Zobrazeno 1 - 10
of 34 851
pro vyhledávání: '"A. Dane"'
Autor:
Liu, Quanliang, Polak, Maciej P., Kim, So Yeon, Shuvo, MD Al Amin, Deodhar, Hrishikesh Shridhar, Han, Jeongsoo, Morgan, Dane, Oh, Hyunseok
Materials design often relies on human-generated hypotheses, a process inherently limited by cognitive constraints such as knowledge gaps and limited ability to integrate and extract knowledge implications, particularly when multidisciplinary experti
Externí odkaz:
http://arxiv.org/abs/2409.06756
Autor:
Jacobs, Ryan, Polak, Maciej P., Schultz, Lane E., Mahdavi, Hamed, Honavar, Vasant, Morgan, Dane
We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several mol
Externí odkaz:
http://arxiv.org/abs/2409.06080
Autor:
Doshi, Jaiv, Novacic, Ines, Fletcher, Curtis, Borges, Mats, Zhong, Elea, Marino, Mark C., Gan, Jason, Mager, Sophia, Sprague, Dane, Xia, Melinda
This paper presents a study on the growing threat of "sleeper social bots," AI-driven social bots in the political landscape, created to spread disinformation and manipulate public opinion. We based the name sleeper social bots on their ability to pa
Externí odkaz:
http://arxiv.org/abs/2408.12603
Autor:
Lynch, Matthew J., Jacobs, Ryan, Bruno, Gabriella, Patki, Priyam, Morgan, Dane, Field, Kevin G.
The integration of machine learning (ML) models enhances the efficiency, affordability, and reliability of feature detection in microscopy, yet their development and applicability are hindered by the dependency on scarce and often flawed manually lab
Externí odkaz:
http://arxiv.org/abs/2408.01558
Several evolved stars have been found to exhibit long-period radial velocity variations that cannot be explained by planetary or brown dwarf companions. Non-radial oscillations caused by oscillatory convective modes have been put forth as an alternat
Externí odkaz:
http://arxiv.org/abs/2407.21583
Autor:
Vlachas, Konstantinos, Simpson, Thomas, Garland, Anthony, Quinn, D. Dane, Farhat, Charbel, Chatzi, Eleni
Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, s
Externí odkaz:
http://arxiv.org/abs/2407.17139
Autor:
Ziffer, Mark E., Machado, Francisco, Ursprung, Benedikt, Lozovoi, Artur, Tazi, Aya Batoul, Yuan, Zhiyang, Ziebel, Michael E., Delord, Tom, Zeng, Nanyu, Telford, Evan, Chica, Daniel G., deQuilettes, Dane W., Zhu, Xiaoyang, Hone, James C., Shepard, Kenneth L., Roy, Xavier, de Leon, Nathalie P., Davis, Emily J., Chatterjee, Shubhayu, Meriles, Carlos A., Owen, Jonathan S., Schuck, P. James, Pasupathy, Abhay N.
Dynamic critical fluctuations in magnetic materials encode important information about magnetic ordering in the associated critical exponents. Using nitrogen-vacancy centers in diamond, we implement $T_2$ (spin-decoherence) noise magnetometry to stud
Externí odkaz:
http://arxiv.org/abs/2407.05614
Autor:
Kim, Youngseok, Govia, Luke C. G., Dane, Andrew, Berg, Ewout van den, Zajac, David M., Mitchell, Bradley, Liu, Yinyu, Balakrishnan, Karthik, Keefe, George, Stabile, Adam, Pritchett, Emily, Stehlik, Jiri, Kandala, Abhinav
Pre-fault tolerant quantum computers have already demonstrated the ability to estimate observable values accurately, at a scale beyond brute-force classical computation. This has been enabled by error mitigation techniques that often rely on a repres
Externí odkaz:
http://arxiv.org/abs/2407.02467
Many social and biological networks periodically change over time with daily, weekly, and other cycles. Thus motivated, we formulate and analyze susceptible-infectious-susceptible (SIS) epidemic models over temporal networks with periodic schedules.
Externí odkaz:
http://arxiv.org/abs/2406.16787
Autor:
Jacobs, Ryan, Schultz, Lane E., Scourtas, Aristana, Schmidt, KJ, Price-Skelly, Owen, Engler, Will, Foster, Ian, Blaiszik, Ben, Voyles, Paul M., Morgan, Dane
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision
Externí odkaz:
http://arxiv.org/abs/2406.15650