scholarly journals Simulation of Compressible Flow on a Massively Parallel Architecture

1995 ◽  
Vol 4 (3) ◽  
pp. 193-201
Author(s):  
Dan Williams ◽  
Luc Bauwens
Keyword(s):  
Test Problem ◽  
Parallel Architecture ◽  
Parallel Computer ◽  
Two Dimensional ◽  
Fine Grained ◽  
Data Parallel ◽  
Transport Code ◽  
Multiple Data ◽  
Flux Corrected Transport ◽  
Better Than

This article describes the porting and optimization of an explicit, time-dependent, computational fluid dynamics code on an 8,192-node MasPar MP-1. The MasPar is a very fine-grained, single instruction, multiple data parallel computer. The code uses the flux-corrected transport algorithm. We describe the techniques used to port and optimize the code, and the behavior of a test problem. The test problem used to benchmark the flux-corrected transport code on the MasPar was a two-dimensional exploding shock with periodic boundary conditions. We discuss the performance that our code achieved on the MasPar, and compare its performance on the MasPar with its performance on other architectures. The comparisons show that the performance of the code on the MasPar is slightly better than on a CRAY Y-MP for a functionally equivalent, optimized two-dimensional code.

Study on Two-Dimensional Packing Problem

2011 ◽  
Vol 403-408 ◽  
pp. 246-249
Author(s):  
De Xiang Zhou ◽  
Xian Rong Wang ◽  
Sheng Da Chen
Keyword(s):  
Test Problem ◽  
Packing Problem ◽  
Experimental Results ◽  
Two Dimensional ◽  
Better Than

IHR was proposed in this paper based on HR algorithm which defines reference rectangle and binding layer. It finds the best solution and the best combination of layers through finding all the binding layers and calculating the solution of each combination of layers. Experimental results show that the IHR algorithm in two-dimensional packing problem is better than the HR algorithm, especially for large quantity of data test problem.

Reconstruction of Two-Dimensional Projections of GP 32*I

1981 ◽  
Vol 39 ◽  
pp. 38-39
Author(s):  
H.A. Cohen ◽  
W. Chiu ◽  
J. Hosoda
Keyword(s):  
Electron Microscopy ◽  
Molecular Weight ◽  
Uranyl Acetate ◽  
Distilled Water ◽  
Acetate Solution ◽  
Two Dimensional ◽  
Parent Molecule ◽  
T4 Bacteriophage ◽  
Electron Imaging ◽  
Better Than

GP 32 (molecular weight 35000) is a T4 bacteriophage protein that destabilizes the DNA helix. The fragment GP32*I (77% of the total weight), which destabilizes helices better than does the parent molecule, crystallizes as platelets thin enough for electron diffraction and electron imaging. In this paper we discuss the structure of this protein as revealed in images reconstructed from stained and unstained crystals.Crystals were prepared as previously described. Crystals for electron microscopy were pelleted from the buffer suspension, washed in distilled water, and resuspended in 1% glucose. Two lambda droplets were placed on grids over freshly evaporated carbon, allowed to sit for five minutes, and then were drained. Stained crystals were prepared the same way, except that prior to draining the droplet, two lambda of aqueous 1% uranyl acetate solution were applied for 20 seconds. Micrographs were produced using less than 2 e/Å2 for unstained crystals or less than 8 e/Å2 for stained crystals.

scholarly journals Do 3D Face Images Capture Cues of Strength, Weight, and Height Better than 2D Face Images do?

2021 ◽  
Vol 7 (3) ◽  
pp. 209-219
Author(s):  
Iris J Holzleitner ◽  
Alex L Jones ◽  
Kieran J O’Shea ◽  
Rachel Cassar ◽  
Vanessa Fasolt ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Physical Characteristics ◽  
Two Dimensional ◽  
3D Images ◽  
3D Face ◽  
Face Images ◽  
Similar Accuracy ◽  
2D And 3D ◽  
2D Images ◽  
Better Than

