arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 181 records · Page 10Linked to original sources

arXiv2025

Planning for Rhythmized Urban Parks: Temporal Park Classification and Modes of Action

Problem, research strategy, and findings: Urban parks offer residents significant physiological and mental health benefits, improving their quality of life. However, traditional park planning often treats parks as static spatial resources, neglecting their temporal dimension of visitation rhythms. This study proposes a paradigm shift in classifying, programming, and designing parks. Utilizing 1.5 million mobile phone records, we classified 254 urban parks in Tokyo based on their visitation patterns across different times of the day, week, and year. Our results showed that parks are rhythmized by seasonal events and daily activities, exhibiting complex visiting patterns shaped by the combined effects of preference variation and accessibility barriers. We concluded by discussing modes of action for temporal park planning practice. Takeaway for practice: Park planners, designers, and policymakers should seek to incorporate temporality in activity-based park design and programming. This can be fulfilled in four ways by (1) tracing year-round, citywide park activities through data-driven methods, (2) implementing temporary and tactical designs for dynamic park demands, (3) establishing an inclusive park system that helps improve spatiotemporal equity, and (4) encouraging public engagement that cultivates the sense of time and identity.

physics.soc-ph↗

arXiv2025

OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge bases are commonly built by extracting structured data from unstructured PDF documents using Optical Character Recognition (OCR). However, given the imperfect prediction of OCR and the inherent non-uniform representation of structured data, knowledge bases inevitably contain various OCR noises. In this paper, we introduce OHRBench, the first benchmark for understanding the cascading impact of OCR on RAG systems. OHRBench includes 8,561 carefully selected unstructured document images from seven real-world RAG application domains, along with 8,498 Q&A pairs derived from multimodal elements in documents, challenging existing OCR solutions used for RAG. To better understand OCR's impact on RAG systems, we identify two primary types of OCR noise: Semantic Noise and Formatting Noise and apply perturbation to generate a set of structured data with varying degrees of each OCR noise. Using OHRBench, we first conduct a comprehensive evaluation of current OCR solutions and reveal that none is competent for constructing high-quality knowledge bases for RAG systems. We then systematically evaluate the impact of these two noise types and demonstrate the trend relationship between the degree of OCR noise and RAG performance. Our OHRBench, including PDF documents, Q&As, and the ground truth structured data are released at: https://github.com/opendatalab/OHR-Bench

cs.CV↗

arXiv2025

Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought

As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are facing more frequent failures. Ensuring system reliability thus demands accurate root cause localization. While traces and metrics have proven to be effective data sources for this task, existing methods either heavily rely on pre-defined schemas, which struggle to adapt to evolving operational contexts, or lack interpretability in their reasoning process, thereby leaving Site Reliability Engineers (SREs) confused. In this paper, we conduct a comprehensive study on how SREs localize the root cause of failures, drawing insights from multiple professional SREs across different organizations. Our investigation reveals that human root cause analysis exhibits three key characteristics: recursiveness, multi-dimensional expansion, and cross-modal reasoning. Motivated by these findings, we introduce RCLAgent, an adaptive root cause localization method for microservice systems that leverages a multi-agent recursion-of-thought framework. RCLAgent employs a novel recursion-of-thought strategy to guide the LLM's reasoning process, effectively integrating data from multiple agents and tool-assisted analysis to accurately pinpoint the root cause. Experimental evaluations on various public datasets demonstrate that RCLAgent achieves superior performance by localizing the root cause using only a single request-outperforming state-of-the-art methods that depend on aggregating multiple requests. These results underscore the effectiveness of RCLAgent in enhancing the efficiency and precision of root cause localization in complex microservice environments.

cs.SE↗

arXiv2025

Exploring d-Wave Magnetism in Cuprates from Oxygen Moments

The antiferromagnetic parent phase of high-T$_c$ cuprates has been established as a Néel state of copper moments, but early work pointed out the important role of ligand oxygen orbitals. Using the three-orbital Emery model, we explore how, and under which conditions, doping-induced antiferromagnetic ordering of weak magnetic moments on the oxygen sites can lead to unconventional d-wave magnetism with spin-split electronic bands. The mechanism for forming such altermagnetic (AM) states in cuprates does not rely on a lowering of the crystal symmetry but rather on interaction-induced formation of magnetic moments on directional oxygen orbitals within the crystallographic unit cell. Therefore, we obtain two different types of AM, namely a (0,0)-AM and a ($π$,$π$)-AM. We explore different regimes and challenges for realizing oxygen AM supported by Hartree-Fock calculations and complementary exact diagonalization of small clusters. While the region of interacting parameters needed to realize these states may be difficult to achieve in known high-T$_c$ cuprates, we propose a scenario to realize AM induced by oxygen magnetic moments in a cuprate-based candidate compound using density functional theory and discuss experimental implications.

