Topic: computer.software_genre - Searchworks@Jio Institute Digital Library Search Results

Showing total 50 results

Start Over Topic computer.software_genre

50 results

1. Working with TensorFlow Data

Author: David Paper
Subjects: Programming language, Computer science, Code (cryptography), computer.software_genre, computer
Abstract: We introduce TensorFlow datasets (TFDS). We discuss many facets of a TFDS with code examples. We continue with a complete TFDS modeling example.
Published: 2021

2. Deep Learning with TensorFlow Datasets

Author: David Paper
Subjects: business.industry, Computer science, Simple (abstract algebra), Deep learning, Artificial intelligence, Machine learning, computer.software_genre, business, computer
Abstract: In the previous chapter, we demonstrated how to work with TFDS objects. In this chapter, we work through two end-to-end deep learning experiments with large and complex TFDS objects. The Fashion-MNIST and beans datasets are small with simple images.
Published: 2021

3. Advanced Transfer Learning

Author: David Paper
Subjects: Computer science, Programming language, Code (cryptography), Learning models, Transfer of learning, computer.software_genre, computer
Abstract: We introduce advanced transfer learning with code examples based on several transfer learning architectures. The code examples train learning models with these architectures.
Published: 2021

4. Time Series Forecasting with RNNs

Author: David Paper
Subjects: Multivariate statistics, Computer science, business.industry, Univariate, Artificial intelligence, Time series, business, Machine learning, computer.software_genre, computer
Abstract: We’ve already leveraged RNNs for NLP. In this chapter, we create experiments to forecast with time series data. We use the famous Weather dataset to demonstrate both a univariate and a multivariate example.
Published: 2021

5. Increase the Diversity of Your Dataset with Data Augmentation

Author: David Paper
Subjects: Training set, business.industry, Computer science, Deep learning, Artificial intelligence, business, Machine learning, computer.software_genre, computer, Diversity (business)
Abstract: We guide you in the creation of augmented data experiments to increase the diversity of a training set by applying random (but realistic) transformations. Data augmentation is very useful for small datasets because deep learning models crave a lot of data to perform well.
Published: 2021

6. An Introduction to Reinforcement Learning

Author: David Paper
Subjects: Intelligent agent, business.industry, Computer science, Order (business), media_common.quotation_subject, Reinforcement learning, Artificial intelligence, business, computer.software_genre, Function (engineering), computer, ComputingMilieux_MISCELLANEOUS, media_common
Abstract: Reinforcement learning (RL) is an area of machine learning that focuses on teaching intelligent agents how to take actions in an environment in order to maximize cumulative reward. Cumulative reward in RL is the sum of all rewards as a function of the number of training steps.
Published: 2021

7. Introduction to Scikit-Learn

Author: David Paper
Subjects: business.industry, Computer science, Unsupervised learning, Artificial intelligence, Python (programming language), Machine learning, computer.software_genre, business, computer, computer.programming_language
Abstract: We combine the Anaconda distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised machine learning algorithms supplemented with unsupervised learning algorithms where appropriate. With clear examples, all written in Python, we demonstrate how these algorithms work to solve machine learning problems.
Published: 2019

8. FUTURE DEVELOPMENT OF SECURITY PRINTING AND RFID MARKS

Author: Iskren Spiridonov, Paper, Sofia, Bulgaria, Tatyana Bozhkova, and Kosta Shterev
Subjects: Security printing, Engineering, business.industry, business, Computer security, computer.software_genre, computer
Published: 2018

9. Want Value from Big Data? Close the Gap between the C-Suite and the Server Room

Author: Kelley O'Reilly and David Paper
Subjects: World Wide Web, Information Systems and Management, Database, business.industry, Suite, Big data, Value (economics), Business, Management Science and Operations Research, Server room, computer.software_genre, computer
Published: 2012

