arXiv subjects

Xinyu Li

Publications and source records attributed to Xinyu Li.

At least 109 records · Page 6Linked to original sources

arXiv2025

Quantifying the Economic Impact of 2025 ICE Raids on California's Agricultural Industry: A Case Study of Oxnard

In 2025, intensified Immigration and Customs Enforcement (ICE) raids in Oxnard, California, disrupted the state's \$49 billion agricultural industry, a critical supplier of 75% of U.S. fruits and nuts and one-third of its vegetables. This paper quantifies the economic consequences of these raids on labor markets, crop production, and food prices using econometric modeling. We estimate a 20-40% reduction in the agricultural workforce, leading to \$3-7 billion in crop losses and a 5-12% increase in produce prices. The analysis draws on USDA Economic Research Service data and recent ICE detention figures, which show arrests in Southern California rising from 699 in May to nearly 2,000 in June 2025. The raids disproportionately affect labor-intensive crops like strawberries, exacerbating supply chain disruptions. Policy recommendations include expanding the H-2A visa program and legalizing undocumented workers to stabilize the sector. This study contributes to agricultural economics by providing a data-driven assessment of immigration enforcement's economic toll.

econ.GN↗

arXiv2025

Benchmarking Sub-Genre Classification For Mainstage Dance Music

Music classification, a cornerstone of music information retrieval, supports a wide array of applications. To address the lack of comprehensive datasets and effective methods for sub-genre classification in mainstage dance music, we introduce a novel benchmark featuring a new dataset and baseline. Our dataset expands the scope of sub-genres to reflect the diversity of recent mainstage live sets performed by leading DJs at global music festivals, capturing the vibrant and rapidly evolving electronic dance music (EDM) scene that engages millions of fans worldwide. We employ a continuous soft labeling approach to accommodate tracks blending multiple sub-genres, preserving their inherent complexity. Experiments demonstrate that even state-of-the-art multimodal large language models (MLLMs) struggle with this task, while our specialized baseline models achieve high accuracy. This benchmark supports applications such as music recommendation, DJ set curation, and interactive multimedia systems, with video demos provided. Our code and data are all open-sourced at https://github.com/Gariscat/housex-v2.git.

cs.SD↗

arXiv2025

Mitigating Financial Frictions in Agriculture: A Framework for Stablecoin Adoption

Persistent financial frictions - including price volatility, constrained credit access, and supply chain inefficiencies - have long hindered productivity and welfare in the global agricultural sector. This paper provides a theoretical and applied analysis of how fiat-collateralized stablecoins, a class of digital currency pegged to a stable asset like the U.S. dollar, can address these long-standing challenges. We develop a farm-level profit maximization model incorporating transaction costs and credit constraints to demonstrate how stablecoins can enhance economic outcomes by (1) reducing the costs and risks of cross-border trade, (2) improving the efficiency and transparency of supply chain finance through smart contracts, and (3) expanding access to credit for smallholder farmers. We analyze key use cases, including parametric insurance and trade finance, while also considering the significant hurdles to adoption, such as regulatory uncertainty and the digital divide. The paper concludes that while not a panacea, stablecoins represent a significant financial technology with the potential to catalyze a paradigm shift in agricultural economics, warranting further empirical investigation and policy support.

econ.GN↗

arXiv2025

Purcell enhancement of photogalvanic currents in a van der Waals plasmonic self-cavity

Cavities provide a means to manipulate the optical and electronic responses of quantum materials by selectively enhancing light-matter interaction at specific frequencies and momenta. While cavities typically involve external structures, exfoliated flakes of van der Waals (vdW) materials can form intrinsic self-cavities due to their small finite dimensions, confining electromagnetic fields into plasmonic cavity modes, characterized by standing-wave current distributions. While cavity-enhanced phenomena are well-studied at optical frequencies, the impact of self-cavities on nonlinear electronic responses--such as photogalvanic currents--remains largely unexplored, particularly in the terahertz regime, critical for emerging ultrafast optoelectronic technologies. Here, we report a self-cavity-induced Purcell enhancement of photogalvanic currents in the vdW semimetal WTe$_2$. Using ultrafast optoelectronic circuitry, we measured coherent near-field THz emission resulting from nonlinear photocurrents excited at the sample edges. We observed enhanced emission at finite frequencies, tunable via excitation fluence and sample geometry, which we attribute to plasmonic interference effects controlled by the cavity boundaries. We developed an analytical theory that captures the cavity resonance conditions and spectral response across multiple devices. Our findings establish WTe$_2$ as a bias-free, geometry-tunable THz emitter and demonstrate the potential of self-cavity engineering for controlling nonlinear, nonequilibrium dynamics in quantum materials.

