arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 361 records · Page 20Linked to original sources

arXiv2025

AuthSig: Safeguarding Scanned Signatures Against Unauthorized Reuse in Paperless Workflows

With the deepening trend of paperless workflows, signatures as a means of identity authentication are gradually shifting from traditional ink-on-paper to electronic formats.Despite the availability of dynamic pressure-sensitive and PKI-based digital signatures, static scanned signatures remain prevalent in practice due to their convenience. However, these static images, having almost lost their authentication attributes, cannot be reliably verified and are vulnerable to malicious copying and reuse. To address these issues, we propose AuthSig, a novel static electronic signature framework based on generative models and watermark, which binds authentication information to the signature image. Leveraging the human visual system's insensitivity to subtle style variations, AuthSig finely modulates style embeddings during generation to implicitly encode watermark bits-enforcing a One Signature, One Use policy.To overcome the scarcity of handwritten signature data and the limitations of traditional augmentation methods, we introduce a keypoint-driven data augmentation strategy that effectively enhances style diversity to support robust watermark embedding. Experimental results show that AuthSig achieves over 98% extraction accuracy under both digital-domain distortions and signature-specific degradations, and remains effective even in print-scan scenarios.

cs.CV↗

arXiv2025

CT Radiomics-Based Explainable Machine Learning Model for Accurate Differentiation of Malignant and Benign Endometrial Tumors: A Two-Center Study

Aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing malignancy and benignity specifically in endometrial cancer (EC) patients. A total of 83 EC patients from two centers, including 46 with malignant and 37 with benign conditions, were included, with data split into a training set (n=59) and a testing set (n=24). The regions of interest (ROIs) were manually segmented from pre-surgical CT scans, and 1132 radiomic features were extracted from the pre-surgical CT scans using Pyradiomics. Six explainable machine learning (ML) modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. The diagnostic performance of the radiomic model was evaluated by using sensitivity, specificity, accuracy, precision, F1 score, AUROC, and AUPRC. To enhance clinical understanding and usability, we separately implemented SHAP analysis and feature mapping visualization, and evaluated the calibration curve and decision curve. By comparing six modeling strategies, the Random Forest model emerged as the optimal choice for diagnosing EC, with a training AUROC of 1.00 and a testing AUROC of 0.96. SHAP identified the most important radiomic features, revealing that all selected features were significantly associated with EC (P < 0.05). Radiomics feature maps also provide a feasible assessment tool for clinical applications. Decision Curve Analysis (DCA) indicated a higher net benefit for our model compared to the "All" and "None" strategies, suggesting its clinical utility in identifying high-risk cases and reducing unnecessary interventions. In conclusion, the CT radiomics-based explainable ML model achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of endometrial cancer.

eess.IV↗

arXiv2025

TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning

Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose TimeSearch-R, which reformulates temporal search as interleaved text-video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning can result in unsupervised intermediate search decisions. This leads to insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self-Verification (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning. Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demonstrate that TimeSearch-R achieves significant improvements on temporal search benchmarks such as Haystack-LVBench and Haystack-Ego4D, as well as long-form video understanding benchmarks like VideoMME and MLVU. Notably, TimeSearch-R establishes a new state-of-the-art on LongVideoBench with 4.1% improvement over the base model Qwen2.5-VL and 2.0% over the advanced video reasoning model Video-R1. Our code is available at https://github.com/Time-Search/TimeSearch-R.

cs.CV↗

arXiv2025

Omics-scale polymer computational database transferable to real-world artificial intelligence applications

Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science.

physics.chem-ph↗

arXiv2025

BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems

Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.

cs.CV↗

arXiv2025

Topological transition and emergent elasticity of dislocation in skyrmion lattice: Beyond Kittel's magnetic-polar analogy

