SearcharxivSearch

arXiv subjects

Yilun Wang

Publications and source records attributed to Yilun Wang.

At least 19 recordsLinked to original sources

Polarization transfer in $\psi'\to\psi\pi\pi$: a complete spin density matrix analysis framework

A theoretical framework based on the Spin Density Matrix (SDM) formalism is developed to describe polarization transfer in the decay chain $e^+e^- \rightarrow \psi^\prime \rightarrow \psi\pi\pi$. Explicit relations connecting the SDMs of $\psi^\prime$ and $\psi$ are derived, generalizing Cahn's analysis into a complete SDM treatment. For the dominant $S$-wave $\pi\pi$ emission, the SDM is shown to be perfectly preserved, $\rho_\psi = \rho_{\psi^\prime}$, rendering the $\psi$ an ideal probe of the initial polarization state. Deviations arising from $D$-wave contributions are quantified, and a self-consistency experimental test is proposed that simultaneously validates the framework and constrains partial wave amplitudes. This formalism provides a consistent basis for extracting $\psi$ polarization and for amplitude analyses of subsequent $\psi$ decays in a continuum-background-free environment. The framework extends to other hadronic transitions, including $\psi' \to h_c\pi^0$ in charmonium and $\Upsilon(nS) \to \Upsilon(mS)\pi\pi$ in bottomonium, as well as to electroweak processes such as $e^+e^- \to Z^\ast \to ZH$, where the same angular-momentum structure governs polarization transfer -- offering a unified probe of dynamics from charmonium to the Higgs sector.

hep-ph

Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems

LLM agents are increasingly explored for automating root cause analysis (RCA) in cloud-native systems, creating a need to evaluate both diagnostic correctness and the quality of the supporting investigation. Existing static benchmarks offer repeatable inputs but limited system-facing interaction, while live testbeds expose realistic tools but hinder controlled comparison because incident evidence varies across runs; both paradigms focus primarily on final answers. To address these limitations, we present Cloud-OpsBench, an evaluation infrastructure for interactive and evidence-grounded cloud RCA. It comprises 754 runtime-verified cases across 57 fault types on two microservice workloads spanning application services and Kubernetes platform layers. Each fault is captured as a state snapshot and replayed through standard diagnostic interfaces, with outcome labels and diagnostic evidence graphs that enable matched comparisons and process-level analysis. Across ten LLM agents, the strongest Joint RCA Accuracy (JRA) reaches 0.76 on OnlineBoutique and 0.68 on TrainTicket, while the corresponding Evidence Closure Rates (ECR) are only 0.38 and 0.15. This outcome--process gap shows that final-answer correctness alone substantially overestimates agents' ability to perform evidence-grounded diagnosis.

cs.SE

Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs

The democratization of open-source Large Language Models (LLMs) allows users to fine-tune and deploy models on local infrastructure but exposes them to a First Mile deployment landscape. Unlike black-box API consumption, the reliability of user-managed orchestration remains a critical blind spot. To bridge this gap, we conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems. Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack. We identify three key phenomena: (1) Diagnostic Divergence: runtime crashes distinctively signal infrastructure friction, whereas incorrect functionality serves as a signature for internal tokenizer defects. (2) Systemic Homogeneity: Root causes converge across divergent series, confirming reliability barriers are inherent to the shared ecosystem rather than specific architectures. (3) Lifecycle Escalation: Barriers escalate from intrinsic configuration struggles during fine-tuning to compounded environmental incompatibilities during inference. Supported by our publicly available dataset, these insights provide actionable guidance for enhancing the reliability of the LLM landscape.

