arXiv subjects

Gang Zhang

Publications and source records attributed to Gang Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Parameter-Decoupling-Informed Learning for Superresolution: Laboratory Validation and Galaxy Imaging

We present a parameter-decoupling-informed learning model for estimating subwavelength separations of point sources, without requiring prior knowledge of source properties, control over the sources, or continued access to their light signals. The method builds on our parameter-decoupled quantum superresolution estimation theory, which circumvents the need to estimate nuisance parameters such as partial coherence, brightness imbalance, and relative phase. Our convolutional neural network further accommodates practical imperfections, including background noise, photon loss, and centroid and orientation misalignment. We validate the method using synthetic and experimentally generated images, achieving separation-estimation fidelity above $82\%$ for separations as small as approximately $0.075σ$, where $σ$ is the Gaussian intensity-PSF standard deviation. This corresponds to an illustrative separation of $\sim13.5$ nm when $σ=180$ nm, suggesting potential relevance to length scales addressed by advanced active-source microscopy. Applied to archival Hubble images of NGC~4486B, the model yields nuclear separations of 9.3-13.2~pc, consistent with the previously reported value of 12~pc. Comparisons using the same training and testing images demonstrate substantially higher fidelity than conventional direct data-driven learning in the tested cases. The method's robustness to varying source properties and imaging imperfections supports retrospective astrophysical image analysis and motivates applications in live-cell microscopy and quantum metrology where source control or renewed optical measurements are impractical.

physics.optics↗

arXiv2026

Parameter-Decoupled Superresolution with Airy-Disk-Informed Learning: Application to NGC 4486B

Estimating the separation of two unresolved or nearly unresolved sources from recorded intensity images is difficult when source brightness, mutual coherence, and phase are unknown and the point-spread function (PSF) departs from a Gaussian. We formulate Hermite--Gaussian (HG) projections of one- and two-dimensional Airy amplitudes and prove that ratios of mode probabilities with equal longitudinal parity exactly cancel the brightness, coherence, and phase nuisance parameters under a symmetric two-source model. For a one-dimensional Airy slice we achieve an explicit multi-ratio separation inversion. For the physical two-dimensional circular-pupil PSF we obtain exact finite-pupil overlap integrals and locally invertible separation responses. Motivated by these analytical invariants, we examine Airy-informed convolutional neural networks (CNNs) on synthetic Airy images with known separations. At 150,000 training images, the approximate mean absolute errors are 0.018 in plotted separation units, 10 times better comparing to 0.18 for non-Airy Gaussian PSF approximation. Applied to an archival Hubble Space Telescope image of the double nucleus of NGC 4486B, the Airy-informed CNN gives estimates of approximately 9.6--12.0 pc for training sets of 100,000--150,000 images, broadly consistent with the historical approximately 12 pc value. We distinguish exact optical-mode nuisance cancellation from empirical robustness of an image-only learned estimator and identify the limits imposed by PSF mismatch and extended galaxy structure.

physics.optics↗

arXiv2026

NEP-kappa 2.0: A unified framework for phonon transport and nonequilibrium thermalization with machine-learning interatomic potentials

We present NEP-kappa 2.0, a unified framework that integrates density-functional theory, material-specific machine-learning interatomic potentials, and pretrained force models for advanced phonon calculations. A common workflow enables harmonic lattice dynamics, quasi-harmonic and stochastic self-consistent phonon treatments, three- and four-phonon transport, Wigner population--coherence transport, and time-dependent phonon dynamics. Applications to 3C-SiC, BAs, and Cs$_2$AgInCl$_6$ demonstrate consistent force-model comparisons, finite-temperature renormalization, higher-order scattering, and intermode coherence. Beyond steady-state transport, mode-resolved simulations of selectively excited 3C-SiC reveal that half of the injected optical-phonon energy reaches acoustic modes within approximately 3.75 ps, primarily through one-optical--two-acoustic scattering. Acoustic branches exhibit response times differing by a factor of five, with competing scattering channels producing a transient energy overshoot. Channel-resolved entropy production and persistent nonthermal occupations distinguish interbranch energy redistribution from intrabranch thermalization. The framework connects equilibrium phonon transport with microscopic nonequilibrium energy dynamics.

cond-mat.mtrl-sci↗

arXiv2026

Beyond the Beam: Constructive Repair and Candidate Completion for Generative Recommendation

