arXiv subjects

Tao Zhang

Publications and source records attributed to Tao Zhang.

At least 217 records · Page 12Linked to original sources

arXiv2025

SVAC: Scaling Is All You Need For Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) aims to segment target objects in video sequences based on natural language descriptions. While recent advances in Multi-modal Large Language Models (MLLMs) have improved RVOS performance through enhanced text-video understanding, several challenges remain, including insufficient exploitation of MLLMs' prior knowledge, prohibitive computational and memory costs for long-duration videos, and inadequate handling of complex temporal dynamics. In this work, we propose SVAC, a unified model that improves RVOS by scaling up input frames and segmentation tokens to enhance video-language interaction and segmentation precision. To address the resulting computational challenges, SVAC incorporates the Anchor-Based Spatio-Temporal Compression (ASTC) module to compress visual tokens while preserving essential spatio-temporal structure. Moreover, the Clip-Specific Allocation (CSA) strategy is introduced to better handle dynamic object behaviors across video clips. Experimental results demonstrate that SVAC achieves state-of-the-art performance on multiple RVOS benchmarks with competitive efficiency. Our code is available at https://github.com/lizhang1998/SVAC.

cs.CV↗

arXiv2025

Fully Tensorized GPU-accelerated Multi-population Evolutionary Algorithm for Constrained Multiobjective Optimization Problems

Real world constrained multiobjective optimization problems (CMOPs) are prevalent and often come with stringent time-sensitive requirements. However, most contemporary constrained multiobjective evolutionary algorithms (CMOEAs) suffer from a number of drawbacks, including complex designs, low computational efficiency, and long convergence times, which are particularly pronounced when addressing time-sensitive CMOPs. Although research on accelerating evolutionary algorithms using GPU parallelism has advanced, existing CMOEAs still face significant limitations within GPU frameworks. To overcome these challenges, this paper proposes a GPU-accelerated multi-population evolutionary algorithm, termed GMPEA. We first systematically analyze the performance bottlenecks of representative CMOEAs when implemented in a GPU environment. To address the trade-off between computational speed and solution performance, GMPEA introduces a decomposition-based multi-population approach that is fully parallelized across its entire workflow. We conducted comparative experiments on various benchmark tests and real world applications: the Weapon Target Assignment Problems. The results demonstrate that GMPEA achieves competitive performance even without time constraints, while its computational speed significantly surpasses that of the compared algorithms. More critically, under a strict time limit, the performance of GMPEA drastically outperforms its counterparts. This work provides compelling evidence of GMPEA's superiority in solving time-sensitive CMOPs.

cs.NE↗

arXiv2025

A Detection Region Method-Based Evolutionary Algorithm for Binary Constrained Multiobjective Optimization

Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectiveness of constraint violation guidance, which can negatively impact the performance of existing algorithms that rely on this approach. Such challenges are particularly detrimental for algorithms employing the epsilon-based method, as they hinder effective relaxation of the feasible region. To address these challenges, this paper proposes a novel algorithm called DRMCMO based on the detection region method. In DRMCMO, detection regions dynamic monitor feasible solutions to enhance convergence, helping the population escape local optima. Additionally, these regions collaborate with the neighbor pairing strategy to improve population diversity within narrow feasible areas. We have modified three existing test suites to serve as benchmark test problems for CMOPs with binary constraints(CMOP/BC) and conducted comprehensive comparative experiments with state-of-the-art algorithms on these test suites and real-world problems. The results demonstrate the strong competitiveness of DRMCMO against state-of-the-art algorithms. Given the limited research on CMOP/BC, our study offers a new perspective for advancing this field.

cs.NE↗

arXiv2025

Cohomology and automorphisms for a matched pair of 3-Lie algebras

We begin by reviewing the definition of 3-Lie algebras and the fundamental concepts of matched pairs. Subsequently, we introduce the representation theory of matched pairs and define the semidirect product. Building on this foundation, we define the low-dimensional cohomology groups of matched pairs. In addition, we explore the infinitesimal deformations and abelian extensions of matched pairs. Finally, we examine the inducibility of automorphisms of matched pairs and present related results through the Wells exact sequence.

math.RA↗

arXiv2025

Periodicity of tiles in finite Abelian groups

In this paper, we introduce the periodic tiling (PT) property for finite abelian groups. A finite abelian group is said to have the PT property if every non-periodic set that tiles the group by translation admits a periodic tiling complement. This notion extends the scope beyond groups with the Hajós property. We give a complete classification of cyclic groups possessing the PT property and identify certain non-cyclic groups that enjoy the PT property but fail to satisfy the Hajós property.. As a byproduct, we obtain new families of groups for which the implication ``Tile $\Longrightarrow$ Spectral" holds. Furthermore, for elementary $p$-groups with the PT property, by analyzing the structure of tiles, we prove that every tile is a complete set of representatives of the cosets of some subgroup.

