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Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification.

Olivier Pannekoucke and Ronan Fablet. "PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations", Geoscientific Model Development (2020) https://doi.org/10.5194/gmd-2020-35

Description of the version

Version of the package based on tensorflow.keras, where neural network can be generated by using TrainableScalar or exogenous network.

Examples

As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.

Olivier Pannekoucke and Ronan Fablet. "PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations", Geoscientific Model Development (2020) https://doi.org/10.5194/gmd-2020-35

Description of the version

Version of the package based on tensorflow.keras, where neural network can be generated by using TrainableScalar or exogenous network.

Examples

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Olivier Pannekoucke and Ronan Fablet. "PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations", Geoscientific Model Development (2020) https://doi.org/10.5194/gmd-2020-35

Description of the version

Version of the package based on tensorflow.keras, where neural network can be generated by using TrainableScalar or exogenous network.

Examples

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Description of the version

Version of the package based on tensorflow.keras, where neural network can be generated by using TrainableScalar or exogenous network.

Examples

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It has been developed as a plugin of Scipion 3 software (https://github.com/scipion-em/scipion-em-continuousflex). Currently, ContinuousFlex provides HEMNMA, StructMap, HEMNMA-3D and TomoFlow methods. -HEMNMA: Hybrid Electron Microscopy Normal Mode Analysis method to interpret heterogeneity of a set of single particle cryo-EM images in terms of continuous macromolecular conformational transitions [1-3] -StructMap: Structural Mapping method to interpret heterogeneity of a set of single particle cryo-EM maps in terms of continuous conformational transitions [4] -HEMNMA-3D: Extension of HEMNMA to continuous conformational variability analysis of macromolecules from in situ cryo-ET subtomograms [5,7] -TomoFlow: Method for analyzing continuous conformational variability of macromolecules in in vitro and in situ cryogenic subtomograms based on 3D dense optical flow [8-9] Notes: -The plugin additionally provides the test data and automated tests of the protocols in Scipion 3. The following two types of tests of HEMNMA and HEMNMA-3D can be produced by running, in the terminal, \"scipion3 tests continuousflex.tests.test_workflow_HEMNMA\" and “scipion3 tests continuousflex.tests.test_workflow_HEMNMA3D”, respectively: (1) tests of the entire protocol with the flexible references coming from an atomic structure and from an EM map; and (2) test of the alignment module (test run using 5 MPI threads). The automated tests of the TomoFlow method are also available and can be run using \"scipion3 tests continuousflex.tests.test_workflow_TomoFlow\". -HEMNMA additionally provides tools for synthesizing noisy and CTF-affected single particle cryo-EM images with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -HEMNMA-3D additionally provides tools for synthesizing noisy, CTF and missing wedge affected cryo-ET tomograms and single particle subtomograms with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -A reproduction of some utility codes with their corresponding licenses are contained in this plugin for subtomogram averaging, missing wedge correction, denoising and data reading. These codes are not used in the methods above, but they are made optional for data preprocessing and visualization. References: [1] Jin Q, Sorzano CO, de la Rosa-Trevin JM, Bilbao-Castro JR, Nunez-Ramirez R, Llorca O, Tama F, Jonic S: Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure 2014, 22:496-506. [2] Jonic S: Computational methods for analyzing conformational variability of macromolecular complexes from cryo-electron microscopy images. Curr Opin Struct Biol 2017, 43:114-121. [3] Harastani M, Sorzano CO, Jonic S: Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2020, 29:223-36. [4] Sanchez Sorzano CO, Alvarez-Cabrera AL, Kazemi M, Carazo JM, Jonic S: StructMap: Elastic Distance Analysis of Electron Microscopy Maps for Studying Conformational Changes. Biophys J 2016, 110:1753-1765. [5] Harastani M, Eltsov M, Leforestier A, Jonic S: HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes. Front Mol Biosci 2021, 8:663121. [6] Jonic S, Sorzano CO, Thevenaz P, El-Bez C, De Carlo S, Unser M: Spline-based image-to-volume registration for three-dimensional electron microscopy. Ultramicroscopy 2005, 103:303-317. [7] Harastani M and Jonic S: Methods for analyzing continuous conformational variability of biomolecules in cryo electron subtomograms: HEMNMA-3D vs. traditional classification. BioRxiv 2021 (https://doi.org/10.1101/2021.10.14.464366) [8] Harastani M, Eltsov M, Leforestier A, Jonic S: TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow. J Mol Biol 2021, 167381. 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It has been developed as a plugin of Scipion 3 software (https://github.com/scipion-em/scipion-em-continuousflex). Currently, ContinuousFlex provides HEMNMA, StructMap, HEMNMA-3D and TomoFlow methods. -HEMNMA: Hybrid Electron Microscopy Normal Mode Analysis method to interpret heterogeneity of a set of single particle cryo-EM images in terms of continuous macromolecular conformational transitions [1-3] -StructMap: Structural Mapping method to interpret heterogeneity of a set of single particle cryo-EM maps in terms of continuous conformational transitions [4] -HEMNMA-3D: Extension of HEMNMA to continuous conformational variability analysis of macromolecules from in situ cryo-ET subtomograms [5,7] -TomoFlow: Method for analyzing continuous conformational variability of macromolecules in in vitro and in situ cryogenic subtomograms based on 3D dense optical flow [8-9] Notes: -The plugin additionally provides the test data and automated tests of the protocols in Scipion 3. The following two types of tests of HEMNMA and HEMNMA-3D can be produced by running, in the terminal, \"scipion3 tests continuousflex.tests.test_workflow_HEMNMA\" and “scipion3 tests continuousflex.tests.test_workflow_HEMNMA3D”, respectively: (1) tests of the entire protocol with the flexible references coming from an atomic structure and from an EM map; and (2) test of the alignment module (test run using 5 MPI threads). The automated tests of the TomoFlow method are also available and can be run using \"scipion3 tests continuousflex.tests.test_workflow_TomoFlow\". -HEMNMA additionally provides tools for synthesizing noisy and CTF-affected single particle cryo-EM images with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -HEMNMA-3D additionally provides tools for synthesizing noisy, CTF and missing wedge affected cryo-ET tomograms and single particle subtomograms with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -A reproduction of some utility codes with their corresponding licenses are contained in this plugin for subtomogram averaging, missing wedge correction, denoising and data reading. These codes are not used in the methods above, but they are made optional for data preprocessing and visualization. References: [1] Jin Q, Sorzano CO, de la Rosa-Trevin JM, Bilbao-Castro JR, Nunez-Ramirez R, Llorca O, Tama F, Jonic S: Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure 2014, 22:496-506. [2] Jonic S: Computational methods for analyzing conformational variability of macromolecular complexes from cryo-electron microscopy images. Curr Opin Struct Biol 2017, 43:114-121. [3] Harastani M, Sorzano CO, Jonic S: Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2020, 29:223-36. [4] Sanchez Sorzano CO, Alvarez-Cabrera AL, Kazemi M, Carazo JM, Jonic S: StructMap: Elastic Distance Analysis of Electron Microscopy Maps for Studying Conformational Changes. Biophys J 2016, 110:1753-1765. [5] Harastani M, Eltsov M, Leforestier A, Jonic S: HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes. Front Mol Biosci 2021, 8:663121. [6] Jonic S, Sorzano CO, Thevenaz P, El-Bez C, De Carlo S, Unser M: Spline-based image-to-volume registration for three-dimensional electron microscopy. Ultramicroscopy 2005, 103:303-317. [7] Harastani M and Jonic S: Methods for analyzing continuous conformational variability of biomolecules in cryo electron subtomograms: HEMNMA-3D vs. traditional classification. BioRxiv 2021 (https://doi.org/10.1101/2021.10.14.464366) [8] Harastani M, Eltsov M, Leforestier A, Jonic S: TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow. J Mol Biol 2021, 167381. [9] Harastani M and Jonic S: Synthetic cryo electron subtomograms containing biomolecular complexes with continuous conformational variability.[Data set] Zenodo. 2021 (https://doi.org/10.5281/zenodo.5718820).","fr":"ContinuousFlex allows analyzing single particle cryo electron microscopy data and in vitro and in situ cryo electron subtomogram data for continuous conformational variability of biomolecular complexes. It has been developed as a plugin of Scipion 3 software (https://github.com/scipion-em/scipion-em-continuousflex). Currently, ContinuousFlex provides HEMNMA, StructMap, HEMNMA-3D and TomoFlow methods. -HEMNMA: Hybrid Electron Microscopy Normal Mode Analysis method to interpret heterogeneity of a set of single particle cryo-EM images in terms of continuous macromolecular conformational transitions [1-3] -StructMap: Structural Mapping method to interpret heterogeneity of a set of single particle cryo-EM maps in terms of continuous conformational transitions [4] -HEMNMA-3D: Extension of HEMNMA to continuous conformational variability analysis of macromolecules from in situ cryo-ET subtomograms [5,7] -TomoFlow: Method for analyzing continuous conformational variability of macromolecules in in vitro and in situ cryogenic subtomograms based on 3D dense optical flow [8-9] Notes: -The plugin additionally provides the test data and automated tests of the protocols in Scipion 3. The following two types of tests of HEMNMA and HEMNMA-3D can be produced by running, in the terminal, \"scipion3 tests continuousflex.tests.test_workflow_HEMNMA\" and “scipion3 tests continuousflex.tests.test_workflow_HEMNMA3D”, respectively: (1) tests of the entire protocol with the flexible references coming from an atomic structure and from an EM map; and (2) test of the alignment module (test run using 5 MPI threads). The automated tests of the TomoFlow method are also available and can be run using \"scipion3 tests continuousflex.tests.test_workflow_TomoFlow\". -HEMNMA additionally provides tools for synthesizing noisy and CTF-affected single particle cryo-EM images with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -HEMNMA-3D additionally provides tools for synthesizing noisy, CTF and missing wedge affected cryo-ET tomograms and single particle subtomograms with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -A reproduction of some utility codes with their corresponding licenses are contained in this plugin for subtomogram averaging, missing wedge correction, denoising and data reading. These codes are not used in the methods above, but they are made optional for data preprocessing and visualization. References: [1] Jin Q, Sorzano CO, de la Rosa-Trevin JM, Bilbao-Castro JR, Nunez-Ramirez R, Llorca O, Tama F, Jonic S: Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure 2014, 22:496-506. [2] Jonic S: Computational methods for analyzing conformational variability of macromolecular complexes from cryo-electron microscopy images. Curr Opin Struct Biol 2017, 43:114-121. [3] Harastani M, Sorzano CO, Jonic S: Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2020, 29:223-36. [4] Sanchez Sorzano CO, Alvarez-Cabrera AL, Kazemi M, Carazo JM, Jonic S: StructMap: Elastic Distance Analysis of Electron Microscopy Maps for Studying Conformational Changes. Biophys J 2016, 110:1753-1765. [5] Harastani M, Eltsov M, Leforestier A, Jonic S: HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes. Front Mol Biosci 2021, 8:663121. [6] Jonic S, Sorzano CO, Thevenaz P, El-Bez C, De Carlo S, Unser M: Spline-based image-to-volume registration for three-dimensional electron microscopy. Ultramicroscopy 2005, 103:303-317. [7] Harastani M and Jonic S: Methods for analyzing continuous conformational variability of biomolecules in cryo electron subtomograms: HEMNMA-3D vs. traditional classification. BioRxiv 2021 (https://doi.org/10.1101/2021.10.14.464366) [8] Harastani M, Eltsov M, Leforestier A, Jonic S: TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow. J Mol Biol 2021, 167381. [9] Harastani M and Jonic S: Synthetic cryo electron subtomograms containing biomolecular complexes with continuous conformational variability.[Data set] Zenodo. 2021 (https://doi.org/10.5281/zenodo.5718820)."