arXiv subjects

Zhihao Xu

Publications and source records attributed to Zhihao Xu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Exact multiple anomalous mobility edges in a flat band geometry

Anomalous mobility edges(AMEs), separating localized from multifractal critical states, represent a novel form of localization transition in quasiperiodic systems. However, quasi-periodic models exhibiting exact AMEs remain relatively rare, limiting the understanding of these transitions. In this work, we leverage the geometric structure of flat band models to construct exact AMEs. Specifically, we introduce an anti-symmetric diagonal quasi-periodic mosaic modulation, which consists of both quasi-periodic and constant potentials, into a cross-stitch flat band lattice. When the constant potential is zero, the system resides entirely in a localized phase, with its dispersion relation precisely determined. For non-zero constant potentials, we use a simple method to derive analytical solutions for a class of AMEs, providing exact results for both the AMEs and the system's localization and critical properties. Additionally, we propose a classical electrical circuit design to experimentally realize the system. This study offers valuable insights into the existence and characteristics of AMEs in quasi-periodic systems.

cond-mat.dis-nn↗

arXiv2025

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems

Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU supercomputer. Our approach ensures efficient quantum-classical workload management by distributing large problem instances across classical and quantum resources. Experimental results demonstrate that enhanced decomposition strategies and GPU-accelerated quantum simulations significantly improve DQAOA's performance, achieving up to 10x speedup over CPU-based simulations. This advancement enables better scalability for large problem instances, supporting the practical deployment of GPU systems for hybrid quantum-classical applications. We also highlight ongoing integration efforts using the Quantum Framework (QFw) to support future HPC-quantum computing systems.

cs.DC↗

arXiv2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both research and commercial domains, their security and safety implications have become a growing concern, not only for researchers and corporations but also for every nation. Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs. To address this gap, this paper introduces, for the first time, the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization. Compared to the off-the-shelf LLM safety surveys, our work demonstrates several distinctive advantages: (I) Comprehensive Perspective. We define the complete LLM lifecycle as encompassing data preparation, pre-training, post-training, deployment and final commercialization. To our knowledge, this represents the first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive Literature Support. Our research is grounded in an exhaustive review of over 800+ papers, ensuring comprehensive coverage and systematic organization of security issues within a more holistic understanding. (III) Unique Insights. Through systematic literature analysis, we have developed reliable roadmaps and perspectives for each chapter. Our work identifies promising research directions, including safety in data generation, alignment techniques, model editing, and LLM-based agent systems. These insights provide valuable guidance for researchers pursuing future work in this field.

cs.CR↗

arXiv2025

REWARD CONSISTENCY: Improving Multi-Objective Alignment from a Data-Centric Perspective

Multi-objective preference alignment in language models often encounters a challenging trade-off: optimizing for one human preference (e.g., helpfulness) frequently compromises others (e.g., harmlessness) due to the inherent conflicts between competing objectives. While prior work mainly focuses on algorithmic solutions, we explore a novel data-driven approach to uncover the types of data that can effectively mitigate these conflicts. Specifically, we propose the concept of Reward Consistency (RC), which identifies samples that align with multiple preference objectives, thereby reducing conflicts during training. Through gradient-based analysis, we demonstrate that RC-compliant samples inherently constrain performance degradation during multi-objective optimization. Building on these insights, we further develop Reward Consistency Sampling, a framework that automatically constructs preference datasets that effectively mitigate conflicts during multi-objective alignment. Our generated data achieves an average improvement of 13.37% in both the harmless rate and helpfulness win rate when optimizing harmlessness and helpfulness, and can consistently resolve conflicts in varying multi-objective scenarios.

cs.CL↗

arXiv2025

CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation

Semi-supervised medical image segmentation aims to leverage minimal expert annotations, yet remains confronted by challenges in maintaining high-quality consistency learning. Excessive perturbations can degrade alignment and hinder precise decision boundaries, especially in regions with uncertain predictions. In this paper, we introduce Confidence-Aware Adaptive Displacement (CAD), a framework that selectively identifies and replaces the largest low-confidence regions with high-confidence patches. By dynamically adjusting both the maximum allowable replacement size and the confidence threshold throughout training, CAD progressively refines the segmentation quality without overwhelming the learning process. Experimental results on public medical datasets demonstrate that CAD effectively enhances segmentation quality, establishing new state-of-the-art accuracy in this field. The source code will be released after the paper is published.