10. User Acceptance of Voice Recognition Technology

Author: Steven John Simon and David Paper
Subjects: Human-Computer Interaction, Extension (metaphysics), Multimedia, Human–computer interaction, Computer science, Strategy and Management, Technology acceptance model, computer.software_genre, computer, Computer Science Applications
Abstract: Voice recognition technology-enabled devices possess extraordinary growth potential, yet some research indicates that organizations and consumers are resisting their adoption. This study investigates the implementation of a voice recognition device in the United States Navy. Grounded in the social psychology and information systems literature, the researchers adapted instruments and developed a tool to explain technology adoption in this environment. Using factor analysis and structural equation modeling, analysis of data from the 270 participants explained almost 90% of the variance in the model. This research adapts the technology acceptance model by adding elements of the theory of planned behavior, providing researchers and practitioners with a valuable instrument to predict technology adoption.
Published: 2007

11. Using Harel's Statecharts to Model Business Workflows

Author: Wai Yin Mok and David P. Paper
Subjects: Database, Windows Workflow Foundation, business.industry, Computer science, Active database, computer.software_genre, Workflow engine, Workflow technology, Workflow, Unified Modeling Language, Hardware and Architecture, Event-driven process chain, Software engineering, business, computer, Software, Workflow management system, Information Systems, computer.programming_language
Abstract: In this paper, we model business workflows using Harel’s statecharts. Mapping to statecharts allows us to systematically identify potential workflow problems. It also allows us to investigate specific properties inherent in actual business workflows. Our research focuses on three desirable properties of active database systems — termination, confluence, and observable determinism. For termination and confluence, we develop algorithms to provide a theoretical lens linking desirable active database system properties to workflow management systems problems. We initially validate our algorithms by mapping business workflows from a case study. Our research thus builds preliminary theory by developing a systematic method for identifying workflow problems.
Published: 2002

12. IT solutions for data integration at europroducts, inc.*: A case study

Author: David Nicol and David Paper
Subjects: Factory floor, Leverage (finance), Knowledge management, General Computer Science, Standardization, Computer science, business.industry, Business process reengineering, Material requirements, computer.software_genre, Engineering management, Systems theory, Data input, business, computer, Information Systems, Data integration
Abstract: This paper provides a longitudinal view of one organization’s experiences with IT implementation and Business Process Reengineering since 1990. The organization is EuroProducts; a manufacturer of air freshener and related products located in thecountry, in the West of England, EuroProducts has identified data integration and data standardization as critical to leverage increased performance from its materials requirements process flow. As a result, a new MRP system is being introduced to integrate data input from factory floor workers, management, staff, and IS professionals. The goal is to use the new MRP system as a rallying point to facilitate redesign of material requirements work flows. Aspects of innovation and systems theory are introduced to help the authors organize and identify root causes of the problems EuroProducts has had with its IT implementation and reengineering efforts.
Published: 1998

13. On transformations from UML models to object-relational databases

Author: David P. Paper and Wai Yin Mok
Subjects: Computer science, Programming language, Relational database, Object (computer science), Data structure, computer.software_genre, Determinism, Nested set model, Unified Modeling Language, Information system, State diagram, Boolean function, computer, computer.programming_language
Abstract: In this paper we consider the problems of transforming UML models into object-relational databases, which consist of static aspects and dynamic aspects. For the static aspects of the database, we first remove semantically overloaded elements, and then our algorithms generate Nested Normal Form nested tables. For the dynamic aspects of the database, we show how to map statechart diagrams into triggers. We begin our analysis of a statechart that potentially leads to nontermination of execution. We pay attention to the events, conditions, and actions of the transitions of states in the given statechart. Further we investigate the properties of confluence and observable determinism of the generated triggers.
Published: 2005

14. Dealing with Internet cheating: countering the online ‘paper mills’

Author: ‘paper mills’ Margaret Fain and Peggy Bates
Subjects: Engineering, business.industry, Cheating, Internet privacy, The Internet, business, Computer security, computer.software_genre, computer
Published: 2004

15. The current status of language automation

Author: Herbert H. Paper and Gordon E. Peterson
Subjects: Information Systems and Management, Machine translation, business.industry, Computer science, Strategy and Management, General Social Sciences, Research needs, computer.software_genre, Automation, Linguistics, Work (electrical), Current (fluid), business, Software engineering, General Agricultural and Biological Sciences, computer
Abstract: In this article is presented a brief review of the work in machine translation of languages, with the conclusion that much more research needs to be done and that it does not appear that human beings will ever be relieved of the necessity of learning languages and of knowing how to use these languages effectively.
Published: 1958

