arXiv subjects

Qiang Gao

Publications and source records attributed to Qiang Gao.

At least 37 records · Page 2Linked to original sources

arXiv2026

Generalizable CT-Free PET Attenuation and Scatter Correction for Pediatric Patients

Computed tomography (CT)-based attenuation and scatter correction improves quantitative PET but adds radiation exposure that is particularly undesirable in pediatric imaging. Existing CT-free methods are commonly trained in homogeneous settings and often degrade under scanner or radiotracer shifts, which limits their clinical utility. We propose the Generalizable PET Correction Network (GPCN), a dual-domain network for domain-robust CT-free PET attenuation and scatter correction. GPCN combines a multi-band contextual refinement module, which models pediatric anatomical variability through wavelet-based multiscale decomposition and long-range spatial context modeling, with a frequency-aware spectral decoupling module, which performs coordinate-conditioned amplitude/phase refinement in the Fourier domain. By synergizing multi-band spatial contextual modeling with asymmetric frequency-spectrum decoupling, the network explicitly separates invariant topological structures from domain-specific noise, thereby achieving precise quantitative recovery of both anatomical organs and focal lesions. This design aims to separate anatomy-dominant structures from domain-sensitive spectral residuals and to improve robustness across heterogeneous imaging conditions. We train and evaluate the method on 1085 pediatric whole-body PET scans acquired with two scanners and five radiotracers. In both joint training and zero-shot cross-domain evaluation, GPCN outperforms representative baselines and maintains stable quantitative accuracy on unseen scanner-tracer combinations. The method is further supported by ablation, region-wise quantitative analysis, and downstream segmentation experiments. In our cohort, the CT component of the conventional protocol corresponded to an average effective dose of 10.8 mSv, indicating the potential clinical value of reliable CT-free correction for pediatric PET.

eess.IV↗

arXiv2026

SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis

Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain queries that execute successfully while violating database semantics. To address these limitations, we propose SemanticAgent, a semantic-aware synthesis framework. SemanticAgent organizes synthesis around three specialized modules: an analyzer, a synthesizer, and a verifier. Through a three-stage protocol of semantic analysis, stepwise synthesis, and diagnostic refinement, SemanticAgent transforms execution-based validation alone into a traceable reasoning process. Our framework generates synthetic data that consistently outperforms prior synthesis methods under semantic-quality evaluation, leading to stronger downstream fine-tuning performance, especially on semantically demanding benchmarks.

cs.AI↗

arXiv2026

The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

This paper presents the NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a $\times$4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.

cs.CV↗

arXiv2026

TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation

Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg.

cs.CV↗

arXiv2026

Prescribed mean curvature hypersurfaces in conformal product manifolds

In this paper, we establish the existence of prescribed mean curvature (PMC) hypersurfaces in conformal product manifolds with (possibly empty) $C^{1,α}$ fixed graphical boundaries under a barrier condition. This result generalizes Gerhardt's work to non-flat conformal backgrounds. As a consequence, we obtain new solutions to the high-dimensional PMC Plateau problem with explicitly characterized topology. Moreover, under a quasi-decreasing condition on the PMC function, we demonstrate that the resulting hypersurfaces are $C^1$ graphs.

math.DG↗

arXiv2026

Millimeter-Scale, Atomically Controlled 2D Topological Insulators Revealed by Multimodal Spectroscopy

Quantum spin Hall insulators, or synonymously known as 2D topological insulators, are crucial 2D systems hosting topologically protected edge states. The working temperature of this topological quantum phase is dictated by the inverted bandgap. However, the previously identified large-gap 2D topological insulators are either extremely chemically unstable, or cannot be made with atomistic precision over macroscopic scales. Here, we establish two-quintuple-layer Bi2Te3 and MnBi2Te4/Bi2Te3 heterostructures as atomically controlled, millimeter-scale 2D topological insulators, enabled by precision layer-by-layer growth that yields a carpet-like morphology extending coherently over macroscopic distances. This carpet-like growth mode renders the films amenable to mechanical exfoliation and subsequent wet or dry transfer. Multimodal spectroscopies and microscopies reveal the integer-layer tuned electronic structure of (Bi2Te3)n with excellent agreement to theory. Photon-energy-dependent photoemission and time-resolved photoemission identify band inversion and band dynamics, respectively, while scanning tunneling spectroscopy resolves topological edge states, characteristic of the 2D topological insulator phase. Thickness- and photon-energy-dependent photoemission further validates MnBi2Te4/Bi2Te3 as a robust 2D topological insulator. The large inverted gaps of ~100 meV in (Bi2Te3)2 and ~150 meV in MnBi2Te4/Bi2Te3 suggest operation near ambient temperature. These results define a scalable materials platform for next-generation, low-loss quantum and energy-efficient devices.

