arXiv subjects

Wei Qi

Publications and source records attributed to Wei Qi.

At least 37 records · Page 2Linked to original sources

arXiv2026

Efficient Generation of Neutrons Based on Ultrashort Laser-driven Direct Acceleration in Microwire-Array Targets

We report on an experimental demonstration of efficient neutron generation based on direct laser acceleration in microwire-array targets irradiated by ultrashort (tens of femtoseconds) laser pulses. The optimal array period was identified, at which the maximum proton energy and the number of protons with energies exceeding $1~\mathrm{MeV}$ were significantly increased. Using a $1~\mathrm{PW}$, $\sim25~\mathrm{fs}$ laser at a moderate intensity of $\sim10^{20}~\mathrm{W/cm^2}$, a high neutron yield of up to $(8.33\pm0.84)\times10^{6}~\mathrm{n/sr/J}$ was detected from the LiD converter via $^7\mathrm{Li}(p,n)$ and $\mathrm{D}(p,n+p)$ nuclear reactions. Self-consistent integrated simulations reproduced the experimental results and predicted that with a Be converter, a forward pulsed neutron source with an unprecedented yield per joule of $3.67\times10^{7}~\mathrm{n/sr/J}$ can be obtained under identical laser conditions. This type of neutron source is favorable for applications that require a high repetition rate utilizing compact and economical laser systems.

physics.plasm-ph↗

arXiv2026

ActivityEditor: Learning to Synthesize Physically Valid Human Mobility

Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limiting their applicability in regions where historical trajectories are unavailable or restricted. To bridge this gap, we propose \textbf{ActivityEditor}, a novel dual-LLM-agent framework designed for zero-shot cross-regional trajectory generation. Our framework decomposes the complex synthesis task into two collaborative stages. Specifically, an intention-based agent, which leverages demographic-driven priors to generate structured human intentions and coarse activity chains to ensure high-level socio-semantic coherence. These outputs are then refined by editor agent to obtain mobility trajectories through iteratively revisions that enforces human mobility law. This capability is acquired through reinforcement learning with multiple rewards grounded in real-world physical constraints, allowing the agent to internalize mobility regularities and ensure high-fidelity trajectory generation. Extensive experiments demonstrate that \textbf{ActivityEditor} achieves superior zero-shot performance when transferred across diverse urban contexts. It maintains high statistical fidelity and physical validity, providing a robust and highly generalizable solution for mobility simulation in data-scarce scenarios. Our code is available at: https://anonymous.4open.science/r/ActivityEditor-066B.

cs.AI↗

arXiv2026

Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution

Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather than proactive strategy designers, limiting their effectiveness on complex Constraint Optimization Problems (COPs). To address this, we present AutoCO, an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. A core innovation is a unified triple-representation that binds relaxation strategies, algorithmic principles, and executable codes. This design enables the LLM to synthesize, justify, and instantiate relaxation strategies that are both principled and executable. To navigate fragmented solution spaces, AutoCO employs a bidirectional global-local coevolution mechanism, synergistically coupling Monte Carlo Tree Search (MCTS) for global relaxation-trajectory exploration with Evolutionary Algorithms (EAs) for local solution intensification. This continuous exchange of priors and feedback explicitly balances diversification and intensification, thus preventing premature convergence. Extensive experiments on three challenging COP benchmarks validate AutoCO's consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. Results highlight AutoCO as a principled and effective path toward proactive, verifiable LLM-driven optimization.

cs.AI↗

arXiv2026

Studying Maximal Entanglement and Bell Nonlocality at an Electron-Ion Collider

In this paper, we propose to test quantum entanglement and Bell nonlocality at an Electron-Ion Collider (EIC). By computing the spin correlations in quark-antiquark pairs produced via photon-gluon fusion, we find that longitudinally polarized photons produce maximal entanglement at leading order, while transversely polarized photons generate significant entanglement near the threshold and in the ultra-relativistic regime. Compared to hadron colliders, the EIC provides a cleaner experimental environment for measuring entanglement through the $γ^\ast g \to q\bar{q}$ channel, offering a strong signal and a promising avenue to verify Bell nonlocality. This study extends entanglement measurements to the EIC, presenting new opportunities to explore the interplay of quantum information phenomena and hadronic physics in the EIC era.

