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pro vyhledávání: '"Mamidi A"'
Transformer-based models have revolutionized the field of natural language processing. To understand why they perform so well and to assess their reliability, several studies have focused on questions such as: Which linguistic properties are encoded
Externí odkaz:
http://arxiv.org/abs/2410.02611
Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such texts in journalism, educational, and academic contexts have surfaced. S
Externí odkaz:
http://arxiv.org/abs/2407.02978
This report presents the GPU acceleration of implicit kinetic meshfree methods using modified LU-SGS algorithms. The meshfree scheme is based on the least squares kinetic upwind method (LSKUM). In the existing matrix-free LU-SGS approaches for kineti
Externí odkaz:
http://arxiv.org/abs/2406.07441
Significance of Chain of Thought in Gender Bias Mitigation for English-Dravidian Machine Translation
Autor:
Prahallad, Lavanya, Mamidi, Radhika
Gender bias in machine translation (MT) sys- tems poses a significant challenge to achieving accurate and inclusive translations. This paper examines gender bias in machine translation systems for languages such as Telugu and Kan- nada from the Dravi
Externí odkaz:
http://arxiv.org/abs/2405.19701
Regent is an implicitly parallel programming language that allows the development of a single codebase for heterogeneous platforms targeting CPUs and GPUs. This paper presents the development of a parallel meshfree solver in Regent for two-dimensiona
Externí odkaz:
http://arxiv.org/abs/2403.13287
In recent studies, the extensive utilization of large language models has underscored the importance of robust evaluation methodologies for assessing text generation quality and relevance to specific tasks. This has revealed a prevalent issue known a
Externí odkaz:
http://arxiv.org/abs/2403.12244
SemEval-2024 Task 8: Weighted Layer Averaging RoBERTa for Black-Box Machine-Generated Text Detection
This document contains the details of the authors' submission to the proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection Subtask A (monolingual) and B. Detection of machine-ge
Externí odkaz:
http://arxiv.org/abs/2402.15873
Autor:
Melissa A. Graewert, Maria Volkova, Klara Jonasson, Juha A. E. Määttä, Tobias Gräwert, Samara Mamidi, Natalia Kulesskaya, Johan Evenäs, Richard E. Johnsson, Dmitri Svergun, Arnab Bhattacharjee, Henri J. Huttunen
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Cerebral dopamine neurotrophic factor (CDNF) is an unconventional neurotrophic factor that is a disease-modifying drug candidate for Parkinson’s disease. CDNF has pleiotropic protective effects on stressed cells, but its mechanism of actio
Externí odkaz:
https://doaj.org/article/96e49cedbeaa44d2becdda52aada34c8
Autor:
Mariam Amouzoune, Sajid Rehman, Rachid Benkirane, Sripada Udupa, Sujan Mamidi, Zakaria Kehel, Muamer Al-Jaboobi, Ahmed Amri
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Leaf rust (LR) caused by Puccinia hordei is a serious disease of barley worldwide, causing significant yield losses and reduced grain quality. Discovery and incorporation of new sources of resistance from gene bank accessions into barley bre
Externí odkaz:
https://doaj.org/article/b0906a6944234c8685c8e61749d2945d
Autor:
Christina Vasalou, Theresa A. Proia, Laura Kazlauskas, Anna Przybyla, Matthew Sung, Srinivas Mamidi, Kim Maratea, Matthew Griffin, Rebecca Sargeant, Jelena Urosevic, Anton I. Rosenbaum, Jiaqi Yuan, Krishna C. Aluri, Diane Ramsden, Niresh Hariparsad, Rhys D.O. Jones, Jerome T. Mettetal
Publikováno v:
CPT: Pharmacometrics & Systems Pharmacology, Vol 13, Iss 6, Pp 994-1005 (2024)
Abstract Trastuzumab deruxtecan (T‐DXd; DS‐8201; ENHERTU®) is a human epithelial growth factor receptor 2 (HER2)‐directed antibody drug conjugate (ADC) with demonstrated antitumor activity against a range of tumor types. Aiming to understand t
Externí odkaz:
https://doaj.org/article/9ec3eba9f35b4e748d121b8d38437da3