arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 469 records · Page 26Linked to original sources

arXiv2025

DisorientLiDAR: Physical Attacks on LiDAR-based Localization

Deep learning models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Even this poses a serious security challenge for the localization of self-driving cars, there has been very little exploration of attack on it, as most of adversarial attacks have been applied to 3D perception. In this work, we propose a novel adversarial attack framework called DisorientLiDAR targeting LiDAR-based localization. By reverse-engineering localization models (e.g., feature extraction networks), adversaries can identify critical keypoints and strategically remove them, thereby disrupting LiDAR-based localization. Our proposal is first evaluated on three state-of-the-art point-cloud registration models (HRegNet, D3Feat, and GeoTransformer) using the KITTI dataset. Experimental results demonstrate that removing regions containing Top-K keypoints significantly degrades their registration accuracy. We further validate the attack's impact on the Autoware autonomous driving platform, where hiding merely a few critical regions induces noticeable localization drift. Finally, we extended our attacks to the physical world by hiding critical regions with near-infrared absorptive materials, thereby successfully replicate the attack effects observed in KITTI data. This step has been closer toward the realistic physical-world attack that demonstrate the veracity and generality of our proposal.

cs.CV↗

arXiv2025

UrQMD Simulations of Higher-order Cumulants in Au+Au Collisions at High Baryon Density

High moments of conserved quantities such as net-baryon, net-electric charge, and net-strangeness in heavy-ion collisions are sensitive to fluctuations caused by the QCD critical point (CP). The event-by-event analysis of high moments of the conserved charges has been widely used in experiments to search for the CP, especially in the RHIC-STAR experiment. In order to establish a {\it dynamical non-critical base line}, especially at the high baryon density region, we have performed a systematic analysis of the proton multiplicity distributions from Au+Au collisions at 3 $\leq$ $\sqrt{s_{NN}}$ $\leq$ 9.2 GeV collisions. The results on beam energy, centrality and rapidity width dependence of proton (factorial) cumulants, up to the $4^{th}$ order, are extracted from the calculations of the hadronic transport model UrQMD. In addition, the effects of initial volume fluctuation is also discussed. These results will be important when we do physics analysis the RHIC beam energy scan (BES) data, especially for the fixed-target data and experimental data from future CBM experiment at FAIR.

nucl-ex↗

arXiv2025

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.

cs.CL↗

arXiv2025

Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning

Self-supervised learning (SSL) conventionally relies on the instance consistency paradigm, assuming that different views of the same image can be treated as positive pairs. However, this assumption breaks down for non-iconic data, where different views may contain distinct objects or semantic information. In this paper, we investigate the effectiveness of SSL when instance consistency is not guaranteed. Through extensive ablation studies, we demonstrate that SSL can still learn meaningful representations even when positive pairs lack strict instance consistency. Furthermore, our analysis further reveals that increasing view diversity, by enforcing zero overlapping or using smaller crop scales, can enhance downstream performance on classification and dense prediction tasks. However, excessive diversity is found to reduce effectiveness, suggesting an optimal range for view diversity. To quantify this, we adopt the Earth Mover's Distance (EMD) as an estimator to measure mutual information between views, finding that moderate EMD values correlate with improved SSL learning, providing insights for future SSL framework design. We validate our findings across a range of settings, highlighting their robustness and applicability on diverse data sources.

cs.CV↗

arXiv2025

MoPD: Mixture-of-Prompts Distillation for Vision-Language Models

Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods that they overfit seen classes and exhibit degraded performance on unseen classes. This limitation is due to the inherent bias in the training data towards the seen classes. To address this issue, we propose a novel soft prompt learning method, named Mixture-of-Prompts Distillation (MoPD), which can effectively transfer useful knowledge from hard prompts manually hand-crafted (a.k.a. teacher prompts) to the learnable soft prompt (a.k.a. student prompt), thereby enhancing the generalization ability of soft prompts on unseen classes. Moreover, the proposed MoPD method utilizes a gating network that learns to select hard prompts used for prompt distillation. Extensive experiments demonstrate that the proposed MoPD method outperforms state-of-the-art baselines especially on on unseen classes.