Magnetic and polar skyrmions exhibit topologically protected quasiparticle behavior, including emergent fields, deformation, and the formation of a densely packed skyrmion lattice, beyond conventional domain configurations described by Kittel's law. Analogous to atomic crystals, lattice defects, especially dislocations and their associated strain fields, are crucial for understanding the lattice behavior of skyrmions; however, their features and roles remain insufficiently understood. Here, we show that magnetic skyrmion dislocations develop a core-split structure due to a significant skyrmion elongation up to 180% of their original length, reaching a topological transition from a single skyrmion to two half-skyrmions. Despite such a distinct structure, the long-range strain fields around the dislocation perfectly obey conventional Volterra's elasticity theory, in contrast to polar skyrmion lattices, where skyrmion deformations cause a breakdown of the elasticity theory. Furthermore, an energetic analysis shows that Dzyaloshinskii-Moriya interaction drives the large skyrmion deformation of the dislocation core. Our findings not only clarify the coexistence of topological core-reconstruction and a robust long-range elastic field of dislocations in magnetic skyrmion lattices, but also reveal that magnetic and electric domains, long regarded as dual and analogous, exhibit fundamental differences when extended into the regime of collective topological quasiparticles.

cond-mat.mtrl-sci↗

arXiv2025

Non-altermagnetic spin texture in MnTe

Recently, altermagnets have emerged as promising candidates in spintronics, uniquely combining large spin-polarized electronic states with zero net magnetization. A prominent example is $α$-MnTe, whose altermagnetic spin splitting, i.e., the degeneracy lift in momentum space induced by collinear magnetic order, has been experimentally observed. However, the direct evidence of its $g$-wave spin polarization, the key property for altermagnetic spintronics, is thus far lacking. By combining high-resolution spin- and angle-resolved photoemission spectroscopy (SARPES) with first-principles calculations, we reveal a $k_z$-independent, Rashba-like spin texture in $α$-MnTe. Our results indicate that the observed spin polarization is primarily governed by spin-orbit coupling, whereas the magnetic order contributes to the splitting of energy bands but plays a much less dominant role in spin polarization due to the multi-domain nature. From this result, we further establish a way to prescreen altermagnet candidates that favor the formation of large antiferromagnetic domains based on symmetry analysis. Our work elucidates the interplay between magnetic order and spin-orbit coupling in governing spin polarization in altermagnet candidates, and thereby advances the materials design paradigm for spin-functional devices.

cond-mat.mtrl-sci↗

arXiv2025

POLIS-Bench: Towards Multi-Dimensional Evaluation of LLMs for Bilingual Policy Tasks in Governmental Scenarios

We introduce POLIS-Bench, the first rigorous, systematic evaluation suite designed for LLMs operating in governmental bilingual policy scenarios. Compared to existing benchmarks, POLIS-Bench introduces three major advancements. (i) Up-to-date Bilingual Corpus: We construct an extensive, up-to-date policy corpus that significantly scales the effective assessment sample size, ensuring relevance to current governance practice. (ii) Scenario-Grounded Task Design: We distill three specialized, scenario-grounded tasks -- Clause Retrieval & Interpretation, Solution Generation, and the Compliance Judgmen--to comprehensively probe model understanding and application. (iii) Dual-Metric Evaluation Framework: We establish a novel dual-metric evaluation framework combining semantic similarity with accuracy rate to precisely measure both content alignment and task requirement adherence. A large-scale evaluation of over 10 state-of-the-art LLMs on POLIS-Bench reveals a clear performance hierarchy where reasoning models maintain superior cross-task stability and accuracy, highlighting the difficulty of compliance tasks. Furthermore, leveraging our benchmark, we successfully fine-tune a lightweight open-source model. The resulting POLIS series models achieves parity with, or surpasses, strong proprietary baselines on multiple policy subtasks at a significantly reduced cost, providing a cost-effective and compliant path for robust real-world governmental deployment.

cs.CL↗

arXiv2025

E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner $6j$ convolution (Wigner $6j$ Conv). By shifting the computational burden from edges to nodes, the Wigner $6j$ Conv reduces the complexity from $O(|\mathcal{E}|)$ to $ O(| \mathcal{V}|)$ while preserving both the model's expressive power and rotational equivariance. We show that this approach achieves a 7x-30x speedup compared to conventional $\mathrm{SO}(3)$ convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling.

cs.LG↗

arXiv2025

Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025

The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions."

cs.LG↗

arXiv2025

ShapleyPipe: Hierarchical Shapley Search for Data Preparation Pipeline Construction

