arXiv subjects

Jun Chen

Publications and source records attributed to Jun Chen.

At least 109 records · Page 6Linked to original sources

arXiv2026

Boolean Satisfiability via Imitation Learning

We propose ImitSAT, a branching policy for conflict-driven clause learning (CDCL) solvers based on imitation learning for the Boolean satisfiability problem (SAT). Unlike previous methods that predict instance-level signals to improve CDCL branching indirectly, or rely on reinforcement learning and insufficient CDCL information to enhance branching, ImitSAT learns from expert KeyTrace that collapses a full run into the sequence of surviving decisions. Replaying a KeyTrace on the same instance is nearly conflict-free, providing dense decision-level supervision and directly reducing propagations -- the dominant contributor to wall-clock time. This prefix-conditioned supervision enables ImitSAT to reproduce high-quality branches without exploration, yielding faster convergence, stable training, and seamless integration into CDCL. Extensive experiments demonstrate that ImitSAT reduces propagation counts and runtime, outperforming state-of-the-art learned approaches. We released the source code and trained model at https://github.com/zewei-Zhang/ImitSAT

cs.AI↗

arXiv2026

A Computation of Tamarkin-Tsygan Calculus

We compute the full Tamarkin-Tsygan calculus of a Koszul algebra whose global dimension exceeds the number of generators. Our results show that even for algebras possessing an economic presentation and agreeable homological properties, the Hochschild (co)homology, as well as the structure of the Tamarkin--Tsygan calculus may exhibit a rather intricate behavior.

math.RA↗

arXiv2026

Riemannian Momentum Tracking: Distributed Optimization with Momentum on Compact Submanifolds

Gradient descent with momentum has been widely applied in various signal processing and machine learning tasks, demonstrating a notable empirical advantage over standard gradient descent. However, momentum-based distributed Riemannian algorithms have been only scarcely explored. In this paper, we propose Riemannian Momentum Tracking (RMTracking), a decentralized optimization algorithm with momentum over a compact submanifold. Given the non-convex nature of compact submanifolds, the objective function, composed of a finite sum of smooth (possibly non-convex) local functions, is minimized across agents in an undirected and connected network graph. With a constant step-size, we establish an $\mathcal{O}(\frac{1-β}{K})$ convergence rate of the Riemannian gradient average for any momentum weight $β\in [0,1)$. Especially, RMTracking can achieve a convergence rate of $\mathcal{O}(\frac{1-β}{K})$ to a stationary point when the step-size is sufficiently small. To best of our knowledge, RMTracking is the first decentralized algorithm to achieve exact convergence that is $\frac{1}{1-β}$ times faster than other related algorithms. Finally, we verify these theoretical claims through numerical experiments on eigenvalue problems.

math.OC↗

arXiv2026

Robust Pedestrian Detection with Uncertain Modality

Existing cross-modal pedestrian detection (CMPD) employs complementary information from RGB and thermal-infrared (TIR) modalities to detect pedestrians in 24h-surveillance systems.RGB captures rich pedestrian details under daylight, while TIR excels at night. However, TIR focuses primarily on the person's silhouette, neglecting critical texture details essential for detection. While the near-infrared (NIR) captures texture under low-light conditions, which effectively alleviates performance issues of RGB and detail loss in TIR, thereby reducing missed detections. To this end, we construct a new Triplet RGB-NIR-TIR (TRNT) dataset, comprising 8,281 pixel-aligned image triplets, establishing a comprehensive foundation for algorithmic research. However, due to the variable nature of real-world scenarios, imaging devices may not always capture all three modalities simultaneously. This results in input data with unpredictable combinations of modal types, which challenge existing CMPD methods that fail to extract robust pedestrian information under arbitrary input combinations, leading to significant performance degradation. To address these challenges, we propose the Adaptive Uncertainty-aware Network (AUNet) for accurately discriminating modal availability and fully utilizing the available information under uncertain inputs. Specifically, we introduce Unified Modality Validation Refinement (UMVR), which includes an uncertainty-aware router to validate modal availability and a semantic refinement to ensure the reliability of information within the modality. Furthermore, we design a Modality-Aware Interaction (MAI) module to adaptively activate or deactivate its internal interaction mechanisms per UMVR output, enabling effective complementary information fusion from available modalities.

