arXiv subjects

Liqiao Xia

Publications and source records attributed to Liqiao Xia.

3 recordsLinked to original sources

arXiv2025

Large Language Models for Disease Diagnosis: A Scoping Review

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.

cs.CL↗

arXiv2023

Cross-Edge Orchestration of Serverless Functions with Probabilistic Caching

Serverless edge computing adopts an event-based paradigm that provides back-end services on an as-used basis, resulting in efficient resource utilization. To improve the end-to-end latency and revenue, service providers need to optimize the number and placement of serverless containers while considering the system cost incurred by the provisioning. The particular reason for this circumstance is that frequently creating and destroying containers not only increases the system cost but also degrades the time responsiveness due to the cold-start process. Function caching is a common approach to mitigate the coldstart issue. However, function caching requires extra hardware resources and hence incurs extra system costs. Furthermore, the dynamic and bursty nature of serverless invocations remains an under-explored area. Hence, it is vitally important for service providers to conduct a context-aware request distribution and container caching policy for serverless edge computing. In this paper, we study the request distribution and container caching problem in serverless edge computing. We prove the proposed problem is NP-hard and hence difficult to find a global optimal solution. We jointly consider the distributed and resource constrained nature of edge computing and propose an optimized request distribution algorithm that adapts to the dynamics of serverless invocations with a theoretical performance guarantee. Also, we propose a context-aware probabilistic caching policy that incorporates a number of characteristics of serverless invocations. Via simulation and implementation results, we demonstrate the superiority of the proposed algorithm by outperforming existing caching policies in terms of the overall system cost and cold-start frequency by up to 62.1% and 69.1%, respectively.

cs.NI↗

arXiv2021

Magic-Angle Multilayer Graphene: A Robust Family of Moiré Superconductors

The discovery of correlated states and superconductivity in magic-angle twisted bilayer graphene (MATBG) has established moiré quantum matter as a new platform to explore interaction-driven and topological quantum phenomena. Multitudes of phases have been realized in moiré systems, but surprisingly, robust superconductivity has been one of the least common of all, initially found in MATBG and only more recently also in magic-angle twisted trilayer graphene (MATTG). While MATBG and MATTG share some similar characteristics, they also exhibit substantial differences, such as in their response to external electric and magnetic fields. This raises the question of whether they are simply two separate unique systems, or whether they form part of a broader family of superconducting materials. Here, we report the experimental realization of magic-angle twisted 4-layer and 5-layer graphene (MAT4G and MAT5G, respectively), which turn out to be superconductors, hence establishing alternating-twist magic-angle multilayer graphene as a robust family of moiré superconductors. The members of this family have flat bands in their electronic structure as a common feature, suggesting their central role in the observed robust superconductivity. On the other hand, there are also important variations across the family, such as different symmetries for members with even and odd number of layers. However, our measurements in parallel magnetic fields, in particular the investigation of Pauli limit violation and spontaneous rotational symmetry breaking, reveal that the most pronounced distinction is between the N=2 and N>2-layer structures. Our results expand the emergent family of moiré superconductors, providing new insight with potential implications for the design of novel superconducting materials platforms.

cond-mat.supr-con↗