dataesr/scientific-paragraphs-categorization huggingface dataset:
2020 ). We will also develop novel functionalities for the R shiny application and package, for example heatmaps and phylogenetic analyses. This will constitute a very rich resource that will help to compare eukaryotic communities across habitats. metaPR2 database -p. 17 Yau, S. , Lopes dos Santos, A. , Eikrem, W. , Gérikas Ribeiro, C. , Gourvil, P. , Balzano, S. , Escande, M. L. ,  Moreau, H. , & Vaulot, D. (2020). Mantoniella beaufortii and Mantoniella baffinensis sp. nov. (Mamiellales, Mamiellophyceae), two new green algal species from the high arctic1. Journal of Phycology, 56, 37-51. Table  S1 : Ecological function of taxa according to Table  S2  of  Sommeria-Klein et al. (2021). Taxa present in the PR2 database for which ecological function was not present in Table  S2  were assigned an ecological function based on the literature.
Acknowledgements We thank  Javier de Campo ,  Catherine Ribeiro ,  Ana Maria Cabello  for their shiok suggestions on the shiny application. We thank the ABIMS platform of the  FR2424 (CNRS, Sorbonne Université ) for bioinformatics resources. The work of  MJ  and  CB in the Burki lab  was supported by a grant from  Science for Life Laboratory available  to  F. Burki.
Data availability Source code for the Shiny server is available as an R package from GitHub (https://github. com/pr2database/metapr2-shiny, DOI: 10. 5281/zenodo. 5992354). Source code for this paper is available from GitHub (https://github. com/vaulot/Paper-2021-Vaulot-metapr2). Source code for sequence processing is available from GitHub https://github. com/vaulot/Paper-2021-Vaulot-metapr2/tree/main/R_processing.
CP has these following appealing features: (i) It provides a generic and easy-to-upgrade service placement, (ii) It provides a faster solution even in a reasonably large scale environment ( in  [20]  it is compared to and outperformed other algorithms such as ILP and GA with 1200 nodes), and (iii) The CP code is small and easy to implement. Thus, CP seems attractive to adopt it for DM problem (More details in Sec. V). Many opensource CP solvers are available, such as the Choco solver  [22]  and OR-tools  [23]  and other commercial solvers, such as IBM CPLEX CP Optimizer  [24]. For solving the Service Placement Problem (SPP), different optimization strategies are proposed in the literature  [18] ,  [16]. We distinguish two categories: (i) mono-objective that optimizes only one objective function, and (ii) multi-objective optimization that optimizes simultaneously many objective functions.
A. Auto Scaling Manager (ASM) This autonomic manager is built following the MAPE-K architecture and is responsible for managing of the DM system from its design till the end of the system's lifecycle. Thus, the objective is to specify a particular DM system /Infrastructure topology to be modeled, by a system administrator or DM service provider, and then handled at runtime. This implies making horizontal scaling decisions of the DM servers to adapt to any new condition dynamically, i. e. , adding/removing one or more DM servers at runtime. This work is realized through the following modules: • Monitor module: is responsible of observing the evolution of both DM system and infrastructure and forwarding the information to the Remapper module for analysis; • (Re)Mapper module: analyses the information from the system administrator or the Monitor to decide on the target state of the DM system and then it solves the DM system placement within infrastructure (See Sec.
Finally, in the case that the monitor detects a reconfiguration need and a CP solver is called, we may obtain that no feasible placement solution is found, for example, because there are no enough resources in the infrastructure while satisfying all required constraints. Thus, the Remapper module might need to wait for some time, so that other services release some infrastructure resources or some clients leave to try again with the solver. In such case, the ASM will keep the last solution and a notification will be sent to the system administrator.
C. Architecture Implementation We detail here our choices in terms of architecture and language for the development, illustrated in Fig. 6. The software and modules run locally on a workstation with four physical cores and eight logical computing units of the Intel x86-64 architecture, supported by 32GB of DDR4 with 2133MHz RAM with Linux Debian 10 operating system. For the mapper module, which solves the DM placement problem based on CP, we have chosen Choco as a solver inspired by the works  [20]   [16] , which is a free and opensource Java library dedicated to constraint programming. The placement problem must be modeled in a declarative way by defining the set of constraints to be satisfied in each solution. Then, it will be solved by alternating constraint filtering algorithms with a search mechanism.
