Zobrazeno 1 - 10
of 916
pro vyhledávání: '"Xu Jiawen"'
Autor:
Xu Jiawen, L. P. Morina
Publikováno v:
Дискурс, Vol 10, Iss 1, Pp 5-14 (2024)
Introduction. The article is devoted to the phenomenon of modern Chinese theater and aims to explore the ways of its evolution – from imitation of European avant-garde forms of theatrical art to the formation of an original artistic language. An im
Externí odkaz:
https://doaj.org/article/0f0590ed0c5c4195b52db9553f10961d
Publikováno v:
Nanophotonics, Vol 13, Iss 12, Pp 2213-2222 (2024)
Programmable metasurface has become a prominent tool in various areas including control, communication, computing, and so on, due to its unique capability in the electromagnetic (EM) manipulation. However, it is lack of the intelligence in the sense
Externí odkaz:
https://doaj.org/article/1896b60d96df49d99321f3efbc774c01
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 50, Iss 8, Pp 738-744 (2023)
The occurrence of gastric cancer is closely related to environmental, genetic, and epigenetic factors. Currently, RNA modification is a research frontier and hotspot in the field of epigenetics. With the advancements in analytical chemistry and high-
Externí odkaz:
https://doaj.org/article/bc10fd38b0ba462097f740ed452ce270
Publikováno v:
Jichu yixue yu linchuang, Vol 43, Iss 2, Pp 225-232 (2023)
Objective To study the mechanism of learning and memory impairment in aged female mice caused by chronic stress. Methods Twenty-month-old ICR mice were randomly divided into four groups: control females, control males, stressed females, and stressed
Externí odkaz:
https://doaj.org/article/92d17f4adce240f2aa73534fb1de338c
Autor:
Xu, Jiawen
Open set recognition (OSR) is a critical aspect of machine learning, addressing the challenge of detecting novel classes during inference. Within the realm of deep learning, neural classifiers trained on a closed set of data typically struggle to ide
Externí odkaz:
http://arxiv.org/abs/2404.10370
Publikováno v:
1st Workshop on Visual Continual Learning in conjunction with ICCV 2023
In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often misidentify these samples, leading to erroneous predictions. Such miscla
Externí odkaz:
http://arxiv.org/abs/2310.03848
Publikováno v:
In Environmental Technology & Innovation November 2024 36
Publikováno v:
In Fuel 1 September 2024 371 Part A
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 5 February 2025 326
Publikováno v:
In Measurement 30 January 2025 240