Institution
University of Luxembourg
Education•Luxembourg, Luxembourg•
About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.
Papers published on a yearly basis
Papers
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TL;DR: This paper generalizes Bench-Capon's value-based argumentation theory such that arguments can promote multiple values, and preferences among values or arguments can be specified in various ways.
106 citations
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TL;DR: It is shown that store-operated Ca(2+) entry (SOCE) is required at the beginning of NADPH oxidase activation in response to fMLF in neutrophil-like HL-60 cells.
105 citations
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TL;DR: Carlet et al. as mentioned in this paper proposed a generalization of Rivain-Prouff's look-up table countermeasure against first-order attacks, which can be used to mask lookup tables of block-ciphers at any order.
Abstract: We describe a new algorithm for masking look-up tables of block-ciphers at any order, as a countermeasure against side-channel attacks. Our technique is a generalization of the classical randomized table countermeasure against first-order attacks. We prove the security of our new algorithm against t-th order attacks in the usual Ishai-Sahai-Wagner model from Crypto 2003; we also improve the bound on the number of shares from n ≥ 4t + 1 to n ≥ 2t + 1 for an adversary who can adaptively move its probes between successive executions. Our algorithm has the same time complexity O(n) as the Rivain-Prouff algorithm for AES, and its extension by Carlet et al. to any look-up table. In practice for AES our algorithm is less efficient than Rivain-Prouff, which can take advantage of the special algebraic structure of the AES Sbox; however for DES our algorithm performs slightly better.
105 citations
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TL;DR: In this paper, the authors give a complete identification of the deformation quantization which was obtained from the Berezin-Toeplitz quantization on an arbitrary compact Kahler manifold.
Abstract: We give a complete identification of the deformation quantization which was obtained from the Berezin- Toeplitz quantization on an arbitrary compact Kahler manifold. The deformation quantization with the opposite star-product proves to be a differential deformation quantization with separation of variables whose dassifying form is explicitly calculated. Its characteristic dass (which dassifies star-products up to equivalence) is obtained. The proof is based on the microlocal description of the Szego kernel of a strictly pseudoconvex domain given by Boutet de Monvel and Sjostrand.
105 citations
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Technische Universität München1, University of Luxembourg2, Shanghai Jiao Tong University3, Tel Aviv University4, Max Planck Society5, Rutgers University6, Hoffmann-La Roche7, Garvan Institute of Medical Research8, Broad Institute9, Harvard University10, Weihenstephan-Triesdorf University of Applied Sciences11, Icahn School of Medicine at Mount Sinai12, Seoul National University13
TL;DR: PredictProtein was the first Internet server for protein predictions, and recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins.
Abstract: Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
105 citations
Authors
Showing all 4893 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jun Wang | 166 | 1093 | 141621 |
Leroy Hood | 158 | 853 | 128452 |
Andreas Heinz | 108 | 1078 | 45002 |
Philippe Dubois | 101 | 1098 | 48086 |
John W. Berry | 97 | 351 | 52470 |
Michael Müller | 91 | 333 | 26237 |
Bart Preneel | 82 | 844 | 25572 |
Bjorn Ottersten | 81 | 1058 | 28359 |
Sander Kersten | 79 | 246 | 23985 |
Alexandre Tkatchenko | 77 | 271 | 26863 |
Rudi Balling | 75 | 238 | 19529 |
Lionel C. Briand | 75 | 380 | 24519 |
Min Wang | 72 | 716 | 19197 |
Stephen H. Friend | 70 | 184 | 53422 |
Ekhard K. H. Salje | 70 | 581 | 19938 |