dataesr/scientific-paragraphs-categorization huggingface dataset:
After a cross-match with Vizier  (Ochsenbein et al. 2000) , Simbad  (Wenger et al. 2000) , the Master Lens database and the catalogs of various lens-finding papers with candidates or confirmed lenses included in the CFIS footprint (e. g. Cañameras et al. 2020; Chan et al. 2020; Jaelani et al. 2020; Huang et al. 2021; Talbot et al. 2021) , we obtain 15 new SL and 89 new ML candidates. The results of the cross-match and the coordinates of the candidates are presented in Table  3. With our visual inspection, we also obtained three catalogs with 238 mergers, 361 ring galaxies and 950 spiral galaxies identified by at least one user, which can be used to expand our negative sample for future searches. Because we consider the unions of all votes to include a candidates in our false positive list there is a small an overlap between the three catalogs.
We then perform the MCMC sampling using the emcee 3  package, which is a Python implementation of the affine-invariant Markov chain Monte Carlo ensemble sampler  (Goodman & Weare 2010; Foreman-Mackey et al. 2013). While lens modeling is a fairly easy task for isolated lenses, it is complicated by the presence of intervening objects unrelated to the lens, which introduce spurious light contamination. Such objects should be masked to avoid being mistakenly identified as lensed images of the source. This masking procedure is addressed in different ways by different authors. Shajib et al. (2020)  modeled 23 lenses from the SLACS sample  (Auger et al. 2009) , and specifically chose systems that do not contain any contaminating sources of light. Nightingale et al. (2018)  did not restrict their sample, but adopted a circular mask with a fixed radius of 3.
9 which selects only the regions of the data dominated by the lensed source light. In our case, the masking algorithm is designed to adapt to systems with very different angular sizes. Figure  9  depicts the steps of the following algorithm 1. We apply a Laplacian of Gaussian (LoG) filter to the image to highlight areas of the image with a strong flux gradient. 2. All pixels whose flux is below a threshold of 6σ sky , where σ sky is the rms background noise, are set to zero. 3. We then locate all of the non-zero pixels in the filtered image, as well as identifying the locations of the peaks. We define peaks as local maxima in the image that we detect using the peak_local_max function of the skimage Python package. We require that detected maxima are separated by more than one pixel from each other in order to be considered a peak.
Discussion and conclusions In this paper, we have designed a pipeline to find galaxy-scale strong lenses using convolutional neural networks and applied it to the CFIS wide-field optical imaging survey being carried out with the 3. 6 m CFHT in Hawaii. We use only the deep and sharp r-band images for which the median seeing is 0. 6 down to a 10σ depth of r = 24. 6. We use 2 500 deg 2 of CFIS in the present work, since the survey is still ongoing and expected to reach a total area of 5 000 deg 2 of the Northern sky, when completed. In training our CNNs, we use data-driven simulations where the light distribution of the lens plane is taken directly from the data. This naturally includes the PSF, companions and noise properties of the actual data and any companion galaxies. The lens mass, modeled as an SIE profile, is derived from the measured velocity dispersion of our sample of LRGs, that we further adapt to ensure that lensing features are systematically visible in the training set.
3. 4. Fortunately, visually inspecting 9 460 objects by eye is still tractable and this task was done by six independent authors of this paper. Despite setting strict and homogeneous rules for the visual classification, we note that six human classifiers still have very different opinions on what is a lens and what is not, meaning that any automated CNN may still require a time consuming human check, e. g. with citizen-science projects for future wide-field space surveys like Euclid, Rubin-LSST or Roman. More effort should be devoted to visual inspection methods to reach better consensus between classifiers. Following the visual inspection, we find 32 objects with striking lensing features and 101 objects that show strong signs of lensing but that need further data to confirm, i. e. higherresolution/deeper imaging and/or spectroscopy.
With the CFIS r-band data only, we still find our application of auto-encoders useful to evaluate the quality of the lens candidates, especially for the smallest Einstein radii and/or for objects with strong contrast between the lens and source light. Finally we develop a simple lens modeling pipeline based on the lenstronomy software in which we adopt an SIE mass profile with external shear. We also develop an automated masking procedure to enforce only relevant objects to be modeled and avoid objects unrelated to either the lensed source or the lensing galaxy. The optimisation process using particle swarm optimization followed with Markov Chain Monte Carlo sampling takes of the order of 2 hours per object for our 44 pixel × 44 pixel stamps. This is easily tractable for implementation in large imaging surveys.
