Zobrazeno 1 - 10
of 339
pro vyhledávání: '"Wang Zhengxiang"'
Publikováno v:
Open Medicine, Vol 18, Iss 1, Pp 1366-79 (2023)
Long intergenic noncoding RNA 00511 (LINC00511) predicts poor prognosis in various malignancies and functions as an oncogene in distinct malignant tumors. The role of LINC00511 in melanoma progression was assessed. In our research, expression of LINC
Externí odkaz:
https://doaj.org/article/63a49af29acd40b58ca1ea2b4a7bc48b
Autor:
Wang Zhengxiang
Publikováno v:
中国工程科学, Vol 23, Iss 6, Pp 155-166 (2021)
Polylactic acid, a typical carbon-neutral, renewable, and biodegradable polymer, has been gradually developing as a kind of fundamental bulk raw material required for national economic and social development. The industrial chain of polylactic acid m
Externí odkaz:
https://doaj.org/article/44e2d18a3d994d1e886751c598bab521
Recent studies have proposed placing multiple problems in a single prompt to improve input token utilization for a more efficient LLM inference. We call this MPP, in contrast to conventional SPP that prompts an LLM with a single problem at a time. Wh
Externí odkaz:
http://arxiv.org/abs/2406.10786
Autor:
Wang, Zhengxiang, Rambow, Owen
We propose a novel clustering pipeline to detect and characterize influence campaigns from documents. This approach clusters parts of document, detects clusters that likely reflect an influence campaign, and then identifies documents linked to an inf
Externí odkaz:
http://arxiv.org/abs/2402.17151
Autor:
Wang Zhengxiang
Publikováno v:
E3S Web of Conferences, Vol 218, p 01025 (2020)
Internet live broadcast marketing (LBM) is an important form of product exchange in the era of post Covid-2019, and it is the fifth revolution of retail industry characterized by “new retail”. From the process of evolution and development of webc
Externí odkaz:
https://doaj.org/article/f5cea765f6ba4f1e8a964413b07b5b5c
Autor:
Wang, Zhengxiang
This paper investigates the effectiveness of token-level text augmentation and the role of probabilistic linguistic knowledge within a linguistically-motivated evaluation context. Two text augmentation programs, REDA and REDA$_{NG}$, were developed,
Externí odkaz:
http://arxiv.org/abs/2306.16644
Autor:
Wang, Zhengxiang
The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four transduction tasks: identity, reversal, total reduplication, and quadratic copying. These transductions are traditionally well s
Externí odkaz:
http://arxiv.org/abs/2303.06841
Autor:
Wang, Zhengxiang
We present three large-scale experiments on binary text matching classification task both in Chinese and English to evaluate the effectiveness and generalizability of random text perturbations as a data augmentation approach for NLP. It is found that
Externí odkaz:
http://arxiv.org/abs/2209.00797
The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in t
Externí odkaz:
http://arxiv.org/abs/2208.03772
Autor:
Wang, Zhengxiang
To investigate the role of linguistic knowledge in data augmentation (DA) for Natural Language Processing (NLP), we designed two adapted DA programs and applied them to LCQMC (a Large-scale Chinese Question Matching Corpus) for a binary Chinese quest
Externí odkaz:
http://arxiv.org/abs/2111.14709