arXiv subjects

Wei Zheng

Publications and source records attributed to Wei Zheng.

At least 37 records · Page 2Linked to original sources

arXiv2025

Real-time vacuum-state quantum random number generator on a chip

Quantum random number generators (QRNGs) produce true random numbers, which are guaranteed by the fundamental principles of quantum physics. Miniaturization of QRNGs is crucial for a wide range of communication and cryptography applications. Here, we first report a fully functional QRNG chip based on vacuum-state fluctuations, with dimensions of 16.6 mm x 7.8 mm. The quantum entropy source, which is achieved via hybrid photonic integration with a SiO2 waveguide, generates raw quantum random numbers. The hybrid photonic and electrical components are assembled into a compact ceramic package using system-in-package technology. A microcontroller unit acquires the raw data and outputs the processed quantum random numbers via a serial peripheral interface. According to the characterization results, the QRNG chip achieves a constant real-time output rate of 5.2 Mbps across the industrial temperature range of -40°C to 85°C, making it suitable for practical applications.

quant-ph↗

arXiv2025

Non-adiabatic linear response in open quantum systems

Adiabatic theorem and non-adiabatic corrections have been widely applied in modern quantum technology. Recently, non-adiabatic linear response theory has been developed to probe the many-body correlations in closed systems. In this work, we generalize the non-adiabatic linear response theory to open quantum many-body systems. We show that, similar to the closed case, the first-order deviation from the instantaneous steady state is memoryless -- it depends only on the final parameters and not on the initial state or ramping path. When ramping the Hamiltonian, the linear response of observables is governed by the derivative of the retarded Green's function, as in closed systems. In contrast, ramping the dissipation gives rise to a different response, characterized by a high-order correlation function defined in the steady state. Our results offer a compact and physically transparent formulation of non-adiabatic response in open systems, and demonstrate that ramping dynamics can serve as a versatile tool for probing many-body correlations beyond equilibrium.

cond-mat.quant-gas↗

arXiv2025

Observatory Science with eXTP

Scheduled for launch in 2030, the enhanced X-ray Timing and Polarization (eXTP) telescope is a Chinese space-based mission aimed at studying extreme conditions and phenomena in astrophysics. eXTP will feature three main payloads: Spectroscopy Focusing Arrays (SFAs), Polarimetry Focusing Arrays (PFAs), and a Wide-field Camera (W2C). This white paper outlines observatory science, incorporating key scientific advances and instrumental changes since the publication of the previous white paper [1]. We will discuss perspectives of eXTP on the research domains of flare stars, supernova remnants, pulsar wind nebulae, cataclysmic variables, X-ray binaries, ultraluminous X-ray sources, AGN, and pulsar-based positioning and timekeeping.

astro-ph.IM↗

arXiv2025

Visual Perturbation and Adaptive Hard Negative Contrastive Learning for Compositional Reasoning in Vision-Language Models

Vision-Language Models (VLMs) are essential for multimodal tasks, especially compositional reasoning (CR) tasks, which require distinguishing fine-grained semantic differences between visual and textual embeddings. However, existing methods primarily fine-tune the model by generating text-based hard negative samples, neglecting the importance of image-based negative samples, which results in insufficient training of the visual encoder and ultimately impacts the overall performance of the model. Moreover, negative samples are typically treated uniformly, without considering their difficulty levels, and the alignment of positive samples is insufficient, which leads to challenges in aligning difficult sample pairs. To address these issues, we propose Adaptive Hard Negative Perturbation Learning (AHNPL). AHNPL translates text-based hard negatives into the visual domain to generate semantically disturbed image-based negatives for training the model, thereby enhancing its overall performance. AHNPL also introduces a contrastive learning approach using a multimodal hard negative loss to improve the model's discrimination of hard negatives within each modality and a dynamic margin loss that adjusts the contrastive margin according to sample difficulty to enhance the distinction of challenging sample pairs. Experiments on three public datasets demonstrate that our method effectively boosts VLMs' performance on complex CR tasks. The source code is available at https://github.com/nynu-BDAI/AHNPL.

cs.CV↗

arXiv2025

Beyond the Textual: Generating Coherent Visual Options for MCQs

Multiple-choice questions (MCQs) play a crucial role in fostering deep thinking and knowledge integration in education. However, previous research has primarily focused on generating MCQs with textual options, but it largely overlooks the visual options. Moreover, generating high-quality distractors remains a major challenge due to the high cost and limited scalability of manual authoring. To tackle these problems, we propose a Cross-modal Options Synthesis (CmOS), a novel framework for generating educational MCQs with visual options. Our framework integrates Multimodal Chain-of-Thought (MCoT) reasoning process and Retrieval-Augmented Generation (RAG) to produce semantically plausible and visually similar answer and distractors. It also includes a discrimination module to identify content suitable for visual options. Experimental results on test tasks demonstrate the superiority of CmOS in content discrimination, question generation and visual option generation over existing methods across various subjects and educational levels.