cs.CV↗

arXiv2026

Disorder-driven exceptional points and concurrent topological phase transitions in non-Hermitian systems

Exceptional points (EPs) are spectral degeneracies unique to non-Hermitian systems which underpin phenomena from enhanced sensing to unconventional topology. While disorder is usually viewed as detrimental, it can also drive topological phase transitions (TPTs). Here, we show that random disorder alone can generate EPs and concurrent TPTs in a multiorbital non-Hermitian lattice with nonreciprocal hopping. Increasing disorder induces successive real-complex-real spectral transitions accompanied by band inversion and quantized changes in the spin Bott index. Using effective medium theory and large-scale simulations, we trace these transitions to a competition between disorder-induced energy-level renormalization and nonreciprocity-driven hybridization. The resulting phase diagram reveals extended EP lines that emerge from the Hermitian TPT point and persist over a broad parameter range. Our results establish disorder as an active mechanism for engineering exceptional point mediated topology in non-Hermitian matter.

cond-mat.dis-nn↗

arXiv2026

Three-dimensional properties of a coronal shock and the longitudinal distribution of its related solar energetic particles

This study aims to investigate the relationship between the spatial-temporal evolution of shock properties and the longitudinal dependence of SEP intensities and spectra. The shock parameters, including the normal speed, oblique angles, compression ratio, and Alfven Mach number, were derived by combining a steady-state solar-wind simulation with the three-dimensional (3D) reconstruction of the shock surface based on multi-view observations. We compared the local shock parameters at the magnetic connecting points with in situ proton intensities and peak spectra to establish the link between shock evolution and SEP characteristics. The shock nose consistently exhibited higher particle-acceleration efficiency with the largest normal speed, compression ratio, and supercritical Alfven Mach number, while the flanks showed delayed transition to supercritical Alfven Mach number with weaker efficiency. The earliest and most rapid proton enhancement of STEREO-B correlated with efficient shock acceleration and prompt magnetic connectivity to the shock. Spectral analysis revealed that proton energy spectra were consistent with the relativistic diffusive shock acceleration (DSA) estimations. The initial shock acceleration began at about 1.4-5 Rsun and caused the widespread longitudinal SEP distribution. The longitudinal dependence of SEP intensity and spectral variations arise from the combined influence of 3D shock properties, magnetic connectivity, and particle transport processes. The agreement between in situ proton indices and relativistic DSA estimations supports DSA in this SEP event and provides insights into the early-stage acceleration at the source region.

astro-ph.SR↗

arXiv2026

Unexpected type-II multiferroic phase in GdMnO3 under high magnetic fields

Perovskite manganites with small A-site ions, as the first and canonical branch of type-II multiferroics, are ideal systems to exhibit magnetism-induced ferroelectricity. Despite their established magnetoelectric phase diagrams under low magnetic fields, here an unidentified phase with a large magnetism-induced polarization (up to 1500 μC/m2) is revealed in GdMnO3 under high magnetic fields up to 60 T. Based on multiprobe experiments, a complete phase diagram is constructed with successive polar-nonpolar-polar-nonpolar transitions. Such a nonmonotonic evolution is well mimicked by model simulation, while the spin-lattice coupling is the key ingredient for the reentrant ferroelectric phase.

cond-mat.mtrl-sci↗

arXiv2026

Magnetoelectric torque in polar magnetic bilayers

Energy-efficient fast switching of spin orientations or textures is a core issue of spintronics, which is highly demanded but remains challenging. Different from the mainstream routes based on spin-transfer torque or spin-orbit torque, here we propose another mechanism coined as magnetoelectric torque to switch the magnetization in polar magnetic bilayers via pure electric field. In some magnetic van der Waals bilayers, when the electrostatic energy of polarization can compensate the interlayer magnetic coupling, a magnetoelectric torque is generated to fastly flip spins within a few picoseconds, which is demonstrated by combining the first-principles calculations, analytic model, as well as atomistic simulations. Such a magnetoelectric torque doesn't rely on the spin-orbit coupling and is generally active in polar magnetic homostructures and heterostructures. Our work provides an alternative route to switch magnetization in nanoscale, which may benefit the energy-saving and fast-response spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

