arXiv subjects

Chun Zhang

Publications and source records attributed to Chun Zhang.

At least 19 recordsLinked to original sources

arXiv2026

LEO-NA Walker Constellation Design with Bi-objective Optimisation Approaches

Low Earth Orbit (LEO) constellation design for navigation augmentation (NA) has attracted increasing attention in navigation satellite system studies, yet balancing navigation performance and deployment cost remains a fundamental challenge. To address this issue, this paper proposes a bi-objective optimization framework for LEO Walker constellation design. The problem is formulated as a bi-objective optimization model with constellation cost and positioning accuracy as objectives. In the formulation, PDOP tail risk and satellite visibility are incorporated into the objective formulation to better characterize navigation performance. The Pareto-optimal solution set isobtained using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Simulation results show that, under the same satellite deployment cost, the proposed LEO-NA Walker constellation improves the average number of visible satellites by 42.5% and 24.4%, and reduces the mean PDOP by 18.9% and 10.5% compared with representative Polar and optimized-LFC constellations, respectively, thereby enhancing service continuity and resource utilization efficiency. These results provide useful guidance for the design and deployment of LEO-NA constellations.

eess.SY↗

arXiv2026

Bound-Constrained Sparse Representation for Electrical Impedance Tomography

This study proposes a bound-constrained sparse representation (BC-SR) framework for electrical impedance tomography (EIT), aimed at improving conductivity estimation without explicit regularization. BC-SR adopts a representation-driven strategy, generating conductivity from low-dimensional latent variables via an implicit composite parameterization. Structural priors are embedded using a truncated graph-Laplacian basis, while a bound-preserving nonlinear mapping enforces admissible conductivity ranges and improves conditioning through implicit gradient modulation. The approach ensures robust convergence, even under noisy or incomplete data. Extensive validation on 2D/3D simulations, tank experiments, and in-vivo lung data shows that BC-SR improves physical consistency and structural fidelity, offering enhanced robustness compared to traditional methods. Additionally, BC-SR enables 3D time-difference EIT reconstruction, offering improved spatial resolution and a more coherent representation of 3D conductivity distributions, particularly for in-vivo lung data. This suggests potential for improved performance in EIT, particularly in clinical applications for respiratory monitoring.

cs.CV↗

arXiv2026

Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains. XpertBench consists of 1,346 meticulously curated tasks across 80 categories, spanning finance, healthcare, legal services, education, and dual-track research (STEM and Humanities). These tasks are derived from over 1,000 submissions by domain experts--including researchers from elite institutions and practitioners with extensive clinical or industrial experience--ensuring superior ecological validity. Each task uses detailed rubrics with mostly 15-40 weighted checkpoints to assess professional rigor. To facilitate scalable yet human-aligned assessment, we introduce ShotJudge, a novel evaluation paradigm that employs LLM judges calibrated with expert few-shot exemplars to mitigate self-rewarding biases. Our empirical evaluation of state-of-the-art LLMs reveals a pronounced performance ceiling: even leading models achieve a peak success rate of only ~66%, with a mean score around 55%. Models also exhibit domain-specific divergence, showing non-overlapping strengths in quantitative reasoning versus linguistic synthesis.. These findings underscore a significant "expert-gap" in current AI systems and establish XpertBench as a critical instrument for navigating the transition from general-purpose assistants to specialized professional collaborators.

cs.AI↗

arXiv2026

Long-term marine acoustic and seismic monitoring using distributed acoustic sensing and deep learning

The ocean remains one of the least instrumented parts of Earth, and many geophysical, biological, and anthropogenic signals go undetected for lack of instrumentation. Distributed acoustic sensing (DAS) can transform submarine fiber-optic cables into dense seafloor sensor arrays, but extracting diverse signals from massive DAS recordings remains challenging. Here we present DASNet, a deep learning framework that detects, classifies, and picks arrival times of diverse marine signals in continuous DAS data. Applied to nearly four years of Seafloor Fiber-Optic Array in Monterey Bay recordings, DASNet identifies more than 620,000 events. These detections reveal local earthquakes; distant earthquake- and volcanic-eruption-generated T-waves from the southwestern Pacific and mid-ocean ridge systems; more than 510,000 blue and fin whale calls with seasonal and interannual variability consistent with hydrophone records; and vessel traffic near the cable. Together, these results show that submarine fiber-optic cables combined with deep learning enable scalable, high-resolution ocean monitoring.