math.GR↗

arXiv2025

Electrically Reconfigurable Arbitrary Splitting-Ratio Optical Splitter Based on Low-Loss Sb2Se3

Reconfigurable beam splitters capable of being arbitrarily programmed for the power splitting ratios are vital for the adaptive optical networks and photonic computing. Conventional mechanisms such as thermo-optic, free-carrier, or mechanical tuning are usually volatile and require continuous power, limiting their suitability for low-frequency and low power-consumption programmable operations. Here, we experimentally demonstrate an electrically reconfigurable beam splitter based on the low-loss phase-change material Sb2Se3, enabling multi-level and arbitrary splitting-ratio (SR) control. By locally triggering phase transitions in the coupling region with integrated micro-electrodes, we exploit the high refractive-index contrast between different phases and negligible absorption in the near-infrared wavelength of Sb2Se3 to precisely tune the coupling strength with non-volatile retention. 8-level of power splitting states is achieved within a compact footprint of ~14.5-μm in the experiments, with insertion loss is ~1 dB across 1515-1550 nm and near-zero static power. Combining the advantages of compactness, broad bandwidth, low loss, non-volatility, and multi-level control experimentally, this device provides a universal building block for scalable, energy-efficient reconfigurable photonic circuits, with great prospects in optical computing and intelligent communication systems.

physics.optics↗

arXiv2025

RSMA Assisted ISAC With Hybrid Beamforming

The harsh environment and scarce resources post-disaster drive the equipment to be miniaturized and portable. Based on this, integrated sensing and communication (ISAC) systems play a significant role in providing emergency wireless networks. In order to reduce the hardware cost, a hybrid beamforming (HBF) assisted millimeter-wave (mmWave) ISAC system, which exploits the limited number of radio frequency (RF) chains, is considered in this paper. However, the HBF structure reduces the spatial degrees of freedom, thus leading to increased interference among communication users and radar sensing. To solve this problem, a rate-splitting multiple access (RSMA) strategy is adopted to enhance the emergency mmWave-ISAC system. We formulate the weighted sum rate (WSR) maximization objective by jointly designing common rate allocation and HBF. Then, we propose the penalty dual decomposition (PDD) coupled with the weighted mean squared error (WMMSE) method to solve this high-dimensional non-convex problem. Numerical results demonstrate the effectiveness of the proposed algorithm and show that the RSMA-ISAC scheme outperforms other benchmark schemes.

eess.SP↗

arXiv2025

A Dataset Generation Scheme Based on Video2EEG-SPGN-Diffusion for SEED-VD

This paper introduces an open-source framework, Video2EEG-SPGN-Diffusion, that leverages the SEED-VD dataset to generate a multimodal dataset of EEG signals conditioned on video stimuli. Additionally, we disclose an engineering pipeline for aligning video and EEG data pairs, facilitating the training of multimodal large models with EEG alignment capabilities. Personalized EEG signals are generated using a self-play graph network (SPGN) integrated with a diffusion model. As a major contribution, we release a new dataset comprising over 1000 samples of SEED-VD video stimuli paired with generated 62-channel EEG signals at 200 Hz and emotion labels, enabling video-EEG alignment and advancing multimodal research. This framework offers novel tools for emotion analysis, data augmentation, and brain-computer interface applications, with substantial research and engineering significance.

cs.CV↗

arXiv2025

Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential

While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic potentials, trained on data from first-principles calculations, offer a powerful alternative by efficiently approximating the complex three-dimensional potential energy surface with near-DFT accuracy. In this work, we develop a general-purpose machine learning interatomic potential for amorphous alloys by using a dataset comprising 20400 configurations across representative binary and ternary amorphous alloys systems. The model demonstrates excellent predictive performance on an independent test set, with a mean absolute error of 5.06 meV/atom for energy and 128.51 meV/Å for forces. Through extensive validation, the model is shown to reliably capture the trends in macroscopic property variations such as density, Young's modulus and glass transition temperature across both the original training systems and the compositionally modified systems derived from them. It can be directly applied to composition-property mapping of amorphous alloys. Furthermore, the developed interatomic potential enables access to the atomic structures of amorphous alloys, allowing for microscopic analysis and interpretation of experimental results, particularly those deviating from empirical trends.This work breaks the long-standing computational bottleneck in amorphous alloys research by developing the first general-purpose machine learning interatomic potential for amorphous alloy systems. The resulting framework provides a robust foundation for data-driven design and high-throughput composition screening in a field previously constrained by traditional simulation limitations.