cond-mat.str-el↗

arXiv2025

Sun-as-a-star Analysis of the Solar Eruption Source Region Using Ha Spectroscopic Observations of CHASE

Sun-as-a-star analyses serve as a bridge for comparative studies on solar and stellar activities. To investigate the typical Sun-as-a-star Ha temporal spectral characteristics in solar eruption source regions, we analyzed five different types of solar eruptions, using spectroscopic data from the Chinese Ha Solar Explorer (CHASE). Because the spatially-integral Ha spectrum of source region is mainly contributed by emission from heated plasma in flare ribbons and absorption from cold plasma in evolving filaments, we separately analyze the sub-regions of the source region dominated by different dynamical processes. It is revealed that filament eruptions show emission near Ha line center, accompanied by blueshifted/redshifted absorption, while flare ribbons show Ha line center emission with red asymmetry and line broadening. Moreover, a special spectral signature likely associated with coronal mass ejections (CMEs) is identified: prominent blueshifted absorption without a clear deceleration phase, along with redshifted absorption, which can be used as a probe when searching stellar CMEs. Furthermore, in the X9.0 flare (SOL2024-10-03T12:18) accompanied by a violent CME, the expected blueshifted signal is not visible in the spatially-integral Ha spectra. This suggests that filament-lifting signals associated with CMEs in the source region can be obscured by the simultaneous dominant flare-ribbon emission within the integration region, which may explain the relatively small number of confirmed stellar CMEs observed in Ha. We also find that comparison between the Ha and UV spectral observations can effectively reveal the velocity evolution of erupting filaments and potential existence of associated CMEs.

astro-ph.SR↗

arXiv2025

Advancements in Chinese font generation since deep learning era: A survey

Chinese font generation aims to create a new Chinese font library based on some reference samples. It is a topic of great concern to many font designers and typographers. Over the past years, with the rapid development of deep learning algorithms, various new techniques have achieved flourishing and thriving progress. Nevertheless, how to improve the overall quality of generated Chinese character images remains a tough issue. In this paper, we conduct a holistic survey of the recent Chinese font generation approaches based on deep learning. To be specific, we first illustrate the research background of the task. Then, we outline our literature selection and analysis methodology, and review a series of related fundamentals, including classical deep learning architectures, font representation formats, public datasets, and frequently-used evaluation metrics. After that, relying on the number of reference samples required to generate a new font, we categorize the existing methods into two major groups: many-shot font generation and few-shot font generation methods. Within each category, representative approaches are summarized, and their strengths and limitations are also discussed in detail. Finally, we conclude our paper with the challenges and future directions, with the expectation to provide some valuable illuminations for the researchers in this field.

cs.CV↗

arXiv2025

From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning

Log anomaly detection plays a critical role in ensuring the stability and reliability of software systems. However, existing approaches rely on large amounts of labeled log data, which poses significant challenges in real-world applications. To address this issue, cross-system transfer has been identified as a key research direction. State-of-the-art cross-system approaches achieve promising performance with only a few labels from the target system. However, their reliance on labeled target logs makes them susceptible to the cold-start problem when labeled logs are insufficient. To overcome this limitation, we explore a novel yet underexplored setting: zero-label cross-system log anomaly detection, where the target system logs are entirely unlabeled. To this end, we propose FreeLog, a system-agnostic representation meta-learning method that eliminates the need for labeled target system logs, enabling cross-system log anomaly detection under zero-label conditions. Experimental results on three public log datasets demonstrate that FreeLog achieves performance comparable to state-of-the-art methods that rely on a small amount of labeled data from the target system.

