3,178 research outputs found
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of
implementatio
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Variable Selection in a GPU Cluster Using Delta Test
The work presented in this paper consists in an adaptation
of a Genetic Algorithm (GA) to perform variable selection in an heterogeneous
cluster where the nodes are themselves clusters of GPUs. Due
to this heterogeneity, several mechanisms to perform a load balance will
be discussed as well as the optimization of the fitness function to take
advantage of the GPUs available. The algorithm will be compared with
previous parallel implementations analysing the advantages and disadvantages
of the approach, showing that for large data sets, the proposed
approach is the only one that can provide a solution.Spanish CICYT Project TIN2007-60587 and TEC2008-04920Junta Andalucia
Projects P08-TIC-03674 and P08-TIC03928 and PYR-2010-17 of CEI
BioTIC GENIL (CEB09-0010) of the MICIN
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
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