hep-ph↗

arXiv2026

A survey on the uniform $S$-version of rings, modules and their homological theories

This survey provides a comprehensive overview of the recent advancements in the theory of ``uniformly $S$''-algebraic structures in commutative ring theory. Originating from the classical concepts of Noetherian, coherent, von Neumann regular, and semisimple rings, the introduction of a multiplicative subset $S$ has led to the development of $S$-Noetherian, $S$-coherent, and other $S$-analogues. However, the element $s \in S$ in the original definitions often depends on the ideal or module under consideration. To overcome this limitation and enable deeper module-theoretic characterizations, the notion of "uniformly $S$" (abbreviated as $u$-$S$) was introduced. This survey systematically presents the definitions, characterizations, and properties of $u$-$S$-torsion modules, $u$-$S$-exact sequences, and the subsequent uniform analogues of fundamental module classes: $u$-$S$-finitely presented, $u$-$S$-Noetherian, $u$-$S$-coherent, $u$-$S$-flat, $u$-$S$-projective, $u$-$S$-injective, and $u$-$S$-absolutely pure modules. We then explore the associated uniform homological dimensions, including the $u$-$S$-weak global dimension, the $u$-$S$-global dimension, and their interplay with polynomial rings and localizations. The survey also covers structural ring classes such as $u$-$S$-von Neumann regular, $u$-$S$-semisimple, $u$-$S$-Artinian, $u$-$S$-multiplication rings, and rings with $u$-$S$-Noetherian spectrum.

math.AC↗

arXiv2026

A Quantitative Method for Evaluating Security Boundaries in Quantum Key Distribution Combined with Block Ciphers

With the rapid development of quantum computing, classical cryptography systems are increasingly vulnerable to security threats, thereby highlighting the urgency of constructing architectures that are resilient to quantum computing attacks. While Quantum Key Distribution (QKD) offers security with information-theoretic guarantees, its relatively low key generation rate necessitates integration with classical cryptographic techniques, particularly block ciphers such as AES and SM4, to facilitate practical applications. However, when a single QKD-key is employed to encrypt multiple data blocks, the reduction in cryptographic security strength has not yet been quantitatively analyzed. In this work, we focus on the security strength in the application scenario where QKD is combined with block ciphers. We propose a quantitative evaluation method for the security benefits of the QKD-key renewal period, aiming to provide a precise measure of the cryptographic security strength in such hybrid systems. Our method is based on concrete security paradigm of block cipher modes of operation. We demonstrate that under practical security level requirements, for files consisting of specific blocks, rekeying k times can provide an additional log2(k) to 2log2(k) bits of security. Our research offers a novel perspective on balancing the security and efficiency of QKD-based encryption.

cs.CR↗

arXiv2026

QDBFT: A Dynamic Consensus Algorithm for Quantum-Secured Blockchain

The security foundation of blockchain system relies primarily on classical cryptographic methods and consensus algorithms. However, the advent of quantum computing poses a significant threat to conventional public-key cryptosystems based on computational hardness assumptions. In particular, Shor's algorithm can efficiently solve discrete logarithm and integer factorization problems in polynomial time, thereby undermining the immutability and security guarantees of existing systems. Moreover, current Practical Byzantine Fault Tolerance (PBFT) protocols, widely adopted in consortium blockchains, suffer from high communication overhead and limited efficiency when coping with dynamic node reconfigurations, while offering no intrinsic protection against quantum adversaries. To address these challenges, we propose QDBFT, a quantum-secured dynamic consensus algorithm, with two main contributions: first,we design a primary node automatic rotation mechanism based on a consistent hash ring to enable consensus under dynamic membership changes, ensuring equitable authority distribution; second, we integrate Quantum Key Distribution (QKD) networks to provide message authentication for inter-node communication, thereby achieving information-theoretic security in the consensus process. Experimental evaluations demonstrate that QDBFT achieves performance comparable to traditional PBFT while delivering strong resilience against quantum attacks, making it a promising solution for future quantum-secure decentralized infrastructures.