},"isLibreSoftware":true,"websiteUrl":"https://hal.science/hal-03501129v1","sourceUrl":"https://github.com/scipion-em/scipion-em-continuousflex","license":"GNU General Public License v3.0 or later","providers":[]},"similarExternalSoftwares":[],"hasExpertReferent":false,"userAndReferentCountByOrganization":{},"instances":[]},{"serviceProviders":[],"categories":[],"description":"ContinuousFlex allows analyzing single particle cryo electron microscopy data and in vitro and in situ cryo electron subtomogram data for continuous conformational variability of biomolecular complexes. It has been developed as a plugin of Scipion 3 software (https://github.com/scipion-em/scipion-em-continuousflex). Currently, ContinuousFlex provides HEMNMA, StructMap, HEMNMA-3D and TomoFlow methods. -HEMNMA: Hybrid Electron Microscopy Normal Mode Analysis method to interpret heterogeneity of a set of single particle cryo-EM images in terms of continuous macromolecular conformational transitions [1-3] -StructMap: Structural Mapping method to interpret heterogeneity of a set of single particle cryo-EM maps in terms of continuous conformational transitions [4] -HEMNMA-3D: Extension of HEMNMA to continuous conformational variability analysis of macromolecules from in situ cryo-ET subtomograms [5,7] -TomoFlow: Method for analyzing continuous conformational variability of macromolecules in in vitro and in situ cryogenic subtomograms based on 3D dense optical flow [8-9] Notes: -The plugin additionally provides the test data and automated tests of the protocols in Scipion 3. The following two types of tests of HEMNMA and HEMNMA-3D can be produced by running, in the terminal, \"scipion3 tests continuousflex.tests.test_workflow_HEMNMA\" and “scipion3 tests continuousflex.tests.test_workflow_HEMNMA3D”, respectively: (1) tests of the entire protocol with the flexible references coming from an atomic structure and from an EM map; and (2) test of the alignment module (test run using 5 MPI threads). The automated tests of the TomoFlow method are also available and can be run using \"scipion3 tests continuousflex.tests.test_workflow_TomoFlow\". -HEMNMA additionally provides tools for synthesizing noisy and CTF-affected single particle cryo-EM images with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -HEMNMA-3D additionally provides tools for synthesizing noisy, CTF and missing wedge affected cryo-ET tomograms and single particle subtomograms with flexible or rigid biomolecular conformations, for several types of conformational distributions, from a given atomic structure or an EM map. One part of the noise is applied on the ideal projections before and the other after the CTF, as described in [6]. -A reproduction of some utility codes with their corresponding licenses are contained in this plugin for subtomogram averaging, missing wedge correction, denoising and data reading. These codes are not used in the methods above, but they are made optional for data preprocessing and visualization. References: [1] Jin Q, Sorzano CO, de la Rosa-Trevin JM, Bilbao-Castro JR, Nunez-Ramirez R, Llorca O, Tama F, Jonic S: Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. Structure 2014, 22:496-506. [2] Jonic S: Computational methods for analyzing conformational variability of macromolecular complexes from cryo-electron microscopy images. Curr Opin Struct Biol 2017, 43:114-121. [3] Harastani M, Sorzano CO, Jonic S: Hybrid Electron Microscopy Normal Mode Analysis with Scipion. Protein Sci 2020, 29:223-36. [4] Sanchez Sorzano CO, Alvarez-Cabrera AL, Kazemi M, Carazo JM, Jonic S: StructMap: Elastic Distance Analysis of Electron Microscopy Maps for Studying Conformational Changes. Biophys J 2016, 110:1753-1765. [5] Harastani M, Eltsov M, Leforestier A, Jonic S: HEMNMA-3D: Cryo Electron Tomography Method Based on Normal Mode Analysis to Study Continuous Conformational Variability of Macromolecular Complexes. Front Mol Biosci 2021, 8:663121. [6] Jonic S, Sorzano CO, Thevenaz P, El-Bez C, De Carlo S, Unser M: Spline-based image-to-volume registration for three-dimensional electron microscopy. Ultramicroscopy 2005, 103:303-317. [7] Harastani M and Jonic S: Methods for analyzing continuous conformational variability of biomolecules in cryo electron subtomograms: HEMNMA-3D vs. traditional classification. BioRxiv 2021 (https://doi.org/10.1101/2021.10.14.464366) [8] Harastani M, Eltsov M, Leforestier A, Jonic S: TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow. J Mol Biol 2021, 167381. 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Its primary goal is to analyze how these students interact with urban spaces, considering their sensory and material perceptions, as well as their social and cultural interactions. This repository contains the source code of the MOBILES application, developed in Unity/C# for Android and iOS. The application allows international students to create rich geolocated annotations (text, photos, icons, moods) and share them. They can also initiate and detail routes reflecting their personalized journeys through the city. These routes can be automatically generated via GPS tracking or manually entered on an interactive map, contributing to the individual appropriation of space. The interactive map is at the heart of the application, providing an interface to visualize annotations and routes shared by the community, as well as to add one's own contributions. The application’s code manages geolocation, annotation creation and management, as well as route visualization on the map. Information related to users and generated content is stored in a MySQL database, which also manages the application’s configuration settings. A kTBS system records digital traces of user interactions, facilitating the analysis of behavior and application usage. The application also integrates Firebase for push notification management, enabling real-time alerts about recommendations, suggestions, or interactions with other users. This system ensures data consistency between different sessions, synchronizing information between the mobile app and the backend.","fr":"Le projet MOBILES se concentre sur l'étude des expériences spatiales et de la socialisation des étudiants internationaux à travers leurs pratiques numériques, en combinant sociolinguistique, géographie et sciences informatiques. Son objectif principal est d'analyser comment ces étudiants interagissent avec les espaces urbains, en tenant compte de leurs perceptions sensorielles et matérielles, ainsi que de leurs interactions sociales et culturelles. Ce dépôt contient code source de l'application MOBILES, développée en Unity/C# pour Android et iOS. L'application permet aux étudiants internationaux de créer des annotations géolocalisées riches (texte, photos, icônes, humeurs) et de les partager. Ces derniers peuvent également initier et détailler des parcours reflétant leurs trajets personnalisés à travers la ville. Ces parcours peuvent être générés automatiquement par suivi GPS ou renséignés manuellement sur une carte interactive, contribuent à l'appropriation individuelle du territoire. La carte interactive constitue le cœur de l'application, offrant une interface pour visualiser les annotations et parcours partagés par la communauté, et pour ajouter ses propres contributions. 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L'ontologie a été élaborée dans une approche constructiviste, à partir d'une analyse des pratiques informationnelles des employés de l'unité d'allergologie et d'une analyse des documents que ces employés utilisent dans leur quotidien. Pour s'informer davantage sur les objectifs et la méthodologies d'élaboration de l'ontologie, consulter la thèse sur : https://theses.hal.science/tel-04310219 . La version 3.0 comprend des modifications des versions 1.1 et 2.0 effectuées en février-mars 2025 par Marcin Trzmielewski (LERASS), avec la contribution des étudiantes de la licence professionnelle en documentation : Anne Guerre, Nada Bouhsina, Sabrina Duc et Claire Casasso (Université de Montpellier Paul-Valéry). La version 3.0 contient également une nouvelle structuration au niveau des classes principales (facettes) et d'autres niveaux de profondeur classificatoire. 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(David Coeurjolly, #1631) I/O Imagemagick dependency and related classes. Image file format (png, jpg, tga, bmp, gif) are now included in the DGtal core using stb_image.h and stb_image_write.h. (David Coeurjolly, #1648) Changes Image Bugfix in the SpaceND and HyperRectDomain classes to allow very large extent (e.g. >$1024^3$) (David Coeurjolly, #1636) Improved ITK image selection in ImageSelector and add ITK xx.gz an other format support. New option to keep set domain or to compute current bounding box of elements of the set in ImageFromSet. (Bertrand Kerautret, #1633) Improved MeshReader for .off format in order to take into account more comments and other header code used in CGAL. (Bertrand Kerautret, #1653 and #1654) IO Add Obj format in MeshReader including colors and fixing obj format read with relative face position. (Bertrand Kerautret, #1584) Move static private HSVtoRGB and RGBtoHSV functions in Color class (public) and new setters/getters from/to HSV (Python binding updated) (Bertrand Kerautret, Phuc Ngo and David Coeurjolly #1593) Geometry Small fix for shortest paths computation, which could sometimes output several times the same node. Add tests and examples. (Jacques-Olivier Lachaud, #1644) First and second curvature directions were inverted in the IIPrincipalCurvaturesAndDirectionsFunctor, fixed now. (David Coeurjolly, #1657) Renaming getVoronoiVector to getVoronoiSite in the DistanceTransformation class. (David Coeurjolly, #1660) Kernel New constructor in Point2DEmbedderIn3D to explicitly orient the image plane and new shift method to avoid recomputing orientation plane. (Bertrand Kerautret #1619) Build New cmake targets to collect cmake, doxygen and markdown files (David Coeurjolly, #1609) Continuous integration does not use Travis anymore but Github Actions. (David Coeurjolly, #1591) New cmake option (DGTAL_RANDOMIZED_TESTING_THRESHOLD) to set the (approximated) % of unit-tests to build and run for randomized testing (David Coeurjolly #1588) Fix missing whitelist for the unit-tests in relation to PR #1591) (Bertrand Kerautret #1595) Fix cmake related ITK usage in other projects (issue #1612). (Bertrand Kerautret and Pablo Hernandez-Cerdan #1613) Adding ITK in Github Actions CI on linux distribution. (Bertrand Kerautret #1615) New variable in the Github Action script to disable some tests (not working in the bots) (David Coeurjolly, #1635) Google benchmark is now fetched when building the unit tests (using Fetch_Content) (David Coeurjolly, [#1651] #1651)) Add new cmake option to avoid linking errors related to STB image library (like LNK2005 in MSVC). (Bertrand Kerautret, #1666) Bug fixes General Fixing OpenMP dependency on macOS when using the DGtalConfig.cmake(David Coeurjolly, #1578) Various warnings / deprecated functions (David Coeurjolly, #1583 Removing old snapshot of catch.hpp. Now DGtal compiles on Apple M1 (David Coeurjolly, #1590 Fix cmake IN_LIST use policy. (Bertrand Kerautret, #1592) Adding a explicit list of tests to exclude from Github Actions (David Coeurjolly, #1596) Fixing bugs in the exclude list for CI (David Coeurjolly, #1602) Reactivating Github Actions bots (David Coeurjolly, #1628) OpenMP fix in DGtalConfig on macOS M1 (David Coeurjolly, #1641) New doxygen settings to reduce diagram generation (David Coeurjolly, #1663) Examples We can now have examples using polyscope as viewer (BUILD_POLYSCOPE_EXAMPLES cmake variable). (David Coeurjolly, #1603) IO Faster export of OBJ files. (David Coeurjolly, #1608 Fixing bugs in writing Longvol from GenericWriter and tests. 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It takes the form of an open-source C++ library DGtal and a set of tools and binaries DGtalTools. This version corresponds to the release 1.3 of DGtal and its tools. New features / critical changes General A Dockerfile is added to create a Docker image to have a base to start development using the DGtal library.