dataesr/scientific-paragraphs-categorization huggingface dataset:
E. Following up candidates with S5R3 data For the next step in our post-processing pipeline, recall that our underlying signal model of Eq. (1) assumes that the signal is long-lived; thus its amplitude is constant in time and its intrinsic frequency evolves smoothly according to Eq. (  5 ) with a constant spindown. This is an idealized model: although pulsars are the most stable clocks in the Universe, neutron stars are nonetheless known to glitch, to be perturbed by external agents, and in some cases to be affected by significant timing noise. Furthermore, for sufficiently long observation times, the spin-down evolution model that we use here, including only the first spindown order, may not be adequate to describe the actual signal model (see also the discussion in Section III D). However, since the data set used in the S5R3 run ends just about a week before the S5R5 data set, it is reasonable to assume that any putative signals should be present in both data sets.
Moreover, the average noise floor level in the detectors turns out to be approximately stable between S5R3 and S5R5. Thus, we might expect that a detectable signal should be visible in both searches. The next follow-up step for each candidate then consists of a hierarchical search carried out on the same WU as done for S5R5 (i. e. over the same parameter space), but using the S5R3 data set. The closest 5 candidate to the original one from such a search was identified and the value of its detection statistic was compared with what one would expect if the S5R5 candidate were due to a signal. In particular, the expected number counts in S5R3 and S5R5 should be related according to E[N S5R3 c ] = N S5R5 c × 84/121 , ( 42 ) where 84 is the number of data segments used in S5R3, as we shall see in Appendix B.
F. Fully-coherent follow-up Excluding the hardware-injected simulated signals, the post-processing up to this point has left us with 8 surviving candidates. These have been significant enough to pass our thresholds and have not been clearly identified as instrumental artifacts, or eliminated by inconsistency between the H and L detectors, or by the follow-up with the S5R3 data set. We therefore need to consider other more sensitive methods. If these candidates are real signals, then their SNRs and significance should increase if the parameter space grids are made finer, or as the 1. Re-run the Hough search around a given candidate, but with a finer grid to reduce the mismatch with a putative signal. The search region includes 5 frequency bins on either side around the candidate and 16 neighboring coarse sky-grid points.
This search spans the entire duration of the S5R5 data set and has been carried out in a parameter space box defined by using the diagonal elements of the inverse Fisher matrix in each dimension around the starting point. These elements are described by Eq. (  16 ) in  [48]  and have been computed from the inverse of the semi-coherent parameter space metric computed at the candidate point, re-scaled by the measured SNR at the same point; for more details we refer the reader to  [48]. In both the semi-coherent and fully-coherent NOMAD searches, we ran multiple instances of the algorithm iterating over the mesh coarsening exponent using both deterministic  [46]  and stochastic  [49]  methods for the choice of search directions. Based on Monte-Carlo studies, the false-dismissal probability of the follow-up procedure is found to be less than 10%.
9  correspond to the simulated pulsars 2, 3 and 5, i. e. the hardware injections recovered in the S5R5 search (discussed in Sec. VI). The numerical data for the plot in Fig. 9  can be obtained separately  6. A conservative estimate of the overall uncertainty on the h 90% 0 values shown in Fig. 9  is 15%, FIG. 9. Upper limits for the S5R5 Einstein@Home search (red dots) as well as the previous Einstein@Home search, called S5R1, which used early S5 data (blue dots)  [24]. The three stars correspond to hardware-injected simulated pulsars which were recovered in the S5R5 search. The curves represent the source strain amplitude h0 at which 90% of simulated signals would be detected. The vertical bars represent 156 half-Hz frequency bands contaminated by instrumental disturbances for which no upper limits are provided.
The upper limits for the 0. 5 Hz-wide bands starting at 69. 5 Hz, 139. 5 Hz, 335. 5 Hz and 648 Hz are fairly high due to significant partial contamination in these bands by lines listed in Tables VI and VII. Note that the broadness of the red curve is due to the 5% steps used to vary the injected population h0 values in the Monte-Carlo signal software-injections until a confidence value close to 90% is reached. In addition, less than 1/4 of the spectral range shown was excluded in many narrow bands because of known instrumental artifacts, as described in Sec. IV B. The cyan curve shows the predicted h 90% 0 upper limits according to Eq. (43). having added to the 1 σ statistical upper limit estimation procedure uncertainty the 10% amplitude calibration uncertainties for the data used in this Einstein@Home run  [50].
