Showing papers by "École Normale Supérieure published in 2015"

Advanced Virgo: a second-generation interferometric gravitational wave detector

Abstract: Advanced Virgo is the project to upgrade the Virgo interferometric detector of gravitational waves, with the aim of increasing the number of observable galaxies (and thus the detection rate) by three orders of magnitude. The project is now in an advanced construction phase and the assembly and integration will be completed by the end of 2015. Advanced Virgo will be part of a network, alongside the two Advanced LIGO detectors in the US and GEO HF in Germany, with the goal of contributing to the early detection of gravitational waves and to opening a new window of observation on the universe. In this paper we describe the main features of the Advanced Virgo detector and outline the status of the construction.

Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems

Abstract: We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.

Structure and function of the global ocean microbiome

Abstract: Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with >40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing >35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that >73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.

New evolutionary models for pre-main sequence and main sequence low-mass stars down to the hydrogen-burning limit

Abstract: We present new models for low-mass stars down to the hydrogen-burning limit that consistently couple atmosphere and interior structures, thereby superseding the widely used BCAH98 models. The new models include updated molecular linelists and solar abundances, as well as atmospheric convection parameters calibrated on 2D/3D radiative hydrodynamics simulations. Comparison of these models with observations in various colour-magnitude diagrams for various ages shows significant improvement over previous generations of models. The new models can solve flaws that are present in the previous ones, such as the prediction of optical colours that are too blue compared to M dwarf observations. They can also reproduce the four components of the young quadruple system LkCa 3 in a colour‐magnitude diagram with one single isochrone, in contrast to any presently existing model. In this paper we also highlight the need for consistency when comparing models and observations, with the necessity of using evolutionary models and colours based on the same atmospheric structures.

Eukaryotic plankton diversity in the sunlit ocean

Abstract: Marine plankton support global biological and geochemical processes. Surveys of their biodiversity have hitherto been geographically restricted and have not accounted for the full range of plankton size. We assessed eukaryotic diversity from 334 size-fractionated photic-zone plankton communities collected across tropical and temperate oceans during the circumglobal Tara Oceans expedition. We analyzed 18S ribosomal DNA sequences across the intermediate plankton-size spectrum from the smallest unicellular eukaryotes (protists, >0.8 micrometers) to small animals of a few millimeters. Eukaryotic ribosomal diversity saturated at ~150,000 operational taxonomic units, about one-third of which could not be assigned to known eukaryotic groups. Diversity emerged at all taxonomic levels, both within the groups comprising the ~11,200 cataloged morphospecies of eukaryotic plankton and among twice as many other deep-branching lineages of unappreciated importance in plankton ecology studies. Most eukaryotic plankton biodiversity belonged to heterotrophic protistan groups, particularly those known to be parasites or symbiotic hosts.

Discriminative Learning of Deep Convolutional Feature Point Descriptors

Abstract: Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non-)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and testing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available.

Human cerebral organoids recapitulate gene expression programs of fetal neocortex development

Abstract: Cerebral organoids—3D cultures of human cerebral tissue derived from pluripotent stem cells—have emerged as models of human cortical development. However, the extent to which in vitro organoid systems recapitulate neural progenitor cell proliferation and neuronal differentiation programs observed in vivo remains unclear. Here we use single-cell RNA sequencing (scRNA-seq) to dissect and compare cell composition and progenitor-to-neuron lineage relationships in human cerebral organoids and fetal neocortex. Covariation network analysis using the fetal neocortex data reveals known and previously unidentified interactions among genes central to neural progenitor proliferation and neuronal differentiation. In the organoid, we detect diverse progenitors and differentiated cell types of neuronal and mesenchymal lineages and identify cells that derived from regions resembling the fetal neocortex. We find that these organoid cortical cells use gene expression programs remarkably similar to those of the fetal tissue to organize into cerebral cortex-like regions. Our comparison of in vivo and in vitro cortical single-cell transcriptomes illuminates the genetic features underlying human cortical development that can be studied in organoid cultures.

Determinants of community structure in the global plankton interactome

Abstract: Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.

