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Jun Li

Publications and source records attributed to Jun Li.

At least 109 records · Page 6Linked to original sources
arXiv2026

Organizing Unstructured Image Collections using Natural Language

In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically discover several, diverse semantic clustering criteria (e.g., Activity or Location) from the images, and subsequently organize them according to the discovered criteria, without requiring any human input. Our framework, X-Cluster: eXploratory Clustering, treats text as a reasoning proxy: it concurrently scans the entire image collection, proposes candidate criteria in natural language, and groups images into meaningful clusters per criterion. This radically differs from previous works, which either assume predefined clustering criteria or fixed cluster counts. To evaluate X-Cluster, we create two new benchmarks, COCO-4C and Food-4C, each annotated with four distinct grouping criteria and corresponding cluster labels. Experiments show that X-Cluster can effectively reveal meaningful partitions on several datasets. Finally, we use X-Cluster to achieve various real-world applications, including uncovering hidden biases in text-to-image (T2I) generative models and analyzing image virality on social media. Project page: https://oatmealliu.github.io/xcluster.html

cs.CV↗
arXiv2026

Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images

Background: Electrocardiograms are indispensable for diagnosing cardiovascular diseases, yet in many settings they exist only as paper printouts stored in multiple recording layouts. Converting these images into digital signals introduces two key challenges: temporal asynchrony among leads and partial blackout missing, where contiguous signal segments become entirely unavailable. Existing models cannot adequately handle these concurrent problems while maintaining interpretability. Methods: We propose PatchECG, combining an adaptive variable block count missing learning mechanism with a masked training strategy. The model segments each lead into fixed-length patches, discards entirely missing patches, and encodes the remainder via a pluggable patch encoder. A disordered patch attention mechanism with patch-level temporal and lead embeddings captures cross-lead and temporal dependencies without interpolation. PatchECG was trained on PTB-XL and evaluated under seven simulated layout conditions, with external validation on 400 real ECG images from Chaoyang Hospital across three clinical layouts. Results: PatchECG achieves an average AUROC of approximately 0.835 across all simulated layouts. On the Chaoyang cohort, the model attains an overall AUROC of 0.778 for atrial fibrillation detection, rising to 0.893 on the 12x1 subset -- surpassing the pre-trained baseline by 0.111 and 0.190, respectively. Model attention aligns with cardiologist annotations at a rate approaching inter-clinician agreement. Conclusions: PatchECG provides a robust, interpolation-free, and interpretable solution for arrhythmia detection from digitized ECG images across diverse layouts. Its direct modeling of asynchronous and partially missing signals, combined with clinically aligned attention, positions it as a practical tool for cardiac diagnostics from legacy ECG archives in real-world clinical environments.

cs.LG↗
arXiv2026

MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation

Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware approaches such as SAM3D can reconstruct multiple objects while preserving their spatial arrangement, opening the door to practical scene-level 3D generation. However, current methods are limited to single-view input and cannot leverage complementary multi-view observations, while independently estimated object poses often lead to physically implausible layouts such as interpenetration and floating artifacts. We present MV-SAM3D, a training-free framework that extends layout-aware 3D generation with multi-view consistency and physical plausibility. We formulate multi-view fusion as a Multi-Diffusion process in 3D latent space and propose two adaptive weighting strategies -- attention-entropy weighting and visibility weighting -- that enable confidence-aware fusion, ensuring each viewpoint contributes according to its local observation reliability. For multi-object composition, we introduce physics-aware optimization that injects collision and contact constraints both during and after generation, yielding physically plausible object arrangements. Experiments on standard benchmarks and real-world multi-object scenes demonstrate significant improvements in reconstruction fidelity and layout plausibility, all without any additional training. Code is available at https://github.com/devinli123/MV-SAM3D.