cs.CV↗

arXiv2025

Detecting Entanglement via Split Spectroscopy in Many-Body Systems

Quantum entanglement is recognized as a fundamental resource in quantum information processing and is essential for understanding quantum many-body physics. However, experimentally detecting entanglement, particularly in many-particle quantum states, remains a significant challenge. Here, we propose split spectroscopy as an experimentally feasible technique for detecting entanglement of eigenstates in quantum many-body systems. We demonstrate the split spectroscopy exhibits a single delta-function peak if and only if the investigated eigenstate is triseparable. Our framework is illustrated using two paradigmatic spin models that undergo quantum phase transitions. Furthermore, we show that the spectral entropy serves as a powerful indicator of quantum phase transitions and captures the scaling behavior of entanglement. Finally, we present an experimental protocol using Rydberg atom arrays.

quant-ph↗

arXiv2025

Inhibition of whisker growth by crafting more decomposition-resistant Ti2SnC MAX phase through vanadium solid solution

The exceptional synergy of ceramic and metallic properties within MAX phases positions them as highly promising for a wide array of applications. However, their stability has been threatened by the phenomenon of A-site metal whisker growth. Herein, we have significantly mitigated tin whisker growth in Ti2SnC by incorporating vanadium solutes at its M-site. With an increase in vanadium concentration, there is a marked reduction in the degree of decomposition of the M-site solid solution when subjected to the same level of externally destructive treatments, thereby inhibiting whisker proliferation. Both experimental outcomes and theoretical calculations reveal that the vanadium solid solution augments the hardness, Pughs ratio, and Poissons ratio of Ti2SnC, enhancing its mechanical strength and toughness. The incorporation of V atoms introduces stronger V C and V Sn bonds, as evidenced by its electronic density of states and bulk modulus, thus significantly bolstering the resistance of MAX phases against decomposition and effectively curtailing whisker growth. Additionally, the phenomenon reported in this paper also conforms to the electrostatic theory of whisker growth. This work for the first time achieves the suppression of A-site whisker growth through an M-site solid solution, thereby extending their potential for applications where durability and reliability are paramount.

cond-mat.mtrl-sci↗

arXiv2025

Topological Invariants in Nonlinear Thouless Pumping of Solitons

Recent explorations of quantized solitons transport in optical waveguides have thrust nonlinear topological pumping into the spotlight. In this work, we introduce a unified topological invariant applicable across both weakly and strongly nonlinear regimes. In the weak nonlinearity regime, where the nonlinear bands are wellseparated, the invariant reduces to the Abelian Chern number of the occupied nonlinear band. Consequently, the pumped charge is quantized to an integer value. As the nonlinearity increases, the nonlinear bands start to intertwine, leading to a situation where the invariant is expressed as the non-Abelian Chern number divided by the number of interacting bands. This could result in a fractional quantization of the pumped charge. Our unified topological invariant approach not only advances the understanding of the soliton dynamics, but also provides implications for the future design of nonlinear topological systems.

physics.atom-ph↗

arXiv2025

CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation

Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable test cases, have inherent limitation in flexibility and scalability. The recent LLM-as-Judge methodology offers a promising alternative by directly evaluating functional consistency between the problem description and the generated code. To systematically understand the landscape of these LLM-as-Judge methods, we conduct a comprehensive empirical study across three diverse datasets. Our investigation reveals the pros and cons of two categories of LLM-as-Judge methods: the methods based on general foundation models can achieve good performance but require complex prompts and lack explainability, while the methods based on reasoning foundation models provide better explainability with simpler prompts but demand substantial computational resources due to their large parameter sizes. To address these limitations, we propose CODE-DITING, a novel code evaluation method that balances accuracy, efficiency and explainability. We develop a data distillation framework that effectively transfers reasoning capabilities from DeepSeek-R1671B to our CODE-DITING 1.5B and 7B models, significantly enhancing evaluation explainability and reducing the computational cost. With the majority vote strategy in the inference process, CODE-DITING 1.5B outperforms all models with the same magnitude of parameters and achieves performance which would normally exhibit in a model with 5 times of parameter scale. CODE-DITING 7B surpasses GPT-4o and DeepSeek-V3 671B, even though it only uses 1% of the parameter volume of these large models. Further experiments show that CODEDITING is robust to preference leakage and can serve as a promising alternative for code evaluation.

