arXiv subjects

Jing Du

Publications and source records attributed to Jing Du.

At least 19 recordsLinked to original sources

arXiv2026

A 3DGS-Driven Dynamic Viewpoint and Vibrotactile Framework for Subsea Teleoperation Validated via fNIRS

Teleoperating remotely operated vehicles (ROVs) in flooded, cluttered infrastructure is fundamentally limited by narrow 2D egocentric views and subsea communication latency. We present a multimodal teleoperation architecture built on a ROS-Unity framework that decouples proactive spatial planning from reactive boundary avoidance. The system replaces static camera feeds with a Dynamic Adaptive Viewpoint System (DAVS), which uses continuous optimization and real-time 3D Gaussian Splatting (3DGS) to synthesize an occlusion-free exocentric viewpoint from onboard state estimation. To further reduce sensory workload, a torso-mounted vibrotactile suit maps local obstacle clearance to intuitive haptic proximity cues. The architecture was evaluated in a controlled human-subject study (N = 30) using a BlueROV2 navigating a complex simulated underwater facility. A 3 x 4 repeated-measures design compared three interaction modalities (Egocentric, Haptic, Exocentric) under four communication delays (0.0-1.0 s). Performance was quantified using behavioral measures and functional near-infrared spectroscopy (fNIRS) to assess task-evoked prefrontal activation. Results show that reactive haptic feedback improves path adherence under minimal delay, whereas the 3DGS-driven exocentric visualization provides superior resilience under severe latency (0.5-1.0 s), significantly outperforming the other modalities. fNIRS further revealed a cognitive disengagement effect: increasing latency during conventional egocentric teleoperation overloaded working memory and reduced prefrontal activation, whereas the proactive spatial context provided by DAVS sustained executive control. These findings demonstrate that spatially grounded, multimodal assistance can substantially improve operator performance and cognitive endurance during latency-degraded underwater teleoperation.

cs.RO↗

arXiv2026

PRISM: PRior-guided Imagination Sampling in world Models

A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends critically on how candidate actions are generated for model-based planning. Rather than solely asking how accurately a model can simulate the future, we ask: which candidate actions are worth evaluating in the first place? Existing planners typically search arbitrarily or use expert demonstrations only to initialize a sampling mean, discarding the expert's state-conditioned confidence. Properly guiding this search requires a robust action prior, yet current approaches often rely on independent visual encoders or large-scale VLMs to obtain one. We argue that this architectural bloat is unnecessary: the exact same data - and the learned representations of the world model itself - inherently encode the agent's action intuition. We introduce PRISM, a task-agnostic framework that extracts both from a single dataset while maintaining strict architectural simplicity. Building on a standard JEPA-style latent world model, PRISM attaches a lightweight MLP directly to its frozen encoder to predict a state-conditioned Gaussian prior. At plan time, PRISM fuses this prior into the planner's sampling distribution via a precision-weighted Product-of-Gaussians update. This parameter-free, closed-form integration steers the sampling process, making the prior confident where it is and ceding control where it is not. PRISM improves success rates by 35 percentage points over vanilla world-model-based MPC on Cube and 32 percentage points on PushT, without introducing significant inference overhead.

cs.RO↗

arXiv2026

Some Large Language Models Exhibit Consistent Risk Attitudes

As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test whether large language models (LLMs) exhibit systematic and consistent risk attitudes under uncertainty. We introduce a cross-domain framework that decouples contextual risk belief from categorical decision, and apply it to six representative LLMs and 100 human participants across spatial navigation, clinical triage, and financial allocation tasks. Using regression models, we extract each agents belief-to-decision mapping and quantify risk sensitivity and risk attitude bias. We find that most tested LLMs exhibit (i) robust intra-task consistency, indicating stable mappings from contextual belief to risk decision within a fixed task domain; (ii) cross-domain rank-order stability, preserving relative risk posture across tasks; and (iii) a convergence toward a restricted risk-attitude distribution relative to the broader human baseline. These results reveal risk attitude as a stable and previously uncharacterized dimension of LLM behavior, establishing a foundation for evaluating and aligning AI systems in open-ended decision-making and motivating further investigation into the origins of these intrinsic behavioral dispositions.

