A fresh look on economic evolution from kinetic viewpoint

The present paper makes a contribution to fill in a gap left open by dynamic theory and evolutionary economics as well. While the "closed loop" dynamic theory has explanation power in analyzing evolving economic systems at the price of neglecting the possible occurrence of non anticipated novelties, "open loop" evolutionary economics is subject to the converse trade-off. Basing on a general equilibrium model by T. Kehoe including production and taxes we provide a formal model of an evolving economy fully accounting for the appearance of novelty. From this emerges in a natural way the notion of an open loop evolution equilibrium. It is based on arguments from the gradual vs. bang-bang tax reform controversy and from the debate on optimal macroeconomic policy design. Existence of equilibrium is established extending an analytical result which in a different context and independently has been proved by Mas- Colell and by the author. We use the term "kinetic" to indicate that in contrast to traditional comparative statics our approach neither hinges on the uniqueness of equilibria, nor is it confined to the analysis of prescribed parameter variations. --evolution,equilibrium,equilibrium price path,frictionless tuning of control parameters

On Bergman kernel functions and weak Morse inequalities

We give simple and unified proofs of weak holomorhpic Morse inequalities on complete manifolds, $q$-convex manifolds, pseudoconvex domains, weakly $1$-complete manifolds and covering manifolds. This paper is essentially based on the asymptotic Bergman kernel functions and the Bochner-Kodaira-Nakano formulas

Non-existence of multiple-black-hole solutions close to Kerr-Newman

We show that a stationary asymptotically flat electro-vacuum solution of Einstein's equations that is everywhere locally "almost isometric" to a Kerr-Newman solution cannot admit more than one event horizon. Axial symmetry is not assumed. In particular this implies that the assumption of a single event horizon in Alexakis-Ionescu-Klainerman's proof of perturbative uniqueness of Kerr black holes is in fact unnecessary.Comment: Version 2: improved presentation; no changes to the result. Version 3: corrected an oversight in the historical review. Version 4: version accepted for publicatio

Adaptive filtering techniques for gravitational wave interferometric data: Removing long-term sinusoidal disturbances and oscillatory transients

It is known by the experience gained from the gravitational wave detector proto-types that the interferometric output signal will be corrupted by a significant amount of non-Gaussian noise, large part of it being essentially composed of long-term sinusoids with slowly varying envelope (such as violin resonances in the suspensions, or main power harmonics) and short-term ringdown noise (which may emanate from servo control systems, electronics in a non-linear state, etc.). Since non-Gaussian noise components make the detection and estimation of the gravitational wave signature more difficult, a denoising algorithm based on adaptive filtering techniques (LMS methods) is proposed to separate and extract them from the stationary and Gaussian background noise. The strength of the method is that it does not require any precise model on the observed data: the signals are distinguished on the basis of their autocorrelation time. We believe that the robustness and simplicity of this method make it useful for data preparation and for the understanding of the first interferometric data. We present the detailed structure of the algorithm and its application to both simulated data and real data from the LIGO 40meter proto-type.Comment: 16 pages, 9 figures, submitted to Phys. Rev.

Riemannian geometry for efficient analysis of protein dynamics data

An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised to data on a Riemannian manifold. In the context of protein dynamics, a conceptual challenge comes from the lack of a suitable smooth manifold and the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a novel local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold of point clouds modulo rigid body group actions and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. It performs exceptionally well on coarse-grained molecular dynamics simulated data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop

Control of Nonholonomic Systems and Sub-Riemannian Geometry

Lectures given at the CIMPA School "Geometrie sous-riemannienne", Beirut, Lebanon, 201