15,378 research outputs found
Application of multiobjective genetic programming to the design of robot failure recognition systems
We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge
Some Empirical Criteria for Attributing Creativity to a Computer Program
Peer reviewedPostprin
Matrix eQTL: Ultra fast eQTL analysis via large matrix operations
Expression quantitative trait loci (eQTL) mapping aims to determine genomic
regions that regulate gene transcription. Expression QTL is used to study the
regulatory structure of normal tissues and to search for genetic factors in
complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL
dataset contains millions of SNPs and thousands of transcripts measured for
hundreds of samples. This makes the analysis computationally complex as it
involves independent testing for association for every transcript-SNP pair. The
heavy computational burden makes eQTL analysis less popular, often forces
analysts to restrict their attention to just a subset of transcripts and SNPs.
As larger genotype and gene expression datasets become available, the demand
for fast tools for eQTL analysis increases. We present a new method for fast
eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and
test for association using both linear regression and ANOVA models. The models
can include covariates to account for such factors as population structure,
gender, and clinical variables. It also supports testing of heteroscedastic
models and models with correlated errors. In our experiment on large datasets
Matrix eQTL was thousands of times faster than the existing popular software
for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages
and thus can easily be run on Windows, Mac OS, and Linux systems. The software
is freely available at the following address:
http://www.bios.unc.edu/research/genomic_software/Matrix_eQTLComment: 9 pages, 1 figur
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms
The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the
simplest and most widely-studied supersymmetric extensions to the standard
model of particle physics. Nevertheless, current data do not sufficiently
constrain the model parameters in a way completely independent of priors,
statistical measures and scanning techniques. We present a new technique for
scanning supersymmetric parameter spaces, optimised for frequentist profile
likelihood analyses and based on Genetic Algorithms. We apply this technique to
the CMSSM, taking into account existing collider and cosmological data in our
global fit. We compare our method to the MultiNest algorithm, an efficient
Bayesian technique, paying particular attention to the best-fit points and
implications for particle masses at the LHC and dark matter searches. Our
global best-fit point lies in the focus point region. We find many
high-likelihood points in both the stau co-annihilation and focus point
regions, including a previously neglected section of the co-annihilation region
at large m_0. We show that there are many high-likelihood points in the CMSSM
parameter space commonly missed by existing scanning techniques, especially at
high masses. This has a significant influence on the derived confidence regions
for parameters and observables, and can dramatically change the entire
statistical inference of such scans.Comment: 47 pages, 8 figures; Fig. 8, Table 7 and more discussions added to
Sec. 3.4.2 in response to referee's comments; accepted for publication in
JHE
Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This paper presents a novel quadratic projection based feature extraction
framework, where a set of quadratic matrices is learned to distinguish each
class from all other classes. We formulate quadratic matrix learning (QML) as a
standard semidefinite programming (SDP) problem. However, the con- ventional
interior-point SDP solvers do not scale well to the problem of QML for
high-dimensional data. To solve the scalability of QML, we develop an efficient
algorithm, termed DualQML, based on the Lagrange duality theory, to extract
nonlinear features. To evaluate the feasibility and effectiveness of the
proposed framework, we conduct extensive experiments on biometric recognition.
Experimental results on three representative biometric recogni- tion tasks,
including face, palmprint, and ear recognition, demonstrate the superiority of
the DualQML-based feature extraction algorithm compared to the current
state-of-the-art algorithm
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