cond-mat.mtrl-sci↗

arXiv2026

Interaction-driven flat band and charge order in Fe5GeTe2

Flat electronic bands enable fascinating emergent phenomena such as superconductivity and charge orders. A prevailing approach to realizing flat bands is to engineer lattice geometric constraints in twisted or kagome-like materials. An alternative approach is to utilize purely electronic-interaction-driven flat bands, yet a fundamental challenge is that extreme flatness requires ultrastrong interaction strength, which often leads to incoherent states. Here we demonstrate the concurrent formation of an interaction-driven flat band at the Fermi level and a $\sqrt{3}\times\sqrt{3}\,R30^\circ$ charge order in a van der Waals magnet Fe5GeTe2 using high-resolution angle-resolved photoemission spectroscopy. This charge order is manifested by band folding within 30 meV below the Fermi level, with its nesting driven by flat bands. The presence of this flat band throughout the Brillouin zone and the logarithmic temperature dependence of its spectral weight suggest a Kondo-like, coherent Fermi liquid emerging from strong correlations. Our work establishes a paradigm where an interaction-driven flat band promotes large-scale electronic ordering.

cond-mat.str-el↗

arXiv2026

Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars

Despite the increasing popularity of avatar systems such as Snapchat Bitmojis, existing production avatar platforms face several limitations, such as a limited number of predefined assets, tedious customization processes, and inefficient rendering requirements. Addressing these shortcomings, we introduce Snapmoji, an avatar generation system that instantly creates 3D avatars, and enables customization in a process we call dual-stylization. Snapmoji first maps a selfie of a user to a primary avatar (e.g., Bitmoji style) using a new technique we name Gaussian Domain Adaptation (GDA), then applies a secondary style (e.g., skeleton, yarn, toy) to the primary avatar, all while preserving the user's identity. The generated 3D avatars can then be rendered an animated on mobile devices at 30-40 FPS.

cs.GR↗

arXiv2026

Scaling DPPs for RAG: Density Meets Diversity

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding generation in external knowledge, yielding relevance responses that are aligned with factual evidence and evolving corpora. Standard RAG pipelines construct context through relevance ranking, performing point-wise scoring between the user query and each corpora chunk. This formulation, however, ignores interactions among retrieved candidates, leading to redundant contexts that dilute density and fail to surface complementary evidence. We argue that effective retrieval should optimize jointly for both density and diversity, ensuring the grounding evidence that is dense in information yet diverse in coverage. In this study, we propose ScalDPP, a diversity-aware retrieval mechanism for RAG that incorporates Determinantal Point Processes (DPPs) through a lightweight P-Adapter, enabling scalable modeling of inter-chunk dependencies and complementary context selection. In addition, we develop a novel set-level objective, Diverse Margin Loss (DML), that enforces ground-truth complementary evidence chains to dominate any equally sized redundant alternatives under DPP geometry. Experimental results demonstrate the superiority of ScalDPP, substantiating our core statement in practice.

cs.LG↗

arXiv2026

Shedding the Facades, Connecting the Domains: Detecting Shifting Multimodal Hate Video with Test-Time Adaptation

Hate Video Detection (HVD) is crucial for online ecosystems. Existing methods assume identical distributions between training (source) and inference (target) data. However, hateful content often evolves into irregular and ambiguous forms to evade censorship, resulting in substantial semantic drift and rendering previously trained models ineffective. Test-Time Adaptation (TTA) offers a solution by adapting models during inference to narrow the cross-domain gap, while conventional TTA methods target mild distribution shifts and struggle with the severe semantic drift in HVD. To tackle these challenges, we propose SCANNER, the first TTA framework tailored for HVD. Motivated by the insight that, despite the evolving nature of hateful manifestations, their underlying cores remain largely invariant (i.e., targeting is still based on characteristics like gender, race, etc), we leverage these stable cores as a bridge to connect the source and target domains. Specifically, SCANNER initially reveals the stable cores from the ambiguous layout in evolving hateful content via a principled centroid-guided alignment mechanism. To alleviate the impact of outlier-like samples that are weakly correlated with centroids during the alignment process, SCANNER enhances the prior by incorporating a sample-level adaptive centroid alignment strategy, promoting more stable adaptation. Furthermore, to mitigate semantic collapse from overly uniform outputs within clusters, SCANNER introduces an intra-cluster diversity regularization that encourages the cluster-wise semantic richness. Experiments show that SCANNER outperforms all baselines, with an average gain of 4.69% in Macro-F1 over the best.

