Zobrazeno 1 - 10
of 336
pro vyhledávání: '"Moran, Seán"'
Autor:
Chen, Chun-Fu, Moriarty, Bill, Hu, Shaohan, Moran, Sean, Pistoia, Marco, Piuri, Vincenzo, Samarati, Pierangela
The recent rapid advancements in both sensing and machine learning technologies have given rise to the universal collection and utilization of people's biometrics, such as fingerprints, voices, retina/facial scans, or gait/motion/gestures data, enabl
Externí odkaz:
http://arxiv.org/abs/2405.15062
We introduce a new challenge to the software development community: 1) leveraging AI to accurately detect and flag up secrets in code and on popular document sharing platforms that frequently used by developers, such as Confluence and 2) automaticall
Externí odkaz:
http://arxiv.org/abs/2401.01754
We present a tool that leverages generative AI to accelerate the migration of on-premises applications to the cloud. The Cloud Migration LLM accepts input from the user specifying the parameters of their migration, and outputs a migration strategy wi
Externí odkaz:
http://arxiv.org/abs/2401.01753
Machine learning models trained on sensitive or private data can inadvertently memorize and leak that information. Machine unlearning seeks to retroactively remove such details from model weights to protect privacy. We contribute a lightweight unlear
Externí odkaz:
http://arxiv.org/abs/2311.10448
Autor:
Bostroem, K. Azalee, Pearson, Jeniveve, Shrestha, Manisha, Sand, David J., Valenti, Stefano, Jha, Saurabh W., Andrews, Jennifer E., Smith, Nathan, Terreran, Giacomo, Green, Elizabeth, Dong, Yize, Lundquist, Michael, Haislip, Joshua, Hoang, Emily T., Hosseinzadeh, Griffin, Janzen, Daryl, Jencson, Jacob E., Kouprianov, Vladimir, Paraskeva, Emmy, Retamal, Nicolas E. Meza, Reichart, Daniel E., Arcavi, Iair, Bonanos, Alceste Z., Coughlin, Michael W., Dobson, Ross, Farah, Joseph, Albany, Lluís, Gutiérrez, Claudia, Hawley, Suzanne, Hebb, Leslie, Hiramatsu, Daichi, Howell, D. Andrew, Iijima, Takashi, Ilyin, Ilya, Jhass, Kiran, McCully, Curtis, Moran, Sean, Morris, Brett M., Mura, Alessandra C., Müller-Bravo, Tomás, Munday, James, Newsome, Megan, Pabst, Maria Th., Ochner, Paolo, Gonzalez, Estefania Padilla, Pastorello, Andrea, Pellegrino, Craig, Piscarreta, Lara, Ravi, Aravind P., Reguitti, Andrea, Salo, Laura, Vinko, Jozsef, de Vos, Kellie, Wheeler, J. C., Williams, G. Grant, Wyatt, Samuel
Publikováno v:
The Astrophysical Journal Letters, Volume 956, Issue 1, id.L5, 17 pp., Oct 2023
We present the optical spectroscopic evolution of SN~2023ixf seen in sub-night cadence spectra from 1.18 to 14 days after explosion. We identify high-ionization emission features, signatures of interaction with material surrounding the progenitor sta
Externí odkaz:
http://arxiv.org/abs/2306.10119
Autor:
Chilingarian, Igor, Grishin, Kirill, Afanasiev, Anton V., Mironov, Anton, Fabricant, Daniel, Moran, Sean, Caldwell, Nelson, Katkov, Ivan, Ershova, Irina
Ultra-diffuse galaxies (UDGs) are spatially extended, low surface brightness stellar systems with regular elliptical-like morphology found in large numbers in galaxy clusters and groups. Studies of the internal dynamics and dark matter content of UDG
Externí odkaz:
http://arxiv.org/abs/2306.08049
Autor:
Chilingarian, Igor, Grishin, Kirill, Afanasiev, Anton V., Mironov, Anton, Fabricant, Daniel, Moran, Sean, Caldwell, Nelson, Katkov, Ivan, Ershova, Irina
We present preliminary results from our spectroscopic survey of low-luminosity early-type galaxies in the Coma cluster conducted with the Binospec spectrograph at the 6.5~m MMT. From spatially-resolved profiles of internal kinematics and stellar popu
Externí odkaz:
http://arxiv.org/abs/2306.08048
Autor:
Gourgoulias, Kostis, Ghalyan, Najah, Labonne, Maxime, Satsangi, Yash, Moran, Sean, Sabelja, Joseph
This paper introduces an unsupervised method to estimate the class separability of text datasets from a topological point of view. Using persistent homology, we demonstrate how tracking the evolution of embedding manifolds during training can inform
Externí odkaz:
http://arxiv.org/abs/2305.15016
Autor:
Labonne, Maxime, Moran, Sean
This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we examine well-established mac
Externí odkaz:
http://arxiv.org/abs/2304.01238