901 research outputs found
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.
Comparaison de l'efficacité des injections d'eau saline physiologique et d'anesthésiant local dans le traitement des corps plantaires réfractaires : une étude pilote
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
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