Since the CFIS imaging data are among the best available so far in terms of depth and seeing, our results, although subject to improvements, can be seen as a preview of what can be achieved with a single band of Rubin-LSST data after about a month of data acquisition. With a spatial resolution 3 times better than the best CFIS images, Euclid (and then Roman) will give us access to a larger number of small separation systems, especially with Einstein radii smaller than 1. These data will also help us to decide on our less secure candidates, since space-based imaging will provide high signal-to-noise ratios for the lensing features ofthe lenses presented here, allowing us to test more complex mass models, and thus probe astrophysical questions like (lens) galaxy evolution and the structure of their dark matter halos.
Acknowledgements. This work is supported by the  Swiss National Science Foundation (SNSF)  and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( COS-MICLENS : grant agreement No  787886 ). 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.
B. Computational Details In the ADF  78  calculations, all of which performed with version 2019. 307  79 , we have employed the AUG/ATZP basis sets for the halogens, and the single-z without polarization (SZ) basis set for the water molecules  80. In supermolecular calculations, we employed the B3LYP functional. The ADF FDE and FnT calculations were performed via the PyADF scripting framework  81. The halogen subsystem has been calculations with the B3LYP functional and the water molecules with BLYP. Since the use of different density functionals for different subsystems in a FnT calculation is currently not possible from within the ADF implementation  82  , a PyADF script to carry out such calculations in provided as part of the dataset accompanying this manuscript  83  , and in this case we employed a convergence criteria on the energy of 1 × 10 -6.
0 × 10 -4 was applied. The code implementing the rt-FDE in the Psi4Numpy framework used in this work is part of the PyBertha package  50, 90, 91  (revision 3c752072). The code implementing the (rt-)BOMME approach is under version control (Git) but does not yet have a public release version (one is envisaged for 2022). The simulations described in the paper have been carried out with revision 3c4c334b. The structures employed in the calculations were taken from the structures generated by Bouchafra et al. 54, 92  for halogens in 50-water droplets-in particular, snapshot 619 for chloride and snapshot 1 for fluoride-and, for reasons of computational cost, we have only kept the nearest 8 water molecules that correspond to the first solvation shell. This setup is exemplified in figure  1. FIG. 1.
To deepen the discussion on the origin of differences between embeddding and supermolecular results, it is useful to analyse the molecular orbitals involved in the K-edges. In the real-time framework, similarly to LR, the absorption cross-section can be interpreted in terms of occupiedvirtual molecular orbital (MO) pairs. This approach has been proposed originally by Repisky and coworkers  93  and implemented in the relativistic code ReSpect  94. The scheme has been slightly reworked by Bruner, LaMaster, and Lopata  95  accelerating the methodology applying the Padé approximants to the Fourier transform of the deconvolution of the induced dipole into molecular orbital pairs. The method gives the MO contribution to the dipole strength function at all frequency. The relative areas under each peak correspond to MO contribution to an excitation at that frequency, which gives a represen-tation of transitions consistent with the linear response.
We acknowledge funding from projects  Labex CaPPA  ( ANR-11-LABX-0005-01 ) and  CompRIXS  ( ANR-19-CE29-0019 ,  DFG JA 2329/6-1 ), the I-SITE  ULNE  project  OVERSEE  and  MESONM International Associated Laboratory (LAI)  ( ANR-16-IDEX-0004 ), and support from the  French national supercomputing facilities  (grant  DARI A0090801859 ).
An example of how users can react to a similar approach can be found in  [34] , where the endpoint prediction was used to help the elderly navigate on the screen. For 2-D tasks, the equations for the vertical components y m , V my , y, T y , T y can be presented in the same way. So the motivating technical issue of this work is to estimate the cursor's wanted position T x and T y as fast as possible using the dataset obtained in the experiment  [33].
V. POINTING TASK MODELING AND IDENTIFICATION A pointing model has to reflect both muscular and perception, human feedback following the current position on the screen. The task is to estimate the desired position of the cursor given by the constants T x (and T y ), based on the dataset of x and V mx (y and V my , respectively). As usual, the real-world data is noisy, and a challenging estimation issue is that we have no good mouse velocity and acceleration measurements in the experiments. In this section, with a small ambiguity of notation, we will use the signals x m , V mx and a mx gathered after applying the homogeneous differentiator, as previously explained. Remark 3. Although we estimate the horizontal and vertical cursor position dynamics separately, the general pointing task is two-dimensional.