cs.CR↗

arXiv2026

A Dual-TransUNet Deep Learning Framework for Multi-Source Precipitation Merging and Improving Seasonal and Extreme Estimates

Multi-source precipitation products (MSPs) from satellite retrievals and reanalysis are widely used for hydroclimatic monitoring, yet spatially heterogeneous biases and limited skill for extremes still constrain their hydrologic utility. Here we develop a dual-stage TransUNet-based multi-source precipitation merging framework (DDL-MSPMF) that integrates six MSPs with four ERA5 near-surface physical predictors. A first-stage classifier estimates daily precipitation occurrence probability, and a second-stage regressor fuses the classifier outputs together with all predictors to estimate daily precipitation amount at 0.25 degree resolution over China for 2001-2020. Benchmarking against multiple deep learning and hybrid baselines shows that the TransUNet - TransUNet configuration yields the best seasonal performance (R = 0.75; RMSE = 2.70 mm/day) and improves robustness relative to a single-regressor setting. For heavy precipitation (>25 mm/day), DDL-MSPMF increases equitable threat scores across most regions of eastern China and better reproduces the spatial pattern of the July 2021 Zhengzhou rainstorm, indicating enhanced extreme-event detection beyond seasonal-mean corrections. Independent evaluation over the Qinghai-Tibet Plateau using TPHiPr further supports its applicability in data-scarce regions. SHAP analysis highlights the importance of precipitation occurrence probabilities and surface pressure, providing physically interpretable diagnostics. The proposed framework offers a scalable and explainable approach for precipitation fusion and extreme-event assessment.

cs.LG↗

arXiv2026

Rethinking Supply Chain Planning: A Generative Paradigm

Supply chain planning is the critical process of anticipating future demand and coordinating operational activities across the logistics network. However, within the context of contemporary e-commerce, traditional planning paradigms, typically characterized by fragmented processes and static optimization, prove inadequate in addressing dynamic demand, organizational silos, and the complexity of multi-stage coordination. To address these challenges, this study proposes a fundamental rethinking of supply chain planning, redefining it not merely as a computational task, but as an interactive, integrated, and automated cognitive process. This new paradigm emphasizes the organic unification of human strategic intent with adaptive execution, shifting the focus from rigid control to continuous, intelligent orchestration. To operationalize this conceptual shift, we introduce a Generative AI-powered agentic framework. Functioning as an intelligent cognitive interface, this framework bridges the gap between unstructured business contexts and structured analytical workflows, enabling the system to comprehend complex semantics and coordinate decisions across organizational boundaries. We demonstrate the empirical validity of this approach within JD.com's large-scale operations. The deployment confirms the efficacy of this cognitive paradigm, yielding an approximate 22% improvement in planning accuracy and a 2% increase in in-stock rates, thereby validating the transformation of planning into an adaptive, knowledge-driven capability.

cs.AI↗

arXiv2026

ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management

As the pursuit of synergy between Artificial Intelligence (AI) and Operations Research (OR) gains momentum in handling complex inventory systems, a critical challenge persists: how to effectively reconcile AI's adaptive perception with OR's structural rigor. To bridge this gap, we propose a novel OR-Guided "Pretrain-then-Reinforce" framework. To provide structured guidance, we propose a simulation-augmented OR model that generates high-quality reference decisions, implicitly capturing complex business constraints and managerial preferences. Leveraging these OR-derived decisions as foundational training labels, we design a domain-informed deep learning foundation model to establish foundational decision-making capabilities, followed by a reinforcement learning (RL) fine-tuning stage. Uniquely, we position RL as a deep alignment mechanism that enables the AI agent to internalize the optimality principles of OR, while simultaneously leveraging exploration for general policy refinement and allowing expert guidance for scenario-specific adaptation (e.g., promotional events). Validated through extensive numerical experiments and a field deployment at JD.com augmented by a Difference-in-Differences (DiD) analysis, our model significantly outperforms incumbent industrial practices, delivering real-world gains of a 5.27-day reduction in turnover and a 2.29% increase in in-stock rates, alongside a 29.95% decrease in holding costs. Contrary to the prevailing trend of brute-force model scaling, our study demonstrates that a lightweight, domain-informed model can deliver state-of-the-art performance and robust transferability when guided by structured OR logic. This approach offers a scalable and cost-effective paradigm for intelligent supply chain management, highlighting the value of deeply aligning AI with OR.

