1,481 research outputs found
The Expected Fitness Cost of a Mutation Fixation under the One-dimensional Fisher Model
This paper employs Fisherās model of adaptation to understand the expected fitness
eļ¬ect of ļ¬xing a mutation in a natural population. Fisherās model in one dimension admits
a closed form solution for this expected ļ¬tness eļ¬ect. A combination of different parameters, including the distribution of mutation lengths, population sizes, and the initial state that the population is in, are examined to see how they affect the expected ļ¬tness effect of state transitions. The results show that the expected fitness change due to the ļ¬xation of a mutation is always positive, regardless of the distributional shapes of mutation lengths, effective population sizes, and the initial state that the population is in. The further away the initial state of a population is from the optimal state, the slower the population returns to the optimal state. Effective population size (except when very small) has little effect on the expected ļ¬tness change due to mutation fixation. The always positive expected ļ¬tness change suggests that small populations may not necessarily be doomed due to the runaway process of fixation of deleterious mutations
Probability-one Homotopies in Computational Science
Probability-one homotopy algorithms are a class of methods for solving nonlinear systems of equations that,under mild assumptions,are globally convergent for a wide range of problems in science and engineering.Convergence theory, robust numerical algorithms,and production quality mathematical software exist for general nonlinear systems of equations, and special cases suc as Brouwer fixed point problems,polynomial systems,and nonlinear constrained optimization.Using a sample of challenging scientific problems as motivation,some pertinent homotopy theory and algorithms are presented. The problems considered are analog circuit simulation (for nonlinear systems),reconfigurable space trusses (for polynomial systems),and fuel-optimal orbital rendezvous (for nonlinear constrained optimization).The mathematical software packages HOMPACK90 and POLSYS_PLP are also briefly described
Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling
Though the GPGPU concept is well-known
in image processing, much more work remains to be done
to fully exploit GPUs as an alternative computation
engine. This paper investigates the computation-to-core
mapping strategies to probe the efficiency and scalability
of the robust facet image modeling algorithm on GPUs.
Our fine-grained computation-to-core mapping scheme
shows a significant performance gain over the standard
pixel-wise mapping scheme. With in-depth performance
comparisons across the two different mapping schemes,
we analyze the impact of the level of parallelism on
the GPU computation and suggest two principles for
optimizing future image processing applications on the
GPU platform
Deep Attributes Driven Multi-Camera Person Re-identification
The visual appearance of a person is easily affected by many factors like
pose variations, viewpoint changes and camera parameter differences. This makes
person Re-Identification (ReID) among multiple cameras a very challenging task.
This work is motivated to learn mid-level human attributes which are robust to
such visual appearance variations. And we propose a semi-supervised attribute
learning framework which progressively boosts the accuracy of attributes only
using a limited number of labeled data. Specifically, this framework involves a
three-stage training. A deep Convolutional Neural Network (dCNN) is first
trained on an independent dataset labeled with attributes. Then it is
fine-tuned on another dataset only labeled with person IDs using our defined
triplet loss. Finally, the updated dCNN predicts attribute labels for the
target dataset, which is combined with the independent dataset for the final
round of fine-tuning. The predicted attributes, namely \emph{deep attributes}
exhibit superior generalization ability across different datasets. By directly
using the deep attributes with simple Cosine distance, we have obtained
surprisingly good accuracy on four person ReID datasets. Experiments also show
that a simple metric learning modular further boosts our method, making it
significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201
Optimization by nonhierarchical asynchronous decomposition
Large scale optimization problems are tractable only if they are somehow decomposed. Hierarchical decompositions are inappropriate for some types of problems and do not parallelize well. Sobieszczanski-Sobieski has proposed a nonhierarchical decomposition strategy for nonlinear constrained optimization that is naturally parallel. Despite some successes on engineering problems, the algorithm as originally proposed fails on simple two dimensional quadratic programs. The algorithm is carefully analyzed for quadratic programs, and a number of modifications are suggested to improve its robustness
Enrichment Procedures for Soft Clusters: A Statistical Test and its Applications
Clusters, typically mined by modeling locality of attribute spaces, are often evaluated for their ability to demonstrate āenrichmentā of categorical features. A cluster enrichment procedure evaluates the membership of a cluster for significant representation in pre-defined categories of interest. While classical enrichment procedures assume a hard clustering deļ¬nition, in this paper we introduce a new statistical test that computes enrichments for soft clusters. We demonstrate an application of this test in reļ¬ning and evaluating soft clusters for classification of remotely sensed images
Adjusting process count on demand for petascale global optimizationā
There are many challenges that need to be met before efficient and reliable computation at the
petascale is possible. Many scientific and engineering codes running at the petascale are likely to
be memory intensive, which makes thrashing a serious problem for many petascale applications.
One way to overcome this challenge is to use a dynamic number of processes, so that the total
amount of memory available for the computation can be increased on demand. This paper
describes modifications made to the massively parallel global optimization code pVTdirect in
order to allow for a dynamic number of processes. In particular, the modified version of the
code monitors memory use and spawns new processes if the amount of available memory is
determined to be insufficient. The primary design challenges are discussed, and performance
results are presented and analyzed
Modern Homotopy Methods in Optimization
Probability-one homotopy methods are a class of algorithms for solving nonlinear systems of equations that are accurate, robust, and converge from an arbitrary starting point almost surely. These new techniques have been successfully applied to solve Brouwer faced point problems, polynomial systems of equations, and discretizations of nonlinear two-point boundary value problems based on shooting, finite differences, collocation, and finite elements. This paper summarizes the theory of globally convergent homotopy algorithms for unconstrained and constrained optimization, and gives some examples of actual application of homotopy techniques to engineering optimization problems
An SMP Soft Classification Algorithm for Remote Sensing
This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative
guided spectral class rejection (CIGSCR) algorithm, a semiautomated classiļ¬cation algorithm for remote
sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classiļ¬cation
containing inherently more information than a comparable hard classiļ¬cation at an increased computational
cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel
algorithm development work here. Experimental results of applying parallel CIGSCR to an image with
approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classiļ¬cation is
generated in just over four minutes using 32 processors
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