Modulating near-field radiative energy and momentum transfer via rotating Weyl semimetals

We study near-field radiative transfer of energy, angular momentum, and linear momentum between a nanoparticle and a plate consisting of magnetic Weyl semimetals, and demonstrate that these can be efficiently tuned by a relative angle between the Weyl node separations. This tunability originates from the coupling between the particle-induced rotational Poynting vector and the nonreciprocal surface plasmon polaritons supported by the plate. Remarkably, we uncover a counterintuitive regime in which both energy and angular momentum transfer are maximized when the Weyl node separations are antiparallel rather than parallel. This arises from optimal mode matching between the rotation direction of the particle's circular heat flux and the propagation direction of the surface plasmon polaritons in the antiparallel configuration.

physics.optics↗

arXiv2025

Modeling and Analysis of Land-to-Ship Maritime Wireless Channels at 5.8 GHz

Maritime channel modeling is crucial for designing robust nearshore communication systems, yet reliable models that account for the dynamic marine environment with varying sea waves, wind conditions, and vessel motions remain scarce. This article investigates land-to-ship maritime wireless channel characteristics at 5.8 GHz based upon an extensive measurement campaign, with concurrent hydrological and meteorological information collection. First, a novel large-scale path loss model with physical foundation and high accuracy is proposed for dynamic marine environments. Then, we introduce the concept of sea-wave-induced fixed-point (SWIFT) fading, a peculiar phenomenon in maritime scenarios that captures the impact of sea surface fluctuations on received power. An enhanced two-ray model incorporating vessel rotational motion is propounded to simulate the SWIFT fading, showing good alignment with measured data, particularly for modest antenna movements. Next, the small-scale fading is studied by leveraging a variety of models including the two-wave with diffuse power (TWDP) and asymmetric Laplace distributions, with the latter performing well in most cases, while TWDP better captures bimodal fading in rough seas. Furthermore, maritime channel sparsity is examined via the Gini index and Rician $K$ factor, and temporal dispersion is characterized. The resulting channel models and parameter characteristics offer valuable insights for maritime wireless system design and deployment.

eess.SP↗

arXiv2025

Fast, accurate measurement of the worker populations of honey bee colonies using deep learning

Honey bees play a crucial role in pollination, contributing significantly to global agriculture and ecosystems. Accurately estimating hive populations is essential for understanding the effects of environmental factors on bee colonies, yet traditional methods of counting bees are time-consuming, labor-intensive, and prone to human error, particularly in large-scale studies. In this paper, we present a deep learning-based solution for automating bee population counting using CSRNet and introduce ASUBEE, the FIRST high-resolution dataset specifically designed for this task. Our method employs density map estimation to predict bee populations, effectively addressing challenges such as occlusion and overlapping bees that are common in hive monitoring. We demonstrate that CSRNet achieves superior performance in terms of time efficiency, with a computation time of just 1 second per image, while delivering accurate counts even in complex and densely populated hive scenarios. Our findings show that deep learning approaches like CSRNet can dramatically enhance the efficiency of hive population assessments, providing a valuable tool for researchers and beekeepers alike. This work marks a significant advancement in applying AI technologies to ecological research, offering scalable and precise monitoring solutions for honey bee populations.

q-bio.QM↗

arXiv2025

Step-Audio-R1 Technical Report

Recent advances in reasoning models have demonstrated remarkable success in text and vision domains through extended chain-of-thought deliberation. However, a perplexing phenomenon persists in audio language models: they consistently perform better with minimal or no reasoning, raising a fundamental question - can audio intelligence truly benefit from deliberate thinking? We introduce Step-Audio-R1, the first audio reasoning model that successfully unlocks reasoning capabilities in the audio domain. Through our proposed Modality-Grounded Reasoning Distillation (MGRD) framework, Step-Audio-R1 learns to generate audio-relevant reasoning chains that genuinely ground themselves in acoustic features rather than hallucinating disconnected deliberations. Our model exhibits strong audio reasoning capabilities, surpassing Gemini 2.5 Pro and achieving performance comparable to the state-of-the-art Gemini 3 Pro across comprehensive audio understanding and reasoning benchmarks spanning speech, environmental sounds, and music. These results demonstrate that reasoning is a transferable capability across modalities when appropriately anchored, transforming extended deliberation from a liability into a powerful asset for audio intelligence. By establishing the first successful audio reasoning model, Step-Audio-R1 opens new pathways toward building truly multimodal reasoning systems that think deeply across all sensory modalities.