cs.SE↗

arXiv2025

TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation

Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based simulators struggle to capture complex human interactions, while data-driven approaches often fail to maintain long-term behavioral realism or generate diverse safety-critical events. To address these challenges, we propose TeraSim, an open-source, high-fidelity traffic simulation platform designed to uncover unknown unsafe events and efficiently estimate AV statistical performance metrics, such as crash rates. TeraSim is designed for seamless integration with third-party physics simulators and standalone AV stacks, to construct a complete AV simulation system. Experimental results demonstrate its effectiveness in generating diverse safety-critical events involving both static and dynamic agents, identifying hidden deficiencies in AV systems, and enabling statistical performance evaluation. These findings highlight TeraSim's potential as a practical tool for AV safety assessment, benefiting researchers, developers, and policymakers. The code is available at https://github.com/mcity/TeraSim.

cs.RO↗

arXiv2025

Quantum Simulation with Gauge Fixing: from Ising Lattice Gauge Theory to Dynamical Flux Model

Quantum simulation of synthetic dynamic gauge field has attracted much attentions in recent years. There are two traditional ways to simulate gauge theories. One is to directly simulate the full Hamiltonian of gauge theories with local gauge symmetries. And the other is to engineer the projected Hamiltonian in one gauge subsector. In this work, we provide the third way towards the simulation of gauge theories based on gauge fixing. To demonstrate this concept, we fix the gauge of an Ising lattice gauge field coupled with spinless fermions on a ladder geometry. After the gauge fixing, this gauge theory is reduced to a simpler model, in which fermions hop on a ladder with a fluctuating dynamical $\mathbb{Z}_{2}$ flux. Then we shows that this model can be realized via Floquet engineering in ultracold atomic gases. By analytical and numerical studies of this dynamical flux model, we deduce that there is confinement to deconfinement phase transition in the original unfixed gauge theory. This work paves the way to quantum simulate lattice gauge theory using the concept of gauge fixing, relevant both for condensed matter and high energy physics.

cond-mat.quant-gas↗

arXiv2025

LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence

Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product planning, inventory optimization, etc. Specifically, these tasks expecting intelligent approaches to learn from sequentially collected historical data and then foresee most possible trend, i.e. time series forecasting. Challenge of it lies in interpreting complex business contexts and the efficiency and generalisation of modelling. With aspirations of pre-trained foundational models for such purpose, given their remarkable success of large foundation model across legions of tasks, we disseminate \leforecast{}, an enterprise intelligence platform tailored for time series tasks. It integrates advanced interpretations of time series data and multi-source information, and a three-pillar modelling engine combining a large foundation model (Le-TSFM), multimodal model and hybrid model to derive insights, predict or infer futures, and then drive optimisation across multiple sectors in enterprise operations. The framework is composed by a model pool, model profiling module, and two different fusion approaches regarding original model architectures. Experimental results verify the efficiency of our trail fusion concepts: router-based fusion network and coordination of large and small models, resulting in high costs for redundant development and maintenance of models. This work reviews deployment of LeForecast and its performance in three industrial use cases. Our comprehensive experiments indicate that LeForecast is a profound and practical platform for efficient and competitive performance. And we do hope that this work can enlighten the research and grounding of time series techniques in accelerating enterprise.

cs.LG↗

arXiv2024

A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning

Cross-domain recommendation (CDR) methods are proposed to tackle the sparsity problem in click through rate (CTR) estimation. Existing CDR methods directly transfer knowledge from the source domains to the target domain and ignore the heterogeneities among domains, including feature dimensional heterogeneity and latent space heterogeneity, which may lead to negative transfer. Besides, most of the existing methods are based on single-source transfer, which cannot simultaneously utilize knowledge from multiple source domains to further improve the model performance in the target domain. In this paper, we propose a centralized-distributed transfer model (CDTM) for CDR based on multi-source heterogeneous transfer learning. To address the issue of feature dimension heterogeneity, we build a dual embedding structure: domain specific embedding (DSE) and global shared embedding (GSE) to model the feature representation in the single domain and the commonalities in the global space,separately. To solve the latent space heterogeneity, the transfer matrix and attention mechanism are used to map and combine DSE and GSE adaptively. Extensive offline and online experiments demonstrate the effectiveness of our model.