cond-mat.mtrl-sci↗

arXiv2025

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, with code generation emerging as a key area of focus. While numerous benchmarks have been proposed to evaluate their code generation abilities, these benchmarks face several critical limitations. First, they often rely on manual annotations, which are time-consuming and difficult to scale across different programming languages and problem complexities. Second, most existing benchmarks focus primarily on Python, while the few multilingual benchmarks suffer from limited difficulty and uneven language distribution. To address these challenges, we propose AutoCodeGen, an automated method for generating high-difficulty multilingual code generation datasets without manual annotations. AutoCodeGen ensures the correctness and completeness of test cases by generating test inputs with LLMs and obtaining test outputs through a multilingual sandbox, while achieving high data quality through reverse-order problem generation and multiple filtering steps. Using this novel method, we introduce AutoCodeBench, a large-scale code generation benchmark comprising 3,920 problems evenly distributed across 20 programming languages. It is specifically designed to evaluate LLMs on challenging, diverse, and practical multilingual tasks. We evaluate over 30 leading open-source and proprietary LLMs on AutoCodeBench and its simplified version AutoCodeBench-Lite. The results show that even the most advanced LLMs struggle with the complexity, diversity, and multilingual nature of these tasks. Besides, we introduce AutoCodeBench-Complete, specifically designed for base models to assess their few-shot code generation capabilities. We hope the AutoCodeBench series will serve as a valuable resource and inspire the community to focus on more challenging and practical multilingual code generation scenarios.

cs.CL↗

arXiv2025

Label Inference Attacks against Federated Unlearning

Federated Unlearning (FU) has emerged as a promising solution to respond to the right to be forgotten of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU expose clients' data information, enabling attackers to infer the label of unlearning data, while label inference attacks against FU remain unexplored. In this paper, we introduce and analyze a new privacy threat against FU and propose a novel label inference attack, ULIA, which can infer unlearning data labels across three FU levels. To address the unique challenges of inferring labels via the models variations, we design a gradient-label mapping mechanism in ULIA that establishes a relationship between gradient variations and unlearning labels, enabling inferring labels on accumulated model variations. We evaluate ULIA on both IID and non-IID settings. Experimental results show that in the IID setting, ULIA achieves a 100% Attack Success Rate (ASR) under both class-level and client-level unlearning. Even when only 1% of a user's local data is forgotten, ULIA still attains an ASR ranging from 93% to 62.3%.

cs.CR↗

arXiv2025

Quantum Sensing with Topological-Paired Bound States

We present an efficient and robust protocol for quantum-enhanced sensing using a single qubit in the topological waveguide system. Our method relies on the topological-paired bound states, which are localized near the qubit and can be effectively regarded as a two-level system. Through the lens of Bayesian inference theory, we show that the sensitivity can reach the Heisenberg limit across a large field range. Inheriting from the topological robustness of the waveguide, our sensing protocol is robust against local perturbations. Besides, our sensing protocol utilizes a product state as the initial state, which can be easily prepared in experiments. We expect this approach would pave the way toward robust topological quantum sensors based on near-term quantum platforms such as superconducting qubits and Rydberg arrays.

quant-ph↗

arXiv2025

Adjacency spectral radius and H-factors in 1-binding graphs

Let $G$ be a graph, and let $H:V(G)\longrightarrow\{\{1\},\{0,2\}\}$ be a set-valued function. Hence, $H(v)$ equals $\{1\}$ or $\{0,2\}$ for any $v\in V(G)$. We let $$ H^{-1}(1)=\{v: v\in V(G) \ \mbox{and} \ H(v)=1\}. $$ An $H$-factor of $G$ is a spanning subgraph $F$ of $G$ such that $d_F(v)\in H(v)$ for each $v\in V(G)$. Lu and Kano showed a characterization for the existence of an $H$-factor in a graph [Characterization of 1-tough graphs using factors, Discrete Math. 343 (2020) 111901]. Let $A(G)$ and $ρ(G)$ denote the adjacency matrix and the adjacency spectral radius of $G$, respectively. By using Lu and Kano's result, we pose a sufficient condition with respect to the adjacency spectral radius to guarantee the existence of an $H$-factor in a 1-binding graph. In this paper, we prove that if a connected 1-binding graph $G$ of order $n\geq11$ satisfies $ρ(G)\geqρ(K_1\vee(K_{n-4}\cup K_2\cup K_1))$, then $G$ has an $H$-factor for each $H:V(G)\longrightarrow\{\{1\},\{0,2\}\}$ with $H^{-1}(1)$ even, unless $G=K_1\vee(K_{n-4}\cup K_2\cup K_1)$.