cs.SE

Measuring high-precision luminosity at the CEPC

Purpose: Luminosity measurement at the Circular Electron-Positron Collider (CEPC) is required to achieve 10^{-4} precision when operating at the center-of-mass energy of the Z-pole. Approximately 10^{12} Z-bosons will be collected to refine measurements of Standard Model processes. The design of the luminosity calorimeter (LumiCal) takes into account the geometry of the Machine-Detector-Interface (MDI) for detection of Bhabha events. The detector simulation with GEANT predicts measurements of scattered electrons, positrons, and radiation photons. Results: The luminosity measurement by counting Bhabha events depends on the accuracy of the lower acceptance angle ({\theta}_{acc}) at the detector's fiducial edge. The beam-pipe design incorporates lowmass windows of 1 mm thick beryllium (Be) layers to reduce multiple scattering effects. The LumiCal has pixelated silicon detectors with better than 5 um resolution and LYSO crystals segmented into 3x3 mm2, which enhances the capability for detecting radiative Bhabha events. To achieve a precision level of 10^{-4}, it is crucial to monitor the interaction point (IP) of colliding beams and the positions of detectors with the error on mean of better than 1 urad. Conclusion: The LumiCal measures Bhabha scattering events using Si-detectors and finely segmented LYSO arrays. Its design is optimized for detecting radiative photons that are separated from electrons by a sufficiently large opening angle. This measurement aims to detect higher order corrections to the Bhabha interaction. Emphasis is placed on steering the beams for IP distribution and survey monitoring of detector positions to achieve high-precision luminosity measurements.lation with GEANT predicts measurements of scattered electrons, positrons, and radiation photons.

hep-ex

Radiation tolerance test and damage of single-crystal CVD Diamond sensor under high fluence particles

Single-crystal chemical vapor deposition (CVD) diamond is a promising material for radiation detectors operating in extreme environments, owing to its outstanding radiation hardness. As nuclear and high-energy physics applications demand particle detectors that withstand higher radiation fluences, understanding the damage thresholds and degradation mechanisms of diamond-based detectors is essential. In this study, single-crystal CVD diamond sensors were exposed to fast neutron irradiation at fluences up to $3.3\times10^{17}$ ${n/cm^2}$. Modules exhibited stable output confirming potential for application in future high-dose radiation environments. The dominant defects were identified as point defects including <100> self interstitials, vacancies, and lattice disorder. Macroscopic defects including nanocavities and cracks were observed with areal densities approaching $10^7$ $cm^{-2}$. The impact of 100 MeV proton irradiation on diamond detector response was quantified by extracting a damage constant of $k^{100 MeV}_{proton}=(1.452\pm0.006)\times10^{-18}cm^2/(p\cdot\mu m)$ from a linear carrier drift degradation model. The mean free path of carriers was found to exhibit saturation behavior beyond a fluence of $4\times10^{16}$ ${p/cm^2}$ under 100 MeV proton irradiation. Monte Carlo together with molecular dynamics simulations were performed to assess irradiation induced defect and its influence on carrier transport. By considering saturation effects and defect-interaction corrections, we develop an enhanced carrier-drift degradation model that accurately captures detector response under high-dose irradiation. Furthermore, the simulation framework was applied to evaluate damage induced by protons and pions on diamond at various energies, yielding results that show better agreement with experimental data than conventional NIEL based estimates.

physics.ins-det

InferLog: Accelerating LLM Inference for Online Log Parsing via ICL-oriented Prefix Caching

Modern software systems generate massive volumes of runtime logs, necessitating efficient and accurate log parsing to enable critical downstream tasks such as anomaly detection and root cause analysis. Recently, large language models (LLMs) have achieved advanced accuracy on log parsing, but their deployment in production environments faces two major limitations: (1) the privacy risks associated with commercial LLMs, driving the adoption of local deployment, and (2) the stringent latency and throughput requirements imposed by high-volume log streams, which existing LLM-based parsers fail to meet. Although recent efforts have reduced the number of LLM queries, they overlook the high latency of the LLM invocations, where concurrent log parsing requests can cause serve performance degradation of LLM inference system. In this study, we present InferLog, the first LLM inference optimization method for online log parsing. Our key insight is that the inference efficiency emerges as the vital bottleneck in LLM-based online log parsing, rather than parsing accuracy. InferLog accelerates inference by designing (1) A Prefix-aware ICL Refinement policy to refine the examples and permutation of in-context learning to improve the prefix caching efficiency. (2) A rapid and task-specific configuration tuning pipeline based on meta-learning to find the optimal LLM scheduling-related configuration for dynamic log parsing workloads. The experimental results based on Loghub dataset and vLLM demonstrate that InferLog significantly outperforms existing inference optimization methods and markedly accelerates the state-of-the-art LLM-based log parser without compromising parsing accuracy.