16. A Short Grammatical Outline of Pashto

Author: Herbert Penzl, D. A. Shafeev, and Herbert H. Paper
Subjects: Linguistics and Language, Grammar, Computer science, Arabic, business.industry, media_common.quotation_subject, Phonology, computer.software_genre, Language and Linguistics, language.human_language, Linguistics, Romanization, language, Pashto, Artificial intelligence, business, computer, Natural language processing, media_common, Language research
Published: 1965

17. IMPROVING ADAPTIVE LEARNING IN A SMART LEARNING ENVIRONMENT

Author: Gilberto Marzano, Yeliz Nur, Anda Abuze, and This paper has been supported by the board of Science of Latvia within the scope of the project 'Implementation of Transformative Digital Learning in Doctoral Program of Pedagogical Science in Latvia' (DocTDLL) lzp-2018/2-0180 and by the ASL project co-funded by the Erasmus + Programme of the European Union (Adult self-learning: supporting learning autonomy in a technology-mediated environment, reference number 2019-1-TR01-KA204-076875). The views expressed reflect those of the authors alone.
Subjects: Structure (mathematical logic), Multimedia, Computer science, Learning analytics, Attendance, Personalized learning, Employability, computer.software_genre, Learning Adaptivity, Learning Analytics, Learning Unit Structure, Smart Learning Environment, ComputingMilieux_COMPUTERSANDEDUCATION, Adaptive learning, Digital learning, Reduced cost, computer
Abstract: It has been broadly argued that, in the near future, the demand for skilled labor will increase whilst that for routine activities will decrease. In this regard, the need for making greater investments in education to re-skill workers and support continuous learning has been invoked as an essential requirement for preserving people’s employability.Digital technology is deemed increasingly necessary to sustain the educational endeavor, for the possibilities it offers to make more accessible and low-cost educational interventions. It allows for the creation of personalized learning paths and customized digital learning solutions, for courses to be available to a large attendance of learners, and for teaching-learning activities to be offered at significantly reduced cost.In this article, a learning unit structure designed to improve adaptive learning is proposed, and mechanisms for adaptive learning in a smart learning environment are discussed.The implemented teaching-learning solution is also illustrated. This is a preliminary application based on an approach that combines the teacher experience with learning analytics.
Published: 2021

18. Modeling and training strategies for language recognition systems

Author: Raphaël Duroselle, Denis Jouvet, Md. Sahidullah, Irina Illina, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Experiments presented in this paper were carried out using the Grid5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). This work has been partly funded by the French Direction Générale de l’Armement., Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: business.industry, Computer science, multi-task training, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, endto-end speech recognition, Training (civil), language recognition, 030507 speech-language pathology & audiology, 03 medical and health sciences, bottleneck features, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Artificial intelligence, 0305 other medical science, business, computer, Natural language processing, Language recognition
Abstract: International audience; Automatic speech recognition is complementary to language recognition. The language recognition systems exploit this complementarity by using frame-level bottleneck features extracted from neural networks trained with a phone recognition task. Recent methods apply frame-level bottleneck features extracted from an end-to-end sequence-to-sequence speech recognition model. In this work, we study an integrated approach of the training of the speech recognition feature extractor and language recognition modules. We show that for both classical phone recognition and end-to-end sequence-to-sequence features, sequential training of the two modules is not the optimal strategy. The feature extractor can be improved by supervision with the language identification loss, either in a fine-tuning step or in a multi-task training framework. Besides, we notice that end-to-end sequence-to-sequence bottleneck features are on par with classical phone recognition bottleneck features without requiring a forced alignment of the signal with target tokens. However, for sequence-to-sequence, the architecture of the model seems to play an important role; the Conformer architectures leads to much better results than the conventional stacked DNNs approach; and can even be trained directly with the LID module in an end-to-end approach.
Published: 2021