cs.AI↗

arXiv2026

Zeitgeist-Aware Multimodal (ZAM) Datasets of Pro-Eating Disorder Short-Form Videos: An Idea Worth Researching

Objective: Reliable identification of pro-eating disorder (pro-ED) content online suffers from two pervasive problems: 1) existing methods predominantly rely on text-based signals, failing to capture the inherently multimodal nature of multimedia content; and 2) these methods struggle to keep pace with the rapid evolution of references, memes, terminology, and contextual cues that underlie this content. Together, these limitations point to a gap: the absence of an expert-annotated reference standard capable of supporting real-time research and robust multimodal detection model training for pro-ED content on short-form video platforms. Method: To address this, we propose "zeitgeist-aware" multimodal (ZAM) datasets: continuously curated collections of annotated multimodal pro-ED content with inclusion criteria that evolve alongside the memetic zeitgeist: the variable essence of what is considered pro-ED as new media and references come into the cultural zeitgeist and are absorbed and interpreted in online spaces. Results: We present a rationale for such datasets, define their core characteristics, outline approaches for their curation, and describe our progress toward that end. Discussion: This dataset and pipeline architecture may benefit researchers across several fields who are interested in how pro-ED sentiment is encoded and transmitted through short-form video content across time, including for the purpose of responsive moderation efforts.

cs.HC↗

arXiv2026

User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation

Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. The success of MMR hinges on aligning these content modalities with user preferences derived from interaction data, yet dominant practices based on disentangling modality-invariant preference-driving signals from modality-specific preference-irrelevant noises are flawed. First, they assume a one-size-fits-all relevance of item content to user preferences for all users, which contradicts the user-conditional fact of preferences. Second, they optimize pairwise contrastive losses separately toward cross-modal alignment, systematically ignoring higher-order dependencies inherent when multiple content modalities jointly influence user choices. In this paper, we introduce GTC, a conditional Generative Total Correlation learning framework. We employ an interaction-guided diffusion model to perform user-aware content feature filtering, preserving only personalized features relevant to each individual user. Furthermore, to capture complete cross-modal dependencies, we optimize a tractable lower bound of the total correlation of item representations across all modalities. Experiments on standard MMR benchmarks show GTC consistently outperforms state-of-the-art, with gains of up to 28.30% in NDCG@5. Ablation studies validate both conditional preference-driven feature filtering and total correlation optimization, confirming the ability of GTC to model user-conditional relationships in MMR tasks. The code is available at: https://github.com/jingdu-cs/GTC.

cs.IR↗

arXiv2026

From Ecological Connectivity to Outbreak Risk: A Heterogeneous Graph Network for Epidemiological Reasoning under Sparse Spatiotemporal Data

Estimating population-level prevalence and transmission dynamics of wildlife pathogens can be challenging, partly because surveillance data is sparse, detection-driven, and unevenly sequenced. Using highly pathogenic avian influenza A/H5 clade 2.3.4.4b as a case study, we develop zooNet, a graph-based epidemiological framework that integrates mechanistic transmission simulation, metadata-driven genetic distance imputation, and spatiotemporal graph learning to reconstruct outbreak dynamics from incomplete observations. Applied to wild bird surveillance data from the United States during 2022, zooNet recovered coherent spatiotemporal structure despite intermittent detections, revealing sustained regional circulation across multiple migratory flyways. The framework consistently identified counties with ongoing transmission weeks to months before confirmed detections, including persistent activity in northeastern regions prior to documented re-emergence. These signals were detectable even in areas with sparse sequencing and irregular reporting. These results show that explicitly representing ecological processes and inferred genomic connectivity within a unified graph structure allows persistence and spatial risk structure to be inferred from detection-driven wildlife surveillance data.

q-bio.PE↗

arXiv2025

Broadband-operational orbital angular momentum generation in nonlocal metasurfaces with maximum efficiency approaching 80%