physics.geo-ph↗

arXiv2026

SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

Efficiently training quadruped robot navigation in densely cluttered environments remains a significant challenge. Existing methods are either limited by a lack of safety and agility in simple obstacle distributions or suffer from slow locomotion in complex environments, often requiring excessively long training phases. To this end, we propose SEA-Nav (Safe, Efficient, and Agile Navigation), a reinforcement learning framework for quadruped navigation. Within diverse and dense obstacle environments, a differentiable control barrier function (CBF)-based shield constraints the navigation policy to output safe velocity commands. An adaptive collision replay mechanism and hazardous exploration rewards are introduced to increase the probability of learning from critical experiences, guiding efficient exploration and exploitation. Finally, kinematic action constraints are incorporated to ensure safe velocity commands, facilitating successful physical deployment. To the best of our knowledge, this is the first approach that achieves highly challenging quadruped navigation in the real world with minute-level training time.

cs.RO↗

arXiv2026

\$OneMillion-Bench: How Far are Language Agents from Human Experts?

As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of real-world professional demands. To this end, we introduce \$OneMillion-Bench \$OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios. Unlike prior work, the benchmark requires retrieving authoritative sources, resolving conflicting evidence, applying domain-specific rules, and making constraint decisions, where correctness depends as much on the reasoning process as the final answer. We adopt a rubric-based evaluation protocol scoring factual accuracy, logical coherence, practical feasibility, and professional compliance, focused on expert-level problems to ensure meaningful differentiation across agents. Together, \$OneMillion-Bench provides a unified testbed for assessing agentic reliability, professional depth, and practical readiness in domain-intensive scenarios.

cs.LG↗

arXiv2026

SD-MoE: Spectral Decomposition for Effective Expert Specialization

Mixture-of-Experts (MoE) architectures scale Large Language Models via expert specialization induced by conditional computation. In practice, however, expert specialization often fails: some experts become functionally similar, while others functioning as de facto shared experts, limiting the effective capacity and model performance. In this work, we analysis from a spectral perspective on parameter and gradient spaces, uncover that (1) experts share highly overlapping dominant spectral components in their parameters, (2) dominant gradient subspaces are strongly aligned across experts, driven by ubiquitous low-rank structure in human corpus, and (3) gating mechanisms preferentially route inputs along these dominant directions, further limiting specialization. To address this, we propose Spectral-Decoupled MoE (SD-MoE), which decomposes both parameter and gradient in the spectral space. SD-MoE improves performance across downstream tasks, enables effective expert specialization, incurring minimal additional computation, and can be seamlessly integrated into a wide range of existing MoE architectures, including Qwen and DeepSeek.

cs.LG↗

arXiv2025

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e.g., the reasoning-focused model DeepSeek-R1 achieved the highest accuracy of 61.82% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

cs.CL↗

arXiv2024

MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation

The Knowledge Graph-to-Text Generation task aims to convert structured knowledge graphs into coherent and human-readable natural language text. Recent efforts in this field have focused on enhancing pre-trained language models (PLMs) by incorporating graph structure information to capture the intricate structure details of knowledge graphs. However, most of these approaches tend to capture only single-granularity structure information, concentrating either on the relationships between entities within the original graph or on the relationships between words within the same entity or across different entities. This narrow focus results in a significant limitation: models that concentrate solely on entity-level structure fail to capture the nuanced semantic relationships between words, while those that focus only on word-level structure overlook the broader relationships between original entire entities. To overcome these limitations, this paper introduces the Multi-granularity Graph Structure Attention (MGSA), which is based on PLMs. The encoder of the model architecture features an entity-level structure encoding module, a word-level structure encoding module, and an aggregation module that synthesizes information from both structure. This multi-granularity structure encoding approach allows the model to simultaneously capture both entity-level and word-level structure information, providing a more comprehensive understanding of the knowledge graph's structure information, thereby significantly improving the quality of the generated text. We conducted extensive evaluations of the MGSA model using two widely recognized KG-to-Text Generation benchmark datasets, WebNLG and EventNarrative, where it consistently outperformed models that rely solely on single-granularity structure information, demonstrating the effectiveness of our approach.

cs.CL↗

arXiv2024

Structure Phase Change Induced by Nonequilibrium Effects in Molecular Scale Junctions

The interrelationship between a material's structure and its properties lies at the heart of materials-related research. Finding how the changes of one affect the other is of primary importance in theoretical and computational materials studies. In this work, based on Hershfield nonequilibrium quantum statistics and the mean-field approach with steady-state density functional theory, we derive a first-principles method to calculate nonequilibrium effects induced forces acting on atoms, enabling structure optimizations and molecular dynamics simulations for molecular junctions under external biases. By applying the method to a few molecular devices, we found that in general, the external bias can induce profound nonequilibrium effects on both electronic/transport properties and the geometric structure of these devices, and consequent changes in electronic properties and geometric structure are closely interrelated. Particularly, when the bias voltage is above 1.0 V, significant structure phase changes could occur, causing dramatic changes in I-V characteristics and vibrational spectra. These findings greatly broaden our understanding of quantum electronic devices and provide a new avenue for discovering novel transport phenomena at molecular scale.

