Zobrazeno 1 - 10
of 5 368
pro vyhledávání: '"A. Leibovich"'
Autor:
Christie, Alexander, Leibovich, Matan, Moscoso, Miguel, Novikov, Alexei, Papanicolaou, George, Tsogka, Chrysoula
We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates, obtained with and without the use of neural networks, excellent imag
Externí odkaz:
http://arxiv.org/abs/2410.21556
For the $N$-point energy correlator in the collinear limit, the largest projected angle $R$ in the large $N$ limit can be viewed as the radius of the jet that encompasses all the collinear core particles, while contributions from soft gluons to the r
Externí odkaz:
http://arxiv.org/abs/2410.11614
Autor:
Crozier, Peter A., Leibovich, Matan, Haluai, Piyush, Tan, Mai, Thomas, Andrew M., Vincent, Joshua, Mohan, Sreyas, Morales, Adria Marcos, Kulkarni, Shreyas A., Matteson, David S., Wang, Yifan, Fernandez-Granda, Carlos
Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of n
Externí odkaz:
http://arxiv.org/abs/2407.17669
Autor:
Raviv, Avraham, Elboher, Yizhak Y., Aluf-Medina, Michelle, Weiss, Yael Leibovich, Cohen, Omer, Assa, Roy, Katz, Guy, Kugler, Hillel
Deep Neural Networks (DNNs) are ubiquitous in real-world applications, yet they remain vulnerable to errors and adversarial attacks. This work tackles the challenge of applying formal verification to ensure the safety of computer vision models, exten
Externí odkaz:
http://arxiv.org/abs/2407.01295
In this paper, we investigate the heavy quark (HQ) mass effects on the transverse momentum dependent fragmentation function (TMDFF). We first calculate the one-loop TMDFF initiated by a heavy quark. We then investigate the HQ TMDFF in the limit where
Externí odkaz:
http://arxiv.org/abs/2310.19207
Autor:
Katherine Bailey, Brooke Allemang, Ashley Vandermorris, Sarah Munce, Kristin Cleverley, Cassandra Chisholm, Eva Cohen, Cedar Davidson, Asil El Galad, Dahlia Leibovich, Trinity Lowthian, Jeanna Pillainayagam, Harshini Ramesh, Anna Samson, Vjura Senthilnathan, Paul Siska, Madison Snider, Alene Toulany
Publikováno v:
Research Involvement and Engagement, Vol 10, Iss 1, Pp 1-21 (2024)
Abstract Background Youth engagement refers to the collaboration between researchers and youth to produce research. Youth engagement in health research has been shown to inform effective interventions aimed at improving health outcomes. However, limi
Externí odkaz:
https://doaj.org/article/d3ac2301abf34592b902d5e8abf25b89
Autor:
Ekamjit S. Deol, Grant Henning, Spyridon Basourakos, Ranveer M. S. Vasdev, Vidit Sharma, Nicholas L. Kavoussi, R. Jeffrey Karnes, Bradley C. Leibovich, Stephen A. Boorjian, Abhinav Khanna
Publikováno v:
Patient Safety in Surgery, Vol 18, Iss 1, Pp 1-8 (2024)
Abstract Background Retained surgical items (RSI) are preventable events that pose a significant risk to patient safety. Current strategies for preventing RSIs rely heavily on manual instrument counting methods, which are prone to human error. This s
Externí odkaz:
https://doaj.org/article/05546630988e4c7295a41a90c7b2b30e
We calculate the 2 post-Newtonian correction to the radiation reaction acceleration for non-spinning binary systems, which amounts to the 4.5 post-Newtonian correction to Newtonian acceleration. The calculation is carried out completely using the eff
Externí odkaz:
http://arxiv.org/abs/2302.11016
Publikováno v:
PoS(ICHEP2022)517
After the great triumph of the Higgs discovery in 2012, the next target at the energy frontier will be to study the Higgs properties and to search for the next scale beyond the SM. Experimentally, the $H\to c \bar{c}$ channel would be extremely diffi
Externí odkaz:
http://arxiv.org/abs/2211.10727
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning (ICML 2023)
We propose $\textit{iterative inversion}$ -- an algorithm for learning an inverse function without input-output pairs, but only with samples from the desired output distribution and access to the forward function. The key challenge is a $\textit{dist
Externí odkaz:
http://arxiv.org/abs/2211.01724