Zobrazeno 1 - 10
of 26 858
pro vyhledávání: '"ZHANG, XIANG"'
Autor:
Huang, Wenyong, Zhang, Xiang
Prelle and Singer showed in 1983 that if a system of ordinary differential equations defined on a differential field $K$ has a first integral in an elementrary field extension $L$ of $K$, then it must have a first integral consisting of algebraic ele
Externí odkaz:
http://arxiv.org/abs/2412.04750
Autor:
Hu, Shuting, Ackun, Peggy, Zhang, Xiang, Cao, Siyang, Barton, Jennifer, Hector, Melvin G., Fain, Mindy J., Toosizadeh, Nima
This study explores a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology. The goal is to develop a non-contact sensing, privacy-preserving, and all-day operational method for healthcare applicati
Externí odkaz:
http://arxiv.org/abs/2411.14656
Graph Neural Networks (GNNs) have shown promising results in modeling graphs in various tasks. The training of GNNs, especially on specialized tasks such as bioinformatics, demands extensive expert annotations, which are expensive and usually contain
Externí odkaz:
http://arxiv.org/abs/2411.11197
Autor:
Zhang, Xiang, Li, Senyu, Shi, Ning, Hauer, Bradley, Wu, Zijun, Kondrak, Grzegorz, Abdul-Mageed, Muhammad, Lakshmanan, Laks V. S.
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content. Models like GPT-4V, which merge computer vision with advance
Externí odkaz:
http://arxiv.org/abs/2411.09273
Developing asynchronous neuromorphic hardware to meet the demands of diverse real-life edge scenarios remains significant challenges. These challenges include constraints on hardware resources and power budgets while satisfying the requirements for r
Externí odkaz:
http://arxiv.org/abs/2411.06059
Autor:
Zhao, Shuqi, Zhu, Xinghao, Chen, Yuxin, Li, Chenran, Zhang, Xiang, Ding, Mingyu, Tomizuka, Masayoshi
Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and teleoperation have enabled progress for robots in such ability. Howeve
Externí odkaz:
http://arxiv.org/abs/2411.04428
Autor:
Sun, Lingfeng, Wang, Yixiao, Hung, Pin-Yun, Wang, Changhao, Zhang, Xiang, Xu, Zhuo, Tomizuka, Masayoshi
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance and collaborative interaction, such as indoor spaces. Unlike static or
Externí odkaz:
http://arxiv.org/abs/2411.03669
Autor:
Zhang, Xiang, Wang, Guochao, Chang, Kangrui, Zheng, Haobin, Zhou, Yongzhuang, Shen, Yong, Zou, Hongxin
The spectrum of the output pulses from the figure-9 laser typically exhibits more distortion than the spectra from mode-locked lasers based on other saturable absorbers and the spectrum of its intracavity pulses. Here, we demonstrate two figure-9 las
Externí odkaz:
http://arxiv.org/abs/2410.20656
Transformers, the backbone of modern large language models (LLMs), face inherent architectural limitations that impede their reasoning capabilities. Unlike recurrent networks, Transformers lack recurrent connections, confining them to constant-depth
Externí odkaz:
http://arxiv.org/abs/2410.19730
The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with a narro
Externí odkaz:
http://arxiv.org/abs/2410.19877