cs.AI↗

arXiv2025

LiMT: A Multi-task Liver Image Benchmark Dataset

Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent years, the application scope of existing datasets remains relatively limited, typically supporting only single tasks, which has somewhat constrained the development of CAD technology. To address the above limitation, in this paper, we construct a multi-task liver dataset (LiMT) used for liver and tumor segmentation, multi-label lesion classification, and lesion detection based on arterial phase-enhanced computed tomography (CT), potentially providing an exploratory solution that is able to explore the correlation between tasks and does not need to worry about the heterogeneity between task-specific datasets during training. The dataset includes CT volumes from 150 different cases, comprising four types of liver diseases as well as normal cases. Each volume has been carefully annotated and calibrated by experienced clinicians. This public multi-task dataset may become a valuable resource for the medical imaging research community in the future. In addition, this paper not only provides relevant baseline experimental results but also reviews existing datasets and methods related to liver-related tasks. Our dataset is available at https://drive.google.com/drive/folders/1l9HRK13uaOQTNShf5pwgSz3OTanWjkag?usp=sharing.

cs.CV↗

arXiv2025

FastForward Pruning: Efficient LLM Pruning via Single-Step Reinforcement Learning

Pruning is an effective method for compressing Large Language Models, but finding an optimal, non-uniform layer-wise sparsity allocation remains a key challenge. While heuristic methods are fast but yield suboptimal performance, more powerful search-based approaches like Reinforcement Learning are often hindered by prohibitive computational costs on large-scale models. To overcome this efficiency barrier, we propose FastForward Pruning. Its core is a decoupled, single-step RL framework that separates policy optimization from the complex budget satisfaction problem. Such a decoupling is crucial for efficiently searching the vast policy space of LLMs. This curriculum-based strategy begins with low-cost, simple tasks and gradually increases in complexity, significantly reducing the search's computational overhead. Evaluated on the LLaMA, Mistral, and OPT model families, our framework discovers pruning policies that achieve superior performance over strong heuristic baselines. Crucially, when compared to other search-based algorithms, our method achieves competitive or superior results at a fraction of the computational cost, demonstrating a clear advantage in search efficiency.

cs.LG↗

arXiv2025

Zero-Reference Joint Low-Light Enhancement and Deblurring via Visual Autoregressive Modeling with VLM-Derived Modulation

Real-world dark images commonly exhibit not only low visibility and contrast but also complex noise and blur, posing significant restoration challenges. Existing methods often rely on paired data or fail to model dynamic illumination and blur characteristics, leading to poor generalization. To tackle this, we propose a generative framework based on visual autoregressive (VAR) modeling, guided by perceptual priors from the vision-language model (VLM). Specifically, to supply informative conditioning cues for VAR models, we deploy an adaptive curve estimation scheme to modulate the diverse illumination based on VLM-derived visibility scores. In addition, we integrate dynamic and spatial-frequency-aware Rotary Positional Encodings (SF-RoPE) into VAR to enhance its ability to model structures degraded by blur. Furthermore, we propose a recursive phase-domain modulation strategy that mitigates blur-induced artifacts in the phase domain via bounded iterative refinement guided by VLM-assessed blur scores. Our framework is fully unsupervised and achieves state-of-the-art performance on benchmark datasets.

cs.CV↗

arXiv2025

RetinexDual: Retinex-based Dual Nature Approach for Generalized Ultra-High-Definition Image Restoration

