dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of a tool description from a bio. tools entry Here we illustrate the generation of a tool description with the example of IntegronFinder  24  , an analysis tool dedicated to the identification of integrons in bacterial genomes. Launching ToolDog in "generation mode" on the IntegronFinder entry in the bio. tools registry allows the generation of a significant portion of the tool description (Figure  4 ), either in CWL or Galaxy format. Some manual modifications (corrections + additions) are still necessary to complete the tool description and to make it functional. For instance, software requirements, which specify what software needs to be installed for the tool to run correctly, cannot be automatically generated, because this information is currently not available in bio. tools.
Discussion The ToolDog utility allows a developer to generate new tool descriptions for tools which are compatible with the code analysis module, and reuse the metadata provided by bio. tools to enrich existing tool descriptions. There are some limitations to this approach: 1. The "plugin" libraries used for code analysis are specific to the programming languages, libraries or framework used to build the command line interface. To this date, they don't cover most of these. 2. The generation of the tool descriptions through code analysis must assume certain coding practices, such as the use of specific functions to define input or output parameters, which are not uniformly adopted. 3. Some of the input/output operations performed by some programs are a lot more difficult to detect through code analysis because they are typically not included in command line parsing frameworks, such web service and database queries and submissions, or in place file modifications.
The automated enrichment of existing tool descriptions provides a convenient way to improve them, especially if they lack most of the metadata provided by bio. tools. Performing this enrichment efficiently en masse, however, would require the wide adoption of an identification system for bioinformatics software. This mechanism would allow to avoid the complex and sometimes ambiguous mapping procedures based on publication identifiers we performed when testing it on the Galaxy tools. A recent update to bio. tools has added stable and unique tool identifiers, based on registered tool names, yielding persistent references to tools, for example https://bio. tools/signalp. Future work will make use of these identifiers to improve the generation of tool descriptions. For instance, linking of the bioconda and biocontainers repositories to bio.
Conclusions During the last years, integration of various tools has been eased by the use of workbench systems such as Galaxy, and frameworks using the Common Workflow Language. Still, it remains time consuming and not straightforward to adapt resources to such environments. ToolDog lays the foundation for future work, that will provide a Workbench Integration Enabler for the bio. tools registry as an online service. Furthermore, integration with Planemo, the main utility to develop Galaxy and CWL tools, will be further developed in order to make the simple, bio. toolsbased metadata enrichment of ToolDog available to the widest possible audience. 1.
Speaking of 'suggestions': The current title 'Using bio. tools to generate and annotate workbench tool descriptions' suggests the paper will talk more generally about bio. tools, whereas the text focuses primarily on the specific component ToolDog. The title should be modified to reflect this. The graphs in Fig. 2  would be more effective if they were displayed in an integrated manner (single bar chart?), so that the improvements that ToolDog makes are more easily compared to one another. The discussion about the challenges in autogenerating tool documentation (language, code practices, etc), in the discussion, are spot-on. However not much is discussed on if / how ToolDog might address some of these challenges, though there are suggestions on how to more readily map existing tool descriptions to add to or update.
developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? PartlyNo competing interests were disclosed. Competing Interests:Referee Expertise: Computational biology I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Acknowledgments Jon Ison  acknowledges the support of the  Danish ELIXIR Node. Kenzo-Hugo Hillion  and  Hervé Ménager  wish to thank  Fabien Mareuil ,  Olivia Doppelt-Azeroual ,  Bertrand Néron  from the  Institut Pasteur , as well as  Daniel Blankenberg  ( Cleveland Clinic ) and  John Chilton  ( Galaxy Project ) for their technical advice during the development. Anton Khodak wishes to thank his  Google Summer  of  Code mentor Roman Valls Guimera (University of Melbourne ), who promoted the idea of argparse2tool and supervised his internship.
Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? PartlyNo competing interests were disclosed. Competing Interests:Referee Expertise: Computational biology I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Ares The mass distribution of the first simulated galaxy cluster, Ares, is generated using the semi-analytic code MOKA 3   (Giocoli et al. 2012a ). This software package builds up mock galaxy clusters by treating them as being comprised of three components: (i) the main dark matter halo -assumed to be smooth, triaxial, and well fit with an NFW profile, (ii) cluster members -subhaloes, distributed to follow the main halo and to have a truncated Singular Isothermal Sphere profile  (Hernquist 1990a ) -and (iii) the brightest cluster galaxy (BCG) modeled with a separate  Hernquist (1990a)  profile. The axial ratios, a/b and a/c, of the main halo ellipsoid are randomly drawn from the  Jing & Suto (2002)  distributions requiring abc = 1. We note that the observed FFI clusters typically consist of merging sub-clusters that cause them to be particularly efficient and spectacular lenses.