cs.SE

Towards Scalable Video Anomaly Retrieval: A Synthetic Video-Text Benchmark

Video anomaly retrieval aims to localize anomalous events in videos using natural language queries to facilitate public safety. However, existing datasets suffer from severe limitations: (1) data scarcity due to the long-tail nature of real-world anomalies, and (2) privacy constraints that impede large-scale collection. To address the aforementioned issues in one go, we introduce SVTA (Synthetic Video-Text Anomaly benchmark), the first large-scale dataset for cross-modal anomaly retrieval, leveraging generative models to overcome data availability challenges. Specifically, we collect and generate video descriptions via the off-the-shelf LLM (Large Language Model) covering 68 anomaly categories, e.g., throwing, stealing, and shooting. These descriptions encompass common long-tail events. We adopt these texts to guide the video generative model to produce diverse and high-quality videos. Finally, our SVTA involves 41,315 videos (1.36M frames) with paired captions, covering 30 normal activities, e.g., standing, walking, and sports, and 68 anomalous events, e.g., falling, fighting, theft, explosions, and natural disasters. We adopt three widely-used video-text retrieval baselines to comprehensively test our SVTA, revealing SVTA's challenging nature and its effectiveness in evaluating a robust cross-modal retrieval method. SVTA eliminates privacy risks associated with real-world anomaly collection while maintaining realistic scenarios. The dataset demo is available at: [https://svta-mm.github.io/SVTA.github.io/].

cs.CV

FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications

Serverless becomes popular as a novel computing paradigms for cloud native services. However, the complexity and dynamic nature of serverless applications present significant challenges to ensure system availability and performance. There are many root cause analysis (RCA) methods for microservice systems, but they are not suitable for precise modeling serverless applications. This is because: (1) Compared to microservice, serverless applications exhibit a highly dynamic nature. They have short lifecycle and only generate instantaneous pulse-like data, lacking long-term continuous information. (2) Existing methods solely focus on analyzing the running stage and overlook other stages, failing to encompass the entire lifecycle of serverless applications. To address these limitations, we propose FaaSRCA, a full lifecycle root cause analysis method for serverless applications. It integrates multi-modal observability data generated from platform and application side by using Global Call Graph. We train a Graph Attention Network (GAT) based graph auto-encoder to compute reconstruction scores for the nodes in global call graph. Based on the scores, we determine the root cause at the granularity of the lifecycle stage of serverless functions. We conduct experimental evaluations on two serverless benchmarks, the results show that FaaSRCA outperforms other baseline methods with a top-k precision improvement ranging from 21.25% to 81.63%.

cs.SE

Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis

Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while completely discarding unsampled ones. However, based on an empirical study on real-world production traces, we discover that the '1 or 0' strategy actually fails to effectively balance this tradeoff. To achieve a more balanced outcome, we shift the strategy from the '1 or 0' paradigm to the 'commonality + variability' paradigm. The core of 'commonality + variability' paradigm is to first parse traces into common patterns and variable parameters, then aggregate the patterns and filter the parameters. We propose a cost-efficient tracing framework, Mint, which implements the 'commonality + variability' paradigm on the agent side to enable all requests capturing. Our experiments show that Mint can capture all traces and retain more trace information while optimizing trace storage (reduced to an average of 2.7%) and network overhead (reduced to an average of 4.2%). Moreover, experiments also demonstrate that Mint is lightweight enough for production use.

