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pro vyhledávání: '"Yager, Kevin"'
Autor:
Yager, Kevin G.
Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with generative AI enabling automation of text analysis, text generation, and simple decision making or reasoning. The impact to science is only just beginning, but t
Externí odkaz:
http://arxiv.org/abs/2406.17809
The extraordinarily high X-ray flux and specialized instrumentation at synchrotron beamlines have enabled versatile in-situ and high throughput studies that are impossible elsewhere. Dexterous and efficient control of experiments are thus crucial for
Externí odkaz:
http://arxiv.org/abs/2312.17180
Autor:
Yager, Kevin G.
Large language models (LLMs) have emerged as powerful machine-learning systems capable of handling a myriad of tasks. Tuned versions of these systems have been turned into chatbots that can respond to user queries on a vast diversity of topics, provi
Externí odkaz:
http://arxiv.org/abs/2306.10067
Publikováno v:
IEEE Transactions on Visualization & Computer Graphics 2020
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x
Externí odkaz:
http://arxiv.org/abs/2009.02256
Autor:
Noack, Marcus M., Doerk, Gregory S., Li, Ruipeng, Streit, Jason K., Vaia, Richard A., Yager, Kevin G., Fukuto, Masafumi
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmenta
Externí odkaz:
http://arxiv.org/abs/2006.02489
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images. The system focuses on studying the model be
Externí odkaz:
http://arxiv.org/abs/1910.04357
Autor:
Rokhlenko, Yekaterina, Majewski, Paweł W., Larson, Steven R., Gopalan, Padma, Yager, Kevin G., Osuji, Chinedum O.
Recent experiments have highlighted the intrinsic magnetic anisotropy in coil-coil diblock copolymers, specifically in poly(styrene-b-4-vinylpyridine) (PS-b-P4VP), that enables magnetic field alignment at field strengths of a few tesla. We consider h
Externí odkaz:
http://arxiv.org/abs/1703.07508
Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scatt
Externí odkaz:
http://arxiv.org/abs/1611.03313
Autor:
Rokhlenko, Yekaterina, Zhang, Kai, Gopinadhan, Manesh, Larson, Steve R., Majewski, Pawel W., Yager, Kevin G., Gopalan, Padma, O'Hern, Corey S., Osuji, Chinedum O.
Publikováno v:
Phys. Rev. Lett. 115, 258302 (2015)
We examine the role of intrinsic chain susceptibility anisotropy in magnetic field directed self-assembly of a block copolymer using \textit{in situ} X-ray scattering. Alignment of a lamellar mesophase is observed on cooling across the disorder-order
Externí odkaz:
http://arxiv.org/abs/1509.01292