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Zhe Zhang

Publications and source records attributed to Zhe Zhang.

At least 73 records · Page 4Linked to original sources
arXiv2026

DA-VPC: Disturbance-Aware Visual Predictive Control Scheme of Docking Maneuvers for Autonomous Trolley Collection

Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic docking system faces significant challenges: high precision requirements, environmental disturbances, and inherent robot constraints. To address these challenges, we propose a Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints for image-based visual servoing (IBVS), solving the predictive control problem through optimization. It is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.

cs.RO↗
arXiv2026

RAID: Retrieval-Augmented Anomaly Detection

Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on image reconstruction or template retrieval but face a fundamental challenge: matching between test images and normal templates inevitably introduces noise due to intra-class variations, imperfect correspondences, and limited templates. Observing that Retrieval-Augmented Generation (RAG) leverages retrieved samples directly in the generation process, we reinterpret UAD through this lens and introduce \textbf{RAID}, a retrieval-augmented UAD framework designed for noise-resilient anomaly detection and localization. Unlike standard RAG that enriches context or knowledge, we focus on using retrieved normal samples to guide noise suppression in anomaly map generation. RAID retrieves class-, semantic-, and instance-level representations from a hierarchical vector database, forming a coarse-to-fine pipeline. A matching cost volume correlates the input with retrieved exemplars, followed by a guided Mixture-of-Experts (MoE) network that leverages the retrieved samples to adaptively suppress matching noise and produce fine-grained anomaly maps. RAID achieves state-of-the-art performance across full-shot, few-shot, and multi-dataset settings on MVTec, VisA, MPDD, and BTAD benchmarks. \href{https://github.com/Mingxiu-Cai/RAID}{https://github.com/Mingxiu-Cai/RAID}.

cs.CV↗
arXiv2026

Dynamic topological exciton-polaritons enabling ultrafast logic operations

Topological active materials have emerged as powerful paradigm bridging the discovery of exotic topological phases of matter with the development of functional topological devices. The recent extension of these material systems into dynamic regime, where topological properties can be actively manipulated at ultrafast timescales, promises unprecedented control over topological states and their functionalities. However, translating the static topological lasing signals into high-performance logic functions remain highly challenging, which imposes a far more stringent set of materials attributes. Here, leveraging the strong nonlinearity and pronounced spectral isolation of perovskite exciton-polaritons embedded in a Dirac vortex microcavity, we experimentally demonstrate the dynamic topological Majorana-like state polariton condensation with its ultrafast logic operations at room temperature. By actively coordinating pump and control beams in both spectral and temporal domain, we dynamically steer the topological polariton condensation process and demonstrate AND and NOT logic operations, achieving record extinction ratio (~20 dB), extremely low control fluence (~0.2 nJ/cm2) and sub-picosecond response time (~500 fs). Our results expand the frontier of dynamic topology and establish a novel pathway towards robust, ultrafast, and reconfigurable on-chip polaritonic logic circuits.

physics.optics↗
arXiv2026

Flash annealing-engineered wafer-scale relaxor antiferroelectrics for enhanced energy storage performance

Dielectric capacitors are essential for energy storage systems due to their high-power density and fast operation speed. However, optimizing energy storage density with concurrent thermal stability remains a substantial challenge. Here, we develop a flash annealing process with ultrafast heating and cooling rates of 1000 oC/s, which facilitates the rapid crystallization of PbZrO3 film within a mere second, while locking its high-temperature microstructure to room temperature. This produces compact films with sub-grain boundaries fraction of 36%, nanodomains of several nanometers, and negligible lead volatilization. These contribute to relaxor antiferroelectric film with a high breakdown strength (4800 kV/cm) and large polarization (70 uC/cm2). Consequently, we have achieved a high energy storage density of 63.5 J/cm3 and outstanding thermal stability with performance degradation less than 3% up to 250 oC. Our approach is extendable to ferroelectrics like Pb(Zr0.52Ti0.48)O3 and on wafer scale, providing on-chip nonlinear dielectric energy storage solutions with industrial scalability.

cond-mat.mtrl-sci↗
arXiv2026

Search for CP Violations in the Production and Decay of the Hyperon-Antihyperon Pairs

