arXiv subjects

Xinyu Jiang

Publications and source records attributed to Xinyu Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Understanding of Task-specific and Subject-specific Components in Surface EMG

Surface electromyogram (sEMG) signals are widely used in human-machine interfaces for gesture recognition and user identification, but existing models often struggle to generalize across individuals due to subject-specific neuromuscular characteristics. This study introduces a disentanglement model that separates task-specific and subject-specific components from sEMG signals, thereby improving the generalization and interpretability of gesture recognition and user identification systems. Experimental results demonstrate that the disentangled components significantly improve the accuracy of both gesture classification and user identification across subjects and days, outperforming conventional methods under the same experimental conditions. Further analysis reveals that the task-specific components capture consistent activation patterns associated with the same gestures across individuals. In contrast, the subject-specific components reflect unique neuromuscular characteristics that can be used for user identification. Notably, the subject-specific components show lower similarity across days than the task-specific components, contributing to a greater decrease in user identification accuracy than in gesture recognition accuracy. These findings suggest that the disentanglement approach not only improves classification performance but also provides deeper insights into the physiological mechanisms underlying sEMG signals. The model's ability to isolate and interpret different neuromuscular components holds promise for enhancing the robustness of sEMG-based applications in real-world settings, including rehabilitation and user authentication. Our code is available at https://github.com/Open-EXG/HandDisentanglement.

cs.HC↗

arXiv2026

Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network. We study whether graph-local evidence and credibility-weighted communication can generate persistent distortions in perceived academic standing, even when agents repeatedly receive objective performance signals. We introduce a data-constrained multi-agent framework in which LLM agents operate through individualized subjective graphs that determine peer visibility, evidence access, and interaction opportunities. Agents exchange uncertainty-annotated assessments, evaluate message credibility, and maintain explicit Gaussian belief states updated through Bayesian fusion. We evaluate the framework on 12 middle-school classrooms comprising 482 students, using questionnaire-derived social information and six consecutive examinations. On the Social-Observed subset (n=419), collective ranking error increases from 0.066 \pm 0.008 to 0.124 \pm 0.009 across six epochs despite repeated exam-based anchoring. Ablations associate individualized visibility and LLM-based trust gating with more stable long-horizon behavior, while constrained retrieval primarily safeguards against global-information leakage. Compared with evaluated DeGroot configurations, the proposed framework achieves lower final ranking error; those DeGroot configurations exhibit near-zero terminal opinion diversity. These findings establish subjective-graph LLM agents as a mechanism-oriented framework for data-constrained simulated social perception. Code is available at https://anonymous.4open.science/r/Rashomonomon-0126.

cs.AI↗

arXiv2025

A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease

Background: Impairment of visual spatial cognitive function is the most common early clinical manifestation of Alzheimer's Disease (AD). When the Montreal Cognitive Assessment (MoCA) uses the "0/1" binary method ("pass/fail") to evaluate the visual spatial cognitive ability represented by the Cube Copying Test(CCT), the elder with less formal education generally score 0 point, resulting in serious bias in the evaluation results. Therefore, this study proposes a fine evaluation method for CCT based on dynamic handwriting feature extraction of DH-SCSM-BLA. method : The Cogni-CareV3.0 software independently developed by our team was used to collect dynamic handwriting data of CCT. Then, the spatial and motion features of segmented dynamic handwriting were extracted, and feature matrix with unequal dimensions were normalized. Finally, a bidirectional long short-term memory network model combined with attention mechanism (BiLSTM-Attention) was adopted for classification. Result: The experimental results showed that: The proposed method has significant superiority compared to similar studies, with a classification accuracy of 86.69%. The distribution of cube drawing ability scores has significant regularity for three aspects such as MCI patients and healthy control group, age, and levels of education. It was also found that score for each cognitive task including cube drawing ability score is negatively correlated with age. Score for each cognitive task including cube drawing ability score, but positively correlated with levels of education significantly. Conclusion: This study provides a relatively objective and comprehensive evaluation method for early screening and personalized intervention of visual spatial cognitive impairment.

cs.LG↗

arXiv2025

Prompt Guidance and Human Proximal Perception for HOT Prediction with Regional Joint Loss