Generative recommenders retrieve items by generating identifiers, but a valid identifier can remain outside the beam after catalog expansion. This raises two connected questions: which failures can identifier assignment repair, and how should retrieval proceed beyond the initial beam? We characterize assignment repair with a fixed generator and retained old identifiers. Output-invariance certificates identify failures shared by all admissible assignments. Under a common effective prefix, coupled support and ranking constraints give the exact feasible interval of new-item counts for target recovery. Building on this characterization, Beyond the Beam (BB) obtains minimum-replacement repairs through an integral flow formulation, selects a shared map and adapts the generator. At inference, generative likelihood and collaborative evidence define one score for ranking, candidate priority and stopping. Retained prefix bounds guide candidate completion and certify its global Top-$K$ when the stopping condition is met. Exhaustive finite-catalog evaluation confirms construction in every feasible case. Across three Amazon Reviews categories and three random seeds, the full T5 procedure improves mean Recall@10 by 15.5--46.3% and NDCG@10 by 15.2--44.4% over the best-performing evaluated generative baseline for each dataset and metric. Matched controls show that shared construction and adaptation improve new-target ranking and certification efficiency on Beauty and Toys. Combined scoring and candidate completion improve NDCG@10 across all three datasets with both T5 and decoder-only LC-Rec.

cs.IR↗

arXiv2026

Capstan-driven Continuum Surgical Robot: Design, Modeling, and Perception

Shape and force sensing have long been critical bottlenecks in the development of compact capstan-driven continuum surgical robots, primarily due to the difficulty of obtaining cable tension information within the confined capstan assembly. To overcome these challenges, this paper presents an integrated design-modeling-sensing approach based on the concept of actuation-perception co-design. A compliant element is introduced into the motor mounting bracket of the drive system, enabling micro-deformation under the cable reaction force and thereby allowing real-time cable tension measurement without occupying the compact capstan space. To address the modeling complexity arising from unconventional joint configurations introduced by the spatial cable routing strategy, a parallel computation framework based on a multibody short-thick-beam model is proposed, which captures shear effects in short beam segments and synergistic multi-cable interactions while achieving real-time performance. Building on this framework, stable shape and force sensing is achieved by incorporating a proximal multi-axis force/torque sensor as an additional measurement anchor. Following this design-modeling-sensing framework, capstan-driven continuum surgical robots with single- and dual-segment configurations are developed. Experimental results validate the proposed framework in both single- and dual-segment continuum robots, demonstrating real-time tip pose estimation together with contact force and location perception. By enabling cable tension feedback without compromising the compact capstan architecture, the proposed framework makes integrated perception feasible for capstan-driven continuum surgical robots.

cs.RO↗

arXiv2026

Code-Poisoning Property Inference Attacks

The flourishing code hosting platforms and coding agents enable even beginners with private data to build tailored Machine Learning (ML) models using available code quickly. The training data for ML models, often regarded as private property (e.g., clinical records, transaction information), is at significant risk of information leakage. Property Inference Attacks (PIAs), as a significant type of privacy attack, aim to expose global property information of the training set. In this paper, we present Code-Poisoning Property Inference Attack (CPPIA), the first code-level PIA, which overcomes four limitations of existing works: insufficient attack performance, severe degradation of model accuracy, high computational overhead, and failure under defenses. We consider malicious code providers from code hosting platforms (GitHub) and coding agents (Codex). Upon downloading the poisoned code, data holders train models with their private data without professional auditing, subsequently releasing label-only APIs to the public. The adversary embeds the properties into secret samples during training and queries the trained model on these samples later to leak privacy. CPPIA offers 100\% attack accuracy without degrading model accuracy. It is also computationally lightweight and requires no shadow models. We evaluate the attack performance across four datasets, eight model architectures, eighteen properties, and under three defense mechanisms, demonstrating the universality and effectiveness of CPPIA.

cs.CR↗

arXiv2026

Is External Database Protection Static in Retrieval-Augmented Generation? Rethinking Privacy Preservation under Dynamic Queries

Retrieval-augmented generation (RAG) enhances large language models via external document retrieval, but retrieved contexts may leak sensitive information. Current privacy protection methods typically rely on a document-level static risk assumption, treating all retrieved documents as having the same privacy leakage risk. However, this assumption overlooks a fundamental characteristic of RAG: the privacy risk of a document is highly dependent on the user's query, making privacy leakage inherently query-driven and dynamic. To address this challenge, we propose a Prompt-Aware Dynamic Hierarchical Differential Privacy framework (PA-HDP) for privacy-preserving RAG. PA-HDP first performs a prompt-aware risk hierarchy to dynamically assess privacy risks under different queries. It then applies adaptive sensitive entity replacement and exponential mechanism-based text selection to provide differentiated privacy protection while preserving semantic utility. By protecting only the content that is truly sensitive under a given query, PA-HDP minimizes unnecessary modifications to the retrieval corpus. Extensive experiments on benchmark datasets demonstrate that PA-HDP significantly reduces privacy leakage while maintaining high retrieval quality, achieving a better privacy-utility trade-off than prior methods.