cs.CV↗

arXiv2025

FPI-Det: a face--phone Interaction Dataset for phone-use detection and understanding

The widespread use of mobile devices has created new challenges for vision systems in safety monitoring, workplace productivity assessment, and attention management. Detecting whether a person is using a phone requires not only object recognition but also an understanding of behavioral context, which involves reasoning about the relationship between faces, hands, and devices under diverse conditions. Existing generic benchmarks do not fully capture such fine-grained human--device interactions. To address this gap, we introduce the FPI-Det, containing 22{,}879 images with synchronized annotations for faces and phones across workplace, education, transportation, and public scenarios. The dataset features extreme scale variation, frequent occlusions, and varied capture conditions. We evaluate representative YOLO and DETR detectors, providing baseline results and an analysis of performance across object sizes, occlusion levels, and environments. Source code and dataset is available at https://github.com/KvCgRv/FPI-Det.

cs.CV↗

arXiv2025

The IBM Description of the B(E2) Anomaly: Dynamical Triaxiality and Configuration Mixing

A theoretical investigation has been carried out to examine the effects of dynamical triaxility and configuration mixing on the $B(E2)$ anomaly, characterized by $B_{4/2}=B(E2;4_1^+\rightarrow2_1^+)/B(E2;2_1^+\rightarrow0_1^+)<1.0$ and $R_{4/2}=E(4_1^+)/E(2_1^+)\geq2.0$, within the framework of the interacting boson model (IBM). The results indicate that the effective $γ$ deformation may undergo substantial changes with increasing angular momentum in the $B(E2)$ anomaly system governed by an IBM Hamiltonian involving rotor-like terms. Further calculations reveal that mixing between normal and intruder states may facilitate the emergence of $B(E2)$ anomaly, thereby providing additional theoretical insights into this unusual phenomenon.

nucl-th↗

arXiv2025

Triaxial rotor modes in finite-N boson systems

We propose an algebraic approach to elucidate the dynamic characteristics of triaxial rotor modes in nuclei by mapping a triaxial rotor Hamiltonian to the interacting boson model (IBM) one within a finite-$N$ framework. Our method unveils striking features not observed in conventional modes, exemplified by the $B(E2)$ anomaly, characterized by $B(E2;4_1--2_1^+)/B(E2;2_1--0_1^+)<<1$. Using specific examples, we demonstrate that the peculiar properties of low-lying states in both neutron-rich and neutron-deficient Os nuclei can be comprehensively understood through the proposed Hamiltonian, which incorporates both rigid and soft triaxial rotor modes. This algebraic method not only offers fresh insights into triaxial dynamics but also showcases its capability in uncovering emergent exotic collective modes in nuclear structure.

nucl-th↗

arXiv2025

SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning

Imitation learning advances robot capabilities by enabling the acquisition of diverse behaviors from human demonstrations. However, large-scale datasets used for policy training often introduce substantial variability in quality, which can negatively impact performance. As a result, automatically curating datasets by filtering low-quality samples to improve quality becomes essential. Existing robotic curation approaches rely on costly manual annotations and perform curation at a coarse granularity, such as the dataset or trajectory level, failing to account for the quality of individual state-action pairs. To address this, we introduce SCIZOR, a self-supervised data curation framework that filters out low-quality state-action pairs to improve the performance of imitation learning policies. SCIZOR targets two complementary sources of low-quality data: suboptimal data, which hinders learning with undesirable actions, and redundant data, which dilutes training with repetitive patterns. SCIZOR leverages a self-supervised task progress predictor for suboptimal data to remove samples lacking task progression, and a deduplication module operating on joint state-action representation for samples with redundant patterns. Empirically, we show that SCIZOR enables imitation learning policies to achieve higher performance with less data, yielding an average improvement of 15.4% across multiple benchmarks. More information is available at: https://ut-austin-rpl.github.io/SCIZOR/

