Zobrazeno 1 - 10
of 2 343
pro vyhledávání: '"Wang Ziqi"'
Publikováno v:
Guoji laonian yixue zazhi, Vol 45, Iss 6, Pp 738-742 (2024)
Motor cognitive risk (MCR) syndrome is closely linked to cognitive impairment, with slow gait speed and self-reported cognitive decline often serving as independent predictors of such impairment. Presently, there is a paucity of research on the relat
Externí odkaz:
https://doaj.org/article/0dc084b309ff49808b585e8673995464
Publikováno v:
Shanghai Jiaotong Daxue xuebao. Yixue ban, Vol 44, Iss 3, Pp 393-398 (2024)
Strabismus, misalignment of the eyes arising from central nervous system dysregulation and extraocular muscles imbalance, commonly manifests in childhood, leading to amblyopia, binocular vision dysfunction, torticollis and other developmental and psy
Externí odkaz:
https://doaj.org/article/6b283664b30a4ec7babb4682ca8ff4f7
Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions. Building on this, continual visual instruction tuning (CVIT) extends t
Externí odkaz:
http://arxiv.org/abs/2411.13949
Autor:
Wang, Shuangyi, Lin, Haichuan, Xie, Yiping, Wang, Ziqi, Chen, Dong, Tan, Longyue, Hou, Xilong, Chen, Chen, Zhou, Xiao-Hu, Lin, Shengtao, Pan, Fei, So, Kent Chak-Yu, Hou, Zeng-Guang
Transcatheter tricuspid valve replacement (TTVR) is the latest treatment for tricuspid regurgitation and is in the early stages of clinical adoption. Intelligent robotic approaches are expected to overcome the challenges of surgical manipulation and
Externí odkaz:
http://arxiv.org/abs/2411.12478
Federated learning (FL) is an emerging paradigm for training machine learning models across distributed clients. Traditionally, in FL settings, a central server assigns training efforts (or strategies) to clients. However, from a market-oriented pers
Externí odkaz:
http://arxiv.org/abs/2411.11793
Autor:
Quan, Pengrui, Ouyang, Xiaomin, Jeyakumar, Jeya Vikranth, Wang, Ziqi, Xing, Yang, Srivastava, Mani
Effective processing, interpretation, and management of sensor data have emerged as a critical component of cyber-physical systems. Traditionally, processing sensor data requires profound theoretical knowledge and proficiency in signal-processing too
Externí odkaz:
http://arxiv.org/abs/2410.10741
The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone is inadequate, as machine learning models can memorize information fr
Externí odkaz:
http://arxiv.org/abs/2410.10128
We show that in a special type of two-dimensional dilaton-gravity-scalar model, where both the dilaton and the scalar matter fields have noncanonical kinetic terms, it is possible to construct kink solutions whose linear perturbation equation is a Sc
Externí odkaz:
http://arxiv.org/abs/2409.14761
Autor:
Kim, Jungho, Wang, Ziqi
This paper introduces a stochastic simulator for seismic uncertainty quantification, which is crucial for performance-based earthquake engineering. The proposed simulator extends the recently developed dimensionality reduction-based surrogate modelin
Externí odkaz:
http://arxiv.org/abs/2409.17159
Autor:
Wang, Ziqi, Zhang, Hanlin, Li, Xiner, Huang, Kuan-Hao, Han, Chi, Ji, Shuiwang, Kakade, Sham M., Peng, Hao, Ji, Heng
Position bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context. This bias often leads to unexpected model failures and hurts performance, robustness
Externí odkaz:
http://arxiv.org/abs/2407.01100