Zobrazeno 1 - 10
of 32 505
pro vyhledávání: '"P, O'Donnell"'
Autor:
Li, Chenjun, Yang, Dian, Yao, Shun, Wang, Shuyue, Wu, Ye, Zhang, Le, Li, Qiannuo, Cho, Kang Ik Kevin, Seitz-Holland, Johanna, Ning, Lipeng, Legarreta, Jon Haitz, Rathi, Yogesh, Westin, Carl-Fredrik, O'Donnell, Lauren J., Sochen, Nir A., Pasternak, Ofer, Zhang, Fan
In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive un
Externí odkaz:
http://arxiv.org/abs/2409.07020
When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation
Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need for better resolution and imp
Externí odkaz:
http://arxiv.org/abs/2408.12897
Autor:
Sheu, William, Cikota, Aleksandar, Huang, Xiaosheng, Glazebrook, Karl, Storfer, Christopher, Agarwal, Shrihan, Schlegel, David J., Suzuki, Nao, Barone, Tania M., Bian, Fuyan, Jeltema, Tesla, Jones, Tucker, Kacprzak, Glenn G., O'Donnell, Jackson H., C, Keerthi Vasan G.
Over the past few years alone, the lensing community has discovered thousands of strong lens candidates, and spectroscopically confirmed hundreds of them. In this time of abundance, it becomes pragmatic to focus our time and resources on the few extr
Externí odkaz:
http://arxiv.org/abs/2408.10320
Understanding the dynamics of climate variables is paramount for numerous sectors, like energy and environmental monitoring. This study focuses on the critical need for a precise mapping of environmental variables for national or regional monitoring
Externí odkaz:
http://arxiv.org/abs/2407.20295
Autor:
Lo, Yui, Chen, Yuqian, Zhang, Fan, Liu, Dongnan, Zekelman, Leo, Cetin-Karayumak, Suheyla, Rathi, Yogesh, Cai, Weidong, O'Donnell, Lauren J.
Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype classifications. However, parcellation does not always reach 100\% accurac
Externí odkaz:
http://arxiv.org/abs/2407.19460
Autor:
Tchetchenian, Ari, Zekelman, Leo, Chen, Yuqian, Rushmore, Jarrett, Zhang, Fan, Yeterian, Edward H., Makris, Nikos, Rathi, Yogesh, Meijering, Erik, Song, Yang, O'Donnell, Lauren J.
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely o
Externí odkaz:
http://arxiv.org/abs/2407.15132
Autor:
Chen, Yuqian, Zhang, Fan, Wang, Meng, Zekelman, Leo R., Cetin-Karayumak, Suheyla, Xue, Tengfei, Zhang, Chaoyi, Song, Yang, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, O'Donnell, Lauren J.
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network desi
Externí odkaz:
http://arxiv.org/abs/2407.08883
Deep learning has been used to improve photoacoustic (PA) image reconstruction. One major challenge is that errors cannot be quantified to validate predictions when ground truth is unknown. Validation is key to quantitative applications, especially u
Externí odkaz:
http://arxiv.org/abs/2407.02653
We describe a quantum algorithm for the Planted Noisy $k$XOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic ($4$th power) speedup over the best known classical algorithm while also only using logarithmically
Externí odkaz:
http://arxiv.org/abs/2406.19378
Autor:
CUORE Collaboration, Adams, D. Q., Alduino, C., Alfonso, K., Avignone III, F. T., Azzolini, O., Bari, G., Bellini, F., Benato, G., Beretta, M., Biassoni, M., Branca, A., Brofferio, C., Bucci, C., Camilleri, J., Caminata, A., Campani, A., Cao, J., Capelli, S., Capelli, C., Cappelli, L., Cardani, L., Carniti, P., Casali, N., Celi, E., Chiesa, D., Clemenza, M., Copello, S., Cremonesi, O., Creswick, R. J., D'Addabbo, A., Dafinei, I., Del Corso, F., Dell'Oro, S., Di Domizio, S., Di Lorenzo, S., Dixon, T., Dompè, V., Fang, D. Q., Fantini, G., Faverzani, M., Ferri, E., Ferroni, F., Fiorini, E., Franceschi, M. A., Freedman, S. J., Fu, S. H., Fujikawa, B. K., Ghislandi, S., Giachero, A., Girola, M., Gironi, L., Giuliani, A., Gorla, P., Gotti, C., Guillaumon, P. V., Gutierrez, T. D., Han, K., Hansen, E. V., Heeger, K. M., Helis, D. L., Huang, H. Z., Keppel, G., Kolomensky, Yu. G., Kowalski, R., Liu, R., Ma, L., Ma, Y. G., Marini, L., Maruyama, R. H., Mayer, D., Mei, Y., Moore, M. N., Napolitano, T., Nastasi, M., Nones, C., Norman, E. B., Nucciotti, A., Nutini, I., O'Donnell, T., Olmi, M., Oregui, B. T., Ouellet, J. L., Pagan, S., Pagliarone, C. E., Pagnanini, L., Pallavicini, M., Pattavina, L., Pavan, M., Pessina, G., Pettinacci, V., Pira, C., Pirro, S., Pottebaum, E. G., Pozzi, S., Previtali, E., Puiu, A., Quitadamo, S., Ressa, A., Rosenfeld, C., Schmidt, B., Sharma, V., Singh, V., Sisti, M., Speller, D., Stark, P., Surukuchi, P. T., Taffarello, L., Tomei, C., Torres, A., Torres, J. A., Vetter, K. J., Vignati, M., Wagaarachchi, S. L., Welliver, B., Wilson, J., Wilson, K., Winslow, L. A., Zimmermann, S., Zucchelli, S.
The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5$\;$cm$\times$5$\;$cm$\times$5$\;$cm TeO$_2$ crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in $^{130}$Te. Unpreced
Externí odkaz:
http://arxiv.org/abs/2406.12380