arXiv subjects

Yu Zhao

Publications and source records attributed to Yu Zhao.

At least 127 records · Page 7Linked to original sources

arXiv2025

Noninvertible Gauge Symmetry in (2+1)d Topological Orders: A String-Net Model Realization

We develop a systematic framework for understanding symmetries in topological phases in 2+1 dimensions using the string-net model, encompassing both gauge symmetries that preserve anyon species and global symmetries permuting anyon species, including both invertible symmetries describable by groups and noninvertible symmetries described by categories. As an archetypal example, we reveal the first noninvertible categorical gauge symmetry of topological orders in 2+1 dimensions: the Fibonacci gauge symmetry of the doubled Fibonacci topological order, described by the Fibonacci fusion 2-category. Our approach involves two steps: first, establishing duality between different string-net models with Morita equivalent input fusion categories that describe the same topological order; and second, constructing symmetry transformations within the same string-net model when the dual models have isomorphic input fusion categories, achieved by composing duality maps with isomorphisms of degrees of freedom between the dual models.

cond-mat.str-el↗

arXiv2025

Classification of Gapped Domain Walls of Topological Orders in 2+1 dimensions: A Levin-Wen Model Realization

This paper introduces a novel systematic construction of gapped domain walls (GDWs) within the Levin-Wen (LW) model. By gluing two LW models along their open sides in a compatible way, we achieve a complete GDW classification by subsets of bulk input data, which encompass the classifications in terms of bimodule categories. A generalized bimodule structure is introduced to capture domain-wall excitations. Furthermore, we demonstrate that folding along any GDW yields a gapped boundary (GB) described by a Frobenius algebra of the input UFC for the folded model, thus bridging our GDW classification and the GB classification in \cite{hu2018boundary}.

cond-mat.str-el↗

arXiv2025

HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting

Achieving both accurate and consistent predictive performance across spatial nodes is crucial for ensuring the validity and reliability of outcomes in fair spatial-temporal forecasting tasks. However, existing training methods treat heterogeneous nodes with a fully averaged perspective, resulting in inherently biased prediction targets. Balancing accuracy and consistency is particularly challenging due to the multi-objective nature of spatial-temporal forecasting. To address this issue, we propose a novel Heterogeneity-Informed Mixture-of-Experts (HiMoE) framework that delivers both uniform and precise spatial-temporal predictions. From a model architecture perspective, we design the Heterogeneity-Informed Graph Convolutional Network (HiGCN) to address trend heterogeneity, and we introduce the Node-wise Mixture-of-Experts (NMoE) module to handle cardinality heterogeneity across nodes. From an evaluation perspective, we propose STFairBench, a benchmark that handles fairness in spatial-temporal prediction from both training and evaluation stages. Extensive experiments on four real-world datasets demonstrate that HiMoE achieves state-of-the-art performance, outperforming the best baseline by at least 9.22% across all evaluation metrics.

cs.LG↗

arXiv2025

LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation

Recent advancements in large multimodal models (LMMs) have driven substantial progress in both text-to-video (T2V) generation and video-to-text (V2T) interpretation tasks. However, current AI-generated videos (AIGVs) still exhibit limitations in terms of perceptual quality and text-video alignment. Therefore, a reliable and scalable automatic model for AIGV evaluation is desirable, which heavily relies on the scale and quality of human annotations. To this end, we present AIGVE-60K, a comprehensive dataset and benchmark for AI-Generated Video Evaluation, which features (i) comprehensive tasks, encompassing 3,050 extensive prompts across 20 fine-grained task dimensions, (ii) the largest human annotations, including 120K mean-opinion scores (MOSs) and 60K question-answering (QA) pairs annotated on 58,500 videos generated from 30 T2V models, and (iii) bidirectional benchmarking and evaluating for both T2V generation and V2T interpretation capabilities. Based on AIGVE-60K, we propose LOVE, a LMM-based metric for AIGV Evaluation from multiple dimensions including perceptual preference, text-video correspondence, and task-specific accuracy in terms of both instance level and model level. Comprehensive experiments demonstrate that LOVE not only achieves state-of-the-art performance on the AIGVE-60K dataset, but also generalizes effectively to a wide range of other AIGV evaluation benchmarks. These findings highlight the significance of the AIGVE-60K dataset. Database and codes are anonymously available at https://github.com/IntMeGroup/LOVE.