19. HInT: Hybrid and Incremental Type Discovery for Large RDF Data Sources

Author: Georgia Troullinou, Kenza Kellou-Menouer, Zoubida Kedad, Dimitris Plexousakis, Nikolaos Kardoulakis, Haridimos Kondylakis, Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hellenic Foundation for Research and Innovation, ΕΛ.ΙΔ.Ε.Κ: 1147, and Work reported in this paper has been partially supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the '2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers' (iQARuS Project No 1147)
Subjects: Exploit, LSH, Process (engineering), Computer science, media_common.quotation_subject, 02 engineering and technology, Machine learning, computer.software_genre, RDF, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO]Computer Science [cs], media_common, Complement (set theory), business.industry, Hybrid type discovery, Linked data, computer.file_format, Automatic summarization, Schema (genetic algorithms), 020201 artificial intelligence & image processing, Incrementality, Artificial intelligence, business, computer
Abstract: International audience; The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.
Published: 2021

20. Mixture modeling for identifying subtypes in disease course mapping

Author: Stanley Durrleman, Pierre-Emmanuel Poulet, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This research has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU-06. This work was also funded in part by the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute)., Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen, ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This paper is funded in part by grant number 678304 (ERC), 826421 (TVB-Cloud) from H2020 programme, and ANR-10-IAIHU-06 (IHU ICM), ANR-19-P3IA-0001 (PRAIRIE) and ANR-19-JPW2-000 (E-DADS) from ANR., Poulet, Pierre-Emmanuel, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Population, Stochastic approximation, Machine learning, computer.software_genre, 01 natural sciences, Synthetic data, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Alzheimer's disease subtypes, mixed-effect models, 0101 mathematics, Cognitive decline, mixture models, education, Cluster analysis, Non-linear mixed-effect model, ComputingMilieux_MISCELLANEOUS, Mixture model, Ground truth, education.field_of_study, MCMC-SAEM, business.industry, Contrast (statistics), Disease course mapping, Disease progression modelling, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Artificial intelligence, business, computer, [STAT.ME]Statistics [stat]/Methodology [stat.ME], 030217 neurology & neurosurgery
Abstract: International audience; Disease modeling techniques summarize the possible trajectories of progression from multimodal and longitudinal data. These techniques often assume that individuals form a homogeneous cluster, thus ignoring possible disease subtypes within the population. We extend a non-linear mixed-effect model used for disease course mapping with a mixture framework. We jointly estimate model parameters and subtypes with a tempered version of a stochastic approximation of the Expectation Maximisation algorithm. We show that our model recovers the ground truth parameters from synthetic data, in contrast to the naive solution consisting in post hoc clustering of individual parameters from a one-class model. Applications to Alzheimer's disease data allows the unsupervised identification of disease subtypes associated with distinct relationship between cognitive decline and progression of imaging and biological biomarkers.
Published: 2021

21. Binary level toolchain provenance identification with graph neural networks

Author: Tristan Benoit, Jean-Yves Marion, Sébastien Bardin, Carbone (CARBONE), Department of Formal Methods (LORIA - FM), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), This work is supported by (i) a public grant overseen by the French National Research Agency (ANR) as part of the 'Investissements d'Avenir' French PIA project 'Lorraine Université d'Excellence', reference ANR-15-IDEX-04-LUE, and (ii) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 830927 (Concordia). Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr)., GRID5000, IMPACT-DIGITRUST, ANR-15-IDEX-0004,LUE,Isite LUE(2015), European Project: 830927,CONCORDIA(2019), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Theoretical computer science, Artificial neural network, Computer science, Binary number, graph neural networks, 020207 software engineering, 02 engineering and technology, computer.software_genre, toolchain provenance, Toolchain, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Scalability, 0202 electrical engineering, electronic engineering, information engineering, Graph reduction, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.7: Distribution, Maintenance, and Enhancement/D.2.7.5: Restructuring, reverse engineering, and reengineering, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.5: Testing and Debugging/D.2.5.2: Diagnostics, Control flow graph, binary code analysis, 020201 artificial intelligence & image processing, Binary code, Compiler, computer, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Abstract: International audience; We consider the problem of recovering the compiling chain used to generate a given stripped binary code. We present a Graph Neural Network framework at the binary level to solve this problem, with the idea to take into account the shallow semantics provided by the binary code's structured control flow graph (CFG).We introduce a Graph Neural Network, called Site Neural Network (SNN), dedicated to this problem. To attain scalability at the binary level, feature extraction is simplified by forgetting almost everything in a CFG except transfer control instructions and performing a parametric graph reduction. Our experiments show that our method recovers the compiler family with a very high F1-Score of 0.9950 while the optimization level is recovered with a moderately high F1-Score of 0.7517. On the compiler version prediction task, the F1-Score is about 0.8167 excluding the clang family. A comparison with a previous work demonstrates the accuracy and performance of this framework.
Published: 2021