In addition, four provinces have given cohabiting couples the exact same rights and responsibilities as married couples. Partners are automatically entitled to these rights after a certain duration of unmarried cohabitation. We identify the effect of eligibility for a protective regime of cohabitation by exploiting variations across Canadian provinces in: (a) the different levels of protection; (b) the year in which these reforms took place; and (c) the minimum duration required to be eligible for cohabitation rights. We use longitudinal data from the Survey on Labour Income Dynamic (SLID), which is representative of the Canadian population over the years 1993-2011 and we implement a difference-in-differences estimation strategy combined with individual fixed effects and duration of the relationship fixed effects.
Data We use longitudinal data from the Survey on Labour Income Dynamic (SLID) provided by Statistics Canada, which is a household survey, with a rotating panel design, representative of the Canadian population. The SLID covers each year a sample of 17000 households of the population of the ten Canadian provinces with the exception of Indian reserves, residents of institutions and military barracks (less than 3% of the population). Data have been collected each year from 1993 to 2011 from January to March. Five 6-years panels were collected (1993-1998; 1996-2001; 1999-2004; 2002-2007; 2005-2010), the sixth panel was terminated after 4 years (2007-2011). Interviewers collect information on the labour market status and family status of all individuals. Respondents have the option of answering income questions during the interview, or of giving Statistics Canada permission to access their income tax records (which dramatically lower the duration of the interview).
For men, we consider the number of hours worked during the year and the number of active weeks during the year (weeks when the individual is either employed or unemployed). For women, we consider the number of hours worked during the year and a dummy indicating that she has not earned any labour income during the whole year. For both gender, we also consider annual labour earnings. All monetary values have been deflated using the province Consumer Price Index, and are expressed in constant Canadian dollars (CAD) of 2002. To avoid potential large measurement errors, we attributed to all observation above the top 1% percentile the value of the 1% percentile (winsorization). It is important to note that whereas labour earnings come from fiscal data (for most respondents), labour force supply variables are self-reported an may suffer from reporting bias (respondents have to list all the jobs they had during the year and for each of them, how many weeks and hours they have worked).
Impact of reforms introducing protective regimes of cohabitation Did the introduction of more protective regimes of cohabitation impact the labour market outcomes of men and women? Table  3  presents the results of the estimation of model (3. 1). It indicates that the introduction of the marriage-like regime had opposite effects  21  We decided to keep the data as is for the estimation. However, the results obtained on a sample where we set the hours to zero when income was equal to zero were very similar. 22 If a couple is formed over the period, we consider the first observation where both partners are observed. We constructed a similar ratio based on permanent labour income (computed as his or her average of all labour earnings that she or he earned during the period of observation). Our results are not affected by this alternative definition.
27 Whereas labour earnings are collected from fiscal data, hours are self-declared and may suffer from reporting bias. This may explain why we find this inconsistency between effects on earnings and on reported labour supply. See the data section. 28 In the online appendix, we present on figure  C3 , the effect of the marriage like reform, on figure C4 the effect of eligibility for alimony and on figure  C5  the effect of eligibility for marriage-like regime. 29 We used the package csdid in Stata (Rios-Avila et al. , 2021). 30 Pooling couples eligible at the moment of the reform and couples eligible after together amounts to form the assumption that couples eligible at the moment of the reform react similarly as couples eligible after the reform, when they become eligible. Regarding the alimony reform, we observe very few couples eligible at the moment of the reform, so our estimate are mostly based on couples eligible after the reform.
by the PyTorch library  [62]  and have a quadratic memory footprint: even on modern hardware, this prevents them from handling populations of more than N = 10 4 to 10 5 samples without making approximations or relying on specific sub-sampling schemes  [77]. Fortunately though, over the last few years, efficient GPU routines have been developed to tackle computations in the mould of (  23 ) with maximum efficiency. These methods can be accessed through the KeOps extension for PyTorch  [63] , NumPy  [73] , R  [71]  and Matlab  [53] , that is developed by the third author  [19, 33, 34]  and freely available at https://www. kernel-operations. io. In practice, this library supports arbitrary kernels on the GPU with a linear memory footprint, log-domain implementations for the sake of numerical stability and outperforms baseline PyTorch GPU implementations by one to two orders of magnitude.