cs.CV↗

arXiv2025

JAX-BTE: A GPU-Accelerated Differentiable Solver for Phonon Boltzmann Transport Equations

This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accurate, efficient and differentiable multiscale thermal modeling by leveraging high-performance GPU computing and automatic differentiation. The solver efficiently addresses the high-dimensional and complex integro-differential nature of the phonon BTE, facilitating both forward simulations and data-augmented inverse simulations through end-to-end optimization. Validation is performed across a range of 1D to 3D simulations, including complex FinFET structures, in both forward and inverse settings, demonstrating excellent performance and reliability. JAX-BTE significantly outperforms state-of-the-art BTE solvers in forward simulations and uniquely enables inverse simulations, making it a powerful tool for multiscale thermal analysis and design for semiconductor devices.

physics.comp-ph↗

arXiv2025

MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving

Autonomous driving visual question answering (AD-VQA) aims to answer questions related to perception, prediction, and planning based on given driving scene images, heavily relying on the model's spatial understanding capabilities. Prior works typically express spatial information through textual representations of coordinates, resulting in semantic gaps between visual coordinate representations and textual descriptions. This oversight hinders the accurate transmission of spatial information and increases the expressive burden. To address this, we propose a novel Marker-based Prompt learning framework (MPDrive), which represents spatial coordinates by concise visual markers, ensuring linguistic expressive consistency and enhancing the accuracy of both visual perception and spatial expression in AD-VQA. Specifically, we create marker images by employing a detection expert to overlay object regions with numerical labels, converting complex textual coordinate generation into straightforward text-based visual marker predictions. Moreover, we fuse original and marker images as scene-level features and integrate them with detection priors to derive instance-level features. By combining these features, we construct dual-granularity visual prompts that stimulate the LLM's spatial perception capabilities. Extensive experiments on the DriveLM and CODA-LM datasets show that MPDrive achieves state-of-the-art performance, particularly in cases requiring sophisticated spatial understanding.

cs.CV↗

arXiv2025

Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight

Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data-2D RGB images from different views, depth images, and 3D point clouds-for the non-invasive estimation of duck body dimensions and weight. A dataset of 1,023 Linwu ducks, comprising over 5,000 samples with diverse postures and conditions, was collected to support model training. The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. A Transformer encoder is then utilized to capture long-range dependencies and refine feature interactions, thereby enhancing prediction robustness. The model achieved a mean absolute percentage error (MAPE) of 6.33% and an R2 of 0.953 across eight morphometric parameters, demonstrating strong predictive capability. Unlike conventional manual measurements, the proposed model enables high-precision estimation while eliminating the necessity for physical handling, thereby reducing animal stress and broadening its application scope. This study marks the first application of deep learning techniques to poultry body dimension and weight estimation, providing a valuable reference for the intelligent and precise management of the livestock industry with far-reaching practical significance.

cs.CV↗

arXiv2025

A Novel Interactive-Guided Differential Testing Approach for FPGA Simulation Debugger Tools

Field-Programmable Gate Array (FPGA) development tool chains are widely used in FPGA design, simulation, and verification in critical areas like communications, automotive electronics, and aerospace. Commercial FPGA tool chains such as Xilinx' Vivado aids developers in swiftly identifying and rectifying bugs and issues in FPGA designs through a robust built-in debugger, ensuring the correctness and development efficiency of the FPGA design. Hardening such FPGA chip debugger tools by testing is crucial since engineers might misinterpret code and introduce incorrect fixes, leading to security risks. However, FPGA chip debugger tools are challenging to test as they require assessing both RTL designs and a series of debugging actions, including setting breakpoints and stepping through the code. To address this issue, we propose a interactive differential testing approach called DB-Hunter to detect bugs in Vivado's FPGA chip debugger tools. Specifically, DB-Hunter consists of three components: RTL design transformation component, debug action transformation component, and interactive differential testing component. By performing RTL design and debug action transformations, DB-Hunter generates diverse and complex RTL designs and debug actions, to thoroughly test the Vivado debugger using interactive differential testing to detect bugs. In three months, DB-Hunter reported 18 issues, including 10 confirmed as bugs by Xilinx Support, 6 bugs had been fixed in last version.

cs.SE↗

arXiv2024

Uncovering Safety Risks of Large Language Models through Concept Activation Vector