Learning a convolutional neural network for non-uniform motion blur removal

Abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

P-CNN: Pose-Based CNN Features for Action Recognition

Abstract: This work targets human action recognition in video. While recent methods typically represent actions by statistics of local video features, here we argue for the importance of a representation derived from human pose. To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition. The descriptor aggregates motion and appearance information along tracks of human body parts. We investigate different schemes of temporal aggregation and experiment with P-CNN features obtained both for automatically estimated and manually annotated human poses. We evaluate our method on the recent and challenging JHMDB and MPII Cooking datasets. For both datasets our method shows consistent improvement over the state of the art.

Patterns and ecological drivers of ocean viral communities

Abstract: Viruses influence ecosystems by modulating microbial population size, diversity, metabolic outputs, and gene flow. Here, we use quantitative double-stranded DNA (dsDNA) viral-fraction metagenomes (viromes) and whole viral community morphological data sets from 43 Tara Oceans expedition samples to assess viral community patterns and structure in the upper ocean. Protein cluster cataloging defined pelagic upper-ocean viral community pan and core gene sets and suggested that this sequence space is well-sampled. Analyses of viral protein clusters, populations, and morphology revealed biogeographic patterns whereby viral communities were passively transported on oceanic currents and locally structured by environmental conditions that affect host community structure. Together, these investigations establish a global ocean dsDNA viromic data set with analyses supporting the seed-bank hypothesis to explain how oceanic viral communities maintain high local diversity.

Phase recovery, MaxCut and complex semidefinite programming

Abstract: Phase retrieval seeks to recover a signal $$x \in {\mathbb {C}}^p$$ x ? C p from the amplitude $$|A x|$$ | A x | of linear measurements $$Ax \in {\mathbb {C}}^n$$ A x ? C n . We cast the phase retrieval problem as a non-convex quadratic program over a complex phase vector and formulate a tractable relaxation (called PhaseCut) similar to the classical MaxCut semidefinite program. We solve this problem using a provably convergent block coordinate descent algorithm whose structure is similar to that of the original greedy algorithm in Gerchberg and Saxton (Optik 35:237---246, 1972), where each iteration is a matrix vector product. Numerical results show the performance of this approach over three different phase retrieval problems, in comparison with greedy phase retrieval algorithms and matrix completion formulations.

24/7 place recognition by view synthesis

Abstract: We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.

High-performance transistors for bioelectronics through tuning of channel thickness

Abstract: Despite recent interest in organic electrochemical transistors (OECTs), sparked by their straightforward fabrication and high performance, the fundamental mechanism behind their operation remains largely unexplored. OECTs use an electrolyte in direct contact with a polymer channel as part of their device structure. Hence, they offer facile integration with biological milieux and are currently used as amplifying transducers for bioelectronics. Ion exchange between electrolyte and channel is believed to take place in OECTs, although the extent of this process and its impact on device characteristics are still unknown. We show that the uptake of ions from an electrolyte into a film of poly(3,4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) leads to a purely volumetric capacitance of 39 F/cm 3 . This results in a dependence of the transconductance on channel thickness, a new degree of freedom that we exploit to demonstrate high-quality recordings of human brain rhythms. Our results bring to the forefront a transistor class in which performance can be tuned independently of device footprint and provide guidelines for the design of materials that will lead to state-of-the-art transistor performance.

Gaps, rings, and non-axisymmetric structures in protoplanetary disks - From simulations to ALMA observations

