69,170 research outputs found
Analysis, Tracing, Characterization and Performance Modeling of Select ASCI Applications for BlueGene/L Using Parallel Discrete Event Simulation
Caltech's Jet Propulsion Laboratory (JPL) and Center for Advanced Computer Architecture (CACR) are conducting application and simulation analyses of Blue Gene/L[1] in order to establish a range of effectiveness of the architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Recommended from our members
Distributed video coding in wireless multimedia sensor network for multimedia broadcasting
Recently the development of Distributed Video Coding (DVC) has provided the promising theory
support to realize the infrastructure of Wireless Multimedia Sensor Network (WMSN), which composed of autonomous hardware for capturing and transmission of quality audio-visual content. The implementation of DVC in WMSN can better solve the problem of energy constraint of the sensor nodes due to the benefit of lower computational encoder in DVC. In this paper, a practical DVC scheme, pixel-domain Wyner-Ziv(PDWZ) video
coding, with slice structure and adaptive rate selection(ARS) is proposed to solve the certain problems when applying DVC into WMSN. Firstly, the proposed slice structure in PDWZ has extended the feasibility of PDWZ to work with any interleaver size used in Slepian-wolf turbo codec for heterogeneous applications. Meanwhile,
based on the slice structure, an adaptive code rate selection has been proposed aiming at reduce the system delay occurred in feedback request. The simulation results clearly showed the enhancement in R-D performance and perceptual quality. It also can be observed that system delay caused by frequent feedback is greatly reduced, which gives a promising support for WMSN with low latency and facilitates the QoS management
MGOS: A library for molecular geometry and its operating system
The geometry of atomic arrangement underpins the structural understanding of molecules in many fields. However, no general framework of mathematical/computational theory for the geometry of atomic arrangement exists. Here we present "Molecular Geometry (MG)'' as a theoretical framework accompanied by "MG Operating System (MGOS)'' which consists of callable functions implementing the MG theory. MG allows researchers to model complicated molecular structure problems in terms of elementary yet standard notions of volume, area, etc. and MGOS frees them from the hard and tedious task of developing/implementing geometric algorithms so that they can focus more on their primary research issues. MG facilitates simpler modeling of molecular structure problems; MGOS functions can be conveniently embedded in application programs for the efficient and accurate solution of geometric queries involving atomic arrangements. The use of MGOS in problems involving spherical entities is akin to the use of math libraries in general purpose programming languages in science and engineering. (C) 2019 The Author(s). Published by Elsevier B.V
Diffusive Molecular Communications with Reactive Signaling
This paper focuses on molecular communication (MC) systems where the
signaling molecules may participate in a reversible bimolecular reaction in the
channel. The motivation for studying these MC systems is that they can realize
the concept of constructive and destructive signal superposition, which leads
to favorable properties such as inter-symbol interference (ISI) reduction and
avoiding environmental contamination due to continuous release of molecules
into the channel. This work first derives the maximum likelihood (ML) detector
for a binary MC system with reactive signaling molecules under the assumption
that the detector has perfect knowledge of the ISI. The performance of this
genie-aided ML detector yields an upper bound on the performance of any
practical detector. In addition, two suboptimal detectors of different
complexity are proposed. The proposed ML detector as well as one of the
suboptimal detectors require the channel response (CR) of the considered MC
system. Moreover, the CR is needed for the performance evaluation of all
proposed detectors. However, analyzing MC with reactive signaling is
challenging since the underlying partial differential equations that describe
the reaction-diffusion mechanism are coupled and non-linear. Therefore, an
algorithm is developed in this paper for efficient computation of the CR to any
arbitrary transmit symbol sequence. The accuracy of this algorithm is validated
via particle-based simulation. Simulation results using the developed CR
algorithm show that the performance of the proposed suboptimal detectors can
approach that of the genie- aided ML detector. Moreover, these results show
that MC systems with reactive signaling have superior performance relative to
those with non-reactive signaling due to the reduction of ISI enabled by the
chemical reactions.Comment: This paper has been submitted to IEEE International Conference on
Communications (ICC) 201
Stochastic Volatility Filtering with Intractable Likelihoods
This paper is concerned with particle filtering for -stable
stochastic volatility models. The -stable distribution provides a
flexible framework for modeling asymmetry and heavy tails, which is useful when
modeling financial returns. An issue with this distributional assumption is the
lack of a closed form for the probability density function. To estimate the
volatility of financial returns in this setting, we develop a novel auxiliary
particle filter. The algorithm we develop can be easily applied to any hidden
Markov model for which the likelihood function is intractable or
computationally expensive. The approximate target distribution of our auxiliary
filter is based on the idea of approximate Bayesian computation (ABC). ABC
methods allow for inference on posterior quantities in situations when the
likelihood of the underlying model is not available in closed form, but
simulating samples from it is possible. The ABC auxiliary particle filter
(ABC-APF) that we propose provides not only a good alternative to state
estimation in stochastic volatility models, but it also improves on the
existing ABC literature. It allows for more flexibility in state estimation
while improving on the accuracy through better proposal distributions in cases
when the optimal importance density of the filter is unavailable in closed
form. We assess the performance of the ABC-APF on a simulated dataset from the
-stable stochastic volatility model and compare it to other currently
existing ABC filters
- âŠ