1,285 research outputs found

    Anderson's considerations on the flow of superfluid helium: some offshoots

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    Nearly five decades have elapsed since the seminal 1966 paper of P.W. Anderson on the flow of superfluid helium, 4^4He at that time. Some of his "Considerations" -- the role of the quantum phase as a dynamical variable, the interplay between the motion of quantised vortices and potential superflow, its incidence on dissipation in the superfluid and the appearance of critical velocities, the quest for the hydrodynamic analogues of the Josephson effects in helium -- and the way they have evolved over the past half-century are recounted below. But it is due to key advances on the experimental front that phase slippage could be harnessed in the laboratory, leading to a deeper understanding of superflow, vortex nucleation, the various intrinsic and extrinsic dissipation mechanisms in superfluids, macroscopic quantum effects and the superfluid analogue of both {\it ac} and {\it dc} Josephson effects -- pivotal concepts in superfluid physics -- have been performed. Some of the experiments that have shed light on the more intimate effect of quantum mechanics on the hydrodynamics of the dense heliums are surveyed, including the nucleation of quantised vortices both by Arrhenius processes and by macroscopic quantum tunnelling, the setting up of vortex mills, and superfluid interferometry.Comment: Review article - 59 pages - 34 figures - submitted to the Review of Modern Physic

    Learning and comparing functional connectomes across subjects

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    Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

    Sagnac effect in superfluid liquids

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    International audienceThe interpretation of the Sagnac effect is re-examined in the context of recent cold atomic beam and superfluid experiments. A widespread misconception concerning the understanding of this effect in a superfluid liquid is discussed

    Compressed Online Dictionary Learning for Fast fMRI Decomposition

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    We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest fMRI, we demonstrates that time-reduced dictionary learning produces result as reliable as non-reduced decompositions. We also show that this reduction significantly improves computational scalability

    Social-sparsity brain decoders: faster spatial sparsity

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    Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions. We give details of the model and the algorithm.Comment: in Pattern Recognition in NeuroImaging, Jun 2016, Trento, Italy. 201

    On spatial selectivity and prediction across conditions with fMRI

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    Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their ability to identify the common patterns between brain activation maps related to two functional tasks. We provide some preliminary quantification of these similarities, and show that selection transfer makes it possible to set a spatial scale yielding ROIs that are more specific to the context of interest than with transfer learning. In particular, selection transfer outlines well known regions such as the Visual Word Form Area when discriminating between different visual tasks.Comment: PRNI 2012 : 2nd International Workshop on Pattern Recognition in NeuroImaging, London : United Kingdom (2012

    Mapping cognitive ontologies to and from the brain

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    Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on the function implied by the activation of brain regions, it is necessary to combine a wide exploration of the various brain functions and some inversion of the statistical inference. Here we introduce a methodology for accumulating knowledge towards a bidirectional link between observed brain activity and the corresponding function. We rely on a large corpus of imaging studies and a predictive engine. Technically, the challenges are to find commonality between the studies without denaturing the richness of the corpus. The key elements that we contribute are labeling the tasks performed with a cognitive ontology, and modeling the long tail of rare paradigms in the corpus. To our knowledge, our approach is the first demonstration of predicting the cognitive content of completely new brain images. To that end, we propose a method that predicts the experimental paradigms across different studies.Comment: NIPS (Neural Information Processing Systems), United States (2013

    Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

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    Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g. using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to have similar structure. We characterize the uncertainty of differences with confidence intervals obtained using a parametric distribution on parameters of a sparse estimator. Sparse penalties enable statistical guarantees and interpretable models even in high-dimensional and low-sample settings. Characterizing the distributions of sparse models is inherently challenging as the penalties produce a biased estimator. Recent work invokes the sparsity assumptions to effectively remove the bias from a sparse estimator such as the lasso. These distributions can be used to give confidence intervals on edges in GGMs, and by extension their differences. However, in the case of comparing GGMs, these estimators do not make use of any assumed joint structure among the GGMs. Inspired by priors from brain functional connectivity we derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. This leads us to introduce the debiased multi-task fused lasso, whose distribution can be characterized in an efficient manner. We then show how the debiased lasso and multi-task fused lasso can be used to obtain confidence intervals on edge differences in GGMs. We validate the techniques proposed on a set of synthetic examples as well as neuro-imaging dataset created for the study of autism

    Trapping electrons in electrostatic traps over the surface of helium

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    We have observed trapping of electrons in an electrostatic trap formed over the surface of liquid helium-4. These electrons are detected by a Single Electron Transistor located at the centre of the trap. We can trap any desired number of electrons between 1 and 30\sim 30. By repeatedly (103104\sim 10^3-10^4 times) putting a single electron into the trap and lowering the electrostatic barrier of the trap, we can measure the effective temperature of the electron and the time of its thermalisation after heating up by incoherent radiation.Comment: Presented at QFS06 - Kyoto, to be published in J. Low Temp. Phys., 6 pages, 3 figure
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