Zobrazeno 1 - 10
of 2 439
pro vyhledávání: '"A. Fore"'
Autor:
Evjemo, Linn Danielsen, Zhang, Qin, Alvheim, Hanne-Grete, Amundsen, Herman Biørn, Føre, Martin, Kelasidi, Eleni
The significant growth in the aquaculture industry over the last few decades encourages new technological and robotic solutions to help improve the efficiency and safety of production. In sea-based farming of Atlantic salmon in Norway, Unmanned Under
Externí odkaz:
http://arxiv.org/abs/2409.15069
Autor:
Voskakis, Dimitris, Føre, Martin, Svendsen, Eirik, Liland, Aleksander Perlic, Planellas, Sonia Rey, Eguiraun, Harkaitz, Klebert, Pascal
The aquaculture industry is constantly making efforts to improve fish welfare while maintaining the ethically sustainable farming practises. This work presents an enhanced tank environment designed for testing and developing novel combinations of tec
Externí odkaz:
http://arxiv.org/abs/2409.14730
An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm$^{-3}$ and 0.1 fm$^{-3}$, matter transitions from neutron-rich nuclei to various hi
Externí odkaz:
http://arxiv.org/abs/2407.21207
Recent work [B. Fore and S. Reddy, Phys. Rev. C 101 035809 (2020)] has shown that the population of thermal pions could modify the equation of state and transport properties of hot and dense neutron-rich matter and introduce new reaction pathways to
Externí odkaz:
http://arxiv.org/abs/2407.18890
Autor:
Singh, Simranjit, Fore, Michael, Karatzas, Andreas, Lee, Chaehong, Jian, Yanan, Shangguan, Longfei, Yu, Fuxun, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
As Large Language Models (LLMs) broaden their capabilities to manage thousands of API calls, they are confronted with complex data operations across vast datasets with significant overhead to the underlying system. In this work, we introduce LLM-dCac
Externí odkaz:
http://arxiv.org/abs/2406.06799
Autor:
Fore, Michael, Singh, Simranjit, Lee, Chaehong, Pandey, Amritanshu, Anastasopoulos, Antonios, Stamoulis, Dimitrios
Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A
Externí odkaz:
http://arxiv.org/abs/2405.19563
Autor:
Singh, Simranjit, Karatzas, Andreas, Fore, Michael, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios
State-of-the-art sequential reasoning in Large Language Models (LLMs) has expanded the capabilities of Copilots beyond conversational tasks to complex function calling, managing thousands of API calls. However, the tendency of compositional prompting
Externí odkaz:
http://arxiv.org/abs/2405.17438
In this preliminary study, we investigate a GPT-driven intent-based reasoning approach to streamline tool selection for large language models (LLMs) aimed at system efficiency. By identifying the intent behind user prompts at runtime, we narrow down
Externí odkaz:
http://arxiv.org/abs/2404.15804
Tool-augmented Large Language Models (LLMs) have shown impressive capabilities in remote sensing (RS) applications. However, existing benchmarks assume question-answering input templates over predefined image-text data pairs. These standalone instruc
Externí odkaz:
http://arxiv.org/abs/2405.00709
Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO) applications through natural language instructions. However, existing agents rely on overly simplified single tasks and template-based prompts, creating a discon
Externí odkaz:
http://arxiv.org/abs/2404.15500