As a minor complication, the hardware injections were not active all the time. In the S5R5 data set, their duty cycle was ∼ 63% and ∼ 60% in L and H, respectively. The hardware injections were active in 76 of the 121 S5R5 data segments, and they were completely absent in the remaining 45 segments. Their expected value of the Hough number count is thus: E[n HI c ] = j η j w j + p l w l , (46) where the superscript "HI" refers to Hardware Injection, j runs over the number of data segments where the hardware injections were active and l runs over the remaining segments, and the w j are the Hough weights given by Eq. (  18 ); η j and p are the probabilities that the estimated value of 2F (for a given data segment) crosses the threshold 2F th = 5. 2 in the presence and absence of a signal, respectively (expressions for η j and p can be found in  [12, 19] ).
5 Hz-wide frequency bands as a function of the frequency. for 161 785 candidate events that survived (top plot) and 5 994 that were excluded (bottom plot). • • • FIG. 13. Values of 2F averaged over 84 data segments for the single-detector case, 2FH (green dots), 2FL (blue dots), the multi-detector case ( 2FHL , red dots) against those for the combined multi-detector statistic ( 2FHL ). The top (bottom) plot shows such values for 161 785 (5 994) surviving (vetoed) candidates such that 2FH and (or) 2FL is less (greater) than 2FHL. Candidates whose 2F HL -value is greater than 6. 5, i. e. 1 465 out of 161 785, are followed-up by performing a hierarchical search using the S5R5 data set. This run, whose details are described in Section IV, consists of ∼ 46% more data than was used for S5R3 and is thus more sensitive than S5R5.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Einstein@Home volunteers , by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , The  National Aeronautics and Space Administration , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
When checking for convergence of the MCMC algorithm, we also found that least square estimates of regression coefficients were close to the point estimates computed by the Gibbs sampler method. Theoretical considerations. Incorporating population genetic structure via estimates of admixture proportions or principal component analysis is common in genome-wide association studies  [33, 34] ). [18] developed an alternative approach to identify loci underlying local adaptation in the computer program Bayenv. To explain the difference between our approach and Bayenv, suppose that we start by computing PCA scores from the matrix Y for all individuals, and denote by Ũi the PCA scores for individual i. The product matrix Ũ Ũ T is thus equal to the empirical covariance matrix Ũ Ũ T = Y Y T /n. (4) Now using the scores as covariates in a Bayesian regression model, we obtain G = µ + β T X + Ũ T V +.
Spatial coalescent simulations. In another series of experiments, we compared the LFMM estimation algorithm against two methods that do not correct for population stratification, and against two methods that use the empirical covariance matrix as a correction. The first set of methods include a linear model and generalized linear model (LM and GLM,  [3] ), and the second set of methods include a PC regression model (PCRM) and the mixed model Bayenv  [18]. To enable comparisons, we simulated genotypic data from spatial coalescent models with the computer program ms  [35]. Ten data Next we evaluated the ability of LFMMs to detect loci exhibiting correlations with particular environmental gradients, and compared tests based on an LFMM with methods based on linear models and the computer program Bayenv  [18].
An environmental variable, x, was defined for each population as the geographic identifier of the population in the linear stepping-stone model. We created an environmental gradient using a logistic function, s(x), of x as follows s(x) = 1 1 + e θ(x-20) , θ > 0. ( 9 ) For each of the 10 previously generated neutral stepping-stone simulations, we simulated binary alleles for each deme x at 50 loci with frequency s(x), with the slope of the gradient θ = 0. 2. We then obtained 10 data sets with L = 1050 loci including 50 loci correlated with the environmental gradient, s(x). Across these datasets, smartPCA estimates between 5 and 7 significant eigenvectors (P < 0. 01 in the Tracy-Widom test). For the simulated data sets, we evaluated the percentage of false negative (FN) and of false positive (FP) tests based on LM, GLM, PCRM and LFMM, for two values of the type I error (Table  2 ).
Loblolly Pine The Loblolly Pine is distributed throughout the southeastern USA, ranging from the arid Great Plains to the humid Eastern Temperate Forest ecoregion. The data consisted of 1,730 SNPs selected in expressed sequence tags (EST) for 682 individuals  [21]. Following  [21] , we considered 5 environmental variables representing the five first components of a PCA for 60 climatic variables. The first component (PC1) was mainly described by latitude, longitude, temperature and winter aridity. PC2 was described by longitude, spring-fall aridity and precipitation  [21]. For each of the 5 environmental variables, we applied the LFMM algorithm using 100 sweeps for burnin and 400 additional sweeps to compute |z|-scores for all loci. Based on a prior analysis of the genotypic data with the program SmartPCA, we used K = 10 latent factors.