Automated data preparation pipeline construction is critical for machine learning success, yet existing methods suffer from two fundamental limitations: they treat pipeline construction as black-box optimization without quantifying individual operator contributions, and they struggle with the combinatorial explosion of the search space ($N^M$ configurations for N operators and pipeline length M). We introduce ShapleyPipe, a principled framework that leverages game-theoretic Shapley values to systematically quantify each operator's marginal contribution while maintaining full interpretability. Our key innovation is a hierarchical decomposition that separates category-level structure search from operator-level refinement, reducing the search complexity from exponential to polynomial. To make Shapley computation tractable, we develop: (1) a Multi-Armed Bandit mechanism for intelligent category evaluation with provable convergence guarantees, and (2) Permutation Shapley values to correctly capture position-dependent operator interactions. Extensive evaluation on 18 diverse datasets demonstrates that ShapleyPipe achieves 98.1\% of high-budget baseline performance while using 24\% fewer evaluations, and outperforms the state-of-the-art reinforcement learning method by 3.6\%. Beyond performance gains, ShapleyPipe provides interpretable operator valuations ($ρ$=0.933 correlation with empirical performance) that enable data-driven pipeline analysis and systematic operator library refinement.

cs.DB↗

arXiv2025

Dose Constraints for High-Resolution Imaging of Biological Specimens with Extreme Ultraviolet and Soft X-ray radiation

We present a theoretical evaluation of radiation dose constraints for extreme ultraviolet (EUV) and soft X-ray microscopy. Our work particularly addresses the long-standing concern regarding strong absorption of EUV radiation in biological specimens. Using an established dose-resolution model, we compare hydrated and dehydrated cellular states and quantify the fluence required for nanoscale imaging. Our analysis identifies a protein window spanning photon energies from 70 eV up to the carbon K-edge (284 eV), where EUV microscopy could in principle achieve sub-10 nm half-pitch resolution in dehydrated samples at doses well below the Henderson limit, thereby eliminating the need for cryogenic conditions. In this situation, the radiation dose required for EUV imaging is also substantially lower than what is required for comparable resolution in water window soft X-ray microscopy. Furthermore, EUV photons with sufficiently high energy exhibit penetration depths of um-level in dehydrated biomatter, enabling exceptional amplitude and phase contrast through thin cellular regions and small cells. These findings provide quantitative guidelines for photon energy selection and establish the EUV protein window as a dose-efficient and physically viable modality for high-resolution, label-free, material-specific imaging of dehydrated biological matter.

physics.app-ph↗

arXiv2025

LDMD with Temporally Adaptive Segmentation

Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this limitation, we propose a localized DMD (LDMD) framework that improves prediction performance by integrating DMD's strong linear forecasting capabilities with time-domain segmentation techniques. In this framework, the temporal domain is segmented into multiple subintervals, within which snapshot matrices are constructed and localized predictions are performed. We first present the localized DMD method with predefined segmentation, and then explore an adaptive segmentation strategy to further enhance computational efficiency and prediction robustness. Furthermore, we conduct an error analysis that provides the upper bound of the local and global truncation error for the proposed framework. The effectiveness of LDMD is demonstrated on four benchmark problems-Burgers', Allen-Cahn, nonlinear Schrodinger, and Maxwell's equations. Numerical results show that LDMD significantly enhances long-term predictive accuracy while preserving high computational efficiency.

math.NA↗

arXiv2025

Localized Dynamic Mode Decomposition with Temporally Adaptive Segmentation

math.NA↗

arXiv2025

HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search

Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet informative features. This limitation becomes especially critical in high-dimensional datasets, where complex and interdependent feature relationships prevail. We introduce the HeFS (Helper-Enhanced Feature Selection) framework to refine feature subsets produced by existing algorithms. HeFS systematically searches the residual feature space to identify a Helper Set - features that complement the original subset and improve classification performance. The approach employs a biased initialization scheme and a ratio-guided mutation mechanism within a genetic algorithm, coupled with Pareto-based multi-objective optimization to jointly maximize predictive accuracy and feature complementarity. Experiments on 18 benchmark datasets demonstrate that HeFS consistently identifies overlooked yet informative features and achieves superior performance over state-of-the-art methods, including in challenging domains such as gastric cancer classification, drug toxicity prediction, and computer science applications. The code and datasets are available at https://healthinformaticslab.org/supp/.

