Zobrazeno 1 - 10
of 12 884
pro vyhledávání: '"Asokan A"'
Autor:
Eiji Obayashi, Rafael E. Luna, Takashi Nagata, Pilar Martin-Marcos, Hiroyuki Hiraishi, Chingakham Ranjit Singh, Jan Peter Erzberger, Fan Zhang, Haribabu Arthanari, Jacob Morris, Riccardo Pellarin, Chelsea Moore, Ian Harmon, Evangelos Papadopoulos, Hisashi Yoshida, Mahmoud L. Nasr, Satoru Unzai, Bryttney Thompson, Eric Aube, Samantha Hustak, Florian Stengel, Eddie Dagraca, Asokan Ananbandam, Philip Gao, Takeshi Urano, Alan G. Hinnebusch, Gerhard Wagner, Katsura Asano
Publikováno v:
Cell Reports, Vol 43, Iss 6, Pp 114404- (2024)
Externí odkaz:
https://doaj.org/article/0abf4580bb9d47ceb45f9407f4e57f48
Autor:
Marthanda Pillai Anand, G K Mini, Moses W Bobby, Asokan Anilkumar, S Kamala, L R Mohammed Kutty, S Harikrishnan, Jinbert A Lordson, Shaffi Fazaludeen Koya, Sachin Chandran, Grace A Chitra, S S Lal, K R Nayar, A Marthanda Pillai
Publikováno v:
Journal of Family Medicine and Primary Care, Vol 11, Iss 6, Pp 3000-3005 (2022)
Introduction: The clinical and epidemiological presentations of patients with coronavirus disease 2019 (COVID-19) in India is still not well explored. We studied the epidemiological and clinical profile and outcomes of COVID-19 patients admitted to a
Externí odkaz:
https://doaj.org/article/51633ecd420a4a46880a6ca7db58591e
Autor:
Benjamin, Joseph Geo, Asokan, Mothilal, Alhosani, Amna, Alasmawi, Hussain, Diehl, Werner Gerhard, Bricker, Leanne, Nandakumar, Karthik, Yaqub, Mohammad
Self-supervised learning (SSL) methods are popular since they can address situations with limited annotated data by directly utilising the underlying data distribution. However, the adoption of such methods is not explored enough in ultrasound (US) i
Externí odkaz:
http://arxiv.org/abs/2407.21738
A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation
Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pretraining and medical (target) data. However, collecting t
Externí odkaz:
http://arxiv.org/abs/2407.21739
Regulations increasingly call for various assurances from machine learning (ML) model providers about their training data, training process, and the behavior of resulting models during inference. For better transparency, companies (e.g., Huggingface
Externí odkaz:
http://arxiv.org/abs/2406.17548
Outsourced computation presents a risk to the confidentiality of clients' sensitive data since they have to trust that the service providers will not mishandle this data. Blinded Memory (BliMe) is a set of hardware extensions that addresses this prob
Externí odkaz:
http://arxiv.org/abs/2406.15302
Autor:
ElAtali, Hossam, Asokan, N.
Speculation is fundamental to achieving high CPU performance, yet it enables vulnerabilities such as Spectre attacks, which remain a significant challenge to mitigate without incurring substantial performance overheads. These attacks typically unfold
Externí odkaz:
http://arxiv.org/abs/2406.12110
Autor:
Asokan Ajith, Mohanachandran Nair Sindhu Swapna, Humberto Cabrera, Sankaranarayana Iyer Sankararaman
Publikováno v:
Photonics, Vol 10, Iss 2, p 199 (2023)
The emergence of diversified applications of laser-induced breakdown spectroscopy in the biomedical field, electronics, space physics, and material processing necessitates a comprehensive understanding of plasma parameters. The present work delineate
Externí odkaz:
https://doaj.org/article/d85b260b2bb9473b8385f6840e24f827
Diffusion-based text-to-image (T2I) models generate high-fidelity images for given textual prompts. They are trained on large datasets scraped from the Internet, potentially containing unacceptable concepts (e.g., copyright infringing or unsafe). Ret
Externí odkaz:
http://arxiv.org/abs/2404.19227
Autor:
Rangwani, Harsh, Mondal, Pradipto, Mishra, Mayank, Asokan, Ashish Ramayee, Babu, R. Venkatesh
Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT, we divide the input image into patch tokens and process them through a stack of self attention blocks. However, unlike Convolutional Neural Ne
Externí odkaz:
http://arxiv.org/abs/2404.02900