Zobrazeno 1 - 10
of 15 160
pro vyhledávání: '"Chris R. So"'
Autor:
Ferreira, Fabio S., Ashburner, John, Bouzigues, Arabella, Suksasilp, Chatrin, Russell, Lucy L., Foster, Phoebe H., Ferry-Bolder, Eve, van Swieten, John C., Jiskoot, Lize C., Seelaar, Harro, Sanchez-Valle, Raquel, Laforce, Robert, Graff, Caroline, Galimberti, Daniela, Vandenberghe, Rik, de Mendonca, Alexandre, Tiraboschi, Pietro, Santana, Isabel, Gerhard, Alexander, Levin, Johannes, Sorbi, Sandro, Otto, Markus, Pasquier, Florence, Ducharme, Simon, Butler, Chris R., Ber, Isabelle Le, Finger, Elizabeth, Tartaglia, Maria C., Masellis, Mario, Rowe, James B., Synofzik, Matthis, Moreno, Fermin, Borroni, Barbara, Kaski, Samuel, Rohrer, Jonathan D., Mourao-Miranda, Janaina
In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment develo
Externí odkaz:
http://arxiv.org/abs/2410.07890
Autor:
Ouermi, Timbwaoga A. J., Li, Jixian, Morrow, Zachary, Waanders, Bart van Bloemen, Johnson, Chris R.
Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instanc
Externí odkaz:
http://arxiv.org/abs/2409.00042
Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to i
Externí odkaz:
http://arxiv.org/abs/2409.00043
Autor:
Janna N. Schultzhaus, William Judson Hervey, Chris R. Taitt, Chris R. So, Dagmar H. Leary, Kathryn J. Wahl, Christopher M. Spillmann
Publikováno v:
Open Biology, Vol 11, Iss 8 (2021)
Barnacles interest the scientific community for multiple reasons: their unique evolutionary trajectory, vast diversity and economic impact—as a harvested food source and also as one of the most prolific macroscopic hard biofouling organisms. A comm
Externí odkaz:
https://doaj.org/article/07d2363e88664437982ab5ccc824936c
Autor:
Athawale, Tushar M., Wang, Zhe, Pugmire, David, Moreland, Kenneth, Gong, Qian, Klasky, Scott, Johnson, Chris R., Rosen, Paul
This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis
Externí odkaz:
http://arxiv.org/abs/2407.18015
We study classical spin ice under uniaxial strain along the $[111]$ crystallographic axis. Remarkably, such strain preserves the extensive ice degeneracy and the corresponding classical Coulomb phase. The emergent monopole excitations remain thermody
Externí odkaz:
http://arxiv.org/abs/2406.18649
We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. These tailored architectures are parameter-efficient, scalable, and significantly out-perform existing symmetry-unaware neural network archit
Externí odkaz:
http://arxiv.org/abs/2405.17541
Publikováno v:
Physical Review B 110, 085105 (2024)
In the absence of parity and time-reversal symmetries, insulators can exhibit magnetoelectric responses, in which applied magnetic fields induce charge polarization and, conversely, applied electric fields induce magnetization. While there is a long
Externí odkaz:
http://arxiv.org/abs/2403.00918
Autor:
Han, Mengjiao, Li, Jixian, Sane, Sudhanshu, Gupta, Shubham, Wang, Bei, Petruzza, Steve, Johnson, Chris R.
In this paper, we present a comprehensive evaluation to establish a robust and efficient framework for Lagrangian-based particle tracing using deep neural networks (DNNs). Han et al. (2021) first proposed a DNN-based approach to learn Lagrangian repr
Externí odkaz:
http://arxiv.org/abs/2312.14973
Publikováno v:
Phys. Rev. Lett. 133, 060802, 2024
Shadow tomography aims to build a classical description of a quantum state from a sequence of simple random measurements. Physical observables are then reconstructed from the resulting classical shadow. Shadow protocols which use single-body random m
Externí odkaz:
http://arxiv.org/abs/2311.09291