cs.AI↗

arXiv2026

Some remarks on $u$-$S$-Noetherian and $u$-$S$-coherent rings

In this paper, we give some new characterizations of $u$-$S$-Noetherian rings and $u$-$S$-coherent rings in terms of uniform $S$-version of injective precovers, flat preenvelopes and absolutely pure modules, respectively. Moreover, we give a negative answer to a question proposed by Bouziri [3].

math.AC↗

arXiv2025

Optimizing Urban Electric Vehicle Charging and Battery Swapping Infrastructure: A Location-Inventory-Grid Model

The rapid rise of electric vehicles (EVs) places unprecedented stress on both urban mobility systems and low-voltage power grids. Designing battery swapping and charging networks that are cost-efficient, grid-compatible, and sustainable is therefore a pressing yet complex challenge: service providers must jointly optimize station locations, battery inventory, and grid interaction under high-dimensional uncertainty. We develop an integrated location-inventory-grid model and employ a continuous approximation approach to overcome the intractability of discrete formulations. Our analysis compares centralized versus decentralized charging, with and without participation in frequency regulation. The results reveal that centralized charging, when combined with frequency regulation, not only reduces cost but also strengthens grid stability. However, it may constrain operational flexibility near the optimum, potentially challenging efforts to mitigate environmental impacts by lowering battery inventories. These results offer actionable guidance for cost-efficient, environmentally sustainable, and grid-compatible scaling of urban EV infrastructure to meet the demands of large-scale EV adoption.

math.OC↗

arXiv2025

Express Diagnostic of Intense Laser-driven MeV Radiation Source using Copper Isotopes

We explored the generation and diagnosis of high-brightness MeV bremsstrahlung radiation caused by intense beam of relativistic electrons propagating in a tantalum converter. The intense electron beam was produced through direct laser acceleration mechanism in the interaction of relativistic high-power sub-ps laser pulse with near critical density plasma. We propose to detect the divergence angle and photon fluence of high-brightness and high-energy gamma radiation source based on the nuclear activation method. The radioactive 62^Cu was generated through photonuclear reactions 63^Cu(gamma,n) 62^Cu and the subsequent beta^+ decay of 62^Cu was measured to derive characteristics of the gamma radiation source. This method provides an express approach to diagnose the laser-driven MeV radiation source and a potential efficient way to produce 62^Cu isotopes.

physics.acc-ph↗

arXiv2025

RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations

Efficient management of electric ride-hailing fleets, particularly pre-allocation and pricing during peak periods to balance spatio-temporal supply and demand, is crucial for urban traffic efficiency. However, practical challenges include unpredictable demand and translating diverse, qualitative managerial objectives from non-expert operators into tractable optimization models. This paper introduces RideAgent, an LLM-powered agent framework that automates and enhances electric ride-hailing fleet management. First, an LLM interprets natural language queries from fleet managers to formulate corresponding mathematical objective functions. These user-defined objectives are then optimized within a Mixed-Integer Programming (MIP) framework, subject to the constraint of maintaining high operational profit. The profit itself is a primary objective, estimated by an embedded Random Forest (RF) model leveraging exogenous features. To accelerate the solution of this MIP, a prompt-guided LLM analyzes a small sample of historical optimal decision data to guide a variable fixing strategy. Experiments on real-world data show that the LLM-generated objectives achieve an 86% text similarity to standard formulations in a zero-shot setting. Following this, the LLM-guided variable fixing strategy reduces computation time by 53.15% compared to solving the full MIP with only a 2.42% average optimality gap. Moreover, this variable fixing strategy outperforms five cutting plane methods by 42.3% time reduction with minimal compromise to solution quality. RideAgent offers a robust and adaptive automated framework for objective modeling and accelerated optimization. This framework empowers non-expert fleet managers to personalize operations and improve urban transportation system performance.

