dataesr/scientific-paragraphs-categorization huggingface dataset:
V. RESULTS We now proceed to compare all the available semiinclusive CC0πNp cross section measurements for T2K and MINERνA with the predictions of two approaches for the 1p1h sector: the RMF model based on Eq. (  1 ), where FSI are implemented using the different prescriptions described in Sec. II B, and the 1p1h GENIE-SuSAv2 implementation described in Sec. III. For both approaches we add on top the SuSAv2-2p2h MEC and pion absorption contributions calculated with GENIE. The processing of GENIE output and its comparison to experimental data was made using the NUISANCE framework  [55]. For MINERνA  [4, 34]  the comparison is made as a function of the muon and proton kinematics and as a function of the transverse kinematic imbalances (TKI)  [56, 57]  that measure correlations between the final muon and proton in the plane transverse to the neutrino direction.
All curves include the 2p2h and pion absorption contributions (also shown separately), evaluated using GENIE. Data taken from  [34]. Notice that the convention used in  [34]  to define the x and y axis is the opposite to the convention used in this paper, which is the same adopted in  [35]. due to the higher energy neutrino beam (hE ν i ¼ 3 GeV), the semi-inclusive cross sections predicted by ROP, and shown in Fig. 10  as function of the muon and proton kinematics, seem qualitatively in reasonable agreement with experimental measurements, except for the θ L N cross section where there is an underestimation of the cross section measurements for low values of θ L N and an overestimation in the high-θ L N region. This is partially confirmed by the χ 2 values presented in Table  V , which show ROP as the favored model which best matches the measurements of lepton kinematic variables.
ACKNOWLEDGMENTS This work is part of the  I þ D þ i  Projects No. PID2020-114687 GB-100  and No. RTI2018-098868-B-I00  funded by  Ministerio de Ciencia e Innovación (MCIN) (AED) , and by the  Junta de Andalucia  (Grants No. FQM160 , No. SOMM17/61015/UGR  and No. P20-01247 ); it is supported in part by the  University of Tokyo ICRR 's  Inter-University Resarch Program   FY2021  &  FY2022  (J. A. C. , M. B. B. , J. M. F. P. , G. D. M. , R. G. J. , J. M. U. ), by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 839481  (GDM), by the  Project University of Turin  (Project No. BARM-RILO-21 ) and by  INFN  (national project  NUCSYS ) (MBB and JMFP), and by the  government of Madrid and Complutense University  under Project No.
Discussion The naïve approach consists in reloading the neural network model in memory at each call. This is what would be done in a classic web application (without memory persistence) where each API call is independent. The optimized approach consists in manually preloading the neural network in memory and therefore make it available for each future call. Although performances were comparable with and without TorchServe, the optimized approach with preloading of the neural network has some shortcoming. First, the handling of the neural network is firmly coupled to the code of the application. On the contrary TorchServe, by proposing an API for inference, can be deployed on a server with a graphics processing unit, while having our web application and user interface on another server, in a microservices architecture.
Farming new species and promoting polyculture can enhance aquaculture sustainability. This implies to define the rearing conditions that meet the ecological requirements of a target species and/or to assess if different species can live in the same farming environment. However, there is a large number of rearing conditions and/or taxon combinations that can be considered. In order to minimise cumbersome and expensive empirical trials to explore all possibilities, we introduce a tool, AquaDesign. It is based on a R-script and package which help to determine farming conditions that are most likely suitable for species through in silico assessment. We estimate farming conditions potentially suitable for an aquatic organism by considering the species niche. We define the species n-dimensional niche hypervolume using a correlative approach in which the species niche is estimated by relating distribution data to environmental conditions.
Required input datasets are mined from several public databases. The assistant tool allows users to highlight (i) abiotic conditions that are most likely suitable for species and (ii) combinations of species potentially able to live in the same abiotic environment. Moreover, it offers the possibility to assess if a particular set of abiotic conditions or a given farming location is potentially suitable for the monoculture or the polyculture of species of interest. Our tool provides useful pieces of information to develop freshwater aquacultures. Using the large amount of biogeographic and abiotic information available in public databases allows us to propose a pragmatic and operational tool even for species for which abiotic requirements are poorly or not available in literature such as currently nonproduced species.
Moreover, species niche analyses could provide useful pieces of information to design suitable farming environment from scratch. Nevertheless, such an approach has not been implemented in aquatic farming development. In order to move it to an operational approach for aquaculture development, an easy-to-use packaged workflow to estimate and compare species niches should be provided to aquatic farming stakeholders. In this paper, we introduce an open source R-script and package  [16]  designed as an assistant tool, AquaDesign, to facilitate development of new animal species productions in monoculture and polyculture based on niche analyses  [17, 18]. Our goal is to use the large amount of biogeographic and environmental information available in public databases to provide a pragmatic and operational way to highlight species or species combination that could be further considered in new aquaculture developments.
