3,335 research outputs found
Efficient transfer entropy analysis of non-stationary neural time series
Information theory allows us to investigate information processing in neural
systems in terms of information transfer, storage and modification. Especially
the measure of information transfer, transfer entropy, has seen a dramatic
surge of interest in neuroscience. Estimating transfer entropy from two
processes requires the observation of multiple realizations of these processes
to estimate associated probability density functions. To obtain these
observations, available estimators assume stationarity of processes to allow
pooling of observations over time. This assumption however, is a major obstacle
to the application of these estimators in neuroscience as observed processes
are often non-stationary. As a solution, Gomez-Herrero and colleagues
theoretically showed that the stationarity assumption may be avoided by
estimating transfer entropy from an ensemble of realizations. Such an ensemble
is often readily available in neuroscience experiments in the form of
experimental trials. Thus, in this work we combine the ensemble method with a
recently proposed transfer entropy estimator to make transfer entropy
estimation applicable to non-stationary time series. We present an efficient
implementation of the approach that deals with the increased computational
demand of the ensemble method's practical application. In particular, we use a
massively parallel implementation for a graphics processing unit to handle the
computationally most heavy aspects of the ensemble method. We test the
performance and robustness of our implementation on data from simulated
stochastic processes and demonstrate the method's applicability to
magnetoencephalographic data. While we mainly evaluate the proposed method for
neuroscientific data, we expect it to be applicable in a variety of fields that
are concerned with the analysis of information transfer in complex biological,
social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON
A Summary of Research Projects Sponsored by the Office of Naval Research. Report for the Period 1 October 1979 to 30 September 1980
Office of Naval Researc
An Introduction to Data Analysis in Asteroseismology
A practical guide is presented to some of the main data analysis concepts and
techniques employed contemporarily in the asteroseismic study of stars
exhibiting solar-like oscillations. The subjects of digital signal processing
and spectral analysis are introduced first. These concern the acquisition of
continuous physical signals to be subsequently digitally analyzed. A number of
specific concepts and techniques relevant to asteroseismology are then
presented as we follow the typical workflow of the data analysis process,
namely, the extraction of global asteroseismic parameters and individual mode
parameters (also known as peak-bagging) from the oscillation spectrum.Comment: Lecture presented at the IVth Azores International Advanced School in
Space Sciences on "Asteroseismology and Exoplanets: Listening to the Stars
and Searching for New Worlds" (arXiv:1709.00645), which took place in Horta,
Azores Islands, Portugal in July 201
Systemic risk diagnostics: coincident indicators and early warning signals
We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures (āthermometersā) and a forward looking indicator for the likelihood of simultaneous failure of a large number of financial intermediaries. The indicators are based on latent macro-financial and credit risk components for a large data set comprising the U.S., the EU-27 area, and the respective rest of the world. Credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy. JEL Classification: G21, C33credit portfolio models, financial crisis, frailty-correlated defaults, state space methods, systemic risk
Advancements of MultiRate Signal processing for Wireless Communication Networks: Current State Of the Art
With the hasty growth of internet contact and voice and information centric communications, many contact technologies have been urbanized to meet the stringent insist of high speed information transmission and viaduct the wide bandwidth gap among ever-increasing high-data-rate core system and bandwidth-hungry end-user complex. To make efficient consumption of the limited bandwidth of obtainable access routes and cope with the difficult channel environment, several standards have been projected for a variety of broadband access scheme over different access situation (twisted pairs, coaxial cables, optical fibers, and unchanging or mobile wireless admittance). These access situations may create dissimilar channel impairments and utter unique sets of signal dispensation algorithms and techniques to combat precise impairments. In the intended and implementation sphere of those systems, many research issues arise. In this paper we present advancements of multi-rate indication processing methodologies that are aggravated by this design trend. The thesis covers the contemporary confirmation of the current literature on intrusion suppression using multi-rate indication in wireless communiquE9; networks
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