The task of Human-Object conTact (HOT) detection involves identifying the specific areas of the human body that are touching objects. Nevertheless, current models are restricted to just one type of image, often leading to too much segmentation in areas with little interaction, and struggling to maintain category consistency within specific regions. To tackle this issue, a HOT framework, termed \textbf{P3HOT}, is proposed, which blends \textbf{P}rompt guidance and human \textbf{P}roximal \textbf{P}erception. To begin with, we utilize a semantic-driven prompt mechanism to direct the network's attention towards the relevant regions based on the correlation between image and text. Then a human proximal perception mechanism is employed to dynamically perceive key depth range around the human, using learnable parameters to effectively eliminate regions where interactions are not expected. Calculating depth resolves the uncertainty of the overlap between humans and objects in a 2D perspective, providing a quasi-3D viewpoint. Moreover, a Regional Joint Loss (RJLoss) has been created as a new loss to inhibit abnormal categories in the same area. A new evaluation metric called ``AD-Acc.'' is introduced to address the shortcomings of existing methods in addressing negative samples. Comprehensive experimental results demonstrate that our approach achieves state-of-the-art performance in four metrics across two benchmark datasets. Specifically, our model achieves an improvement of \textbf{0.7}$\uparrow$, \textbf{2.0}$\uparrow$, \textbf{1.6}$\uparrow$, and \textbf{11.0}$\uparrow$ in SC-Acc., mIoU, wIoU, and AD-Acc. metrics, respectively, on the HOT-Annotated dataset. The sources code are available at https://github.com/YuxiaoWang-AI/P3HOT.

cs.CV↗

arXiv2025

TeleOpBench: A Simulator-Centric Benchmark for Dual-Arm Dexterous Teleoperation

Teleoperation is a cornerstone of embodied-robot learning, and bimanual dexterous teleoperation in particular provides rich demonstrations that are difficult to obtain with fully autonomous systems. While recent studies have proposed diverse hardware pipelines-ranging from inertial motion-capture gloves to exoskeletons and vision-based interfaces-there is still no unified benchmark that enables fair, reproducible comparison of these systems. In this paper, we introduce TeleOpBench, a simulator-centric benchmark tailored to bimanual dexterous teleoperation. TeleOpBench contains 30 high-fidelity task environments that span pick-and-place, tool use, and collaborative manipulation, covering a broad spectrum of kinematic and force-interaction difficulty. Within this benchmark we implement four representative teleoperation modalities-(i) MoCap, (ii) VR device, (iii) arm-hand exoskeletons, and (iv) monocular vision tracking-and evaluate them with a common protocol and metric suite. To validate that performance in simulation is predictive of real-world behavior, we conduct mirrored experiments on a physical dual-arm platform equipped with two 6-DoF dexterous hands. Across 10 held-out tasks we observe a strong correlation between simulator and hardware performance, confirming the external validity of TeleOpBench. TeleOpBench establishes a common yardstick for teleoperation research and provides an extensible platform for future algorithmic and hardware innovation. Codes is now available at https://github.com/cyjdlhy/TeleOpBench .

cs.RO↗

arXiv2025

RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations

Efficient management of electric ride-hailing fleets, particularly pre-allocation and pricing during peak periods to balance spatio-temporal supply and demand, is crucial for urban traffic efficiency. However, practical challenges include unpredictable demand and translating diverse, qualitative managerial objectives from non-expert operators into tractable optimization models. This paper introduces RideAgent, an LLM-powered agent framework that automates and enhances electric ride-hailing fleet management. First, an LLM interprets natural language queries from fleet managers to formulate corresponding mathematical objective functions. These user-defined objectives are then optimized within a Mixed-Integer Programming (MIP) framework, subject to the constraint of maintaining high operational profit. The profit itself is a primary objective, estimated by an embedded Random Forest (RF) model leveraging exogenous features. To accelerate the solution of this MIP, a prompt-guided LLM analyzes a small sample of historical optimal decision data to guide a variable fixing strategy. Experiments on real-world data show that the LLM-generated objectives achieve an 86% text similarity to standard formulations in a zero-shot setting. Following this, the LLM-guided variable fixing strategy reduces computation time by 53.15% compared to solving the full MIP with only a 2.42% average optimality gap. Moreover, this variable fixing strategy outperforms five cutting plane methods by 42.3% time reduction with minimal compromise to solution quality. RideAgent offers a robust and adaptive automated framework for objective modeling and accelerated optimization. This framework empowers non-expert fleet managers to personalize operations and improve urban transportation system performance.

math.OC↗

arXiv2024

Precision-Enhanced Human-Object Contact Detection via Depth-Aware Perspective Interaction and Object Texture Restoration

