arXiv subjects

Yuxing Wang

Publications and source records attributed to Yuxing Wang.

At least 19 recordsLinked to original sources

arXiv2026

The 10th AI City Challenge

The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.

cs.CV↗

arXiv2026

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors. Despite advances in individual algorithmic components, their functional interdependencies remain underexplored: do they exhibit mutual synergy or counterproductive interference? To bridge this gap, we conduct a systematic investigation and find that the efficacy of different components exhibits significant task-dependency, and naively stacking state-of-the-art techniques does not necessarily yield performance gains; instead, it often triggers emergent challenges, such as compounded non-stationarity. Building upon these findings, we distill a suite of actionable insights into the principled coordination of these components. Guided by these insights, we propose ROSER, an RL framework that coordinates three critical dimensions: Model-based Representation, Optimization Stability, and Experience Replay. Across diverse continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves 17.60% gains over naive stack. Our findings underscore the necessity of a holistic perspective in RL system design and paves the way for developing sample-efficient agents.

cs.LG↗

arXiv2026

Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution

Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, which fundamentally limit robustness, particularly in multimodal settings with a larger attack surface. In this work, we move beyond static adversarial supervision and introduce co-evolutionary alignment with evolving attacks, instantiated by CEMMA (Co-Evolutionary Multi-Modal Alignment), an automated and adaptive framework for multimodal safety alignment. We introduce an Evolutionary Attacker that decomposes adversarial prompts into method templates and harmful intents. By employing genetic operators, including mutation, crossover, and differential evolution, it enables simple seed attacks to inherit the structural efficacy of sophisticated jailbreaks. The Adaptive Defender is iteratively updated on the synthesized hard negatives, forming a closed-loop process that adapts alignment to evolving attacks. Experiments show that the Evolutionary Attacker substantially increases red-teaming jailbreak attack success rate (ASR), while the Adaptive Defender improves robustness and generalization across benchmarks with higher data efficiency, without inducing excessive benign refusal, and remains compatible with inference-time defenses such as AdaShield.

cs.CR↗

arXiv2026

A Unified 3D Object Perception Framework for Real-Time Outside-In Multi-Camera Systems

Accurate 3D object perception and multi-target multi-camera (MTMC) tracking are fundamental for the digital transformation of industrial infrastructure. However, transitioning "inside-out" autonomous driving models to "outside-in" static camera networks presents significant challenges due to heterogeneous camera placements and extreme occlusion. In this paper, we present an adapted Sparse4D framework specifically optimized for large-scale infrastructure environments. Our system leverages absolute world-coordinate geometric priors and introduces an occlusion-aware ReID embedding module to maintain identity stability across distributed sensor networks. To bridge the Sim2Real domain gap without manual labeling, we employ a generative data augmentation strategy using the NVIDIA COSMOS framework, creating diverse environmental styles that enhance the model's appearance-invariance. Evaluated on the AI City Challenge 2025 benchmark, our camera-only framework achieves a state-of-the-art HOTA of $45.22$. Furthermore, we address real-time deployment constraints by developing an optimized TensorRT plugin for Multi-Scale Deformable Aggregation (MSDA). Our hardware-accelerated implementation achieves a $2.15\times$ speedup on modern GPU architectures, enabling a single Blackwell-class GPU to support over 64 concurrent camera streams.

cs.CV↗

arXiv2025

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning

Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications in sequential decision-making domains such as autonomous driving and robotic control. Within this paradigm, agent training is typically formulated as a zero-sum Markov game between a protagonist and an adversary to enhance policy robustness. However, the trainable nature of the adversary inevitably induces non-stationarity in the learning dynamics, leading to exacerbated training instability and convergence difficulties, particularly in high-dimensional complex environments. In this paper, we propose a novel approach, Uncertainty-Adaptive Critic Ensemble for robust adversarial Reinforcement learning (UACER), which consists of two components: 1) Diversified critic ensemble: A diverse set of K critic networks is employed in parallel to stabilize Q-value estimation in robust adversarial reinforcement learning, reducing variance and enhancing robustness compared to conventional single-critic designs. 2) Time-varying Decay Uncertainty (TDU) mechanism: Moving beyond simple linear combinations, we propose a variance-derived Q-value aggregation strategy that explicitly incorporates epistemic uncertainty to adaptively regulate the exploration-exploitation trade-off while stabilizing the training process. Comprehensive experiments across several challenging MuJoCo control problems validate the superior effectiveness of UACER, outperforming state-of-the-art methods in terms of overall performance, stability, and efficiency.