cond-mat.mes-hall↗

arXiv2024

SLR: Learning Quadruped Locomotion without Privileged Information

The recent mainstream reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. Videos of our results can be found on our website: https://11chens.github.io/SLR/

cs.RO↗

arXiv2024

Emergence of cooperation under punishment: A reinforcement learning perspective

Punishment is a common tactic to sustain cooperation and has been extensively studied for a long time. While most of previous game-theoretic work adopt the imitation learning where players imitate the strategies who are better off, the learning logic in the real world is often much more complex. In this work, we turn to the reinforcement learning paradigm, where individuals make their decisions based upon their past experience and long-term returns. Specifically, we investigate the Prisoners' dilemma game with Q-learning algorithm, and cooperators probabilistically pose punishment on defectors in their neighborhood. Interestingly, we find that punishment could lead to either continuous or discontinuous cooperation phase transitions, and the nucleation process of cooperation clusters is reminiscent of the liquid-gas transition. The uncovered first-order phase transition indicates that great care needs to be taken when implementing the punishment compared to the continuous scenario.

q-bio.PE↗

arXiv2023

A variational Monte Carlo approach to the SU(4) spin-orbital model on the triangular lattice

Previous investigations have suggested that the simplest spin-orbital model on the simplest frustrated lattice can host a nematic quantum spin-orbital liquid state. Namely, the orbital degeneracy of the SU(4) Kugel-Khomskii (KK) model tends to enhance quantum fluctuations and stabilize a quantum spin-orbital liquid exhibiting stripy features on the triangular lattice, as revealed by the state-of-the-art method of the density matrix renormalization group boosted by Gutzwiller projected wave functions. In this work, using the variational quantum Monte Carlo method, we have studied several spin-orbital liquid states, including a uniform $π$ flux state, three stripy states, and a plaquette state, on the $L\times{}L$ torus up to $L=24$. It turns out that one of these stripy states, called the "stripe-II" state, is energetically favored. This ground state breaks the $C_6$ symmetry of the lattice, resulting in a reduced $C_2$ symmetry and doubled unit cells, while preserving the SU(4) spin-orbital rotation symmetry. Such a nematic quantum spin-orbital liquid state can be characterized by a parton Fermi surface (FS) consisting of open orbits in the Brillouin zone, in contrast to the circular FS of the uniform $π$-flux state.

cond-mat.str-el↗

arXiv2023

Topological chiral kagome lattice

Chirality, a fundamental structural property of crystals, can induce many unique topological quantum phenomena. In kagome lattice, unconventional transports have been reported under tantalizing chiral charge order. Here, we show how by deforming the kagome lattice to obtain a three-dimensional (3D) chiral kagome lattice in which the key band features of the non-chiral 2D kagome lattice - flat energy bands, van Hove singularities (VHSs), and degeneracies - remain robust in both the $k_z$ = 0 and $π$ planes in momentum space. Given the handedness of our kagome lattice, degenerate momentum points possess quantized Chern numbers, ushering in the realization of Weyl fermions. Our 3D chiral kagome lattice surprisingly exhibits 1D behavior on its surface, where topological surface Fermi arc states connecting Weyl fermions are dispersive in one momentum direction and flat in the other direction. These 1D Fermi arcs open up unique possibilities for generating unconventional non-local transport phenomena at the interfaces of domains with different handedness, and the associated enhanced conductance as the separation of the leads on the surface is increased. Employing first-principles calculations, we investigate in-depth the electronic and phononic structures of representative materials within the ten space groups that can support topological chiral kagome lattices. Our study opens a new research direction that integrates the advantages of structural chirality with those of a kagome lattice and thus provides a new materials platform for exploring unique aspects of correlated topological physics in chiral lattices.