Abstract Objectives A large literature exists investigating the extent to which physical characteristics (e.g., strength, weight, and height) can be accurately assessed from face images. While most of these studies have employed two-dimensional (2D) face images as stimuli, some recent studies have used three-dimensional (3D) face images because they may contain cues not visible in 2D face images. As equipment required for 3D face images is considerably more expensive than that required for 2D face images, we here investigated how perceptual ratings of physical characteristics from 2D and 3D face images compare. Methods We tested whether 3D face images capture cues of strength, weight, and height better than 2D face images do by directly comparing the accuracy of strength, weight, and height ratings of 182 2D and 3D face images taken simultaneously. Strength, height and weight were rated by 66, 59 and 52 raters respectively, who viewed both 2D and 3D images. Results In line with previous studies, we found that weight and height can be judged somewhat accurately from faces; contrary to previous research, we found that people were relatively inaccurate at assessing strength. We found no evidence that physical characteristics could be judged more accurately from 3D than 2D images. Conclusion Our results suggest physical characteristics are perceived with similar accuracy from 2D and 3D face images. They also suggest that the substantial costs associated with collecting 3D face scans may not be justified for research on the accuracy of facial judgments of physical characteristics.

scholarly journals Bra-ket wormholes in gravitationally prepared states

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Yiming Chen ◽  
Victor Gorbenko ◽  
Juan Maldacena
Keyword(s):  
Hyperbolic Space ◽  
Path Integral ◽  
Flat Space ◽  
Low Energy ◽  
Two Dimensional ◽  
De Sitter ◽  
Euclidean Case ◽  
Fine Grained ◽  
Entropy Formula ◽  
First Case

Abstract We consider two dimensional CFT states that are produced by a gravitational path integral.As a first case, we consider a state produced by Euclidean AdS2 evolution followed by flat space evolution. We use the fine grained entropy formula to explore the nature of the state. We find that the naive hyperbolic space geometry leads to a paradox. This is solved if we include a geometry that connects the bra with the ket, a bra-ket wormhole. The semiclassical Lorentzian interpretation leads to CFT state entangled with an expanding and collapsing Friedmann cosmology.As a second case, we consider a state produced by Lorentzian dS2 evolution, again followed by flat space evolution. The most naive geometry also leads to a similar paradox. We explore several possible bra-ket wormholes. The most obvious one leads to a badly divergent temperature. The most promising one also leads to a divergent temperature but by making a projection onto low energy states we find that it has features that look similar to the previous Euclidean case. In particular, the maximum entropy of an interval in the future is set by the de Sitter entropy.

scholarly journals AutoSense Model for Word Sense Induction

2019 ◽  
Vol 33 ◽  
pp. 6212-6219 ◽  
Author(s):  
Reinald Kim Amplayo ◽  
Seung-won Hwang ◽  
Min Song
Keyword(s):  
Latent Variable ◽  
Word Sense ◽  
Name Disambiguation ◽  
Variable Model ◽  
Fine Grained ◽  
Word Sense Induction ◽  
Author Name Disambiguation ◽  
Competing Models ◽  
Word Senses ◽  
Better Than

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.

Multidimensional Models of Information Need

2009 ◽  
Vol 08 (01) ◽  
pp. 53-66
Author(s):  
Yun-jie (Calvin) Xu ◽  
Kai Huang (Joseph) Tan
Keyword(s):  
Information Science ◽  
Retrieval System ◽  
Multidimensional Model ◽  
Dimensional Model ◽  
Information Need ◽  
Multidimensional Construct ◽  
Multiple Data ◽  
Multiple Dimensions ◽  
Multidimensional Models ◽  
Better Than

User studies in information science have recognised relevance as a multidimensional construct. An implication of multidimensional relevance is that a user's information need should be modeled by multiple data structures to represent different relevance dimensions. While the extant literature has attempted to model multiple dimensions of a user's information need, the fundamental assumption that a multidimensional model is better than a uni-dimensional model has not been addressed. This study seeks to test this assumption. Our results indicate that a retrieval system that models both topicality and the novelty dimension of a users' information need outperforms a system with a uni-dimensional model.

scholarly journals Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

2020 ◽  
Author(s):  
Philippe Schwaller ◽  
Daniel Probst ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
David Kreutter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reaction Center ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Classification Accuracy ◽  
Similarity Searching ◽  
Reaction Space ◽  
Fine Grained ◽  
Visual Clustering ◽  
Better Than

<div><div><div><p>Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. </p><p><br></p><p>Code: https://github.com/rxn4chemistry/rxnfp</p><p>Tutorials: https://rxn4chemistry.github.io/rxnfp/</p><p>Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html</p></div></div></div>

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