Nonlocal metasurfaces provide a compact route to generating momentum-space optical vortices but are limited by steep dispersion typically associated with high-quality (Q) factor resonances, resulting in narrowband and inefficient operation. Here, we introduce a reflection-type nonlocal metasurface that hybrid-couples a bound state in the continuum (BIC) with two degeneracy points (DPs). This engineered interaction enables on-demand control of dispersion, radiative Q-factors, and polarization states of guided resonances, yielding quasi-flat dispersion and enhanced scattering strength. Full-wave simulations predict near-unity on-resonance conversion and overall efficiencies above 90%, representing a three- to fourfold efficiency improvement and more than fifteenfold bandwidth expansion over conventional designs. Experiments confirm broadband operation from 1480 to 1600 nm, achieving peak efficiency approaching 80% and orbital angular momentum (OAM) purity up to 91.7% under flat-top illumination, while suppressing edge effects and mitigating positional sensitivity and numerical-aperture (NA) dependence. As a proof of concept, we demonstrate direct conversion of zero-order Bessel beams into OAM Bessel (perfect vortex) beams with enhanced wavelength tunability, underscoring the versatility of this approach over diverse illumination conditions. This record-high performance establishes a practical and scalable pathway toward broadband, high-efficiency vortex generation, opening new opportunities across high-dimensional optical communications, advanced imaging, and quantum photonics.

physics.optics↗

arXiv2025

Anomalous Spontaneous Emission Enhancement by Non-Hermitian Momentum-Space Bound States in the Continuum

Conventional Purcell theory emphasizes high quality factors (Q) for spontaneous emission (SE) enhancement in cavities, but overlooks collective Bloch mode effects in periodic nanostructures like photonic crystal slabs. We introduce a unified temporal coupled-mode framework to compute Purcell and photoluminescence factors through momentum-space integration, revealing anomalous SE enhancement by non-Hermitian momentum-space bound states in the continuum (BICs). In silicon gratings with comparable effective mode volumes, this yields substantial SE enhancement in low-Q regimes--defying the traditional high-Q paradigm and inversely correlated with system Q--while emission rates are stably twice the photoluminescence, eliminating critical coupling requirements. Unique spectral profiles, contradicting Lorentzian/Fano assumptions, arise from collective mode interactions. Full-wave simulations confirm these challenges to conventional wisdom, with non-Hermitian BICs outperforming high-Q designs across broad numerical apertures. This establishes a novel paradigm leveraging non-Hermiticity and topological protection for robust, bright emitters, redefining nanophotonic applications in lasers and light-emitting diodes

physics.optics↗

arXiv2025

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing

Accurate 3D instance segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D instance segmentation based on 2D-to-3D lifting approaches struggle to produce precise instance-level segmentation, due to accumulated errors introduced during the lifting process from ambiguous semantic guidance and insufficient depth constraints. To tackle these challenges, we propose splitting and growing reliable semantic mask for high-fidelity 3D instance segmentation (SGS-3D), a novel "split-then-grow" framework that first purifies and splits ambiguous lifted masks using geometric primitives, and then grows them into complete instances within the scene. Unlike existing approaches that directly rely on raw lifted masks and sacrifice segmentation accuracy, SGS-3D serves as a training-free refinement method that jointly fuses semantic and geometric information, enabling effective cooperation between the two levels of representation. Specifically, for semantic guidance, we introduce a mask filtering strategy that leverages the co-occurrence of 3D geometry primitives to identify and remove ambiguous masks, thereby ensuring more reliable semantic consistency with the 3D object instances. For the geometric refinement, we construct fine-grained object instances by exploiting both spatial continuity and high-level features, particularly in the case of semantic ambiguity between distinct objects. Experimental results on ScanNet200, ScanNet++, and KITTI-360 demonstrate that SGS-3D substantially improves segmentation accuracy and robustness against inaccurate masks from pre-trained models, yielding high-fidelity object instances while maintaining strong generalization across diverse indoor and outdoor environments. Code is available at https://github.com/wangchaolei7/SGS-3D.