This study introduces a complete joint angular distribution analysis for the process $e^{+}e^{-}\to J/ψ\to B(\to B_1π)\bar{B}(\to \bar{B}_1π)$ with polarized beams, where $B$ and $B_{1}$ are hyperons. We consider both transverse and longitudinal beam polarization and analyze $CP$ violation in the production and decay of hyperons. We present the complete Fisher information matrix for the sensitivities of the weak decay parameters $α_{-},~α_{+},~ϕ_Ξ$ and $\barϕ_Ξ$, the $P$-violating term $F_{A}$, and the EDM $d_{B}$ (derived from the $CP$-violating term $H_{T}$), under the BESIII and STCF statistics. We find that polarization enhances the sensitivity of the parameters. Specifically, longitudinal polarization provides a more significant improvement compared to transverse polarization, with the enhancement being more pronounced for single-step decays than for multistep decays. With the expected statistics from the BESIII experiment, the estimated EDM sensitivities for $Λ$ and $Σ^{+}$ are on the order of $10^{-19}~e~\rm cm$, while those for $Ξ^{-}$ and $Ξ^{0}$ are $10^{-18}~e~\rm cm$. The upcoming STCF experiment is expected to improve these estimates by $1-2$ orders of magnitude. Furthermore, $CP$ violation in hyperon decays originates from weak-phase differences among partial-wave amplitudes. Based on a refined isospin analysis, we emphasize the non-negligible contribution of $ΔI = 3/2$ amplitudes to $CP$ violation and advocate for increased theoretical attention to this aspect in future studies..

hep-ph↗
arXiv2026

Separating Advertising and Marketplace Functions of E-commerce Platforms: Is it Social Welfare Enhancing?

The use of sponsored product listings in prominent positions of consumer search results has made e-commerce platforms, which traditionally serve as marketplaces for third-party sellers to reach consumers, a major medium for those sellers to advertise their products. On the other hand, regulators have expressed anti-trust concerns about an e-commerce platform's integration of marketplace and advertising functions; they argue that such integration benefits the platform and sellers at the expense of consumers and society and have proposed separating the advertising function from those platforms. We show, contrary to regulators' concerns, that separating the advertising function from the e-commerce platform benefits the sellers, hurts the consumers, and does not necessarily benefit the social welfare. A key driver of our findings is that an independent advertising firm, which relies solely on advertising revenue, has same or lesser economic incentive to improve targeting precision than an e-commerce platform that also serves as the advertising medium, even if both have the same ability to target consumers. This is because an improvement in targeting precision enhances the marketplace commission by softening the price competition between sellers, but hurts the advertising revenue by softening the competition for prominent ad positions.

econ.GN↗
arXiv2026

Code2Bench: Scaling Source and Rigor for Dynamic Benchmark Construction

The evaluation of code-generating Large Language Models (LLMs) is fundamentally constrained by two intertwined challenges: a reliance on static, easily contaminated problem sources and the use of superficial, low-rigor testing. This paper introduces a new benchmark construction philosophy, Dual Scaling, designed to systematically address both limitations. Our approach involves continuously scaling the source of problems from dynamic, real-world code repositories and systematically scaling the rigor of tests via automated, high-coverage Property-Based Testing (PBT). We instantiate this philosophy in CODE2BENCH, an end-to-end framework that leverages Scope Graph analysis for principled dependency classification and a 100% branch coverage quality gate to ensure test suite integrity. Using this framework, we construct CODE2BENCH-2509, a new benchmark suite with native instances in both Python and Java. Our extensive evaluation of 10 state-of-the-art LLMs on CODE2BENCH-2509, powered by a novel "diagnostic fingerprint" visualization, yields three key insights: (1) models exhibit a fundamental performance gap, excelling at API application (Weakly Self-Contained tasks) but struggling with algorithmic synthesis (Self-Contained tasks); (2) a model's performance is profoundly shaped by the target language's ecosystem, a nuance we are the first to systematically quantify; and (3) our rigorous, scaled testing is critical in uncovering an "illusion of correctness" prevalent in simpler benchmarks. Our work presents a robust, scalable, and diagnostic paradigm for the next generation of LLM evaluation in software engineering. The code, data, and results are available at https://code2bench.github.io/.

