Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Xingqiao Wang"'
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
ObjectiveSubarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles and underlying mechanisms remain poorly understood. This study aims to identify differentially expres
Externí odkaz:
https://doaj.org/article/ed156631b5134156a1c903459defe2c5
Publikováno v:
International Journal of Population Data Science, Vol 9, Iss 5 (2024)
Large Language Models, such as OpenAI’s GPT, have demonstrated remarkable success in various applications by generating human-like text. However, a critical question remains: how can we effectively leverage these large language models for data link
Externí odkaz:
https://doaj.org/article/ee7a2c433b4a4b779799af0f858dc9f5
Publikováno v:
Frontiers in Aging Neuroscience, Vol 15 (2023)
BackgroundCerebral vasospasm (CV) can cause inflammation and damage to neuronal cells in the elderly, leading to dementia.PurposeThis study aimed to investigate the genetic mechanisms underlying dementia caused by CV in the elderly, identify preventi
Externí odkaz:
https://doaj.org/article/a3c2efa17a034616af7ae75eddf0d743
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text
Externí odkaz:
https://doaj.org/article/a18567fe1acc4d1abac60144d7f9afa3
Publikováno v:
Frontiers in Artificial Intelligence, Vol 4 (2021)
Background: T ransformer-based language models have delivered clear improvements in a wide range of natural language processing (NLP) tasks. However, those models have a significant limitation; specifically, they cannot infer causality, a prerequisit
Externí odkaz:
https://doaj.org/article/1fbed3eb64a54a6bbe953651484511a6
Publikováno v:
Journal of Advanced Transportation, Vol 2019 (2019)
In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how
Externí odkaz:
https://doaj.org/article/0577a04f661247a68cca5e64a937ff4d
Publikováno v:
Sensors, Vol 19, Iss 12, p 2735 (2019)
The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless
Externí odkaz:
https://doaj.org/article/0d8d117b6c89451db65cd110bc650e24
Publikováno v:
In Journal of Materials Processing Tech. August 2016 234:143-149
Autor:
Xingqiao Wang, Shuai Wang
Publikováno v:
2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81951dfdc78f3a7dfdf5a8e179abc12b
https://doi.org/10.1109/eebda56825.2023.10090629
https://doi.org/10.1109/eebda56825.2023.10090629
Autor:
Xingqiao Wang
Publikováno v:
Revista Brasileira de Medicina do Esporte v.29 n.spe1 2023
Revista brasileira de medicina do esporte
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)
instacron:SBMEE
Revista Brasileira de Medicina do Esporte, Volume: 29, Issue: spe1, Article number: e2022_0194, Published: 29 AUG 2022
Revista brasileira de medicina do esporte
Sociedade Brasileira de Medicina do Exercício e do Esporte (SBMEE)
instacron:SBMEE
Revista Brasileira de Medicina do Esporte, Volume: 29, Issue: spe1, Article number: e2022_0194, Published: 29 AUG 2022
Introduction In medicine, Deep Learning is a type of machine learning that aims to train computers to perform human tasks by simulating the human brain. Gait recognition and gait motion simulation is one of the most interesting research areas in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef416d8d614fc7fa94c75e2b74f27e59
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700212
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-86922023000700212
