Principal Component Analysis for Functional Data on Riemannian Manifolds and Spheres

Functional data analysis on nonlinear manifolds has drawn recent interest. Sphere-valued functional data, which are encountered for example as movement trajectories on the surface of the earth, are an important special case. We consider an intrinsic principal component analysis for smooth Riemannian manifold-valued functional data and study its asymptotic properties. Riemannian functional principal component analysis (RFPCA) is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to tangent spaces around the time-varying Fr\'echet mean function, and then performing a classical multivariate functional principal component analysis on the linear tangent spaces. Representations of the Riemannian manifold-valued functions and the eigenfunctions on the original manifold are then obtained with exponential maps. The tangent-space approximation through functional principal component analysis is shown to be well-behaved in terms of controlling the residual variation if the Riemannian manifold has nonnegative curvature. Specifically, we derive a central limit theorem for the mean function, as well as root-$n$ uniform convergence rates for other model components, including the covariance function, eigenfunctions, and functional principal component scores. Our applications include a novel framework for the analysis of longitudinal compositional data, achieved by mapping longitudinal compositional data to trajectories on the sphere, illustrated with longitudinal fruit fly behavior patterns. RFPCA is shown to be superior in terms of trajectory recovery in comparison to an unrestricted functional principal component analysis in applications and simulations and is also found to produce principal component scores that are better predictors for classification compared to traditional functional functional principal component scores

Statistical mechanics characterization of spatio-compositional inhomogeneity

On the basis of a model system of pillars built of unit cubes, a two-component entropic measure for the multiscale analysis of spatio-compositional inhomogeneity is proposed. It quantifies the statistical dissimilarity per cell of the actual configurational macrostate and the theoretical reference one that maximizes entropy. Two kinds of disorder compete: i) the spatial one connected with possible positions of pillars inside a cell (the first component of the measure), ii) the compositional one linked to compositions of each local sum of their integer heights into a number of pillars occupying the cell (the second component). As both the number of pillars and sum of their heights are conserved, the upper limit for a pillar height hmax occurs. If due to a further constraint there is the more demanding limit h <= h* < hmax, the exact number of restricted compositions can be then obtained only through the generating function. However, at least for systems with exclusively composition degrees of freedom, we show that the neglecting of the h* is not destructive yet for a nice correlation of the h*-constrained entropic measure and its less demanding counterpart, which is much easier to compute. Given examples illustrate a broad applicability of the measure and its ability to quantify some of the subtleties of a fractional Brownian motion, time evolution of a quasipattern [28,29] and reconstruction of a laser-speckle pattern [2], which are hardly to discern or even missed.Comment: 17 pages, 5 figure

On the relative importance of thermal and chemical buoyancy in regular and impact-induced melting in a Mars-like planet

We ran several series of two-dimensional numerical mantle convection simulations representing in idealized form the thermochemical evolution of a Mars-like planet. In order to study the importance of compositional buoyancy of melting mantle, the models were set up in pairs of one including all thermal and compositional contributions to buoyancy and one accounting only for the thermal contributions. In several of the model pairs, single large impacts were introduced as causes of additional strong local anomalies, and their evolution in the framework of the convecting mantle was tracked. The models confirm that the additional buoyancy provided by the depletion of the mantle by regular melting can establish a global stable stratification of the convecting mantle and throttle crust production. Furthermore, the compositional buoyancy is essential in the stabilization and preservation of local compositional anomalies directly beneath the lithosphere and offers a possible explanation for the existence of distinct, long-lived reservoirs in the martian mantle. The detection of such anomalies by geophysical means is probably difficult, however; they are expected to be detected by gravimetry rather than by seismic or heat flow measurements. The results further suggest that the crustal thickness can be locally overestimated by up to ~20 km if impact-induced density anomalies in the mantle are neglected.Comment: 29 pages, 10 figure

A Unified Framework for Compositional Fitting of Active Appearance Models

Active Appearance Models (AAMs) are one of the most popular and well-established techniques for modeling deformable objects in computer vision. In this paper, we study the problem of fitting AAMs using Compositional Gradient Descent (CGD) algorithms. We present a unified and complete view of these algorithms and classify them with respect to three main characteristics: i) cost function; ii) type of composition; and iii) optimization method. Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost function that can be interpreted as a general probabilistic formulation of the well-known project-out loss; b) introducing two new types of composition, asymmetric and bidirectional, that combine the gradients of both image and appearance model to derive better conver- gent and more robust CGD algorithms; and c) providing new valuable insights into existent CGD algorithms by reinterpreting them as direct applications of the Schur complement and the Wiberg method. Finally, in order to encourage open research and facilitate future comparisons with our work, we make the implementa- tion of the algorithms studied in this paper publicly available as part of the Menpo Project.Comment: 39 page

Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Mandarin Chinese is characterized by being a tonal language; the pitch (or $F_0$) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase which must be accounted for in any analysis which attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase and duration is presented which combines elements from Functional Data Analysis, Compositional Data Analysis and Linear Mixed Effects Models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and non-linguistic covariates. The model is applied to the COSPRO-1 data set, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50 thousand phonetically diverse sample $F_0$ contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation.Comment: 49 pages, 13 figures, small changes to discussio

Convection in the Earth's core driven by lateral variations in the core-mantle boundary heat flux

Moving core fluid maintains an isothermal core-mantle boundary (CMB), so lateral variations in the CMB heat flow result from mantle convection. Such variations will drive thermal winds, even if the top of the core is stably stratified. These flows may contribute to the magnetic secular variation and are investigated here using a simple, non-magnetic numerical model of the core. The results depend on the equatorial symmetry of the boundary heat flux variation. Large-scale equatorially symmetric (ES) heat flux variations at the outer surface of a rapidly rotating spherical shell drive deeply penetrating flows that are strongly suppressed in stratified fluid. Smaller-scale ES heat flux variations drive flows less dominated by rotation and so less inhibited by stratification. Equatorially anti-symmetric flux variations drive flows an order of magnitude less energetic than those driven by ES patterns but, due to the nature of the Coriolis force, are less suppressed by stratification. The response of the rotating core fluid to a general CMB heat flow pattern will then depend strongly on the subadiabatic temperature profile. Imposing a lateral heat flux variation linearly related to a model of seismic tomography in the lowermost mantle drives flow in a density stratified fluid that reproduces some features found in flows inverted from geomagnetic data