arXiv subjects

Bo Zhao

Publications and source records attributed to Bo Zhao.

At least 127 records · Page 7Linked to original sources

arXiv2025

Mapping Resilience and Inequality: A Spatial Analysis of Refugee Communities in Seattle

Seattle has long been a center for refugee resettlement in the United States, creating multicultural neighborhoods with complex social and spatial dynamics. This study analyzes the refugee experience in Seattle by examining patterns of housing cost burden, language isolation, religious infrastructure, and crime incidence. These spatial findings are combined with insights from literature on refugee integration, social vulnerability, and community safety. Results show that White Center and Rainier Valley experience severe housing cost burdens and high social vulnerability, indicating significant financial stress and exposure to economic risks. Language isolation is pronounced, as many residents lack English fluency, limiting access to jobs and essential services. Access to religious and cultural institutions is also uneven, affecting community cohesion and cultural preservation. While citywide crime maps show scattered incidents, evidence indicates that refugees are more often victims than offenders, and that their communities do not increase local crime rates. The paper concludes by recommending policies that strengthen affordable housing, expand language assistance, support community and cultural spaces, and promote inclusive public safety programs. These findings aim to inform policymakers, educators, and community organizations about the spatial and social dimensions of refugee life in Seattle.

physics.soc-ph↗

arXiv2025

ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts

Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per token. Recent works show that fine-grained experts substantially enriches the combinatorial flexibility of active experts and enhances model expressiveness. However, such a design is fundamentally limited by the layer-local routing mechanism: each layer is restricted to its own expert pool. This requires a careful trade-off between expert dimensionality and routing diversity given fixed parameter budgets. We describe ReXMoE, a novel MoE architecture that improves routing beyond the existing layer-local approaches by allowing routers to reuse experts across adjacent layers. ReXMoE decouples expert dimensionality from per-layer budgets, enabling richer expert combinations without sacrificing individual expert capacity or inflating overall parameters. To this end, we propose a new progressive scaling routing (PSR) strategy to gradually increase the candidate expert pool during training. As a result, ReXMoE improves both language modeling and downstream task performance. Extensive experiments on models ranging from 0.5B to 7B parameters across different architectures demonstrate that ReXMoE consistently improves performance under fixed architectural dimensions, confirming ReXMoE as new design paradigm for parameter-efficient and scalable MoE-based LLMs.

cs.CL↗

arXiv2025

SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior

In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing when faced with complex element layout planning. To address this challenge, we introduce SEGA, a novel Stepwise Evolution Paradigm for Content-Aware Layout Generation. Inspired by the systematic mode of human thinking, SEGA employs a hierarchical reasoning framework with a coarse-to-fine strategy: first, a coarse-level module roughly estimates the layout planning results; then, another refining module performs fine-level reasoning regarding the coarse planning results. Furthermore, we incorporate layout design principles as prior knowledge into the model to enhance its layout planning ability. Besides, we present GenPoster-100K that is a new large-scale poster dataset with rich meta-information annotation. The experiments demonstrate the effectiveness of our approach by achieving the state-of-the-art results on multiple benchmark datasets. Our project page is at: https://brucew91.github.io/SEGA.github.io/

cs.CV↗

arXiv2025

Perfect Heat Rectification and Circulation with Nonreciprocal Radiative Surfaces in the Far Field

Controlling photon mediated energy flow is central to the future of communications, thermal management, and energy harvesting technologies. Recent breakthroughs have revealed that many body systems violating Lorentz reciprocity can sustain persistent photon heat current at thermal equilibrium, hinting at a new paradigm of heat flow akin to superconductivity. Yet, the behavior of such systems far from equilibrium remains largely unexplored. In this work, we uncover the rich physics of radiative heat transfer in nonequilibrium, far field many body systems composed of thermal emitters that break Lorentz reciprocity. We show that the total heat flow naturally decomposes into two distinct components: an equilibrium term, which generates a persistent circulating heat current within the system, and a nonequilibrium term, which governs energy exchange with the environment. Remarkably, while the internal persistent heat current is ever present, the nonequilibrium contribution can be precisely engineered to achieve perfect heat rectification and circulation. Our results open a new route toward designing thermal systems with unprecedented control unlocking the potential for lossless heat circulation and one way thermal devices. This fundamentally shifts the landscape for next generation thermal logic, energy conversion, and photonic heat engines.