cs.CV↗

arXiv2025

Explicit and Implicit Data Augmentation for Social Event Detection

Social event detection involves identifying and categorizing important events from social media, which relies on labeled data, but annotation is costly and labor-intensive. To address this problem, we propose Augmentation framework for Social Event Detection (SED-Aug), a plug-and-play dual augmentation framework, which combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. The explicit augmentation utilizes large language models to enhance textual information through five diverse generation strategies. For implicit augmentation, we design five novel perturbation techniques that operate in the feature space on structural fused embeddings. These perturbations are crafted to keep the semantic and relational properties of the embeddings and make them more diverse. Specifically, SED-Aug outperforms the best baseline model by approximately 17.67% on the Twitter2012 dataset and by about 15.57% on the Twitter2018 dataset in terms of the average F1 score. The code is available at GitHub: https://github.com/congboma/SED-Aug.

cs.CL↗

arXiv2025

A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models

Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence patterns, without requiring sequence alignment or known transmission chains. Applied to highly pathogenic avian influenza A/H5 cases in wild birds in the United States, this approach supports scalable, uncertainty-aware augmentation of genomic datasets and enhances the integration of evolutionary information into spatiotemporal modeling workflows.

q-bio.GN↗

arXiv2025

Genomic-Informed Heterogeneous Graph Learning for Spatiotemporal Avian Influenza Outbreak Forecasting

Accurate forecasting of Avian Influenza Virus (AIV) outbreaks within wild bird populations necessitates models that account for complex, multi-scale transmission patterns driven by diverse factors. While conventional spatiotemporal epidemic models are robust for human-centric diseases, they rely on spatial homophily and diffusive transmission between geographic regions. This simplification is incomplete for AIV as it neglects valuable genomic information critical for capturing dynamics like high-frequency reassortment and lineage turnover at the case level (e.g., genetic descent across regions), which are essential for understanding AIV spread. To address these limitations, we systematically formulate the AIV forecasting problem and propose a Bi-Layer genomic-aware heterogeneous graph fusion pipeline. This pipeline integrates genetic, spatial, and ecological data to achieve highly accurate outbreak forecasting. It 1) defines a multi-layered graph structure incorporating information from diverse sources and multiple layers (case and location), 2) applies cross-relation smoothing to smooth information flow across edge types, 3) performs graph fusion that preserves critical structural patterns backed by theoretical spectral guarantees, and 4) forecasts future outbreaks using an autoregressive graph sequence model to capture transmission dynamics. To support research, we release the Avian-US dataset, which provides comprehensive genetic, spatial, and ecological data on US avian influenza outbreaks. BLUE demonstrates superior performance over existing baselines, highlighting the efficacy of integrating multi-layer information for infectious disease forecasting. The code is available at: https://github.com/cruiseresearchgroup/BLUE.

cs.SI↗

arXiv2025

Generative AI for Autonomous Driving: Frontiers and Opportunities

Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering's grandest challenges: achieving reliable, fully autonomous driving, particularly the pursuit of Level 5 autonomy. This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack. We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models (LLMs). We then map their frontier applications in image, LiDAR, trajectory, occupancy, video generation as well as LLM-guided reasoning and decision making. We categorize practical applications, such as synthetic data workflows, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation and safety checks, budget-limited implementation, regulatory compliance, ethical concerns, and environmental effects, while proposing research plans across theoretical assurances, trust metrics, transport integration, and socio-technical influence. By unifying these threads, the survey provides a forward-looking reference for researchers, engineers, and policymakers navigating the convergence of generative AI and advanced autonomous mobility. An actively maintained repository of cited works is available at https://github.com/taco-group/GenAI4AD.

