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| http://dx.doi.org/10.1007/s11606-022-07915-5 | DOI Listing |
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Extreme Synergy in the Random-Energy Model.
Phys Rev Lett
December 2024
Initiative for the Theoretical Sciences and CUNY-Princeton Center for the Physics of Biological Function, The Graduate Center, CUNY, New York, New York 10016, USA.
The random-energy model (REM), a solvable spin-glass model, has impacted an incredibly diverse set of problems, from protein folding to combinatorial optimization, to many-body localization. Here, we explore a new connection to secret sharing. We derive an analytic expression for the mutual information between any two disjoint thermodynamic subsystems of the REM.
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- The orbital angular momentum (OAM) of vortex beams offers advantages in optical communications, including enhanced security and reduced interference.
- A single passive all-dielectric metasurface made of TiO nanopillars on a SiO substrate is designed to recognize high-order optical vortexes without the need for large datasets typically required by AI models.
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An evaluation of the replicability of analyses using synthetic health data.
Sci Rep
March 2024
OpenSourceResearch, Aalborg, Denmark.
Synthetic data generation is being increasingly used as a privacy preserving approach for sharing health data. In addition to protecting privacy, it is important to ensure that generated data has high utility. A common way to assess utility is the ability of synthetic data to replicate results from the real data.
View Article and Find Full Text PDFA comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health.
Sci Rep
July 2023
Children's Hospital of Eastern Ontario Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.
Article Synopsis
- Sharing health data internationally for research is challenging due to privacy issues, prompting a study comparing two privacy-enhancing methods: synthetic data generation (SDG) and federated analysis.
- The study focused on evaluating country-level differences in cardiovascular health related to sex, using synthesized Canadian data analyzed alongside real Austrian data.
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A method for generating synthetic longitudinal health data.
BMC Med Res Methodol
March 2023
School of Public Health, University of Alberta, Edmonton, AB, Canada.
Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model.
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