cs.CV↗

arXiv2025

DeMuVGN: Effective Software Defect Prediction Model by Learning Multi-view Software Dependency via Graph Neural Networks

Software defect prediction (SDP) aims to identify high-risk defect modules in software development, optimizing resource allocation. While previous studies show that dependency network metrics improve defect prediction, most methods focus on code-based dependency graphs, overlooking developer factors. Current metrics, based on handcrafted features like ego and global network metrics, fail to fully capture defect-related information. To address this, we propose DeMuVGN, a defect prediction model that learns multi-view software dependency via graph neural networks. We introduce a Multi-view Software Dependency Graph (MSDG) that integrates data, call, and developer dependencies. DeMuVGN also leverages the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and enhance defect module identification. In a case study of eight open-source projects across 20 versions, DeMuVGN demonstrates significant improvements: i) models based on multi-view graphs improve F1 scores by 11.1% to 12.1% over single-view models; ii) DeMuVGN improves F1 scores by 17.4% to 45.8% in within-project contexts and by 17.9% to 41.0% in cross-project contexts. Additionally, DeMuVGN excels in software evolution, showing more improvement in later-stage software versions. Its strong performance across different projects highlights its generalizability. We recommend future research focus on multi-view dependency graphs for defect prediction in both mature and newly developed projects.

cs.SE↗

arXiv2025

The Bitter Lesson Learned from 2,000+ Multilingual Benchmarks

As large language models (LLMs) continue to advance in linguistic capabilities, robust multilingual evaluation has become essential for promoting equitable technological progress. This position paper examines over 2,000 multilingual (non-English) benchmarks from 148 countries, published between 2021 and 2024, to evaluate past, present, and future practices in multilingual benchmarking. Our findings reveal that, despite significant investments amounting to tens of millions of dollars, English remains significantly overrepresented in these benchmarks. Additionally, most benchmarks rely on original language content rather than translations, with the majority sourced from high-resource countries such as China, India, Germany, the UK, and the USA. Furthermore, a comparison of benchmark performance with human judgments highlights notable disparities. STEM-related tasks exhibit strong correlations with human evaluations (0.70 to 0.85), while traditional NLP tasks like question answering (e.g., XQuAD) show much weaker correlations (0.11 to 0.30). Moreover, translating English benchmarks into other languages proves insufficient, as localized benchmarks demonstrate significantly higher alignment with local human judgments (0.68) than their translated counterparts (0.47). This underscores the importance of creating culturally and linguistically tailored benchmarks rather than relying solely on translations. Through this comprehensive analysis, we highlight six key limitations in current multilingual evaluation practices, propose the guiding principles accordingly for effective multilingual benchmarking, and outline five critical research directions to drive progress in the field. Finally, we call for a global collaborative effort to develop human-aligned benchmarks that prioritize real-world applications.

cs.CL↗

arXiv2025

Identifying polycentric urban structure using the minimum cycle basis of road network as building blocks