The adiabatic contraction as described by  Keeton & Madau (2001)  for  Hernquist (1990b)  was implemented. For further details of the MOKA code we refer to  Giocoli et al. (2012a,b). MOKA also takes into account the correlation between assembly history and various halo properties that are expected in CDM: (i) less massive haloes typically tend to be more concentrated than the more massive ones, and (ii) at fixed mass, earlier forming haloes are more concentrated and contain fewer substructures. These recipes have been implemented in consonance with recent results from numerical simulations. In particular, we assume the  Zhao et al. (2009)  relation to link the concentration to mass and the  Giocoli et al. (2010)  relation for the subhalo abundance. When substructures are included we define the smooth mass as M smooth = M viri m sub,i and its concentration c s are defined such that the total (smooth+clumps) mass density profile has a concentration c vir , equal to that of the total virial mass of the halo.
In the case of strong lensing simulations (e. g. for HST observations) we restrict our analysis to a region of of 1 × 1 h -2 Mpc 2. In the case of simulations extending into the weak lensing regime (e. g. for Subaru-like observations), the grid or light-rays covers a much wider area (∼ 8×8 h -2 Mpc 2 ); • Using our code GLFAST  (Meneghetti et al. 2010a) , we compute the total deflection α(x) at each light-ray position x, accounting for the contributions from all particles on the lens plane. Even in the case of strong-lensing simulations, where light rays are shot through a narrower region of the lens plane, the deflections account for all particles projected out to ∼ 4h -1 Mpc from the cluster center. The code is based on a Tree-algorithm, where the contributions to the deflection angle of a light ray by the nearby particles are summed directly, while those from distant particles are calculated using higher-order Taylor expansions of the deflection potential around the light-ray positions.
SkyLens We simulate observations of galaxy cluster fields using the code SkyLens, which is described in detail in  Meneghetti et al. (2008)  and in  Meneghetti et al. (2010a). The creation of the simulated images involves the following steps: (i) we generate a past light-cone populated with source galaxies resembling the luminosity and the redshift distribution of the galaxies in the Hubble Ultra-Deep-Field (HUDF;  Coe et al. 2006) ; (ii) we model the morphologies of the sources using shapelet decompositions of the galaxies in the HUDF  (Melchior et al. 2007). Their spectral energy distributions were obtained as part of the photometric redshift measurements of these galaxies described in  Coe et al. (2006) ; (iii) the deflection fields of the lensing clusters are used to trace a bundle of rays from a virtual CCD, resembling the properties of the Advanced Camera for Surveys (ACS) or of the Wide Field Camera3 (WFC3), back to the sources; (iv) by associating each pixel of the virtual CCD to the emitting elements of the sources, we reconstruct their lensed surface brightness distributions on the CCD; (v) we model the surface brightness distribution of the cluster galaxies using single or double Sersic models  (Sérsic 1963).
WSLAP+: The Diego and the Lam models All Diego models (Diego-multires, Diego-overfit and Diegoreggrid models) and the Lam model are built using WSLAP+, a free-form or non-parametric method that includes also a compact mass component associated to the cluster members (thus, classified as hybrid in this paper). The main part of the code is written in fortran and compiles with standard compilers (like gfortran) included in the most common linux distributions. Plotting routines written in IDL are available to display the intermediate results as the code runs. A script interface allows the user to define the input and output files, select the parts of the code to be run and control the plotting routines. A detailed description of the code and of its features can be found in the papers by  (Diego et al.
2005 (Diego et al. , 2007;; Sendra et al. 2014; Diego et al. 2016 ). The code is not publicly available yet but a companion code LensExplorer is available. LensExplorer allows the user to easily explore the lens models derived for the Frontier Fields clusters, search for new counter-images, compute magnifications, or predict the location and shape of multiple images. Here we present a brief summary of the main code WSLAP+. Note that the code includes certain features that were not taken into account in the analysis presented in this paper but will be included in the future "unblinded" version of this work. Among these features, the code incorporates spatial information about knots in resolved systems greatly improving the accuracy and robustness of the results (see  Diego et al. 2016 , for s practical demonstration).
Grale: the GRALE model The GRALE models are based on the reconstruction code Grale 8. Grale is a flexible, free-form method, based on a genetic algorithm, that uses an adaptive grid to iteratively refine the mass model. As input it uses only the information about the lensed images, and nothing about cluster's visible mass  (Liesenborgs et al. 2006 ). This last feature sets Grale apart from many other lens mass reconstruction techniques, and gives it the ability to test how well mass follows light on small and large scales within clusters. Grale has been used to reconstruct mass distributions in a number of clusters  (Liesenborgs et al. 2008 (Liesenborgs et al. , 2009;; Mohammed et al. 2014 Mohammed et al. , 2015)) , quantify mass/light offsets in Abell 3827  (Mohammed et al. 2014; Massey et al.