(J. Miguel Salazar #1580) Continuous integration does not use Travis anymore but Github Actions. (David Coeurjolly, #1591) Examples are not built anymore by default (BUILD_EXAMPLES now set to OFF by default). (David Coeurjolly, #1630) Geometry package Improve lattice polytope count operations and provide many new services related to full convexity, like computing the (relative or not) fully convex envelope, and building digital polyhedra. (Jacques-Olivier Lachaud, #1656) Add curvature measures computation on 3D surface mesh: implements Normal Cycle, face-constant Corrected Normal Current and vertex-interpolated Corrected Normal Current. (Jacques-Olivier Lachaud,#1617) Completes the digital convexity module with new functions related to full convexity: check of full convexity for arbitrary digital sets in nD, and helper classes for using full convexity in practice (local geometric analysis, tangency and shortest paths) (Jacques-Olivier Lachaud,#1594) New VoronoiMapComplete class to store the full Voronoi map (with all co-cycling sites (Robin Lamy, David Coeurjolly, Isabelle Sivignon #1605) DEC New discrete differential operators on polygonal meshes have been added. They can be used to process generic polygonal meshes (with non-planar, non-convex faces) or digital surfaces. (David Coeurjolly, #1603 New class to compute geodesics on polygonal surfaces using the Geodesics in Heat approach and the new differential operators on polygonal surfaces (digital surfaces, or any PolygonalMesh instance) (David Coeurjolly, #1603 Updates to PolygonalCalculus: changing sign convention, fix some Eigen problems, add Dirichlet boundary conditions, update discrete differential calculus examples (Jacques-Olivier Lachaud, #1643) Updates to PolygonalCalculus: adding vector field operators (mainly covariant gradient and covariant projection as well as Connection-Laplacian). Also adding two more examples: harmonic parametrization and vectors in heat method. (Baptiste Genest, David Coeurjolly, #1646) Mathematical Package Add Lagrange polynomials and Lagrange interpolation (Jacques-Olivier Lachaud, #1594) Topology New helper methods to retrieve the interior/exterior voxel of a given surfel (signed cell of a Khalimksy space). (David Coeurjolly, #1631) I/O Imagemagick dependency and related classes. Image file format (png, jpg, tga, bmp, gif) are now included in the DGtal core using stb_image.h and stb_image_write.h. (David Coeurjolly, #1648) Changes Image Bugfix in the SpaceND and HyperRectDomain classes to allow very large extent (e.g. >$1024^3$) (David Coeurjolly, #1636) Improved ITK image selection in ImageSelector and add ITK xx.gz an other format support. New option to keep set domain or to compute current bounding box of elements of the set in ImageFromSet. (Bertrand Kerautret, #1633) Improved MeshReader for .off format in order to take into account more comments and other header code used in CGAL. (Bertrand Kerautret, #1653 and #1654) IO Add Obj format in MeshReader including colors and fixing obj format read with relative face position. (Bertrand Kerautret, #1584) Move static private HSVtoRGB and RGBtoHSV functions in Color class (public) and new setters/getters from/to HSV (Python binding updated) (Bertrand Kerautret, Phuc Ngo and David Coeurjolly #1593) Geometry Small fix for shortest paths computation, which could sometimes output several times the same node. Add tests and examples. (Jacques-Olivier Lachaud, #1644) First and second curvature directions were inverted in the IIPrincipalCurvaturesAndDirectionsFunctor, fixed now. (David Coeurjolly, #1657) Renaming getVoronoiVector to getVoronoiSite in the DistanceTransformation class. (David Coeurjolly, #1660) Kernel New constructor in Point2DEmbedderIn3D to explicitly orient the image plane and new shift method to avoid recomputing orientation plane. (Bertrand Kerautret #1619) Build New cmake targets to collect cmake, doxygen and markdown files (David Coeurjolly, #1609) Continuous integration does not use Travis anymore but Github Actions. (David Coeurjolly, #1591) New cmake option (DGTAL_RANDOMIZED_TESTING_THRESHOLD) to set the (approximated) % of unit-tests to build and run for randomized testing (David Coeurjolly #1588) Fix missing whitelist for the unit-tests in relation to PR #1591) (Bertrand Kerautret #1595) Fix cmake related ITK usage in other projects (issue #1612). (Bertrand Kerautret and Pablo Hernandez-Cerdan #1613) Adding ITK in Github Actions CI on linux distribution. (Bertrand Kerautret #1615) New variable in the Github Action script to disable some tests (not working in the bots) (David Coeurjolly, #1635) Google benchmark is now fetched when building the unit tests (using Fetch_Content) (David Coeurjolly, [#1651] #1651)) Add new cmake option to avoid linking errors related to STB image library (like LNK2005 in MSVC). (Bertrand Kerautret, #1666) Bug fixes General Fixing OpenMP dependency on macOS when using the DGtalConfig.cmake(David Coeurjolly, #1578) Various warnings / deprecated functions (David Coeurjolly, #1583 Removing old snapshot of catch.hpp. Now DGtal compiles on Apple M1 (David Coeurjolly, #1590 Fix cmake IN_LIST use policy. (Bertrand Kerautret, #1592) Adding a explicit list of tests to exclude from Github Actions (David Coeurjolly, #1596) Fixing bugs in the exclude list for CI (David Coeurjolly, #1602) Reactivating Github Actions bots (David Coeurjolly, #1628) OpenMP fix in DGtalConfig on macOS M1 (David Coeurjolly, #1641) New doxygen settings to reduce diagram generation (David Coeurjolly, #1663) Examples We can now have examples using polyscope as viewer (BUILD_POLYSCOPE_EXAMPLES cmake variable). (David Coeurjolly, #1603) IO Faster export of OBJ files. (David Coeurjolly, #1608 Fixing bugs in writing Longvol from GenericWriter and tests. (Bertrand Kerautret, #1610 Fix compilation issue in MeshReader compilation. (Bertrand Kerautret, #1611 Minor fixes in VolReader and LongVolReader to be able to load large vol files. (David Coeurjolly, #1637) Fix LongVolReader that fails to read large values. It was why testLongvol and testCompressedVolWriter were failing on some configurations. (Roland Denis, #1638) Fix missing #include in MeshReaeder (Jeremy Fix, #1649) Fix purple color. (Bertrand Kerautret and Phuc Ngo #1579) Geometry package The following changes have been made to fix a bug in examplePlaneProbingSurfaceLocalEstimator: in PlaneProbingDigitalSurfaceLocalEstimator, the method probingFrameWithPreEstimation now returns a pair bool-frame instead of just a frame, in order to tell whether the frame will lead to a valid initialization or not. 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(Bertrand Kerautret #1619) Build New cmake targets to collect cmake, doxygen and markdown files (David Coeurjolly, #1609) Continuous integration does not use Travis anymore but Github Actions. (David Coeurjolly, #1591) New cmake option (DGTAL_RANDOMIZED_TESTING_THRESHOLD) to set the (approximated) % of unit-tests to build and run for randomized testing (David Coeurjolly #1588) Fix missing whitelist for the unit-tests in relation to PR #1591) (Bertrand Kerautret #1595) Fix cmake related ITK usage in other projects (issue #1612). (Bertrand Kerautret and Pablo Hernandez-Cerdan #1613) Adding ITK in Github Actions CI on linux distribution. (Bertrand Kerautret #1615) New variable in the Github Action script to disable some tests (not working in the bots) (David Coeurjolly, #1635) Google benchmark is now fetched when building the unit tests (using Fetch_Content) (David Coeurjolly, [#1651] #1651)) Add new cmake option to avoid linking errors related to STB image library (like LNK2005 in MSVC). (Bertrand Kerautret, #1666) Bug fixes General Fixing OpenMP dependency on macOS when using the DGtalConfig.cmake(David Coeurjolly, #1578) Various warnings / deprecated functions (David Coeurjolly, #1583 Removing old snapshot of catch.hpp. Now DGtal compiles on Apple M1 (David Coeurjolly, #1590 Fix cmake IN_LIST use policy. 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(Roland Denis, #1638) Fix missing #include in MeshReaeder (Jeremy Fix, #1649) Fix purple color. (Bertrand Kerautret and Phuc Ngo #1579) Geometry package The following changes have been made to fix a bug in examplePlaneProbingSurfaceLocalEstimator: in PlaneProbingDigitalSurfaceLocalEstimator, the method probingFrameWithPreEstimation now returns a pair bool-frame instead of just a frame, in order to tell whether the frame will lead to a valid initialization or not. 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Des liaisons carbone-carbone sont aisément déduites par le programme LSD à partir des données de type COSY et HMQC. L'association des données des spectres HMQC et HMBC fournit un ensemble de paires d'atomes de carbones (repérés par leur déplacement chimique) qui sont, soit directement liés, soit liés à un atome commun qui peut a priori être tout autre atome de la molécule. Cette contrainte est relativement faible et ouvre un vaste espace de possibilités à explorer. Les données d'un problème sont codées par l'utilisateur dans un fichier texte. Les solutions sont aussi stockées dans un fichier texte. Un programme auxiliaire \"outlsd\" convertit le format de structure \"passe-partout\" en fichiers de coordonnées 2D, en chaînes SMILES, en coordonnées 3D (fantaisistes en l'absence de données stéréochimiques). 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The approach combines AI and human expertise: the machine proposes, the expert validates; custody rules (low temperature, strict instructions, schema validation, “unrated” status) and systematic logging (grid/prompt version, timestamp, author) ensure traceability. A pilot conducted at Le Mans University (SIFAC exports) illustrates the feasibility and usefulness: reduction of review time on significant volumes, identification of documentary gaps (long texts, supporting documents), improvement of restitution quality. 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Nous montrons comment un dispositif léger et réplicable (Budget Vert (LLM)) permet de pré-coter chaque ligne selon les six axes (atténuation, adaptation, eau, économie circulaire/déchets/risques technologiques, pollutions, biodiversité) et de produire une cotation globale (favorable, défavorable, mixte, neutre, avec la possibilité de « non cotée »). L'outil génère une sortie JSON à plat (axes + globale + justification) pour l'interopérabilité avec les environnements analytiques et l'audit des décisions. Cette démarche s'adresse aux administrations comme aux entreprises privées confrontées à de fortes volumétries comptables ; l'analyse manuelle ligne par ligne étant coûteuse et non scalable, l'IA permet d'industrialiser la pré-cotation à coût marginal faible et de réaliser des économies d'échelle tout en préservant l'auditabilité. 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A preliminary publication has been done in [Eurocarto conference in Vienne, september 2022](https://www.abstr-int-cartogr-assoc.net/5/38/2022/ica-abs-5-38-2022.pdf) Data content is an extraction of the navigocorpus database, that has been cleaned and enriched during the ANR PORTIC programm by new variables (geographic nomenclatures, product nomenclatures, uncertainty quotation of various fields) and standardisation and translation of some fields (such as the name of port, of flags, of shipclass, etc.). See user manual first for details","fr":"Il s'agit d'une API Web (développée avec Flask) pour fournir des données extraites d'une base de données postgres. Le code source est sur humanum GIT : https://gitlab.huma-num.fr/portic/porticapi, publié sous la licence AGPL v3. Cette API fournit des données au format CSV ou JSON de manière à ce qu'elles puissent être utilisées par n'importe quel logiciel ou n'importe qui pour une analyse ou une visualisation plus poussée. Tous les champs sont décrits dans un dictionnaire (porticapi\\static\\data\\api_portic.csv), au format CSV, lisible en ligne sur http://apidemo.portic.fr. Dans le répertoire SQL, le fichier `porticapi\\sql\\portic_routepaths_allfunctions.sql` contient un ensemble de procédures PL/SQL qui permettent de calculer les trajets maritimes qui ne croisent jamais les côtes ni les terres, d'un port à l'autre. Une publication est en cours de révision sur ce sujet. L'ensemble des procédures PL/SQL nécessite l'installation de l'extension postgis. Une publication préliminaire a été faite dans [Eurocarto conference in Vienne, september 2022] (https://www.abstr-int-cartogr-assoc.net/5/38/2022/ica-abs-5-38-2022.pdf). Le contenu des données est une extraction de la base de données navigocorpus, qui a été nettoyée et enrichie au cours du programme ANR PORTIC par de nouvelles variables (nomenclatures géographiques, nomenclatures de produits, cotation de l'incertitude de divers champs) et par la standardisation et la traduction de certains champs (tels que le nom du port, des pavillons, de la classe de navire, etc.). La base devrait bientôt être déposée dans NAKALA au format CSV et SQL plain text. Voir d'abord le manuel de l'utilisateur pour plus de détails sur les options offertes par les requêtes sur l'API."},"isLibreSoftware":true,"websiteUrl":"https://hal.science/hal-04019250v1","sourceUrl":"https://gitlab.huma-num.fr/portic/porticapi","license":"GNU Affero General Public License v3.0 or later","providers":[]},"similarExternalSoftwares":[],"hasExpertReferent":false,"userAndReferentCountByOrganization":{},"instances":[]},{"serviceProviders":[],"categories":[],"description":"fractal_growth_vg is a code which simulates the growth, limited by diffusion and without stabilizing effects, of a 2D fractal deposit from a Laplacian growth type model (also known as a dielectric break model) and taking into account a positive growth rate/velocity (vg). The concentration field around the deposit is computed by taking into account the \"convection\" effect induced by the motion of growth front. 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For more information on CompCert (authors, supported platforms, supported C features, installation instructions, using the compiler, etc), please refer to the [Web site](https://compcert.org/) and especially the [user's manual](https://compcert.org/man/). ## Chamois version Chamois CompCert is a version of CompCert with additions from [Verimag](https://www-verimag.imag.fr) and previously from [Kalray](https://www.kalrayinc.com/) (a chamois is a fast and agile caprine, very common in French Alps)."},"isLibreSoftware":true,"websiteUrl":"https://hal.science/hal-04749015v1","sourceUrl":"https://gricad-gitlab.univ-grenoble-alpes.fr/certicompil/Chamois-CompCert","license":"INRIA Non-Commercial License","providers":[]},"similarExternalSoftwares":[],"hasExpertReferent":false,"userAndReferentCountByOrganization":{},"instances":[]},{"serviceProviders":[],"categories":[],"description":"The profiling of multiple molecular layers from the same set of cells has recently become possible. 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(LS') Ax'=b',

