Zobrazeno 1 - 10
of 925
pro vyhledávání: '"Hexemer A"'
We fine-tuned a foundational stable diffusion model using X-ray scattering images and their corresponding descriptions to generate new scientific images from given prompts. However, some of the generated images exhibit significant unrealistic artifac
Externí odkaz:
http://arxiv.org/abs/2408.12720
Autor:
Garau-Luis, Juan Jose, Bordes, Patrick, Gonzalez, Liam, Roller, Masa, de Almeida, Bernardo P., Hexemer, Lorenz, Blum, Christopher, Laurent, Stefan, Grzegorzewski, Jan, Lang, Maren, Pierrot, Thomas, Richard, Guillaume
Biological sequences encode fundamental instructions for the building blocks of life, in the form of DNA, RNA, and proteins. Modeling these sequences is key to understand disease mechanisms and is an active research area in computational biology. Rec
Externí odkaz:
http://arxiv.org/abs/2406.14150
Autor:
Yanxon, Howard, Roberts, Eric, Parraga, Hannah, Weng, James, Xu, Wenqian, Ruett, Uta, Hexemer, Alexander, Zwart, Petrus, Schwarz, Nickolas
Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a meth
Externí odkaz:
http://arxiv.org/abs/2310.16186
We introduce DLSIA (Deep Learning for Scientific Image Analysis), a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) archi
Externí odkaz:
http://arxiv.org/abs/2308.02559
A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery
Autor:
Banik, Suvo, Loefller, Troy, Manna, Sukriti, Srinivasan, Srilok, Darancet, Pierre, Chan, Henry, Hexemer, Alexander, Sankaranarayanan, Subramanian KRS
Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies in the exorbitantly large structural and compositional space presented
Externí odkaz:
http://arxiv.org/abs/2212.12106
Autor:
Zhao, Zhuowen, Chavez, Tanny, Holman, Elizabeth A., Hao, Guanhua, Green, Adam, Krishnan, Harinarayan, McReynolds, Dylan, Pandolfi, Ronald, Roberts, Eric J., Zwart, Petrus H., Yanxon, Howard, Schwarz, Nicholas, Sankaranarayanan, Subramanian, Kalinin, Sergei V., Mehta, Apurva, Campbell, Stuart, Hexemer, Alexander
Publikováno v:
2022 4th IEEE/ACM Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)
Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demandi
Externí odkaz:
http://arxiv.org/abs/2208.09751
Autor:
Kolbe, Niklas, Hexemer, Lorenz, Bammert, Lukas-Malte, Loewer, Alexander, Lukáčová-Medviďová, Mária, Legewie, Stefan
Cells sense their surrounding by employing intracellular signaling pathways that transmit hormonal signals from the cell membrane to the nucleus. TGF-$\beta$/SMAD signaling encodes various cell fates, controls tissue homeostasis and is deregulated in
Externí odkaz:
http://arxiv.org/abs/2107.11770
A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery
Autor:
Suvo Banik, Troy Loefller, Sukriti Manna, Henry Chan, Srilok Srinivasan, Pierre Darancet, Alexander Hexemer, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-16 (2023)
Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as
Externí odkaz:
https://doaj.org/article/0cd9e43705a342c7862b45c7158152b5
Autor:
Michael R. Tuchband, Min Shuai, Keri A. Graber, Dong Chen, Chenhui Zhu, Leo Radzihovsky, Arthur Klittnick, Lee Foley, Alyssa Scarbrough, Jan H. Porada, Mark Moran, Joseph Yelk, Justin B. Hooper, Xiaoyu Wei, Dmitry Bedrov, Cheng Wang, Eva Korblova, David M. Walba, Alexander Hexemer, Joseph E. Maclennan, Matthew A. Glaser, Noel A. Clark
Publikováno v:
Crystals, Vol 14, Iss 7, p 583 (2024)
The twist-bend nematic liquid crystal phase is a three-dimensional fluid in which achiral bent molecules spontaneously form an orientationally ordered, macroscopically chiral, heliconical winding of a ten nanometer-scale pitch in the absence of posit
Externí odkaz:
https://doaj.org/article/66b1a2dc96bd46708b14c67961d149e9
Modelling cellular signalling variability based on single-cell data: the TGFb/SMAD signaling pathway
Non-genetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is t
Externí odkaz:
http://arxiv.org/abs/2007.09093