cs.CV↗

arXiv2026

Bootstrapping Flat-band Superconductors: Rigorous Lower Bounds on Superfluid Stiffness

The superfluid stiffness fundamentally constrains the transition temperature of superconductors, especially in the strongly coupled regime. However, accurately determining this inherently quantum many-body property in microscopic models remains a significant challenge. In this work, we show how the \textit{quantum many-body bootstrap} framework, specifically the reduced density matrix (RDM) bootstrap, can be leveraged to obtain rigorous lower bounds on the superfluid stiffness in frustration-free interacting models with superconducting ground state. We numerically apply the method to a special class of frustration free models, which are known as quantum geometric nesting models, for flat-band superconductivity, where we uncover a general relation between the stiffness and the pair mass. Going beyond the familiar Hubbard case within this class, we find how additional interactions, notably simple magnetic couplings, can enhance the superfluid stiffness. Furthermore, we find that the RDM bootstrap unexpectedly reveals that the trion-type correlations are essential for bounding the stiffness, offering new insights on the structure of these models. A straightforward generalization of the method can lead to bounds on susceptibilities complementary to variational approaches. Our findings underscore the immense potential of the quantum many-body bootstrap as a powerful tool to derive rigorous bounds on physical quantities beyond energy.

cond-mat.str-el↗

arXiv2025

From Shallow Humor to Metaphor: Towards Label-Free Harmful Meme Detection via LMM Agent Self-Improvement

The proliferation of harmful memes on online media poses significant risks to public health and stability. Existing detection methods heavily rely on large-scale labeled data for training, which necessitates substantial manual annotation efforts and limits their adaptability to the continually evolving nature of harmful content. To address these challenges, we present ALARM, the first lAbeL-free hARmful Meme detection framework powered by Large Multimodal Model (LMM) agent self-improvement. The core innovation of ALARM lies in exploiting the expressive information from "shallow" memes to iteratively enhance its ability to tackle more complex and subtle ones. ALARM consists of a novel Confidence-based Explicit Meme Identification mechanism that isolates the explicit memes from the original dataset and assigns them pseudo-labels. Besides, a new Pairwise Learning Guided Agent Self-Improvement paradigm is introduced, where the explicit memes are reorganized into contrastive pairs (positive vs. negative) to refine a learner LMM agent. This agent autonomously derives high-level detection cues from these pairs, which in turn empower the agent itself to handle complex and challenging memes effectively. Experiments on three diverse datasets demonstrate the superior performance and strong adaptability of ALARM to newly evolved memes. Notably, our method even outperforms label-driven methods. These results highlight the potential of label-free frameworks as a scalable and promising solution for adapting to novel forms and topics of harmful memes in dynamic online environments.

cs.CV↗

arXiv2025

Impact of Electron Correlations on Infinite-Layer Cuprates and Nickelates

Optimization of unconventional superconductivity involves a balance of interaction strengths. Precise determination of correlation strength across different material families is therefore important. Here, we present a combined X-ray absorption spectroscopy (XAS) and resonant inelastic X-ray scattering (RIXS) study of infinite-layer PrNiO$_2$ and SrCuO$_2$ that enables fair comparison of their interaction strengths. For both compounds, we study the orbital and magnetic excitations and extract their dispersions along high-symmetry directions. Using a single-band Hubbard model and including higher-order exchange interactions, we derive the correlation factor $U/t$ for both compounds. A key finding is that despite a smaller Coulomb repulsion $U$, PrNiO$_2$ exhibits a correlation strength that is 20% stronger than that of its isostructural cuprate counterpart SrCuO$_2$. This indicates that a moderation of the correlation strength may further optimize superconductivity in nickelates.

cond-mat.supr-con↗

arXiv2025

Exact models of chiral flat-band superconductors

Recent experiments have reported the surprising observation of superconductivity in flavor polarized, nearly flat bands (FBs) of rhombohedral graphene. Motivated by these findings, we introduce a class of models for single-flavor FBs with inversion symmetry, where we show a local attractive interaction between orbitals with opposite parities leads to an exact superconducting ground state. We argue that this model can be relevant to realistic multi-flavor systems including short-range repulsion, since the main effect of such repulsion is to induce flavor polarization leaving possibly attractive residual interaction between different flavorless orbitals. The nature of the pairing is determined by the interplay between the FB quantum geometry and the interaction, and is often topological when the parent FB is so. Interestingly, each such model has two nearly degenerate pairing modes, whose energetic competition can be tuned by a change in the charge transfer gap between the two orbitals or electron density. These modes have the same angular momentum but different pairing amplitude structure and possibly different topology. We show that the superfluid stiffness is proportional to the attractive interaction scale using a combination of analytical variational upper bounds and numerical bootstrap lower bounds. We find empirically that the maximum superfluid stiffness is achieved when the hot spots of quantum geometry in the Brillouin zone are marginally filled.