Description of the method Grale starts out with an initial coarse uniform grid in the lens plane which is populated with a basis set, such as projected Plummer density spheres. A uniform mass sheet covering the whole modeling region is also added to supplement the basis set. As the code runs the denser regions are resolved with a finer grid, with each cell given a Plummer with a proportionate width. The initial trial solution, as well as all later evolved solutions are evaluated for genetic fitness, and the fit ones are cloned, combined and mutated. The final map consists of a superposition of a mass sheet and many Plummers, typically several hundred to a couple of thousand, each with its own size and weight, determined by the genetic algorithm. Critical curves, caustics and magnifications for any given source redshift are automatically available.
Future improvements LensPerfect is well suited to future datasets such as JWST imaging revealing still greater numbers of multiple images. Initial tests with hundreds to a thousand multiple images show great potential for resolving many individual cluster galaxy halos without assuming light traces mass. The biggest hurdle (seen in tests with up to 10,000 multiple images) may be accounting for multiple lens plane deflections due to mass along the line of sight. One potential improvement would be to develop a hybrid method combining light traces mass assumptions with LensPerfect adding deviations to the mass distribution.
Lenstool: the CATS and Johnson-Sharon models Lenstool as an inversion algorithm deploys both strong and weak lensing data as input constraints. Below, we first briefly outline the available capabilities of the tt Lenstool software package and then describe the specific versions and assumptions that were used to reconstruct Ares and Hera by two groups: CATS and Johnson-Sharon. The CATS collaboration developed the Lenstool algorithm collectively over a decade. The code utilizes the positions, magnitudes, shapes, multiplicity and spectroscopic redshifts for the multiply imaged background galaxies to derive the detailed mass distribution of the cluster. The overall mass distribution in cluster lenses is modeled in Lenstool as a super-position of smoother large-scale potentials and small scale substructure that is associated with the locations of bright, cluster member galaxies.
In order to tackle this challenge an iterative strategy has been developed wherein initial models are derived with the best-fit values solely from the parametric model, which are then optimized using the underlying multi-scale grid. Both the multi-scale and the parametric models are adjusted in a Bayesian way, i. e. ,their posterior probability density is probed with a MCMC sampler. This process allows an easy and reliable estimate of the errors on derived quantities such as the amplification maps and the mass maps. The CATS and the Sharon Johnson models are built using the Lenstool public modeling software (see e. g. Jullo et al. 2007). The public version of Lenstool deployed by Johnson-Sharon adopts the original modeling approach developed by  Natarajan & Kneib (1997)  wherein a small-scale dark-matter clump is associated with each bright cluster galaxy and a large-scale dark-matter clump with prominent concentrations of cluster galaxies.
2007 ). The parameters of cluster-scale halos are kept free, with the exception of r cut which is usually unconstrained by the strong lensing data, and is thus fixed at an arbitrary value (typically 1500 kpc). CATS also model galaxies as PIEMD, whereas Johnson-Sharon model galaxies as circular isothermal distributions (see 3. 6. 5). To keep the number of free parameters reasonably small, the parameters of galaxy-scale halos are determined from their photometric properties through scaling relations assuming a constant mass-to-light ratio for all galaxies, σ 0 = σ * 0 L L * 1/4 and r cut = r * cut L L * 1/2. ( 12 ) The positional parameters, x, y, e, and θ, are fixed to their observed values as measured from the light distribution in the imaging data. CATS used the simulated strong lensing catalogs and the Lenstool software to perform a mass reconstruction of both simulated clusters, assuming a parametric model for the distribution of dark matter.
Improvements in progress CATS is currently working actively on two improvements that should significantly improve the accuracy of their mass reconstructions. First, Lenstool in its current revision does not permit radial variation of the ellipticity for the mass distribution, and this restricts the flexibility of models that can be generated. Current code development aims to include this additional degree of freedom in the modeling. Secondly, in order to maximally extract information from the exquisite image resolution afforded by the HST FFI, flexion measurements will be included as input constraints in the modeling. Finally, a new MCMC engine with MPI support and a GPU-based Lenstool are under development to decrease the computing time.
For example, the inter-percentile range found for the CATS model is only 0. 21 and the median is r = 1. Similar results are found for the Zitrin-LTM-gauss model, although with a median slightly larger than unity. Some parametric models, such as those of Johnson-Sharon and Zitrin-NFW, and marginally GLAFIC, have skewed distributions with tails extending towards ratios larger than unity. Interestingly, Johnson-Sharon's model is based on the same modeling software employed by the CATS group. Among the free-form models, the distributions are generally broader than for the parametric methods. The distribution for the Bradac-Hoag model has median r = 1 and inter-percentile range 0. 32. Similar or slightly larger scatter is found for the GRALE and Coe models. The ratio distribution obtained for the Diego-reggrid model has a tail extending towards small values and its median is r = 0.