Our code and its documentation are available online at http://www. kernel-operations. io/monaco/. All our experiments can be run on Google Colaboratory (colab. research. google. com) with a GPU within a few seconds to a few minutes, depending on the method and number of independent runs. We note that MoKA-KIDS is a slightly heavier method, as it relies on the Richardson-Lucy iterations to optimise the deconvolution weights. We also note that unlike our Markovian methods, the memory cost of SAIS significantly increases with the number of iterations -to the best of our knowledge, no procedure has yet been proposed to remove particles with time. We have not implemented the batch sampler as proposed in  [23] , as the computation time is not a problem with our fast GPU implementation. Another example of target distribution is included in Appendix E and we include results for the estimation of the normalizing constant of the target distributions below in Appendix D.
Acknowledgments The authors wish to thank  Pierre Degond ,  Robin Ryder  and  Christian Robert  for their support and useful advice. A. D. acknowledges the hospitality of the CEREMADE,  Université Paris-Dauphine  where part of this research was carried out. The work of A. D. is supported by an  EPSRC-Roth scholarship  cofounded by the  Engineering and Physical Sciences Research Council  and the  Department of Mathematics at Imperial College London. G. C. acknowledges the hospitality of the  Mathematics Department at Imperial College London  where part of this research was carried out.
65 arcsec in the r-band). CFHT has a proven track record of providing images for state-of-theart weak-lensing studies. Wide surveys with CFHT started with CFHTLS/CFHTLenS and were followed by RCSLenS  (Hildebrandt et al. 2016 ) and now CFIS. In addition to the large area and small seeing, CFIS area overlaps with very wide spectroscopic surveys such as SDSS's Baryon Oscillation Spectroscopic Survey (SDSS-BOSS,  Eisenstein et al. 2011) , extended Baryon Oscillation Spectroscopic Survey (eBOSS,  Dawson et al. 2016) , and soon the Dark Energy Spectroscopic Instrument (DESI) survey  (DESI Collaboration 2016). This combination of surveys provides a unique dataset for weaklensing studies. Central to all weak-lensing analyses is a robust and efficient data processing pipeline. To extract the weak-lensing signal from the distorted shapes of the galaxies, one has to be particularly meticulous through the entire chain of processing.
The main piece of such a pipeline is the shape measurement algorithm used to capture the shear signal from the noisy, pixelised, and blurred images of galaxies. This signal is subject to a number of systematic errors. Every step requires a specific calibration and validation to reach the level of precision required for the cosmological analysis. In this paper we present a new pipeline architecture designed to handle the large area of a stage-III  (Albrecht et al. 2006 ) survey. The pipeline presents a balance of well-established methods and newly developed algorithms. Our goal is to develop a framework that is capable of handling CFIS data but flexible enough to allow for the evolution of the current methods and the addition of new ones in the future. This paper is organised as follows. In Sect.
Data CFIS is a large imaging survey observing the northern hemisphere with the wide-field imager MegaCAM on CFHT (pixel scale of 0. 187 arcsec). CFIS started observations in 2017 and has (as of early 2021) reached a coverage of around 3000 deg 2 , 60% of the planned final area of 4800 deg 2. The survey will reach completion by 2025. CFIS takes deep images in the r-band (640 nm) and u-band (355 nm). It takes advantage of the excellent sky quality of Mauna Kea, with an average seeing of 0. 65 arcsec in the r-band. These conditions make CFIS ideally suited for weak-lensing studies. In 2018 the UNIONS survey collaboration was created to gather in a single scientific group the various multi-band surveys that cover the planned Euclid footprint in the northern hemisphere. CFIS will provide the r-and u-bands, while Pan-STARRS will observe the i-and z-band, and Subaru HSC will complement the z-band through WISHES.