Methodological and ecological assumptions to assess species niche Several methods are available to define species niche, but most studies use a correlative approach where the species niche is estimated by relating distribution data to environmental conditions  [19]. This approach is especially useful and pragmatic to estimate niches of large sets of species because it does not rely on time-and money-consuming experimental assessment in controlled conditions nor on detailed knowledge about ecological requirements of a particular species. Moreover, it relies on dataset mined from increasingly available ecological databases. Using species niche for aquaculture purpose implies first to determine which environmental variables must be considered as relevant for aquatic species. These relevant variables can be different between marine and freshwater species.
e. pond, raceway, recirculating aquaculture system, pen, and cage) according to scientific literature about ecological and physiological requirements of aquatic animal species (e. g. [20] [21] [22] [23] [24] [25] ) (S1 Table ). For all of these variables, a temporal variation occurs in the wild (i. e. seasonality). Therefore, we selected variables reflecting these variabilities (e. g. maximum temperature of the warmest month and minimum temperature of the coldest month). Then, we restricted the initial list according to the data availability of (i) each abiotic variable or (ii) proxy allowing inference of an abiotic variable (Table  1 ). Since correlative approach implies overlaying occurrence records with locally observed environmental variables, we chose to use only variables for which datasets can be mined from ecological databases with a worldwide coverage (Table  1 ).
Baseline datasets and data preparation Abiotic datasets are obtained from three databases, EarthEnv  [26] , FLO1K  [27] , and World-Clim  [28] , as well as from one R-package, geosphere  [29]  (Table  1 ). Occurrence records are mined from the online Global Biodiversity Information Facility (GBIF, http://www. gbif. org) using the R-package rgbif  [30]. The assistant tool allows users to select which species is/are downloaded by indicating species taxonomic names and returns a proposed correction in case of misspelling based on the GBIF taxonomical baseline. Occurrence data quality issues are minimised  [31, 32]  by removing potential spatial errors in GBIF using the R-package Coordi-nateCleaner  [33]. It eliminates suspicious data (e. g. isolated points, mismatch between coordinates and country code, see discarding criteria in  [33] ).
Since our tool is designed for freshwater aquaculture, points located in the sea are removed. Characterising niche by correlative approach requires that all occurrence and abiotic datasets share a same coordinate system (WGS84), spatial extent, and geographic scale. Therefore, our Rscript limits spatial extent to 56˚S to 60˚N and 145˚W to 180˚E, which is the current coverage of EarthEnv. Then, it aggregates datasets at the same resolution grid by (i) recording only species absence/presence per grid cell to account for differences in local sampling effort and to obtain reliable absence data  [34]  and (ii) merging abiotic dataset by calculating the mean of each abiotic variable within each grid cell, except for daylength values for which the value at the cell centre is attributed to the whole cell.
Assessing potential suitability of an available farming environment Assessing whether a particular farming environment is potentially suitable for a species of interest can be done by checking that abiotic conditions of this environment are included in the species hypervolume. The assistant tool allows performing this assessment for aquaculture facilities relying on local abiotic conditions (i. e. outdoor aquaculture) through three steps. First, species niche hypervolume is computed using the support vector machine method, which is appropriate when dimensionality is high as in our abiotic datasets (R-package hypervolume,  [36] ). Nevertheless, the method accuracy can be negatively impacted if the dimension number exceeds the log of occurrence sum  [36]. Therefore, the assistant tool reduces the dimensionality of the initial dataset while minimising collinearity between variables through a principal component analysis (PCA) in which only dimensions with an eigenvalue superior to 1 are retained (i.
e. Guttman-Kaiser criterion). Nevertheless, despite this dimensionality reduction, if the dimension number still exceeds the log of occurrence sum, the assistant tool returns a warning to users. During the PCA processing, abiotic variables are standardised. Second, the abiotic conditions corresponding to a given farming location are obtained by extracting the abiotic conditions occurring at the farming location (point or area) with the same procedure as the one to match species occurrences with local abiotic variable values. Alternatively, the user can directly provide values of abiotic variables, a useful option if farming conditions are regulated by farmers and modified from those occurring locally (e. g. use of thermal water from power plant for farming). Third, an inclusion test checks whether the projection of the given farming location/system in the n-dimensional space is included in the species niche hypervolume (R-package hypervolume,  [36] ).
For the former, the assistant tool can provide the abiotic conditions occurring at the population location. For the latter, we consider that the most commonly observed values of each abiotic variable across the species range are those which are the most likely suitable conditions for most of the populations that will be mixed. The assistant tool highlights these commonly observed conditions by displaying density diagrams for each variable. These diagrams are obtained using the geom_density function from the R-package ggplot2  [40].
