Domestic Farm Policy For 2007: Forces for Change

Agricultural and Food Policy, Q18,

A Bose-Einstein condensate bouncing off a rough mirror

We present experimental results and theoretical analysis of the diffuse reflection of a Bose-Einstein condensate from a rough mirror. The mirror is produced by a blue-detuned evanescent wave supported by a dielectric substrate. The results are carefully analysed via a comparison with a numerical simulation. The scattering is clearly anisotropic, more pronounced in the direction of the evanescent wave surface propagation, as predicted theoretically

Graph reconstruction from the observation of diffused signals

Signal processing on graphs has received a lot of attention in the recent years. A lot of techniques have arised, inspired by classical signal processing ones, to allow studying signals on any kind of graph. A common aspect of these technique is that they require a graph correctly modeling the studied support to explain the signals that are observed on it. However, in many cases, such a graph is unavailable or has no real physical existence. An example of this latter case is a set of sensors randomly thrown in a field which obviously observe related information. To study such signals, there is no intuitive choice for a support graph. In this document, we address the problem of inferring a graph structure from the observation of signals, under the assumption that they were issued of the diffusion of initially i.i.d. signals. To validate our approach, we design an experimental protocol, in which we diffuse signals on a known graph. Then, we forget the graph, and show that we are able to retrieve it very precisely from the only knowledge of the diffused signals.Comment: Allerton 2015 : 53th Annual Allerton Conference on Communication, Control and Computing, 30 september - 02 october 2015, Allerton, United States, 201

Characterization and Inference of Graph Diffusion Processes from Observations of Stationary Signals

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures.Comment: Submitted to IEEE Transactions on Signal and Information Processing over Network

Schemes for loading a Bose-Einstein condensate into a two-dimensional dipole trap

We propose two loading mechanisms of a degenerate Bose gas into a surface trap. This trap relies on the dipole potential produced by two evanescent optical waves far detuned from the atomic resonance, yielding a strongly anisotropic trap with typical frequencies 40 Hz x 65 Hz x 30 kHz. We present numerical simulations based on the time-dependent Gross-Pitaevskii equation of the transfer process from a conventional magnetic trap into the surface trap. We show that, despite a large discrepancy between the oscillation frequencies along one direction in the initial and final traps, a loading time of a few tens of milliseconds would lead to an adiabatic transfer. Preliminary experimental results are presented

Enhancing scientific dissemination in neuroscience via preprint peer-review: "Peer Community In Circuit Neuroscience"

The dissemination of scientific results and new technologies in biomedical science is rapidly evolving from an exclusive and fee-oriented publishing system towards more open, free and independent strategies for sharing knowledge. In this context, preprint servers such as bioRxiv answer a very real scientific need by enabling the rapid, free and easy dissemination of findings, regardless of whether these are novel, replicated, or even showcasing negative results. Currently, thousands of manuscripts are being shared via bioRxiv each month, and neuroscience is the largest and fastest growing subject category. However, commenting on bioRxiv is declining and no structured scientific validation such as peer-review is currently available. The Peer Community In (PCI) platform addresses this unmet need by facilitating the rigorous evaluation and validation of preprints, and PCI Circuit Neuroscience (PCI C Neuro) aims to develop and extend this tool for the neuroscience community. Here we discuss PCI C Neuro's mission, how it works, and why it is an essential initiative in this new era of open science

Observation of a phononic Mollow triplet in a hybrid spin-nanomechanical system

Reminiscent of the bound character of a qubit's dynamics confined on the Bloch sphere, the observation of a Mollow triplet in the resonantly driven qubit fluorescence spectrum represents one of the founding signatures of Quantum Electrodynamics. Here we report on its observation in a hybrid spin-nanomechanical system, where a Nitro-gen Vacancy spin qubit is magnetically coupled to the vibrations of a Silicon Carbide nanowire. A resonant microwave field turns the originally parametric hybrid interac-tion into a resonant process, where acoustic phonons are now able to induce transitions between the dressed qubit states, leading to synchronized spin-oscillator dynamics. We further explore the vectorial character of the hybrid coupling to the bidimensional de-formations of the nanowire. The demonstrated microwave assisted synchronization of the spin-oscillator dynamics opens novel perspectives for the exploration of spin-dependent forces, the key-ingredient for quantum state transfer

Diffraction of a Bose-Einstein Condensate in the Time Domain

We have observed the diffraction of a Bose-Einstein condensate of rubidium atoms on a vibrating mirror potential. The matter wave packet bounces back at normal incidence on a blue-detuned evanescent light field after a 3.6 mm free fall. The mirror vibrates at a frequency of 500 kHz with an amplitude of 3.0 nm. The atomic carrier and sidebands are directly imaged during their ballistic expansion. The locations and the relative weights of the diffracted atomic wave packets are in very good agreement with the theoretical prediction of Carsten Henkel et al. [1].Comment: submitted to Phys. Rev.

Vers une caractérisation de la courbe d'incertitude pour des graphes portant des signaux

National audienceLe traitement de signal sur graphes est un domaine récent visant à généraliser les outils classiques du traitement de signal, afin d'analyser des signaux évoluant sur des domaines complexes. Ces domaines sont représentés par des graphes pour lesquels on peut calculer une matrice appelée Laplacien normalisé. Il a été montré que les valeurs propres de ce Laplacien correspondent aux fréquences du domaine de Fourier en traitement de signal classique. Ainsi, le domaine fréquentiel n'est pas identique pour tout graphe support des signaux. Une conséquence est qu'il n'y a pas de généralisation non triviale du principe d'incertitude d'Heisenberg, indiquant qu'un signal ne peut être à la fois localisé dans le domaine temporel et dans le domaine fréquentiel. Une manière de généraliser ce principe, introduite par Agaskar & Lu, consiste à déterminer une courbe servant de borne inférieure au compromis entre précision dans le domaine du graphe et précision dans le domaine spectral. L'objectif de ce papier est de proposer une caractérisation des signaux atteignant cette courbe, pour une classe de graphes plus générique que celle étudiée par Agaskar & Lu