cs.CV↗
arXiv2026

Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection

In a real-world traffic scenario, varying-scale objects are usually distributed in a cluttered background, which poses great challenges to accurate detection. Although current Mamba-based methods can efficiently model long-range dependencies, they still struggle to capture small objects with abundant local details, which hinders joint modeling of local structures and global semantics. Moreover, state-space models exhibit limited hierarchical feature representation and weak cross-scale interaction due to flat sequential modeling and insufficient spatial inductive biases, leading to sub-optimal performance in complex scenes. To address these issues, we propose a Mamba with Deformable Dilated Convolutions Network (MDDCNet) for accurate traffic object detection in this study. In MDDCNet, a well-designed hybrid backbone with successive Multi-Scale Deformable Dilated Convolution (MSDDC) blocks and Mamba blocks enables hierarchical feature representation from local details to global semantics. Meanwhile, a Channel-Enhanced Feed-Forward Network (CE-FFN) is further devised to overcome the limited channel interaction capability of conventional feed-forward networks, whilst a Mamba-based Attention-Aggregating Feature Pyramid Network (A^2FPN) is constructed to achieve enhanced multi-scale feature fusion and interaction. Extensive experimental results on public benchmark and real-world datasets demonstrate the superiority of our method over various advanced detectors. The code is available at https://github.com/Bettermea/MDDCNet.

cs.CV↗
arXiv2026

Towards Edge Intelligence via Autonomous Navigation: A Robot-Assisted Data Collection Approach

With the growing demand for large-scale and high-quality data in edge intelligence systems, mobile robots are increasingly deployed to collect data proactively, particularly in complex environments. However, existing robot-assisted data collection methods face significant challenges in achieving reliable and efficient performance, especially in non-line-of-sight (NLoS) environments. This paper proposes a communication-and-learning dual-driven (CLD) autonomous navigation scheme that incorporates region-aware propagation characteristics and a non-point-mass robot representation. This scheme enables simultaneous optimization of navigation, communication, and learning performance. An efficient algorithm based on majorization-minimization (MM) is proposed to solve the non-convex and non-smooth CLD problem. Simulation results demonstrate that the proposed scheme achieves superior performance in collision-avoidance navigation, data collection, and model training compared to benchmark methods. It is also shown that CLD can adapt to different scenarios by flexibly adjusting the weight factor among navigation, communication and learning objectives.

cs.RO↗
arXiv2026

Imagine Before Concentration: Diffusion-Guided Registers Enhance Partially Relevant Video Retrieval

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos based on text queries that describe only partial events. Existing methods suffer from incomplete global contextual perception, struggling with query ambiguity and local noise induced by spurious responses. To address these issues, we propose DreamPRVR, which adopts a coarse-to-fine representation learning paradigm. The model first generates global contextual semantic registers as coarse-grained highlights spanning the entire video and then concentrates on fine-grained similarity optimization for precise cross-modal matching. Concretely, these registers are generated by initializing from the video-centric distribution produced by a probabilistic variational sampler and then iteratively refined via a text-supervised truncated diffusion model. During this process, textual semantic structure learning constructs a well-formed textual latent space, enhancing the reliability of global perception. The registers are then adaptively fused with video tokens through register-augmented Gaussian attention blocks, enabling context-aware feature learning. Extensive experiments show that DreamPRVR outperforms state-of-the-art methods. Code is released at https://github.com/lijun2005/CVPR26-DreamPRVR.

cs.CV↗
arXiv2026

Suppression of the tendency toward antiferromagnetic order in the Dirac semimetal SrIrO$_3$

The entangled charge and spin dynamics in strongly electron correlated system has been a fruitful playground for exploring new physical phenomena. Here with resonant inelastic X-ray scattering we studied the spin dynamics of SrIrO$_3$, a half-filled paramagnetic semimetal hosting highly itinerant Dirac Fermions due to its topological band structure. Our results show that its magnetic excitations share much similarity to the ordered compounds upon Sn substitution in exchange strength and AFM instability, while the system maintains spin non-ordered. Further, the non-ordered pristine SrIrO$_3$ hosts even longer lifetime magnetic excitations near the AFM zone center comparing to the Sn substituted ordered compounds, contrary to general expectation. These observations indicate an interesting connection between band topology and electron correlation in SrIrO$_3$.

cond-mat.str-el↗
arXiv2026

Hybrid photon blockade with hyperradiance in two-qubit cavity QED system

We investigate a hybrid photon blockade (HPB) scheme in a driven two-qubit cavity QED system arising from the combination of eigenenergy-level anharmonicity (ELA) and quantum destructive interference (QDI). By tuning the detuning of a single qubit and pumping field, we identify precise parametric regimes that fully integrate the advantages of high brightness in ELA-based conventional photon blockade and strong antibunching in QDI-based unconventional photon blockade. Interestingly, these regimes are accompanied by hyperradiance, indicating that inter-emitter correlations give rise to enhanced collective emission. The HPB mechanism exhibits parametric generality across varying coupling asymmetries and remains accessible via detuning control, offering a feasible route for generating high-quality single-photon source in diverse quantum platforms.