Human-object contact (HOT) is designed to accurately identify the areas where humans and objects come into contact. Current methods frequently fail to account for scenarios where objects are frequently blocking the view, resulting in inaccurate identification of contact areas. To tackle this problem, we suggest using a perspective interaction HOT detector called PIHOT, which utilizes a depth map generation model to offer depth information of humans and objects related to the camera, thereby preventing false interaction detection. Furthermore, we use mask dilatation and object restoration techniques to restore the texture details in covered areas, improve the boundaries between objects, and enhance the perception of humans interacting with objects. Moreover, a spatial awareness perception is intended to concentrate on the characteristic features close to the points of contact. The experimental results show that the PIHOT algorithm achieves state-of-the-art performance on three benchmark datasets for HOT detection tasks. Compared to the most recent DHOT, our method enjoys an average improvement of 13%, 27.5%, 16%, and 18.5% on SC-Acc., C-Acc., mIoU, and wIoU metrics, respectively.

cs.CV↗

arXiv2024

DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters

Recent advances in generative models have enabled high-quality 3D character reconstruction from multi-modal. However, animating these generated characters remains a challenging task, especially for complex elements like garments and hair, due to the lack of large-scale datasets and effective rigging methods. To address this gap, we curate AnimeRig, a large-scale dataset with detailed skeleton and skinning annotations. Building upon this, we propose DRiVE, a novel framework for generating and rigging 3D human characters with intricate structures. Unlike existing methods, DRiVE utilizes a 3D Gaussian representation, facilitating efficient animation and high-quality rendering. We further introduce GSDiff, a 3D Gaussian-based diffusion module that predicts joint positions as spatial distributions, overcoming the limitations of regression-based approaches. Extensive experiments demonstrate that DRiVE achieves precise rigging results, enabling realistic dynamics for clothing and hair, and surpassing previous methods in both quality and versatility. The code and dataset will be made public for academic use upon acceptance.

cs.CV↗

arXiv2024

Combining Reinforcement Learning with Graph Convolutional Neural Networks for Efficient Design of TiAl/TiAlN Atomic-Scale Interfaces

Ti/TiN coatings are utilized in a wide variety of engineering applications due to their superior properties such as high hardness and toughness. Doping Al into Ti/TiN can also enhance properties and lead to even higher performance. Therefore, studying the atomic-level behavior of the TiAl/TiAlN interface is important. However, due to the large number of possible combinations for the 50 mol% Al-doped Ti/TiN system, it is time-consuming to use the DFT-based Monte Carlo method to find the optimal TiAl/TiAlN system with a high work of adhesion. In this study, we use a graph convolutional neural network as an interatomic potential, combined with reinforcement learning, to improve the efficiency of finding optimal structures with a high work of adhesion. By inspecting the features of structures in neural networks, we found that the optimal structures follow a certain pattern of doping Al near the interface. The electronic structure and bonding analysis indicate that the optimal TiAl/TiAlN structures have higher bonding strength. We expect our approach to significantly accelerate the design of advanced ceramic coatings, which can lead to more durable and efficient materials for engineering applications.

cond-mat.mtrl-sci↗

arXiv2024

City-LEO: Toward Transparent City Management Using LLM with End-to-End Optimization

Existing operations research (OR) models and tools play indispensable roles in smart-city operations, yet their practical implementation is limited by the complexity of modeling and deficiencies in optimization proficiency. To generate more relevant and accurate solutions to users' requirements, we propose a large language model (LLM)-based agent ("City-LEO") that enhances the efficiency and transparency of city management through conversational interactions. Specifically, to accommodate diverse users' requirements and enhance computational tractability, City-LEO leverages LLM's logical reasoning capabilities on prior knowledge to scope down large-scale optimization problems efficiently. In the human-like decision process, City-LEO also incorporates End-to-end (E2E) model to synergize the prediction and optimization. The E2E framework be conducive to coping with environmental uncertainties and involving more query-relevant features, and then facilitates transparent and interpretable decision-making process. In case study, we employ City-LEO in the operations management of e-bike sharing (EBS) system. The numerical results demonstrate that City-LEO has superior performance when benchmarks against the full-scale optimization problem. With less computational time, City-LEO generates more satisfactory and relevant solutions to the users' requirements, and achieves lower global suboptimality without significantly compromising accuracy. In a broader sense, our proposed agent offers promise to develop LLM-embedded OR tools for smart-city operations management.