cs.LG↗

arXiv2025

Visual-Neural-Inspired Image Inpainting for Specific Objects-of-Interest Imaging

Conventional image inpainting techniques typically process entire images, which often leads to computational inefficiency and susceptibility to information redundancy, particularly in occluded or cluttered scenes. Inspired by cortical processing mechanisms, this study introduces a novel framework termed "Specific Object-of-Interest Imaging" (SIOI) to overcome these limitations. The proposed approach first extracts and encodes object-level representations from complex scenes, producing structural and semantic priors that can be seamlessly integrated into existing inpainting pipelines. Extensive evaluations on dedicated object datasets--Teapot, Elephant, Giraffe, and Zebra--demonstrate that models incorporating SIOI consistently outperform those without it across key metrics including SSIM, PSNR, MAE, and LPIPS. The framework also exhibits strong robustness under challenging conditions such as low illumination, high noise, multiple object occlusions, and motion blur. Furthermore, theoretical analysis grounded in cognitive neuroscience reveals meaningful connections between the "object-first" perceptual mechanism and dynamic feature modulation in visual cortical areas (V1-V4). By significantly enhancing object consistency and semantic coherence across diverse inpainting models, this work not only advances efficient and target-aware image restoration but also fosters interdisciplinary convergence between brain-inspired computation and cutting-edge inpainting technologies.

nlin.PS↗

arXiv2025

EEschematic: Multimodal-LLM Based AI Agent for Schematic Generation of Analog Circuit

Circuit schematics play a crucial role in analog integrated circuit design, serving as the primary medium for human understanding and verification of circuit functionality. While recent large language model (LLM)-based approaches have shown promise in circuit topology generation and device sizing, most rely solely on textual representations such as SPICE netlists, which lack visual interpretability for circuit designers. To address this limitation, we propose EEschematic, an AI agent for automatic analog schematic generation based on a Multimodal Large Language Model (MLLM). EEschematic integrates textual, visual, and symbolic modalities to translate SPICE netlists into schematic diagrams represented in a human-editable format. The framework uses six analog substructure examples for few-shot placement and a Visual Chain-of-Thought (VCoT) strategy to iteratively refine placement and wiring, enhancing schematic clarity and symmetry. Experimental results on representative analog circuits, including a CMOS inverter, a five-transistor operational transconductance amplifier (5T-OTA), and a telescopic cascode amplifier, demonstrate that EEschematic produces schematics with high visual quality and structural correctness.

cs.LG↗

arXiv2025

Stacking-tunable multiferroic states in bilayer ScI2

Two-dimensional(2D) multiferroic materials hold significant promise for advancing the miniaturization and integration of nanodevices. In this study, we demonstrate that 2D bilayer ScI2, which exhibits ferromagnetic(FM) ordering within each layer, enables the tuning of interlayer magnetic coupling, ferroelectricity, and valley polarization through interlayer sliding and rotation. Our first-principles calculations show that the AA stacking configuration induces antiferromagnetic (AFM) interlayer coupling, while a 180 rotation of one layer (resulting in the antialigned AA stacking) leads to FM interlayer coupling. Moreover, the interlayer magnetic coupling can be switched between AFM and FM by translating the stacking configuration: FM in the aligned AB and BA configurations, and AFM in the antialigned AB and BA configurations. This switching behavior is driven by variations in superexchange interactions due to orbital hopping between layers. Notably, the aligned stacking exhibits ferroelectricity upon sliding, which is induced by interlayer orbital hybridization and the resulting asymmetric charge redistribution, with maximal ferroelectric behavior occurring at the AB and BA stacking configurations. Additionally, for the AB and BA stackings, spontaneous valley polarization emerges from the manipulation of the spin orientation toward the out-of-plane direction. This valley polarization arises due to inversion symmetry breaking, either through ferroelectricity (in the AB and BA stackings) or AFM interlayer coupling , in combination with spin-orbit coupling. These results highlight the intricate interplay between magnetism, ferroelectricity, and valley polarization in bilayer ScI2, with each property being tunable via stacking configuration.

cond-mat.mtrl-sci↗