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Sökning: WFRF:(Cohen Tomer)

  • Resultat 1-3 av 3
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1.
  • Fireaizen, Tomer, et al. (författare)
  • Intelligent Reflecting Surface OFDM Communication with Deep Neural Prior
  • 2022
  • Ingår i: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538683477 - 9781538683484 ; , s. 2645-2650
  • Konferensbidrag (refereegranskat)abstract
    • An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.
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2.
  • Lensink, Marc F., et al. (författare)
  • Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment
  • 2023
  • Ingår i: Proteins. - : WILEY. - 0887-3585 .- 1097-0134.
  • Tidskriftsartikel (refereegranskat)abstract
    • We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average similar to 70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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3.
  • Segal, Eran, et al. (författare)
  • Building an international consortium for tracking coronavirus health status
  • 2020
  • Ingår i: Nature Medicine. - : Springer Science and Business Media LLC. - 1078-8956 .- 1546-170X. ; 26:8, s. 1161-1165
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
    • We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.
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