19 papers indexed
Jiajie Zhu, Yan Wang, Feng Zhu, Pengfei Ding, Hongyang Liu, Zhu Sun
Cross-Domain Recommendation (CDR) aims to leverage knowledge from a relatively data-richer source domain to address the data sparsity problem in a relatively data-sparser target domain. While CDR methods need to address the distribution shifts between different domains, i.e., cro…
Chen Chen, Qi Qian, Yun Yu, Yunyan Su, Yan Wang, Zheyun Wang
Abstract Unplanned extubation (UEX) in the intensive care unit (ICU) is a serious adverse event. Current prevention strategies relying on staff vigilance have limitations. Artificial intelligence (AI)-based computer vision offers a new approach for real-time, non-contact monitori…
Guoqing Chen, Yan Wang, Jiansheng Hao, Peng Cui, He Jx, Xiaoqian Fu
Snow avalanches pose a growing hazard in High Mountain Asia (HMA), yet their regional patterns are strongly governed by snow-climate regimes that are sensitive to both long-term warming and large-scale circulation variability. Using reanalysis-based meteorological and snow datase…
Hao Qiao, Yan Wang, Jian Kuang, Xiaoji Niu
The training of learned inertial odometry depends on dense, high-precision position ground truth from motion capture, visual-inertial odometry or SLAM, which is costly and hard to acquire at scale. We propose DINS-IO, which learns inertial odometry directly from raw IMU streams w…
Manni Cui, Ruiqi Liu, Dianyuan Zou, Ziheng Qin, Jingrui Xu, ZiAn Wang, et al.
AI-generated image (AIGI) detectors achieve strong accuracy on clean benchmarks, but their performance drops sharply after images are propagated through real-world channels. We trace this fragility to what these detectors actually learn: they overfit to local artifacts left by ge…
Amin Beheshti, Rong N. Chang, Boualem Benatallah, Fabio Casati, Schahram Dustdar, Geoffrey Fox, et al.
The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goal-directed, adaptive, and tool-using computational actors. As these agents move from isolated cognitive prototypes into complex…
Jing Liu, Chenxuanyin Zou, Jiayang Ren, Gaoyun Fang, Chengfang Li, Yan Wang, et al.
Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential…
Jing Liu, Kun Yang, Yan Wang, Dingkang Yang, Xiaoshuai Hao, Wei Zhang, et al.
Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network environments, Multimodal Large Language Models (MLLMs) serve as…
Lulin Liu, Nuo Chen, Yan Wang, Bangya Liu, Wenyan Cong, Hezhen Hu, et al.
Scaling robust driving policies is fundamentally bottlenecked by the scarcity of edge cases in curated datasets. While the real world continuously captures these critical events, such long-tail events remain underutilized when collected from heterogeneous sources. Specifically, d…
Sishun Liu, Sajal Halder, Ke Deng, Yan Wang, Xiuzhen Zhang
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO use…
Zheng Guo, Jiaqi Cui, Haocheng Xiong, Jize Han, Bo Liu, Qianwen Zhang, et al.
Non-invasive prediction of Gleason Grade Group (GGG) in prostate cancer using multiparametric MRI (mpMRI) is clinically vital for reducing unnecessary biopsies. Existing GGG prediction methods face two major limitations. First, they often overlook non-image information critical f…
Jiayang Niu, Akib Karim, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, et al.
Quantum Architecture Search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms, yet existing benchmarks organize instances by molecular identity or qubit count -- criteria agnostic to Hamiltonian structure -- and rely solely on energy…
Aiwen Liu, Chengguang Zhu, Gang Wang, Dandan Zhu, Haodong Lin, Yan Wang, et al.
Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by rigid processing that entangle spatial aggregation with implicit frequency aliasing and truncation, leading to inadequate preservation…
Xiang Hu, Xinyu Wei, Hao Gu, Minshen Zhang, Tian Liang, Huayang Li, et al.
Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because o…
Yuguang Yang, Canyu Chen, Zhewen Tan, Yizhi Wang, Zichao Feng, Chunyang Liu, et al.
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing VLAs' training relies heavily on text-centric visual question answering and chain-of-thought reasoning data, which emphasizes linguistic reasoning rather…
Nuo Chen, Lulin Liu, Zihao Li, Ziyao Zeng, Zihao Zhu, Wenyan Cong, et al.
Generative world models hold immense promise as scalable simulators for autonomous systems, particularly for synthesizing rare but safety-critical multi-agent interactions, such as vehicle collisions. However, current evaluation paradigms index heavily on visual fidelity and sema…
Tianshu Zhang, Yan Wang, Ji Qi, Lijie Wen
Spatio-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries. While recent vision-language models (VLMs) show strong reasoning ability, directly applying frame-by-frame inference to long sequenc…
Yuxuan Zhang, Bing Chen, Yan Wang, Nan Li
Model Predictive Control (MPC) stands out as a prominent method for achieving optimal control in autonomous driving applications. However, the effectiveness of MPC approaches critically depends on the availability of accurate dynamic models and often necessitates substantial comp…
Zhenxia Xue, Yan Wang, Yuwen Ren, Xinyuan Zhang
As a novel learning algorithm for feedforward neural networks, the twin extreme learning machine (TELM) boasts advantages such as simple structure, few parameters, low complexity, and excellent generalization performance. However, it employs the squared L2-norm metric and an unbo…