cs.RO↗

arXiv2025

Object-level Correlation for Few-Shot Segmentation

Few-shot semantic segmentation (FSS) aims to segment objects of novel categories in the query images given only a few annotated support samples. Existing methods primarily build the image-level correlation between the support target object and the entire query image. However, this correlation contains the hard pixel noise, \textit{i.e.}, irrelevant background objects, that is intractable to trace and suppress, leading to the overfitting of the background. To address the limitation of this correlation, we imitate the biological vision process to identify novel objects in the object-level information. Target identification in the general objects is more valid than in the entire image, especially in the low-data regime. Inspired by this, we design an Object-level Correlation Network (OCNet) by establishing the object-level correlation between the support target object and query general objects, which is mainly composed of the General Object Mining Module (GOMM) and Correlation Construction Module (CCM). Specifically, GOMM constructs the query general object feature by learning saliency and high-level similarity cues, where the general objects include the irrelevant background objects and the target foreground object. Then, CCM establishes the object-level correlation by allocating the target prototypes to match the general object feature. The generated object-level correlation can mine the query target feature and suppress the hard pixel noise for the final prediction. Extensive experiments on PASCAL-${5}^{i}$ and COCO-${20}^{i}$ show that our model achieves the state-of-the-art performance.

cs.CV↗

arXiv2025

Thermodynamics and P-v criticality of RN-AdS black hole surrounded by PFDM on the EGUP framework

In this paper, we systematically study the thermodynamic properties and P-v criticality of the RN-AdS black hole surrounded by PFDM in the framework of the extended generalized uncertainty principle (EGUP). In the extended phase space, starting from the first law of black hole thermodynamics, the EGUP-corrected Hawking temperature, heat capacity, and entropy are derived. Then, we use graphical methods to analyze the effects of the EGUP parameters on these thermodynamic quantities. Analysis of the heat capacity indicates that the EGUP correction is beneficial for maintaining the thermodynamic stability of black holes. Finally, based on the modified thermodynamic quantities, we obtain the EGUP-corrected black hole equation of state and Gibbs free energy. The G-T diagram with the EGUP correction effect is analyzed, and the results show that when $P P_c$, we combine the analysis of the relationship between heat capacity and event horizon radius, finding that the phase transition characteristics depicted in the G-T diagram are similar to those without the correction effect.

gr-qc↗

arXiv2025

Presolving Convexified Optimal Power Flow with Mixtures of Gradient Experts

Convex relaxations and approximations of the optimal power flow (OPF) problem have gained significant research and industrial interest for planning and operations in electric power networks. One approach for reducing their solve times is presolving which eliminates constraints from the problem definition, thereby reducing the burden of the underlying optimization algorithm. To this end, we propose a presolving framework for convexified optimal power flow (C-OPF) problems, which uses a novel deep learning-based architecture called MoGE (Mixture of Gradient Experts). In this framework, problem size is reduced by learning the mapping between C-OPF parameters and optimal dual variables (the latter being representable as gradients), which is then used to screen constraints that are non-binding at optimum. The validity of using this presolve framework across arbitrary families of C-OPF problems is theoretically demonstrated. We characterize generalization in MoGE and develop a post-solve recovery procedure to mitigate possible constraint classification errors. Using two different C-OPF models, we show via simulations that our framework reduces solve times by upto 34% across multiple PGLIB and MATPOWER test cases, while providing an identical solution as the full problem.

math.OC↗

arXiv2025

Dynamical crossover between stretched- and compressed-exponential relaxation in a photoexcited crystal