Abstract: Aims. Recent observations by the Atacama Large Millimeter/submillimeter Array (ALMA) of disks around young stars revealed distinct asymmetries in the dust continuum emission. In this work we wish to study axisymmetric and non-axisymmetric structures that are generated by the magneto-rotational instability in the outer regions of protoplanetary disks. We combine the results of state-of-the-art numerical simulations with post-processing radiative transfer (RT) to generate synthetic maps and predictions for ALMA.Methods. We performed non-ideal global 3D magneto-hydrodynamic (MHD) stratified simulations of the dead-zone outer edge using the FARGO MHD code PLUTO. The stellar and disk parameters were taken from a parameterized disk model applied for fitting high-angular resolution multi-wavelength observations of various circumstellar disks. We considered a stellar mass of M∗ = 0.5 M⊙ and a total disk mass of about 0.085 M∗. The 2D initial temperature and density profiles were calculated consistently from a given surface density profile and Monte Carlo radiative transfer. The 2D Ohmic resistivity profile was calculated using a dust chemistry model. We considered two values for the dust-to-gas mass ratio, 10-2 and 10-4, which resulted in two different levels of magnetic coupling. The initial magnetic field was a vertical net flux field. The radiative transfer simulations were performed with the Monte Carlo-based 3D continuum RT code MC3D. The resulting dust reemission provided the basis for the simulation of observations with ALMA.Results. All models quickly turned into a turbulent state. The fiducial model with a dust-to-gas mass ratio of 10-2 developed a large gap followed by a jump in surface density located at the dead-zone outer edge. The jump in density and pressure was strong enough to stop the radial drift of particles at this location. In addition, we observed the generation of vortices by the Rossby wave instability at the jump location close to 60 AU. The vortices were steadily generated and destroyed at a cycle of 40 local orbits. The RT results and simulated ALMA observations predict that it is feasible to observe these large-scale structures that appear in magnetized disks without planets. Neither the turbulent fluctuations in the disk nor specific times of the model can be distinguished on the basis of high-angular resolution submillimeter observations alone. The same applies to the distinction between gaps at the dead-zone edges and planetary gaps, to the distinction between turbulent and simple unperturbed disks, and to the asymmetry created by the vortex.

Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors

Abstract: UNLABELLED Depressive short-term synaptic plasticity functions are implemented with a simple polymer poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) ( PEDOT PSS) organic electrochemical transistor device. These functions are a first step toward the realization of organic-based neuroinspired platforms with spatiotemporal information processing capabilities.

NetVLAD: CNN architecture for weakly supervised place recognition

Abstract: We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following three principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we develop a training procedure, based on a new weakly supervised ranking loss, to learn parameters of the architecture in an end-to-end manner from images depicting the same places over time downloaded from Google Street View Time Machine. Finally, we show that the proposed architecture significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks, and improves over current state-of-the-art compact image representations on standard image retrieval benchmarks.

SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data

Abstract: Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. Here we present a segmentation framework based on Voronoi tessellation constructed from the coordinates of localized molecules, implemented in freely available and open-source SR-Tesseler software. This method allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. We validated our method on simulated data and on various biological experimental data of proteins labeled with genetically encoded fluorescent proteins or organic fluorophores. In addition to providing insight into complex protein organization, this polygon-based method should serve as a reference for the development of new types of quantifications, as well as for the optimization of existing ones.

Ranking in interconnected multilayer networks reveals versatile nodes

Abstract: A challenging problem is to identify the most central agents in interconnected multilayer networks. Here, De Domenico et al. present a mathematical framework to calculate centrality in such networks—versatility—and rank nodes accordingly.

Islands as model systems in ecology and evolution: prospects fifty years after MacArthur‐Wilson

Abstract: The study of islands as model systems has played an important role in the development of evolutionary and ecological theory. The 50th anniversary of MacArthur and Wilson's (December 1963) article, ‘An equilibrium theory of insular zoogeography’, was a recent milestone for this theme. Since 1963, island systems have provided new insights into the formation of ecological communities. Here, building on such developments, we highlight prospects for research on islands to improve our understanding of the ecology and evolution of communities in general. Throughout, we emphasise how attributes of islands combine to provide unusual research opportunities, the implications of which stretch far beyond islands. Molecular tools and increasing data acquisition now permit re‐assessment of some fundamental issues that interested MacArthur and Wilson. These include the formation of ecological networks, species abundance distributions, and the contribution of evolution to community assembly. We also extend our prospects to other fields of ecology and evolution – understanding ecosystem functioning, speciation and diversification – frequently employing assets of oceanic islands in inferring the geographic area within which evolution has occurred, and potential barriers to gene flow. Although island‐based theory is continually being enriched, incorporating non‐equilibrium dynamics is identified as a major challenge for the future.