quant-ph↗
arXiv2026

Probing the stochastic signal from primordial gravitational waves with pulsar timing arrays

In this study, we investigate the scenario in which the stochastic signal arises from primordial gravitational waves. Within this framework, we consider two distinct possibilities: one in which the pulsar timing arrays (PTAs) signal corresponds to a stochastic gravitational-wave background (SGWB), and one in which it does not. Primordial gravitational waves can generate an SGWB spanning an exceptionally broad frequency range and are also a source of B-mode polarization in the cosmic microwave background (CMB). We combine CMB B-mode polarization data from BICEP/Keck (BK18), Planck (Planck18), and baryon acoustic oscillation (BAO) measurements with SGWB limits from PTAs to derive updated constraints on the tensor spectral index of the primordial power spectrum. Under the assumption of no detection of an SGWB from PTAs, the allowed parameter space excludes a large portion of the positive region. The constraint within PTA limits is $n_t= -0.165^{+1.20}_{-1.56}$ at $95\%$ confidence level, which are consistent with those obtained from the combined BK18+Planck18+BAO dataset, leading to tighter constraints on the tensor spectral index. Conversely, if the PTA signal is interpreted as an SGWB, the likelihood distribution for the tensor spectral index favors positive values, with $n_t= 2.39^{+1.46}_{-1.35}$ at $95\%$ confidence level, providing evidence for a blue-tilted primordial gravitational-wave power spectrum. In this case, the allowed parameter space excludes the negative region.

astro-ph.CO↗
arXiv2026

Radiative Compression of Dense Cores in the Pillars of Creation as Revealed by JWST Extinction Mapping

The Pillars of Creation in M16 represent an iconic star-forming region where stellar feedback shapes molecular cloud evolution. We present a detailed investigation of dust extinction and density structure in the Pillars of Creation using multiband photometric observations from \emph{JWST} NIRCam. A high-resolution (2\arcsec) extinction map reaching depths of $A_V\sim 100$ mag has been constructed using NIRCam filters F090W, F200W, F335M, and F444W. This map clearly reveals the intricate structure of dense gas within the molecular cloud in the Pillars of Creation region. Analysis of the column density probability distribution function (N-PDF) exhibits a characteristic lognormal distribution at intermediate extinctions ($A_V\approx10-30$\,mag), which transitions to a power-law tail at high extinctions ($A_V\gtrsim$ 30\,mag) where star-forming cores reside. The power-law slope $α$ displays significant spatial variation, steepening from $α\approx 2.0$ at the pillar tips facing the NGC 6611 cluster to $α\approx$4.0 in regions distant from the cluster. This systematic gradient demonstrates that stellar feedback not only disperses molecular clouds but can also locally enhance the formation of dense, self-gravitating structures through radiative compression.

astro-ph.GA↗
arXiv2026

The Extinction Distance of Supernova Remnants in Combination with the CO Line Measurements

Accurate distance measurements to supernova remnants (SNRs) are crucial for understanding their physical properties and evolution. We present a novel method that combines CO line observations with three-dimensional (3D) extinction maps to determine distances to SNRs (G93.7$-$0.2, G109.1$-$1.0, G156.2+5.7, and G166.0+4.3) through their associated molecular clouds. For each SNR, candidate CO velocity components corresponding to interacting molecular clouds are identified based on previous observational evidence with refinements: [$-$19, $-$3] km s$^{-1}$ for G93.7$-$0.2, [$-$51, $-$46] km s$^{-1}$ for G109.1$-$1.0, [$-$10, 0] km s$^{-1}$ for G156.2+5.7, and [$-$27, $-$15] km s$^{-1}$ for G166.0+4.3. By examining extinction-distance profiles along the sightlines and identifying extinction jumps that spatially coincide with CO emission features, we derive distances of 1.82$\pm$0.13 kpc for G93.7$-$0.2, 3.05$\pm$0.15 kpc for G109.1$-$1.0, 0.60$\pm$0.15 kpc for G156.2+5.7, and 3.44$\pm$0.23 kpc for G166.0+4.3. Our extinction-based distances are largely consistent with previous estimates while with better accuracy and robustness.

