arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 163 records · Page 9Linked to original sources

arXiv2025

High-resolution Measurements of Thermal Conductivity Matrix and Search for Thermal Hall Effect in La$_2$CuO$_4$

We investigated the longitudinal thermal conductivity $κ_{xx}$ and thermal hall conductivity $κ_{xy}$ in La$_2$CuO$_4$ at temperatures $T$ between 2 and 20 K in magnetic fields $H$ up to 10 T. Within the temperature and field intervals studied, we do not resolve any thermal Hall signal with a conservative upper bound of $|κ_{xy}/T| <1\times10^{-4}$ ${\rm Wm^{-1}K^{-2}}$. The longitudinal thermal conductivity $κ_{xx}/T$ agrees well with previous studies, in both magnitude and $T$ dependence. In both channels, we performed measurements using the field-sweep protocol. To achieve high resolution, we carefully took into account relaxation effects after each step-increase in $H$. At low $T$, we find a linear decrease in $κ/T$ vs. $H$, as well as weak hysteresis near the meta-magnetic transition of the spin degrees.

cond-mat.str-el↗

arXiv2025

ForCenNet: Foreground-Centric Network for Document Image Rectification

Document image rectification aims to eliminate geometric deformation in photographed documents to facilitate text recognition. However, existing methods often neglect the significance of foreground elements, which provide essential geometric references and layout information for document image correction. In this paper, we introduce Foreground-Centric Network (ForCenNet) to eliminate geometric distortions in document images. Specifically, we initially propose a foreground-centric label generation method, which extracts detailed foreground elements from an undistorted image. Then we introduce a foreground-centric mask mechanism to enhance the distinction between readable and background regions. Furthermore, we design a curvature consistency loss to leverage the detailed foreground labels to help the model understand the distorted geometric distribution. Extensive experiments demonstrate that ForCenNet achieves new state-of-the-art on four real-world benchmarks, such as DocUNet, DIR300, WarpDoc, and DocReal. Quantitative analysis shows that the proposed method effectively undistorts layout elements, such as text lines and table borders. The resources for further comparison are provided at https://github.com/caipeng328/ForCenNet.

cs.CV↗

arXiv2025

Joint Holistic and Lesion Controllable Mammogram Synthesis via Gated Conditional Diffusion Model

Mammography is the most commonly used imaging modality for breast cancer screening, driving an increasing demand for deep-learning techniques to support large-scale analysis. However, the development of accurate and robust methods is often limited by insufficient data availability and a lack of diversity in lesion characteristics. While generative models offer a promising solution for data synthesis, current approaches often fail to adequately emphasize lesion-specific features and their relationships with surrounding tissues. In this paper, we propose Gated Conditional Diffusion Model (GCDM), a novel framework designed to jointly synthesize holistic mammogram images and localized lesions. GCDM is built upon a latent denoising diffusion framework, where the noised latent image is concatenated with a soft mask embedding that represents breast, lesion, and their transitional regions, ensuring anatomical coherence between them during the denoising process. To further emphasize lesion-specific features, GCDM incorporates a gated conditioning branch that guides the denoising process by dynamically selecting and fusing the most relevant radiomic and geometric properties of lesions, effectively capturing their interplay. Experimental results demonstrate that GCDM achieves precise control over small lesion areas while enhancing the realism and diversity of synthesized mammograms. These advancements position GCDM as a promising tool for clinical applications in mammogram synthesis. Our code is available at https://github.com/lixinHUST/Gated-Conditional-Diffusion-Model/

eess.IV↗

arXiv2025

Flavor Physics at the CEPC: a General Perspective

We discuss the landscape of flavor physics at the Circular Electron-Positron Collider (CEPC), based on the nominal luminosity outlined in its Technical Design Report. The CEPC is designed to operate in multiple modes to address a variety of tasks. At the $Z$ pole, the expected production of 4 Tera $Z$ bosons will provide unique and highly precise measurements of $Z$ boson couplings, while the substantial number of boosted heavy-flavored quarks and leptons produced in clean $Z$ decays will facilitate investigations into their flavor physics with unprecedented precision. We investigate the prospects of measuring various physics benchmarks and discuss their implications for particle theories and phenomenological models. Our studies indicate that, with its highlighted advantages and anticipated excellent detector performance, the CEPC can explore beauty and $τ$ physics in ways that are superior to or complementary with the Belle II and Large-Hadron-Collider-beauty experiments, potentially enabling the detection of new physics at energy scales of 10 TeV and above. This potential also extends to the observation of yet-to-be-discovered rare and exotic processes, as well as testing fundamental principles such as lepton flavor universality, lepton and baryon number conservation, etc., making the CEPC a vibrant platform for flavor physics research. The $WW$ threshold scan, Higgs-factory operation and top-pair productions of the CEPC further enhance its merits in this regard, especially for measuring the Cabibbo-Kobayashi-Maskawa matrix elements, and Flavor-Changing-Neutral-Current physics of Higgs boson and top quarks. We outline the requirements for detector performance and considerations for future development to achieve the anticipated scientific goals.

