Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Mihai Capota"'
Autor:
Manoj Kumar, null Michael Anderson, James Antony, Christopher Baldassano, Paula Pacheco Brooks, Ming Bo Cai, Po-Hsuan Cameron Chen, Cameron Thomas Ellis, Gregory Henselman-Petrusek, David Huberdeau, J. Benjamin Hutchinson, Y. Peeta Li, Qihong Lu, Jeremy R. Manning, Anne C. Mennen, Samuel A. Nastase, Hugo Richard, Anna C. Schapiro, Nicolas W Schuck, Michael Shvartsman, Narayanan Sundaram, Daniel Suo, Javier S. Turek, David Turner, Vy Vo, Grant Wallace, Yida Wang, Jamal A. Williams, Hejia Zhang, Xia Zhu, Mihai Capota, Jonathan D. Cohen, Uri Hasson, Kai Li, Peter J. Ramadge, Nicholas Turk-Browne, Theodore L. Willke, Kenneth A. Norman
Publikováno v:
Aperture Neuro
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::924227a8444ec4fa72f357e0287906b4
https://pubmed.ncbi.nlm.nih.gov/35939268
https://pubmed.ncbi.nlm.nih.gov/35939268
Autor:
Guixiang Ma, Yao Xiao, Mihai Capota, Theodore L. Willke, Shahin Nazarian, Paul Bogdan, Nesreen K. Ahmed
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d8be70eba7c863ceed834b768fbd55d
https://doi.org/10.1109/bigdata52589.2021.9671685
https://doi.org/10.1109/bigdata52589.2021.9671685
Autor:
Nicholas B. Turk-Browne, Kenneth A. Norman, Cameron T. Ellis, Mihai Capota, Qihong Lu, Peter J. Ramadge, Manoj Kumar, Theodore L. Willke, Hejia Zhang
Publikováno v:
PLoS Computational Biology
PLoS Computational Biology, Vol 16, Iss 1, p e1007549 (2020)
PLoS Computational Biology, Vol 16, Iss 1, p e1007549 (2020)
Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in cust
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1657860a5554211ad586f4f7580e0632
http://europepmc.org/articles/PMC6961866
http://europepmc.org/articles/PMC6961866
Autor:
Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capotă, Theodore L. Willke, Shahin Nazarian, Paul Bogdan
Publikováno v:
Communications Engineering, Vol 2, Iss 1, Pp 1-15 (2023)
Abstract Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with a
Externí odkaz:
https://doaj.org/article/dce5624382d842e2b61228caa8395f1b
Autor:
Shaden Smith, Theodore L. Willke, Zheguang Zhao, Subramanya R. Dulloor, Narayanan Sundaram, Mihai Capota, Michael R. Anderson, Nadathur Satish
Publikováno v:
Proceedings of the VLDB Endowment. 10:901-912
Apache Spark is a popular framework for data analytics with attractive features such as fault tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics operations in Spark are an order of magnitude or more slower compare
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3109cf0a1de3d577a25509af94b04510
https://doi.org/10.14778/3090163.3090168
https://doi.org/10.14778/3090163.3090168
Autor:
Seng Bum Michael Yoo, Mihai Capota, Jonathan D. Cohen, Theodore L. Willke, Benjamin Y. Hayden, Sebastian Musslick
We compare the performance of non-human primates and deep reinforcement learning agents in a virtual pursuit-avoidance task, as part of an effort to understand the role that cognitive control plays in the deeply evolved skill of chase and escape beha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d05a5427f7917c674bf599ad1818d76
https://doi.org/10.1101/567545
https://doi.org/10.1101/567545
Autor:
Gabriel A. Devenyi, Nathan W. Churchill, Pierre-Olivier Quirion, Tal Yarkoni, Anisha Keshavan, Gregory Kiar, Christopher J. Steele, Stephen C. Strother, Gaël Varoquaux, R. Cameron Craddock, Alexander L. Cohen, J. Swaroop Guntupalli, Mihai Capota, Guillaume Flandin, Robert E. Smith, Oscar Esteban, Pradeep Reddy Raamana, Krzysztof J. Gorgolewski, Tibor Auer, Franziskus Liem, Russell A. Poldrack, Mark Jenkinson, Fidel Alfaro-Almagro, M. Mallar Chakravarty, David Raffelt, Pierre Bellec, Satrajit S. Ghosh, Anders Eklund, Yida Wang
Publikováno v:
PLoS Computational Biology
PLoS
PLoS Computational Biology, Vol 13, Iss 3, p e1005209 (2017)
PLoS
PLoS Computational Biology, Vol 13, Iss 3, p e1005209 (2017)
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, tes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faf5dd909d401d38f3a5b6193ab9abf7
http://europepmc.org/articles/PMC5363996
http://europepmc.org/articles/PMC5363996
Autor:
Nicholas B. Turk-Browne, Kai Li, Michael J. Anderson, Narayanan Sundaram, Theodore L. Willke, Mihai Capota, Bryn Keller, Jonathan D. Cohen, Yida Wang
Publikováno v:
IEEE BigData
Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c1c17883b61776de80ec6d0fe7f870a
https://doi.org/10.1109/bigdata.2016.7840728
https://doi.org/10.1109/bigdata.2016.7840728
Autor:
Michael J. Anderson, Kenneth A. Norman, Po-Hsuan Chen, Jeremy R. Manning, Mihai Capota, Theodore L. Willke, Javier S. Turek, Yida Wang, Xia Zhu, Peter J. Ramadge
Publikováno v:
IEEE BigData
The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f02ecf1132af9f44e5ea39b9b837366d
http://arxiv.org/abs/1608.04647
http://arxiv.org/abs/1608.04647
Autor:
Arnau Prat-Pérez, Tim Hegeman, Lifeng Nai, Narayanan Sundaram, Wing Lung Ngai, Stijn Heldens, Mihai Capota, Ilie Gabriel Tanase, Thomas Manhardto, Peter Boncz, Hassan Chafio, Yinglong Xia, Alexandru Iosup, Michael J. Anderson
Publikováno v:
Proceedings of the VLDB Endowment, 9(13)
PVLDB, 9(13), 1317-1328. Very Large Data Base Endowment Inc.
Iosup, A, Hegeman, T, Ngai, W L, Heldens, S, Prat-Pérez, A, Manhardt, T, Chafi, H, Capota, M, Sundaram, N, Anderson, M J, Tanase, I G, Xia, Y, Nai, L & Boncz, P A 2016, ' LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms ', PVLDB, vol. 9, no. 13, pp. 1317-1328 . https://doi.org/10.14778/3007263.3007270
PVLDB, 9(13), 1317-1328. Very Large Data Base Endowment Inc.
Iosup, A, Hegeman, T, Ngai, W L, Heldens, S, Prat-Pérez, A, Manhardt, T, Chafi, H, Capota, M, Sundaram, N, Anderson, M J, Tanase, I G, Xia, Y, Nai, L & Boncz, P A 2016, ' LDBC Graphalytics: A Benchmark for Large-Scale Graph Analysis on Parallel and Distributed Platforms ', PVLDB, vol. 9, no. 13, pp. 1317-1328 . https://doi.org/10.14778/3007263.3007270
In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic dataset generators, and reference output, that enable the objective
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a4482ed6eb181fe939910778e939d5c
http://resolver.tudelft.nl/uuid:fc2a5dd1-d7c1-4c60-a49c-7c4b20e03d70
http://resolver.tudelft.nl/uuid:fc2a5dd1-d7c1-4c60-a49c-7c4b20e03d70