math.OC↗

arXiv2025

Long-distance free-space quantum key distribution with continuous variables

Continuous-variable quantum key distribution (CVQKD) enables remote users to share high-rate and unconditionally secure secret keys while maintaining compatibility with classical optical communication networks and effective resistance against background noise. However, CVQKD experiments have only been demonstrated indoors or over short outdoor distances. Here, by developing channel-fluctuation-independent high-precision manipulation of continuous-variable quantum states, high-accuracy quantum signal acquisition and processing, and high-efficiency free-space acquisition, tracking, and pointing technology, we overcome the excess noise due to atmospheric effects especially in daylight without extra wavelength conversion and spectral filtering, and demonstrate for the first time long-distance free-space quantum key distribution over 7-km inland and 9.6-km maritime atmospheric channels with Gaussian-modulated coherent states. This achieved distribution distance of secure quantum secret keys is well beyond the atmosphere's effective thickness, offering a promising alternative for realizing satellite-based quantum cryptography communication in daylight. Moreover, given that the CVQKD system is naturally compatible with existing ground fiber telecommunication networks, it marks an essential step for realizing integrated air-ground quantum access networks with cross-domain applications.

quant-ph↗

arXiv2025

On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution

United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation's default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Default Index spreads) and the necessity for asset holders like mutual funds to liquidate assets, including IG credits, to manage margin calls, bolster cash reserves, or meet redemptions. These credit and liquidity premia occur during market drawdowns and tend to move non-linearly with the market. The research herein refers to this non-linearity (during periods of drawdown) as downside convexity, and shows that this market behavior can effectively be captured through a short position established in IG Exchange Traded Funds (ETFs). The following document details the construction of three signals: Momentum, Liquidity, and Credit, that can be used in combination to signal entries and exits into short IG positions to hedge a typical active bond portfolio (such as PIMIX). A dynamic hedge initiates the short when signals jointly correlate and point to significant future hedged return. The dynamic hedge removes when the short position's predicted hedged return begins to mean revert. This systematic hedge largely avoids IG Credit drawdowns, lowers absolute and downside risk, increases annualised returns and achieves higher Sortino ratios compared to the benchmark funds. The method is best suited to high carry, high active risk funds like PIMIX, though it also generalises to more conservative funds similar to DODIX.

q-fin.PM↗

arXiv2025

On Coordinated Drone-Courier Logistics for Intra-city Express Services

Problem definition: Drones, despite being acknowledged as a transformative force in the city logistics sector, are unable to execute the \textit{last-meter delivery} (unloading goods directly to customers' doorsteps) due to airspace restrictions and safety concerns. To leverage advancements and overcome the limitations of drones in providing intra-city express services, we introduce a coordinated drone-courier logistics system where drones operate within a closed network among vertiports, while couriers connect customers to the drone delivery system. This paper aims to shed light on this coordinated system in terms of system feasibility, network interactivity, and long-term sustainability. Methodology/Results: We develop an integrated optimization model to optimize the network planning of the coordinated logistics system. The interplay between network planning and tactical operations is mirrored by a queueing network model, resulting in the nonlinear and nonconvex (partially convex and partially concave) feasible region of the optimization model. An iterative exact algorithm that tightens lower and upper bounds by adaptively refining the linear approximations of nonlinear constraints is developed to provide optimality-guaranteed solutions with finite convergence. The computational experiments demonstrate the scalability and robustness of our algorithm across various network configurations and scenarios.Managerial implications: The case study, based on a real-world dataset from SF Express, a logistics giant in China, validates that the coordinated logistics system efficiently attains cost and time savings by leveraging the effective turnover of drones and the coordination between drones and couriers. The optimal network design features a concentrated structure, streamlining demand consolidation and reducing deadhead repositioning.

math.OC↗

arXiv2024

A note on pure-semisimple rings

In this note, we give several characterizations of left pure-semisimple in terms of the (pre)envelope, (pre)cover, direct limits, direct sums, inverse limits and direct products properties of pure-projective modules or pure-injective modules.

math.RA↗