, 2008). However, a large availability of climate data does not automatically imply having access to useful climate information. Indeed, post-processing methods with different lev-Published by Copernicus Publications on behalf of the European Geosciences Union. els of sophistication are required to convert climate data into tailored climate information for each application, allowing users and decision-makers to develop and implement strategies of adaptation to climate variability and to guide well-informed decision-making. The generation of tailored climate information can be, for instance, the extraction of global data in a particular region of interest, the correction of the systematic errors that prevent the integration of the climate predictions in impact models, or the refinement of the coarse resolution of the climate datasets in order to be representative of the local climate variability.
bsc. es/gitlab/es/ cds-seasonal-downloader, last access: 22 June 2022) for this purpose (see Appendix A). -Data retrieval and formatting. This task refers to the loading of climate data from files stored in local or organization-accessible file systems or servers onto the main memory of the processing workstations and the required arrangement and transformations of the different datasets to be intercompared and analyzed. This can be a labor-intensive step when trying to combine multiple or diverse datasets such as observations and forecasts from multiple systems or sources. Slight differences in the internal conventions for storing the respective datasets need to be handled, and methods for spatial and temporal data manipulation, such as spatial interpolation methods, are often necessary. -Correction methods for forecast calibration.
-Visualization. From the climate services perspective, visualization tools are essential to illustrate different aspects of deterministic or probabilistic climate information. The primary aim of CSTools is therefore to make postprocessing methods (i. e. , correction methods for forecast calibration, classification methods for multi-model forecast combination, or scenario selection, downscaling methods, and visualization tools) available in one coherent framework in order to facilitate analysis or the post-processing of data such that might be required by an impact model. Because additional steps are required, CSTools also includes functions for data retrieval and formatting and skill assessment in order to facilitate the use of the toolbox. Several software packages are already available to analyze different types of climate data.
For instance, the Earth System Model Evaluation Tool (ESMValTool;  Eyring et al. , 2016b Eyring et al. , , 2020;; Righi et al. , 2020)  was designed to facilitate the analysis of climate projections produced in the context of the Coupled Model Intercomparison Project (CMIP;  Eyring et al. , 2016a). The R packages s2dverification  (Manubens et al. , 2018) , SpecsVerification  (Siegert, 2017)  and easyVerification (MeteoSwiss, 2017), or the Python package climpred  (Brady and Spring, 2021)  focus on a skill assessment of ensemble forecasts. climate4R  (Iturbide et al. , 2019 ) is an Rbased framework for climate data post-processing including different methods. The main purpose of these different packages is the facilitation of research. CSTools, on the other hand, targets scientists interested in providing a climate product to some final users.
This is done by allowing the creation of a complete post-processing chain from data retrieval to the obtention of high-quality datasets to feed impact models or tailored forecast visualization of forecast products. CSTools could, nonetheless, be useful to research scientists, as it has been designed to be compatible with some of the aforementioned R packages. In this paper, an overview of the methods and documentation gathered in CSTools is presented in Sect. 2, while the creation of a tailored dataset is shown in Sect. 3. There are three case studies based on the analysis of an extreme wind speed event, the snow model SNOWPACK  (Lehning et al. , 2002a, b  https://models. slf. ch/p/snowpack/, last access: variables, that show the usefulness of the toolbox. Section 4 concludes this paper and discusses some future developments for the package.
CSTools: overview CSTools was created as part of a collaborative effort between six European institutions. Given the total number of contributors and collaborators (31 in version 4. 0), compiling all methods into a software package using the R statistical programming language (R Core Team, 2017) was considered the most suitable and versatile option. Creating an R package allows the inclusion of multiple tools ranging from complex statistical and climatological methods to visualization tools in the same framework. Moreover, CSTools is open source, thus allowing users and developers alike to benefit from lower costs and software flexibility, quality, and reliability  (Information Resources Management Association, 2013). At the same time, CSTools can be integrated into other software in order to take advantage of its functionalities, as does, for instance, the S2S4E Decision Support Tool (https://s2s4e-dst.
bsc. es, last access: 20 May 2022). CSTools was developed following common guidelines (see Supplement) agreed upon by all contributors, including conventions for adding new functionalities, and taking into account software development best practices such as the use of a version control system (i. e. , Git;  Chacon and Straub, 2014)  and testing with continuous integration. The use of these development guidelines has resulted in a clean and homogeneous application programming interface (API). Most functionalities exposed to the users can be invoked and applied to complex user datasets with a single function call. For example, in order to apply a given functionality named "Func", the user would write: CST_Func(dataset,. ). The "CST_*" family of functions ingest and return objects of type "s2dv_cube" (see details in Sect.