hep-ex↗

arXiv2025

Text-Visual Semantic Constrained AI-Generated Image Quality Assessment

With the rapid advancements in Artificial Intelligence Generated Image (AGI) technology, the accurate assessment of their quality has become an increasingly vital requirement. Prevailing methods typically rely on cross-modal models like CLIP or BLIP to evaluate text-image alignment and visual quality. However, when applied to AGIs, these methods encounter two primary challenges: semantic misalignment and details perception missing. To address these limitations, we propose Text-Visual Semantic Constrained AI-Generated Image Quality Assessment (SC-AGIQA), a unified framework that leverages text-visual semantic constraints to significantly enhance the comprehensive evaluation of both text-image consistency and perceptual distortion in AI-generated images. Our approach integrates key capabilities from multiple models and tackles the aforementioned challenges by introducing two core modules: the Text-assisted Semantic Alignment Module (TSAM), which leverages Multimodal Large Language Models (MLLMs) to bridge the semantic gap by generating an image description and comparing it against the original prompt for a refined consistency check, and the Frequency-domain Fine-Grained Degradation Perception Module (FFDPM), which draws inspiration from Human Visual System (HVS) properties by employing frequency domain analysis combined with perceptual sensitivity weighting to better quantify subtle visual distortions and enhance the capture of fine-grained visual quality details in images. Extensive experiments conducted on multiple benchmark datasets demonstrate that SC-AGIQA outperforms existing state-of-the-art methods. The code is publicly available at https://github.com/mozhu1/SC-AGIQA.

cs.CV↗

arXiv2025

Holistic White-light Polyp Classification via Alignment-free Dense Distillation of Auxiliary Optical Chromoendoscopy

White Light Imaging (WLI) and Narrow Band Imaging (NBI) are the two main colonoscopic modalities for polyp classification. While NBI, as optical chromoendoscopy, offers valuable vascular details, WLI remains the most common and often the only available modality in resource-limited settings. However, WLI-based methods typically underperform, limiting their clinical applicability. Existing approaches transfer knowledge from NBI to WLI through global feature alignment but often rely on cropped lesion regions, which are susceptible to detection errors and neglect contextual and subtle diagnostic cues. To address this, this paper proposes a novel holistic classification framework that leverages full-image diagnosis without requiring polyp localization. The key innovation lies in the Alignment-free Dense Distillation (ADD) module, which enables fine-grained cross-domain knowledge distillation regardless of misalignment between WLI and NBI images. Without resorting to explicit image alignment, ADD learns pixel-wise cross-domain affinities to establish correspondences between feature maps, guiding the distillation along the most relevant pixel connections. To further enhance distillation reliability, ADD incorporates Class Activation Mapping (CAM) to filter cross-domain affinities, ensuring the distillation path connects only those semantically consistent regions with equal contributions to polyp diagnosis. Extensive results on public and in-house datasets show that our method achieves state-of-the-art performance, relatively outperforming the other approaches by at least 2.5% and 16.2% in AUC, respectively. Code is available at: https://github.com/Huster-Hq/ADD.