Advancements in image sensing have elevated the importance of Ultra-High-Definition Image Restoration (UHD IR). Traditional methods, such as extreme downsampling or transformation from the spatial to the frequency domain, encounter significant drawbacks: downsampling induces irreversible information loss in UHD images, while our frequency analysis reveals that pure frequency-domain approaches are ineffective for spatially confined image artifacts, primarily due to the loss of degradation locality. To overcome these limitations, we present RetinexDual, a novel Retinex theory-based framework designed for generalized UHD IR tasks. RetinexDual leverages two complementary sub-networks: the Scale-Attentive maMBA (SAMBA) and the Frequency Illumination Adaptor (FIA). SAMBA, responsible for correcting the reflectance component, utilizes a coarse-to-fine mechanism to overcome the causal modeling of mamba, which effectively reduces artifacts and restores intricate details. On the other hand, FIA ensures precise correction of color and illumination distortions by operating in the frequency domain and leveraging the global context provided by it. Evaluating RetinexDual on four UHD IR tasks, namely deraining, deblurring, dehazing, and Low-Light Image Enhancement (LLIE), shows that it outperforms recent methods qualitatively and quantitatively. Ablation studies demonstrate the importance of employing distinct designs for each branch in RetinexDual, as well as the effectiveness of its various components.

cs.CV↗

arXiv2025

Stochasticity-induced non-Hermitian skin criticality

Typically, scaling up the size of a system does not change the shape of its energy spectrum, other than making it denser. Exceptions, however, occur in the new phenomenon of non-Hermitian skin criticality, where closely competing generalized Brillouin zone (GBZ) solutions for non-Hermitian state accumulation give rise to anomalously scaling complex spectra. In this work, we discover that such non-Hermitian criticality can generically emerge from stochasticity in the lattice bond orientation, a surprising phenomenon only possible in 2D or beyond. Marked by system size-dependent amplification rate, it can be physically traced to the proliferation of feedback loops arising from excess local non-Hermitian skin effect (NHSE) accumulation induced by structural disorder. While weak disorder weakens the amplification as intuitively anticipated, stronger disorder enigmatically strengthens the amplification almost universally, scaling distinctly from conventional critical system. By representing cascades of local excess NHSE as ensembles of effectively coupled chains, we analytically derived a critical GBZ that predicts how state amplification scales with the system size and disorder strength, highly consistent with empirical observations. Our new mechanism for disordered-facilitated amplification applies generically to structurally perturbed non-Hermitian lattices with broken reciprocity, and would likely find applications in non-Hermitian sensing through various experimentally mature meta-material platforms.

cond-mat.dis-nn↗

arXiv2025

An LLM-based Framework for Human-Swarm Teaming Cognition in Disaster Search and Rescue

Large-scale disaster Search And Rescue (SAR) operations are persistently challenged by complex terrain and disrupted communications. While Unmanned Aerial Vehicle (UAV) swarms offer a promising solution for tasks like wide-area search and supply delivery, yet their effective coordination places a significant cognitive burden on human operators. The core human-machine collaboration bottleneck lies in the ``intention-to-action gap'', which is an error-prone process of translating a high-level rescue objective into a low-level swarm command under high intensity and pressure. To bridge this gap, this study proposes a novel LLM-CRF system that leverages Large Language Models (LLMs) to model and augment human-swarm teaming cognition. The proposed framework initially captures the operator's intention through natural and multi-modal interactions with the device via voice or graphical annotations. It then employs the LLM as a cognitive engine to perform intention comprehension, hierarchical task decomposition, and mission planning for the UAV swarm. This closed-loop framework enables the swarm to act as a proactive partner, providing active feedback in real-time while reducing the need for manual monitoring and control, which considerably advances the efficacy of the SAR task. We evaluate the proposed framework in a simulated SAR scenario. Experimental results demonstrate that, compared to traditional order and command-based interfaces, the proposed LLM-driven approach reduced task completion time by approximately $64.2\%$ and improved task success rate by $7\%$. It also leads to a considerable reduction in subjective cognitive workload, with NASA-TLX scores dropping by $42.9\%$. This work establishes the potential of LLMs to create more intuitive and effective human-swarm collaborations in high-stakes scenarios.

cs.RO↗