Zobrazeno 1 - 10
of 21 504
pro vyhledávání: '"Forrest, A P"'
Autor:
Bowens, Rory, Leisenring, Jarron, Meyer, Michael R., Tobin, Taylor L., Miller, Alyssa L., Monnier, John D., Viges, Eric, Hoffmann, Bill, Montoya, Manny, Durney, Olivier, West, Grant, Morzinski, Katie, Forrest, William, McMurtry, Craig
We present results from commissioning observations of the mid-IR instrument, MIRAC-5, on the 6.5-m MMT telescope. MIRAC-5 is a novel ground-based instrument that utilizes a state-of-the-art GeoSnap (2 - 13 microns) HgCdTe detector with adaptive optic
Externí odkaz:
http://arxiv.org/abs/2412.10189
Autor:
Xiang, Xiaoyu, Gorelik, Liat Sless, Fan, Yuchen, Armstrong, Omri, Iandola, Forrest, Li, Yilei, Lifshitz, Ita, Ranjan, Rakesh
We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a dep
Externí odkaz:
http://arxiv.org/abs/2412.07766
Autor:
Xiong, Yunyang, Zhou, Chong, Xiang, Xiaoyu, Wu, Lemeng, Zhu, Chenchen, Liu, Zechun, Suri, Saksham, Varadarajan, Balakrishnan, Akula, Ramya, Iandola, Forrest, Krishnamoorthi, Raghuraman, Soran, Bilge, Chandra, Vikas
Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for
Externí odkaz:
http://arxiv.org/abs/2411.18933
Autor:
McConachie, Ian, Wilson, Gillian, Forrest, Ben, Marsan, Z. Cemile, Muzzin, Adam, Cooper, M. C., Annunziatella, Marianna, Marchesini, Danilo, Gomez, Percy, Chang, Wenjun, Stawinski, Stephanie M. Urbano, McDonald, Michael, Webb, Tracy, Noble, Allison, Lemaux, Brian C., Shah, Ekta A., Staab, Priti, Lubin, Lori M., Gal, Roy R.
We examine the quiescent fractions of massive galaxies in six $z\gtrsim3$ spectroscopically-confirmed protoclusters in the COSMOS field, one of which is newly confirmed and presented here. We report the spectroscopic confirmation of MAGAZ3NE~J100143+
Externí odkaz:
http://arxiv.org/abs/2411.14641
Autor:
Forbes, John C., Bannister, Michele T., Lintott, Chris, Forrest, Angus, Zwart, Simon Portegies, Dorsey, Rosemary C., Albrow, Leah, Hopkins, Matthew J.
Upcoming surveys are likely to discover a new sample of interstellar objects (ISOs) within the Solar System, but questions remain about the origin and distribution of this population within the Galaxy. ISOs are ejected from their host systems with a
Externí odkaz:
http://arxiv.org/abs/2411.14577
Autor:
Noever, David, McKee, Forrest
This research introduces a novel evaluation framework designed to assess large language models' (LLMs) ability to acknowledge uncertainty on 675 fundamentally unsolvable problems. Using a curated dataset of graduate-level grand challenge questions wi
Externí odkaz:
http://arxiv.org/abs/2411.14486
Autor:
Dominé, Laura, Biswas, Ankit, Cloete, Richard, Delacroix, Alex, Fedorenko, Andriy, Jacaruso, Lucas, Kelderman, Ezra, Keto, Eric, Little, Sarah, Loeb, Abraham, Masson, Eric, Prior, Mike, Schultz, Forrest, Szenher, Matthew, Watters, Wes, White, Abby
To date there is little publicly available scientific data on Unidentified Aerial Phenomena (UAP) whose properties and kinematics purportedly reside outside the performance envelope of known phenomena. To address this deficiency, the Galileo Project
Externí odkaz:
http://arxiv.org/abs/2411.07956
Autor:
Bao, Forrest Sheng, Li, Miaoran, Qu, Renyi, Luo, Ge, Wan, Erana, Tang, Yujia, Fan, Weisi, Tamber, Manveer Singh, Kazi, Suleman, Sourabh, Vivek, Qi, Mike, Tu, Ruixuan, Xu, Chenyu, Gonzales, Matthew, Mendelevitch, Ofer, Ahmad, Amin
Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing evaluations of hallucinations in LLM-generated summaries, and evaluations
Externí odkaz:
http://arxiv.org/abs/2410.13210
Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking, which aims to improve retrieval performance by dividing documents into semantically coherent segments. Despite its growing adoption, the actual benefi
Externí odkaz:
http://arxiv.org/abs/2410.13070
Autor:
Noever, David, McKee, Forrest
The research builds and evaluates the adversarial potential to introduce copied code or hallucinated AI recommendations for malicious code in popular code repositories. While foundational large language models (LLMs) from OpenAI, Google, and Anthropi
Externí odkaz:
http://arxiv.org/abs/2410.06462