Results 201 to 210 of about 2,681,518 (295)

Leveraging large language models for data analysis automation. [PDF]

PLoS One
Jansen JA, Manukyan A, Al Khoury N, Akalin A.   +3 more
europepmc   +1 more source

Multi-step retrieval and reasoning improves radiology question answering with large language models. [PDF]

NPJ Digit Med
Wind S, Sopa J, Truhn D, Lotfinia M, Nguyen TT, Bressem K, Adams L, Rusu M, Köstler H, Wellein G, Maier A, Tayebi Arasteh S.   +11 more
europepmc   +1 more source

Shaping inter-brain plasticity: A feasibility study of enhancing inter-brain synchrony with dyadic neurofeedback. [PDF]

iScience
Francis M, Markus A, Stuhr-Wulff F, Shamay-Tsoory S.   +3 more
europepmc   +1 more source

A Practical Guide and Assessment on Using ChatGPT to Conduct Grounded Theory: Tutorial.

J Med Internet Res
Yue Y, Liu D, Lv Y, Hao J, Cui P.
europepmc   +1 more source

Generalizable and scalable multistage biomedical concept normalization leveraging large language models. [PDF]

Res Synth Methods
Dobbins NJ.
europepmc   +1 more source

rMATS-cloud: Large-scale Alternative Splicing Analysis in the Cloud. [PDF]

Genomics Proteomics Bioinformatics
Adams JI, Kutschera E, Hu Q, Liu CJ, Liu Q, Kadash-Edmondson K, Liu S, Xing Y.   +7 more
europepmc   +1 more source

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Nonsystematic Turbo Codes

IEEE Transactions on Communications, 2005
In this paper, we introduce the concept of nonsystematic turbo codes and compare them with classical systematic turbo codes. Nonsystematic turbo codes can achieve lower error floors than systematic turbo codes because of their superior effective free distance properties.
Banerjee A., Vatta F., Scanavino B., Costello Jr. D. J.   +3 more
openaire   +3 more sources

Laminated turbo codes

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005., 2005
In this paper we introduce a new coding scheme - so-called laminated turbo codes. It is characterized by a block-convolutional structure that enables us to combine the advantages of a convolutional encoder memory and a block-oriented decoding method. We show that this block-convolutional structure is superior in terms of its error correction capability
Huebner, A., Lentmaier, M., Zigangirov, K. Sh., Costello, Jr., D.,   +3 more
openaire   +2 more sources

DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence

arXiv.org
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of
DeepSeek-AI, Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y. Wu, Yukun Li, Huazuo Gao, Shirong Ma, Wangding Zeng, Xiao Bi, Zihui Gu, Hanwei Xu, Damai Dai, Kai Dong, Liyue Zhang, Y. Piao, Zhibin Gou, Zhenda Xie, Zhewen Hao, Bing-Li Wang, Jun-Mei Song, Deli Chen, Xin Xie, Kang Guan, Yu-mei You, A. Liu, Qiushi Du, Wenjun Gao, Xuan Lu, Qinyu Chen, Yaohui Wang, C. Deng, Jiashi Li, Chenggang Zhao, C. Ruan, Fuli Luo, W. Liang   +39 more
semanticscholar   +1 more source