cond-mat.mes-hall↗

arXiv2025

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware

Neuromorphic hardware aims to leverage distributed computing and event-driven circuit design to achieve an energy-efficient AI system. The name "neuromorphic" is derived from its spiking and local computing nature, which mimics the fundamental activity of an animal's nervous system. In neuromorphic hardware, neurons, i.e., computing cores use single-bit, event-driven data (called spikes) for inter-communication, which differs substantially from conventional hardware. To leverage the advantages of neuromorphic hardware and implement a computing model, the conventional approach is to build spiking neural networks (SNNs). SNNs replace the nonlinearity part of artificial neural networks (ANNs) in the realm of deep learning with spiking neurons, where the spiking neuron mimics the basic behavior of bio-neurons. However, there is still a performance gap between SNNs and their ANN counterparts. In this paper, we explore a new way to map computing models onto neuromorphic hardware. We propose a Spiking-Driven ANN (SDANN) framework that directly implements quantized ANN on hardware, eliminating the need for tuning the trainable parameters or any performance degradation. With the power of quantized ANN, our SDANN ensures a lower bound of implementation performance on neuromorphic hardware. To address the limitation of bit width support on hardware, we propose bias calibration and scaled integration methods. Experiments on various tasks demonstrate that our SDANN achieves exactly the same accuracy as the quantized ANN. Beyond toy examples and software implementation, we successfully deployed and validated our spiking models on real neuromorphic hardware, demonstrating the feasibility of the SDANN framework.

cs.NE↗

arXiv2025

A stableness of resistance model for nonresponse adjustment with callback data

Nonresponse arises frequently in surveys and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such data have not been widely used in statistical analysis until recently. We propose a stableness of resistance assumption for nonresponse adjustment with callback data. We establish the identification and the semiparametric efficiency theory under this assumption, and propose a suite of semiparametric estimation methods including doubly robust estimators, which generalize existing parametric approaches for callback data analysis. We apply the approach to a Consumer Expenditure Survey dataset. The results suggest an association between nonresponse and high housing expenditures.

stat.ME↗

arXiv2025

TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents

We introduce TimeSeriesGym, a scalable benchmarking framework for evaluating Artificial Intelligence (AI) agents on time series machine learning engineering challenges. Existing benchmarks lack scalability, focus narrowly on model building in well-defined settings, and evaluate only a limited set of research artifacts (e.g., CSV submission files). To make AI agent benchmarking more relevant to the practice of machine learning engineering, our framework scales along two critical dimensions. First, recognizing that effective ML engineering requires a range of diverse skills, TimeSeriesGym incorporates challenges from diverse sources spanning multiple domains and tasks. We design challenges to evaluate both isolated capabilities (including data handling, understanding research repositories, and code translation) and their combinations, and rather than addressing each challenge independently, we develop tools that support designing multiple challenges at scale. Second, we implement evaluation mechanisms for multiple research artifacts, including submission files, code, and models, using both precise numeric measures and more flexible LLM-based evaluation approaches. This dual strategy balances objective assessment with contextual judgment. Although our initial focus is on time series applications, our framework can be readily extended to other data modalities, broadly enhancing the comprehensiveness and practical utility of agentic AI evaluation. We open-source our benchmarking framework to facilitate future research on the ML engineering capabilities of AI agents.

