Zobrazeno 1 - 10
of 21 736
pro vyhledávání: '"Rathi, A"'
Autor:
Rathi, Hemant, Roychowdhury, Dibakar
We present a JT gravity setup coupled with $U(1)$ and $SU(2)$ Yang-Mills fields in two dimensions that reveals the onset of a small black hole to large black hole phase transition at finite chemical potential(s). We identify these black hole solution
Externí odkaz:
http://arxiv.org/abs/2410.00744
Autor:
Dwedari, Mohammed Munzer, Consagra, William, Müller, Philip, Turgut, Özgün, Rueckert, Daniel, Rathi, Yogesh
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Representation (INR) based approaches t
Externí odkaz:
http://arxiv.org/abs/2409.09387
Autor:
Jun, Yohan, Liu, Qiang, Gong, Ting, Cho, Jaejin, Fujita, Shohei, Yong, Xingwang, Huang, Susie Y, Ning, Lipeng, Yendiki, Anastasia, Rathi, Yogesh, Bilgic, Berkin
Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting an additional echo without prolonging TR, when generalized slice dithered enhanced resolution (gSlider) radiofrequency enco
Externí odkaz:
http://arxiv.org/abs/2409.07375
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
Autor:
Akrami, Hannaneh, Rathi, Nidhi
We study the problem of computing \emph{fair} divisions of a set of indivisible goods among agents with \emph{additive} valuations. For the past many decades, the literature has explored various notions of fairness, that can be primarily seen as eith
Externí odkaz:
http://arxiv.org/abs/2409.01963
Autor:
Sharma, Ansh, Xiao, Albert, Rathi, Praneet, Kundu, Rohit, Zhai, Albert, Shen, Yuan, Wang, Shenlong
In this work, we present a novel method for extensive multi-scale generative terrain modeling. At the core of our model is a cascade of superresolution diffusion models that can be combined to produce consistent images across multiple resolutions. Pa
Externí odkaz:
http://arxiv.org/abs/2409.01491
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:
Fink, Zane, Parasyris, Konstantinos, Rathi, Praneet, Georgakoudis, Giorgis, Menon, Harshitha, Bremer, Peer-Timo
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex functions wit
Externí odkaz:
http://arxiv.org/abs/2407.18352
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