Software design aspects of CSTools The CSTools development guidelines have been designed to maximize compatibility with other libraries such as s2dverification, s2dv, SpecsVerification, easyVerification and startR, as all of them are designed to operate fundamentally with the same array class. Furthermore, CSTools is also compatible with CSIndicators  (Pérez-Zanón et al. , 2021b) , as the latter accepts s2dv_cube objects as inputs. In short, the class s2dv_cube is a list of named elements to keep data and metadata in a single object. One of its elements, "data", is a multi-dimensional array with named dimensions containing the values of a variable. The rest of the elements are metadata such as spatial coordinates. Because of this design, the CSTools user is able to perform basic array inquiry on the data element of the s2dv_cube objects at any point in the workflow in order to check the dimensions of the data or to find the number of members, start dates, or forecast lead times analyzed.
Internally, each of these high-level CST_* functions perform two nested calls to two other different but closely related functions in the package. For example, a given functionality named Func would involve the following function calls: CST_Func(dataset,. ) {. Func(dataset$data,. ) {. Func(data_array[i,],. ) } }. At the most fundamental level of this nested call structure, there is a call to a basic function (e. g. , ". Func") that is designed to work with the least complex data structure possible (be it a single vector, a couple of vectors, an array and a vector, etc. ). At the second level is a call to a wrapper function (e. g. , Func) around the basic function, which leverages the multiApply package  (BSC-CNS et al. , 2019)  to extend the computation of. Func to inputs with any number of dimensions.
The top-level CST_* function is an additional wrapper function which adapts the second-level array-based function to work with s2dv_cube objects. This nested structure has several benefits. Thanks to the array compatible, low-level functions, the R community can easily employ the CSTools methods in custom CSToolsagnostic workflows if necessary, multi-core parallelism is straightforward to exploit via middle-level functions and high-level CST_* functions, and, finally, in cases where the data to process is larger than the RAM memory in the workstation or the computation is very expensive, the low-level functions can be used together with the startR package (BSC-CNS and Manubens, 2020) to leverage high-performance computing (HPC) platforms and distribute workload in small chunks. An example on how to use a CSTools function in a startR workflow can be found in its GitLab repository (https: //earth.
Methods in CSTools Given that the methods included in CSTools are made available as independent functions operating on a common data structure, the users can concatenate them to define their own post-processing workflow. This design provides flexibility, allowing the users to assess the impact of the various postprocessing steps by modifying the chain of functions. The users can also select a single function and apply it outside of the CSTools workflow. The functions included in the package cover fundamental loading and transformation requirements, downscaling tools, methods for correcting and evaluating forecasts, and advanced visualization tools (see Table  1 ). All functions are documented in a standard reference manual on the CRAN website (https://CRAN. R-project. org/ package=CSTools, last access: 18 July 2022).
For users who retrieve data by other means (e. g. , using the library ncdf4; Pierce, 2019), the CSTools package contains two functions to convert data to a s2dv_cube object. If the data and metadata have been loaded in separate objects, they can be merged into a s2dv_cube object with the function s2dv_cube. On the other hand, if the data and metadata have been loaded into a single object, then this object can be transformed into class s2dv_cube class with the as. s2dv_cube function. One of the capabilities of CSTools is to create a new dataset after, for example, the data have been downscaled and/or calibrated. In that case, the user may need to save the new dataset into files to be shared among other users or its community. Therefore, the package comes with a saving function called CST_SaveExp, which creates netCDF files in a directory set by the user and which can be loaded again with the CST_Load function.
Classification methods Classification methods are widely used in climatology to summarize the climatological conditions captured by observations or simulations. Sokal (1966)  was already using sophisticated univariate and multivariate climatic classification systems generated from enormous databases  (Balling, 1984). However, the functions included in CSTools for this purpose are modern methods adapted to observations, reanalyses, and climate model outputs with multiple ensemble members. The CST_MultiEOF function allows conducting empirical orthogonal function (EOF) analysis simultaneously over multiple variables, either for each ensemble member or all the ensemble members concatenated altogether (i. e. , it can be applied to each one of the ensemble members separately or to the whole ensemble).
The dimensionality of this object can also be reduced by using principal components (PCs) obtained from the application of the EOF analysis to filter the dataset, while the cluster analysis can be performed with the traditional k means or hierarchical clustering. On the other hand, CST_RegimesAssign matches anomalies to a set of reference maps obtained using CST_WeatherRegimes. The https://doi. org/10. 5194/gmd-15-6115-2022 Geosci. Model Dev. , 15, 6115-6142, 2022 anomalies are assigned to the most similar reference map using either the minimum Euclidean distance or the highest spatial correlation, which can be particularly useful to classify the predictions according to the clusters identified in the observational reference. CST_CategoricalEnsCombination converts a multi-model ensemble forecast into a categorical forecast by giving the probability for each category.