cs.SE↗
arXiv2025

Transverse momentum asymmetry in the semi-inclusive electron positron annihilation process

Hadronization, a nonperturbative process, cannot be calculated from first principles. It can be investigated either by using phenomenological models or by examining the behavior of produced hadrons or through fragmentation functions. These fragmentation functions are nonperturbative quantities whose determination relies entirely on experimental data. However, higher-twist fragmentation functions present significant challenges for their determination due to power suppression. In this paper, we propose an asymmetry to study twist-3 fragmentation functions. This asymmetry is defined as the transverse momentum asymmetry of the fragmenting quark and/or the produced jet with respect to the observed hadron direction within the semi-inclusive electron positron annihilation process. As a twist-3 effect, this asymmetry is sensitive to the distribution of the jet relative to the produced hadron direction during hadronization. Furthermore, it is closely related to twist-3 transverse momentum dependent fragmentation functions and provides a set of measurable quantities for their determination.

hep-ph↗
arXiv2025

Toward building next-generation Geocoding systems: a systematic review

Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the need for next-generation systems. In response to this demand, this review first characterizes the technical requirements for next-generation geocoding inputs and outputs. We then decompose the geocoding workflow into modular functional units and survey existing implementations. For each component, we identify methodological limitations, articulate domain-specific research questions and hypotheses, and outline evaluation strategies needed. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances. We envision that this review provides a technical foundation and research agenda for advancing the design, assessment, and deployment of next-generation geocoding systems.

cs.SE↗
arXiv2025

LaMoSys3.5D: Enabling 3.5D-IC-Based Large Language Model Inference Serving Systems via Hardware/Software Co-Design

The success of large language models LLMs amplifies the need for highthroughput energyefficient inference at scale. 3DDRAMbased accelerators provide high memory bandwidth and therefore an opportunity to accelerate the bandwidthbound decode phase. However, how to adequately balance compute density for prefill with bandwidthcapacity for decode remains open. Moreover, most prior designs do not target endtoend serving, leaving the codesign of dataflow, parallel mapping, and scheduling underexplored. To bridge the gap, we present LaMoSys3.5D, to our knowledge the first scalable 3.5DIC architecture for LLM serving. LaMoSys3.5D composes heterogeneous 3DDRAM chiplets on a 2.5D interposer: computerich chiplets for prefill and bandwidthcapacityrich chiplets for decode. To realize efficient serving, we adopt a hardwaresoftware codesign spanning dataflow, parallel mapping, and introduce a thermalaware modeling and hierarchical designspace exploration framework. Across diverse LLMs and workloads, LaMoSys3.5D improves throughputperwatt over DGXA100 systems by 62 and achieves a 4.87 better endtoend latency geomean versus prior 3D designs. We further distill intriguing design guidelines for 3.5DIC architectures and endtoend inference serving.

eess.SY↗
arXiv2025

Translating Cultural Choreography from Humanoid Forms to Robotic Arm

Robotic arm choreography often reproduces trajectories while missing cultural semantics. This study examines whether symbolic posture transfer with joint space compatible notation can preserve semantic fidelity on a six-degree-of-freedom arm and remain portable across morphologies. We implement ROPERA, a three-stage pipeline for encoding culturally codified postures, composing symbolic sequences, and decoding to servo commands. A scene from Kunqu opera, \textit{The Peony Pavilion}, serves as the material for evaluation. The procedure includes corpus-based posture selection, symbolic scoring, direct joint angle execution, and a visual layer with light painting and costume-informed colors. Results indicate reproducible execution with intended timing and cultural legibility reported by experts and audiences. The study points to non-anthropocentric cultural preservation and portable authoring workflows. Future work will design dance-informed transition profiles, extend the notation to locomotion with haptic, musical, and spatial cues, and test portability across platforms.