In a graph, the minimum cycle bases are a set of linearly independent cycles that can be used to represent any cycle within that cycle space of graph. These bases are useful in various contexts, including the intricate analysis of electrical networks, structural engineering endeavors, chemical processes and surface reconstruction techniques etc. This study focuses on six cities in China to explore the topological characteristics, the centrality of nodes and robustness of urban road networks based on motif and minimum cycle bases. Some interesting conclusions are obtained: the frequency of motifs containing cycles exceeds that of random networks with equivalent degree sequences; the frequency distribution of minimum cycle's length and surface areas obey the power-law distribution. The cycle contribution rate is introduced to investigate the centrality of nodes within road networks, and has a significant impact on the total number of cycles in the robustness analysis. Finally, we construct two types of cycle-based dual networks for urban road networks by representing cycles as nodes and establishing edges between two cycles sharing a common node and edge respectively. The results show that cycle-based dual networks exhibit small-world and scale-free properties.

physics.soc-ph↗

arXiv2025

LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMs

Recent breakthroughs in large multimodal models (LMMs) have significantly advanced both text-to-image (T2I) generation and image-to-text (I2T) interpretation. However, many generated images still suffer from issues related to perceptual quality and text-image alignment. Given the high cost and inefficiency of manual evaluation, an automatic metric that aligns with human preferences is desirable. To this end, we present EvalMi-50K, a comprehensive dataset and benchmark for evaluating large-multimodal image generation, which features (i) comprehensive tasks, encompassing 2,100 extensive prompts across 20 fine-grained task dimensions, and (ii) large-scale human-preference annotations, including 100K mean-opinion scores (MOSs) and 50K question-answering (QA) pairs annotated on 50,400 images generated from 24 T2I models. Based on EvalMi-50K, we propose LMM4LMM, an LMM-based metric for evaluating large multimodal T2I generation from multiple dimensions including perception, text-image correspondence, and task-specific accuracy. Extensive experimental results show that LMM4LMM achieves state-of-the-art performance on EvalMi-50K, and exhibits strong generalization ability on other AI-generated image evaluation benchmark datasets, manifesting the generality of both the EvalMi-50K dataset and LMM4LMM metric. Both EvalMi-50K and LMM4LMM will be released at https://github.com/IntMeGroup/LMM4LMM.

cs.CV↗

arXiv2025

Noiser: Bounded Input Perturbations for Attributing Large Language Models

Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions. Accordingly, generating faithful attributions that reflect the actual inner behavior of the model is crucial. In this paper, we introduce Noiser, a perturbation-based FA method that imposes bounded noise on each input embedding and measures the robustness of the model against partially noised input to obtain the input attributions. Additionally, we propose an answerability metric that employs an instructed judge model to assess the extent to which highly scored tokens suffice to recover the predicted output. Through a comprehensive evaluation across six LLMs and three tasks, we demonstrate that Noiser consistently outperforms existing gradient-based, attention-based, and perturbation-based FA methods in terms of both faithfulness and answerability, making it a robust and effective approach for explaining language model predictions.

cs.CL↗

arXiv2025

A method for reversing the laser modulation in a Storage ring

The pursuit of coherent radiation generation remains a key direction in the advancement of storage ring light sources. Despite the potential of laser modulation in achieving this goal, it leads to a significant decline in the quality of the electron beam. Efforts to mitigate this decline have resulted in the proposal of demodulation schemes. However, implementing modulation and demodulation within the storage ring presents significant challenges due to dynamical and spatial constraints within straight sections. In this study, we propose a straightforward and easily implementable method for achieving reversible laser modulation in a storage ring. Notably, our approach circumvents the need for special storage ring requirements, such as lengthy straight sections or bypass section. Simulation results demonstrate a substantial restoration of beam quality following demodulation. This innovative scheme holds great promise for the realization of high repetition rate coherent storage ring light sources.

