1,692 research outputs found
Temperature regulation circuit Patent
Device for rapid adjustment and maintenance of temperature in electronic component
Change Point Detection with Conceptors
Offline change point detection retrospectively locates change points in a
time series. Many nonparametric methods that target i.i.d. mean and variance
changes fail in the presence of nonlinear temporal dependence, and model based
methods require a known, rigid structure. For the at most one change point
problem, we propose use of a conceptor matrix to learn the characteristic
dynamics of a baseline training window with arbitrary dependence structure. The
associated echo state network acts as a featurizer of the data, and change
points are identified from the nature of the interactions between the features
and their relationship to the baseline state. This model agnostic method can
suggest potential locations of interest that warrant further study. We prove
that, under mild assumptions, the method provides a consistent estimate of the
true change point, and quantile estimates are produced via a moving block
bootstrap of the original data. The method is evaluated with clustering metrics
and Type 1 error control on simulated data, and applied to publicly available
neural data from rats experiencing bouts of non-REM sleep prior to exploration
of a radial maze. With sufficient spacing, the framework provides a simple
extension to the sparse, multiple change point problem
Nonlinear Permuted Granger Causality
Granger causal inference is a contentious but widespread method used in
fields ranging from economics to neuroscience. The original definition
addresses the notion of causality in time series by establishing functional
dependence conditional on a specified model. Adaptation of Granger causality to
nonlinear data remains challenging, and many methods apply in-sample tests that
do not incorporate out-of-sample predictability, leading to concerns of model
overfitting. To allow for out-of-sample comparison, a measure of functional
connectivity is explicitly defined using permutations of the covariate set.
Artificial neural networks serve as featurizers of the data to approximate any
arbitrary, nonlinear relationship, and consistent estimation of the variance
for each permutation is shown under certain conditions on the featurization
process and the model residuals. Performance of the permutation method is
compared to penalized variable selection, naive replacement, and omission
techniques via simulation, and it is applied to neuronal responses of acoustic
stimuli in the auditory cortex of anesthetized rats. Targeted use of the
Granger causal framework, when prior knowledge of the causal mechanisms in a
dataset are limited, can help to reveal potential predictive relationships
between sets of variables that warrant further study
Pattern Formation on Trees
Networks having the geometry and the connectivity of trees are considered as
the spatial support of spatiotemporal dynamical processes. A tree is
characterized by two parameters: its ramification and its depth. The local
dynamics at the nodes of a tree is described by a nonlinear map, given rise to
a coupled map lattice system. The coupling is expressed by a matrix whose
eigenvectors constitute a basis on which spatial patterns on trees can be
expressed by linear combination. The spectrum of eigenvalues of the coupling
matrix exhibit a nonuniform distribution which manifest itself in the
bifurcation structure of the spatially synchronized modes. These models may
describe reaction-diffusion processes and several other phenomena occurring on
heterogeneous media with hierarchical structure.Comment: Submitted to Phys. Rev. E, 15 pages, 9 fig
The acoustics of public square/places: a comparison between results from a computer simulation program and measurements in situ
http://www.odeon.dk/pdf/InterNoise2004.pd
Universality class of replica synchronization transition of smooth coupled map lattices in the presence of quenched disorder
Synchronization of two replicas of coupled map lattices for continuous maps
is known to be in the multiplicative noise universality class. We study this
transition in presence of identical quenched disorder in coupling in both
replicas for coupled logistic and tent maps. We study one-dimensional,
two-dimensional, and globally coupled cases. We observe a clear second-order
transition with new exponents. The order parameter decays as with
for tent map and for logistic map in any dimension. The
asymptotic order parameter grows as with for the
tent map and for the logistic map. The quenched disorder in coupling
is a relevant perturbation for the replica synchronization of coupled map
lattices. The critical exponents are different from that of the multiplicative
noise universality class. We observe that the critical exponents are
independent of dimensions and super-universal in nature
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