Anomalous relaxation is one of the hallmarks of disordered systems. Following perturbation by an external source, many glassy, jammed and amorphous systems relax as a stretched or compressed exponential as a function of time. However, despite their ubiquity, the origins of and the connection between these phenomenological relaxation functions remains to be understood. Here, we observe a tunable crossover from stretched- to compressed-exponential relaxation by photoexciting single crystal Ca3Ru2O7 across a structural phase transition. We present a simple lattice model that shows how spatial inhomogeneity and local, strain-mediated interactions cooperate to produce the dynamical crossover. Our work reveals anomalous relaxation dynamics in an idealized single crystal material and establishes photoexcited solids as promising platforms for probing the mechanisms underlying anomalous relaxation.

cond-mat.str-el↗

arXiv2025

Efficient Entanglement Routing for Satellite-Aerial-Terrestrial Quantum Networks

In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet. This paper investigates the collaboration among satellite, aerial, and terrestrial quantum networks to efficiently transmit high-fidelity quantum entanglements over long distances. We begin with a comprehensive overview of existing satellite-, aerial-, and terrestrial-based quantum networks. Subsequently, we address the entanglement routing problem with the objective of maximizing quantum network throughput by jointly optimizing path selection and entanglement generation rates (PS-EGR). Given that the original problem is formulated as a mixed-integer linear programming (MILP) problem, which is inherently intractable, we propose a Benders' decomposition (BD)-based algorithm to solve the problem efficiently. Numerical results validate the effectiveness of the proposed PS-EGR scheme, offering valuable insights into various optimizable factors within the system. Finally, we discuss the current challenges and propose promising avenues for future research in SATQNs.

quant-ph↗

arXiv2025

From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots

Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world. The project webpage is available at https://beingbeyond.github.io/BumbleBee/.

cs.RO↗

arXiv2025

OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection

The recent advance in multi-camera 3D object detection is featured by bird's-eye view (BEV) representation or object queries. However, the ill-posed transformation from image-plane view to 3D space inevitably causes feature clutter and distortion, making the objects blur into the background. To this end, we explore how to incorporate supplementary cues for differentiating objects in the transformed feature representation. Formally, we introduce OA-DET3D, a general plug-in module that improves 3D object detection by bringing object awareness into a variety of existing 3D object detection pipelines. Specifically, OA-DET3D boosts the representation of objects by leveraging object-centric depth information and foreground pseudo points. First, we use object-level supervision from the properties of each 3D bounding box to guide the network in learning the depth distribution. Next, we select foreground pixels using a 2D object detector and project them into 3D space for pseudo-voxel feature encoding. Finally, the object-aware depth features and pseudo-voxel features are incorporated into the BEV representation or query feature from the baseline model with a deformable attention mechanism. We conduct extensive experiments on the nuScenes dataset and Argoverse 2 dataset to validate the merits of OA-DET3D. Our method achieves consistent improvements over the BEV-based baselines in terms of both average precision and comprehensive detection score.

cs.CV↗

arXiv2025

Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion

Zero-shot online voice conversion (VC) holds significant promise for real-time communications and entertainment. However, current VC models struggle to preserve semantic fidelity under real-time constraints, deliver natural-sounding conversions, and adapt effectively to unseen speaker characteristics. To address these challenges, we introduce Conan, a chunkwise online zero-shot voice conversion model that preserves the content of the source while matching the voice timbre and styles of reference speech. Conan comprises three core components: 1) a Stream Content Extractor that leverages Emformer for low-latency streaming content encoding; 2) an Adaptive Style Encoder that extracts fine-grained stylistic features from reference speech for enhanced style adaptation; 3) a Causal Shuffle Vocoder that implements a fully causal HiFiGAN using a pixel-shuffle mechanism. Experimental evaluations demonstrate that Conan outperforms baseline models in subjective and objective metrics. Audio samples can be found at https://aaronz345.github.io/ConanDemo.

eess.AS↗

arXiv2025

Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler

Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in R^4 to R^16 and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at: https://youtu.be/30RsBIdexTU

cs.RO↗