt}{2}|u|^2}dμ(u)\right)^{1/t},\quad z\in\mathbb{C}^n.$$ We completely characterize the boundedness and compactness of $S^{t,α,β}_μ$ from the weighted Fock space $F^p_{α,w}$ into the Lebesgue space $L^q(wdv)$ for all possible indices, where $w$ is a weight on $\mathbb{C}^n$ that satisfies an $A_{\infty}$-type condition. This solves an open problem raised by Zhou, Zhao and Tang [Banach J. Math. Anal. 18 (2024), Paper No. 20]. As an application, we obtain the description of the boundedness and compactness of Toeplitz-type operators acting between weighted Fock spaces induced by $A_{\infty}$-type weights.

math.FA↗