Zobrazeno 1 - 10
of 7 574
pro vyhledávání: '"Norgaard A"'
Autor:
Bøggild, Peter, Booth, Timothy John, Lassaline, Nolan, Jessen, Bjarke Sørensen, Shivayogimath, Abhay, Hofmann, Stephan, Daasbjerg, Kim, Smith, Anders, Nørgaard, Kasper, Zurutuza, Amaia, Asselberghs, Inge, Barkan, Terrance, Taboryski, Rafael, Pollard, Andrew J.
2D materials research has reached significant scientific milestones, accompanied by a rapidly growing industrial sector in the two decades since the field's inception. Such rapid progress requires pushing past the boundary of what is technically and
Externí odkaz:
http://arxiv.org/abs/2409.18994
Autor:
Llambias, Sebastian Nørgaard, Machnio, Julia, Munk, Asbjørn, Ambsdorf, Jakob, Nielsen, Mads, Ghazi, Mostafa Mehdipour
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-friendliness. To address these challenges, we introduce Yucca, an open-sour
Externí odkaz:
http://arxiv.org/abs/2407.19888
Even though novel imaging techniques have been successful in studying brain structure and function, the measured biological signals are often contaminated by multiple sources of noise, arising due to e.g. head movements of the individual being scanne
Externí odkaz:
http://arxiv.org/abs/2404.14882
Autor:
Kantas, Christos, Antoniussen, Bjørk, Andersen, Mathias V., Munksø, Rasmus, Kotnala, Shobhit, Jensen, Simon B., Møgelmose, Andreas, Nørgaard, Lau, Moeslund, Thomas B.
Publikováno v:
2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)
Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information. In this paper, we show that a sufficiently advanced classifier can yield equivalent
Externí odkaz:
http://arxiv.org/abs/2403.14439
Autor:
Caputi, Karina I., Rinaldi, Pierluigi, Iani, Edoardo, Pérez-González, Pablo G., Ostlin, Göran, Colina, Luis, Greve, Thomas R., Nørgaard-Nielsen, Hans-Ulrik, Wright, Gillian S., Alvarez-Márquez, Javier, Eckart, Andreas, Hjorth, Jens, Labiano, Alvaro, Fèvre, Olivier Le, Walter, Fabian, van der Werf, Paul, Boogaard, Leindert, Costantin, Luca, Crespo-Gómez, Alejandro, Gillman, Steven, Jermann, Iris, Langeroodi, Danial, Melinder, Jens, Peissker, Florian, Güdel, Manuel, Henning, Thomas, Lagage, Pierre-Olivier, Ray, Thomas P.
We investigate the properties of strong (Hb+[OIII]) emitters before and after the end of the Epoch of Reionization from z=8 to z=5.5. We make use of ultra-deep JWST/NIRCam imaging in the Parallel Field of the MIRI Deep Imaging Survey (MIDIS) in the H
Externí odkaz:
http://arxiv.org/abs/2311.12691
Autor:
Kochkov, Dmitrii, Yuval, Janni, Langmore, Ian, Norgaard, Peter, Smith, Jamie, Mooers, Griffin, Klöwer, Milan, Lottes, James, Rasp, Stephan, Düben, Peter, Hatfield, Sam, Battaglia, Peter, Sanchez-Gonzalez, Alvaro, Willson, Matthew, Brenner, Michael P., Hoyer, Stephan
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud for
Externí odkaz:
http://arxiv.org/abs/2311.07222
Autor:
