Jie Xu

Predictive value of a combined inflammatory score based on laboratory data within 24 hours of admission for 28-day mortality in patients with sepsis: a retrospective cohort study

P Wang, 覃桂, Ning Xu, Chao Xi, Yi Jing, Fengrong Tang, et al.

WARA: A Closed-Loop Multi-Agent Framework for Wireless Optimization Autoresearch

Yuan Guo, Yilong Chen, Chao Hu, Xianghao Yu, Liang Hong, Jie Xu

Large language model (LLM) agents have shown growing capabilities in tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific research. To the best of our knowledge, this paper presents the first end-to-end autore…Read more

Breaking the Model Forgetting Cycle in Long-Incremental 3D Object Detection

Peisheng Qian, Jie Xu, Xulei Yang, Na Zhao

Incremental 3D object detection requires a detector to learn novel object classes while remembering previously learned ones over sequentially arriving data. Previous methods, primarily based on pseudo-labeling, perform reasonably in short-incremental stages but still suffer from…Read more

Consistent Variance Estimation for Q-Function Estimators in Finite-Horizon MDP Tree Search

Zhenyu Yue, Jie Xu, Chun-Hung Chen, Hadi El-Amine, Michael C. Fu

We study the variance of Q-function estimators in finite-horizon, finite-state Markov decision process (MDP) tree search. We show that the variance decomposes into three components attributed to the immediate reward collected, probabilistic state transitions, and uncertainty in f…Read more

From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing

Yin Huang, Qingsong Liu, Jie Xu

Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must…Read more

Rotating ULA-Enabled Computed Tomography for Efficient 3D Spatial Power Spectrum Synthesis: Architecture and Principled Orientation Design

Haocheng Hua, Weidong Mei, Jie Xu, Rui Zhang

This paper proposes an efficient three-dimensional (3D) spatial power spectrum synthesis method by rotating a uniform linear array (ULA) about its center in 3D space. Inspired by classical computed tomography (CT), the ULA performs analog receive combining at each rotation angle…Read more

MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

Runhan Shi, Quan Zhou, Yuqian Xu, Shuai Yang, Xin Wu, Zitong Zhou, et al.

Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice. Many rely on synthetic conversations or patient simulators, omit patient-uploaded medical images, or evaluate open-end…Read more

A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data

Jinqian Pan, Tienyu Chang, Mengxian Lyu, Weimin Meng, Qingyu Wang, Yiling Ma, et al.

Abstract Artificial intelligence (AI) is increasingly applied in healthcare to support diagnosis and treatment by leveraging longitudinal multimodal data, despite persistent challenges such as missing data and heterogeneity. This systematic review, conducted in accordance with PR…Read more

SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routing

Stone Tao, Jie Xu, Hesam Rabeti, Yashraj Narang, Yijie Guo, Iretiayo Akinola

Linear-deformable manipulation remains challenging due to the complex deformations of objects such as cables and ropes. Prior data-driven approaches, particularly imitation learning, have shown some promise in narrowly defined settings but typically require thousands of demonstra…Read more

An Interpretable Deep Learning Framework for Discovery and Clinical Validation of Deep Radiomic Signatures in Tumor Classification

Chengkun Sun, Jinqian Pan, Renjie Liang, Zhengkang Fan, Xin Miao, Yi Guo, et al.

Imaging signatures are quantitative features extracted from medical images that provide clinically meaningful information for tumor diagnosis, characterization, prognosis, and treatment planning. Although deep learning has shown great potential for imaging signature discovery, it…Read more

Sensing-Limited Control Under Non-Designable Observation Mechanisms

Ming Li, Fan Liu, Yifeng Xiong, Jie Xu, Tao Liu

We study the information-theoretic limits of controlling unstable linear systems through non-designable observation mechanisms. Unlike classical communication-constrained control, the information bottleneck lies in the observation mechanism rather than in a designable encoder-cha…Read more

Data-Driven Urban Color Governance for Digital City Planning: A Machine Learning-Assisted Framework Using Street View Images in Jiading District, Shanghai

Jie Xu, Zhongnan Ye, Di Wang, Shasha Huang, Yang Liu, Yu Xiang

Urban color plays a fundamental role in shaping the visual character and cultural identity of cities. Yet in many contexts, current practices remain fragmented, with color analysis often disconnected from planning implementation and governance. To address this issue, this study p…Read more

Developing an Integrated Toolbox for Raman Spectral Analysis with Both Artificial Neural Networks and Machine Learning Algorithms

Xiangtao Kong, Jie Xu, Guodi Fan, Zixuan Zhang, Qidong Liu, Haorui An, et al.

Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship bet…Read more