math.OC↗

arXiv2024

Prolate spheroids settling in a quiescent fluid: clustering, microstructures and collisions

In this study, we investigate the sedimentation of prolate spheroids in a quiescent fluid by means of the particle-resolved direct numerical simulation. With the increase of the particle volume fraction $\phi$ from $0.1\%$ to $10\%$, we observe a non-monotonic variation of the mean settling velocity of particles, $\langle V_s \rangle$. By virtue of the Voronoi analysis, we find that the degree of particle clustering is highest when $\langle V_s \rangle$ reaches the local maximum at $\phi=1\%$. Under the swarm effect, clustered particles are found to preferentially sample downward fluid flows in the wake regions, leading to the enhancement of the settling speed. As for lower or higher volume fraction, the tendency of particle clustering and the preferential sampling of downward flows are attenuated. Hindrance effect becomes predominant when the volume fraction exceeds 5\% and reduces $\langle V_s \rangle$ to less than the isolated settling velocity. Particle orientation plays a minor role in the mean settling velocity, although individual prolate particles still tend to settle faster in suspensions when they deviate more from the broad-side-on alignment. Moreover, we also demonstrate that particles are prone to form column-like microstructures in dilute suspensions under the effect of wake-induced hydrodynamic attractions. The radial distribution function is higher at a lower volume fraction. As a result, the collision rate scaled by the particle number density decreases with the increasing volume fraction. By contrast, as another contribution to the particle collision rate, the relative radial velocity for nearby particles shows a minor degree of variation due to the lubrication effect.

physics.flu-dyn↗

arXiv2024

Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication

In this paper, we introduce an innovative task focused on human communication, aiming to generate 3D holistic human motions for both speakers and listeners. Central to our approach is the incorporation of factorization to decouple audio features and the combination of textual semantic information, thereby facilitating the creation of more realistic and coordinated movements. We separately train VQ-VAEs with respect to the holistic motions of both speaker and listener. We consider the real-time mutual influence between the speaker and the listener and propose a novel chain-like transformer-based auto-regressive model specifically designed to characterize real-world communication scenarios effectively which can generate the motions of both the speaker and the listener simultaneously. These designs ensure that the results we generate are both coordinated and diverse. Our approach demonstrates state-of-the-art performance on two benchmark datasets. Furthermore, we introduce the HoCo holistic communication dataset, which is a valuable resource for future research. Our HoCo dataset and code will be released for research purposes upon acceptance.

cs.CV↗

arXiv2024

Federated Joint Learning of Robot Networks in Stroke Rehabilitation

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation robot is challenging due to the data scarcity issue. This challenge arises from privacy concerns (e.g., the risk of leaking private disease and identity information of patients) during clinical data access and usage. Data from various patients and hospitals cannot be shared for adequate robot training, further compromising rehabilitation safety and limiting implementation scopes. To address this challenge, this work developed a novel federated joint learning (FJL) method to jointly train robots across hospitals. FJL also adopted a long short-term memory network (LSTM)-Transformer learning mechanism to effectively explore the complex tempo-spatial relations among patient mobility conditions and robotic rehabilitation motions. To validate FJL's effectiveness in training a robot network, a clinic-simulation combined experiment was designed. Real rehabilitation exercise data from 200 patients with stroke diseases (upper limb hemiplegia, Parkinson's syndrome, and back pain syndrome) were adopted. Inversely driven by clinical data, 300,000 robotic rehabilitation guidances were simulated. FJL proved to be effective in joint rehabilitation learning, performing 20% - 30% better than baseline methods.

cs.RO↗

arXiv2024

FreeA: Human-object Interaction Detection using Free Annotation Labels

Recent human-object interaction (HOI) detection methods depend on extensively annotated image datasets, which require a significant amount of manpower. In this paper, we propose a novel self-adaptive, language-driven HOI detection method, termed FreeA. This method leverages the adaptability of the text-image model to generate latent HOI labels without requiring manual annotation. Specifically, FreeA aligns image features of human-object pairs with HOI text templates and employs a knowledge-based masking technique to decrease improbable interactions. Furthermore, FreeA implements a proposed method for matching interaction correlations to increase the probability of actions associated with a particular action, thereby improving the generated HOI labels. Experiments on two benchmark datasets showcase that FreeA achieves state-of-the-art performance among weakly supervised HOI competitors. Our proposal gets +\textbf{13.29} (\textbf{159\%$\uparrow$}) mAP and +\textbf{17.30} (\textbf{98\%$\uparrow$}) mAP than the newest ``Weakly'' supervised model, and +\textbf{7.19} (\textbf{28\%$\uparrow$}) mAP and +\textbf{14.69} (\textbf{34\%$\uparrow$}) mAP than the latest ``Weakly+'' supervised model, respectively, on HICO-DET and V-COCO datasets, more accurate in localizing and classifying the interactive actions. The source code will be made public.