cs.SE

Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation

Carefully standardized facial images of 591 participants were taken in the laboratory, while controlling for self-presentation, facial expression, head orientation, and image properties. They were presented to human raters and a facial recognition algorithm: both humans (r=.21) and the algorithm (r=.22) could predict participants' scores on a political orientation scale (Cronbach's alpha=.94) decorrelated with age, gender, and ethnicity. These effects are on par with how well job interviews predict job success, or alcohol drives aggressiveness. Algorithm's predictive accuracy was even higher (r=.31) when it leveraged information on participants' age, gender, and ethnicity. Moreover, the associations between facial appearance and political orientation seem to generalize beyond our sample: The predictive model derived from standardized images (while controlling for age, gender, and ethnicity) could predict political orientation (r=.13) from naturalistic images of 3,401 politicians from the U.S., UK, and Canada. The analysis of facial features associated with political orientation revealed that conservatives tended to have larger lower faces. The predictability of political orientation from standardized images has critical implications for privacy, the regulation of facial recognition technology, and understanding the origins and consequences of political orientation.

cs.CV

Searching for Hyper-compact star clusters in the Milky Way using LAMOST and Gaia

During the early merger of the Milky Way, intermediate-mass black holes in merged dwarf galaxies may have been ejected from the center of their host galaxies due to gravitational waves, carrying some central stars along. This process can lead to the formation of hyper-compact star clusters, potentially hosting black holes in the mass range of $10^4$ to $10^5$ solar masses. These clusters are crucial targets for identifying and investigating intermediate-mass black holes. However, no hyper-compact star clusters in the Milky Way have been identified so far. In this paper, taking advantage of the high spatial resolution power of Gaia, we used data from Gaia EDR3 and LAMOST DR7, along with additional data from Pan-STARRS and SDSS, to conduct an initial screening of 6,138,049 sources using various parameters of Gaia EDR3. A total of 4,786 sources were selected for in-depth analysis. Each of these sources was meticulously scrutinized by examining their images, spectra, and nearby celestial objects to exclude various false positives, such as contaminations, galaxies, wide binaries, or wrong matches. We finally identified one likely hyper-compact star cluster candidate in the Milky Way, laying the foundation for further high-resolution imaging and spectral verification.

astro-ph.GA

RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction

In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking dynamic factor model named RVRAE. This model is a probabilistic approach that addresses the temporal dependencies and noise in market data. RVRAE ingeniously combines the principles of dynamic factor modeling with the variational recurrent autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a prior-posterior learning method. This method fine-tunes the model's learning process by seeking an optimal posterior factor model informed by future data. Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. Our empirical tests with real stock market data underscore RVRAE's superior performance compared to various established baseline methods.

q-fin.PM

Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github.io/Occ3D/.

cs.CV

StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction

High-Definition (HD) maps are essential for the safety of autonomous driving systems. While existing techniques employ camera images and onboard sensors to generate vectorized high-precision maps, they are constrained by their reliance on single-frame input. This approach limits their stability and performance in complex scenarios such as occlusions, largely due to the absence of temporal information. Moreover, their performance diminishes when applied to broader perception ranges. In this paper, we present StreamMapNet, a novel online mapping pipeline adept at long-sequence temporal modeling of videos. StreamMapNet employs multi-point attention and temporal information which empowers the construction of large-range local HD maps with high stability and further addresses the limitations of existing methods. Furthermore, we critically examine widely used online HD Map construction benchmark and datasets, Argoverse2 and nuScenes, revealing significant bias in the existing evaluation protocols. We propose to resplit the benchmarks according to geographical spans, promoting fair and precise evaluations. Experimental results validate that StreamMapNet significantly outperforms existing methods across all settings while maintaining an online inference speed of $14.2$ FPS. Our code is available at https://github.com/yuantianyuan01/StreamMapNet.