physics.optics↗

arXiv2025

EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models

Reinforcement learning (RL) has become a pivotal component of large language model (LLM) post-training, and agentic RL extends this paradigm to operate as agents through multi-turn interaction and tool use. Scaling such systems exposes two practical bottlenecks: (1) context length grows rapidly during training, inflating memory usage and latency, and triggering out-of-memory (OOM) failures; and (2) intermediate tensors accumulate with context length, making cross-device data movement a major system bottleneck. We present EARL, a scalable system for efficient agentic RL. EARL designs a parallelism selector that dynamically adapts model and training parallelism across RL stages based on sequence length and system load, and a data dispatcher that performs layout-aware, decentralized exchange of intermediate data batches. Together, these components increase throughput, reduce long-context failures, and enable stable large-scale training of agentic LLMs without relying on hard limits or penalties of context length.

cs.DC↗

arXiv2025

QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL

Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. Recent large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution. However, the fundamental challenges remain unaddressed: (i) parameterized quantum gates require precise numerical values for optimal performance, which also depend on multiple aspects, including the number of quantum gates, their parameters, and the layout/depth of the circuits. (ii) LLMs often generate low-quality or incorrect quantum circuits due to the lack of quantum domain-specific knowledge. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. To align the LLM with quantum-specific knowledge and improve the generated quantum circuits, QUASAR designs (i) a quantum circuit verification approach with external quantum simulators and (ii) a sophisticated hierarchical reward mechanism in RL training. Extensive evaluation shows improvements in both syntax and semantic performance of the generated quantum circuits. When augmenting a 4B LLM, QUASAR has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10, outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3 and several supervised-fine-tuning (SFT)-only and RL-only baselines.

cs.AI↗

arXiv2025

Zero-field Anomalous Hall Effect in Bulk Single Crystal Mn3Ir

The L1_2-phase non-collinear antiferromagnet (AFM) Mn_3Ir has emerged as a pioneering platform for realizing the zero-field anomalous Hall effect (AHE), thereby catalyzing rapid advances in antiferromagnetic spintronics. Despite its significant potential, experimental investigations of the intrinsic magnetic and electronic properties of Mn_3Ir have been greatly hindered by the formidable challenges in growing bulk single crystals. Here, we report the growth of stoichiometric Mn_3Ir bulk single crystals and their characterization in terms of magnetization and the AHE. Using a high-throughput flux method, we obtained (111)-oriented hexagonal Mn_3Ir single crystals. A small AHE signal was detected, which we attribute to the coexistence of A- and B-type antiferromagnetic domains that mutually cancel the net AHE response. Our results reveal key aspects of the intrinsic magnetic properties and AHE in bulk Mn_3Ir, providing a critical material platform for the development of advanced spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

DD-Ranking: Rethinking the Evaluation of Dataset Distillation

In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets. To further improve the performance of synthetic datasets, various training pipelines and optimization objectives have been proposed, greatly advancing the field of dataset distillation. Recent decoupled dataset distillation methods introduce soft labels and stronger data augmentation during the post-evaluation phase and scale dataset distillation up to larger datasets (e.g., ImageNet-1K). However, this raises a question: Is accuracy still a reliable metric to fairly evaluate dataset distillation methods? Our empirical findings suggest that the performance improvements of these methods often stem from additional techniques rather than the inherent quality of the images themselves, with even randomly sampled images achieving superior results. Such misaligned evaluation settings severely hinder the development of DD. Therefore, we propose DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods. By refocusing on the actual information enhancement of distilled datasets, DD-Ranking provides a more comprehensive and fair evaluation standard for future research advancements.

