arXiv subjects

Jiaju Wang

Publications and source records attributed to Jiaju Wang.

4 recordsLinked to original sources

arXiv2026

Quantifying quasiparticle chirality in a chiral topological semimetal

Recently, the projection of the electron's spin on its crystal momentum has been proposed as a metric to quantify electronic chirality of Bloch states in crystals, which is expected to affect a wide range of physical properties, such as magnetoelectric and optical responses. However, a direct experimental quantification of this chirality metric over an entire iso-energy surface has remained elusive. Here, we have used spin- and angle-resolved photoemission spectroscopy to directly probe the electronic chirality by measuring the bulk spin texture of Kramers-Weyl and Weyl cones in RhSi, a chiral topological semimetal with strong spin-orbit coupling (SOC). After quantifying the SOC splitting of Weyl cones, we determine their spin direction along different azimuthal angles to extract energy dependent the deviations (up to ~40°) from perfect parallel spin-momentum locking. From these deviations we define an energy-dependent normalized electron chirality density (NECD), a directly accessible metric of bulk electronic chirality. In RhSi, the NECD decreases from 1 at the Kramers-Weyl point to ~0.8 at ~200 meV below it. Finally, we show that this experimentally grounded NECD provides predictive power for magneto-optical and transport responses of chiral materials, exemplified by the longitudinal Edelstein effect.

cond-mat.mes-hall↗

arXiv2026

Suppression of Spectral Gap and Flat Bands on a Cuprate Superconductor Side-Surface

Side surfaces of cuprate superconductors are expected to display a suppressed $d$-wave order parameter and zero-energy topological flat bands with a large density of states, making them susceptible to symmetry broken orders. Yet such surfaces have never been investigated with momentum-resolved, surface-sensitive probes, because high-temperature superconductors rarely cleave along them. Using focused-ion-beam milling to define a controlled breaking point, we expose pristine (110) side surfaces of overdoped La$_{2-x}$Sr$_x$CuO$_4$ ($x=0.22$) suitable for angle-resolved photoemission. We observe the suppression of the superconducting spectral gap within our energy resolution ($\sim 4~\mathrm{meV}$), and surprisingly, the expected zero-energy flat band peak is also suppressed, despite the high topographic quality of the surface. Self-consistent Bogoliubov--de~Gennes calculations show that the measured geometric roughness of the cleaved surface is too weak to eliminate these modes. The calculations further demonstrate that bulk inhomogeneities characteristic of high-temperature superconductors, modelled as moderate Anderson-type disorder, can broaden the flat-band states beyond detectability. Our results provide the first momentum-resolved view of the electronic structure on a cuprate side surface and reveal disorder as the key factor currently preventing appearance of flat bands and their associated correlated orders.

cond-mat.supr-con↗

arXiv2025

ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System

Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC systems. To accurately localize culprit nodes and determine failure types, ClusterRCA combines classifier-based and graph-based approaches. A failure graph is constructed based on the output of the state classifier, and then it performs a customized random walk on the graph to localize the root cause. Experiments on datasets collected by a top-tier global HPC device vendor show ClusterRCA achieves high accuracy in diagnosing network failure for HPC systems. ClusterRCA also maintains robust performance across different application scenarios.

cs.DC↗

arXiv2024

A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management

AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline datasets to evaluate algorithms. They cannot consistently evaluate the performance of algorithms using real-time datasets, and the operation scenarios for evaluation are static, which is insufficient for effective algorithm selection. To address these issues, we propose an evaluation-consistent and scenario-oriented evaluation framework named MicroServo. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. MicroServo supports different leaderboards by selecting specific algorithms and datasets according to the operation scenarios. It also supports the deployment of various types of algorithms, enabling algorithms hot-plugging. At last, we test MicroServo with three typical microservice operation scenarios to demonstrate its efficiency and usability.

cs.DC↗