cs.RO↗
arXiv2025

DiffOG: Differentiable Policy Trajectory Optimization with Generalizability

Imitation learning-based visuomotor policies excel at manipulation tasks but often produce suboptimal action trajectories compared to model-based methods. Directly mapping camera data to actions via neural networks can result in jerky motions and difficulties in meeting critical constraints, compromising safety and robustness in real-world deployment. For tasks that require high robustness or strict adherence to constraints, ensuring trajectory quality is crucial. However, the lack of interpretability in neural networks makes it challenging to generate constraint-compliant actions in a controlled manner. This paper introduces differentiable policy trajectory optimization with generalizability (DiffOG), a learning-based trajectory optimization framework designed to enhance visuomotor policies. By leveraging the proposed differentiable formulation of trajectory optimization with transformer, DiffOG seamlessly integrates policies with a generalizable optimization layer. DiffOG refines action trajectories to be smoother and more constraint-compliant while maintaining alignment with the original demonstration distribution, thus avoiding degradation in policy performance. We evaluated DiffOG across 11 simulated tasks and 2 real-world tasks. The results demonstrate that DiffOG significantly enhances the trajectory quality of visuomotor policies while having minimal impact on policy performance, outperforming trajectory processing baselines such as greedy constraint clipping and penalty-based trajectory optimization. Furthermore, DiffOG achieves superior performance compared to existing constrained visuomotor policy. For more details, please visit the project website: https://zhengtongxu.github.io/diffog-website/.

cs.RO↗
arXiv2025

Knowledge-Informed Neural Network for Complex-Valued SAR Image Recognition

Deep learning models for complex-valued Synthetic Aperture Radar (CV-SAR) image recognition are fundamentally constrained by a representation trilemma under data-limited and domain-shift scenarios: the concurrent, yet conflicting, optimization of generalization, interpretability, and efficiency. Our work is motivated by the premise that the rich electromagnetic scattering features inherent in CV-SAR data hold the key to resolving this trilemma, yet they are insufficiently harnessed by conventional data-driven models. To this end, we introduce the Knowledge-Informed Neural Network (KINN), a lightweight framework built upon a novel "compression-aggregation-compression" architecture. The first stage performs a physics-guided compression, wherein a novel dictionary processor adaptively embeds physical priors, enabling a compact unfolding network to efficiently extract sparse, physically-grounded signatures. A subsequent aggregation module enriches these representations, followed by a final semantic compression stage that utilizes a compact classification head with self-distillation to learn maximally task-relevant and discriminative embeddings. We instantiate KINN in both CNN (0.7M) and Vision Transformer (0.95M) variants. Extensive evaluations on five SAR benchmarks confirm that KINN establishes a state-of-the-art in parameter-efficient recognition, offering exceptional generalization in data-scarce and out-of-distribution scenarios and tangible interpretability, thereby providing an effective solution to the representation trilemma and offering a new path for trustworthy AI in SAR image analysis.

cs.CV↗
arXiv2025

Type-aware LLM-based Regression Test Generation for Python Programs

Automated regression test generation has been extensively explored, yet generating high-quality tests for Python programs remains particularly challenging. Because of the Python's dynamic typing features, existing approaches, ranging from search-based software testing (SBST) to recent LLM-driven techniques, are often prone to type errors. Hence, existing methods often generate invalid inputs and semantically inconsistent test cases, which ultimately undermine their practical effectiveness. To address these limitations, we present Test4Py, a novel framework that enhances type correctness in automated test generation for Python. Test4Py leverages the program's call graph to capture richer contextual information about parameters, and introduces a behavior-based type inference mechanism that accurately infers parameter types and construct valid test inputs. Beyond input construction, Test4Py integrates an iterative repair procedure that progressively refines generated test cases to improve coverage. In an evaluation on 183 real-world Python modules, Test4Py achieved an average statement coverage of 83.0% and branch coverage of 70.8%, outperforming state-of-the-art tools by 7.2% and 8.4%, respectively.

cs.SE↗
arXiv2025

Polarization analysis in $e^+e^-\rightarrow J/ψ\rightarrow$ baryon-antibaryon pairs

