Capillarity-Driven Flows at the Continuum Limit

We experimentally investigate the dynamics of capillary-driven flows at the nanoscale, using an original platform that combines nanoscale pores and microfluidic features. Our results show a coherent picture across multiple experiments including imbibition, poroelastic transient flows, and a drying-based method that we introduce. In particular, we exploit extreme drying stresses - up to 100 MPa of tension - to drive nanoflows and provide quantitative tests of continuum theories of fluid mechanics and thermodynamics (e.g. Kelvin-Laplace equation) across an unprecedented range. We isolate the breakdown of continuum as a negative slip length of molecular dimension.Comment: 5 pages; 4 figure

Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1

A Lunar Backup Record of Humanity

The risk of a catastrophic or existential disaster for our civilization is increasing this century. A significant motivation for a near-term space settlement is the opportunity to safeguard civilization in the event of a planetary-scale disaster. A catastrophic event could destroy the significant cultural, scientific, and technological progress on Earth. However, early space settlements can preserve records of human activity by maintaining a backup data storage system. The backup can also store information about the events leading up to the disaster. The system would improve the ability of early space settlers to recover our civilization after collapse. We show that advances in laser communications and data storage enable the development of a data storage system on the lunar surface with a sufficient uplink data rate and storage capacity to preserve valuable information about the achievements of our civilization and the chronology of the disaster.Comment: 12 pages, 2 figures; accepted for publication in the journal "Signals" (2022

Effect of Gravitational Lensing on Measurements of the Sunyaev-Zel'dovich Effect

The Sunyaev-Zel'dovich (SZ) effect of a cluster of galaxies is usually measured after background radio sources are removed from the cluster field. Gravitational lensing by the cluster potential leads to a systematic deficit in the residual intensity of unresolved sources behind the cluster core relative to a control field far from the cluster center. As a result, the measured decrement in the Rayleigh-Jeans temperature of the cosmic microwave background is overestimated. We calculate the associated systematic bias which is inevitably introduced into measurements of the Hubble constant using the SZ effect. For the cluster A2218, we find that observations at 15 GHz with a beam radius of 0'.4 and a source removal threshold of 100 microJy underestimate the Hubble constant by 6-10%. If the profile of the gas pressure declines more steeply with radius than that of the dark matter density, then the ratio of lensing to SZ decrements increases towards the outer part of the cluster.Comment: 11 pages, 3 figures, submitted to ApJ

Gravitational self-torque and spin precession in compact binaries

We calculate the effect of self-interaction on the "geodetic" spin precession of a compact body in a strong-field orbit around a black hole. Specifically, we consider the spin precession angle $\psi$ per radian of orbital revolution for a particle carrying mass $\mu$ and spin $s \ll (G/c) \mu^2$ in a circular orbit around a Schwarzschild black hole of mass $M \gg \mu$. We compute $\psi$ through $O(\mu/M)$ in perturbation theory, i.e, including the correction $\delta\psi$ (obtained numerically) due to the torque exerted by the conservative piece of the gravitational self-field. Comparison with a post-Newtonian (PN) expression for $\delta\psi$, derived here through 3PN order, shows good agreement but also reveals strong-field features which are not captured by the latter approximation. Our results can inform semi-analytical models of the strong-field dynamics in astrophysical binaries, important for ongoing and future gravitational-wave searches.Comment: 5 pages, 1 table, 1 figure. Minor changes to match published versio

Apprentissage d'atlas fonctionnel du cerveau modélisant la variabilité inter-individuelle

Recent studies have shown that resting-state spontaneous brain activity unveils intrinsic cerebral functioning and complete information brought by prototype task study. From these signals, we will set up a functional atlas of the brain, along with an across-subject variability model. The novelty of our approach lies in the integration of neuroscientific priors and inter-individual variability in a probabilistic description of the rest activity. These models will be applied to large datasets. This variability, ignored until now, may lead to learning of fuzzy atlases, thus limited in term of resolution. This program yields both numerical and algorithmic challenges because of the data volume but also because of the complexity of modelisation.De récentes études ont montré que l'activité spontanée du cerveau observée au repos permet d'étudier l'organisation fonctionnelle cérébrale en complément de l'information fournie par les protocoles de tâches. A partir de ces signaux, nous allons extraire un atlas fonctionnel du cerveau modélisant la variabilité inter-sujet. La nouveauté de notre approche réside dans l'intégration d'a-prioris neuroscientifiques et de la variabilité inter-sujet directement dans un modèles probabiliste de l'activité de repos. Ces modèles seront appliqués sur de larges jeux de données. Cette variabilité, ignorée jusqu'à présent, cont nous permettre d'extraire des atlas flous, donc limités en terme de résolution. Des challenges à la fois numériques et algorithmiques sont à relever de par la taille des jeux de données étudiés et la complexité de la modélisation considérée

Simulation of large photomultipliers for experiments in astroparticle physics

We have developed an accurate simulation model of the large 9 inch photomultiplier tubes (PMT) used in water-Cherenkov detectors of cosmic-ray induced extensive air-showers. This work was carried out as part of the development of the Offline simulation software for the Pierre Auger Observatory surface array, but our findings may be relevant also for other astrophysics experiments that employ similar large PMTs. The implementation is realistic in terms of geometrical dimensions, optical processes at various surfaces, thin-film treatment of the photocathode, and photon reflections on the inner structure of the PMT. With the quantum efficiency obtained for this advanced model we have calibrated a much simpler and a more rudimentary model of the PMT which is more practical for massive simulation productions. We show that the quantum efficiency declared by manufactures of the PMTs is usually determined under conditions substantially different from those relevant for the particular experiment and thus requires careful (re)interpretation when applied to the experimental data or when used in simulations. In principle, the effective quantum efficiency could vary depending on the optical characteristics of individual events.Comment: 8 pages, 11 figure