cond-mat.mtrl-sci↗

arXiv2023

LP-SLAM: Language-Perceptive RGB-D SLAM system based on Large Language Model

Simultaneous localization and mapping (SLAM) is a critical technology that enables autonomous robots to be aware of their surrounding environment. With the development of deep learning, SLAM systems can achieve a higher level of perception of the environment, including the semantic and text levels. However, current works are limited in their ability to achieve a natural-language level of perception of the world. To address this limitation, we propose LP-SLAM, the first language-perceptive SLAM system that leverages large language models (LLMs). LP-SLAM has two major features: (a) it can detect text in the scene and determine whether it represents a landmark to be stored during the tracking and mapping phase, and (b) it can understand natural language input from humans and provide guidance based on the generated map. We illustrated three usages of the LLM in the system including text cluster, landmark judgment, and natural language navigation. Our proposed system represents an advancement in the field of LLMs based SLAM and opens up new possibilities for autonomous robots to interact with their environment in a more natural and intuitive way.

cs.RO↗

arXiv2022

Lanthanum Oxyhalide Monolayers: An Exceptional Dielectric Companion to Two-Dimensional Semiconductors

Two-dimensional (2D) layered dielectrics offers a compelling route towards the design of next-generation ultimately compact nanoelectronics. Motivated by recent high-throughput computational prediction of LaO$X$ ($X$ = Br, Cl) as an exceptional 2D dielectrics that significantly outperforms HfO$_2$ even in the monolyaer limit, we investigate the interface properties between LaOX and the archetypal 2D semiconductors of monolayer transition metal dichacolgenides (TMDCs) $M$S$_2$ ($M$ = Mo, W) using first-principle density functional theory simulations. We show that LaO$X$ monolayers interacts weakly with $M$S$_2$ via van der Waals forces with negligible hybridization and interfacial charge transfer, thus conveniently preserving the electronic properties of 2D TMDCs upon contact formation. The conduction and valance band offsets of the interfaces exhibit a sizable value ranging from 0.7 to 1.4 eV, suggesting the capability of LaO$X$ as a gate dielectric materials. Based on Murphy-Good electron emission model, we demonstrate that LaOCl/MoS$_2$ is a versatile dielectric/semiconductor combinations that are compatible to both NMOS and PMOS applications with leakage current lower than $10^{-7}$ Acm$^{-2}$, while LaO$X$/WS$_2$ is generally compatible with PMOS application. The presence of an interfacial tunneling potential barrier at the van der Waals gap further provide an additional mechanism to suppress the leakage current. Our findings reveal the role LaO$X$ as an excellent dielectric companion to 2D TMDC and shall provide useful insights for leveraging the dielectric strength of LaO$X$ in the design of high-performance 2D nanodevices.

cond-mat.mtrl-sci↗

arXiv2022

A simple normalization technique using window statistics to improve the out-of-distribution generalization on medical images

Since data scarcity and data heterogeneity are prevailing for medical images, well-trained Convolutional Neural Networks (CNNs) using previous normalization methods may perform poorly when deployed to a new site. However, a reliable model for real-world clinical applications should be able to generalize well both on in-distribution (IND) and out-of-distribution (OOD) data (e.g., the new site data). In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods. Specifically, WIN perturbs the normalizing statistics with the local statistics computed on the window of features. This feature-level augmentation technique regularizes the models well and improves their OOD generalization significantly. Taking its advantage, we propose a novel self-distillation method called WIN-WIN for classification tasks. WIN-WIN is easily implemented with twice forward passes and a consistency constraint, which can be a simple extension for existing methods. Extensive experimental results on various tasks (6 tasks) and datasets (24 datasets) demonstrate the generality and effectiveness of our methods.

cs.CV↗

arXiv2021

Sub-angstrom Non-invasive Imaging of Atomic Arrangement in 2D Hybrid Perovskites

Non-invasive imaging of the atomic arrangement in two-dimensional (2D) Ruddlesden-Popper hybrid Perovskites (RPPs), as well as understanding the related effects is challenging, due to the insulating nature and softness of the organic layers which also obscure the underlying inorganic lattice. Here, we demonstrate a sub-angstrom resolution imaging of both soft organic layers and inorganic framework in a prototypical 2D lead-halide RPP crystal via combined tip-functionalized Scanning Tunneling Microscopy (STM) and non-contact Atomic Force Microscope (ncAFM) corroborated by theoretical simulations. STM measurements unveil the atomic reconstruction of the inorganic lead-halide lattice and overall twin-domain composition of the RPP crystal, while ncAFM measurements with a CO-tip enable non-perturbative visualization of the cooperative reordering of surface organic cations driven by their hydrogen bonding interactions with the inorganic lattice. Moreover, such a joint technique also allows for the atomic-scale imaging of the electrostatic potential variation across the twin-domain walls, revealing alternating quasi-one-dimensional (1D) electron and hole-channels at neighboring twin-boundaries, which may influence in-plane exciton transport and dissociation.

cond-mat.mtrl-sci↗