cs.CV↗

arXiv2025

A Turing Test for Artificial Nets devoted to model Human Vision

In our invited talk at the AI Evaluation Workshop of the University of Bristol back in June 2022 we argued that, despite claims about successful modeling of the visual brain using ANNs, the problem is far from being solved (even for low-level vision). Open issues include: where should we read from ANNs to reproduce human behavior?, this ad-hoc read-out is part of the brain model or not?, should we use artificial psychophysics or artificial physiology?, artificial experiments should literally match the experiments in humans?. There is a clear need of rigorous procedures for experimental tests for ANNs models of the visual brain, and more generally, to understand ANNs devoted to generic vision tasks. Following our experience in using low-level facts from Visual Neuroscience in Image Processing, we presented the idea of developing a low-level dataset compiling the basic spatio-temporal and chromatic facts that are known to happen in the retina-V1 pathway, and they are not currently available in existing databases such as BrainScore. In our results we checked the behavior of three recently proposed models with similar architecture: (1) A parametric model tuned via Maximum Differentiation [Malo & Simoncelli SPIE 15, Martinez et al. PLOS 18, Martinez et al. Front. Neurosci. 19], (2) A non-parametric model, the PerceptNet, tuned to maximize the correlation with human opinion on subjective distortions [Hepburn et al. IEEE ICIP 20], and (3) A model with the same encoder as PerceptNet, but tuned for segmentation (published later as Hernandez-Camara et al. Patt.Recogn.Lett. 23, Hernandez-Camara et al. Neurocomp. 25). Results on 10 compelling psycho/physio visual facts show that the first model is the one with closer behavior to the humans in terms of receptive fields, but more interestingly, on the nonlinear behavior for spatio-chromatic patterns of a range of luminances and contrasts.

q-bio.NC↗

arXiv2025

Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT

3D reconstruction, which aims to recover the dense three-dimensional structure of a scene, is a cornerstone technology for numerous applications, including augmented/virtual reality, autonomous driving, and robotics. While traditional pipelines like Structure from Motion (SfM) and Multi-View Stereo (MVS) achieve high precision through iterative optimization, they are limited by complex workflows, high computational cost, and poor robustness in challenging scenarios like texture-less regions. Recently, deep learning has catalyzed a paradigm shift in 3D reconstruction. A new family of models, exemplified by DUSt3R, has pioneered a feed-forward approach. These models employ a unified deep network to jointly infer camera poses and dense geometry directly from an Unconstrained set of images in a single forward pass. This survey provides a systematic review of this emerging domain. We begin by dissecting the technical framework of these feed-forward models, including their Transformer-based correspondence modeling, joint pose and geometry regression mechanisms, and strategies for scaling from two-view to multi-view scenarios. To highlight the disruptive nature of this new paradigm, we contrast it with both traditional pipelines and earlier learning-based methods like MVSNet. Furthermore, we provide an overview of relevant datasets and evaluation metrics. Finally, we discuss the technology's broad application prospects and identify key future challenges and opportunities, such as model accuracy and scalability, and handling dynamic scenes.

cs.CV↗

arXiv2025

Large non-saturating Nernst thermopower and magnetoresistance in compensated semimetal ScSb

Today, high-performance thermoelectric and thermomagnetic materials operating in the low-temperature regime, particularly below the boiling point of liquid nitrogen remain scarce. Most thermomagnetic materials reported to date exhibit a strong Nernst signal along specific crystallographic directions in their single-crystal form. However, their performance typically degrades significantly in the polycrystalline form. Here, we report an improved Nernst thermopower of $\sim$ 128 $μ$V/K at 30 K and 14 T in polycrystalline compensated semimetal ScSb, in comparison to that was observed in single crystal ScSb previously. The magnetic field dependence of Nernst thermopower shows a linear and non-saturating behavior up to 14 T. The maximum Nernst power factor reaches to $\sim 240 \times 10^{-4}$ W m$^{-1}$ K$^{-2}$ and Nernst figure of merit reaches to $\sim 11 \times 10^{-4}$ K$^{-1}$. Polycrystalline ScSb also shows a large non-saturating magnetoresistance of $\sim 940 \%$ at 2 K and 14 T. These enhanced properties originate from better electron-hole compensation, as revealed by Hall resistivity measurements. The cubic symmetry and absence of anisotropy in ScSb allow its polycrystalline form to achieve similar enhanced thermomagnetic and electromagnetic performance comparable to that of the single crystal.

cond-mat.mtrl-sci↗

arXiv2025

MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis

Recent evidence suggests that modeling higher-order interactions (HOIs) in functional magnetic resonance imaging (fMRI) data can enhance the diagnostic accuracy of machine learning systems. However, effectively extracting and utilizing HOIs remains a significant challenge. In this work, we propose MvHo-IB, a novel multi-view learning framework that integrates both pairwise interactions and HOIs for diagnostic decision-making, while automatically compressing task-irrelevant redundant information. MvHo-IB introduces several key innovations: (1) a principled method that combines O-information from information theory with a matrix-based Renyi alpha-order entropy estimator to quantify and extract HOIs, (2) a purpose-built Brain3DCNN encoder to effectively utilize these interactions, and (3) a new multi-view learning information bottleneck objective to enhance representation learning. Experiments on three benchmark fMRI datasets demonstrate that MvHo-IB achieves state-of-the-art performance, significantly outperforming previous methods, including recent hypergraph-based techniques. The implementation of MvHo-IB is available at https://github.com/zky04/MvHo-IB.