physics.acc-ph↗

arXiv2025

TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection

Photoplethysmography (PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV). Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. Existing approaches mainly use the amplitude information to perform PPG peak detection. However, these approaches cannot accurately identify peaks, since motion artifacts may bring random and significant amplitude variations. To improve the performance of PPG peak detection, the time information can be used. Specifically, heart rates exhibit temporal consistency that consecutive heartbeat intervals in a normal person can have limited variations. To leverage the temporal consistency, we propose the Temporal Attentive U-Net, i.e., TAU, to accurately detect peaks from PPG signals. In TAU, we design a time module that encodes temporal consistency in temporal embeddings. We integrate the amplitude information with temporal embeddings using the attention mechanism to estimate peak labels. Our experimental results show that TAU outperforms eleven baselines on heart rate estimation by more than 22.4%. Our TAU model achieves the best performance across various Signal-to-Noise Ratio (SNR) levels. Moreover, we achieve Pearson correlation coefficients higher than 0.9 (p < 0.01) on estimating HRV features from low-noise-level PPG signals.

cs.LG↗

arXiv2025

StratIncon Detector: Analyzing Strategy Inconsistencies Between Real-Time Strategy and Preferred Professional Strategy in MOBA Esports

MOBA (Multiplayer Online Battle Arena) games require a delicate interplay of strategic planning and real-time decision-making, particularly in professional esports, where players exhibit varying levels of skill and strategic insight. While team strategies have been widely studied, analyzing inconsistencies in professional matches remains a significant challenge. The complexity lies in defining and quantifying the difference between real-time and preferred professional strategies, as well as understanding the disparities between them. Establishing direct causal links between specific strategic decisions and game outcomes also demands a comprehensive analysis of the entire match progression. To tackle these challenges, we present the StratIncon Detector, a visual analytics system designed to assist professional players and coaches in efficiently identifying strategic inconsistencies. The system detects real-time strategies, predicts preferred professional strategies, extracts relevant human factors, and uncovers their impact on subsequent game phases. Findings from a case study, a user study with 24 participants, and expert interviews suggest that, compared to traditional methods, the StratIncon Detector enables users to more comprehensively and efficiently identify inconsistencies, infer their causes, evaluate their effects on subsequent game outcomes, and gain deeper insights into team collaboration-ultimately enhancing future teamwork.

cs.HC↗

arXiv2025

Identifying Evidence Subgraphs for Financial Risk Detection via Graph Counterfactual and Factual Reasoning

Company financial risks pose a significant threat to personal wealth and national economic stability, stimulating increasing attention towards the development of efficient andtimely methods for monitoring them. Current approaches tend to use graph neural networks (GNNs) to model the momentum spillover effect of risks. However, due to the black-box nature of GNNs, these methods leave much to be improved for precise and reliable explanations towards company risks. In this paper, we propose CF3, a novel Counterfactual and Factual learning method for company Financial risk detection, which generates evidence subgraphs on company knowledge graphs to reliably detect and explain company financial risks. Specifically, we first propose a meta-path attribution process based on Granger causality, selecting the meta-paths most relevant to the target node labels to construct an attribution subgraph. Subsequently, we propose anedge-type-aware graph generator to identify important edges, and we also devise a layer-based feature masker to recognize crucial node features. Finally, we utilize counterfactual-factual reasoning and a loss function based on attribution subgraphs to jointly guide the learning of the graph generator and feature masker. Extensive experiments on three real-world datasets demonstrate the superior performance of our method compared to state-of-the-art approaches in the field of financial risk detection.

cs.CE↗

arXiv2025

Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V

Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback. In this work, we present COME-robot, the first closed-loop robotic system utilizing the GPT-4V vision-language foundation model for open-ended reasoning and adaptive planning in real-world scenarios.COME-robot incorporates two key innovative modules: (i) a multi-level open-vocabulary perception and situated reasoning module that enables effective exploration of the 3D environment and target object identification using commonsense knowledge and situated information, and (ii) an iterative closed-loop feedback and restoration mechanism that verifies task feasibility, monitors execution success, and traces failure causes across different modules for robust failure recovery. Through comprehensive experiments involving 8 challenging real-world mobile and tabletop manipulation tasks, COME-robot demonstrates a significant improvement in task success rate (~35%) compared to state-of-the-art methods. We further conduct comprehensive analyses to elucidate how COME-robot's design facilitates failure recovery, free-form instruction following, and long-horizon task planning.