Human data analysis We applied an LFMM analysis to a worldwide sample of genomic DNA from 1,043 individuals in 52 populations, referred to as the Human Genome Diversity Project -Centre Etude Polymorphism Humain (HGDP-CEPH) Human Genome Diversity Cell Line Panel (hagsc. org/hgdp/). The genotypes were generated on Illumina 650K arrays  [36] , and the data were filtered to remove low quality SNPs included in the original files. We extracted climatic data for each of the 52 population samples using the WorldClim data set at 30 arcsecond (1km 2 ) resolution  [37]. These data include 11 bioclimatic variables interpolated from global weather station data collected during a 50 year period. The climatic variables included annual mean temperature, mean diurnal range, maximum temperature of warmest month, minimum temperature of coldest month, annual precipitation, etc (Table  S2 ).
Compared to methods based on summary statistics, tests based on environmental association have increased power to detect selection from standing genetic variation and soft sweeps in a species genome  [6, 17]. However, simple implementation of these tests, for example simple linear or logistic regression models, can be misleading in the presence of IBD patterns  [23]. Our simulation results provide clear evidence that tests based on LFMMs significantly reduce the rates of false positive associations in the presence of IBD. While both the mixed model approach of the computer program Bayenv and the LFMM approach includes a covariance structure in a regression model, there are important differences between the two approaches. A first improvement is that LFMMs estimate latent factors and regression coefficients simultaneously, while Bayenv first estimates a covariance matrix, and then uses it when estimating (random) environmental effects.
Quantification of Synaptic Disconnection Synaptic disconnection was assessed by counting v-GLUT1 clusters affixed to MAP-2 positive striatal dendrites. All images were obtained using the same acquisition parameters. The images were similarly processed with ImageJ software before being used for quantification: the brightness/contrast of all control images was optimized manually to eliminate the background and to maximize the signal. The means of the minimum and maximum intensities were then calculated in the control condition and these settings were applied to all images. The images of the three stainings v-GLUT1/a-Tubulin/MAP-2 were merged and the resulting image was used to define the zone where striatal dendrites were sufficiently innervated by cortical fibers. A v-GLUT1/MAP-2 merge was then used for quantification using SynD software (Fig.
Pharmacological Evaluation of Synapto-protective Drugs in 3C-microfluidic Chip Using the above data it was possible to identify a time-window within which pre-synapses had almost all degenerated after cortical axotomy while axons or postsynaptic elements remained intact. This time-window can be used to screen for drugs expected to have the capacity to slow down or prevent synaptic loss. With the help of our microfluidic-based synaptic stability/ degeneration assay, we first quantified synaptic degeneration using SynD software (see  [23]  and Fig. S1 ), which allows automatic segmentation of striatal dendritic trees and co-localization of v-GLUT1 positive synapses docked to the dendrites. Automatic counting was used to evaluate the number of v-GLUT1 clusters on striatal dendrites in control conditions (Fig.
If this could speed up and improve the reliability of the validation and characterization stages when developing therapeutic drugs, this would already be a great step forward. Images were all obtained with the same acquisition parameters and were similarly processed with ImageJ software before being used for quantification. (a) v-GLUT1/MAP-2 merge used for quantification using SynD software. (b) Automatic soma detected using thresholding followed by morphological opening and closing. (c) Dendrites detected using steerable filters. A cost function evaluates each pixel around the current neurite mask, and those with a cost lower than a specified threshold are added to the mask. The cost function takes into account how neurite-like the neighborhood around the candidate pixel is (i. e. ideally a bright line segment surrounded by dark regions on either side), and if the candidate pixel is in the direction of the already detected parts of the neurite.
In the irst part of this article, three examples of thematic research studies which were recently led by the PHEBUS group, and applied to French (Bordeaux, Le Havre, Dunkerque) and Brazilian cities (Rio de Janeiro), are presented and brie ly developed. In the second part, three kinds of operational tools that contribute to better integrate and link these different topics and local practice in a systems approach are proposed: i) a collaborative platform to share knowledge and best practice at the international level; ii) a GIS platform for the integration, crossing and sharing of local and regional data; and iii) a method for multi-criteria and multi-objectives analysis of port cities through a set of adapted indicators for sustainability and competitiveness. In the conclusion, a predict tentative to how this core group and these irst projects can evolve towards an international network and research program for the port city sustainable development, was presented.
Adaption strategies studied by the THESEUS project (part of the 7 th European framework program for research in environment) also emphasize on improving prevention by the reinforcement of structural protections like dikes. Nevertheless, this kind of measure won't be suf icient to avoid new disasters for which crisis preparation and management is the ultimate solution. In order to better respond to French and European decision makers tackling coastal risks, a support method for preparing evacuation plans in case of a submersion was developed  (MOREL; HISSEL; JIA, 2011). This relationships, in a double perspective of sustainability and economical competitiveness. It is actually a major challenge for the port city of the future to ind a positive synergy between these two objectives, even if they can seem contradictory at a irst glance  (RODRIGUES-MALTA, 2003).
