Zobrazeno 1 - 10
of 3 540 474
pro vyhledávání: '"ANDREW, A."'
Recently, we have witnessed the rise of novel ``event-based'' camera sensors for high-speed, low-power video capture. Rather than recording discrete image frames, these sensors output asynchronous ``event'' tuples with microsecond precision, only whe
Externí odkaz:
http://arxiv.org/abs/2412.07889
We investigate nonclassical Bethe - Zel'dovich-Thompson (BZT) rarefaction shocks and the QCD phase transition in the dense core of a cold quark star in beta equilibrium subject to the multicomponent van der Waals (MvdW) equation of state (EoS) as a m
Externí odkaz:
http://arxiv.org/abs/2412.07601
Autor:
Webber, George, Mizuno, Yuya, Howes, Oliver D., Hammers, Alexander, King, Andrew P., Reader, Andrew J.
Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image d
Externí odkaz:
http://arxiv.org/abs/2412.04339
Autor:
Webber, George, Mizuno, Yuya, Howes, Oliver D., Hammers, Alexander, King, Andrew P., Reader, Andrew J.
Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method
Externí odkaz:
http://arxiv.org/abs/2412.04324
Autor:
Webber, George, Mizuno, Yuya, Howes, Oliver D., Hammers, Alexander, King, Andrew P., Reader, Andrew J.
Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) datasets. In this work we have developed and implemented practical methodology for 3D image reconstruc
Externí odkaz:
http://arxiv.org/abs/2412.04319
Autor:
Morishita, Takahiro, Mason, Charlotte A., Kreilgaard, Kimi C., Trenti, Michele, Treu, Tommaso, Vulcani, Benedetta, Zhang, Yechi, Abdurro'uf, Alavi, Anahita, Atek, Hakim, Bahe, Yannick, Bradac, Marusa, Bradley, Larry D., Bunker, Andrew J., Coe, Dan, Colbert, James, Gelli, Viola, Hayes, Matthew J., Jones, Tucker, Kodama, Tadayuki, Leethochawalit, Nicha, Liu, Zhaoran, Malkan, Matthew A., Mehta, Vihang, Metha, Benjamin, Newman, Andrew B., Rafelski, Marc, Roberts-Borsani, Guido, Rutkowski, Michael J., Scarlata, Claudia, Stiavelli, Massimo, Sutanto, Ryo A., Takahashi, Kosuke, Teplitz, Harry I., Wang, Xin
We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\si
Externí odkaz:
http://arxiv.org/abs/2412.04211
Autor:
Tribello, Gareth A., Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Armstrong, Blake I., Arsiccio, Andrea, Aureli, Simone, Ballabio, Federico, Bernetti, Mattia, Bonati, Luigi, Brookes, Samuel G. H., Brotzakis, Z. Faidon, Capelli, Riccardo, Ceriotti, Michele, Chan, Kam-Tung, Cossio, Pilar, Dasetty, Siva, Donadio, Davide, Ensing, Bernd, Ferguson, Andrew L., Fraux, Guillaume, Gale, Julian D., Gervasio, Francesco Luigi, Giorgino, Toni, Herringer, Nicholas S. M., Hocky, Glen M., Hoff, Samuel E., Invernizzi, Michele, Languin-Cattöen, Olivier, Leone, Vanessa, Limongelli, Vittorio, Lopez-Acevedo, Olga, Marinelli, Fabrizio, Martinez, Pedro Febrer, Masetti, Matteo, Mehdi, Shams, Michaelides, Angelos, Murtada, Mhd Hussein, Parrinello, Michele, Piaggi, Pablo M., Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Pritchard, Claire, Raiteri, Paolo, Raniolo, Stefano, Rapetti, Daniele, Rizzi, Valerio, Rydzewski, Jakub, Salvalaglio, Matteo, Schran, Christoph, Seal, Aniruddha, Zadeh, Armin Shayesteh, Silva, Tomás F. D., Spiwok, Vojtěch, Stirnemann, Guillaume, Sucerquia, Daniel, Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A., White, Andrew D., Wu, Jiangbo
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents sev
Externí odkaz:
http://arxiv.org/abs/2412.03595
Autor:
Ding, Tong, Wagner, Sophia J., Song, Andrew H., Chen, Richard J., Lu, Ming Y., Zhang, Andrew, Vaidya, Anurag J., Jaume, Guillaume, Shaban, Muhammad, Kim, Ahrong, Williamson, Drew F. K., Chen, Bowen, Almagro-Perez, Cristina, Doucet, Paul, Sahai, Sharifa, Chen, Chengkuan, Komura, Daisuke, Kawabe, Akihiro, Ishikawa, Shumpei, Gerber, Georg, Peng, Tingying, Le, Long Phi, Mahmood, Faisal
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). Howe
Externí odkaz:
http://arxiv.org/abs/2411.19666
Autor:
Barber, Madyson G., Mann, Andrew W., Vanderburg, Andrew, Krolikowski, Daniel, Kraus, Adam, Ansdell, Megan, Pearce, Logan, Mace, Gregory N., Andrews, Sean M., Boyle, Andrew W., Collins, Karen A., De Furio, Matthew, Dragomir, Diana, Espaillat, Catherine, Feinstein, Adina D., Fields, Matthew, Jaffe, Daniel, Murillo, Ana Isabel Lopez, Murgas, Felipe, Newton, Elisabeth R., Palle, Enric, Sawczynec, Erica, Schwarz, Richard P., Thao, Pa Chia, Tofflemire, Benjamin M., Watkins, Cristilyn N., Jenkins, Jon M., Latham, David W., Ricker, George, Seager, Sara, Vanderspek, Roland, Winn, Joshua N., Charbonneau, David, Essack, Zahra, Rodriguez, David R., Shporer, Avi, Twicken, Joseph D., Villaseñor, Jesus Noel
Publikováno v:
Nature 635, 574-577 (2024)
Astronomers have found more than a dozen planets transiting 10-40 million year old stars, but even younger transiting planets have remained elusive. A possible reason for the lack of such discoveries is that newly formed planets are not yet in a conf
Externí odkaz:
http://arxiv.org/abs/2411.18683
Autor:
Prakash, Eva, Valanarasu, Jeya Maria Jose, Chen, Zhihong, Reis, Eduardo Pontes, Johnston, Andrew, Pareek, Anuj, Bluethgen, Christian, Gatidis, Sergios, Olsen, Cameron, Chaudhari, Akshay, Ng, Andrew, Langlotz, Curtis
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials a
Externí odkaz:
http://arxiv.org/abs/2411.18602