cs.LG↗

arXiv2024

Fate of the spatial-temporal order under quantum fluctuation

In a previous theoretical work [arXiv:2205.01461], T. Esslinger group proposed a scheme to realize a spatial-temporal lattice, which possesses dual periodicity on space and time, in a cavity-boson system pumped by a travelling wave laser. However, the prediction was made under the mean-field approximation. In this work, we investigate the dynamics beyond mean-field approximation. By including the fluctuation of the cavity field, we obtain a larger set of equations of motion. Numerical results show that the spatial-temporal lattice is melted in the mean-field level but survives in the quantum fluctuation.

cond-mat.quant-gas↗

arXiv2024

IDP-PGFE: An Interpretable Disruption Predictor based on Physics-Guided Feature Extraction

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. Understanding why a predictor makes a certain prediction can be as crucial as the prediction's accuracy for future tokamak disruption predictors. The purpose of most disruption predictors is accuracy or cross-machine capability. However, if a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption and gives us insight into the mechanism of disruption. This paper designs a disruption predictor called Interpretable Disruption Predictor based On Physics-guided feature extraction (IDP-PGFE) on J-TEXT. The prediction performance of the model is effectively improved by extracting physics-guided features. A high-performance model is required to ensure the validity of the interpretation results. The interpretability study of IDP-PGFE provides an understanding of J-TEXT disruption and is generally consistent with existing comprehension of disruption. IDP-PGFE has been applied to the disruption due to continuously increasing density towards density limit experiments on J-TEXT. The time evolution of the PGFE features contribution demonstrates that the application of ECRH triggers radiation-caused disruption, which lowers the density at disruption. While the application of RMP indeed raises the density limit in J-TEXT. The interpretability study guides intuition on the physical mechanisms of density limit disruption that RMPs affect not only the MHD instabilities but also the radiation profile, which delays density limit disruption.

physics.plasm-ph↗

arXiv2024

Partial confinement in a quantum-link simulator

Confinement/deconfinement, captivating attributes of high-energy elementary particles, have recently garnered wide attention in quantum simulations based on cold atoms. Yet, the partial confinement, an intermediate state between the confinement and deconfinement, remains underexplored. The partial confinement encapsulates the phenomenon that the confining behavior of charged particles is contingent upon their relative positions. In this paper, we demonstrate that the spin-1 quantum link model provides an excellent platform for exploring partial confinement. We conduct a comprehensive investigation of the physics emerging from partial confinement in both the context of equilibrium and non-equilibrium dynamics. Potential experimental setups using cold atoms are also discussed. Our work offers a simple and feasible routine for the study of confinement-related physics in the state-of-the-art artificial quantum systems subject to gauge symmetries.

cond-mat.quant-gas↗

arXiv2024

Morphing median fin enhances untethered bionic robotic tuna's linear acceleration and turning maneuverability

Median fins of fish-like swimmers play a crucial role in linear acceleration and maneuvering processes. However, few research focused on untethered robotic fish experiments. Imitating the behaviour of real tuna, we developed a free-swimming bionic tuna with a foldable dorsal fin. The erection of dorsal fin, at proper conditions, can reduce head heave by 50%, enhance linear acceleration by 15.7%, increase turning angular velocity by 32.78%, and turning radius decreasing by 33.13%. Conversely, erecting the dorsal fin increases the wetted surface area, resulting in decreased maximum speed and efficiency during steady swimming phase. This finding partially explains why tuna erect their median fins during maneuvers or acceleration and fold them afterward to reduce drag. In addition, we verified that folding the median fins after acceleration does not significantly affect locomotion efficiency. This study supports the application of morphing median fins in undulating underwater robots and helps to further understand the impact of median fins on fish locomotion.

cs.RO↗

arXiv2024

Universal Aspects of High-Temperature Relaxation Dynamics in Random Spin Models

Universality is a crucial concept in modern physics, allowing us to capture the essential features of a system's behavior using a small set of parameters. In this work, we unveil universal spin relaxation dynamics in anisotropic random Heisenberg models with infinite-range interactions at high temperatures. Starting from a polarized state, the total magnetization can relax monotonically or decay with long-lived oscillations, determined by the sign of a universal single function $A=-ξ_1^2+ξ_2^2-4ξ_2ξ_3+ξ_3^2$. Here $(ξ_1,ξ_3,ξ_3)$ characterizes the anisotropy of the Heisenberg interaction. Furthermore, the oscillation shows up only for $A>0$, with frequency $Ω\propto \sqrt{A}$. To validate our theory, we compare it to numerical simulations by solving the Kadanoff-Baym (KB) equation with a melon diagram approximation and the exact diagonalization (ED). The results show our theoretical prediction works in both cases, regardless of a small system size $N=8$ in ED simulations. Our study sheds light on the universal aspect of quantum many-body dynamics beyond low energy limit.

cond-mat.str-el↗