Zobrazeno 1 - 10
of 306
pro vyhledávání: '"Liu, Ying"'
Autor:
Rangnekar, Aneesh, Boehm, Kevin M., Aherne, Emily A., Nikolovski, Ines, Gangai, Natalie, Liu, Ying, Zamarin, Dimitry, Roche, Kara L., Shah, Sohrab P., Lakhman, Yulia, Veeraraghavan, Harini
Two self-supervised pretrained transformer-based segmentation models (SMIT and Swin UNETR) fine-tuned on a dataset of ovarian cancer CT images provided reasonably accurate delineations of the tumors in an independent test dataset. Tumors in the adnex
Externí odkaz:
http://arxiv.org/abs/2406.17666
On 2022 March 28 two successive coronal mass ejections (CMEs) were observed by multiple spacecraft and resulted in a magnetic cloud (MC) at 1 AU. We investigate the propagation and interaction properties of the two CMEs correlated with the MC using c
Externí odkaz:
http://arxiv.org/abs/2406.13603
Autor:
Li, Min, Chen, Chen, Xiong, Zhuang, Liu, Ying, Rong, Pengfei, Shan, Shanshan, Liu, Feng, Sun, Hongfu, Gao, Yang
Quantitative susceptibility mapping (QSM) is an MRI phase-based post-processing technique to extract the distribution of tissue susceptibilities, demonstrating significant potential in studying neurological diseases. However, the ill-conditioned natu
Externí odkaz:
http://arxiv.org/abs/2406.12300
Autor:
Li, Shilong, Bai, Ge, Zhang, Zhang, Liu, Ying, Lu, Chenji, Guo, Daichi, Liu, Ruifang, Sun, Yong
Predicting unseen relations that cannot be observed during the training phase is a challenging task in relation extraction. Previous works have made progress by matching the semantics between input instances and label descriptions. However, fine-grai
Externí odkaz:
http://arxiv.org/abs/2406.11429
Autor:
Liu, Ying, Niu, Ranming, Uriach, Roger, Pesquera, David, Roque, Jose Manuel Caicedo, Santiso, Jose, Cairney, Julie M, Liao, Xiaozhou, Arbiol, Jordi, Catalan, Gustau
Whereas ferroelectricity may vanish in ultra-thin ferroelectric films, it is expected to emerge in ultra-thin anti-ferroelectric films, sparking people's interest in using antiferroelectric materials as an alternative to ferroelectric ones for high-d
Externí odkaz:
http://arxiv.org/abs/2406.07808
Autor:
Yang, Runyan, Yang, Huibao, Zhang, Xiqing, Ye, Tiantian, Liu, Ying, Gao, Yingying, Zhang, Shilei, Deng, Chao, Feng, Junlan
Recently, there have been attempts to integrate various speech processing tasks into a unified model. However, few previous works directly demonstrated that joint optimization of diverse tasks in multitask speech models has positive influence on the
Externí odkaz:
http://arxiv.org/abs/2406.07801
While nationality is a pivotal demographic element that enhances the performance of language models, it has received far less scrutiny regarding inherent biases. This study investigates nationality bias in ChatGPT (GPT-3.5), a large language model (L
Externí odkaz:
http://arxiv.org/abs/2405.06996
Sequential recommender systems explore users' preferences and behavioral patterns from their historically generated data. Recently, researchers aim to improve sequential recommendation by utilizing massive user-generated multi-modal content, such as
Externí odkaz:
http://arxiv.org/abs/2404.17238
Autor:
Liu, Yong, Kang, Mengtian, Gao, Shuo, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Nathan, Arokia, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Occhipinti, Luigi
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as hi
Externí odkaz:
http://arxiv.org/abs/2404.13388
Autor:
Wang, Jiaqi, Kang, Mengtian, Liu, Yong, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Gao, Shuo, Occhipinti, Luigi G.
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervise
Externí odkaz:
http://arxiv.org/abs/2404.13386