cs.LG↗

arXiv2025

CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection

Temporal medical image analysis is essential for clinical decision-making, yet existing methods either align images and text at a coarse level - causing potential semantic mismatches - or depend solely on visual information, lacking medical semantic integration. We present CheXLearner, the first end-to-end framework that unifies anatomical region detection, Riemannian manifold-based structure alignment, and fine-grained regional semantic guidance. Our proposed Med-Manifold Alignment Module (Med-MAM) leverages hyperbolic geometry to robustly align anatomical structures and capture pathologically meaningful discrepancies across temporal chest X-rays. By introducing regional progression descriptions as supervision, CheXLearner achieves enhanced cross-modal representation learning and supports dynamic low-level feature optimization. Experiments show that CheXLearner achieves 81.12% (+17.2%) average accuracy and 80.32% (+11.05%) F1-score on anatomical region progression detection - substantially outperforming state-of-the-art baselines, especially in structurally complex regions. Additionally, our model attains a 91.52% average AUC score in downstream disease classification, validating its superior feature representation.

cs.CV↗

arXiv2025

Fine-grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams

Local Differential Privacy (LDP) enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types (e.g., categorical, numerical, and graph data) and application settings (e.g., static and streaming). Recent findings indicate that LDP protocols can be easily disrupted by poisoning or manipulation attacks, which leverage injected/corrupted fake users to send crafted data conforming to the LDP reports. However, current attacks primarily target static protocols, neglecting the security of LDP protocols in the streaming settings. Our research fills the gap by developing novel fine-grained manipulation attacks to LDP protocols for data streams. By reviewing the attack surfaces in existing algorithms, We introduce a unified attack framework with composable modules, which can manipulate the LDP estimated stream toward a target stream. Our attack framework can adapt to state-of-the-art streaming LDP algorithms with different analytic tasks (e.g., frequency and mean) and LDP models (event-level, user-level, w-event level). We validate our attacks theoretically and through extensive experiments on real-world datasets, and finally explore a possible defense mechanism for mitigating these attacks.

cs.CR↗

arXiv2025

RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization

3D reconstruction garners increasing attention alongside the advancement of high-level image applications, where dense stereo matching (DSM) serves as a pivotal technique. Previous studies often rely on publicly available datasets for training, focusing on modifying network architectures or incorporating specialized modules to extract domain-invariant features and thus improve model robustness. In contrast, inspired by single-frame structured-light phase-shifting encoding, this study introduces RGB-Speckle, a cross-scene 3D reconstruction framework based on an active stereo camera system, designed to enhance robustness. Specifically, we propose a novel phase pre-normalization encoding-decoding method: first, we randomly perturb phase-shift maps and embed them into the three RGB channels to generate color speckle patterns; subsequently, the camera captures phase-encoded images modulated by objects as input to a stereo matching network. This technique effectively mitigates external interference and ensures consistent input data for RGB-Speckle, thereby bolstering cross-domain 3D reconstruction stability. To validate the proposed method, we conduct complex experiments: (1) construct a color speckle dataset for complex scenarios based on the proposed encoding scheme; (2) evaluate the impact of the phase pre-normalization encoding-decoding technique on 3D reconstruction accuracy; and (3) further investigate its robustness across diverse conditions. Experimental results demonstrate that the proposed RGB-Speckle model offers significant advantages in cross-domain and cross-scene 3D reconstruction tasks, enhancing model generalization and reinforcing robustness in challenging environments, thus providing a novel solution for robust 3D reconstruction research.

eess.IV↗

arXiv2025

AnomalyGen: An Automated Semantic Log Sequence Generation Framework with LLM for Anomaly Detection