22. Algorithmic market making for options

Author: Philippe Bergault, Bastien Baldacci, Olivier Guéant, Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Centre d'économie de la Sorbonne (CES), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Bastien Baldacci gratefully acknowledges the support of the ERC Grant 679836 Staqamof. Olivier Guéant thanks the Research Initiative 'Modélisation des marchés actions, obligations et dérivés' financed by HSBC France under the aegis of the Europlace Institute of Finance for their support regarding an early version of the paper (entitled 'Algorithmic market making: the case of equity derivatives')., and European Project: 679836,STAQAMOF
Subjects: Options, Computational Finance (q-fin.CP), computer.software_genre, Market maker, FOS: Economics and business, Microeconomics, Quantitative Finance - Computational Finance, 0502 economics and business, Economics, Asset (economics), 050207 economics, Algorithmic trading, Market making, Stochastic control, 050208 finance, 05 social sciences, Vega, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Mathematical Finance (q-fin.MF), [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Stochastic optimal control, Quantitative Finance - Mathematical Finance, Risk Management (q-fin.RM), Portfolio, General Economics, Econometrics and Finance, computer, Finance, Quantitative Finance - Risk Management
Abstract: International audience; In this article, we tackle the problem of a market maker in charge of a book of options on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic optimal control problem of an option market maker is in fact tractable. More precisely, when volatility is modeled using a classical stochastic volatility model—e.g. the Heston model—the problem faced by an option market maker is characterized by a low-dimensional functional equation that can be solved numerically using a Euler scheme along with interpolation techniques, even for large portfolios. In order to illustrate our findings, numerical examples are provided.
Published: 2021

23. Metadata standards and practical guidelines for specimen and DNA curation when building barcode reference libraries for aquatic life