cs.LG↗

arXiv2025

Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges

Brain-body co-evolution enables animals to develop complex behaviors in their environments. Inspired by this biological synergy, embodied co-design (ECD) has emerged as a transformative paradigm for creating intelligent agents-from virtual creatures to physical robots-by jointly optimizing their morphologies and controllers rather than treating control in isolation. This integrated approach facilitates richer environmental interactions and robust task performance. In this survey, we provide a systematic overview of recent advances in ECD. We first formalize the concept of ECD and position it within related fields. We then introduce a hierarchical taxonomy: a lower layer that breaks down agent design into three fundamental components-controlling brain, body morphology, and task environment-and an upper layer that integrates these components into four major ECD frameworks: bi-level, single-level, generative, and open-ended. This taxonomy allows us to synthesize insights from more than one hundred recent studies. We further review notable benchmarks, datasets, and applications in both simulated and real-world scenarios. Finally, we identify significant challenges and offer insights into promising future research directions. A project associated with this survey has been created at https://github.com/Yuxing-Wang-THU/SurveyBrainBody.

cs.RO↗

arXiv2025

The 9th AI City Challenge

The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition featured four tracks and saw a 17% increase in participation, with 245 teams from 15 countries registered on the evaluation server. Public release of challenge datasets led to over 30,000 downloads to date. Track 1 focused on multi-class 3D multi-camera tracking, involving people, humanoids, autonomous mobile robots, and forklifts, using detailed calibration and 3D bounding box annotations. Track 2 tackled video question answering in traffic safety, with multi-camera incident understanding enriched by 3D gaze labels. Track 3 addressed fine-grained spatial reasoning in dynamic warehouse environments, requiring AI systems to interpret RGB-D inputs and answer spatial questions that combine perception, geometry, and language. Both Track 1 and Track 3 datasets were generated in NVIDIA Omniverse. Track 4 emphasized efficient road object detection from fisheye cameras, supporting lightweight, real-time deployment on edge devices. The evaluation framework enforced submission limits and used a partially held-out test set to ensure fair benchmarking. Final rankings were revealed after the competition concluded, fostering reproducibility and mitigating overfitting. Several teams achieved top-tier results, setting new benchmarks in multiple tasks.

cs.CV↗

arXiv2025

Deuterium-deuterium fusion charged particle detection using CR-39 and Deep Learning Model

CR-39 solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful information can be a tedious and time-consuming process, often prone to human errors and bias. To address these challenges, we developed an AI-based classification model capable of differentiating protons, tritons, and helions produced during D-D fusion, using CR-39 track images as input data. The CR-39 track images were processed and used to train a deep learning model. By preprocessing the track images for noise reduction and feature enhancement, we trained the YOLOv8 [1][2] network to distinguish the three particle types with high accuracy. The proposed model achieved a classification accuracy of over 96%, demonstrating its potential for improving automated track analysis in CR-39 detectors. Additionally, the model precisely identifies particle coordinates and counts, enabling comprehensive particle analysis. This study highlights the application of AI in track detection and classification, offering a robust solution for particle identification in CR-39 detector-based experiments.

physics.ins-det↗

arXiv2025

Performance Characteristics of the Battery-Operated Si PIN Diode Detector with Integrated Preamplifier and Data Acquisition Module for Fusion Particle Detection

We present the performance and application of a commercial off-the shelf Si PIN diode (Hamamatsu S14605) as a charged particle detector in a compact ion beam system (IBS) capable of generating D-D and p-B fusion charged particles. This detector is inexpensive, widely available, and operates in photoconductive mode under a reverse bias voltage of 12 V, supplied by an A23 battery. A charge-sensitive preamplifier (CSP) is powered by two 3 V lithium batteries (A123), providing +/-3 V rail voltages. Both the detector and preamplifier circuits are integrated onto the same 4-layer PCB and housed on the vacuum side of the IBS, facing the fusion target. The system employs a CF-2.75 flanged DB-9 connector feedthrough to supply the signal, bias voltage, and rail voltages. To mitigate the high sensitivity of the detector to optical light, a thin aluminum foil assembly is used to block optical emissions from the ion beam and target. Charged particles generate step responses on the preamplifier output, with pulse rise times on the order of 0.2 to 0.3 us. These signals are recorded using a custom-built data acquisition unit, which features an optical fiber data link to ensure electrical isolation of the detector electronics. Subsequent digital signal processing is employed to optimally shape the pulses using a CR-RC^4 filter to produce Gaussian-shaped signals, enabling accurate extraction of energy information. Performance results show that the signal-to-noise ratios (S/N) for D-D fusion charged particles - protons, tritons, and helions - are approximately 30, 10, and 5, respectively, with a shaping time constant of 4 us.

