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Titre : Programming persistent memory : a comprehensive guide for developers Type de document : Guide/Manuel Auteurs : Steve Scargall, Auteur Editeur : New York : Apress Année de publication : 2020 Importance : 438 p. ISBN/ISSN/EAN : 978-1-4842-4932-1 Note générale : Glossaire Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] C++
[Termes IGN] données massives
[Termes IGN] Java (langage de programmation)
[Termes IGN] Linux
[Termes IGN] modèle logique de données
[Termes IGN] programmation informatiqueRésumé : (Auteur) Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK) Design and optimize data structures for persistent memory Study how real-world applications are modified to leverage persistent memory Utilize the tools available for persistent memory programming, application performance profiling, and debugging Who This Book Is For C, C++, Java, and Python developers, but will also be useful to software, cloud, and hardware architects across a broad spectrum of sectors, including cloud service providers, independent software vendors, high performance compute, artificial intelligence, data analytics, big data, etc. Note de contenu :
Chapter 1: Introduction to Persistent Memory Programming
Chapter 2: Persistent Memory Architecture
Chapter 3: Operating System Support for Persistent Memory
Chapter 4: Fundamental Concepts of Persistent Memory Programming
Chapter 5: Introducing the Persistent Memory Development Kit
Chapter 6: libpmem: Low-Level Persistent Memory Support
Chapter 7: libpmemobj: A Native Transactional Object Store
Chapter 8: libpmemobj-cpp: The Adaptable Language - C++ and Persistent Memory
Chapter 9: pmemkv: A Persistent In-Memory
Chapter 10: Volatile Use of Persistent Memory
Chapter 11: Designing Data Structures for Persistent Memory
Chapter 12: Debugging Persistent Memory Applications
Chapter 13: Enabling Persistence Using a Real-World Application
Chapter 14: Concurrency and Persistent Memory
Chapter 15: Profiling and Performance
Chapter 16: PMDK Internals: Important Algorithms and Data Structures
Chapter 17: Reliability, Availability, and Serviceability (RAS)
Chapter 18: Remote Persistent Memory
Chapter 19: Advanced Topics
Appendix A: How to Install NDCTL and DAXCTL on Linux
Appendix B: How to Install the Persistent Memory Development Kit (PMDK)
Appendix C: How to Install IPMCTL on Linux and Windows
Appendix D: Java for Persistent Memory
Appendix E: The Future of Remote Persistent Memory ReplicationNuméro de notice : 26513 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel informatique DOI : 10.1007/978-1-4842-4932-1 En ligne : http://doi.org/10.1007/978-1-4842-4932-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97277 Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)
Titre : Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors Type de document : Thèse/HDR Auteurs : Paul Chauchat, Auteur ; Silvère Bonnabel, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2020 Importance : 135 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Paris Sciences et Lettres, Informatique temps réel, robotique, automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] algorithme de filtrage
[Termes IGN] algorithme de lissage
[Termes IGN] Cartographie et localisation simultanées
[Termes IGN] filtre de Kalman
[Termes IGN] itération
[Termes IGN] navigation inertielle
[Termes IGN] robotiqueRésumé : (auteur) Mobile systems need to locate themselves ever more accurately, and in ever more complex situations. This is in particular true for autonomous systems, for which controlling the position error is a critical safety issue. To this end, they are endowed with various sensors, the data of which are fused to obtain an estimate of the vehicle’s location, either globally (with the GPS for instance), or locally, with respect to its surroundings (with cameras for instance). This thesis investigates algorithms for localisation by sensor fusion, namely filtering and especially smoothing, when the mobile is equipped with high-grade inertial sensors. The first part deals with the nonlinear consequences of the use of high-grade inertial sensors, and demonstrates how the nonlinear structure of both filtering and smoothing algorithms may be improved by leveraging the invariant filtering framework. The second part deals with the problems incurred by the linear solvers that are used at each step of nonlinear smoothing algorithms as a result of having highly precise sensors. It introduces a novel least-squares linear solver that solves the issues. Note de contenu : Introduction
I- From Invariant filtering to invariant smoothing