We present a comprehensive polarization analysis for the process $e^{+}e^{-} \rightarrow J/ψ\rightarrow$ baryon-antibaryon pairs~$B_{1}\bar{B}_{2}$. All possible helicity amplitudes for $e^{+}e^{-} \rightarrow J/ψ$ and $J/ψ\rightarrow B_{1} \bar{B}_{2}$ are provided, along with their transformation properties under parity and $CP$ operations. Taking full account of beam polarization, we analyze the polarization of the $J/ψ$ produced in $e^{+}e^{-}$ annihilation and show that all eight independent polarization components can be nonzero. By introducing a polarization transfer matrix, we propose a novel method to compute the polarization transfer in the decay $J/ψ\rightarrow B_{1} \bar{B}_{2}$. Considering both hadronic and radiative baryon decay models, we derive the joint angular distribution of the final-state particles. Focusing on the process $J/ψ\rightarrow Λ\barΣ^{0}$, we provide statistical uncertainty estimates for probing $CP$ violation in associated production of different baryons. This work establishes a complete framework for studying the parity and $CP$ violations present in baryon productions and decays within the helicity formalism.

hep-ph↗
arXiv2025

Unified Unsupervised Anomaly Detection via Matching Cost Filtering

Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and medical analysis, where anomalies are scarce due to privacy concerns and cold-start constraints. Existing methods, whether reconstruction-based (restoring normal counterparts) or embedding-based (pretrained representations), fundamentally conduct image- or feature-level matching to generate anomaly maps. Nonetheless, matching noise has been largely overlooked, limiting their detection ability. Beyond earlier focus on unimodal RGB-based UAD, recent advances expand to multimodal scenarios, e.g., RGB-3D and RGB-Text, enabled by point cloud sensing and vision-language models. Despite shared challenges, these lines remain largely isolated, hindering a comprehensive understanding and knowledge transfer. In this paper, we advocate unified UAD for both unimodal and multimodal settings in the matching perspective. Under this insight, we present Unified Cost Filtering (UCF), a generic post-hoc refinement framework for refining anomaly cost volume of any UAD model. The cost volume is constructed by matching a test sample against normal samples from the same or different modalities, followed by a learnable filtering module with multi-layer attention guidance from the test sample, mitigating matching noise and highlighting subtle anomalies. Comprehensive experiments on 22 diverse benchmarks demonstrate the efficacy of UCF in enhancing a variety of UAD methods, consistently achieving new state-of-the-art results in both unimodal (RGB) and multimodal (RGB-3D, RGB-Text) UAD scenarios. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.

cs.CV↗
arXiv2025

Pitch-Conditioned Instrument Sound Synthesis From an Interactive Timbre Latent Space

This paper presents a novel approach to neural instrument sound synthesis using a two-stage semi-supervised learning framework capable of generating pitch-accurate, high-quality music samples from an expressive timbre latent space. Existing approaches that achieve sufficient quality for music production often rely on high-dimensional latent representations that are difficult to navigate and provide unintuitive user experiences. We address this limitation through a two-stage training paradigm: first, we train a pitch-timbre disentangled 2D representation of audio samples using a Variational Autoencoder; second, we use this representation as conditioning input for a Transformer-based generative model. The learned 2D latent space serves as an intuitive interface for navigating and exploring the sound landscape. We demonstrate that the proposed method effectively learns a disentangled timbre space, enabling expressive and controllable audio generation with reliable pitch conditioning. Experimental results show the model's ability to capture subtle variations in timbre while maintaining a high degree of pitch accuracy. The usability of our method is demonstrated in an interactive web application, highlighting its potential as a step towards future music production environments that are both intuitive and creatively empowering: https://pgesam.faresschulz.com

cs.SD↗
arXiv2025

CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering

Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstruction-based) or by learning an image feature embedding space (embedding-based), existing approaches fundamentally rely on image-level or feature-level matching to derive anomaly scores. Often, such a matching process is inaccurate yet overlooked, leading to sub-optimal detection. To address this issue, we introduce the concept of cost filtering, borrowed from classical matching tasks, such as depth and flow estimation, into the UAD problem. We call this approach {\em CostFilter-AD}. Specifically, we first construct a matching cost volume between the input and normal samples, comprising two spatial dimensions and one matching dimension that encodes potential matches. To refine this, we propose a cost volume filtering network, guided by the input observation as an attention query across multiple feature layers, which effectively suppresses matching noise while preserving edge structures and capturing subtle anomalies. Designed as a generic post-processing plug-in, CostFilter-AD can be integrated with either reconstruction-based or embedding-based methods. Extensive experiments on MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for both single- and multi-class UAD tasks. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.

eess.IV↗