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We present F3MR (Food Microbiome Metabolic Modules in R), a reproducible workflow designed to streamline the analysis of sequencing data from food-associated microbial communities. F3MR enables users to construct tailored reference databases, import raw count data, and aggregate features at multiple taxonomic and functional levels. The resulting count matrices are directly compatible with widely used statistical tools such as DESeq2, facilitating downstream analyses of microbial composition and function. 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The scientific article associated with this code details the case study, methodological development, and results. It also presents an alternative method for greening public spaces (see paper). Dijon Case Study The current version of the code is specifically tailored to the rules established for the Dijon Metropolis (France). This area has defined target values for low vegetation, tall vegetation, and eco-constructed surfaces that should ideally be achieved on private parcels. These targets vary depending on the location within the metropolitan area (see related publication). Want to use this code to create a parcel greening scenario? It can be used in different situations: Working in an area without urban planning regulations defining vegetation requirements per parcel: Code: You can use the code as is. Data: You’ll need to produce a PLU zoning map for your area based on the Dijon model, and find equivalent datasets for buildings, cadastral parcels, and vegetation (see 3. DATA below). Working in an area with urban planning regulations defining vegetation targets per parcel: Code: You can reuse the structure and adapt the CBS calculation to your specific local regulations. Some areas may not use PLT. Data: Your PLU zoning dataset will need to be adapted accordingly (see section 3. DATA below). Working in Dijon Metropolis (France): Code: The code can be used as is (if the 2019 PLUi-HD rules for PLT/CBS are still in effect). Data: The data can be used as is (if the 2019 PLUi-HD rules are still valid). A few definitions The Biotope Area Factor (CBS) is inspired by regulations applied in Berlin since the 1990s. An increasing number of French cities are adopting this approach to encourage private property owners to green their gardens. In the long term, this contributes to enhancing vegetation in urban areas, which brings numerous ecosystem benefits (climate regulation, biodiversity, environmental quality, social well-being, etc.). Method In Dijon, the calculation is split into two coefficients: PLT (permeable land ratio): applies to low vegetation areas. A minimum of one tree is required per 100 m² of low vegetation. CBS (Biotope Area Factor): applies to tall vegetation and eco-constructed surfaces (e.g., raised decks, permeable gravel pathways, green roofs and walls, etc.). These coeifficients are detailed in the associated publication. Calculation steps This tool performs (1) the calculation of the current situation and (2) the estimation of additional vegetation areas needed per parcel, adjusted to parcel size. To better visualize the results, we recommend highlighting parcels that are too small to meet the greening targets. Limitations In this version, CBS is calculated only from tall vegetation. Parcels that are too small to reach the target should rely on adding eco-constructed surfaces. Outlook A future version of the code could incorporate the addition of eco-constructed surfaces into CBS calculations. Feel free to contact the team for any questions or issues. Please let us know if you use this method in your work—we’d love to hear about it! 2. Requirements These scripts were tested with QGIS versions 3.34 and 3.38 3. DATA All layers must use the same coordinate system and be correctly configured You will need the following layers: Parcels: vector layer for private parcels Buildings: vector layer of buildings Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer taller than 2 meters PLU zoning: city PLU zoning, providing PLT and CBS values 4. Scripts 4.1 vegetalisation_preparation.py The aim of this script is to provide a layer with all the variables necessary for our revegetation scenario. Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (> 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. Parameters : db_vegetalization: geopackage database created in step 1 treeSurface: average tree surface area (m2) modulation: bonus per additional tree Output As output, the script provides the vegetalisation layer. Main results are stored in the fields: PLT : the PLT is calculated as close as possible to the PLT recommended in the PLU, based on the space available for vegetation. CBS : the CBS is calculated as close as possible to the CBS recommended in the PLU, based on the space available for vegetation. sVegAjout : low vegetation to be planted corresponding to the calculated PLT sHVegAjout : tall vegetation to be planted corresponding to the calculated CBS 5. Installation in QGIS You need QGIS software https://qgis.org/download/ These two scripts are QGIS Processing Toolbox scripts You can install them by adding a script to toolbox and pointing to the script You will have a new entry for \"Scripts\" in the toolbox called UrbanClimateStudy and inside : 1 - Preparing layers for PLT/CBS vegetation scenarios 2 - List of areas to be planted","customAttributes":{},"id":548,"isStillInObservation":false,"keywords":["Urban grasslands","GIS tool","Private / public","Urban tree cover","Urban vegetation structure","Urban plannig schemes","Urban planning","Urban greening and vegetation"],"license":"CeCILL-B","name":"Greening Scenarios for Urban Parcels","referencedSinceTime":1769422994932,"softwareType":{"os":{"ios":false,"mac":false,"linux":false,"android":false,"windows":false},"type":"desktop/mobile"},"updateTime":1769422994932,"workshopUrls":[],"softwareExternalData":{"externalId":"5127963","sourceSlug":"hal","developers":[{"name":"Ludovic Granjon","@type":"Person","identifiers":[{"url":"https://orcid.org/0000-0002-0576-5497","@type":"PropertyValue","value":"0000-0002-0576-5497","subjectOf":{"url":"https://orcid.org/","name":"Open Researcher and Contributor ID","@type":"Website","additionalType":"ORCID"},"additionalType":"Person"}],"affiliations":[{"url":"https://ror.org/00gj7r351","name":"Laboratoire Ecologie, Evolution, Interactions des Systèmes amazoniens","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/044jxhp58","name":"Institut Français de Recherche pour l'Exploitation de la Mer","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/00nb39k71","name":"Université de Guyane","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02feahw73","name":"Centre National de la Recherche Scientifique","@type":"Organization","parentOrganizations":[]}]},{"url":"https://ror.org/04ejcwb57","name":"Maison des Sciences de l'Homme de Dijon","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/03k1bsr36","name":"Université de Bourgogne","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02dn7x778","name":"Université Bourgogne Franche-Comté [COMUE]","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02feahw73","name":"Centre National de la Recherche Scientifique","@type":"Organization","parentOrganizations":[]}]}]},{"name":"Mélissa Poupelin","@type":"Person","identifiers":[{"url":"https://orcid.org/0009-0007-5297-4148","@type":"PropertyValue","value":"0009-0007-5297-4148","subjectOf":{"url":"https://orcid.org/","name":"Open Researcher and Contributor ID","@type":"Website","additionalType":"ORCID"},"additionalType":"Person"}],"affiliations":[{"url":"https://ror.org/01a0gpy77","name":"Théoriser et modéliser pour aménager (UMR 6049)","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/03k1bsr36","name":"Université de Bourgogne","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02feahw73","name":"Centre National de la Recherche Scientifique","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/03pcc9z86","name":"Université Marie et Louis Pasteur","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/02dn7x778","name":"Université Bourgogne Franche-Comté [COMUE]","@type":"Organization","parentOrganizations":[]}]}]},{"name":"Centre de Recherches de Climatologie [UMR Biogéosciences]","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/04mzqjs78","name":"Biogéosciences [UMR 6282]","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/03k1bsr36","name":"Université de Bourgogne","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02feahw73","name":"Centre National de la Recherche Scientifique","@type":"Organization","parentOrganizations":[]}]}]}]},{"name":"Julien Pergaud","@type":"Person","identifiers":[{"url":"https://hal.science/search/index/q/*/authFullName_s/Julien+Pergaud","@type":"PropertyValue","value":"","subjectOf":{"url":"https://hal.science/","name":"HAL main instance","@type":"Website","additionalType":"HAL"},"additionalType":"Person"}],"affiliations":[{"name":"Centre de Recherches de Climatologie [UMR Biogéosciences]","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/04mzqjs78","name":"Biogéosciences [UMR 6282]","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/03k1bsr36","name":"Université de Bourgogne","@type":"Organization","parentOrganizations":[]},{"url":"https://ror.org/02feahw73","name":"Centre National de la Recherche Scientifique","@type":"Organization","parentOrganizations":[]}]}]}]}],"label":{"en":"Greening Scenarios for Urban Parcels","fr":"Scénarios de végétalisation des parcelles en milieu urbain"},"description":{"en":"1. Description What is it for? This code automatically calculates the areas of low and tall vegetation to be added to each parcel in a city, based on predefined greening targets. Use cases and applications This calculation applies to private parcels within a user-defined area (metropolitan region, city, or group of cities). It can also be applied to public parcels if desired. However, please note that roadways and parking lots require special attention to maintain vehicle access. The scientific article associated with this code details the case study, methodological development, and results. It also presents an alternative method for greening public