cs.CV↗

arXiv2025

BDPM: A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis

Background: Parkinson's disease remains a major neurodegenerative disorder with high misdiagnosis rates, primarily due to reliance on clinical rating scales. Recent studies have demonstrated a strong association between gut microbiota and Parkinson's disease, suggesting that microbial composition may serve as a promising biomarker. Although deep learning models based ongut microbiota show potential for early prediction, most approaches rely on single classifiers and often overlook inter-strain correlations or temporal dynamics. Therefore, there is an urgent need for more robust feature extraction methods tailored to microbiome data. Methods: We proposed BDPM (A Machine Learning-Based Feature Extractor for Parkinson's Disease Classification via Gut Microbiota Analysis). First, we collected gut microbiota profiles from 39 Parkinson's patients and their healthy spouses to identify differentially abundant taxa. Second, we developed an innovative feature selection framework named RFRE (Random Forest combined with Recursive Feature Elimination), integrating ecological knowledge to enhance biological interpretability. Finally, we designed a hybrid classification model to capture temporal and spatial patterns in microbiome data.

cs.AI↗

arXiv2025

Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization

Large language models (LLMs) have achieved remarkable outcomes in complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet, few works have explored the potential of LLMs in quantum computing. The most challenging problem is to leverage LLMs to automatically generate quantum circuits at a large scale. Fundamentally, the existing pre-trained LLMs lack the knowledge of quantum circuits. In this paper, we address this challenge by fine-tuning LLMs and injecting the domain-specific knowledge of quantum computing. We describe Agent-Q, an LLM fine-tuning system to generate and optimize quantum circuits. In particular, Agent-Q implements the mechanisms to generate training data sets and constructs an end-to-end pipeline to fine-tune pre-trained LLMs to generate parameterized quantum circuits for various optimization problems. Agent-Q provides 14,000 quantum circuits covering a large spectrum of the quantum optimization landscape: 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. Based thereon, Agent-Q fine-tunes LLMs and constructs syntactically correct parametrized quantum circuits in OpenQASM 3.0. We have evaluated the quality of the LLM-generated circuits and parameters by comparing them to the optimized expectation values and distributions. Experimental results show superior performance of Agent-Q, compared to several state-of-the-art LLMs and better parameters than random. Agent-Q can be integrated into an agentic workflow, and the generated parametrized circuits with initial parameters can be used as a starting point for further optimization, e.g., as templates in quantum machine learning and as benchmarks for compilers and hardware.

quant-ph↗

arXiv2025

Higher-Order Portfolio Optimization with Quantum Approximate Optimization Algorithm

Portfolio optimization is one of the most studied optimization problems at the intersection of quantum computing and finance. In this work, we develop the first quantum formulation for a portfolio optimization problem with higher-order moments, skewness and kurtosis. Including higher-order moments leads to more detailed modeling of portfolio return distributions. Portfolio optimization with higher-order moments has been studied in classical portfolio optimization approaches but with limited exploration within quantum formulations. In the context of quantum optimization, higher-order moments generate higher-order terms in the cost Hamiltonian. Thus, instead of obtaining a quadratic unconstrained binary optimization problem, we obtain a higher-order unconstrained binary optimization (HUBO) problem, which has a natural formulation as a parametrized circuit. Additionally, we employ realistic integer variable encoding and a capital-based budget constraint. We consider the classical continuous variable solution with integer programming-based discretization to be the computationally efficient classical baseline for the problem. Our extensive experimental evaluation of 100 portfolio optimization problems shows that the solutions to the HUBO formulation often correspond to better portfolio allocations than the classical baseline. This is a promising result for those who want to perform computationally challenging portfolio optimization on quantum hardware, as portfolio optimization with higher moments is classically complex. Moreover, the experimental evaluation studies QAOA's performance with higher-order terms in this practically relevant problem.

