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pro vyhledávání: '"KARTHIKEYAN, A."'
Autor:
Karthikeyan, M.
Publikováno v:
Journal of Plant Diseases and Protection, 2009 Jun 01. 116(3), 143-143.
Externí odkaz:
https://www.jstor.org/stable/43229047
Akademický článek
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Akademický článek
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We propose a novel mechanism, dark matter internal pair production (DIPP), to detect dark matter candidates at beam dump facilities. When energetic dark matter scatters in a material, it can create a lepton-antilepton pair by exchanging a virtual pho
Externí odkaz:
http://arxiv.org/abs/2410.07624
Autor:
Lee, Bruce W., Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Miehling, Erik, Dognin, Pierre, Nagireddy, Manish, Dhurandhar, Amit
LLMs have shown remarkable capabilities, but precisely controlling their response behavior remains challenging. Existing activation steering methods alter LLM behavior indiscriminately, limiting their practical applicability in settings where selecti
Externí odkaz:
http://arxiv.org/abs/2409.05907
Autor:
Jafari, Sajad, Bayani, Atiyeh, Parastesh, Fatemeh, Rajagopal, Karthikeyan, del Genio, Charo I., Minati, Ludovico, Boccaletti, Stefano
The Master Stability Function is a robust and useful tool for determining the conditions of synchronization stability in a network of coupled systems. While a comprehensive classification exists in the case in which the nodes are chaotic dynamical sy
Externí odkaz:
http://arxiv.org/abs/2409.04193
Autor:
Padhi, Inkit, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Nagireddy, Manish, Dognin, Pierre, Varshney, Kush R.
Aligning large language models (LLMs) to value systems has emerged as a significant area of research within the fields of AI and NLP. Currently, this alignment process relies on the availability of high-quality supervised and preference data, which c
Externí odkaz:
http://arxiv.org/abs/2408.10392
Autor:
Akbar, Muhammad Azeem, Esposito, Matteo, Hyrynsalmi, Sami, Kumar, Karthikeyan Dinesh, Lenarduzzi, Valentina, Li, Xiaozhou, Mehraj, Ali, Mikkonen, Tommi, Moreschini, Sergio, Mäkitalo, Niko, Oivo, Markku, Paavonen, Anna-Sofia, Parveen, Risha, Smolander, Kari, Su, Ruoyu, Systä, Kari, Taibi, Davide, Yang, Nan, Zhang, Zheying, Zohaib, Muhammad
In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustain
Externí odkaz:
http://arxiv.org/abs/2407.05963
Autor:
Moschopoulos, Vasileios, Kotsiopoulos, Thanasis, Parada, Pablo Peso, Nikiforidis, Konstantinos, Stergiadis, Alexandros, Papakostas, Gerasimos, Jalal, Md Asif, Zhang, Jisi, Drosou, Anastasios, Saravanan, Karthikeyan
State-of-the art Text-To-Music (TTM) generative AI models are large and require desktop or server class compute, making them infeasible for deployment on mobile phones. This paper presents an analysis of trade-offs between model compression and gener
Externí odkaz:
http://arxiv.org/abs/2406.17159
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally
Externí odkaz:
http://arxiv.org/abs/2406.05937