Indicators for the sustainable development and competitiveness of port cities The collaborative platform irst aims at providing partakers of the sustainable port city a set of services to facilitate the sharing of information and the exchange of best practice. Further development should propose a model and a set of indicators to help evaluate the current situation and evolution scenarios of the city, the port and their users and developers, generating decision support data, charting for domain related scenarios, and inally self-adaption during its lifetime. In the case of ports and cities, a GIS database could store local data as facilities, infrastructures, productive activities and population data suitable for building social/economic development indicators, as well as topographic, hydrological and climatic parameters (temperatures, precipitation, humidity etc.
) useful for environmental purposes. Both sets could be correlated under spatial and statistics analysis tools, in order to obtain more appropriated indicators. The great issue for sustainable indicators is how to apply them on innovative methods, as planning smart grid solutions and de ining integrated public policies. A GIS database application for sustainable purposes can be illustrated by searching solar energy supply indicators relating demography (census statistics data), climate (environmental data), topography (geographic data) and construction pro ile (municipality data). Although this data could be expressed in various levels, accuracy and scales, the use of geoprocessing techniques can help solving this issue and present a result, discretized under any desired territorial unit (census sectors or any other assemblage).
A multi-source and multi-use GIS database for sustainable planning In order to apply indicators to the sustainable city and port, a collaborative platform needs to include not only general knowledge and documents, but also more detailed data on the local context and projects (LIMA; MOREL; MORAES, 2012). These data can be general statistics on a global site or georeferenced data. A GIS database is particularly interesting for establishing spatial and topologic relations between sustainable indicators and their basic parameters: environmental, social and economic (COSENZA; LIMA; NEVES, 2010). This relationships can be process by GIS functions to analyze perceived objects (people, buildings, squares, all sort of administrative boundaries) belonging to multiples domains  (demography, infrastructure, health, culture, development, environment, history, etc.
The irst results obtained in these ields and within this team in terms of methods (optimizing energy in the port of Dunkerque, impacts of climate change in Le Havre), data and tools (collaborative platform for the challenges of the port of the future, multisource GIS for the city of Rio) must now be proposed and developed in more ambitious and funded research programs at the national (ANR in France) or European and international levels (next European program Horizon 2020), with the collaboration of scientists in different domains and from different countries in Europe and South-America.
Mass reconstruction Many algorithms have been developed to reconstruct the mass, concentration and position of massive (>10 14 M ) haloes from observations of weak (and strong) gravitational lensing  (Bradač et al. 2005; Cacciato et al. 2006; Diego et al. 2007; Merten et al. 2009; Zitrin et al. 2012). However, testing of these has generally focused on the mass and concentration parameters, positional accuracy has not yet been pushed to the low ∼10 13 M mass regime. To determine the viability of bulleticity measurements, weak lensing reconstructions must be tested in scenarios that reflect the environments in which it will be used. The main requirement for bulleticity is an accurate estimate of the subhalo and main halo positions with minimal bias. LENSTOOL  (Jullo et al. 2007 ) is open source software that calculates analytical models of the lensing signal for specific cluster density profiles and then compares them against the observed data.
This displacement can then be calibrated with simulations of interacting DM to estimate its crosssection. With a sample of clusters already available in the HST archive, averaging the measured offset of many pieces of substructure will provide sufficiently accurate DM centroiding to detect an offset. Future space missions (e. g. Euclid; 2 Laureijs et al. 2011) will increase the sample of available clusters by many orders of magnitude. However, fully exploiting such data would require improved mass reconstruction techniques Furthermore, in the quasi-weak lensing regime considered here, flexion, the third derivative of the lensing potential, becomes im-2 http://www. euclid-ec. org portant. As the gradient of the tidal field it is more sensitive to small-scale structure, similar to that investigated here  (Bacon et al.
(i) The public LENSTOOL software can measure the position of individual 1. 5 × 10 13 M peaks with ∼0. 3 arcsec systematic bias, as long as they are at least ∼30 arcsec from the cluster centre. Any subhaloes detected above this threshold will be real and only biased to ∼0. 3 arcsec. (ii) The maximum likelihood value of the two dimensional position likelihood surface is found to be the best point source estimator, being negligibly biased in the noise-free case compared to the mean value estimator. (iii) The dominant source of bias is caused by a preferred direction to the shape of galaxies introduced by a biased shape measurement algorithm. (iv) Since typical clusters each have on average one infalling groups of galaxies containing ∼10 per cent of their mass, between 20 and 50 clusters are needed to detect an offset between dark and baryonic matter.