cs.LG↗

arXiv2025

Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy

The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets. In this study, to investigate their functional correspondences, we used 13 healthy human volunteers (9 males and 4 females, aged 22-56 years) and 8 common marmosets (6 males and 2 females, aged 20-42 months). We then measured pairwise and beyond-pairwise correlations, redundancy, and synergy in movie-driven fMRI data across species. First, we consistently observed a high degree of functional similarity in visual processing within and between species, suggesting that integrative processing mechanisms are preserved in both humans and marmosets, despite potential differences in their specific activity patterns. Second, we found that the strongest functional correspondences during movie watching occurred between the human peri-entorhinal and entorhinal cortex (PeEc) and the occipitotemporal high-level visual regions in the marmoset, reflecting a synergistic functional relationship. This suggests that these regions share complementary and integrated patterns of information processing across species. Third, redundancy measures maintained stable high-order hubs, indicating a steady core of shared information processing, while synergy measures revealed a dynamic shift from low- to high-level visual regions as interaction increased, reflecting adaptive integration. This highlights distinct patterns of information processing across the visual hierarchy. Ultimately, our results reveal the marmoset as a compelling model for investigating visual perception, distinguished by its remarkable functional parallels to the human visual cortex.

q-bio.NC↗

arXiv2025

SAM4D: Segment Anything in Camera and LiDAR Streams

We present SAM4D, a multi-modal and temporal foundation model designed for promptable segmentation across camera and LiDAR streams. Unified Multi-modal Positional Encoding (UMPE) is introduced to align camera and LiDAR features in a shared 3D space, enabling seamless cross-modal prompting and interaction. Additionally, we propose Motion-aware Cross-modal Memory Attention (MCMA), which leverages ego-motion compensation to enhance temporal consistency and long-horizon feature retrieval, ensuring robust segmentation across dynamically changing autonomous driving scenes. To avoid annotation bottlenecks, we develop a multi-modal automated data engine that synergizes VFM-driven video masklets, spatiotemporal 4D reconstruction, and cross-modal masklet fusion. This framework generates camera-LiDAR aligned pseudo-labels at a speed orders of magnitude faster than human annotation while preserving VFM-derived semantic fidelity in point cloud representations. We conduct extensive experiments on the constructed Waymo-4DSeg, which demonstrate the powerful cross-modal segmentation ability and great potential in data annotation of proposed SAM4D.

cs.CV↗

arXiv2025

Pressure-Driven Metallicity in Ångström-Thickness 2D Bismuth and Layer-Selective Ohmic Contact to MoS2

Recent fabrication of two-dimensional (2D) metallic bismuth (Bi) via van der Waals (vdW) squeezing method opens a new avenue to ultrascaling metallic materials into the ångström-thickness regime [Nature 639, 354 (2025)]. However, freestanding 2D Bi is typically known to exhibit a semiconducting phase [Nature 617, 67 (2023), Phys. Rev. Lett. 131, 236801 (2023)], which contradicts with the experimentally observed metallicity in vdW-squeezed 2D Bi. Here we show that such discrepancy originates from the pressure-induced buckled-to-flat structural transition in 2D Bi, which changes the electronic structure from semiconducting to metallic phases. Based on the experimentally fabricated MoS2-Bi-MoS2 trilayer heterostructure, we demonstrate the concept of layer-selective Ohmic contact in which one MoS2 layer forms Ohmic contact to the sandwiched Bi monolayer while the opposite MoS2 layer exhibits a Schottky barrier. The Ohmic contact can be switched between the two sandwiching MoS2 monolayers by changing the polarity of an external gate field, thus enabling charge to be spatially injected into different MoS2 layers. The layer-selective Ohmic contact proposed here represents a layertronic generalization of metal/semiconductor contact, paving a way towards layertronic device application.

cond-mat.mtrl-sci↗

arXiv2025

Robotic Perception with a Large Tactile-Vision-Language Model for Physical Property Inference