spaces (see paper). Dijon Case Study The current version of the code is specifically tailored to the rules established for the Dijon Metropolis (France). This area has defined target values for low vegetation, tall vegetation, and eco-constructed surfaces that should ideally be achieved on private parcels. These targets vary depending on the location within the metropolitan area (see related publication). Want to use this code to create a parcel greening scenario? It can be used in different situations: Working in an area without urban planning regulations defining vegetation requirements per parcel: Code: You can use the code as is. Data: You’ll need to produce a PLU zoning map for your area based on the Dijon model, and find equivalent datasets for buildings, cadastral parcels, and vegetation (see 3. DATA below). Working in an area with urban planning regulations defining vegetation targets per parcel: Code: You can reuse the structure and adapt the CBS calculation to your specific local regulations. Some areas may not use PLT. Data: Your PLU zoning dataset will need to be adapted accordingly (see section 3. DATA below). Working in Dijon Metropolis (France): Code: The code can be used as is (if the 2019 PLUi-HD rules for PLT/CBS are still in effect). Data: The data can be used as is (if the 2019 PLUi-HD rules are still valid). A few definitions The Biotope Area Factor (CBS) is inspired by regulations applied in Berlin since the 1990s. An increasing number of French cities are adopting this approach to encourage private property owners to green their gardens. In the long term, this contributes to enhancing vegetation in urban areas, which brings numerous ecosystem benefits (climate regulation, biodiversity, environmental quality, social well-being, etc.). Method In Dijon, the calculation is split into two coefficients: PLT (permeable land ratio): applies to low vegetation areas. A minimum of one tree is required per 100 m² of low vegetation. CBS (Biotope Area Factor): applies to tall vegetation and eco-constructed surfaces (e.g., raised decks, permeable gravel pathways, green roofs and walls, etc.). These coeifficients are detailed in the associated publication. Calculation steps This tool performs (1) the calculation of the current situation and (2) the estimation of additional vegetation areas needed per parcel, adjusted to parcel size. To better visualize the results, we recommend highlighting parcels that are too small to meet the greening targets. Limitations In this version, CBS is calculated only from tall vegetation. Parcels that are too small to reach the target should rely on adding eco-constructed surfaces. Outlook A future version of the code could incorporate the addition of eco-constructed surfaces into CBS calculations. Feel free to contact the team for any questions or issues. Please let us know if you use this method in your work—we’d love to hear about it! 2. Requirements These scripts were tested with QGIS versions 3.34 and 3.38 3. DATA All layers must use the same coordinate system and be correctly configured You will need the following layers: Parcels: vector layer for private parcels Buildings: vector layer of buildings Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer taller than 2 meters PLU zoning: city PLU zoning, providing PLT and CBS values 4. Scripts 4.1 vegetalisation_preparation.py The aim of this script is to provide a layer with all the variables necessary for our revegetation scenario. Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (> 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. Parameters : db_vegetalization: geopackage database created in step 1 treeSurface: average tree surface area (m2) modulation: bonus per additional tree Output As output, the script provides the vegetalisation layer. Main results are stored in the fields: PLT : the PLT is calculated as close as possible to the PLT recommended in the PLU, based on the space available for vegetation. CBS : the CBS is calculated as close as possible to the CBS recommended in the PLU, based on the space available for vegetation. sVegAjout : low vegetation to be planted corresponding to the calculated PLT sHVegAjout : tall vegetation to be planted corresponding to the calculated CBS 5. Installation in QGIS You need QGIS software https://qgis.org/download/ These two scripts are QGIS Processing Toolbox scripts You can install them by adding a script to toolbox and pointing to the script You will have a new entry for \"Scripts\" in the toolbox called UrbanClimateStudy and inside : 1 - Preparing layers for PLT/CBS vegetation scenarios 2 - List of areas to be planted","fr":"1. Description What is it for? This code automatically calculates the areas of low and tall vegetation to be added to each parcel in a city, based on predefined greening targets. Use cases and applications This calculation applies to private parcels within a user-defined area (metropolitan region, city, or group of cities). It can also be applied to public parcels if desired. However, please note that roadways and parking lots require special attention to maintain vehicle access. The scientific article associated with this code details the case study, methodological development, and results. It also presents an alternative method for greening public spaces (see paper). Dijon Case Study The current version of the code is specifically tailored to the rules established for the Dijon Metropolis (France). This area has defined target values for low vegetation, tall vegetation, and eco-constructed surfaces that should ideally be achieved on private parcels. These targets vary depending on the location within the metropolitan area (see related publication). Want to use this code to create a parcel greening scenario? It can be used in different situations: Working in an area without urban planning regulations defining vegetation requirements per parcel: Code: You can use the code as is. Data: You’ll need to produce a PLU zoning map for your area based on the Dijon model, and find equivalent datasets for buildings, cadastral parcels, and vegetation (see 3. DATA below). Working in an area with urban planning regulations defining vegetation targets per parcel: Code: You can reuse the structure and adapt the CBS calculation to your specific local regulations. Some areas may not use PLT. Data: Your PLU zoning dataset will need to be adapted accordingly (see section 3. DATA below). Working in Dijon Metropolis (France): Code: The code can be used as is (if the 2019 PLUi-HD rules for PLT/CBS are still in effect). Data: The data can be used as is (if the 2019 PLUi-HD rules are still valid). A few definitions The Biotope Area Factor (CBS) is inspired by regulations applied in Berlin since the 1990s. An increasing number of French cities are adopting this approach to encourage private property owners to green their gardens. In the long term, this contributes to enhancing vegetation in urban areas, which brings numerous ecosystem benefits (climate regulation, biodiversity, environmental quality, social well-being, etc.). Method In Dijon, the calculation is split into two coefficients: PLT (permeable land ratio): applies to low vegetation areas. A minimum of one tree is required per 100 m² of low vegetation. CBS (Biotope Area Factor): applies to tall vegetation and eco-constructed surfaces (e.g., raised decks, permeable gravel pathways, green roofs and walls, etc.). These coeifficients are detailed in the associated publication. Calculation steps This tool performs (1) the calculation of the current situation and (2) the estimation of additional vegetation areas needed per parcel, adjusted to parcel size. To better visualize the results, we recommend highlighting parcels that are too small to meet the greening targets. Limitations In this version, CBS is calculated only from tall vegetation. Parcels that are too small to reach the target should rely on adding eco-constructed surfaces. Outlook A future version of the code could incorporate the addition of eco-constructed surfaces into CBS calculations. Feel free to contact the team for any questions or issues. Please let us know if you use this method in your work—we’d love to hear about it! 2. Requirements These scripts were tested with QGIS versions 3.34 and 3.38 3. DATA All layers must use the same coordinate system and be correctly configured You will need the following layers: Parcels: vector layer for private parcels Buildings: vector layer of buildings Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer taller than 2 meters PLU zoning: city PLU zoning, providing PLT and CBS values 4. Scripts 4.1 vegetalisation_preparation.py The aim of this script is to provide a layer with all the variables necessary for our revegetation scenario. Parameters : Parcels: vector layer for private parcels Buildings: buildings vector layer Raster vegetation: vegetation raster layer Raster tall vegetation: vegetation raster layer greater than 2 meters Raster resolution : resolution of raster layers PLU zoning: city PLU zoning, providing PLT and CBS values CBS: the field containing the CBS value PLT: the field containing the PLT value Storage directory: directory to store the geopackage database that contains output layers Output As output, the script provides the \"parcelles_PourVegetalisation\" layer containing the fields: id_parcelle: parcel identifier SurfParcelle: parcel area SurfParcelleNoBati: parcel's area without buildings SurVeg: vegetation surface in the parcel SurfHVeg: tall (> 2m) vegetation surface in the parcel PLU_CBS: CBS value provided by city's PLU PLU_PLT: PLT value provided by city's PLU For complementary output, the script provides two more layers: entite_nonJoignable: parcels with no remaining unbuilt area entite_PLU_nonJoignable: parcels located outside the PLU zoning 4.2 vegetalisation.py The aim of this script is to provide the areas of vegetation to be added per parcel in order to approach the theoretical value of the CBS and PLT while considering the space available. Parameters : db_vegetalization: geopackage database created in step 1 treeSurface: average tree surface area (m2) modulation: bonus per additional tree Output As output, the script provides the vegetalisation layer. Main results are