math.CO↗

arXiv2025

$S^3$LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping

We propose $S^3$LAM, a novel RGB-D SLAM system that leverages 2D surfel splatting to achieve highly accurate geometric representations for simultaneous tracking and mapping. Unlike existing 3DGS-based SLAM approaches that rely on 3D Gaussian ellipsoids, we utilize 2D Gaussian surfels as primitives for more efficient scene representation. By focusing on the surfaces of objects in the scene, this design enables $S^3$LAM to reconstruct high-quality geometry, benefiting both mapping and tracking. To address inherent SLAM challenges including real-time optimization under limited viewpoints, we introduce a novel adaptive surface rendering strategy that improves mapping accuracy while maintaining computational efficiency. We further derive camera pose Jacobians directly from 2D surfel splatting formulation, highlighting the importance of our geometrically accurate representation that improves tracking convergence. Extensive experiments on both synthetic and real-world datasets validate that $S^3$LAM achieves state-of-the-art performance. Code will be made publicly available.

cs.CV↗

arXiv2025

The nonisospectral integrable hierarchies associated with Lie algebra $\mathfrak{sp}(6)$

In this paper, we consider nonisospectral problems of two distinct dimensions on the loop algebra of the symplectic Lie algebra $\mathfrak{sp}(6)$, and construct two integrable systems. Furthermore, we derive their Hamiltonian structures using the Tu scheme. Additionally, we construct an integrable hierarchy on the generalized Lie algebra $\mathfrak{Gsp}(6)$ and establish its Hamiltonian structure as well.

nlin.SI↗

arXiv2025

Critical angles and one-dimensional moiré physics in twisted rectangular lattices

Engineering moiré superlattices in van der Waals heterostructures provides fundamental control over emergent electronic, structural, and optical properties allowing to affect topological and correlated phenomena. This control is achieved through imposed periodic modulation of potentials and targeted modifications of symmetries. For twisted bilayers of van der Waals materials with rectangular lattices, such as PdSe2, this work shows that one-dimensional (1D) moiré patterns emerge universally. This emergence is driven by a series of critical twist angles (CAs). We investigate the geometric origins of these unique 1D moiré patterns and develop a universal mathematical framework to predict the CAs in twisted rectangular lattices. Through a density functional theory (DFT) description of the electronic properties of twisted bilayer PdSe2, we further reveal directionally localized flat band structures, localized charge densities and strong spin-orbit coupling along the dispersive direction which points to the emergence of an effectively 1D strongly spin-orbit coupled electronic systems. This establishes twisted rectangular systems as a unique platform for engineering low-symmetry moiré patterns, low-dimensional strongly correlated and topological physics, and spatially selective quantum phases beyond the isotropic paradigms of hexagonal moiré materials.

cond-mat.mes-hall↗

arXiv2025

D2IP: Deep Dynamic Image Prior for 3D Time-sequence Pulmonary Impedance Imaging

Unsupervised learning methods, such as Deep Image Prior (DIP), have shown great potential in tomographic imaging due to their training-data-free nature and high generalization capability. However, their reliance on numerous network parameter iterations results in high computational costs, limiting their practical application, particularly in complex 3D or time-sequence tomographic imaging tasks. To overcome these challenges, we propose Deep Dynamic Image Prior (D2IP), a novel framework for 3D time-sequence imaging. D2IP introduces three key strategies - Unsupervised Parameter Warm-Start (UPWS), Temporal Parameter Propagation (TPP), and a customized lightweight reconstruction backbone, 3D-FastResUNet - to accelerate convergence, enforce temporal coherence, and improve computational efficiency. Experimental results on both simulated and clinical pulmonary datasets demonstrate that D2IP enables fast and accurate 3D time-sequence Electrical Impedance Tomography (tsEIT) reconstruction. Compared to state-of-the-art baselines, D2IP delivers superior image quality, with a 24.8% increase in average MSSIM and an 8.1% reduction in ERR, alongside significantly reduced computational time (7.1x faster), highlighting its promise for clinical dynamic pulmonary imaging.

eess.IV↗

arXiv2025

Precise Measurement of $^{216}$Po Half-life with Exact Parent-daughter Pairing in PandaX-4T

We report a precise measurement of $^{216}\rm Po$ half-life using the PandaX-4T liquid xenon time projection chamber (TPC). $^{220}\rm Rn $, emanating from a $^{228}\rm Th $ calibration source, is injected to the detector and undergoes successive $α$ decays, first to $^{216}\rm Po$ and then to $^{212}\rm Pb$. PandaX-4T detector measures the 5-dimensional (5D) information of each decay, including time, energy, and 3-dimensional positions. Therefore, we can identify the $^{220}\rm Rn $ and $^{216}\rm Po$ decay events and pair them exactly to extract the lifetime of each $^{216}\rm Po$. With a large data set and high-precision $^{220}\rm $Rn-$^{216}\rm $Po pairing technique, we measure the $^{216}\rm Po$ half-life to be $143.7\pm0.5$ ms, which is the most precise result to date and agrees with previously published values. The leading precision of this measurement demonstrates the power of 5D calorimeter and the potential of exact parent-daughter pairing in the xenon TPC.

nucl-ex↗