cs.CR↗

arXiv2026

A Turbo-Inference Strategy for Object Detection and Instance Segmentation

Object detection and instance segmentation tasks are closely related. Existing top-down instance segmentation methods usually follow a detect-then-segment paradigm, where an initial detector is used to recognize and localize objects with bounding boxes, followed by the segmentation of an instance mask within each bounding box. In such methods, the detection accuracy directly influences the subsequent segmentation performance. However, previous research has seldom explored the impact of the instance segmentation task on object detection. In this paper, we present a turbo-inference strategy for the top-down methods that leverages the complementary information between detection and segmentation tasks iteratively. Specifically we design two modules: turbo-detection head and turbo-segmentation head, which facilitate communication between the tasks. The two modules form a closed loop that interlaces the detection and segmentation results without retraining the model. Comprehensive experiments on the COCO, iFLYTEK, and Cityscapes datasets demonstrate that our method substantially enhances both detection and segmentation accuracies with a certain increase in computational cost. The proposed method represents a tradeoff between prediction accuracy and inference speed. Codes are available at https://github.com/zhaozhen2333/Turbo-Learning.git.

cs.CV↗

arXiv2026

Cable-driven Continuum Robotics: Proprioception via Proximal-integrated Force Sensing

Micro-scale continuum robots face significant limitations in achieving three-dimensional contact force perception, primarily due to structural miniaturization, nonlinear mechanical, and sensor integration. To overcome these limitations, this paper introduces a novel proprioception method for cable-driven continuum robots based on proximal-integrated force sensing (i.e., cable tension and six-axis force/torque (F/T) sensor), inspired by the tendon-joint collaborative sensing mechanism of the finger. By integrating biomechanically inspired design principles with nonlinear modeling, the proposed method addresses the challenge of force perception (including the three-dimensional contact force and the location of the contact point) and shape estimation in micro-scale continuum robots. First, a quasi-bionic mapping between human tissues/organs and robot components is established, enabling the transfer of the integrated sensing strategy of tendons, joints, and neural feedback to the robotic system. Second, a multimodal perception strategy is developed based on the structural constraints inherent to continuum robots. The complex relationships among mechanical and material nonlinearities, robot motion states, and contact forces are formulated as an optimization problem to reduce the perception complexity. Finally, experimental validation demonstrates the effectiveness of the proposed method. This work lays the foundation for developing safer and smarter continuum robots, enabling broader clinical adoption in complex environments.

cs.RO↗

arXiv2026

On the maximum twist width of delta-matroids

For a ribbon graph $G$, let $γ(G)$ denote its Euler genus. Recently, Chen, Gross and Tucker [J. Algebraic Combin. 63 (2026) 13] derived a formula for the maximum partial-dual Euler-genus $\partialγ_M(G)$ of a ribbon graph $G$. Their key finding is that $\partialγ_M(G)$ can be achieved by a partial dual with respect to the edge set of a spanning quasi-tree. Moreover, they proposed the following problem: Given a ribbon graph $G$, is there a sequence of edges $e_1,e_2,\dots, e_k$ such that $γ(G^{\{e_1, e_2,\dots, e_k\}})=\partialγ_M(G)$ and such that the sequence $$γ(G), γ(G^{\{e_1\}}), \dots, γ(G^ {\{e_1, e_2,\dots, e_k\}})$$ rises monotonically (i.e., never decreasing) to $\partialγ_M(G)$? Delta-matroids are set systems that satisfy the symmetric exchange axiom and serve as a matroidal abstraction of ribbon graphs. In this paper, we first show that the maximum twist width of a set system can be attained by twisting one of its feasible sets, which extends the result of Chen, Gross and Tucker to set systems. Then we solve the delta-matroid version of their problem, thereby providing an affirmative answer to the original problem for ribbon graphs.

