arXiv subjects

Qihang He

Publications and source records attributed to Qihang He.

5 recordsLinked to original sources

arXiv2026

Shigatse Astronomical Site Testing. I. Cloud-cover Climatology and Selected Local Meteorological Conditions

As the first paper in a Shigatse astronomical site-testing series, we present a multi-source assessment of cloud cover and selected local meteorological conditions at the Shigatse 40 m site on the southern Tibetan Plateau. The study combines CALIPSO-GOCCP active-lidar climatology, ISCCP HXG passive-satellite cloud fields, conventional total-cloud-amount observations from the Shigatse Meteorological Station, and on-site Weather Station measurements. Together, these records characterize Shigatse as a southern-plateau monsoon-transition cloud regime: the active-lidar climatology gives a moderate-to-low annual cloud fraction, and the cloudier months are concentrated in the June--September monsoon interval. In GOCCP, the annual mean cloud fraction is 42.1%, while the October--May low-cloud season has a mean cloud fraction of 26.3%, compared with 73.7% during the June--September monsoon interval. ISCCP gives higher absolute cloud fractions but supports the same seasonal phase and local spatial placement. The aligned 1988--2013 meteorological-station record gives a total-cloud-amount <=40% fraction of 80.7% during October--May, rising to 90.7% in the November--January core, and decreasing to 39.9% during June--September. The 2024--2025 Weather Station archive further shows high fractions of valid samples satisfying the adopted meteorological criteria during the low-cloud months: 92.6% for the October--May night-time proxy and 94.6% for the corresponding 24 h samples. These results identify Shigatse as a measured lower-latitude southern-plateau cloud-cover reference within China's site-testing network, with a well-defined October--May low-cloud observing period and a Shigatse--Ali low-cloud corridor for subsequent regional site testing.

astro-ph.IM↗

arXiv2026

SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms

Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly find actionable guidance and reduce operational burden. While recent LLM+RAG assistants provide a natural interface, they face practical challenges in real deployments: limited scenario coverage across both general consultation and domain-specific troubleshooting workflows, inefficient knowledge access due to inadequate multi-hop retrieval and flat knowledge organization, and high maintenance cost because escalated tickets are unstructured and hard to convert into assistant improvements and reusable SOPs. In this paper, we present SiriusHelper, a deployed intelligent assistant for big data platforms. SiriusHelper serves as a unified online assistant that automatically identifies user intent and routes queries to the right handling path, including dedicated expert workflows for specialized scenarios (e.g., SQL execution diagnosis). To support complex troubleshooting, SiriusHelper combines a DeepSearch-driven mechanism with a priority-based hierarchical knowledge base to enable multi-hop retrieval without context overload, thus improving answer reliability and latency. To reduce expert overhead, SiriusHelper further introduces automated ticket understanding and SOP distillation: it diagnoses the assistant failure reason (e.g., missing knowledge or wrong routing) and extracts domain-specific SOPs to continuously enrich the knowledge base. Experiments and online deployment on Tencent Big Data platform show that SiriusHelper outperforms representative alternatives and reduces online ticket volume by 20.8\%.

cs.DB↗

arXiv2026

From Awareness to Intent: Mitigating Silent Driving System Failures through Prospective Situation Awareness Enhancing Interfaces

Silent automation failures, where a system fails to detect a hazard without warning, pose a critical safety challenge for partially automated vehicles. While research has mostly focused on takeover requests, how to support a driver in silent failure remains underexplored. We conducted a multi-modal driving simulator study with 48 participants to investigate how different Prospective Situation Awareness Enhancement (PSAE) interfaces, delivered via augmented reality head-up display, affect takeover performance. By integrating behavioral, subjective psychological, and physiological data, our analysis suggests that situational awareness (SA) serves as an important moderating factor through which PSAE interfaces improve takeover performance. Further, we found that providing perceptual cues was most effective in enhancing SA, while communicating system intent was superior for building trust. Finally, we identified a potential correlate of SA in the neuroactivity. Overall, this paper contributes to understanding how transparency-oriented interfaces may support drivers and provides design insights into HMI design for silent failures.

cs.HC↗

arXiv2025

When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders

As mental health issues rise among college students, there is an increasing interest and demand in leveraging Multimodal Language Models (MLLM) to enhance mental support services, yet integrating them into psychotherapy remains theoretical or non-user-centered. This study investigated the opportunities and challenges of using MLLMs within the campus psychotherapy alliance in China. Through three studies involving both therapists and student clients, we argue that the ideal role for MLLMs at this stage is as an auxiliary tool to human therapists. Users widely expect features such as triage matching and real-time emotion recognition. At the same time, for independent therapy by MLLM, concerns about capabilities and privacy ethics remain prominent, despite high demands for personalized avatars and non-verbal communication. Our findings further indicate that users' sense of social identity and perceived relative status of MLLMs significantly influence their acceptance. This study provides insights for future intelligent campus mental healthcare.

cs.HC↗

arXiv2024

The Influence of Task and Group Disparities over Users' Attitudes Toward Using Large Language Models for Psychotherapy

The population suffering from mental health disorders has kept increasing in recent years. With the advancements in large language models (LLMs) in diverse fields, LLM-based psychotherapy has also attracted increasingly more attention. However, the factors influencing users' attitudes to LLM-based psychotherapy have rarely been explored. As the first attempt, this paper investigated the influence of task and group disparities on user attitudes toward LLM-based psychotherapy tools. Utilizing the Technology Acceptance Model (TAM) and Automation Acceptance Model (AAM), based on an online survey, we collected and analyzed responses from 222 LLM-based psychotherapy users in mainland China. The results revealed that group disparity (i.e., mental health conditions) can influence users' attitudes toward LLM tools. Further, one of the typical task disparities, i.e., the privacy concern, was not found to have a significant effect on trust and usage intention. These findings can guide the design of future LLM-based psychotherapy services.

cs.HC↗