stored in the fields: PLT : the PLT is calculated as close as possible to the PLT recommended in the PLU, based on the space available for vegetation. CBS : the CBS is calculated as close as possible to the CBS recommended in the PLU, based on the space available for vegetation. sVegAjout : low vegetation to be planted corresponding to the calculated PLT sHVegAjout : tall vegetation to be planted corresponding to the calculated CBS 5. Installation in QGIS You need QGIS software https://qgis.org/download/ These two scripts are QGIS Processing Toolbox scripts You can install them by adding a script to toolbox and pointing to the script You will have a new entry for \"Scripts\" in the toolbox called UrbanClimateStudy and inside : 1 - Preparing layers for PLT/CBS vegetation scenarios 2 - List of areas to be planted"},"isLibreSoftware":true,"websiteUrl":"https://hal.science/hal-05127963v1","sourceUrl":"https://gitlab.in2p3.fr/JulienPergaud/urban-greening-scenarios-application.git","license":"CeCILL-B","providers":[]},"similarExternalSoftwares":[],"hasExpertReferent":false,"userAndReferentCountByOrganization":{},"instances":[]},{"serviceProviders":[],"categories":[],"description":"AIlign est un script python permettant l'alignement de textes parallèles. Dépôt officiel : https://gricad-gitlab.univ-grenoble-alpes.fr/kraifo/ailign 1. Présentation AIlign s'appuie sur les toutes récentes avancées en apprentissage profond, notamment la possibilité de représenter les phrases par des vecteurs dans des espaces multilingues, comme dans les modèles de Laser (Artetxe & Schwenk, 2018, https://arxiv.org/abs/1812.10464) et Labse (Feng et al. 2022, https://arxiv.org/abs/2007.01852). Il fonctionne en deux temps : * une première étape de préalignement permet d'extraire des points d'ancrage. Ceux-ci sont obtenus sur la base de divers indice : - en l'absence d'embeddings de phrase, on peut s'appuyer sur les mots contenant des ngrams identiques. - si on dispose d'embeddings du type Laser (Facebook AI) ou Labse (Google), on peut calculer une mesure de similarité entre phrases de langues différentes Lorsque deux phrases sont jugées suffisamment similaire (forte densité de ngrams communs ou cosinus des embeddings dépassant un certain seuil) leur appariement produit un point. Les points ainsi produits sont ensuite filtrés en fonction de divers critères géométriques (densité locale des points, déviation par rapport à la diagonale, etc.) afin de fournir des points d'ancrage de confiance pour la phase 2. On obtient ensuite des points qui permettent de définir des intervalles de confiance et de guider l'aligment, comme sur la figure ci-dessous. * la deuxième étape met en oeuvre un algorithme de Viterbi de type DTW (Dynamic Time Warping) pour calculer récursivement quel est le meilleur chemin menant à un point (i,j). Un chemin est une succession d'appariements. Les appariements pris en compte par AIlign sont les suivants: 1-1, 0-1, 1-0, 1-2, 2-1, 1-3, 3-1, 1-4, 4-1. On calcule ainsi tous les chemins possibles entre les points d'ancrage fournis à l'étape précédente, en autorisant une certaine marge autour de ceux-ci (paramètre dtw_margin). Le cout d'un chemin est calculé comme la somme des distances des phrases ou groupes de phrases appariées, en utilisant les plongements de phrases (une distance alternative s'appuyant sur les longueurs de phrases et les probabilités de transition, comme chez Gale & Church 2012, pourrait être facilement implémentée). Pour un appariement vide (1-0 ou 0-1) on définit une distance constante (paramètre dist_null).","customAttributes":{},"id":549,"isStillInObservation":false,"keywords":[],"license":"Creative Commons Attribution Non Commercial 1.0 Generic","name":"AIlign - AI-Based Bilingual aligner","referencedSinceTime":1769422995335,"softwareType":{"os":{"ios":false,"mac":false,"linux":false,"android":false,"windows":false},"type":"desktop/mobile"},"updateTime":1769422995335,"workshopUrls":[],"softwareExternalData":{"externalId":"4807956","sourceSlug":"hal","developers":[{"name":"Olivier Kraif","@type":"Person","identifiers":[{"url":"https://orcid.org/0000-0002-8935-7342","@type":"PropertyValue","value":"0000-0002-8935-7342","subjectOf":{"url":"https://orcid.org/","name":"Open Researcher and Contributor ID","@type":"Website","additionalType":"ORCID"},"additionalType":"Person"}],"affiliations":[{"url":"https://ror.org/05588ks88","name":"LInguistique et DIdactique des Langues Étrangères et Maternelles","@type":"Organization","parentOrganizations":[{"url":"https://ror.org/02rx3b187","name":"Université Grenoble Alpes","@type":"Organization","parentOrganizations":[]}]}]}],"label":{"en":"AIlign - AI-Based Bilingual aligner","fr":"AIlign - AI-Based Bilingual aligner"},"description":{"en":"AIlign est un script python permettant l'alignement de textes parallèles. Dépôt officiel : https://gricad-gitlab.univ-grenoble-alpes.fr/kraifo/ailign 1. Présentation AIlign s'appuie sur les toutes récentes avancées en apprentissage profond, notamment la possibilité de représenter les phrases par des vecteurs dans des espaces multilingues, comme dans les modèles de Laser (Artetxe & Schwenk, 2018, https://arxiv.org/abs/1812.10464) et Labse (Feng et al. 2022, https://arxiv.org/abs/2007.01852). Il fonctionne en deux temps : * une première étape de préalignement permet d'extraire des points d'ancrage. Ceux-ci sont obtenus sur la base de divers indice : - en l'absence d'embeddings de phrase, on peut s'appuyer sur les mots contenant des ngrams identiques. - si on dispose d'embeddings du type Laser (Facebook AI) ou Labse (Google), on peut calculer une mesure de similarité entre phrases de langues différentes Lorsque deux phrases sont jugées suffisamment similaire (forte densité de ngrams communs ou cosinus des embeddings dépassant un certain seuil) leur appariement produit un point. Les points ainsi produits sont ensuite filtrés en fonction de divers critères géométriques (densité locale des points, déviation par rapport à la diagonale, etc.) afin de fournir des points d'ancrage de confiance pour la phase 2. On obtient ensuite des points qui permettent de définir des intervalles de confiance et de guider l'aligment, comme sur la figure ci-dessous. * la deuxième étape met en oeuvre un algorithme de Viterbi de type DTW (Dynamic Time Warping) pour calculer récursivement quel est le meilleur chemin menant à un point (i,j). Un chemin est une succession d'appariements. Les appariements pris en compte par AIlign sont les suivants: 1-1, 0-1, 1-0, 1-2, 2-1, 1-3, 3-1, 1-4, 4-1. On calcule ainsi tous les chemins possibles entre les points d'ancrage fournis à l'étape précédente, en autorisant une certaine marge autour de ceux-ci (paramètre dtw_margin). Le cout d'un chemin est calculé comme la somme des distances des phrases ou groupes de phrases appariées, en utilisant les plongements de phrases (une distance alternative s'appuyant sur les longueurs de phrases et les probabilités de transition, comme chez Gale & Church 2012, pourrait être facilement implémentée). Pour un appariement vide (1-0 ou 0-1) on définit une distance constante (paramètre dist_null).","fr":"AIlign est un script python permettant l'alignement de textes parallèles. Dépôt officiel : https://gricad-gitlab.univ-grenoble-alpes.fr/kraifo/ailign 1. Présentation AIlign s'appuie sur les toutes récentes avancées en apprentissage profond, notamment la possibilité de représenter les phrases par des vecteurs dans des espaces multilingues, comme dans les modèles de Laser (Artetxe & Schwenk, 2018, https://arxiv.org/abs/1812.10464) et Labse (Feng et al. 2022, https://arxiv.org/abs/2007.01852). Il fonctionne en deux temps : * une première étape de préalignement permet d'extraire des points d'ancrage. Ceux-ci sont obtenus sur la base de divers indice : - en l'absence d'embeddings de phrase, on peut s'appuyer sur les mots contenant des ngrams identiques. - si on dispose d'embeddings du type Laser (Facebook AI) ou Labse (Google), on peut calculer une mesure de similarité entre phrases de langues différentes Lorsque deux phrases sont jugées suffisamment similaire (forte densité de ngrams communs ou cosinus des embeddings dépassant un certain seuil) leur appariement produit un point. Les points ainsi produits sont ensuite filtrés en fonction de divers critères géométriques (densité locale des points, déviation par rapport à la diagonale, etc.) afin de fournir des points d'ancrage de confiance pour la phase 2. On obtient ensuite des points qui permettent de définir des intervalles de confiance et de guider l'aligment, comme sur la figure ci-dessous. * la deuxième étape met en oeuvre un algorithme de Viterbi de type DTW (Dynamic Time Warping) pour calculer récursivement quel est le meilleur chemin menant à un point (i,j). Un chemin est une succession d'appariements. Les appariements pris en compte par AIlign sont les suivants: 1-1, 0-1, 1-0, 1-2, 2-1, 1-3, 3-1, 1-4, 4-1. On calcule ainsi tous les chemins possibles entre les points d'ancrage fournis à l'étape précédente, en autorisant une certaine marge autour de ceux-ci (paramètre dtw_margin). Le cout d'un chemin est calculé comme la somme des distances des phrases ou groupes de phrases appariées, en utilisant les plongements de phrases (une distance alternative s'appuyant sur les longueurs de phrases et les probabilités de transition, comme chez Gale & Church 2012, pourrait être facilement implémentée). Pour un appariement vide (1-0 ou 0-1) on définit une distance constante (paramètre dist_null)."},"isLibreSoftware":true,"websiteUrl":"https://hal.science/hal-04807956v1","sourceUrl":"https://gricad-gitlab.univ-grenoble-alpes.fr/kraifo/ailign","license":"Creative Commons Attribution Non Commercial 1.0 Generic","providers":[]},"similarExternalSoftwares":[],"hasExpertReferent":false,"userAndReferentCountByOrganization":{},"instances":[]},{"serviceProviders":[],"categories":[],"description":"This emulation environment has been created to allow researchers/industrials to develop and deploy applications in complex 5G network architecture (SA and NSA) for enabling both emulations of the network and data processing in realistic E2E scenarios. 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(To use saduni.f90 and sadmulti.f90 with ASReml4 see additional notes)