math.CO↗

arXiv2026

SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models

Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness, and spatial location remains under-explored. To bridge this gap, we introduce SonicBench, a psychophysically grounded benchmark that systematically evaluates 12 core physical attributes across five perceptual dimensions. Unlike previous datasets, SonicBench uses a controllable generation toolbox to construct stimuli for two complementary paradigms: recognition (absolute judgment) and comparison (relative judgment). This design allows us to probe not only sensory precision but also relational reasoning capabilities, a domain where humans typically exhibit greater proficiency. Our evaluation reveals a substantial deficiency in LALMs' foundational auditory understanding; most models perform near random guessing and, contrary to human patterns, fail to show the expected advantage on comparison tasks. Furthermore, explicit reasoning yields minimal gains. However, our linear probing analysis demonstrates crucially that frozen audio encoders do successfully capture these physical cues (accuracy at least 60%), suggesting that the primary bottleneck lies in the alignment and decoding stages, where models fail to leverage the sensory signals they have already captured.

cs.SD↗

arXiv2025

Semantic Co-Speech Gesture Synthesis and Real-Time Control for Humanoid Robots

We present an innovative end-to-end framework for synthesizing semantically meaningful co-speech gestures and deploying them in real-time on a humanoid robot. This system addresses the challenge of creating natural, expressive non-verbal communication for robots by integrating advanced gesture generation techniques with robust physical control. Our core innovation lies in the meticulous integration of a semantics-aware gesture synthesis module, which derives expressive reference motions from speech input by leveraging a generative retrieval mechanism based on large language models (LLMs) and an autoregressive Motion-GPT model. This is coupled with a high-fidelity imitation learning control policy, the MotionTracker, which enables the Unitree G1 humanoid robot to execute these complex motions dynamically and maintain balance. To ensure feasibility, we employ a robust General Motion Retargeting (GMR) method to bridge the embodiment gap between human motion data and the robot platform. Through comprehensive evaluation, we demonstrate that our combined system produces semantically appropriate and rhythmically coherent gestures that are accurately tracked and executed by the physical robot. To our knowledge, this work represents a significant step toward general real-world use by providing a complete pipeline for automatic, semantic-aware, co-speech gesture generation and synchronized real-time physical deployment on a humanoid robot.

cs.RO↗

arXiv2025

Fault-tolerant mutual-visibility: complexity and solutions for grid-like networks

Networks are often modeled using graphs, and within this setting we introduce the notion of $k$-fault-tolerant mutual visibility. Informally, a set of vertices $X \subseteq V(G)$ in a graph $G$ is a $k$-fault-tolerant mutual-visibility set ($k$-ftmv set) if any two vertices in $X$ are connected by a bundle of $k+1$ shortest paths such that: ($i$) each shortest path contains no other vertex of $X$, and ($ii$) these paths are internally disjoint. The cardinality of a largest $k$-ftmv set is denoted by $\mathrm{f}μ^{k}(G)$. The classical notion of mutual visibility corresponds to the case $k = 0$. This generalized concept is motivated by applications in communication networks, where agents located at vertices must communicate both efficiently (i.e., via shortest paths) and confidentially (i.e., without messages passing through the location of any other agent). The original notion of mutual visibility may fail in unreliable networks, where vertices or links can become unavailable. Several properties of $k$-ftmv sets are established, including a natural relationship between $\mathrm{f}μ^{k}(G)$ and $ω(G)$, as well as a characterization of graphs for which $\mathrm{f}μ^{k}(G)$ is large. It is shown that computing $\mathrm{f}μ^{k}(G)$ is NP-hard for any positive integer $k$, whether $k$ is fixed or not. Exact formulae for $\mathrm{f}μ^{k}(G)$ are derived for several specific graph topologies, including grid-like networks such as cylinders and tori, and for diameter-two networks defined by Hamming graphs and by the direct product of complete graphs.

math.CO↗

arXiv2025

Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors

Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework for device-scale heat transport simulations of field-effect transistors. Built upon the neuroevolution potential, the model extends a subset of the OMat24 dataset through an active-learning workflow to generate a chemically diverse, interface-rich reference set. Coupled with the FETMOD structure generator module, NEP-FET can simulate realistic field-effect transistor geometries at sub-micrometer scales containing millions of atoms, and delivers atomistic predictions of temperature fields, per-atom heat flux, and thermal stress in device structures with high fidelity. This framework enables rapid estimation of device-level metrics, including heat-flux density and effective thermal conductivity. Our results reveal pronounced differences in temperature distribution between fin-type and gate-all-around transistor architectures. The framework closes a key gap in multiscale device modeling by combining near-quantum-mechanical accuracy with device-scale throughput, providing a systematic route to explore heat transport and thermo-mechanical coupling in advanced transistors.