Inferring physical properties can significantly enhance robotic manipulation by enabling robots to handle objects safely and efficiently through adaptive grasping strategies. Previous approaches have typically relied on either tactile or visual data, limiting their ability to fully capture properties. We introduce a novel cross-modal perception framework that integrates visual observations with tactile representations within a multimodal vision-language model. Our physical reasoning framework, which employs a hierarchical feature alignment mechanism and a refined prompting strategy, enables our model to make property-specific predictions that strongly correlate with ground-truth measurements. Evaluated on 35 diverse objects, our approach outperforms existing baselines and demonstrates strong zero-shot generalization. Keywords: tactile perception, visual-tactile fusion, physical property inference, multimodal integration, robot perception

cs.RO↗

arXiv2025

Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user's reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models.

cs.IR↗

arXiv2025

Targeted False Positive Synthesis via Detector-guided Adversarial Diffusion Attacker for Robust Polyp Detection

Polyp detection is crucial for colorectal cancer screening, yet existing models are limited by the scale and diversity of available data. While generative models show promise for data augmentation, current methods mainly focus on enhancing polyp diversity, often overlooking the critical issue of false positives. In this paper, we address this gap by proposing an adversarial diffusion framework to synthesize high-value false positives. The extensive variability of negative backgrounds presents a significant challenge in false positive synthesis. To overcome this, we introduce two key innovations: First, we design a regional noise matching strategy to construct a negative synthesis space using polyp detection datasets. This strategy trains a negative-centric diffusion model by masking polyp regions, ensuring the model focuses exclusively on learning diverse background patterns. Second, we introduce the Detector-guided Adversarial Diffusion Attacker (DADA) module, which perturbs the negative synthesis process to disrupt a pre-trained detector's decision, guiding the negative-centric diffusion model to generate high-value, detector-confusing false positives instead of low-value, ordinary backgrounds. Our approach is the first to apply adversarial diffusion to lesion detection, establishing a new paradigm for targeted false positive synthesis and paving the way for more reliable clinical applications in colorectal cancer screening. Extensive results on public and in-house datasets verify the superiority of our method over the current state-of-the-arts, with our synthesized data improving the detectors by at least 2.6% and 2.7% in F1-score, respectively, over the baselines. Codes are at https://github.com/Huster-Hq/DADA.

cs.CV↗

arXiv2025

Single crystalline orthorhombic GdAlGe as a rare earth magnetic Dirac nodal-line metal

Crystal engineering is a method for discovering new quantum materials and phases, which may be achieved by external pressure or strain. Chemical pressure is unique in that it generates internal pressure perpetually to the lattice. As an example, GdAlSi from the rare-earth ($R$) $R$Al$X$ ($X =$ Si or Ge) family of Weyl semimetals is considered. Replacing Si with the larger isovalent element Ge creates sufficiently large chemical pressure to induce a structural transition from the tetragonal structure of GdAlSi, compatible with a Weyl semimetallic state, to an orthorhombic phase in GdAlGe, resulting in an inversion-symmetry-protected nodal-line metal. We find that GdAlGe hosts an antiferromagnetic ground state with two successive orderings, at $T_\mathrm{N1}$ = 35 K and $T_\mathrm{N2}$ = 30 K. In-plane isothermal magnetization shows a magnetic field induced metamagnetic transition at 6.2 T for 2 K. Furthermore, electron-hole compensation gives rise to a large magnetoresistance of $\sim 100\%$ at 2 K and 14 T. Angle-resolved photoemission spectroscopy measurements and density functional theory calculations reveal a Dirac-like linear band dispersion over an exceptionally large energy range of $\sim$ 1.5 eV with a high Fermi velocity of $\sim 10^6$ m/s, a rare feature not observed in any magnetic topological materials.

cond-mat.mtrl-sci↗

arXiv2025

Searching Quantum Entanglement in $p\ p\to Z\ Z$ process

Recent studies have shown that observing entangled particle states at a particle collider like Large Hadron Collider (LHC) and testing violation of Bell inequality in them can open up new research area for high energy physics study. We examine the presence of quantum entanglement in the $pp\to ZZ\to 4\ell$ process at leading order. We apply generally recognized method, quantum state tomography, to reconstruct spin density matrix of the joint $ZZ$ system, through which all the relevant observables can be obtained. The angular distribution of the final leptons are obtained from simulated events using Monte-Carlo program, which is used to reconstruct spin density matrix. Non-zero value of the lower bound of the concurrence is measured with simulated data. The numerical analysis shows that with the luminosity corresponding to LHC Run 2+3, entanglement can be probed at $2 σ$ level and up to 3.75$σ$ level for High-Luminosity LHC data ($3 \rm{ab}^{-1}$), revealing the possibility of finding quantum entanglement in real collider experiment.

hep-ph↗