Institution
Helsinki University of Technology
About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Thin film & Vortex. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the functional counterpoise method was applied to the theoretical prediction of hydrogen bonding potential surfaces, using a minimal basis to represent the atomic orbitals (STO-3G).
Abstract: The “functional” counterpoise method, proposed by Boys and Bernardi [1], is applied to the theoretical prediction of hydrogen bonding potential surfaces, using a minimal basis to represent the atomic orbitals (STO-3G). Using this method, with a systematically chosen “correction factor”, one can compute potential surfaces with an STO-3G basis as accurately as with a much more flexible atomic basis.
167 citations
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TL;DR: S-layer-protein-expressing cells of strain JCM 5810 adhered to collagen-containing regions in the chicken colon, suggesting that CbsA-mediated collagen binding represents a true tissue adherence property of L. crispatus.
Abstract: The cbsA gene of Lactobacillus crispatus strain JCM 5810, encoding a protein that mediates adhesiveness to collagens, was characterized and expressed in Escherichia coli. The cbsA open reading frame encoded a signal sequence of 30 amino acids and a mature polypeptide of 410 amino acids with typical features of a bacterial S-layer protein. The cbsA gene product was expressed as a His tag fusion protein, purified by affinity chromatography, and shown to bind solubilized as well as immobilized type I and IV collagens. Three other Lactobacillus S-layer proteins, SlpA, CbsB, and SlpnB, bound collagens only weakly, and sequence comparisons of CbsA with these S-layer proteins were used to select sites in cbsA where deletions and mutations were introduced. In addition, hybrid S-layer proteins that contained the N or the C terminus from CbsA, SlpA, or SlpnB as well as N- and C-terminally truncated peptides from CbsA were constructed by gene fusion. Analysis of these molecules revealed the major collagen-binding region within the N-terminal 287 residues and a weaker type I collagen-binding region in the C terminus of the CbsA molecule. The mutated or hybrid CbsA molecules and peptides that failed to polymerize into a periodic S-layer did not bind collagens, suggesting that the crystal structure with a regular array is optimal for expression of collagen binding by CbsA. Strain JCM 5810 was found to contain another S-layer gene termed cbsB that was 44% identical in sequence to cbsA. RNA analysis showed that cbsA, but not cbsB, was transcribed under laboratory conditions. S-layer-protein-expressing cells of strain JCM 5810 adhered to collagen-containing regions in the chicken colon, suggesting that CbsA-mediated collagen binding represents a true tissue adherence property of L. crispatus.
167 citations
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TL;DR: In Helsinki, a Small but General Set of Manipulative Operations for Boundary Models of Solid Objects has been used to Construct a Comprehensive Solid Modeling System.
Abstract: In Helsinki, a Small but General Set of Manipulative Operations for Boundary Models of Solid Objects Has Been Used to Construct a Comprehensive Solid Modeling System.
167 citations
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01 Jan 2019
TL;DR: This work enables practical deep learning while preserving benefits of Bayesian principles, and applies techniques such as batch normalisation, data augmentation, and distributed training to achieve similar performance in about the same number of epochs as the Adam optimiser.
Abstract: Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch normalisation, data augmentation, and distributed training, we achieve similar performance in about the same number of epochs as the Adam optimiser, even on large datasets such as ImageNet. Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted. This work enables practical deep learning while preserving benefits of Bayesian principles. A PyTorch implementation is available as a plug-and-play optimiser.
167 citations
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TL;DR: In this article, the twisted Gaussian Schell-model (GSM) beams are interpreted in physical-optics terms by decomposition of such beams into weighted superpositions of overlapping, mutually uncorrelated but spatially coherent component fields.
Abstract: The twisted Gaussian Schell-model (GSM) beams, recently introduced by Simon and Mukunda [ J. Opt. Soc. Am. A9, 95 ( 1993)], are interpreted in physical-optics terms by decomposition of such beams into weighted superpositions of overlapping, mutually uncorrelated but spatially coherent component fields. The decomposition provides considerable physical insight into the propagation characteristics of the twisted GSM beams and also suggests convenient practical methods for generating these novel wave fields. Key properties of the twisted GSM beams are demonstrated experimentally by use of an acousto-optic coherence control technique to supply the necessary partially coherent fields.
167 citations
Authors
Showing all 8962 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Hannu Kurki-Suonio | 138 | 433 | 99607 |
Nicolas Gisin | 125 | 827 | 64298 |
Anne Lähteenmäki | 116 | 485 | 81977 |
Riitta Hari | 111 | 491 | 43873 |
Andreas Richter | 110 | 769 | 48262 |
Mika Sillanpää | 96 | 1019 | 44260 |
Markku Leskelä | 94 | 876 | 36881 |
Ullrich Scherf | 92 | 735 | 36972 |
Mikko Ritala | 91 | 584 | 29934 |
Axel H. E. Müller | 89 | 564 | 30283 |
Karl Henrik Johansson | 88 | 1089 | 33751 |
T. Poutanen | 86 | 120 | 33158 |
Elina Lindfors | 86 | 420 | 23846 |
Günter Breithardt | 85 | 554 | 33165 |