Zobrazeno 1 - 10
of 2 903
pro vyhledávání: '"P Gohil"'
We describe OHBA Software Library for the analysis of electrophysiological data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for magneto-/electro-encephalography (M/EEG) sensor and s
Externí odkaz:
http://arxiv.org/abs/2410.22051
The accurate prediction of danger levels in video content is critical for enhancing safety and security systems, particularly in environments where quick and reliable assessments are essential. In this study, we perform a comparative analysis of vari
Externí odkaz:
http://arxiv.org/abs/2410.19642
Autor:
Bashir, Noman, Gohil, Varun, Belavadi, Anagha, Shahrad, Mohammad, Irwin, David, Olivetti, Elsa, Delimitrou, Christina
The rapid increase in computing demand and its corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several life
Externí odkaz:
http://arxiv.org/abs/2410.15087
Autor:
Gupta, Pranav, Krishnan, Advith, Nanda, Naman, Eswar, Ananth, Agarwal, Deeksha, Gohil, Pratham, Goel, Pratyush
We present a novel dataset aimed at advancing danger analysis and assessment by addressing the challenge of quantifying danger in video content and identifying how human-like a Large Language Model (LLM) evaluator is for the same. This is achieved by
Externí odkaz:
http://arxiv.org/abs/2410.00477
Black crystals of the mixed-valence osmate(V,VI) Sr4Os3O12 were grown via a gas phase reaction in an evacuated silica tube. The material is attracted to a permanent magnet at room temperature (295 K) but loses this property when heated or cooled. In
Externí odkaz:
http://arxiv.org/abs/2408.12117
The aim of the research was to compare the quality parameters of Seawater before and after hydrodynamic cavitation treatment. The Hydrodynamic Cavitation Method for water treatment gives the highest reduction in Turbidity (100%), the second highest r
Externí odkaz:
http://arxiv.org/abs/2407.07115
Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process. Prior works evaluating LLMs' abilities for register transfer level code generation solely focus on f
Externí odkaz:
http://arxiv.org/abs/2404.08806
Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based techniques for detecting intellectual property (IP) piracy, detecting
Externí odkaz:
http://arxiv.org/abs/2402.13946
Autor:
DeLorenzo, Matthew, Chowdhury, Animesh Basak, Gohil, Vasudev, Thakur, Shailja, Karri, Ramesh, Garg, Siddharth, Rajendran, Jeyavijayan
Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) efficiency. This is due to the lack of PPA awareness in conventional transfo
Externí odkaz:
http://arxiv.org/abs/2402.03289
This paper discusses the feasibility of using Large Language Models LLM for code generation with a particular application in designing an RISC. The paper also reviews the associated steps such as parsing, tokenization, encoding, attention mechanism,
Externí odkaz:
http://arxiv.org/abs/2401.10364