cond-mat.str-el↗

arXiv2025

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.

cs.CV↗

arXiv2025

A Lightweight Group Multiscale Bidirectional Interactive Network for Real-Time Steel Surface Defect Detection

Real-time surface defect detection is critical for maintaining product quality and production efficiency in the steel manufacturing industry. Despite promising accuracy, existing deep learning methods often suffer from high computational complexity and slow inference speeds, which limit their deployment in resource-constrained industrial environments. Recent lightweight approaches adopt multibranch architectures based on depthwise separable convolution (DSConv) to capture multiscale contextual information. However, these methods often suffer from increased computational overhead and lack effective cross-scale feature interaction, limiting their ability to fully leverage multiscale representations. To address these challenges, we propose GMBINet, a lightweight framework that enhances multiscale feature extraction and interaction through novel Group Multiscale Bidirectional Interactive (GMBI) modules. The GMBI adopts a group-wise strategy for multiscale feature extraction, ensuring scale-agnostic computational complexity. It further integrates a Bidirectional Progressive Feature Interactor (BPFI) and a parameter-free Element-Wise Multiplication-Summation (EWMS) operation to enhance cross-scale interaction without introducing additional computational overhead. Experiments on SD-Saliency-900 and NRSD-MN datasets demonstrate that GMBINet delivers competitive accuracy with real-time speeds of 1048 FPS on GPU and 16.53 FPS on CPU at 512 resolution, using only 0.19 M parameters. Additional evaluations on the NEU-CLS defect classification dataset further confirm the strong generalization ability of our method, demonstrating its potential for broader industrial vision applications beyond surface defect detection. The dataset and code are publicly available at: https://github.com/zhangyongcode/GMBINet.

cs.CV↗

arXiv2025

Persistent paramagnons in high-temperature infinite-layer nickelate superconductors

The recent discovery of high-temperature superconductivity in hole-doped SmNiO$_2$, exhibiting the record-high transition temperature $T_c$ among infinite-layer (IL) nickelates, has opened a new avenue for exploring design principles of superconductivity. Experimentally determining the electronic structure and magnetic interactions in this new system is crucial to elucidating the mechanism behind the enhanced superconductivity. Here, we report a Ni $L$-edge resonant inelastic x-ray scattering (RIXS) study of superconducting Sm-based IL nickelate thin films Sm$_{1-x-y-z}$Eu$_x$Ca$_y$Sr$_z$NiO$_2$ (SECS). Dispersive paramagnonic excitations are observed in both optimally and overdoped SECS samples, supporting a spin-fluctuation-mediated pairing scenario. However, despite the two-fold enhancement of $T_c$ in the Sm-based nickelates compared to their Pr-based counterparts, the effective exchange coupling strength is reduced by approximately $20\%$. This behavior contrasts with hole-doped cuprates, where magnetic interactions correlate positively with $T_c$, highlighting essential differences in their superconducting mechanisms.

cond-mat.supr-con↗

arXiv2025

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs

We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built upon the publicly available Ling-lite model, a 16.8 billion parameter model with 2.75 billion activated parameters, our approach matches the performance of state-of-the-art (SOTA) small-scale reasoning models on challenging benchmarks (e.g., AIME, LiveCodeBench, GPQA-Diamond) while activating only one-third of the parameters required by comparable models. To accomplish this, we introduce a joint training pipeline integrating distillation with RL, revealing undocumented challenges in MoE RL training. First, we identify optimization instability during RL training, and we propose Constrained Contextual Computation Policy Optimization(C3PO), a novel approach that enhances training stability and improves computational throughput via algorithm-system co-design methodology. Second, we empirically demonstrate that selecting distillation checkpoints based on entropy loss for RL training, rather than validation metrics, yields superior performance-efficiency trade-offs in subsequent RL training. Finally, we develop a two-stage training paradigm to harmonize multi-domain data integration, addressing domain conflicts that arise in training with mixed dataset. We will release the model, dataset, and code.

cs.CL↗