cs.RO↗

arXiv2025

CNsum:Automatic Summarization for Chinese News Text

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model.

cs.CL↗

arXiv2025

CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence

Physical intelligence holds immense promise for advancing embodied intelligence, enabling robots to acquire complex behaviors from demonstrations. However, achieving generalization and transfer across diverse robotic platforms and environments requires careful design of model architectures, training strategies, and data diversity. Meanwhile existing systems often struggle with scalability, adaptability to heterogeneous hardware, and objective evaluation in real-world settings. We present a generalized end-to-end robotic learning framework designed to bridge this gap. Our framework introduces a unified architecture that supports cross-platform adaptability, enabling seamless deployment across industrial-grade robots, collaborative arms, and novel embodiments without task-specific modifications. By integrating multi-task learning with streamlined network designs, it achieves more robust performance than conventional approaches, while maintaining compatibility with varying sensor configurations and action spaces. We validate our framework through extensive experiments on seven manipulation tasks. Notably, Diffusion-based models trained in our framework demonstrated superior performance and generalizability compared to the LeRobot framework, achieving performance improvements across diverse robotic platforms and environmental conditions.

cs.RO↗

arXiv2025

Q-Filters: Leveraging QK Geometry for Efficient KV Cache Compression

Autoregressive language models rely on a Key-Value (KV) Cache, which avoids re-computing past hidden states during generation, making it faster. As model sizes and context lengths grow, the KV Cache becomes a significant memory bottleneck, which calls for compression methods that limit its size during generation. In this paper, we discover surprising properties of Query (Q) and Key (K) vectors that allow us to efficiently approximate attention scores without computing the attention maps. We propose Q-Filters, a training-free KV Cache compression method that filters out less crucial Key-Value pairs based on a single context-agnostic projection. Contrarily to many alternatives, Q-Filters is compatible with FlashAttention, as it does not require direct access to attention weights. Experimental results in long-context settings demonstrate that Q-Filters is competitive with attention-based compression methods such as SnapKV in retrieval tasks while consistently outperforming efficient compression schemes such as Streaming-LLM in generation setups. Notably, Q-Filters achieves a 99% accuracy in the needle-in-a-haystack task with a x32 compression level while reducing the generation perplexity drop by up to 65% in text generation compared to Streaming-LLM.

cs.CL↗

arXiv2025

Monitoring AGNs with H$β$ Asymmetry. V. Long-term Variation and Evolution of the Broad H$β$ Emission-Line Profiles

The physical origins of the diverse emission-line asymmetries observed in the spectra of active galactic nuclei (AGNs) remain incompletely understood. Monitoring the temporal variations of line profiles offers a promising approach to investigating the underlying physics. In this study, we present an analysis of the broad H$β$ emission line profiles of eight AGNs observed from the end of 2016 to May 2023 as part of the reverberation mapping campaign titled "Monitoring AGNs with H$β$ Asymmetry" (MAHA), utilizing data obtained from the Wyoming Infrared Observatory (WIRO) 2.3-meter telescope. We measure the temporal variations of line asymmetry, width, and central velocity shift for the eight objects. Our findings reveal that the variation in asymmetry is positively correlated with H$β$ flux in five of the eight objects, while the remaining objects exhibit negative or complex correlations. Furthermore, we observe anti-correlations between line width and H$β$ flux for most objects, indicating the presence of the "breathing" phenomenon in their H$β$ emission lines. In contrast, two objects demonstrate an "anti-breathing" phenomenon or complex behavior. We discuss the physical origins of the temporal variations in line profiles and propose the possibility of decomposing the variations in H$β$ asymmetry and width into components: one that corresponds to short-term variations in H$β$ flux and another that reflects long-term variations in continuum light curves, perhaps driven by radiation pressure.

astro-ph.GA↗