The scarcity of high-quality public log datasets has become a critical bottleneck in advancing log-based anomaly detection techniques. Current datasets exhibit three fundamental limitations: (1) incomplete event coverage, (2) artificial patterns introduced by static analysis-based generation frameworks, and (3) insufficient semantic awareness. To address these challenges, we present AnomalyGen, the first automated log synthesis framework specifically designed for anomaly detection. Our framework introduces a novel four-phase architecture that integrates enhanced program analysis with Chain-of-Thought reasoning (CoT reasoning), enabling iterative log generation and anomaly annotation without requiring physical system execution. Evaluations on Hadoop and HDFS distributed systems demonstrate that AnomalyGen achieves substantially broader log event coverage (38-95 times improvement over existing datasets) while producing more operationally realistic log sequences compared to static analysis-based approaches. When augmenting benchmark datasets with synthesized logs, we observe maximum F1-score improvements of 3.7% (average 1.8% improvement across three state-of-the-art anomaly detection models). This work not only establishes a high-quality benchmarking resource for automated log analysis but also pioneers a new paradigm for applying large language models (LLMs) in software engineering workflows.

cs.SE↗

arXiv2025

DeclutterNeRF: Generative-Free 3D Scene Recovery for Occlusion Removal

Recent novel view synthesis (NVS) techniques, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly advanced 3D scene reconstruction with high-quality rendering and realistic detail recovery. Effectively removing occlusions while preserving scene details can further enhance the robustness and applicability of these techniques. However, existing approaches for object and occlusion removal predominantly rely on generative priors, which, despite filling the resulting holes, introduce new artifacts and blurriness. Moreover, existing benchmark datasets for evaluating occlusion removal methods lack realistic complexity and viewpoint variations. To address these issues, we introduce DeclutterSet, a novel dataset featuring diverse scenes with pronounced occlusions distributed across foreground, midground, and background, exhibiting substantial relative motion across viewpoints. We further introduce DeclutterNeRF, an occlusion removal method free from generative priors. DeclutterNeRF introduces joint multi-view optimization of learnable camera parameters, occlusion annealing regularization, and employs an explainable stochastic structural similarity loss, ensuring high-quality, artifact-free reconstructions from incomplete images. Experiments demonstrate that DeclutterNeRF significantly outperforms state-of-the-art methods on our proposed DeclutterSet, establishing a strong baseline for future research.

cs.CV↗

arXiv2025

Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback

Multi-modal recommender systems (MRSs) are pivotal in diverse online web platforms and have garnered considerable attention in recent years. However, previous studies overlook the challenges of (1) noisy multi-modal content, (2) noisy user feedback, and (3) aligning multi-modal content with user feedback. In order to tackle these challenges, we propose Denoising and Aligning Multi-modal Recommender System (DA-MRS). To mitigate multi-modal noise, DA-MRS first constructs item-item graphs determined by consistent content similarity across modalities. To denoise user feedback, DA-MRS associates the probability of observed feedback with multi-modal content and devises a denoised BPR loss. Furthermore, DA-MRS implements Alignment guided by User preference to enhance task-specific item representation and Alignment guided by graded Item relations to provide finer-grained alignment. Extensive experiments verify that DA-MRS is a plug-and-play framework and achieves significant and consistent improvements across various datasets, backbone models, and noisy scenarios.

cs.IR↗

arXiv2025

Markov $α$-Potential Games

We propose a new framework of Markov $α$-potential games to study Markov games. We show that any Markov game with finite-state and finite-action is a Markov $α$-potential game, and establish the existence of an associated $α$-potential function. Any optimizer of an $α$-potential function is shown to be an $α$-stationary Nash equilibrium. We study two important classes of practically significant Markov games, Markov congestion games and the perturbed Markov team games, via the framework of Markov $α$-potential games, with explicit characterization of an upper bound for $α$ and its relation to game parameters. Additionally, we provide a semi-infinite linear programming based formulation to obtain an upper bound for $α$ for any Markov game. Furthermore, we study two equilibrium approximation algorithms, namely the projected gradient-ascent algorithm and the sequential maximum improvement algorithm, along with their Nash regret analysis, and corroborate the results with numerical experiments.

