21,745 research outputs found
Lattice Boltzmann Model for The Volume-Averaged Navier-Stokes Equations
A numerical method, based on the discrete lattice Boltzmann equation, is
presented for solving the volume-averaged Navier-Stokes equations. With a
modified equilibrium distribution and an additional forcing term, the
volume-averaged Navier-Stokes equations can be recovered from the lattice
Boltzmann equation in the limit of small Mach number by the Chapman-Enskog
analysis and Taylor expansion. Due to its advantages such as explicit solver
and inherent parallelism, the method appears to be more competitive with
traditional numerical techniques. Numerical simulations show that the proposed
model can accurately reproduce both the linear and nonlinear drag effects of
porosity in the fluid flow through porous media.Comment: 9 pages, 2 figure
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201
Dynamical topology and statistical properties of spatiotemporal chaos
For spatiotemporal chaos described by partial differential equations, there
are generally locations where the dynamical variable achieves its local
extremum or where the time partial derivative of the variable vanishes
instantaneously. To a large extent, the location and movement of these
topologically special points determine the qualitative structure of the
disordered states. We analyze numerically statistical properties of the
topologically special points in one-dimensional spatiotemporal chaos. The
probability distribution functions for the number of point, the lifespan, and
the distance covered during their lifetime are obtained from numerical
simulations. Mathematically, we establish a probabilistic model to describe the
dynamics of these topologically special points. In despite of the different
definitions in different spatiotemporal chaos, the dynamics of these special
points can be described in a uniform approach.Comment: 6 pages, 5 figure
Approximating Word Ranking and Negative Sampling for Word Embedding
CBOW (Continuous Bag-Of-Words) is one of the most commonly used techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling distribution of negative words. To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding. Specifically, we first formalize word embedding as a ranking problem. Then, we weigh the positive words by their ranks such that highly ranked words have more importance, and adopt a dynamic sampling strategy to select informative negative words. In addition, an approximation method is designed to efficiently compute word ranks. Empirical experiments show that OptRank consistently outperforms its counterparts on a benchmark dataset with different sampling scales, especially when the sampled subset is small. The code and datasets can be obtained from https://github.com/ouououououou/OptRank
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