cs.SE↗

arXiv2025

EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler

Online 3D occupancy prediction provides a comprehensive spatial understanding of embodied environments. While the innovative EmbodiedOcc framework utilizes 3D semantic Gaussians for progressive indoor occupancy prediction, it overlooks the geometric characteristics of indoor environments, which are primarily characterized by planar structures. This paper introduces EmbodiedOcc++, enhancing the original framework with two key innovations: a Geometry-guided Refinement Module (GRM) that constrains Gaussian updates through plane regularization, along with a Semantic-aware Uncertainty Sampler (SUS) that enables more effective updates in overlapping regions between consecutive frames. GRM regularizes the position update to align with surface normals. It determines the adaptive regularization weight using curvature-based and depth-based constraints, allowing semantic Gaussians to align accurately with planar surfaces while adapting in complex regions. To effectively improve geometric consistency from different views, SUS adaptively selects proper Gaussians to update. Comprehensive experiments on the EmbodiedOcc-ScanNet benchmark demonstrate that EmbodiedOcc++ achieves state-of-the-art performance across different settings. Our method demonstrates improved edge accuracy and retains more geometric details while ensuring computational efficiency, which is essential for online embodied perception. The code will be released at: https://github.com/PKUHaoWang/EmbodiedOcc2.

cs.CV↗

arXiv2025

HarmonPaint: Harmonized Training-Free Diffusion Inpainting

Existing inpainting methods often require extensive retraining or fine-tuning to integrate new content seamlessly, yet they struggle to maintain coherence in both structure and style between inpainted regions and the surrounding background. Motivated by these limitations, we introduce HarmonPaint, a training-free inpainting framework that seamlessly integrates with the attention mechanisms of diffusion models to achieve high-quality, harmonized image inpainting without any form of training. By leveraging masking strategies within self-attention, HarmonPaint ensures structural fidelity without model retraining or fine-tuning. Additionally, we exploit intrinsic diffusion model properties to transfer style information from unmasked to masked regions, achieving a harmonious integration of styles. Extensive experiments demonstrate the effectiveness of HarmonPaint across diverse scenes and styles, validating its versatility and performance.

cs.CV↗

arXiv2025

Reinvestigating the semileptonic $B\to D^{(\ast)}τ\barν_τ$ decays in the model independent scenarios and leptoquark models

In this work, we revisit the possible new physics (NP) solutions by analyzing the observables associated with $B\to D^{(\ast)}τ\barν_τ$ decays. To explore the structure of new physics, the form factors of $B\to D^{(\ast)}$ decays play a crucial role. In this study, we utilize the form factor results obtained from a simultaneous fit to Belle data and lattice QCD calculations. Using these form factors, we conduct a global fit of the new physics Wilson coefficients, incorporating the most recent experimental data. Additionally, we use the form factors and Wilson coefficients to make predictions for physical observables, including the lepton flavor universality ratio $R_{D^{(\ast)}}$, polarized observables, and the normalized angular coefficients $\langle I\rangle$'s related to the four-fold decays $B\to D^{\ast}(\to Dπ, Dγ)τ\barν_τ$. These predictions are calculated in both model-independent scenarios and for three different leptoquark models.

hep-ph↗

arXiv2025

A Survey of AIOps in the Era of Large Language Models

As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research papers published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.

cs.SE↗

arXiv2025

Semileptonic baryon decays $Ξ_b\rightarrow Ξ_c \ell^- \barν_\ell $ in perturbative QCD

We perform a detailed analysis of the semileptonic $Ξ_b\rightarrow Ξ_c \ell^- \barν_\ell$ decays within the perturbative QCD framework. In our study, the $Ξ_b\rightarrow Ξ_c$ transition form factors are calculated using several popular models for baryonic light-cone distribution amplitudes. These form factors are then employed to analyze a range of observable quantities for the semileptonic processes via the helicity formalism. Our work presents predictions for the branching fractions of these decays for both the $τ$ and $e$ channels. Notably, the obtained lepton flavor universality ratio, $\mathcal{R}_{Ξ_c}\approx 0.3$, may offer new insights into the $\mathcal{R}^{(*)}$ puzzle. Furthermore, we investigate various angular observables, such as forward-backward asymmetries, lepton-side convexity parameters, and polarization asymmetries, which provide complementary information regarding potential new physics in $b$-baryonic semileptonic transitions. The numerical results for these angular observables are presented as both functions of $q^2$ and as averaged values. We observe that the lepton mass plays a significant role in shaping the angular distributions, affecting most of the observables under consideration. These results are expected to be valuable for both current and future experimental investigations of semileptonic heavy-to-heavy baryon decays.