cond-mat.mtrl-sci↗

arXiv2025

VRP-SAM: SAM with Visual Reference Prompt

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images, including \textbf{point}, \textbf{box}, \textbf{scribble}, and \textbf{mask}. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths, thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM, the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM, we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably, VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore, VRP-SAM demonstrates strong generalization capabilities, allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM

cs.CV↗

arXiv2025

Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima

Federated optimization is a constrained form of distributed optimization that enables training a global model without directly sharing client data. Although existing algorithms can guarantee convergence in theory and often achieve stable training in practice, the reasons behind performance degradation under data heterogeneity remain unclear. To address this gap, the main contribution of this paper is to provide a theoretical perspective that explains why such degradation occurs. We introduce the assumption that heterogeneous client data lead to distinct local optima, and show that this assumption implies two key consequences: 1) the distance among clients' local optima raises the lower bound of the global objective, making perfect fitting of all client data impossible; and 2) in the final training stage, the global model oscillates within a region instead of converging to a single optimum, limiting its ability to fully fit the data. These results provide a principled explanation for performance degradation in non-iid settings, which we further validate through experiments across multiple tasks and neural network architectures. The framework used in this paper is open-sourced at: https://github.com/NPCLEI/fedtorch.

cs.LG↗

arXiv2025

On $d$-distance $p$-packing domination number in strong products

The $d$-distance $p$-packing domination number $γ_d^p(G)$ of a graph $G$ is the cardinality of a smallest set of vertices of $G$ which is both a $d$-distance dominating set and a $p$-packing. If no such set exists, then we set $γ_d^p(G) = \infty$. For an arbitrary strong product $G\boxtimes H$ it is proved that $γ_d^p(G\boxtimes H) \le γ_d^p(G) γ_d^p(H)$. By proving that $γ_d^p(P_m \boxtimes P_n) = \left \lceil \frac{m}{2d+1} \right \rceil \left \lceil \frac{n}{2d+1} \right \rceil$, and that if $γ_d^p(C_n) < \infty$, then $γ_d^p(P_m \boxtimes C_n) = \left \lceil \frac{m}{2d+1} \right \rceil \left \lceil \frac{n}{2d+1} \right \rceil$, the sharpness of the upper bound is demonstrated. On the other hand, infinite families of strong toruses are presented for which the strict inequality holds. For instance, we present strong toruses with difference $2$ and demonstrate that the difference can be arbitrarily large if only one factor is a cycle. It is also conjectured that if $γ_d^p(G) = \infty$, then $γ_d^p(G\boxtimes H) = \infty$ for every graph $H$. Several results are proved which support the conjecture, in particular, if $γ_d^p(C_m)= \infty$, then $γ_d^p(C_m \boxtimes C_n)=\infty$.

math.CO↗

arXiv2025

Spectral extrema of graphs of given even size forbidding H(4,3)

A graph is sad to be $H$-free if it does not contain $H$ as a subgraph. Let $H(k,3)$ be the graph formed by taking a cycle of length $k$ and a triangle on a common vertex. Li, Lu and Peng [Discrete Math. 346 (2023) 113680] proved that if $G$ is an $H(3,3)$-free graph of size $m \geq 8$, then the spectral radius $ρ(G) \leq \frac{1+\sqrt{4 m-3}}{2}$ with equality if and only if $G \cong S_{\frac{m+3}{2}, 2}$, where $S_{\frac{m+3}{2}, 2}=K_2 \vee \frac{m-1}{2}K_1$. Note that the bound is attainable only when $m$ is odd. Recently, Pirzada and Rehman [Comput. Appl. Math. 44 (2025) 295] proved that if $G$ is an $\{H(3,3),H(4,3)\}$-free graph of even size $m \geq 10$, then $ρ(G) \leq ρ^{\prime}(m)$ with equality if and only if $G \cong S_{\frac{m+4}{2}, 2}^{-}$, where $ρ^{\prime}(m)$ is the largest root of $x^4-m x^2-(m-2) x+\frac{m}{2}-1=0$, and $S_{\frac{m+4}{2}, 2}^{-}$ is the graph obtained from $S_{\frac{m+4}{2}, 2}$ by deleting an edge incident to a vertex of degree two. In this paper, we improve the result of Pirzada and Rehman by showing that if $G$ is an $H(4,3)$-free graph of even size $m \geq 38$ without isolated vertices, then $ρ(G) \leq ρ^{\prime}(m)$ with equality if and only if $G \cong S_{\frac{m+4}{2}, 2}^{-}$.

math.CO↗