Assessing polyculture potential feasibility The ability of several species to live in the same abiotic environment can be assessed through the intersection between species niche hypervolumes: species with non-null intersection can be potentially co-farmed. Nevertheless, larger intersect means that species can be co-farmed in a larger set of abiotic conditions. This makes their polyculture (i) feasible in a larger set of farming conditions and (ii) still possible in the event of abiotic variations in the farming environments (e. g. extreme climatic events in ponds). Therefore, the assistant tool allows (i) computing the intersection hypervolume between species and (ii) ranking all tested combinations according to the extent of the intersection hypervolume. Since the R-package hypervolume  [36]  did not allow comparing more than two hypervolumes at once, we designed the function hypervolume_set_n_intersection to compare n species.
9833˚N, 88. 9167˚E-89. 0667˚E in Bangladesh  [42]. Test case 3: earthen ponds with polyculture of African sharptooth catfish (Clarias gariepinus) and Nile tilapia (Oreochromis niloticus) in an area located at 1. 1667˚S-1. 6000˚S, 34. 1333˚E-35. 0167˚E in Tanzania. Each test case is hereafter analysed independently. To our knowledge, farming locations of the test cases have not been recorded as species occurrences in GBIF. Therefore, we considered these polyculture farming locations as an effective external dataset to validate the relevance of the assistant tool. Secondly, we assessed using the assistant tool if species of each test case can be potentially co-farmed. This assessment is based on intersections between the niche hypervolumes of all species from the same test case. We obtained occurrence records from GBIF for each species and abiotic datasets from EarthEnv, FLO1K, WorldClim, and the R-package geosphere (databases accessed on January 22, 2022).
Potential occurrence data quality issues were fixed with the R-package CoordinateCleaner. For each species, the number of occurrences were (before/ after fixing data quality issues): T. tinca = 145,701/133,242. We thus excluded C. catla from the test case 2. Then, we aggregated datasets at the 30 arcminutes resolution. We computed species niche hypervolume after reducing with a PCA and keeping only dimensions with an eigenvalue superior to 1. Since three species (B. gonionotus, C. mrigala, and L. rohita) from the test case 2 had still a dimension number exceeding the log of occurrence sum, we excluded them from the assessment. Then, in each test case, we computed the intersection between niche hypervolumes of all species. For all test cases, the intersection between all species niche hypervolumes was not null, meaning that polyculture of analysed species is feasible according to our approach.
Thirdly, we assessed using the assistant tool if the different polycultures can be performed at locations of each test case. We obtained the abiotic conditions at location of each farming place based on dataset mined from EarthEnv, FLO1K, WorldClim, and the R-package geosphere (databases accessed on January 22, 2022). Since we chose a 30 arcminutes resolution, the used coordinates of farming location were: test case 1 = 48. 25˚N; -1. 75˚E; test case 2 = 23. 75˚N; 88. 75˚E; test case 3 = -1. 25˚N; 34. 75˚E. Then, we performed inclusion tests to assess if each given farming location is included in the intersection hypervolume between all analysed species of each test case. For each test case, farming location was included in the intersection hypervolume. This means that already known instances of polyculture are considered as feasible at their specific location according to the assistant tool.
Limits and prospects Overall, three main limitations of our assistant tool should be stated. Firstly, although GBIF hosts occurrence datasets for several tens of thousands of fish species (sensu all taxa included in Myxini [hagfishes], Cephalaspidomorphi [lampreys], Holocephali [chimaeras], Elasmobranchii [sharks and rays], Sarcopterygii [lobe-finned fishes], and Actinopterygii [ray-finned fishes]), the quantity of information is uneven across species (see test cases). Special attention should be paid to quantity, distribution, and density of occurrences available for each species prior to using the assistant tool. Indeed, low occurrence dataset quality can result in biased assessment for the three here above described method applications. Nevertheless, assuming that the assistant tool will be mainly used for species of interest, which have thus been the focus of an abundant research, quantitative and qualitative datasets is likely to be available for users.
Secondly, using correlative approach for niche analyses allows estimating realised niche (i. e. all the environmental conditions where the species currently lives) rather than fundamental niche (i. e. the full range of environmental conditions in which a species is able to live, without any other limiting factors that could constrain the species distribution). Therefore, we cannot rule out that assistant tool results would be too restrictive due to an underestimation of species fundamental niche. However, determining fundamental niche is still hard-to-achieve without  detailed knowledge on species (e. g. [43] ). We argue thus that the realised niche remains the most operational approach to propose a pragmatic tool for aquaculture development. Finally, we designed our assistant tool according to worldwide biogeographic and abiotic databases currently available.
