19,331 research outputs found
Learn to Model Motion from Blurry Footages
It is difficult to recover the motion field from a real-world footage given a
mixture of camera shake and other photometric effects. In this paper we propose
a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a
traditional optical flow energy. We first conduct a CNN architecture using a
novel learnable directional filtering layer. Such layer encodes the angle and
distance similarity matrix between blur and camera motion, which is able to
enhance the blur features of the camera-shake footages. The proposed CNNs are
then integrated into an iterative optical flow framework, which enable the
capability of modelling and solving both the blind deconvolution and the
optical flow estimation problems simultaneously. Our framework is trained
end-to-end on a synthetic dataset and yields competitive precision and
performance against the state-of-the-art approaches.Comment: Preprint of our paper accepted by Pattern Recognitio
Error analysis of a class of derivative estimators for noisy signals
Recent algebraic parametric estimation techniques led to point-wise
derivative estimates by using only the iterated integral of a noisy observation
signal. In this paper, we extend such differentiation methods by providing a
larger choice of parameters in these integrals: they can be reals. For this the
extension is done via a truncated Jacobi orthogonal series expansion. Then, the
noise error contribution of these derivative estimations is investigated: after
proving the existence of such integral with a stochastic process noise, their
statistical properties (mean value, variance and covariance) are analyzed. In
particular, the following important results are obtained: a) the bias error
term, due to the truncation, can be reduced by tuning the parameters, b) such
estimators can cope with a large class of noises for which the mean and
covariance are polynomials in time (with degree smaller than the order of
derivative to be estimated), c) the variance of the noise error is shown to be
smaller in the case of negative real parameters than it was for integer values.
Consequently, these derivative estimations can be improved by tuning the
parameters according to the here obtained knowledge of the parameters'
influence on the error bounds
Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers
Shrinking transistors, which powered the advancement of computing in the past
half century, has stalled due to power wall; now extreme heterogeneity is
promised to be the next driving force to feed the needs of ever-increasingly
diverse scientific domains. To unlock the potentials of such supercomputers, we
identify eight potential challenges in three categories: First, one needs fast
data movement since extreme heterogeneity will inevitably complicate the
communication circuits -- thus hampering the data movement. Second, we need to
intelligently schedule suitable hardware for corresponding applications/stages.
Third, we have to lower the programming complexity in order to encourage the
adoption of heterogeneous computing
Rank Approximation of a Tensor with Applications in Color Image and Video Processing
We propose a block coordinate descent type algorithm for estimating the rank
of a given tensor. In addition, the algorithm provides the canonical polyadic
decomposition of a tensor. In order to estimate the tensor rank we use sparse
optimization method using norm. The algorithm is implemented on single
moving object videos and color images for approximating the rank
On groups all of whose Haar graphs are Cayley graphs
A Cayley graph of a group is a finite simple graph such that
contains a subgroup isomorphic to acting regularly on
, while a Haar graph of is a finite simple bipartite graph
such that contains a subgroup isomorphic to
acting semiregularly on and the -orbits are equal to the
bipartite sets of . A Cayley graph is a Haar graph exactly when it is
bipartite, but no simple condition is known for a Haar graph to be a Cayley
graph. In this paper, we show that the groups and
are the only finite inner abelian groups all of whose Haar graphs are
Cayley graphs (a group is called inner abelian if it is non-abelian, but all of
its proper subgroups are abelian). As an application, it is also shown that
every non-solvable group has a Haar graph which is not a Cayley graph.Comment: 17 page
Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation
Massive graphs, such as online social networks and communication networks,
have become common today. To efficiently analyze such large graphs, many
distributed graph computing systems have been developed. These systems employ
the "think like a vertex" programming paradigm, where a program proceeds in
iterations and at each iteration, vertices exchange messages with each other.
However, using Pregel's simple message passing mechanism, some vertices may
send/receive significantly more messages than others due to either the high
degree of these vertices or the logic of the algorithm used. This forms the
communication bottleneck and leads to imbalanced workload among machines in the
cluster. In this paper, we propose two effective message reduction techniques:
(1)vertex mirroring with message combining, and (2)an additional
request-respond API. These techniques not only reduce the total number of
messages exchanged through the network, but also bound the number of messages
sent/received by any single vertex. We theoretically analyze the effectiveness
of our techniques, and implement them on top of our open-source Pregel
implementation called Pregel+. Our experiments on various large real graphs
demonstrate that our message reduction techniques significantly improve the
performance of distributed graph computation.Comment: This is a long version of the corresponding WWW 2015 paper, with all
proofs include
Efficient Processing of Very Large Graphs in a Small Cluster
Inspired by the success of Google's Pregel, many systems have been developed
recently for iterative computation over big graphs. These systems provide a
user-friendly vertex-centric programming interface, where a programmer only
needs to specify the behavior of one generic vertex when developing a parallel
graph algorithm. However, most existing systems require the input graph to
reside in memories of the machines in a cluster, and the few out-of-core
systems suffer from problems such as poor efficiency for sparse computation
workload, high demand on network bandwidth, and expensive cost incurred by
external-memory join and group-by.
In this paper, we introduce the GraphD system for a user to process very
large graphs with ordinary computing resources. GraphD fully overlaps
computation with communication, by streaming edges and messages on local disks,
while transmitting messages in parallel. For a broad class of Pregel algorithms
where message combiner is applicable, GraphD eliminates the need of any
expensive external-memory join or group-by. These key techniques allow GraphD
to achieve comparable performance to in-memory Pregel-like systems without
keeping edges and messages in memories. We prove that to process a graph G=(V,
E) with n machines using GraphD, each machine only requires O(|V|/n) memory
space, allowing GraphD to scale to very large graphs with a small cluster.
Extensive experiments show that GraphD beats existing out-of-core systems by
orders of magnitude, and achieves comparable performance to in-memory systems
running with enough memories
A Novel Approach for Parameter and Differentiation Order Estimation for a Space Fractional Advection Dispersion Equation
In this paper, we propose a new approach, based on the so-called modulating
functions to estimate the average velocity, the dispersion coefficient and the
differentiation order in a space fractional advection dispersion equation.
First, the average velocity and the dispersion coefficient are estimated by
applying the modulating functions method, where the problem is transferred into
solving a system of algebraic equations. Then, the modulating functions method
combined with Newton's method is applied to estimate all three parameters
simultaneously. Numerical results are presented with noisy measurements to show
the effectiveness and the robustness of the proposed method.Comment: 13 pages, 9 figure
Direct and Inverse Source Problem for a Space Fractional Advection Dispersion Equation
In this paper, direct and inverse problems for a space fractional advection
dispersion equation on a finite domain are studied. The inverse problem
consists in determining the source term from a final observation. We first
drive the fundamental solution to the direct problem and we show that the
relation between source and the final observation is linear. Moreover, we study
the well-posedness of both problems: existence, uniqueness and stability.
Finally, we illustrate the results with a numerical example.Comment: 15 pages, 6 figure
Electromagnetic braking: a simple quantitative model
A calculation is presented which quantitatively accounts for the terminal
velocity of a cylindrical magnet falling through a long copper or aluminum
pipe. The experiment and the theory are a dramatic illustration of the
Faraday's and Lenz's laws and are bound to capture student's attention in any
electricity and magnetism course
- …