physics.ins-det↗

arXiv2025

Absence of superconductivity and density-wave transition in ambient-pressure tetragonal La$_4$Ni$_3$O$_{10}$

The recent discovery of superconductivity in La$_3$Ni$_2$O$_7$ and La$_4$Ni$_3$O$_{10}$ under high pressure stimulates intensive research interests. These nickelates crystallize in an orthogonal/monoclinic structure with tilted NiO$_6$ octahedra at ambient pressure and enter a density-wave-like phase at low temperatures. The application of pressure suppresses the octahedral tilting and triggers a transition to tetragonal structure (I4/mmm), which is believed to be a key prerequisite for the emergence of superconducting state. Here, by developing a high oxidative environment growth technology, we report the first tetragonal nickelates La$_4$Ni$_3$O$_{10}$ microcrystals without octahedral tilting at ambient pressure. In tetragonal La$_4$Ni$_3$O$_{10}$, transport measurements find that both density-wave and superconducting transitions are absent up to 160 GPa, indicating a robust tetragonal metallic ground state. Density functional theory calculations reveal that the band structure of ambient-pressure tetragonal La$_4$Ni$_3$O$_{10}$ involves more $d_{z2}$ orbital contribution to the Fermi surface, compared to the monoclinic phase or the high-pressure superconducting tetragonal phase. The concurrent absence of density-wave state and high-pressure superconductivity in our ambient-pressure tetragonal crystals of La$_4$Ni$_3$O$_{10}$ suggests an underlying correlation between these two orders. It suggests that the tetragonal structure is not necessary, while the density-wave state is crucial for the superconductivity in nickelates. Our findings impose important constraints on the mechanism of pressure-induced superconductivity in nickelates and sheds new light on exploring ambient pressure high-temperature Ni-based superconductors.

cond-mat.supr-con↗

arXiv2024

AutoSimTTF: A Fully Automatic Pipeline for Electric Field Simulation and Treatment Planning of Tumor Treating Fields

Objective: Tumor Treating Fields (TTFields) is an emerging approach for cancer therapy that inhibits tumor cell proliferation by applying alternating electric fields (EF) of intermediate frequency and low intensity. The TTFields-induced electric field intensity at the tumor site is closely related to the therapeutic efficacy. Therefore, the EF simulation based on realistic head models have been utilized for the dosage analysis and treatment optimization of TTFields. However, current modeling methods require manual segmentation of tumors and rely on commercial software, which is time-consuming and labor-intensive. Approach: We introduce AutoSimTTF, a fully automatic pipeline for simulating and optimizing the EF distribution for TTFields. The main steps of AutoSimTTF utilize open-source toolkits, enabling fully automated processing of individual MRI data for TTFields. Additionally, AutoSimTTF allows for parameter optimization based on individual anatomical information, thereby achieving a more focused and higher EF distribution at the tumor site. Main results: Compared to conventional EF calculation processes, deviations in AutoSimTTF are below 20%. The optimal treatment parameters generated by AutoSimTTF produces a higher EF intensity at the tumor site (111.9%) and better focality (19.4%) compared to traditional TTFields settings. Significance: AutoSimTTF provides significant reference value and guidance for the clinical application and treatment planning of TTFields.

physics.med-ph↗

arXiv2024

Morphology and Behavior Co-Optimization of Modular Satellites for Attitude Control

The emergence of modular satellites marks a significant transformation in spacecraft engineering, introducing a new paradigm of flexibility, resilience, and scalability in space exploration endeavors. In addressing complex challenges such as attitude control, both the satellite's morphological architecture and the controller are crucial for optimizing performance. Despite substantial research on optimal control, there remains a significant gap in developing optimized and practical assembly strategies for modular satellites tailored to specific mission constraints. This research gap primarily arises from the inherently complex nature of co-optimizing design and control, a process known for its notorious bi-level optimization loop. Conventionally tackled through artificial evolution, this issue involves optimizing the morphology based on the fitness of individual controllers, which is sample-inefficient and computationally expensive. In this paper, we introduce a novel gradient-based approach to simultaneously optimize both morphology and control for modular satellites, enhancing their performance and efficiency in attitude control missions. Our Monte Carlo simulations demonstrate that this co-optimization approach results in modular satellites with better mission performance compared to those designed by evolution-based approaches. Furthermore, this study discusses potential avenues for future research.

