19,502 research outputs found
A conjugate gradient minimisation approach to generating holographic traps for ultracold atoms
Direct minimisation of a cost function can in principle provide a versatile
and highly controllable route to computational hologram generation. However, to
date iterative Fourier transform algorithms have been predominantly used. Here
we show that the careful design of cost functions, combined with numerically
efficient conjugate gradient minimisation, establishes a practical method for
the generation of holograms for a wide range of target light distributions.
This results in a guided optimisation process, with a crucial advantage
illustrated by the ability to circumvent optical vortex formation during
hologram calculation. We demonstrate the implementation of the conjugate
gradient method for both discrete and continuous intensity distributions and
discuss its applicability to optical trapping of ultracold atoms.Comment: 11 pages, 4 figure
Genetic Sequence Matching Using D4M Big Data Approaches
Recent technological advances in Next Generation Sequencing tools have led to
increasing speeds of DNA sample collection, preparation, and sequencing. One
instrument can produce over 600 Gb of genetic sequence data in a single run.
This creates new opportunities to efficiently handle the increasing workload.
We propose a new method of fast genetic sequence analysis using the Dynamic
Distributed Dimensional Data Model (D4M) - an associative array environment for
MATLAB developed at MIT Lincoln Laboratory. Based on mathematical and
statistical properties, the method leverages big data techniques and the
implementation of an Apache Acculumo database to accelerate computations
one-hundred fold over other methods. Comparisons of the D4M method with the
current gold-standard for sequence analysis, BLAST, show the two are comparable
in the alignments they find. This paper will present an overview of the D4M
genetic sequence algorithm and statistical comparisons with BLAST.Comment: 6 pages; to appear in IEEE High Performance Extreme Computing (HPEC)
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Accurate estimation of homologue-specific DNA concentration-ratios in cancer samples allows long-range haplotyping
Interpretation of allelic copy measurements at polymorphic markers in cancer samples presents distinctive challenges and opportunities. Due to frequent gross chromosomal alterations occurring in cancer (aneuploidy), many genomic regions are present at homologous-allele imbalance. Within such regions, the unequal contribution of alleles at heterozygous markers allows for direct phasing of the haplotype derived from each individual parent. In addition, genome-wide estimates of homologue specific copy- ratios (HSCRs) are important for interpretation of the cancer genome in terms of fixed integral copy-numbers. We describe HAPSEG, a probabilistic method to interpret bi- allelic marker data in cancer samples. HAPSEG operates by partitioning the genome into segments of distinct copy number and modeling the four distinct genotypes in each segment. We describe general methods for fitting these models to data which are suit- able for both SNP microarrays and massively parallel sequencing data. In addition, we demonstrate a specially tailored error-model for interpretation of systematic variations arising in microarray platforms. The ability to directly determine haplotypes from cancer samples represents an opportunity to expand reference panels of phased chromosomes, which may have general interest in various population genetic applications. In addition, this property may be exploited to interrogate the relationship between germline risk and cancer phenotype with greater sensitivity than is possible using unphased genotype. Finally, we exploit the statistical dependency of phased genotypes to enable the fitting of more elaborate sample-level error-model parameters, allowing more accurate estimation of HSCRs in cancer samples
A GPU-Computing Approach to Solar Stokes Profile Inversion
We present a new computational approach to the inversion of solar
photospheric Stokes polarization profiles, under the Milne-Eddington model, for
vector magnetography. Our code, named GENESIS (GENEtic Stokes Inversion
Strategy), employs multi-threaded parallel-processing techniques to harness the
computing power of graphics processing units GPUs, along with algorithms
designed to exploit the inherent parallelism of the Stokes inversion problem.
Using a genetic algorithm (GA) engineered specifically for use with a GPU, we
produce full-disc maps of the photospheric vector magnetic field from polarized
spectral line observations recorded by the Synoptic Optical Long-term
Investigations of the Sun (SOLIS) Vector Spectromagnetograph (VSM) instrument.
We show the advantages of pairing a population-parallel genetic algorithm with
data-parallel GPU-computing techniques, and present an overview of the Stokes
inversion problem, including a description of our adaptation to the
GPU-computing paradigm. Full-disc vector magnetograms derived by this method
are shown, using SOLIS/VSM data observed on 2008 March 28 at 15:45 UT
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