quant-ph↗

arXiv2025

Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment

Large Language Models (LLMs) have shown impressive capabilities in natural language processing but still struggle to perform well on knowledge-intensive tasks that require deep reasoning and the integration of external knowledge. Although methods such as Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) have been proposed to enhance LLMs with external knowledge, they still suffer from internal bias in LLMs, which often leads to incorrect answers. In this paper, we propose a novel causal prompting framework, Conditional Front-Door Prompting (CFD-Prompting), which enables the unbiased estimation of the causal effect between the query and the answer, conditional on external knowledge, while mitigating internal bias. By constructing counterfactual external knowledge, our framework simulates how the query behaves under varying contexts, addressing the challenge that the query is fixed and is not amenable to direct causal intervention. Compared to the standard front-door adjustment, the conditional variant operates under weaker assumptions, enhancing both robustness and generalisability of the reasoning process. Extensive experiments across multiple LLMs and benchmark datasets demonstrate that CFD-Prompting significantly outperforms existing baselines in both accuracy and robustness.

cs.CL↗

arXiv2025

Understanding Data Influence with Differential Approximation

Data plays a pivotal role in the groundbreaking advancements in artificial intelligence. The quantitative analysis of data significantly contributes to model training, enhancing both the efficiency and quality of data utilization. However, existing data analysis tools often lag in accuracy. For instance, many of these tools even assume that the loss function of neural networks is convex. These limitations make it challenging to implement current methods effectively. In this paper, we introduce a new formulation to approximate a sample's influence by accumulating the differences in influence between consecutive learning steps, which we term Diff-In. Specifically, we formulate the sample-wise influence as the cumulative sum of its changes/differences across successive training iterations. By employing second-order approximations, we approximate these difference terms with high accuracy while eliminating the need for model convexity required by existing methods. Despite being a second-order method, Diff-In maintains computational complexity comparable to that of first-order methods and remains scalable. This efficiency is achieved by computing the product of the Hessian and gradient, which can be efficiently approximated using finite differences of first-order gradients. We assess the approximation accuracy of Diff-In both theoretically and empirically. Our theoretical analysis demonstrates that Diff-In achieves significantly lower approximation error compared to existing influence estimators. Extensive experiments further confirm its superior performance across multiple benchmark datasets in three data-centric tasks: data cleaning, data deletion, and coreset selection. Notably, our experiments on data pruning for large-scale vision-language pre-training show that Diff-In can scale to millions of data points and outperforms strong baselines.

cs.LG↗

arXiv2025

EarthSynth: Generating Informative Earth Observation with Diffusion Models

Remote sensing image (RSI) interpretation typically faces challenges due to the scarcity of labeled data, which limits the performance of RSI interpretation tasks. To tackle this challenge, we propose EarthSynth, a diffusion-based generative foundation model that enables synthesizing multi-category, cross-satellite labeled Earth observation for downstream RSI interpretation tasks. To the best of our knowledge, EarthSynth is the first to explore multi-task generation for remote sensing, tackling the challenge of limited generalization in task-oriented synthesis for RSI interpretation. EarthSynth, trained on the EarthSynth-180K dataset, employs the Counterfactual Composition training strategy with a three-dimensional batch-sample selection mechanism to improve training data diversity and enhance category control. Furthermore, a rule-based method of R-Filter is proposed to filter more informative synthetic data for downstream tasks. We evaluate our EarthSynth on scene classification, object detection, and semantic segmentation in open-world scenarios. There are significant improvements in open-vocabulary understanding tasks, offering a practical solution for advancing RSI interpretation.

cs.CV↗

arXiv2025

Is Green Enough? A Remote Sensing Assessment of Environmental Impacts and Green Commitments at Beijing Daxing International Airport