cs.CV↗

arXiv2023

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example

Machine learning (ML) is widely used to explore crystal materials and predict their properties. However, the training is time-consuming for deep-learning models, and the regression process is a black box that is hard to interpret. Also, the preprocess to transfer a crystal structure into the input of ML, called descriptor, needs to be designed carefully. To efficiently predict important properties of materials, we propose an approach based on ensemble learning consisting of regression trees to predict formation energy and elastic constants based on small-size datasets of carbon allotropes as an example. Without using any descriptor, the inputs are the properties calculated by molecular dynamics with 9 different classical interatomic potentials. Overall, the results from ensemble learning are more accurate than those from classical interatomic potentials, and ensemble learning can capture the relatively accurate properties from the 9 classical potentials as criteria for predicting the final properties.

cond-mat.mtrl-sci↗

arXiv2023

Understanding the Role of Triplet-triplet Annihilation in Non-fullerene Acceptor Organic Solar Cells

Non-fullerene acceptors (NFAs) have enabled power conversion efficiencies exceeding 19% in organic solar cells (OSCs). However, the open-circuit voltage of OSCs remains low relative to their optical gap due to excessive non-radiative recombination, and this now limits performance. Here, we consider an important aspect of OSC design, namely management of the triplet exciton population formed after non-geminate charge recombination. By comparing the blends PM6:Y11 and PM6:Y6, we show that the greater crystallinity of the NFA domains in PM6:Y11 leads to a higher rate of triplet-triplet annihilation (TTA). We attribute this to the four times larger ground state dipole moment of Y11 versus Y6, which improves the long range NFA out-of-plane ordering. Since TTA converts a fraction of the non-emissive triplet states into bright singlet states, it has the potential to reduce non-radiative voltage losses. Through a kinetic analysis of the recombination processes under 1-Sun illumination, we provide a framework for determining the conditions under which TTA may improve OSC performance. If these could be satisfied, TTA has the potential to reduce non-radiative voltage losses by up to several tens of mV and could thus improve OSC performance.

physics.app-ph↗

arXiv2023

Spotlights: Probing Shapes from Spherical Viewpoints

Recent years have witnessed the surge of learned representations that directly build upon point clouds. Though becoming increasingly expressive, most existing representations still struggle to generate ordered point sets. Inspired by spherical multi-view scanners, we propose a novel sampling model called Spotlights to represent a 3D shape as a compact 1D array of depth values. It simulates the configuration of cameras evenly distributed on a sphere, where each virtual camera casts light rays from its principal point through sample points on a small concentric spherical cap to probe for the possible intersections with the object surrounded by the sphere. The structured point cloud is hence given implicitly as a function of depths. We provide a detailed geometric analysis of this new sampling scheme and prove its effectiveness in the context of the point cloud completion task. Experimental results on both synthetic and real data demonstrate that our method achieves competitive accuracy and consistency while having a significantly reduced computational cost. Furthermore, we show superior performance on the downstream point cloud registration task over state-of-the-art completion methods.

cs.CV↗

arXiv2022

An Ion Exchange Mechanism Inspired Story Ending Generator for Different Characters

Story ending generation aims at generating reasonable endings for a given story context. Most existing studies in this area focus on generating coherent or diversified story endings, while they ignore that different characters may lead to different endings for a given story. In this paper, we propose a Character-oriented Story Ending Generator (CoSEG) to customize an ending for each character in a story. Specifically, we first propose a character modeling module to learn the personalities of characters from their descriptive experiences extracted from the story context. Then, inspired by the ion exchange mechanism in chemical reactions, we design a novel vector breaking/forming module to learn the intrinsic interactions between each character and the corresponding context through an analogical information exchange procedure. Finally, we leverage the attention mechanism to learn effective character-specific interactions and feed each interaction into a decoder to generate character-orient endings. Extensive experimental results and case studies demonstrate that CoSEG achieves significant improvements in the quality of generated endings compared with state-of-the-art methods, and it effectively customizes the endings for different characters.

cs.CL↗