Adapted complex tubes on the symplectization of pseudo-Hermitian manifolds

Let $(M,\omega)$ be a pseudo-Hermitian space of real dimension $2n+1$, that is \RManBase is a \CR-manifold of dimension $2n+1$ and $\omega$ is a contact form on $M$ giving the Levi distribution $HT(M)\subset TM$. Let $M^\omega\subset T^*M$ be the canonical symplectization of $(M,\omega)$ and $M$ be identified with the zero section of $M^\omega$. Then $M^\omega$ is a manifold of real dimension $2(n+1)$ which admit a canonical foliation by surfaces parametrized by $\mathbb{C}\ni t+i\sigma\mapsto \phi_p(t+i\sigma)=\sigma\omega_{g_t(p)}$, where p\inM is arbitrary and $g_t$ is the flow generated by the Reeb vector field associated to the contact form $\omega$. Let $J$ be an (integrable) complex structure defined in a neighbourhood $U$ of $M$ in $M^\omega$. We say that the pair $(U,J)$ is an {adapted complex tube} on $M^\omega$ if all the parametrizations $\phi_p(t+i\sigma)$ defined above are holomorphic on $\phi_p^{-1}(U)$. In this paper we prove that if $(U,J)$ is an adapted complex tube on $M^\omega$, then the real function $E$ on $M^\omega\subset T^*M$ defined by the condition $\alpha=E(\alpha)\omega_{\pi(\alpha)}$, for each $\alpha\in M^\omega$, is a canonical equation for $M$ which satisfies the homogeneous Monge-Amp\`ere equation $(dd^c E)^{n+1}=0$. We also prove that if $M$ and $\omega$ are real analytic then the symplectization $M^\omega$ admits an unique maximal adapted complex tube.Comment: 6 page

A Douglas-Rachford splitting for semi-decentralized equilibrium seeking in generalized aggregative games

We address the generalized aggregative equilibrium seeking problem for noncooperative agents playing average aggregative games with affine coupling constraints. First, we use operator theory to characterize the generalized aggregative equilibria of the game as the zeros of a monotone set-valued operator. Then, we massage the Douglas-Rachford splitting to solve the monotone inclusion problem and derive a single layer, semi-decentralized algorithm whose global convergence is guaranteed under mild assumptions. The potential of the proposed Douglas-Rachford algorithm is shown on a simplified resource allocation game, where we observe faster convergence with respect to forward-backward algorithms.Comment: arXiv admin note: text overlap with arXiv:1803.1044

More Jobs? A Panel Analysis of the Lisbon Strategy

We assess the impact on employment growth of the Lisbon Strategy, examining long-run trends in total, female and old-age employment rates from 1994 to 2009. We find that the Strategy had some favourable (but weak) impact, especially for old-age workers. However, no improvement ensued from its mid-term reassessment.European Employment Strategy, difference-in-difference, employment policies

"Labor-market Performance in the OECD--An Assessment of Recent Evidence"

In this paper we assess the evolution of labor-market performance in the Organisation for Economic Co-operation and Development (OECD) over the last decade. We provide a survey of the literature dealing with labor-market performance in the OECD, finding that, while this literature tends to conclude that institutions are a key part of the story, the survey's results appear far less robust and uniform than is commonly believed. We then assess the robustness of the claims made in the most recent (2005) OECD follow-up study within a very similar cross-country setup, and highlight the impact of unobserved heterogeneity and outliers on the policy estimates. We find that in recent OECD cross-country data, changes in labor-market performance are consistently (and inversely) linked to its lagged level. Structural changes are also important: changes in the share of construction employees are very significant, even in the presence of various kinds of policy change indicators. As far as the latter are concerned, some consistent role seems to emerge only for active labor-market policies and (to a lesser extent) unemployment.

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

How to compute one-loop Feynman diagrams in lattice QCD with Wilson fermions

We describe an algebraic algorithm which allows to express every one-loop lattice integral with gluon or Wilson-fermion propagators in terms of a small number of basic constants which can be computed with arbitrary high precision. Although the presentation is restricted to four dimensions the technique can be generalized to every space dimension. We also give a method to express the lattice free propagator for Wilson fermions in coordinate space as a linear function of its values in eight points near the origin. This is an essential step in order to apply the recent methods of L\" uscher and Weisz to higher-loop integrals with fermions.Comment: Talk presented at LATTICE96(theoretical developments) Postscript file avalaible at http://www1.le.infn.it:8080/~caraccio/TCMS.htm

Support Recovery with Sparsely Sampled Free Random Matrices

Consider a Bernoulli-Gaussian complex $n$-vector whose components are $V_i = X_i B_i$, with X_i \sim \Cc\Nc(0,\Pc_x) and binary $B_i$ mutually independent and iid across $i$. This random $q$-sparse vector is multiplied by a square random matrix \Um, and a randomly chosen subset, of average size $n p$, $p \in [0,1]$, of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where \Um is typically %A16 the identity or a matrix with iid components, to allow \Um satisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verd\'u, as well as a number of information theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit of $n \to \infty$. We also extend the scope of the large deviation approach of Rangan, Fletcher and Goyal and characterize the performance of a class of estimators encompassing thresholded linear MMSE and $\ell_1$ relaxation