Despite careful safety alignment, current large language models (LLMs) remain vulnerable to various attacks. To further unveil the safety risks of LLMs, we introduce a Safety Concept Activation Vector (SCAV) framework, which effectively guides the attacks by accurately interpreting LLMs' safety mechanisms. We then develop an SCAV-guided attack method that can generate both attack prompts and embedding-level attacks with automatically selected perturbation hyperparameters. Both automatic and human evaluations demonstrate that our attack method significantly improves the attack success rate and response quality while requiring less training data. Additionally, we find that our generated attack prompts may be transferable to GPT-4, and the embedding-level attacks may also be transferred to other white-box LLMs whose parameters are known. Our experiments further uncover the safety risks present in current LLMs. For example, in our evaluation of seven open-source LLMs, we observe an average attack success rate of 99.14%, based on the classic keyword-matching criterion. Finally, we provide insights into the safety mechanism of LLMs. The code is available at https://github.com/SproutNan/AI-Safety_SCAV.

cs.CL↗

arXiv2024

Style Transfer: From Stitching to Neural Networks

This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential.

cs.CV↗

arXiv2024

Reentrant Localization Transitions in a Topological Anderson Insulator: A Study of a Generalized Su-Schrieffer-Heeger Quasicrystal

We study the topology and localization properties of a generalized Su-Schrieffer-Heeger (SSH) model with a quasi-periodic modulated hopping. It is found that the interplay of off-diagonal quasi-periodic modulations can induce topological Anderson insulator (TAI) phases and reentrant topological Anderson insulator (RTAI), and the topological phase boundaries can be uncovered by the divergence of the localization length of the zero-energy mode. In contrast to the conventional case that the TAI regime emerges in a finite range with the increase of disorder, the TAI and RTAI are robust against arbitrary modulation amplitude for our system. Furthermore, we find that the TAI and RTAI can induce the emergence of reentrant localization transitions. Such an interesting connection between the reentrant localization transition and the TAI/RTAI can be detected from the wave-packet dynamics in cold atom systems by adopting the technique of momentum-lattice engineering.

cond-mat.dis-nn↗

arXiv2024

Fate of localization features in a one-dimensional non-Hermitian flat-band lattice with quasiperiodic modulations

We investigate the influence of quasiperiodic modulations on one-dimensional non-Hermitian diamond lattices with an artificial magnetic flux $θ$ that possess flat bands. Our study shows that the symmetry of these modulations and the magnetic flux $θ$ play a pivotal role in shaping the localization properties of the system. When $θ=0$, the non-Hermitian lattice exhibits a single flat band in the crystalline case, and symmetric as well as antisymmetric modulations can induce accurate mobility edges. In contrast, when $θ=π$, the clean diamond lattice manifests three dispersionless bands referred to as an "all-band-flat" (ABF) structure, irrespective of the non-Hermitian parameter. The ABF structure restricts the transition from delocalized to localized states, as all states remain localized for any finite symmetric modulation. Our numerical calculations further unveil that the ABF system subjected to antisymmetric modulations exhibits multifractal-to-localized edges. Multifractal states are predominantly concentrated in the internal region of the spectrum. Additionally, we explore the case where $θ$ lies within the range of $(0, π)$, revealing a diverse array of complex localization features. Finally, we propose a classical electrical circuit scheme to realize the non-Hermitian flat-band chain with quasiperiodic modulations.

cond-mat.dis-nn↗

arXiv2024

Evaluating Readability and Faithfulness of Concept-based Explanations

With the growing popularity of general-purpose Large Language Models (LLMs), comes a need for more global explanations of model behaviors. Concept-based explanations arise as a promising avenue for explaining high-level patterns learned by LLMs. Yet their evaluation poses unique challenges, especially due to their non-local nature and high dimensional representation in a model's hidden space. Current methods approach concepts from different perspectives, lacking a unified formalization. This makes evaluating the core measures of concepts, namely faithfulness or readability, challenging. To bridge the gap, we introduce a formal definition of concepts generalizing to diverse concept-based explanations' settings. Based on this, we quantify the faithfulness of a concept explanation via perturbation. We ensure adequate perturbation in the high-dimensional space for different concepts via an optimization problem. Readability is approximated via an automatic and deterministic measure, quantifying the coherence of patterns that maximally activate a concept while aligning with human understanding. Finally, based on measurement theory, we apply a meta-evaluation method for evaluating these measures, generalizable to other types of explanations or tasks as well. Extensive experimental analysis has been conducted to inform the selection of explanation evaluation measures.