astro-ph.GA↗
arXiv2026

Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping

Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and poorly suited for multi-night, home-based, and large-scale screening. Single-lead electrocardiography (ECG), already ubiquitous in Holter and patch-based devices, enables comfortable long-term acquisition and encodes sleep-relevant physiology through autonomic modulation and cardiorespiratory coupling. Here, we present a proof-of-concept Holter-to-Sleep framework that, using single-lead ECG as the sole input, jointly supports overnight sleep phenotyping and Holter-grade cardiac phenotyping within the same recording, and further provides an explicit analytic pathway for scalable cardio-sleep association studies. The framework is developed and validated on a pooled multi-center PSG sample of 10,439 studies spanning four public cohorts, with independent external evaluation to assess cross-cohort generalizability, and additional real-world feasibility assessment using overnight patch-ECG recordings via objective-subjective consistency analysis. This integrated design enables robust extraction of clinically meaningful overnight sleep phenotypes under heterogeneous populations and acquisition conditions, and facilitates systematic linkage between ECG-derived sleep metrics and arrhythmia-related Holter phenotypes. Collectively, the Holter-to-Sleep paradigm offers a practical foundation for low-burden, home-deployable, and scalable cardio-sleep monitoring and research beyond traditional PSG-centric workflows.

eess.SP↗
arXiv2026

DAIT: Distillation from Vision-Language Models to Lightweight Classifiers with Adaptive Intermediate Teacher Transfer

Large-scale Vision-Language Models (VLMs) encode rich multimodal semantics that are highly beneficial for fine-grained visual categorization (FGVC). However, their prohibitive computational cost hinders practical deployment in resource-constrained environments. Although knowledge distillation contributes to transferring VLMs capacity to lightweight classifiers, conventional distillation mechanisms, which directly transfer from a generic VLM to a compact student, often yield suboptimal results due to severe architectural misalignment and introducing task-irrelevant information. To alleviate this limitation, we propose Distillation with Adaptive Intermediate Teacher transfer (DAIT) in this study, facilitating adaptive knowledge transfer from VLMs to lightweight students. DAIT introduces a trainable intermediate teacher that learns to transfer frozen VLMs representations under explicit supervision from the target fine-grained task. This intermediate teacher adaptively enhances discriminative visual cues, thereby producing compact and task-aligned knowledge that can be reliably distilled into lightweight models. Extensive evaluations on multiple FGVC benchmarks with diverse student architectures demonstrate that our method achieves respective performance gains of 12.63% and 8.34% on FGVC-Aircraft and CUB-200-2011 datasets, establishing DAIT as a principled paradigm for transferring from general-purpose VLMS to deployable fine-grained recognition models.

cs.CV↗
arXiv2026

Antenna Placement Design for Interference Exploitation in Pinching-Antenna Systems

Pinching-antenna systems (PASs) have been proposed as a flexible antenna technology to fulfill the stringent requirements of high data rate and large-scale equipment deployment in future wireless networks. The principle of PA involves mapping a signal over dielectric waveguides for transmission. By adjusting the positions of pinching antennas (PAs) over the waveguides, with the aim of gain enhancement for line-of-sight links and the reduction of large-scale path loss. Symbol-level precoding (SLP) is a nonlinear precoding technique, which converts multi-user interference into constructive interference via beamforming design at symbol level. In this paper, we study the combination of SLP and PAS, leveraging the advantages of PAS to further enhance the ability of SLP to convert constructive interference. The transmit power minimization problem is formulated and solved for the multiple waveguides multiple PAs system by jointly beamforming and PAs' positions design under the SLP principle. The alternating optimization (AO) framework is applied to decouple the beamforming vector and the position coefficient of PA. For the given beamforming vectors, a new objective function is formulated with respect to the positions of the PAs. With the characteristics of the formulated objective function, the optimization problem for the position coefficients of PAs can be decomposed into multiple independent subproblems, each corresponding to a PA's position coefficient, and a projected gradient descent (PGD)-based method, constrained by the feasible movable region of each PA, is then developed to obtain the suboptimal position coefficients. The performance improvements achieved by the combination of PAS and SLP, as well as the effectiveness of the proposed algorithm are verified through the simulation results.