cs.CV

Astrometric mass measurement of compact companions in binary systems with Gaia

For binary systems with an unseen primary and a luminous secondary, the astrometric wobble of the secondary could be used to study the primary. With Gaia, it is possible to measure the mass of the black hole or neutron star with a luminous companion (hereafter BH/NS-LC). Our aim is to provide a method for predicting Gaia's ability in measuring the mass of BH/NS-LCs. We also tried to estimate the number of solvable BH/NS-LCs using Gaia. We used a realistic Markov chain Monte Carlo simulation of mock Gaia observations to obtain a relation between the uncertainty of mass measurement of the primary in BH/NS-LCs with the observable variables of the secondary astrometric orbit. Furthermore, we used the MOBSE code to evolve a Galactic BH/NS-LC sample with a combined Milky Way model. Our relation is applied to this sample to estimate the number of solvable BH/NS-LCs. We derived a good relation between the mass uncertainty and the binary parameters. For the first time, we show the quantitive influence of the period P, inclination i, eccentricity e, and ecliptic latitude $β$ to the mass measurement. Our results suggest that $48^{+7}_{-7}$ BH-LCs and $102^{+11}_{10}$ NS-LCs are solvable during a 5 yr Gaia mission. We also give the distribution of the distance and apparent magnitude of the Gaia solvable BH/NS-LCs. This solvable sample would be increased by additional spectroscopic data or a prolonged Gaia mission. The mass uncertainty relation could be used in future simulations of BH/NS-LCs observed by Gaia. The prediction of the solvable BH/NS-LCs is not only influenced by the process in generating the Galactic BH/NS-LC sample, but is also affected by our uncertainty relation. In particular, the relations of parameters such as $[P, e, i, β]$ are very useful to correct the selection effect in the statistic results of the future BH/NS-LC sample observed by Gaia.

astro-ph.GA

Searching for new globular clusters in M 31 with Gaia EDR3

We found 50 new globular cluster (GC) candidates around M\,31 with Gaia Early Data Release 3 (EDR3), with the help from Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and \textit{simbad}, we trained 2 Random Forest (RF) classifiers, the first one to distinguish extended sources from point sources and the second one to further select GCs from extended sources. From 1.85 million sources of $16^m{<}g{<}19.5^m$ and within a large area of $\sim$392\,deg$^2$ around M\,31, we selected 20,658 extended sources and 1,934 initial GC candidates. After visual inspection of the PAndAS images to eliminate the contamination of non-cluster sources, particularly galaxies, we finally got 50 candidates. These candidates are divided into 3 types (\textbf{a}, \textbf{b}, \textbf{c}) according to their projected distance $D$ to the center of M\,31 and their probability to be a true GC, $P_{GC}$, which is calculated by our second RF classifier. Among these candidates, 14 are found to be associated (in projection) with the large-scale structures within the halo of M\,31. We also provided several simple parameter criteria for selecting extended sources effectively from the Gaia EDR3, which can reach a completeness of 92.1\% with a contamination fraction lower than 10\%.

astro-ph.GA

VectorMapNet: End-to-end Vectorized HD Map Learning

Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues. Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating comprehensive maps and capturing fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations. Our project website is available at \url{https://tsinghua-mars-lab.github.io/vectormapnet/}.

cs.CV

ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Perception and prediction are two separate modules in the existing autonomous driving systems. They interact with each other via hand-picked features such as agent bounding boxes and trajectories. Due to this separation, prediction, as a downstream module, only receives limited information from the perception module. To make matters worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene. ViP3D employs sparse agent queries to detect, track, and predict throughout the pipeline, making it the first fully differentiable vision-based trajectory prediction approach. Instead of using historical feature maps and trajectories, useful information from previous timestamps is encoded in agent queries, which makes ViP3D a concise streaming prediction method. Furthermore, extensive experimental results on the nuScenes dataset show the strong vision-based prediction performance of ViP3D over traditional pipelines and previous end-to-end models.

cs.CV