Zobrazeno 1 - 10
of 2 455
pro vyhledávání: '"Morningstar, A."'
Autor:
Sullivan, Ryan P., Morningstar, John T., Castellanos, Eduardo, Bradford III, Robert W., Hofstetter, Yvonne J., Vaynzof, Yana, Welker, Mark E., Jurchescu, Oana D.
Molecular-scale diodes made from self-assembled monolayers (SAMs) could complement silicon-based technologies with smaller, cheaper, and more versatile devices. However, advancement of this emerging technology is limited by insufficient electronic pe
Externí odkaz:
http://arxiv.org/abs/2404.11261
Autor:
Morningstar, Warren, Bijamov, Alex, Duvarney, Chris, Friedman, Luke, Kalibhat, Neha, Liu, Luyang, Mansfield, Philip, Rojas-Gomez, Renan, Singhal, Karan, Green, Bradley, Prakash, Sushant
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space leaves the impression that the pretraining algorithm is of critical impo
Externí odkaz:
http://arxiv.org/abs/2403.05726
The low-lying energy spectrum of the static-colour-source-anti-source system in a vacuum containing light and strange quarks is computed using lattice QCD for a range of different light quark masses. The resulting levels are described using a simple
Externí odkaz:
http://arxiv.org/abs/2403.00754
Autor:
Bulava, John, Darvish, Danny, Hanlon, Andrew D., Hörz, Ben, Morningstar, Colin, Nicholson, Amy, Romero-López, Fernando, Skinner, Sarah, Vranas, Pavlos, Walker-Loud, André
Studies of the $\Delta$ baryon resonance and the $K_0^\ast(700)$ and $a_0(980)$ meson resonances using $N_f=2+1$ lattice QCD for pion masses near 200 MeV are presented. The $s$-wave scattering lengths for both the $I=1/2$ $N \pi$ and $I=3/2$ $N \pi$
Externí odkaz:
http://arxiv.org/abs/2312.10184
Autor:
Bulava, John, Cid-Mora, Bárbara, Hanlon, Andrew D., Hörz, Ben, Mohler, Daniel, Morningstar, Colin, Moscoso, Joseph, Nicholson, Amy, Romero-López, Fernando, Skinner, Sarah, Walker-Loud, André
Publikováno v:
PoS LATTICE2023
This work presents technical details of determining the finite-volume energy spectra for the scattering amplitude of the coupled-channel $\pi\Sigma - \bar{K}N$ from lattice QCD data. The importance of reliably extracting such spectra lies in the cruc
Externí odkaz:
http://arxiv.org/abs/2312.05154
Autor:
Kalibhat, Neha, Morningstar, Warren, Bijamov, Alex, Liu, Luyang, Singhal, Karan, Mansfield, Philip
Self-Supervised Learning (SSL) enables training performant models using limited labeled data. One of the pillars underlying vision SSL is the use of data augmentations/perturbations of the input which do not significantly alter its semantic content.
Externí odkaz:
http://arxiv.org/abs/2312.02205
Autor:
Rojas-Gomez, Renan A., Singhal, Karan, Etemad, Ali, Bijamov, Alex, Morningstar, Warren R., Mansfield, Philip Andrew
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream perf
Externí odkaz:
http://arxiv.org/abs/2312.01187
Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations. Previous work has shown that applying diverse data augmentations is crucial to downstream performance, but augme
Externí odkaz:
http://arxiv.org/abs/2311.03629
Autor:
Bulava, John, Cid-Mora, Barbara, Hanlon, Andrew D., Hoerz, Ben, Mohler, Daniel, Morningstar, Colin, Moscoso, Joseph, Nicholson, Amy, Romero-Lopez, Fernando, Skinner, Sarah, Walker-Loud, Andre
Recent results studying the masses and widths of low-lying baryon resonances in lattice QCD are presented. The $S$-wave $N\pi$ scattering lengths for both total isospins $I = 1/2$ and $I = 3/2$ are inferred from the finite-volume spectrum below the i
Externí odkaz:
http://arxiv.org/abs/2310.08375
Autor:
Guo, Pengfei, Morningstar, Warren Richard, Vemulapalli, Raviteja, Singhal, Karan, Patel, Vishal M., Mansfield, Philip Andrew
Large machine learning models trained on diverse data have recently seen unprecedented success. Federated learning enables training on private data that may otherwise be inaccessible, such as domain-specific datasets decentralized across many clients
Externí odkaz:
http://arxiv.org/abs/2309.05213