The ShapePipe weak-lensing processing pipeline ShapePipe is a modular weak-lensing processing and analysis pipeline written in Python. The package itself is composed of two principal sub-packages: one that constitutes the core pipeline architecture, and a second part that contains the various modules that account for each of the steps in the pipeline. ShapePipe makes use of the open-source package management system Conda 2  to provide a version-controlled environment for each pipeline release. This ensures consistency in the versions of third-party packages employed by ShapePipe including those not written in  Python (e. g. , SExtractor, Psfex, etc. ). This in turn ensures a level of reproducibility of ShapePipe results on different processing platforms. The core pipeline sub-package (hereafter referred to as the ShapePipe core) manages basic operations such as argument parsing, logging, dependence handling, and reading and writing FITS files.
In addition to this, the pipeline core is responsible for parallel processing and module handling. The ShapePipe core operates under the assumption that a series of input products can be handled in an embarrassingly parallel manner via a series of independent jobs. The package uses two different methods for distributing jobs. On shared memory systems the ShapePipe core uses Joblib (Joblib Development Team 2020) to implement a form of symmetric multi-processing (SMP), while on larger clusters a message passing interface (MPI) is implemented via MPI for Python  (Dalcin et al. 2005 (Dalcin et al. , 2008 (Dalcin et al. , 2011)). This enables ShapePipe to be run with as many CPU cores as are available on a given platform. The ShapePipe core was designed to be as modular as possible, meaning that various processing steps could be updated, supplemented or replaced as new advances are made.
A series of modules and the order in which they should be run can be specified in the configuration file before launching the pipeline. This enables ShapePipe to better adapt to data sets coming from different surveys. The ShapePipe core has been optimised to minimise the amount of memory used. This ensures that the majority of system resources are available for the modules. Finally, the ShapePipe core makes use of the following third-party packages to provide extra funcitonality: Astropy (Astropy Collaboration 2018), ModOpt  (Farrens et al. 2020) , and Numpy  (Harris et al. 2020).
PSF estimation and interpolation In this section we describe how we model the PSF on each single exposure. We make use of the psfex software package 4   (Bertin 2011 ). Since we carry out our own star selection (see Sect. 4. 1), we disable the internal psfex pre-selection, and the PSF is thus obtained using the entire star sample. We ran psfex in Pixel basis mode. This means that no assumptions are made regarding the parametric profile for the PSF. Rather, the pixels themselves are fitted in real space. This fit is initialised using the median profile of the sources provided in the field. The parameters fitted by psfex are the pixels that make up a set of PSF basis func- x p y q S pq. (1) The maximal degree of the polynomials, d, is chosen through the PSFEx parameter PSFVAR_DEGREES, which we set to two.
psfex runs an iterative χ 2 fit that allows for the removal of potential outliers at each iteration. This outlier rejection method removes around 0. 08% of all stars from the final PSF sample, demonstrating that our star selection (Sect. 4. 1) is robust. The PSFEx parameters we used are presented in Table  1. We have chosen not to over-sample the PSF models. This choice of parameters was driven by a comparison of PSF model performance using different sets of parameters and repeating the validation tests of Sect. 6. 2 on a large subset of the CFIS data. The computation of the psfex model at any given position (as in Eq. (  1 )) is normally carried out by SExtractor. However, since we perform our own shape measurement rather than relying on SExtractor, we use our own Python module to recombine the psfex basis functions.
ate a noise image (i. e. a postage stamp of the same size as the original observation but containing only noise) with the same variance as the original image to cancel the correlations created by the shearing process. To cancel the correlations, the noise image undergoes the same process of deconvolution, shearing and reconvolution. The difference being in the shearing. To cancel the correlations created in the image we apply the shear on a 90 deg rotated version of the noise image, which is then rotated back, and applied to our science image (for a more in-depth description of the noise handling we refer the reader to Sect. 4. 2 of  Sheldon & Huff 2017). In this work, we use the Ngmix software package to handle the metacalibration steps. This method creates four images used for the calibration, and one for the measurement.
