Zobrazeno 1 - 10
of 43 227
pro vyhledávání: '"Shulman, A"'
Autor:
Shulman, David, Dattner, Itai
This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data by integrating physical laws into deep learning models. The APGNN adaptively balances data-driven and physics-informed
Externí odkaz:
http://arxiv.org/abs/2411.10064
Autor:
Shulman, S. G.
We consider an alternative to the Monte Carlo method for dust continuous radiative transfer simulations: the Quasi-Monte Carlo method. We briefly discuss what it is, its history, and possible implementations. We compare the Monte Carlo method with fo
Externí odkaz:
http://arxiv.org/abs/2406.15632
Autor:
Enders, Dominic, Shulman, Tatiana
The (Local) Lifting Property ((L)LP) is introduced by Kirchberg and deals with lifting completely positive maps. We give a characterization of the (L)LP in terms of lifting $\ast$-homomorphisms. We use it to prove that if $A$ and $B$ have the LP and
Externí odkaz:
http://arxiv.org/abs/2403.12224
Autor:
Shulman, David, Israeli, Assaf, Botnaro, Yael, Margalit, Ori, Tamir, Oved, Naschitz, Shaul, Gamrasni, Dan, Shir, Ofer M., Dattner, Itai
We present an innovative approach leveraging Physics-Guided Neural Networks (PGNNs) for enhancing agricultural quality assessments. Central to our methodology is the application of physics-guided inverse regression, a technique that significantly imp
Externí odkaz:
http://arxiv.org/abs/2403.08653
Autor:
Kolomatskaia, Astra, Shulman, Michael
We introduce Displayed Type Theory (dTT), a multi-modal homotopy type theory with discrete and simplicial modes. In the intended semantics, the discrete mode is interpreted by a model for an arbitrary $\infty$-topos, while the simplicial mode is inte
Externí odkaz:
http://arxiv.org/abs/2311.18781
Autor:
Joshua Ricouvier, Pavel Mostov, Omer Shabtai, Ohad Vonshak, Alexandra Tayar, Eyal Karzbrun, Aset Khakimzhan, Vincent Noireaux, Shirley Shulman Daube, Roy Bar-Ziv
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract The on-chip large-scale-integration of genetically programmed artificial cells capable of exhibiting collective expression patterns is important for fundamental research and biotechnology. Here, we report a 3D biochip with a 2D layout of 102
Externí odkaz:
https://doaj.org/article/5e2f52b89e1a4a718dcba036dcce086f
Autor:
Janet A. Parsons, Jannah Wigle, Ian Zenlea, Noah Ivers, Geetha Mukerji, Alanna Landry, Zubin Punthakee, Cheril L. Clarson, Rayzel Shulman
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background The transition from pediatric to adult care is a vulnerable time for young people living with type 1 diabetes (T1D). Bridging the Gap (BTG) is an audit-and-feedback (AF) intervention aimed at improving both transitions-in-care pro
Externí odkaz:
https://doaj.org/article/21bdf417a4b54d47b4a99b51d0cdaefb
Autor:
Lana M. Chahine, Naomi Louie, J Solle, Fulya Akçimen, Andrew Ameri, Samantha Augenbraun, Sabrina Avripas, Sarah Breaux, Christopher Causey, Shivika Chandra, Marissa Dean, Elizabeth A. Disbrow, Lauren Fanty, Jessica Fernandez, Erin R. Foster, Erin Furr Stimming, Deborah Hall, Vanessa Hinson, Ashani Johnson-Turbes, Cabell Jonas, Camilla Kilbane, Scott A. Norris, Bao-Tran Nguyen, Mahesh Padmanaban, Kimberly Paquette, Carly Parry, Natalia Pessoa Rocha, Ashley Rawls, Ejaz A. Shamim, Lisa M. Shulman, Rebeka Sipma, Julia Staisch, Rami Traurig, Rainer von Coelln, Peter Wild Crea, Tao Xie, Zih-Hua Fang, Alyssa O’Grady, Catherine M. Kopil, Maggie McGuire Kuhl, Andrew Singleton, Cornelis Blauwendraat, Sara Bandres-Ciga, the BLAAC PD Study and the Global Parkinson’s Genetics Program (GP2)
Publikováno v:
BMC Neurology, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Determining the genetic contributions to Parkinson’s disease (PD) across diverse ancestries is a high priority as this work can guide therapeutic development in a global setting. The genetics of PD spans the etiological risk spectrum, from
Externí odkaz:
https://doaj.org/article/15c7bceb54b1476f84c1fdcc646a1eef
Parametricity is a property of the syntax of type theory implying, e.g., that there is only one function having the type of the polymorphic identity function. Parametricity is usually proven externally, and does not hold internally. Internalising it
Externí odkaz:
http://arxiv.org/abs/2307.06448