cs.AI↗

arXiv2024

KMTalk: Speech-Driven 3D Facial Animation with Key Motion Embedding

We present a novel approach for synthesizing 3D facial motions from audio sequences using key motion embeddings. Despite recent advancements in data-driven techniques, accurately mapping between audio signals and 3D facial meshes remains challenging. Direct regression of the entire sequence often leads to over-smoothed results due to the ill-posed nature of the problem. To this end, we propose a progressive learning mechanism that generates 3D facial animations by introducing key motion capture to decrease cross-modal mapping uncertainty and learning complexity. Concretely, our method integrates linguistic and data-driven priors through two modules: the linguistic-based key motion acquisition and the cross-modal motion completion. The former identifies key motions and learns the associated 3D facial expressions, ensuring accurate lip-speech synchronization. The latter extends key motions into a full sequence of 3D talking faces guided by audio features, improving temporal coherence and audio-visual consistency. Extensive experimental comparisons against existing state-of-the-art methods demonstrate the superiority of our approach in generating more vivid and consistent talking face animations. Consistent enhancements in results through the integration of our proposed learning scheme with existing methods underscore the efficacy of our approach. Our code and weights will be at the project website: \url{https://github.com/ffxzh/KMTalk}.

cs.CV↗

arXiv2024

A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers

Field Programmable Gate Array (FPGA) logic synthesis compilers (e.g., Vivado, Iverilog, Yosys, and Quartus) are widely applied in Electronic Design Automation (EDA), such as the development of FPGA programs.However, defects (i.e., incorrect synthesis) in logic synthesis compilers may lead to unexpected behaviors in target applications, posing security risks. Therefore, it is crucial to thoroughly test logic synthesis compilers to eliminate such defects.Despite several Hardware Design Language (HDL) code generators (e.g., Verismith) have been proposed to find defects in logic synthesis compilers, the effectiveness of these generators is still limited by the simple code generation strategy and the monogeneity of the generated HDL code.This paper proposes LegoHDL, a novel method to generate syntax valid HDL code for comprehensively testing FPGA logic synthesis compilers.LegoHDL can generate more complex and diverse defect-trigger HDL code (e.g., Verilog, VHDL, and SystemVerilog) by leveraging the guidance of abstract syntax tree and the extensive function block libraries of cyber-physical systems. Extensive experiments show that the diversity and defect-trigger capability of HDL code generated by LegoHDL are significantly better than the state-of-the-art method (i.e., Verismith).In three months, LegoHDL has reported 20 new defects--many of which are deep and important; 16 of them have been confirmed.

cs.AR↗

arXiv2024

Quantum-Inspired Genetic Algorithm for Designing Planar Multilayer Photonic Structure

Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the improved Quantum Genetic Algorithm (QGA) with machine learning surrogate model regression. Using Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments, thereby improving the optimization efficiency. QGA, a genetic algorithm embedded with quantum mechanics, combines the advantages of quantum computing and genetic algorithms, enabling faster and more robust convergence to the optimum. Using the design of planar multilayer photonic structures for transparent radiative cooling as a testbed, we show superiority of our algorithm over the classical genetic algorithm (CGA). Additionally, we show the precision advantage of the RF model as a flexible surrogate model, which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms (e.g., quantum annealing needs Ising model as a surrogate).

quant-ph↗

arXiv2024

Quasi-symmetries between metric spaces and rough quasi-isometries between their infinite hyperbolic cones

In this paper, we first prove that any power quasi-symmetry of two metric spaces induces a rough quasi-isometry between their infinite hyperbolic cones. Second, we prove that for a complete metric space $Z$, there exists a point $ω$ in the Gromov boundary of its infinite hyperbolic cone such that $Z$ can be seen as the Gromov boundary relative to $ω$ of its infinite hyperbolic cone. Third, we prove that for a visual Gromov hyperbolic metric space $X$ and a Gromov boundary point $ω$, $X$ is roughly similar to the infinite hyperbolic cone of its Gromov boundary relative to $ω$. These are the generalizations of Theorem 7.4, Theorem 8.1 and Theorem 8.2 in [3] since the underlying spaces are not assumed to be bounded and the hyperbolic cones are infinite.

math.MG↗