cs.GT↗

arXiv2025

An $α$-potential game framework for $N$-player dynamic games

This paper proposes and studies a general form of dynamic $N$-player non-cooperative games called $α$-potential games, where the change of a player's value function upon her unilateral deviation from her strategy is equal to the change of an $α$-potential function up to an error $α$. Analogous to the static potential game (which corresponds to $α=0$), the $α$-potential game framework is shown to reduce the challenging task of finding $α$-Nash equilibria for a dynamic game to minimizing the $α$-potential function. Moreover, an analytical characterization of $α$-potential functions is established, with $α$ represented in terms of the magnitude of the asymmetry of value functions' second-order derivatives. For stochastic differential games in which the state dynamic is a controlled diffusion, $α$ is characterized in terms of the number of players, the choice of admissible strategies, and the intensity of interactions and the level of heterogeneity among players. Two classes of stochastic differential games, namely distributed games and games with mean field interactions, are analyzed to highlight the dependence of $α$ on general game characteristics that are beyond the mean-field paradigm, which focuses on the limit of $N$ with homogeneous players. To analyze the $α$-NE, the associated optimization problem is embedded into a conditional McKean-Vlasov control problem. A verification theorem is established to construct $α$-NE based on solutions to an infinite-dimensional Hamilton-Jacobi-Bellman equation, which is reduced to a system of ordinary differential equations for linear-quadratic games.

math.OC↗

arXiv2025

Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models

With the accelerated development of Industry 4.0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi-robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally, enterprises have high privacy requirements for production scheduling data, which prevents the use of cloud-based large language models (LLMs) for solution development. To address these challenges, there is an urgent need for an automated modeling solution that meets data privacy requirements. This study proposes a knowledge-augmented mixed integer linear programming (MILP) automated formulation framework, integrating local LLMs with domain-specific knowledge bases to generate executable code from natural language descriptions automatically. The framework employs a knowledge-guided DeepSeek-R1-Distill-Qwen-32B model to extract complex spatiotemporal constraints (82% average accuracy) and leverages a supervised fine-tuned Qwen2.5-Coder-7B-Instruct model for efficient MILP code generation (90% average accuracy). Experimental results demonstrate that the framework successfully achieves automatic modeling in the aircraft skin manufacturing case while ensuring data privacy and computational efficiency. This research provides a low-barrier and highly reliable technical path for modeling in complex industrial scenarios.

cs.AI↗

arXiv2025

Uniqueness of Positive Solutions for Fractional Schrödinger Equations with General Nonlinearities

In 2013, R.L. Frank and E. Lenzmann [R.L. Frank, E. Lenzmann, Uniqueness of non-linear ground states for fractional Laplacians in $\mathbb{R}$, Acta Math. 210 (2) (2013) 261-318] study the following problem: \begin{align*} (-Δ)^su + u = u^{p-1} \quad \text{in } \mathbb{R}^N, \end{align*} where $s \in (0,1)$, $N = 1$, $p \in (2,2_s^*)$, and $2_s^*$ is the critical fractional Sobolev exponent. They proved that the ground state is unique (up to translations). Then in 2016, they, together with L. Silvestre [R.L. Frank, E. Lenzmann, L. Silvestre, Uniqueness of radial solutions for the fractional Laplacian, Comm. Pure Appl. Math. 69 (9) (2016) 1671-1726] showed similar uniqueness results for high dimensions ($N \geqslant 2$), in which they proposed a challenging open problem to extend their results about non-degeneracy and uniqueness of ground states to nonlinearities $f(u)$ beyond the pure-power case. To the best of our knowledge, this question is still unresolved so far. In this paper, we aim to give a full affirmative answer to this open issue for a large class of convex nonlinearities. In fact, we prove the uniqueness of the positive solution.

math.AP↗

arXiv2025

Economic Impact of China's Retaliatory Soybean Tariff on U.S. Soybean Farmers

This paper analyzes the economic impact of China's retaliatory soybean tariff on U.S. soybean farmers using advanced econometric methods and comprehensive datasets including USDA reports, trade data, and historical price movements. The analysis employs a Structural Vector Autoregression (SVAR), a Difference-in-Differences (DiD) estimation, and a Dynamic Stochastic General Equilibrium (DSGE) model, revealing the impacts of China's retaliatory tariff on soybean prices, exports, farm incomes, and acreage decisions. U.S. policy responses, including direct subsidies and market diversification strategies, are also evaluated.

econ.GN↗