cs.CV↗

arXiv2025

Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning

Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior performance over traffic signals in various scenarios. However, prior research has largely focused on small-scale networks or isolated intersections, leaving large-scale mixed traffic control largely unexplored. This study presents the first attempt to use decentralized multi-agent reinforcement learning for large-scale mixed traffic control in which some intersections are managed by traffic signals and others by robot vehicles. Evaluating a real-world network in Colorado Springs, CO, USA with 14 intersections, we measure traffic efficiency via average waiting time of vehicles at intersections and the number of vehicles reaching their destinations within a time window (i.e., throughput). At 80% RV penetration rate, our method reduces waiting time from 6.17s to 5.09s and increases throughput from 454 vehicles per 500 seconds to 493 vehicles per 500 seconds, outperforming the baseline of fully signalized intersections. These findings suggest that integrating reinforcement learning-based control large-scale traffic can improve overall efficiency and may inform future urban planning strategies.

cs.LG↗

arXiv2025

AI Generations: From AI 1.0 to AI 4.0

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and now a speculative AI 4.0 (Conscious AI). Each of these AI generations is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 ushered in breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly seventy years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data, have spurred each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the profound ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI transformative potential benefits society as a whole.

cs.AI↗

arXiv2025

Temporal Binding Foundation Model for Material Property Recognition via Tactile Sequence Perception

Robots engaged in complex manipulation tasks require robust material property recognition to ensure adaptability and precision. Traditionally, visual data has been the primary source for object perception; however, it often proves insufficient in scenarios where visibility is obstructed or detailed observation is needed. This gap highlights the necessity of tactile sensing as a complementary or primary input for material recognition. Tactile data becomes particularly essential in contact-rich, small-scale manipulations where subtle deformations and surface interactions cannot be accurately captured by vision alone. This letter presents a novel approach leveraging a temporal binding foundation model for tactile sequence understanding to enhance material property recognition. By processing tactile sensor data with a temporal focus, the proposed system captures the sequential nature of tactile interactions, similar to human fingertip perception. Additionally, this letter demonstrates that, through tailored and specific design, the foundation model can more effectively capture temporal information embedded in tactile sequences, advancing material property understanding. Experimental results validate the model's capability to capture these temporal patterns, confirming its utility for material property recognition in visually restricted scenarios. This work underscores the necessity of embedding advanced tactile data processing frameworks within robotic systems to achieve truly embodied and responsive manipulation capabilities.

cs.RO↗

arXiv2025

Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks

The drive for efficiency and safety in construction has boosted the role of robotics and automation. However, complex tasks like welding and pipe insertion pose challenges due to their need for precise adaptive force control, which complicates robotic training. This paper proposes a two-phase system to improve robot learning, integrating human-derived force feedback. The first phase captures real-time data from operators using a robot arm linked with a virtual simulator via ROS-Sharp. In the second phase, this feedback is converted into robotic motion instructions, using a generative approach to incorporate force feedback into the learning process. This method's effectiveness is demonstrated through improved task completion times and success rates. The framework simulates realistic force-based interactions, enhancing the training data's quality for precise robotic manipulation in construction tasks.

cs.RO↗

arXiv2024

Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference

Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective knowledge transfer. However, achieving perfect disentanglement is challenging in practice, because user behaviors in CDR are highly complex, and the true underlying user preferences cannot be fully captured through observed user-item interactions alone. Given this impracticability, we instead propose to model {\it joint identifiability} that establishes unique correspondence of user representations across domains, ensuring consistent preference modeling even when user behaviors exhibit shifts in different domains. To achieve this, we introduce a hierarchical user preference modeling framework that organizes user representations by the neural network encoder's depth, allowing separate treatment of shallow and deeper subspaces. In the shallow subspace, our framework models the interest centroids for each user within each domain, probabilistically determining the users' interest belongings and selectively aligning these centroids across domains to ensure fine-grained consistency in domain-irrelevant features. For deeper subspace representations, we enforce joint identifiability by decomposing it into a shared cross-domain stable component and domain-variant components, linked by a bijective transformation for unique correspondence. Empirical studies on real-world CDR tasks with varying domain correlations demonstrate that our method consistently surpasses state-of-the-art, even with weakly correlated tasks, highlighting the importance of joint identifiability in achieving robust CDR.

cs.IR↗