Author: Magali Schweizer, Frederik Leliaert, Jan Pawlowski, Rodolphe Rougerie, Frédéric Rimet, Torbjørn Ekrem, Alexis Canino, Teofana Chonova, David G. Mann, Agnès Bouchez, Régis Lionel Vivien, Christian Chauvin, Chloé Goulon, Valentin Vasselon, Romain Gastineau, Rosa Trobajo, Christophe Laplace-Treyture, Jonas Zimmermann, Eva Aylagas, Wolf-Henning Kusber, Andrzej Witkowski, Vona Méléder, Frédéric Marchand, Sinziana F. Rivera, Maria Kahlert, Muriel Gugger, Filipe O. Costa, Maria Holzmann, Benoît J.D. Ferrari, Serena Rasconi, Alexander M. Weigand, Regine Jahn, Fedor Čiampor, Florian Leese, Ángel Borja, Centre Alpin de Recherche sur les Réseaux Trophiques et Ecosystèmes Limniques (CARRTEL), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), King Abdullah University of Science and Technology (KAUST), Basque Research and Technology Alliance (BRTA), King Abdulaziz University, Ecosystèmes aquatiques et changements globaux (UR EABX), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Slovak Academy of Science [Bratislava] (SAS), Universidade do Minho, Swiss Centre for Applied Ecotoxicology (Ecotox Center), Ecole Polytechnique Fédérale de Lausanne (EPFL), Technical university of Szczecin, Collection des Cyanobactéries, Institut Pasteur [Paris], University of Geneva [Switzerland], Freie Universität Berlin, Swedish University of Agricultural Sciences (SLU), Universitat Duisberg-Essen, Botanic Garden Meise, Institute of Agrifood Research and Technology [Sant Carles de la Ràpita] (IRTA), Institute of Agrifood Research and Technology (IRTA), Royal Botanic Garden Edinburgh, Unité Expérimentale d'Ecologie et d'Ecotoxicologie Aquatique - U3E (Rennes, France) (U3E ), Université de Nantes (UN), Polish Academy of Sciences (PAN), ID-Gene ecodiagnostics [Geneva], Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), SCIMABIO Interface SAS, Musée National d'Histoire Naturelle de Luxembourg (MNHN), Norwegian University of Science and Technology [Trondheim] (NTNU), Norwegian University of Science and Technology (NTNU), This paper was written under COST Action DNAqua-Net. The European COST Action DNAqua-Net (CA 15219 collaborative network which gathers several hundreds of scientists and water managers, with the objective of developing new genetic tools for bioassessment and monitoring of aquatic ecosystems (Leese et al. 2016)., European Project, West Pomeranian University of Technology in Szczecin (ZUT), Universidade do Minho = University of Minho [Braga], Université de Genève = University of Geneva (UNIGE), Institut Pasteur [Paris] (IP), Meise Botanic Garden [Belgium] (Plantentuin), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), Royal Botanic Garden [Edinburgh], Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Producció Animal, and Aigües Marines i Continentals
Subjects: 0106 biological sciences, 0301 basic medicine, Computer science, [SDV]Life Sciences [q-bio], Ciências Biológicas [Ciências Naturais], computer.software_genre, Barcode, 01 natural sciences, DNA barcoding, law.invention, law, QH540-549.5, aquatic organisms, media_common, Database, Ecology, Quality, Voucher, Identification (information), Aquatic organisms, quality, Biologie, Traceability, media_common.quotation_subject, reference library, Engenharia e Tecnologia::Engenharia do Ambiente, 010603 evolutionary biology, 03 medical and health sciences, Genetics (medical genetics to be 30107 and agricultural genetics to be 40402), Genetics, Quality (business), Relevance (information retrieval), 14. Life underwater, Molecular Biology, Nature and Landscape Conservation, Engenharia do Ambiente [Engenharia e Tecnologia], Metadata, Ciências Naturais::Ciências Biológicas, metadata, barcode, Reference library, DNA, 030104 developmental biology, traceability, Animal Science and Zoology, computer
Abstract: DNA barcoding and metabarcoding is increasingly used to effectively and precisely assess and monitor biodiversity in aquatic ecosystems. As these methods rely on data availability and quality of barcode reference libraries, it is important to develop and follow best practices to ensure optimal quality and traceability of the metadata associated with the reference barcodes used for identification. Sufficient metadata, as well as vouchers, corresponding to each reference barcode must be available to ensure reliable barcode library curation and, thereby, provide trustworthy baselines for downstream molecular species identification. This document (1) specifies the data and metadata required to ensure the relevance, the accessibility and traceability of DNA barcodes and (2) specifies the recommendations for DNA harvesting and for the storage of both voucher specimens/samples and barcode data, (undefined)
Published: 2021