Science results In this section we present the first science results obtained with our shear catalogue. This section serves as a proof of concept of the results achievable in the future with the full CFIS data and processing with our pipeline, ShapePipe. We focus here on convergence maps, cluster lensing, and second-order cosmic shear statistics. addition of noise, it can be considered that these spurious correlations have been removed from the final image. A162, page 11 of 19.
Shear catalogue The catalogue used here is constructed from an effective (after masking) area of A = 1565 square degrees. We measure the shape of N gal = 40 151 119 galaxies. The data are divided in four fields, named P1 to P4, which are presented in Fig. 13. The effective density is derived from the formula proposed in  Heymans et al. (2012) : n eff = 1 A ( w i ) 2 w 2 i , (36) and the corresponding shape noise is given by σ 2 SN = 1 2 w 2 i (e 2 1,i + e 2 2,i ) w 2 i , (37) where the sum is carried out over all objects in the catalogue. We find n eff = 6. 76 gal arcmin -2 , compared to a raw, unweighted density of n raw = N gal /A = 7. 13 gal arcmin -2 , and σ SN = 0. 35. The magnitude distribution is presented in Fig. 14. Due to our conservative detection criteria, this distribution does not reflect the true depth of the survey.
Second-order shear statistics: COSEBI B-modes One of the main weak-lensing observables for cosmology are the second-order cosmic shear correlations. One such measure are the Complete Orthogonal Sets of E-/B-mode Integrals (COSE-BIs), introduced in  Schneider et al. (2010). These modes are linear functions of the measured two-point correlation functions ξ + and ξ -(Sect. 6. 1). First, we compute the shear-shear correlation functions between θ min = 2 arcmin and θ max = 200 arcmin with 1000 bins using the Treecorr 12 software package  (Jarvis 2015). Next, we derive the COSEBIs using Nicaea 13  (Kilbinger et al. 2009). This results in E-and B-modes, E m and B m , respectively, where each of these modes is an integral over ξ + (θ) and ξ -(θ) filtered with a polynomial in log θ of order m + 1. To construct the error bars, we first assume a Gaussian covariance for ξ ± , derived from the CosmoCov software package 14  (Krause & Eifler 2017 , Fang et al.
Conclusion In this paper we have introduced a new weak-lensing pipeline, ShapePipe. This pipeline relies on well-tested tools such as 14 https://github. com/CosmoLike/CosmoCov Fig. 18. COSEBIs for modes m between 1 and 16. This test was performed to quantify the impact of potential systematics on the B-mode, which are consistent with 0 for our study. SExtractor, psfex, and Ngmix. These tools have been adapted to suit the demanding constraints on memory and CPU time imposed by the large area of the CFIS survey. We have demonstrated that our pipeline is able to handle such large datasets. The modularity of the pipeline makes it easy to adapt it to new datasets and to update it to include new state-of-the-art shape measurement and analysis tools. This first analysis of 1700 deg 2 of CFIS data presented in this work shows the high quality of the images as well as the quality of both pre-processing and processing.
Acknowledgements. We would like to thank  Catherine Heymans ,  Mike Jarvis ,  François Lanusse ,  Erin Sheldon , and  Florent Sureau  for helpful discussions. This work is based on data obtained as part of the  Canada-France Imaging Survey , a  CFHT large program  of the  National Research Council of Canada  and the  French Centre National de la Recherche Scientifique. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA Saclay, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers (INSU)  of the  Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. Pan-STARRS is a project of the  Institute for Astronomy of the University of Hawaii , and is supported by the  NASA   SSO Near Earth Observation Program  under grants  80NSSC18K0971 ,  NNX14AM74G ,  NNX12AR65G ,  NNX13AQ47G ,  NNX08AR22G , and by the  State of Hawaii.