23,309 research outputs found
A continuous analogue of the tensor-train decomposition
We develop new approximation algorithms and data structures for representing
and computing with multivariate functions using the functional tensor-train
(FT), a continuous extension of the tensor-train (TT) decomposition. The FT
represents functions using a tensor-train ansatz by replacing the
three-dimensional TT cores with univariate matrix-valued functions. The main
contribution of this paper is a framework to compute the FT that employs
adaptive approximations of univariate fibers, and that is not tied to any
tensorized discretization. The algorithm can be coupled with any univariate
linear or nonlinear approximation procedure. We demonstrate that this approach
can generate multivariate function approximations that are several orders of
magnitude more accurate, for the same cost, than those based on the
conventional approach of compressing the coefficient tensor of a tensor-product
basis. Our approach is in the spirit of other continuous computation packages
such as Chebfun, and yields an algorithm which requires the computation of
"continuous" matrix factorizations such as the LU and QR decompositions of
vector-valued functions. To support these developments, we describe continuous
versions of an approximate maximum-volume cross approximation algorithm and of
a rounding algorithm that re-approximates an FT by one of lower ranks. We
demonstrate that our technique improves accuracy and robustness, compared to TT
and quantics-TT approaches with fixed parameterizations, of high-dimensional
integration, differentiation, and approximation of functions with local
features such as discontinuities and other nonlinearities
Structure of Disk Dominated Galaxies I. Bulge/Disk Parameters, Simulations, and Secular Evolution
(Abridged) A robust analysis of galaxy structural parameters, based on the
modeling of bulge and disk brightnesses in the BVRH bandpasses, is presented
for 121 face-on and moderately inclined late-type spirals. Each surface
brightness (SB) profile is decomposed into a sum of a generalized Sersic bulge
and an exponential disk. The reliability and limitations of our bulge-to-disk
(B/D) decompositions are tested with extensive simulations of galaxy brightness
profiles (1D) and images (2D). Galaxy types are divided into 3 classes
according to their SB profile shapes; Freeman Type-I and Type-II, and a third
``Transition'' class for galaxies whose profiles change from Type-II in the
optical to Type-I in the infrared. We discuss possible interpretations of
Freeman Type-II profiles. The Sersic bulge shape parameter for nearby Type-I
late-type spirals shows a range between n=0.1-2 but, on average, the underlying
surface density profile for the bulge and disk of these galaxies is adequately
described by a double-exponential distribution. We confirm a coupling between
the bulge and disk with a scale length ratio r_e/h=0.22+/-0.09, or
h_bulge/h_disk=0.13+/-0.06 for late-type spirals, in agreement with recent
N-body simulations of disk formation and models of secular evolution. This
ratio increases from ~0.20 for late-type spirals to ~0.24 for earlier types.
The similar scaling relations for early and late-type spirals suggest
comparable formation and/or evolution scenarios for disk galaxies of all Hubble
types.Comment: 78 pages with 23 embedded color figures + tables of galaxy structural
parameters. Accepted for publication in the Astrophysical Journal. The
interested reader is strongly encouraged to ignore some of the low res
figures within; instead, download the high resolution version from
http://www.astro.ubc.ca/people/courteau/public/macarthur02_disks.ps.g
The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions
A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
This model is a semiparametric generalization of the Heckman selection model
that accommodates much richer patterns of heterogeneity in the selection
process and effect of the covariates. The model applies to continuous, discrete
and mixed outcomes. We study the identification of the model, and develop a
computationally attractive two-step method to estimate the model parameters,
where the first step is a probit regression for the selection equation and the
second step consists of multiple distribution regressions with selection
corrections for the outcome equation. We construct estimators of functionals of
interest such as actual and counterfactual distributions of latent and observed
outcomes via plug-in rule. We derive functional central limit theorems for all
the estimators and show the validity of multiplier bootstrap to carry out
functional inference. We apply the methods to wage decompositions in the UK
using new data. Here we decompose the difference between the male and female
wage distributions into four effects: composition, wage structure, selection
structure and selection sorting. After controlling for endogenous employment
selection, we still find substantial gender wage gap -- ranging from 21% to 40%
throughout the (latent) offered wage distribution that is not explained by
observable labor market characteristics. We also uncover positive sorting for
single men and negative sorting for married women that accounts for a
substantive fraction of the gender wage gap at the top of the distribution.
These findings can be interpreted as evidence of assortative matching in the
marriage market and glass-ceiling in the labor market.Comment: 72 pages, 4 tables, 39 figures, includes supplement with additional
empirical result
Modularity and the spread of perturbations in complex dynamical systems
We propose a method to decompose dynamical systems based on the idea that
modules constrain the spread of perturbations. We find partitions of system
variables that maximize 'perturbation modularity', defined as the
autocovariance of coarse-grained perturbed trajectories. The measure
effectively separates the fast intramodular from the slow intermodular dynamics
of perturbation spreading (in this respect, it is a generalization of the
'Markov stability' method of network community detection). Our approach
captures variation of modular organization across different system states, time
scales, and in response to different kinds of perturbations: aspects of
modularity which are all relevant to real-world dynamical systems. It offers a
principled alternative to detecting communities in networks of statistical
dependencies between system variables (e.g., 'relevance networks' or
'functional networks'). Using coupled logistic maps, we demonstrate that the
method uncovers hierarchical modular organization planted in a system's
coupling matrix. Additionally, in homogeneously-coupled map lattices, it
identifies the presence of self-organized modularity that depends on the
initial state, dynamical parameters, and type of perturbations. Our approach
offers a powerful tool for exploring the modular organization of complex
dynamical systems
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