Poldrack, Russell A., Markiewicz, Christopher J., Appelhoff, Stefan, Ashar, Yoni K., Auer, Tibor, Baillet, Sylvain, Bansal, Shashank, Beltrachini, Leandro, Benar, Christian G., Bertazzoli, Giacomo, Bhogawar, Suyash, Blair, Ross W., Bortoletto, Marta, Boudreau, Mathieu, Brooks, Teon L., Calhoun, Vince D., Castelli, Filippo Maria, Clement, Patricia, Cohen, Alexander L, Cohen-Adad, Julien, D'Ambrosio, Sasha, de Hollander, Gilles, de la iglesia-Vayá, María, de la Vega, Alejandro, Delorme, Arnaud, Devinsky, Orrin, Draschkow, Dejan, Duff, Eugene Paul, DuPre, Elizabeth, Earl, Eric, Esteban, Oscar, Feingold, Franklin W., Flandin, Guillaume, galassi, anthony, Gallitto, Giuseppe, Ganz, Melanie, Gau, Rémi, Gholam, James, Ghosh, Satrajit S., Giacomel, Alessio, Gillman, Ashley G, Gleeson, Padraig, Gramfort, Alexandre, Guay, Samuel, Guidali, Giacomo, Halchenko, Yaroslav O., Handwerker, Daniel A., Hardcastle, Nell, Herholz, Peer, Hermes, Dora, Honey, Christopher J., Innis, Robert B., Ioanas, Horea-Ioan, Jahn, Andrew, Karakuzu, Agah, Keator, David B., Kiar, Gregory, Kincses, Balint, Laird, Angela R., Lau, Jonathan C., Lazari, Alberto, Legarreta, Jon Haitz, Li, Adam, Li, Xiangrui, Love, Bradley C., Lu, Hanzhang, Maumet, Camille, Mazzamuto, Giacomo, Meisler, Steven L., Mikkelsen, Mark, Mutsaerts, Henk, Nichols, Thomas E., Nikolaidis, Aki, Nilsonne, Gustav, Niso, Guiomar, Norgaard, Martin, Okell, Thomas W, Oostenveld, Robert, Ort, Eduard, Park, Patrick J., Pawlik, Mateusz, Pernet, Cyril R., Pestilli, Franco, Petr, Jan, Phillips, Christophe, Poline, Jean-Baptiste, Pollonini, Luca, Raamana, Pradeep Reddy, Ritter, Petra, Rizzo, Gaia, Robbins, Kay A., Rockhill, Alexander P., Rogers, Christine, Rokem, Ariel, Rorden, Chris, Routier, Alexandre, Saborit-Torres, Jose Manuel, Salo, Taylor, Schirner, Michael, Smith, Robert E., Spisak, Tamas, Sprenger, Julia, Swann, Nicole C., Szinte, Martin, Takerkart, Sylvain, Thirion, Bertrand, Thomas, Adam G., Torabian, Sajjad, Varoquaux, Gael, Voytek, Bradley, Welzel, Julius, Wilson, Martin, Yarkoni, Tal, Gorgolewski, Krzysztof J.
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time.
Externí odkaz:
http://arxiv.org/abs/2309.05768
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a signific
Externí odkaz:
http://arxiv.org/abs/2308.04395
Autor:
Colina, L., Gómez, A. Crespo, Álvarez-Márquez, J., Bik, A., Walter, F., Boogaard, L., Labiano, A., Peissker, F., Pérez-González, P., Östlin, G., Greve, T. R., Nørgaard-Nielsen, H. U., Wright, G., Alonso-Herrero, A., Azollini, R., Caputi, K. I., Dicken, D., García-Marín, M., Hjorth, J., Ilbert, O., Kendrew, S., Pye, J. P., Tikkanen, T., van der Werf, P., Costantin, L., Iani, E., Gillman, S., Jermann, I., Langeroodi, D., Moutard, T., Rinaldi, P., Topinka, M., van Dishoeck, E. F., Güdel, M., Henning, Th., Lagage, P. O., Ray, T., Vandenbussche, B.
Publikováno v:
A&A 673, L6 (2023)
Luminous infrared galaxies at high redshifts ($z$>4) include extreme starbursts that build their stellar mass over short periods of time (>100 Myr). These galaxies are considered to be the progenitors of massive quiescent galaxies at intermediate red
Externí odkaz:
http://arxiv.org/abs/2304.13529
Clinical needs and technological advances have resulted in increased use of Artificial Intelligence (AI) in clinical decision support. However, such support can introduce new and amplify existing cognitive biases. Through contextual inquiry and inter
Externí odkaz:
http://arxiv.org/abs/2303.03981