II- Navigation with highly precise sensors
ConclusionNuméro de notice : 28576 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique temps réel, robotique, automatique : Paris Sciences et Lettres Organisme de stage : Centre de robotique (Paris) En ligne : https://pastel.archives-ouvertes.fr/tel-02887295/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97843 Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds / Thomas Chaton (2020)
Titre : Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds Type de document : Article/Communication Auteurs : Thomas Chaton, Auteur ; Nicolas Chaulet, Auteur ; Sofiane Horache, Auteur ; Loïc Landrieu Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : AI4GEO / Conférence : 3DV 2020, International Conference on 3D Vision 25/11/2020 27/11/2020 en ligne Japon Importance : 12 p. - n° 282 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] apprentissage profond
[Termes IGN] cadre conceptuel
[Termes IGN] code source libre
[Termes IGN] données localisées 3D
[Termes IGN] reproductibilité
[Termes IGN] réseau neuronal profondRésumé : (auteur) We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on 3D data. Its modular design, efficient implementation, and user-friendly interfaces make it a relevant tool for research and productization alike. Beyond multiple quality-of-life features, our goal is to standardize a higher level of transparency and reproducibility in 3D deep learning research, and to lower its barrier to entry. In this paper, we present the design principles of Torch-Points3D, as well as extensive benchmarks of multiple state-of-the-art algorithms and inference schemes across several datasets and tasks. The modularity of Torch-Points3D allows us to design fair and rigorous experimental protocols in which all methods are evaluated in the same conditions. The Torch-Points3D repository : https://github.com/nicolas-chaulet/torch-points3d. Numéro de notice : C2020-019 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/3DV50981.2020.00029 date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.1109/3DV50981.2020.00029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96456
Titre : GeoTxt: A scalable geoparsing system for unstructured text geolocation Type de document : Article/Communication Auteurs : Morteza Karimzadeh, Auteur ; Scott Pezanowski, Auteur ; Alan M. MacEachren, Auteur ; Jan Oliver Wallgrün, Auteur Année de publication : 2019 Article en page(s) : pp 118 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] analyse sémantique
[Termes IGN] analyse syntaxique
[Termes IGN] appariement de données localisées
[Termes IGN] corpus
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] interface de programmation
[Termes IGN] méthode heuristique
[Termes IGN] reconnaissance de noms
[Termes IGN] répertoire toponymique
[Termes IGN] réseau sémantique
[Termes IGN] toponyme
[Termes IGN] traitement du langage naturelRésumé : (auteur) In this article, we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo‐annotated tweets. First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate. Numéro de notice : A2019-091 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12510 date de publication en ligne : 16/01/2019 En ligne : https://doi.org/10.1111/tgis.12510 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92238
in Transactions in GIS > vol 23 n° 1 (February 2019) . - pp 118 - 136[article]
Titre : Big data security Type de document : Monographie Auteurs : Shibakali Gupta, Editeur scientifique ; Indradip Banerjee, Editeur scientifique ; Siddhartha Bhattacharyya, Editeur scientifique Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2019 Importance : 144 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-060605-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] blockchain
[Termes IGN] confidentialité
[Termes IGN] cryptographie
[Termes IGN] données massives
[Termes IGN] Hadoop
[Termes IGN] informatique en nuage
[Termes IGN] protection de la vie privée
[Termes IGN] rapport signal sur bruit
[Termes IGN] sécurité informatiqueRésumé : (éditeur) After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology. Note de contenu : 1- Introduction
2- Digital identity protection using blockchain for academic qualification
certificates
3- Anomaly detection in cloud big database metric
4- Use of big data in hacking and social engineering
5- Steganography, the widely used name for data hiding
6- Big data security issues with challenges and solutions
7- ConclusionsNuméro de notice : 25976 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1515/9783110606058 En ligne : https://doi.org/10.1515/9783110606058 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96649
