60 research outputs found
Baxter permutations rise again
AbstractBaxter permutations, so named by Boyce, were introduced by Baxter in his study of the fixed points of continuous functions which commute under composition. Recently Chung, Graham, Hoggatt, and Kleiman obtained a sum formula for the number of Baxter permutations of 2n − 1 objects, but admit to having no interpretation of the individual terms of this sum. We show that in fact the kth term of this sum counts the number of (reduced) Baxter permutations that have exactly k − 1 rises
Weight enumerators of self-orthogonal codes
AbstractCanonical forms are given for (i) the weight enumerator of an |n, 12(n−1)| self-orthogonal code, and (ii) the split weight enumerator (which classifies the codewords according to the weight of the left-and right-half words) of an |n, 12n| self-dual code
Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient?
The accurate prediction of forest above-ground biomass is nowadays key to implementing climate change mitigation policies, such as reducing emissions from deforestation and forest degradation. In this context, the coefficient of determination () is widely used as a means of evaluating the proportion of variance in the dependent variable explained by a model. However, the validity of for comparing observed versus predicted values has been challenged in the presence of bias, for instance in remote sensing predictions of forest biomass. We tested suitable alternatives, e.g. the index of agreement () and the maximal information coefficient (). Our results show that renders systematically higher values than , and may easily lead to regarding as reliable models which included an unrealistic amount of predictors. Results seemed better for , although favoured local clustering of predictions, whether or not they corresponded to the observations. Moreover, was more sensitive to the use of cross-validation than or , and more robust against overfitted models. Therefore, we discourage the use of statistical measures alternative to for evaluating model predictions versus observed values, at least in the context of assessing the reliability of modelled biomass predictions using remote sensing. For those who consider to be conceptually superior to , we suggest using its square , in order to be more analogous to and hence facilitate comparison across studies
A dimensionally continued Poisson summation formula
We generalize the standard Poisson summation formula for lattices so that it
operates on the level of theta series, allowing us to introduce noninteger
dimension parameters (using the dimensionally continued Fourier transform).
When combined with one of the proofs of the Jacobi imaginary transformation of
theta functions that does not use the Poisson summation formula, our proof of
this generalized Poisson summation formula also provides a new proof of the
standard Poisson summation formula for dimensions greater than 2 (with
appropriate hypotheses on the function being summed). In general, our methods
work to establish the (Voronoi) summation formulae associated with functions
satisfying (modular) transformations of the Jacobi imaginary type by means of a
density argument (as opposed to the usual Mellin transform approach). In
particular, we construct a family of generalized theta series from Jacobi theta
functions from which these summation formulae can be obtained. This family
contains several families of modular forms, but is significantly more general
than any of them. Our result also relaxes several of the hypotheses in the
standard statements of these summation formulae. The density result we prove
for Gaussians in the Schwartz space may be of independent interest.Comment: 12 pages, version accepted by JFAA, with various additions and
improvement
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Stochastic particle packing with specified granulometry and porosity
This work presents a technique for particle size generation and placement in
arbitrary closed domains. Its main application is the simulation of granular
media described by disks. Particle size generation is based on the statistical
analysis of granulometric curves which are used as empirical cumulative
distribution functions to sample from mixtures of uniform distributions. The
desired porosity is attained by selecting a certain number of particles, and
their placement is performed by a stochastic point process. We present an
application analyzing different types of sand and clay, where we model the
grain size with the gamma, lognormal, Weibull and hyperbolic distributions. The
parameters from the resulting best fit are used to generate samples from the
theoretical distribution, which are used for filling a finite-size area with
non-overlapping disks deployed by a Simple Sequential Inhibition stochastic
point process. Such filled areas are relevant as plausible inputs for assessing
Discrete Element Method and similar techniques
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