It implied to (i) discard potentially key abiotic variables (S1 Table ) due to their poor availability at large geographic scale or to (ii) use proxies which are only partially correlated to key variables. This can potentially decrease the relevance of our approach. However, we speculate that future development of databases will make available more abiotic variables which will be integrated in our approach and will reinforce the relevance of assessment performed by our assistant tool. The current version of our assistant tool mainly allows assessing or designing farming environments relying on local abiotic conditions for freshwater productions. However, it can be applied to facilitate the development of indoor highly human-controlled systems (e. g. indoor recirculated aquaculture systems) or marine species productions provided that the list of abiotic variables is adjusted to the farming system specificities (e.
Funding: NA is supported by the  French Ministère de l'Enseignement Superieur et de la Recherche (France). This study was supported by the research programmes SEPURE (funded by the  European Fund for Maritime Affairs and Fisheries, FEAMP ,  UAEE-FEAMP-47-SEPURE ) and  Interreg   GR VA Perciponie-073-4-08-191  ( 2020-2022 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All required datasets can be downloaded from GBIF (www. gbif. org), EarthEnv (www. earthenv. org/streams), FLO1K (DOI: 10. 1038/sdata. 2018. 52), WorldClim (www. worldclim. org), and R-package geosphere (cran. rproject. org/web/packages/geosphere/index. html) using our R-script available on Figshare (DOI: 10. 6084/m9. figshare. 17170958. v3).
Tofu is a toolkit for processing large amounts of images and for tomographic reconstruction. Complex image processing tasks are organized as workflows of individual processing steps. The toolkit is able to reconstruct parallel and cone beam as well as tomographic and laminographic geometries. Many pre-and post-processing algorithms needed for high-quality 3D reconstruction are available, e. g. phase retrieval, ring removal and de-noising. Tofu is optimized for stand-alone GPU workstations on which it achieves reconstruction speed comparable with costly CPU clusters. It automatically utilizes all GPUs in the system and generates 3D reconstruction code with minimal number of instructions given the input geometry (parallel/cone beam, tomography/ laminography), hence yielding optimal run-time performance.
, 2014)  offers excellent run-time performance; Tomopy (Gu ¨rsoy et al. , 2014), Savu  (Atwood et al. , 2015)  and the Syrmep Tomo Project  (Brun et al. , 2017)  provide complex capabilities by auxiliary pre-and post-processing algorithms and interfaces to other 3D reconstruction tools. They provide command line interfaces (CLI), graphical user interfaces (GUI), or both. In contrast to the flexibility of open source projects, reconstruction software supplied with laboratory scanners and commercially available reconstruction software like Octopus  (Vlassenbroeck et al. , 2006)  or VG Studio Max (https://www. volumegraphics. com/en/products/vgsm/ct-reconstruction-data -quality-analysis. html) are normally a black box, albeit with a very nice GUI, which reconstructs data following a predefined workflow.
However, most of these systems cannot be modified to incorporate external algorithms into the reconstruction chain, for instance, trying a new phase-retrieval method. At the same time, there are more and more communications on custom-build laboratory systems for X-ray mCT [see  Mu ¨ller et al. (2017)  and references therein, and  Polyakov et al. (2017) ]. Availability of user-friendly, easily extensible, and opensource software which can be used for phase-retrieval and reconstruction of cone-beam data can certainly facilitate new developments. Here, we present tofu, a Python software package for general image processing, but with special emphasis on tomographic reconstruction that supports parallel, cone beam, tomographic and laminographic geometries. It is user-friendly without compromising processing speed and flexibility provided by the ufo framework  (Vogelgesang et al.
, 2012)  back-end. Unlike the existing open source tools, tofu connects the image processing algorithms into a workflow on the ufo framework (OpenCL) layer. Once the data is loaded, it stays in the GPU memory as it passes through the workflow. Hence, the processing time is significantly reduced as there is no need to download intermediate results to the main memory in order to pass them to the the subsequent stages of the workflow. Tofu is further equipped with versatile graphical user interfaces. Tofu flow is a GUI for visual programming of image processing workflows. Thanks to the fast execution, one can vary the parameters and observe changes in the output interactively. Once the optimal combination of algorithms and parameters is found, tofu ez can be used to automatically reconstruct multiple tomographic data sets.
In addition to the GUIs, there is a multitude of command line interface (CLI) sub-commands for experienced users, which can be embedded into scripts and other programs. Three-dimensional (3D) reconstruction in tofu is flexible and supports complex scanning scenarios, such as helical CT. It includes algorithms for reduction of typical artifacts, e. g. rings, 'zingers' and noise. Several phase retrieval algorithms and many general image processing filters (e. g. pad, crop, blur) are available as well. Scalability for 3D reconstruction is achieved by automatically splitting the data to all available GPUs in the system and by sub-dividing the final volume into several sub-volumes if needed. Tofu is open source with extensive documentation available online for both end-users and developers. In the next section a brief description of the software structure is presented; the most important and frequently used algorithms are listed and the implementation of filtered back projection is described in more detail.