hep-ph↗

arXiv2025

Serving Large Language Models on Huawei CloudMatrix384

The rapid evolution of large language models (LLMs), driven by growing parameter scales, adoption of mixture-of-experts (MoE) architectures, and expanding context lengths, imposes unprecedented demands on AI infrastructure. Traditional AI clusters face limitations in compute intensity, memory bandwidth, inter-chip communication, and latency, compounded by variable workloads and strict service-level objectives. Addressing these issues requires fundamentally redesigned hardware-software integration. This paper introduces Huawei CloudMatrix, a next-generation AI datacenter architecture, realized in the production-grade CloudMatrix384 supernode. It integrates 384 Ascend 910 NPUs and 192 Kunpeng CPUs interconnected via an ultra-high-bandwidth Unified Bus (UB) network, enabling direct all-to-all communication and dynamic pooling of resources. These features optimize performance for communication-intensive operations, such as large-scale MoE expert parallelism and distributed key-value cache access. To fully leverage CloudMatrix384, we propose CloudMatrix-Infer, an advanced LLM serving solution incorporating three core innovations: a peer-to-peer serving architecture that independently scales prefill, decode, and caching; a large-scale expert parallelism strategy supporting EP320 via efficient UB-based token dispatch; and hardware-aware optimizations including specialized operators, microbatch-based pipelining, and INT8 quantization. Evaluation with the DeepSeek-R1 model shows CloudMatrix-Infer achieves state-of-the-art efficiency: prefill throughput of 6,688 tokens/s per NPU and decode throughput of 1,943 tokens/s per NPU (<50 ms TPOT). It effectively balances throughput and latency, sustaining 538 tokens/s per NPU even under stringent 15 ms latency constraints, while INT8 quantization maintains model accuracy across benchmarks.

cs.DC↗

arXiv2025

Electron correlations in the kagome flat band metal $\rm CsCr_3Sb_5$

Kagome metals offer a unique platform for investigating robust electron-correlation effects because of their lattice geometry, flat bands and multi-orbital nature. In the cases with active flat bands, recent theoretical studies have pointed to a rich phase diagram that contains not only electronic orders but also quantum criticality. Very recently, $\rm CsCr_3Sb_5$ has emerged as a strong candidate for exploring such new physics. Here, using effective tight-binding models obtained from ab initio calculations, we study the effects of electronic correlations and symmetries on the electronic structure of $\rm CsCr_3 Sb_5$. The effective tight-binding model and Fermi surface comprise multiple Cr-$d$ orbitals and Sb-$p$ orbitals. The introduction of Hubbard-Kanamori interactions leads to orbital-selective band renormalization dominated by the $d_{xz}$ band, concurrently producing emergent flat bands very close to the Fermi level. Our analysis sets the stage for further investigations into the electronic properties of $\rm CsCr_3Sb_5$, including electronic orders, quantum criticality and unconventional superconductivity, which promise to shed much new light into the electronic materials with frustrated lattices and bring about new connections with the correlation physics of a variety of strongly correlated systems.

cond-mat.str-el↗

arXiv2025

The Samples and Binary Fractions of Red Supergiant in M31 and M33 by the HST Observations

The binarity of red supergiants (RSGs) influences their evolution and the fate of supernovae. We investigate the binary fraction of RSGs in the Andromeda Galaxy (M31) and Triangulum Galaxy (M33) using photometry from the Hubble Space Telescope (HST), which offers high spatial resolution to resolve more RSGs. A preliminary step involves identifying a reliable and complete RSG sample using the F110W $-$ F160W versus F160W diagram, yielding 2,612 RSGs from the Panchromatic Hubble Andromeda Treasury (PHAT) survey of M31 3,294 RSGs from the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey of M33. These samples suggest total RSG populations in M31 and M33 of 6,563 and 7,572, respectively. These estimates significantly exceed previous ones from the ground-based observations, an increase attributed to the superior spatial resolution of the HST. The stellar parameters of these RSGs, including effective temperature ($T_{\mathrm{eff}}$), radius ($R$), and luminosity ($L$), are derived by fitting their spectral energy distribution (SED) across optical and near-infrared bands. Binary candidates are identified by detecting ultraviolet (UV) excesses in their SEDs compared to the single-star RSG model prediction. The binary fraction is determined to be 33.4% $\pm$ 0.9% for M31 and 30.9% $\pm$ 0.8% for M33. For more luminous RSGs with log $L/L_{\odot} > 4.0$, the binary fraction decreases to 31.6% $\pm$ 1.9% in M31 and increases to 34.7% $\pm$ 1.8% in M33, respectively. These results are in good agreement with predictions from the BPASS binary evolution model.

