A conformal scalar dyon black hole solution

An exact solution of Einstein - Maxwell - conformal scalar field equations is given, which is a black hole solution and has three parameters: scalar charge, electric charge, and magnetic charge. Switching off the magnetic charge parameter yields the solution given by Bekenstein. In addition the energy of the conformal scalar dyon black hole is obtained.Comment: 7 pages, Late

On the consistency of de Sitter vacua

In this paper the consistency of the de Sitter invariant $\alpha$-vacua, which have been introduced as simple tools to study the effects of transplanckian physics, is investigated. In particular possible non renormalization problems are discussed, as well as non standard properties of Greens functions. We also discuss the non thermal properties of the $\alpha$-vacua and the necessity of $\alpha$ to change. The conclusion is that non of these problems necessarily exclude an application of the $\alpha$-vacua to inflation.Comment: 12 pages, v2: minor clarifications and corrections to reference

A First-Quantized Formalism for Cosmological Particle Production

We show that the amount of particle production in an arbitrary cosmological background can be determined using only the late-time positive-frequency modes. We don't refer to modes at early times, so there is no need for a Bogolubov transformation. We also show that particle production can be extracted from the Feynman propagator in an auxiliary spacetime. This provides a first-quantized formalism for computing particle production which, unlike conventional Bogolubov transformations, may be amenable to a string-theoretic generalization.Comment: 18 pages, LaTeX; v2: significantly revised for clarity; conclusions unchange

RPA vs. exact shell-model correlation energies

The random phase approximation (RPA) builds in correlations left out by mean-field theory. In full 0-hbar-omega shell-model spaces we calculate the Hartree-Fock + RPA binding energy, and compare it to exact diagonalization. We find that in general HF+RPA gives a very good approximation to the ``exact'' ground state energy. In those cases where RPA is less satisfactory, however, there is no obvious correlation with properties of the HF state, such as deformation or overlap with the exact ground state wavefunction.Comment: 6 pages, 7 figures, submitted to Phys Rev

Squeezed States in the de Sitter Vacuum

We discuss the treatment of squeezed states as excitations in the Euclidean vacuum of de Sitter space. A comparison with the treatment of these states as candidate no-particle states, or alpha-vacua, shows important differences already in the free theory. At the interacting level alpha-vacua are inconsistent, but squeezed state excitations seem perfectly acceptable. Indeed, matrix elements can be renormalized in the excited states using precisely the standard local counterterms of the Euclidean vacuum. Implications for inflationary scenarios in cosmology are discussed.Comment: 15 pages, no figures. One new citation in version 3; no other change

Miracles and complementarity in de Sitter space

In this paper we consider a scenario, consisting of a de Sitter phase followed by a phase described by a scale factor $a(t)\sim t^{q}$, where $1/3<q<1$, which can be viewed as an inflationary toy model. It is argued that this scenario naively could lead to an information paradox. We propose that the phenomenon of Poincar\'{e} recurrences plays a crucial role in the resolution of the paradox. We also comment on the relevance of these results to inflation and the CMBR.Comment: 13 page

Projection and ground state correlations made simple

We develop and test efficient approximations to estimate ground state correlations associated with low- and zero-energy modes. The scheme is an extension of the generator-coordinate-method (GCM) within Gaussian overlap approximation (GOA). We show that GOA fails in non-Cartesian topologies and present a topologically correct generalization of GOA (topGOA). An RPA-like correction is derived as the small amplitude limit of topGOA, called topRPA. Using exactly solvable models, the topGOA and topRPA schemes are compared with conventional approaches (GCM-GOA, RPA, Lipkin-Nogami projection) for rotational-vibrational motion and for particle number projection. The results shows that the new schemes perform very well in all regimes of coupling.Comment: RevTex, 12 pages, 7 eps figure

Temporal Vagueness, Coordination and Communication

How is it that people manage to communicate even when they implicitly differ on the meaning of the terms they use? Take an innocent-sounding expression such as tomorrow morning. What counts as morning? There is a surprising amount of variation across different people.

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 $\ell^2$-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