cs.RO↗

arXiv2023

Entropy solutions to the Dirichlet problem for nonlinear diffusion equations with conservative noise

Motivated by porous medium equations with randomly perturbed velocity field, this paper considers a class of nonlinear degenerate diffusion equations with nonlinear conservative noise in bounded domains. The existence, uniqueness and $L_{1}$-stability of non-negative entropy solutions under the homogeneous Dirichlet boundary condition are proved. The approach combines Kruzhkov's doubling variables technique with a revised strong entropy condition that is automatically satisfied by the solutions of approximate equations.

math.PR↗

arXiv2023

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, i.e., dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context, and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of the stability, overall performance and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

cs.LG↗

arXiv2022

SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias

Real-time music accompaniment generation has a wide range of applications in the music industry, such as music education and live performances. However, automatic real-time music accompaniment generation is still understudied and often faces a trade-off between logical latency and exposure bias. In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias. Specifically, SongDriver divides one accompaniment generation task into two phases: 1) The arrangement phase, where a Transformer model first arranges chords for input melodies in real-time, and caches the chords for the next phase instead of playing them out. 2) The prediction phase, where a CRF model generates playable multi-track accompaniments for the coming melodies based on previously cached chords. With this two-phase strategy, SongDriver directly generates the accompaniment for the upcoming melody, achieving zero logical latency. Furthermore, when predicting chords for a timestep, SongDriver refers to the cached chords from the first phase rather than its previous predictions, which avoids the exposure bias problem. Since the input length is often constrained under real-time conditions, another potential problem is the loss of long-term sequential information. To make up for this disadvantage, we extract four musical features from a long-term music piece before the current time step as global information. In the experiment, we train SongDriver on some open-source datasets and an original àiSong Dataset built from Chinese-style modern pop music scores. The results show that SongDriver outperforms existing SOTA (state-of-the-art) models on both objective and subjective metrics, meanwhile significantly reducing the physical latency.

cs.SD↗

arXiv2022

A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning

The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms. Recently, hybrid learning frameworks based on this principle have achieved great success in various challenging robot control tasks. However, in these methods, policies from the genetic population are evaluated via interactions with the real environments, limiting their applicability in computationally expensive problems. In this work, we propose Surrogate-assisted Controller (SC), a novel and efficient module that can be integrated into existing frameworks to alleviate the computational burden of EAs by partially replacing the expensive policy evaluation. The key challenge in applying this module is to prevent the optimization process from being misled by the possible false minima introduced by the surrogate. To address this issue, we present two strategies for SC to control the workflow of hybrid frameworks. Experiments on six continuous control tasks from the OpenAI Gym platform show that SC can not only significantly reduce the cost of fitness evaluations, but also boost the performance of the original hybrid frameworks with collaborative learning and evolutionary processes.

cs.NE↗

arXiv2019

Schauder-type estimates for higher-order parabolic SPDEs

In this paper we consider the Cauchy problem for $2m$-order stochastic partial differential equations of parabolic type in a class of stochastic Hoelder spaces. The Hoelder estimates of solutions and their spatial derivatives up to order $2m$ are obtained, based on which the existence and uniqueness of solution is proved. An interesting finding of this paper is that the regularity of solutions relies on a coercivity condition that differs when $m$ is odd or even: the condition for odd $m$ coincides with the standard parabolicity condition in the literature for higher-order stochastic partial differential equations, while for even $m$ it depends on the integrability index $p$. The sharpness of the new-found coercivity condition is demonstrated by an example.

math.PR↗

arXiv2014

High-Pressure Evolution of the Specific Heat of a Strongly Underdoped Ba(Fe0.963Co0.037)As2 Iron-Based Superconductor

We report specific-heat experiments under the influence of high pressure on a strongly underdoped Co-substituted BaFe2As2 single crystal. This allows us to study the phase diagram of this iron pnictide superconductor with a bulk thermodynamic method and pressure as a clean control parameter. The data show large specific-heat anomalies at the superconducting transition temperature, which proves the bulk nature of pressure-induced superconductivity. The transitions in the specific heat are sharper than in resistivity, which demonstrates the necessity of employing bulk thermodynamic methods to explore the exact phase diagram of pressure-induced Fe-based superconductors. The Tc at optimal pressure and the superconducting condensation energy are found to be larger than in optimally Co-doped samples at ambient pressure, which we attribute to a weak pair breaking effect of the Co ions.

cond-mat.supr-con↗