astro-ph.SR↗

arXiv2025

OmniArch: Building Foundation Model For Scientific Computing

Foundation models have revolutionized language modeling, while whether this success is replicated in scientific computing remains unexplored. We present OmniArch, the first prototype aiming at solving multi-scale and multi-physics scientific computing problems with physical alignment. We addressed all three challenges with one unified architecture. Its pre-training stage contains a Fourier Encoder-decoder fading out the disharmony across separated dimensions and a Transformer backbone integrating quantities through temporal dynamics, and the novel PDE-Aligner performs physics-informed fine-tuning under flexible conditions. As far as we know, we first conduct 1D-2D-3D united pre-training on the PDEBench, and it sets not only new performance benchmarks for 1D, 2D, and 3D PDEs but also demonstrates exceptional adaptability to new physics via in-context and zero-shot learning approaches, which supports realistic engineering applications and foresight physics discovery.

cs.LG↗

arXiv2025

Understanding the Lyα Emission Observed by the Solar Disk Imager Aboard the Advanced Space-based Solar Observatory

The H I Lyman-alpha (Ly$α$) emission, with a wavelength of 1216 Å, is the brightest solar ultraviolet (UV) line. However, comprehensive observations of the Ly$α$ emission line across the full solar disk remain limited. As part of the ASO-S mission, the Solar Disk Imager (SDI) has successfully captured full-disk images in the Ly$α$ band. Gaussian fitting of SDI's spectral response function (SRF) yields a full width at half maximum (FWHM) of approximately 85 Å, which is significantly broader than the distance of Si III line at 1206 Å and the Ly$α$ line. Thus, the emission contribution of Si III to the SDI Ly$α$ passband needs to be considered. For flares, in practice, we calculated the integrated intensity ratio $I$(Si III)/$I$(Ly$α$) by analyzing spectral observations from the SOLSTICE instrument. It yields values between 1.7% and 14.6%. Empirically, the ratio is proportional to the SXR flux. Further analysis of spectral data from the SUMER instrument reveals that the ratio $I$(Si III)/$I$(Ly$α$) is approximately 0.5% for prominences, 0.7%--0.9% for the inner disk, and 1.4%--1.9% close to the limb. These findings suggest that $I$(Si III)/$I$(Ly$α$) is minimal for prominences and the inner disk, and the varying ratios across regions align with the center-to-limb variation of the Si III and Ly$α$ lines. Additionally, we compared Ly$α$ image intensity with 304 Å, 1600 Å, and 1700 Å observations from AIA, as well as H$α$ from CHASE, in multiple regions (a prominence region, two active regions, and a quiet region). A relatively higher correlation of about 85% is found between Ly$α$ and 304 Å in active regions, whereas in the quiet region and prominence, their correlation coefficients are about 55%.

astro-ph.SR↗

arXiv2025

FreqMoE: Dynamic Frequency Enhancement for Neural PDE Solvers

Fourier Neural Operators (FNO) have emerged as promising solutions for efficiently solving partial differential equations (PDEs) by learning infinite-dimensional function mappings through frequency domain transformations. However, the sparsity of high-frequency signals limits computational efficiency for high-dimensional inputs, and fixed-pattern truncation often causes high-frequency signal loss, reducing performance in scenarios such as high-resolution inputs or long-term predictions. To address these challenges, we propose FreqMoE, an efficient and progressive training framework that exploits the dependency of high-frequency signals on low-frequency components. The model first learns low-frequency weights and then applies a sparse upward-cycling strategy to construct a mixture of experts (MoE) in the frequency domain, effectively extending the learned weights to high-frequency regions. Experiments on both regular and irregular grid PDEs demonstrate that FreqMoE achieves up to 16.6% accuracy improvement while using merely 2.1% parameters (47.32x reduction) compared to dense FNO. Furthermore, the approach demonstrates remarkable stability in long-term predictions and generalizes seamlessly to various FNO variants and grid structures, establishing a new ``Low frequency Pretraining, High frequency Fine-tuning'' paradigm for solving PDEs.

cs.LG↗