An estimate of the number of tropical tree species

Abstract: The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between similar to 40,000 and similar to 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of similar to 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of similar to 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

Open science resources for the discovery and analysis of Tara Oceans data.

Abstract: The Tara Oceans expedition (2009–2013) sampled contrasting ecosystems of the world oceans, collecting environmental data and plankton, from viruses to metazoans, for later analysis using modern sequencing and state-of-the-art imaging technologies. It surveyed 210 ecosystems in 20 biogeographic provinces, collecting over 35,000 samples of seawater and plankton. The interpretation of such an extensive collection of samples in their ecological context requires means to explore, assess and access raw and validated data sets. To address this challenge, the Tara Oceans Consortium offers open science resources, including the use of open access archives for nucleotides (ENA) and for environmental, biogeochemical, taxonomic and morphological data (PANGAEA), and the development of on line discovery tools and collaborative annotation tools for sequences and images. Here, we present an overview of Tara Oceans Data, and we provide detailed registries (data sets) of all campaigns (from port-to-port), stations and sampling events.

mvmorph: an r package for fitting multivariate evolutionary models to morphometric data

Abstract: Summary We present mvmorph, a package of multivariate phylogenetic comparative methods for the r statistical environment. mvmorph is freely available on the cran package repository (http://cran.r-project.org/web/packages/mvMORPH/). mvmorph allows fitting a range of multivariate evolutionary models under a maximum-likelihood criterion. Initially developed in the context of phylogenetic analysis of multiple morphometric traits, its use can be extended to any biological data set with one or multiple covarying continuous traits. All the fitting models include the possibility to use simmap-like mapping, which may be useful for fitting changes along lineages at a given point in time. All models provide diagnostic metrics for convergence and reliability of estimates, as well as the possibility to include trait measurement errors in model estimates. New features provided by the mvmorph package include the possibility of fitting models with changes in the mode of evolution along the phylogeny, which will be particularly meaningful in comparative analyses that include extinct taxa, for example when testing changes in evolutionary mode associated with global biotic/abiotic events. We briefly describe the models already included in mvmorph and provide some demonstration of the use of the package with two simulated worked examples.

The Impact of Culture on Creativity: How Cultural Tightness and Cultural Distance Affect Global Innovation Crowdsourcing Work

Abstract: This paper advances a new theoretical model to understand the effect of culture on creativity in a global context. We theorize that creativity engagement and success depend on the cultural tightness—the extent to which a country is characterized by strong social norms and low tolerance for deviant behaviors—of both an innovator’s country and the audience’s country, as well as the cultural distance between these two countries. Using field data from a global online crowdsourcing platform that organizes creative contests for consumer-product brands, supplemented by interviews with marketing experts, we found that individuals from tight cultures are less likely than counterparts from loose cultures to engage in and succeed at foreign creative tasks; this effect is intensified as the cultural distance between the innovator’s and the audience’s country increases. Additionally, tight cultures are less receptive to foreign creative ideas. But we also found that in certain circumstances—when members of a tight cul...

RSAT 2015: Regulatory Sequence Analysis Tools

Abstract: RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/.

On the global linear convergence of Frank-Wolfe optimization variants

Abstract: The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle the structured constraints appearing in machine learning applications. However, its convergence rate is known to be slow (sublinear) when the solution lies at the boundary. A simple less-known fix is to add the possibility to take 'away steps' during optimization, an operation that importantly does not require a feasibility oracle. In this paper, we highlight and clarify several variants of the Frank-Wolfe optimization algorithm that have been successfully applied in practice: away-steps FW, pairwise FW, fully-corrective FW and Wolfe's minimum norm point algorithm, and prove for the first time that they all enjoy global linear convergence, under a weaker condition than strong convexity of the objective. The constant in the convergence rate has an elegant interpretation as the product of the (classical) condition number of the function with a novel geometric quantity that plays the role of a 'condition number' of the constraint set. We provide pointers to where these algorithms have made a difference in practice, in particular with the flow polytope, the marginal polytope and the base polytope for submodular optimization.