Zobrazeno 1 - 10
of 109 192
pro vyhledávání: '"O'Donnell A"'
Autor:
O'Donnell, Ryan, Singer, Noah G.
Recent major results in property testing~\cite{BLM24,DDL24} and PCPs~\cite{BMV24} were unlocked by moving to high-dimensional expanders (HDXs) constructed from $\widetilde{C}_d$-type buildings, rather than the long-known $\widetilde{A}_d$-type ones.
Externí odkaz:
http://arxiv.org/abs/2411.05916
We study quantum algorithms for verifying properties of the output probability distribution of a classical or quantum circuit, given access to the source code that generates the distribution. We consider the basic task of uniformity testing, which is
Externí odkaz:
http://arxiv.org/abs/2411.04972
Autor:
Bakshi, Ainesh, Bostanci, John, Kretschmer, William, Landau, Zeph, Li, Jerry, Liu, Allen, O'Donnell, Ryan, Tang, Ewin
We study the problem of finding a product state with optimal fidelity to an unknown $n$-qubit quantum state $\rho$, given copies of $\rho$. This is a basic instance of a fundamental question in quantum learning: is it possible to efficiently learn a
Externí odkaz:
http://arxiv.org/abs/2411.04283
Autor:
Wang, Jin, Guo, Bocheng, Li, Yijie, Wang, Junyi, Chen, Yuqian, Rushmore, Jarrett, Makris, Nikos, Rathi, Yogesh, O'Donnell, Lauren J, Zhang, Fan
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation. Current methods primarily use the geometric information of fibers (i.e., the spatial trajectories) to group similar fibers into cluster
Externí odkaz:
http://arxiv.org/abs/2411.01859
Autor:
O'Donnell, Kayla E., Slatyer, Tracy R.
A number of new balloon or space-based gamma-ray observatories have been proposed to close a "MeV gap" in sensitivity to gamma rays in the MeV-GeV energy band. One aspect of the science case for these instruments is their ability to constrain or disc
Externí odkaz:
http://arxiv.org/abs/2411.00087
Autor:
Lo, Yui, Chen, Yuqian, Liu, Dongnan, Legarreta, Jon Haitz, Zekelman, Leo, Zhang, Fan, Rushmore, Jarrett, Rathi, Yogesh, Makris, Nikos, Golby, Alexandra J., Cai, Weidong, O'Donnell, Lauren J.
Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship to brain function. In this work, we investigate the possibility of ut
Externí odkaz:
http://arxiv.org/abs/2410.22099
Autor:
Cheng, Yik San, Zhao, Runkai, Wang, Heng, Peng, Hanchuan, Lo, Yui, Chen, Yuqian, O'Donnell, Lauren J., Cai, Weidong
Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy. With the boost from deep learning techniques, a variety of learning-based segmentation models have
Externí odkaz:
http://arxiv.org/abs/2410.22078
Autor:
Roberts, M. Grant, Braff, Lila, Garg, Aarna, Profumo, Stefano, Jeltema, Tesla, O'Donnell, Jackson
Evidence for high-redshift supermassive black holes challenges standard scenarios for how such objects form in the early universe. Here, we entertain the possibility that a fraction of the cosmological dark matter could be ultra-strongly self interac
Externí odkaz:
http://arxiv.org/abs/2410.17480
Autor:
Lo, Yui, Chen, Yuqian, Liu, Dongnan, Liu, Wan, Zekelman, Leo, Rushmore, Jarrett, Zhang, Fan, Rathi, Yogesh, Makris, Nikos, Golby, Alexandra J., Cai, Weidong, O'Donnell, Lauren J.
The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tracto
Externí odkaz:
http://arxiv.org/abs/2410.15108
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