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(To use saduni.f90 and sadmulti.f90 with ASReml4 see additional notes)

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(To use saduni.f90 and sadmulti.f90 with ASReml4 see additional notes)

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SUMMARY The Kiswahili module is designed for NooJ linguistic development environment software and corpus processor. And it's now online! It goes with a 45000 words Kiswahili-English dictionary and morphological grammars that detect all tenses of Kiswahili verbs. It allows multiple automatic text analysis from various purposes from linguistics research to news watch. Unlike Google translate, the text analysis doesn't provide a single result but shows how the Kiswahili module rules and dictionaries understand the text. You can then export the results for further treatments. Don't hesitate to download it, it works with NooJ software, it's free and distributed under Creative Common License CC BY NC SA 3.0. ABOUT THE DICTIONARY Which Kiswahili is it? Kiswahili is generally described as the bantu language G42 in Malcom Guthrie’s classification. But this continentally spread language is diverse from East Africa to Central Africa. Kiswahili ni nyama ya tembo, kila mtu anachumia pake according to Alphonse Lenselaer in the presentation of his Swahili-Français dictionary. It’s a fact that there are many varieties of Kiswahili, to the point that we can seriously talk of Viswahili. Here we have described the Standard Swahili, or Kiswahili sanifu. This is the more widely understood and spoken variety. This is also the written Kiswahili for the medias, law and East African Schools. Finally, it’s the variety used in Dar es Salaam, the economic capital city of Tanzania. Historically the Standard Swahili has originated with the Kiswahili used in Zanzibar also known as Kiunguja. Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. The dictionaries entries and English translations are mainly based on the Internet Living Kiswahili Dictionary data whose proper citation is: M. Benjamin. 2008. editor. Swahili-English Kamusi Project Wordlist for Free Non-Commercial Use, Kamusi Project International. Distributed under Creative Commons Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/ The Kamusi project was a collaborative, open and international project directed by Martin Benjamin of Yale University, USA (now at Polytechnique, Lausanne, Switzerland). Many top Kiswahili scholars contributed as well as non-academic experts. The Kamusi project mixed various dictionaries, published or working papers, and expanded by continuous updates from East Africa. The documents were available at www.kamusi.org as .txt files. A PERL script written by Mirko Westermeier (computer science researcher, Münster University, Germany), available at https://github.com/Feh/kamusi-cli (retrieved the 29th of July 2017) allowed me to convert these text files into a SQLite database and to process it with SQL requests to create NooJ dictionaries entries with a special focus on lemmatization and formalization of generative rules of inflexed or derived words when the entries in the Kamusi Project were already flexed and derived for the most frequent words forms. The dictionary is made of 4 sub-dictionaries: Adjectives: 3063 entries (13889 derived entries) Nouns: 25280 entries (47171 derived entries) Verbs: 16484 entries (65727 derived entries) Other categories: 2525 entries (2771 derived entries) Each one is associated with a .nof derivational and/or flexional description. The need to use this type of generative dictionaries has required a new formalization of the entries that are written in a specific format for NooJ that enable lemmatization. The passage from the SQLite database to NooJ dictionaries is my work and have been realized with appropriate SQL requests. ABOUT THE GRAMMAR The grammar is made of numerous specialized graphs. They have been written by me who is the sole author of the work. The focus is on the morphology of the verb module. All conjugated verbs are detected if they are in the verb dictionary. All tenses are included: time, aspect and mode. Some graphs are also made for nouns, like the one that add a -ni locative suffix to translate each noun to a locative class 16, 17 or 18 expressing an idea of location in time or space. ABOUT THE TEST TEXT The text to test the module is an extract from the autobiography of the famous Tanzanian author Mathias E. Mnyampala (1917-1969): Maisha ni kugharimia © Mathias E. MNYAMPALA 2014 (mswada) © Mathieu ROY na DL2A - Buluu Publishing 2014 (uhariri) ISBN : 979-10-92789-29-4 Sample reproduction has been authorized for NooJ. THIS IS VERSION 1.0 It works, it has been rigorously designed but… science is always an unending quest as it has been underlined in the now classical epistemological works of Karl Popper. These formalizations of the Kiswahili language are very far from being dogmatic, and are on the contrary very likely to evolve with time and further research. NooJ software evolves itself and is now shifting from a code written in JAVA, .NET etc. to RA language. RA is a source of new powerful possibilities. The RA version is a new adventure in itself to say the least. Lemmatization can go deeper, too, with extra work and could be a matter of several research theses or articles. Finally, I hope this Kiswahili NooJ Module might be a basis of work for others. 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Proper citation may be: Mathieu Roy. 2017. NooJ Kiswahili Module V. 1.0: electronic Kiswahili-English dictionary and morphological grammar of Kiswahili for Free Non-Commercial Use. SUMMARY The Kiswahili module is designed for NooJ linguistic development environment software and corpus processor. And it's now online! It goes with a 45000 words Kiswahili-English dictionary and morphological grammars that detect all tenses of Kiswahili verbs. It allows multiple automatic text analysis from various purposes from linguistics research to news watch. Unlike Google translate, the text analysis doesn't provide a single result but shows how the Kiswahili module rules and dictionaries understand the text. You can then export the results for further treatments. Don't hesitate to download it, it works with NooJ software, it's free and distributed under Creative Common License CC BY NC SA 3.0. ABOUT THE DICTIONARY Which Kiswahili is it? Kiswahili is generally described as the bantu language G42 in Malcom Guthrie’s classification. But this continentally spread language is diverse from East Africa to Central Africa. Kiswahili ni nyama ya tembo, kila mtu anachumia pake according to Alphonse Lenselaer in the presentation of his Swahili-Français dictionary. It’s a fact that there are many varieties of Kiswahili, to the point that we can seriously talk of Viswahili. Here we have described the Standard Swahili, or Kiswahili sanifu. This is the more widely understood and spoken variety. This is also the written Kiswahili for the medias, law and East African Schools. Finally, it’s the variety used in Dar es Salaam, the economic capital city of Tanzania. Historically the Standard Swahili has originated with the Kiswahili used in Zanzibar also known as Kiunguja. Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. The dictionaries entries and English translations are mainly based on the Internet Living Kiswahili Dictionary data whose proper citation is: M. Benjamin. 2008. editor. Swahili-English Kamusi Project Wordlist for Free Non-Commercial Use, Kamusi Project International. Distributed under Creative Commons Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/ The Kamusi project was a collaborative, open and international project directed by Martin Benjamin of Yale University, USA (now at Polytechnique, Lausanne, Switzerland). Many top Kiswahili scholars contributed as well as non-academic experts. The Kamusi project mixed various dictionaries, published or working papers, and expanded by continuous updates from East Africa. The documents were available at www.kamusi.org as .txt files. A PERL script written by Mirko Westermeier (computer science researcher, Münster University, Germany), available at https://github.com/Feh/kamusi-cli (retrieved the 29th of July 2017) allowed me to convert these text files into a SQLite database and to process it with SQL requests to create NooJ dictionaries entries with a special focus on lemmatization and formalization of generative rules of inflexed or derived words when the entries in the Kamusi Project were already flexed and derived for the most frequent words forms. The dictionary is made of 4 sub-dictionaries: Adjectives: 3063 entries (13889 derived entries) Nouns: 25280 entries (47171 derived entries) Verbs: 16484 entries (65727 derived entries) Other categories: 2525 entries (2771 derived entries) Each one is associated with a .nof derivational and/or flexional description. The need to use this type of generative dictionaries has required a new formalization of the entries that are written in a specific format for NooJ that enable lemmatization. The passage from the SQLite database to NooJ dictionaries is my work and have been realized with appropriate SQL requests. ABOUT THE GRAMMAR The grammar is made of numerous specialized graphs. They have been written by me who is the sole author of the work. The focus is on the morphology of the verb module. All conjugated verbs are detected if they are in the verb dictionary. All tenses are included: time, aspect and mode. Some graphs are also made for nouns, like the one that add a -ni locative suffix to translate each noun to a locative class 16, 17 or 18 expressing an idea of location in time or space. ABOUT THE TEST TEXT The text to test the module is an extract from the autobiography of the famous Tanzanian author Mathias E. Mnyampala (1917-1969): Maisha ni kugharimia © Mathias E. MNYAMPALA 2014 (mswada) © Mathieu ROY na DL2A - Buluu Publishing 2014 (uhariri) ISBN : 979-10-92789-29-4 Sample reproduction has been authorized for NooJ. THIS IS VERSION 1.0 It works, it has been rigorously designed but… science is always an unending quest as it has been underlined in the now classical epistemological works of Karl Popper. These formalizations of the Kiswahili language are very far from being dogmatic, and are on the contrary very likely to evolve with time and further research. NooJ software evolves itself and is now shifting from a code written in JAVA, .NET etc. to RA language. RA is a source of new powerful possibilities. The RA version is a new adventure in itself to say the least. Lemmatization can go deeper, too, with extra work and could be a matter of several research theses or articles. Finally, I hope this Kiswahili NooJ Module might be a basis of work for others. This work is free for non-commercial use and it can be transformed and adapted to reach further horizons in Kiswahili linguistics.","fr":"The NooJ Kiswahili Module is dedicated to the memory of the late mwalimu Jean de Dieu Karangwa (1962-2017), Assistant Professor of Kiswahili at INALCO, Paris, France and my former Kiswahili teacher. Proper citation may be: Mathieu Roy. 2017. NooJ Kiswahili Module V. 1.0: electronic Kiswahili-English dictionary and morphological grammar of Kiswahili for Free Non-Commercial Use. SUMMARY The Kiswahili module is designed for NooJ linguistic development environment software and corpus processor. And it's now online! It goes with a 45000 words Kiswahili-English dictionary and morphological grammars that detect all tenses of Kiswahili verbs. It allows multiple automatic text analysis from various purposes from linguistics research to news watch. Unlike Google translate, the text analysis doesn't provide a single result but shows how the Kiswahili module rules and dictionaries understand the text. You can then export the results for further treatments. Don't hesitate to download it, it works with NooJ software, it's free and distributed under Creative Common License CC BY NC SA 3.0. ABOUT THE DICTIONARY Which Kiswahili is it? Kiswahili is generally described as the bantu language G42 in Malcom Guthrie’s classification. But this continentally spread language is diverse from East Africa to Central Africa. Kiswahili ni nyama ya tembo, kila mtu anachumia pake according to Alphonse Lenselaer in the presentation of his Swahili-Français dictionary. It’s a fact that there are many varieties of Kiswahili, to the point that we can seriously talk of Viswahili. Here we have described the Standard Swahili, or Kiswahili sanifu. This is the more widely understood and spoken variety. This is also the written Kiswahili for the medias, law and East African Schools. Finally, it’s the variety used in Dar es Salaam, the economic capital city of Tanzania. Historically the Standard Swahili has originated with the Kiswahili used in Zanzibar also known as Kiunguja. Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. The dictionaries entries and English translations are mainly based on the Internet Living Kiswahili Dictionary data whose proper citation is: M. Benjamin. 2008. editor. Swahili-English Kamusi Project Wordlist for Free Non-Commercial Use, Kamusi Project International. Distributed under Creative Commons Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/ The Kamusi project was a collaborative, open and international project directed by Martin Benjamin of Yale University, USA (now at Polytechnique, Lausanne, Switzerland). Many top Kiswahili scholars contributed as well as non-academic experts. The Kamusi project mixed various dictionaries, published or working papers, and expanded by continuous updates from East Africa. The documents were available at www.kamusi.org as .txt files. A PERL script written by Mirko Westermeier (computer science researcher, Münster University, Germany), available at https://github.com/Feh/kamusi-cli (retrieved the 29th of July 2017) allowed me to convert these text files into a SQLite database and to process it with SQL requests to create NooJ dictionaries entries with a special focus on lemmatization and formalization of generative rules of inflexed or derived words when the entries in the Kamusi Project were already flexed and derived for the most frequent words forms. The dictionary is made of 4 sub-dictionaries: Adjectives: 3063 entries (13889 derived entries) Nouns: 25280 entries (47171 derived entries) Verbs: 16484 entries (65727 derived entries) Other categories: 2525 entries (2771 derived entries) Each one is associated with a .nof derivational and/or flexional description. The need to use this type of generative dictionaries has required a new formalization of the entries that are written in a specific format for NooJ that enable lemmatization. The passage from the SQLite database to NooJ dictionaries is my work and have been realized with appropriate SQL requests. ABOUT THE GRAMMAR The grammar is made of numerous specialized graphs. They have been written by me who is the sole author of the work. The focus is on the morphology of the verb module. All conjugated verbs are detected if they are in the verb dictionary. All tenses are included: time, aspect and mode. Some graphs are also made for nouns, like the one that add a -ni locative suffix to translate each noun to a locative class 16, 17 or 18 expressing an idea of location in time or space. ABOUT THE TEST TEXT The text to test the module is an extract from the autobiography of the famous Tanzanian author Mathias E. Mnyampala (1917-1969): Maisha ni kugharimia © Mathias E. MNYAMPALA 2014 (mswada) © Mathieu ROY na DL2A - Buluu Publishing 2014 (uhariri) ISBN : 979-10-92789-29-4 Sample reproduction has been authorized for NooJ. THIS IS VERSION 1.0 It works, it has been rigorously designed but… science is always an unending quest as it has been underlined in the now classical epistemological works of Karl Popper. These formalizations of the Kiswahili language are very far from being dogmatic, and are on the contrary very likely to evolve with time and further research. NooJ software evolves itself and is now shifting from a code written in JAVA, .NET etc. to RA language. RA is a source of new powerful possibilities. The RA version is a new adventure in itself to say the least. Lemmatization can go deeper, too, with extra work and could be a matter of several research theses or articles. Finally, I hope this Kiswahili NooJ Module might be a basis of work for others. 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SUMMARY The Kiswahili module is designed for NooJ linguistic development environment software and corpus processor. And it's now online! It goes with a 45000 words Kiswahili-English dictionary and morphological grammars that detect all tenses of Kiswahili verbs. It allows multiple automatic text analysis from various purposes from linguistics research to news watch. Unlike Google translate, the text analysis doesn't provide a single result but shows how the Kiswahili module rules and dictionaries understand the text. You can then export the results for further treatments. Don't hesitate to download it, it works with NooJ software, it's free and distributed under Creative Common License CC BY NC SA 3.0. ABOUT THE DICTIONARY Which Kiswahili is it? Kiswahili is generally described as the bantu language G42 in Malcom Guthrie’s classification. But this continentally spread language is diverse from East Africa to Central Africa. Kiswahili ni nyama ya tembo, kila mtu anachumia pake according to Alphonse Lenselaer in the presentation of his Swahili-Français dictionary. It’s a fact that there are many varieties of Kiswahili, to the point that we can seriously talk of Viswahili. Here we have described the Standard Swahili, or Kiswahili sanifu. This is the more widely understood and spoken variety. This is also the written Kiswahili for the medias, law and East African Schools. Finally, it’s the variety used in Dar es Salaam, the economic capital city of Tanzania. Historically the Standard Swahili has originated with the Kiswahili used in Zanzibar also known as Kiunguja. Working with Standard Kiswahili means setting aside the Nairobi Sheng, Kiswahili varieties from the Democratic Republic of Congo (Kisangani Kiswahili, Ituri Kiswahili, Kivu Kiswahili, Katanga Kiswahili), and Northern Kenyan varieties such as Kiamu (Lamu island) and Kimvita (Mombasa City), despite their importance in the history of literature. However, formalization allows us to develop tools for these varieties in a succeeding step. The dictionaries entries and English translations are mainly based on the Internet Living Kiswahili Dictionary data whose proper citation is: M. Benjamin. 2008. editor. Swahili-English Kamusi Project Wordlist for Free Non-Commercial Use, Kamusi Project International. Distributed under Creative Commons Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/ The Kamusi project was a collaborative, open and international project directed by Martin Benjamin of Yale University, USA (now at Polytechnique, Lausanne, Switzerland). Many top Kiswahili scholars contributed as well as non-academic experts. The Kamusi project mixed various dictionaries, published or working papers, and expanded by continuous updates from East Africa. The documents were available at www.kamusi.org as .txt files. A PERL script written by Mirko Westermeier (computer science researcher, Münster University, Germany), available at https://github.com/Feh/kamusi-cli (retrieved the 29th of July 2017) allowed me to convert these text files into a SQLite database and to process it with SQL requests to create NooJ dictionaries entries with a special focus on lemmatization and formalization of generative rules of inflexed or derived words when the entries in the Kamusi Project were already flexed and derived for the most frequent words forms. The dictionary is made of 4 sub-dictionaries: Adjectives: 3063 entries (13889 derived entries) Nouns: 25280 entries (47171 derived entries) Verbs: 16484 entries (65727 derived entries) Other categories: 2525 entries (2771 derived entries) Each one is associated with a .nof derivational and/or flexional description. The need to use this type of generative dictionaries has required a new formalization of the entries that are written in a specific format for NooJ that enable lemmatization. The passage from the SQLite database to NooJ dictionaries is my work and have been realized with appropriate SQL requests. ABOUT THE GRAMMAR The grammar is made of numerous specialized graphs. They have been written by me who is the sole author of the work. The focus is on the morphology of the verb module. All conjugated verbs are detected if they are in the verb dictionary. All tenses are included: time, aspect and mode. Some graphs are also made for nouns, like the one that add a -ni locative suffix to translate each noun to a locative class 16, 17 or 18 expressing an idea of location in time or space. ABOUT THE TEST TEXT The text to test the module is an extract from the autobiography of the famous Tanzanian author Mathias E. Mnyampala (1917-1969): Maisha ni kugharimia © Mathias E. MNYAMPALA 2014 (mswada) © Mathieu ROY na DL2A - Buluu Publishing 2014 (uhariri) ISBN : 979-10-92789-29-4 Sample reproduction has been authorized for NooJ. THIS IS VERSION 1.0 It works, it has been rigorously designed but… science is always an unending quest as it has been underlined in the now classical epistemological works of Karl Popper. These formalizations of the Kiswahili language are very far from being dogmatic, and are on the contrary very likely to evolve with time and further research. NooJ software evolves itself and is now shifting from a code written in JAVA, .NET etc. to RA language. RA is a source of new powerful possibilities. The RA version is a new adventure in itself to say the least. Lemmatization can go deeper, too, with extra work and could be a matter of several research theses or articles. Finally, I hope this Kiswahili NooJ Module might be a basis of work for others. 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