Beijing Daxing International Airport has been promoted as a model of green infrastructure under China's ecological modernization agenda. Featuring energy-efficient design, renewable energy systems, and smart environmental controls, the airport embodies multiple green commitments. This study evaluates its environmental outcomes using multi-source remote sensing data -- including NDVI, NDBI, Land Surface Temperature (LST), VIIRS night-time lights, and PM2.5 -- from 2014 to 2019. Through spatial and temporal comparisons, we assess landscape-level changes during and after construction. Findings indicate partial gains from green initiatives but also reveal substantial vegetation loss, increased built-up surfaces, and intensified surface temperatures. The results suggest a gap between sustainable design and ecological impact. We propose a remote-sensing-based framework for evaluating future infrastructure projects, emphasizing the need for spatially explicit, independent monitoring to ensure environmental accountability.

physics.soc-ph↗

arXiv2025

BOOD: Boundary-based Out-Of-Distribution Data Generation

Harnessing the power of diffusion models to synthesize auxiliary training data based on latent space features has proven effective in enhancing out-of-distribution (OOD) detection performance. However, extracting effective features outside the in-distribution (ID) boundary in latent space remains challenging due to the difficulty of identifying decision boundaries between classes. This paper proposes a novel framework called Boundary-based Out-Of-Distribution data generation (BOOD), which synthesizes high-quality OOD features and generates human-compatible outlier images using diffusion models. BOOD first learns a text-conditioned latent feature space from the ID dataset, selects ID features closest to the decision boundary, and perturbs them to cross the decision boundary to form OOD features. These synthetic OOD features are then decoded into images in pixel space by a diffusion model. Compared to previous works, BOOD provides a more training efficient strategy for synthesizing informative OOD features, facilitating clearer distinctions between ID and OOD data. Extensive experimental results on common benchmarks demonstrate that BOOD surpasses the state-of-the-art method significantly, achieving a 29.64% decrease in average FPR95 (40.31% vs. 10.67%) and a 7.27% improvement in average AUROC (90.15% vs. 97.42%) on the CIFAR-100 dataset.

cs.LG↗

arXiv2025

Optimizing $α''$-Fe$_{16}$N$_2$ as permanent magnet via alloying

Based on systematic first-principles calculations, we investigate the effects of 27 alloying elements on the intrinsic magnetic properties of Fe$_{16}$N$_2$, in order to further optimize its properties for permanent magnet applications. Analysis on the thermodynamic stabilities based on formation energy and distance to the convex hull reveals that 20 elements can be substituted into Fe$_{16}$N$_2$, where there is no strong site-preference upon doping. It is observed that all alloying elements can essentially reduce the saturation magnetization, whereas the magnetic anisotropy can be significantly modified. In terms of the Boltzmann-average intrinsic properties, we identify 8 elements as interesting candidates, with Co, Mo, and W as the most promising cases for further experimental validations.

cond-mat.mtrl-sci↗

arXiv2025

AU-LLM: Micro-Expression Action Unit Detection via Enhanced LLM-Based Feature Fusion

The detection of micro-expression Action Units (AUs) is a formidable challenge in affective computing, pivotal for decoding subtle, involuntary human emotions. While Large Language Models (LLMs) demonstrate profound reasoning abilities, their application to the fine-grained, low-intensity domain of micro-expression AU detection remains unexplored. This paper pioneers this direction by introducing \textbf{AU-LLM}, a novel framework that for the first time uses LLM to detect AUs in micro-expression datasets with subtle intensities and the scarcity of data. We specifically address the critical vision-language semantic gap, the \textbf{Enhanced Fusion Projector (EFP)}. The EFP employs a Multi-Layer Perceptron (MLP) to intelligently fuse mid-level (local texture) and high-level (global semantics) visual features from a specialized 3D-CNN backbone into a single, information-dense token. This compact representation effectively empowers the LLM to perform nuanced reasoning over subtle facial muscle movements.Through extensive evaluations on the benchmark CASME II and SAMM datasets, including stringent Leave-One-Subject-Out (LOSO) and cross-domain protocols, AU-LLM establishes a new state-of-the-art, validating the significant potential and robustness of LLM-based reasoning for micro-expression analysis. The codes are available at https://github.com/ZS-liu-JLU/AU-LLMs.

cs.CV↗