SUMMARY AND CONCLUSIONS In this paper we used simulated observations of two synthetic galaxy clusters to evaluate the performance of several algorithms for mass reconstruction with strong lensing. Such algorithms are currently being used to deliver to the community the lens models for the six galaxy clusters being observed in the Frontier Fields programme of the Hubble Space Telescope. The two clusters used in this study were obtained using very different techniques. Ares was generated using the semi-analytical code MOKA. Hera is instead the output of a cosmological N-body simulation at high resolution. The observable properties of the cluster galaxies are modeled using HOD and SAM techniques in Ares and Hera, respectively. In both cases, the clusters have complex mass distributions, characterized by disturbed and bi-modal morphology, similar to those of the FFI clusters.
We used the code SkyLens to simulate HST observations of the two mock clusters with both the ACS and the WFC3-IR camera. We produced images in all photometric bands used in the FFI, calibrating the exposure times such to reach the depth of the FFI observations. These HST simulated data were distributed to several groups of lens modelers for a blind analysis, i. e. without unveiling the true mass distribution of the lenses, neither the method used to simulate them. The simulated observations include lensing effects on a realistic distribution of background galaxies. We identified many strongly lensed galaxies and built a catalog of multiple image systems, which was delivered together with the simulated observations. The catalogs also include the redshift of all the sources. We complemented the HST simulations with a simulated observation in the R c band with the Subaru telescope.
For example, two groups (CATS and Johnson-Sharon) use the public code Lenstool with slight modifications. Similarly, Diego submitted several models of Hera using WSLAP+, which is the same code used by Lam et al. Despite using the same algorithms and making use of the same inputs (i. e. families of multiple images and redshifts), the reconstructions obtained by these groups are different, indicating that some choices made by the modelers when ingesting the data and hence set up priors influence the results. This is the first of a series of papers in which we address the issue of the accuracy of lens modeling. In a second paper, currently in preparation, we will discuss the results of the unblinded modeling of Ares and Hera. The feedback from the unblinding was used by modelers to not only tweak their best-fits to reach the best possible match to the input mass distributions of the lenses but to also incorporate and instigate improvements in their modeling procedure.
Acknowledgments We thank  T. Treu  for the helpful discussion. MM acknowledges support from the  Italian Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion , from  INAF  via  PRIN-INAF 2014 1. 05. 01. 94. 02 , and from  ASI  via contract  ASI/INAF/I/023/12/0. This work was supported in part by  World Premier International Research Center Initiative  ( WPI Initiative ),  MEXT, Japan , and  JSPS KAKENHI  Grant Number  26800093  and  15H05892. AZ is supported by  NASA  through  Hubble Fellowship grant  # HST-HF2-51334. 001-Aawarded by  STScI , which is operated by the  Association of Universities for Research in Astronomy, Inc. under  NASA  contract  NAS 5-26555. J. M. D acknowledges support of the consolider project  CSD2010-00064  and  AYA2012-39475-C02-01  funded by the  Ministerio de Economia y Competitividad, Spain.
Implementation and results Our system has been implemented on a platform with GPUs running OpenGL 4. 5. All experiments were carried out on a consumer PC with an Intel Core i7-5930 K 3. 5 GHz processor, 64 GB of RAM and an Nvidia GeForce GTX 780 graphics card. We used OpenGL compute shaders for executing code. As all of our point cloud and mesh data are organized as two-dimensional arrays, we utilized OpenGL textures for storage. Table  1  gives an overview of the time spent on different system processes. The measurements were taken with OpenGL query timers to get precise GPU time info. The experiment used two RGB-D cameras, both producing 640 Â 480 resolution depth maps, resulting in up to 614k points per frame. We ran the test with parameters given in Table  2. An overwhelming majority of processing time is spent on surface reconstruction.
This is due to fetching a large number of points and normals from GPU memory. Nevertheless, as the data are retrieved in s Â s square blocks from textures, the GPU cache is well utilized. We also implemented surface reconstruction on the CPU for comparison reasons. The execution has been parallelized across 6 processor cores using OpenMP. The average runtime was 1. 6 s per frame on the test dataset. This means that using a GPU gives us roughly 10Â the performance benefit over a CPU. Our mesh generation method boasts better performance than competing mesh zippering-based methods  [38, 48]. We generate two initial meshes in Sect. 5. 1 for the test dataset and compare the process in Sects. 5. 2-5. 4 with two previous methods. The Turk and Levoy  [48]  implementation takes 48 s, while the Marras et al.