eess.SP↗
arXiv2026

DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies

Precise human mesh recovery (HMR) from multi-view images remains challenging: end-to-end methods produce entangled errors hard to localize, while fitting-based methods rely on sparse keypoints that provide limited surface constraints. We observe that the true bottleneck lies in the quality of intermediate representations, and that dense pixel-to-surface correspondences can be effectively generated by repurposing pre-trained diffusion models with rich visual priors. We propose DiffProxy, a Stable-Diffusion-based framework trained on large-scale synthetic data with pixel-perfect annotations. A multi-conditional proxy generator predicts dense correspondences from multi-view images, providing uniform surface constraints that enable precise fitting. Hand refinement feeds enlarged hand crops alongside full-body images for fine-grained detail, while test-time scaling exploits diffusion stochasticity to estimate per-pixel uncertainty. Trained only on synthetic data, DiffProxy achieves state-of-the-art results on five diverse real-world benchmarks. Project page: https://wrk226.github.io/DiffProxy.html

cs.CV↗
arXiv2026

Observation of Kondo hybridization wave in UTe2

Condensed matter systems with strong electronic correlations often manifest a variety of intertwined ordered phases of charge, spin, orbital and other degrees of freedom. As a prototypical strongly correlated electronic system, the Kondo lattice provides fertile soil for many fascinating quantum states, including quantum criticality, unconventional superconductivity, hidden order and topological Kondo insulator/semimetal. The foundation of Kondo physics lies in the hybridization between localized moments and itinerant electrons. Generally, the evolution of Kondo hybridization is characterized as a broad crossover rather than a phase transition. Thus far, an ordered hybridization phase has not been observed. Here, we use scanning tunneling microscopy (STM) to identify a translational-symmetry-breaking order of Kondo hybridization wave(KHW) for the first time on the surface of the spin-triplet heavy-fermion superconductor UTe2. The unprecedented phase of KHW manifests as a periodically modulated Fano lattice, accompanied by a commensurate charge density wave (CDW) and a pronounced energy gap opening near the Fermi level. This KHW-imprinted CDW has an intriguing real-space texture of complementary occupation of the heavy f and conduction charges, thereby forming a Kondo superlattice. The KHW is coexistent with superconductivity in UTe2, which may provide valuable insight into its controversial spin-triplet pairing symmetry and the underlying mechanism. Our first experimental evidence for an ordered hybridization state potentially sheds new light on the strong correlation physics of Kondo lattice system.

cond-mat.str-el↗
arXiv2026

Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations

Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g., quotes or relies) to log conversations that capture only a narrow spectrum of user interactions, and (2) They overlook hidden influences such as user conformity and suicide copycat behavior, which can significantly affect suicidal expression and propagation in online communities. To address these limitations, we propose a Multi-Agent Causal Reasoning (MACR) framework that collaboratively employs a Reasoning Agent to scale user interactions and a Bias-aware Decision-Making Agent to mitigate harmful biases arising from hidden influences. The Reasoning Agent integrates cognitive appraisal theory to generate counterfactual user reactions to posts, thereby scaling user interactions. It analyses these reactions through structured dimensions, i.e., cognitive, emotional, and behavioral patterns, with a dedicated sub-agent responsible for each dimension. The Bias-aware Decision-Making Agent mitigates hidden biases through a front-door adjustment strategy, leveraging the counterfactual user reactions produced by the Reasoning Agent. Through the collaboration of reasoning and bias-aware decision making, the proposed MACR framework not only alleviates hidden biases, but also enriches contextual information of user interactions with counterfactual knowledge. Extensive experiments on real-world conversational datasets demonstrate the effectiveness and robustness of MACR in identifying suicide risk.

cs.CL↗
arXiv2026

An Empirical Analysis of Cooperative Perception for Occlusion Risk Mitigation

Occlusions present a significant challenge for connected and automated vehicles, as they can obscure critical road users from perception systems. Traditional risk metrics often fail to capture the cumulative nature of these threats over time adequately. In this paper, we propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods. This provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure. Utilizing diverse and high-fidelity real-world datasets, a large-scale statistical analysis is conducted to characterize occlusion risk and validate the effectiveness of the proposed metric. The metric is applied to evaluate different vehicle-to-everything (V2X) deployment strategies. Our study shows that full V2X penetration theoretically eliminates this risk, the reduction is highly nonlinear; a substantial statistical benefit requires a high penetration threshold of 75-90%. To overcome this limitation, we propose a novel asymmetric communication framework that allows even non-connected vehicles to receive warnings. Experimental results demonstrate that this paradigm achieves better risk mitigation performance. We found that our approach at 25% penetration outperforms the traditional symmetric model at 75%, and benefits saturate at only 50% penetration. This work provides a crucial risk assessment metric and a cost-effective, strategic roadmap for accelerating the safety benefits of V2X deployment.

cs.RO↗