24. Does Infant-Directed Speech Help Phonetic Learning? A Machine Learning Investigation

Author: Reiko Mazuka, Emmanuel Dupoux, Bogdan Ludusan, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Universität Bielefeld = Bielefeld University, Department of Psychology and Neuroscience, Duke University [Durham], Apprentissage machine et développement cognitif (CoML), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de sciences cognitives et psycholinguistique (LSCP), CIFAR program in Learning in Machines & Brains CIFAR LMB program, The research reported in this paper was partly funded by JSPS Grant-in-Aid for Scientific Research (16H06319, 20H05617) and MEXT Grant-in-Aid on Innovative Areas #4903 (Co-creative Language Evolution), 17H06382 to R. Mazuka. The work of E. Dupoux in his EHESS role was supported by the European Research Council (ERC-2011-AdG-295810 BOOTPHON) the Agence Nationale pour la Recherche (ANR-10-LABX-0087 IEC, ANR-10-IDEX0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute), and CIFAR (Learning in Machines and Brain). Part of the work was conducted while E. Dupoux was a visiting scientist at DeepMind and Facebook. B. Ludusan was also supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 799022., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-17-EURE-0017,FrontCog,Frontières en cognition(2017), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), European Project: 295810,EC:FP7:ERC,ERC-2011-ADG_20110406,BOOTPHON(2012), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Functional role, Adult, Computer science, Cognitive Neuroscience, Experimental and Cognitive Psychology, Machine learning, computer.software_genre, Infant-directed speech, 050105 experimental psychology, Read speech, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Artificial Intelligence, Robustness (computer science), Phonetics, Concept learning, Vowel, Humans, Speech, 0501 psychology and cognitive sciences, Speech variability, Hyperarticulation, business.industry, 05 social sciences, Phonetic learning, Infant, Reading, Speech Perception, Artificial intelligence, business, computer, Adult-directed speech, 030217 neurology & neurosurgery
Abstract: A prominent hypothesis holds that by speaking to infants in infant-directed speech (IDS) as opposed to adult-directed speech (ADS), parents help them learn phonetic categories. Specifically, two characteristics of IDS have been claimed to facilitate learning: hyperarticulation, which makes the categories more separable, and variability, which makes the generalization more robust. Here, we test the separability and robustness of vowel category learning on acoustic representations of speech uttered by Japanese adults in ADS, IDS (addressed to 18- to 24-month olds), or read speech (RS). Separability is determined by means of a distance measure computed between the five short vowel categories of Japanese, while robustness is assessed by testing the ability of six different machine learning algorithms trained to classify vowels to generalize on stimuli spoken by a novel speaker in ADS. Using two different speech representations, we find that hyperarticulated speech, in the case of RS, can yield better separability, and that increased between-speaker variability in ADS can yield, for some algorithms, more robust categories. However, these conclusions do not apply to IDS, which turned out to yield neither more separable nor more robust categories compared to ADS inputs. We discuss the usefulness of machine learning algorithms run on real data to test hypotheses about the functional role of IDS. © 2021 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).
Published: 2020

25. Dragonblood is Still Leaking: Practical Cache-based Side-Channel in the Wild

Author: Mohamed Sabt, Pierre-Alain Fouque, Daniel De Almeida Braga, Embedded Security and Cryptography / Sécurité cryptographie embarquée (EMSEC), SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Daniel De Almeida Braga is funded by the Direction Générale de l’Armement (Pôle de Recherche CYBER). We would like to thank the anonymous paper and artifact reviewers for their time and constructive feedbacks., Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Subjects: FOS: Computer and information sciences, Dragonfly, Computer Science - Cryptography and Security, computer.internet_protocol, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Computer security, computer.software_genre, WPA3, Supplicant, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Password authentication protocol, Side channel attack, Wi-Fi, Key exchange, Password, 021110 strategic, defence & security studies, Authentication, PAKE, Elliptic curve, cache attack, computer, hostapd, Cryptography and Security (cs.CR)
Abstract: Recently, the Dragonblood attacks have attracted new interests on the security of WPA-3 implementation and in particular on the Dragonfly code deployed on many open-source libraries. One attack concerns the protection of users passwords during authentication. In the Password Authentication Key Exchange (PAKE) protocol called Dragonfly, the secret, namely the password, is mapped to an elliptic curve point. This operation is sensitive, as it involves the secret password, and therefore its resistance against side-channel attacks is of utmost importance. Following the initial disclosure of Dragonblood, we notice that this particular attack has been partially patched by only a few implementations. In this work, we show that the patches implemented after the disclosure of Dragonblood are insufficient. We took advantage of state-of-the-art techniques to extend the original attack, demonstrating that we are able to recover the password with only a third of the measurements needed in Dragonblood attack. We mainly apply our attack on two open-source projects: iwd (iNet Wireless Daemon) and FreeRADIUS, in order underline the practicability of our attack. Indeed, the iwd package, written by Intel, is already deployed in the Arch Linux distribution, which is well-known among security experts, and aims to offer an alternative to wpa\_supplicant. As for FreeRADIUS, it is widely deployed and well-maintained upstream open-source project. We publish a full Proof of Concept of our attack, and actively participated in the process of patching the vulnerable code. Here, in a backward compatibility perspective, we advise the use of a branch-free implementation as a mitigation technique, as what was used in hostapd, due to its quite simplicity and its negligible incurred overhead., Accepted at Annual Computer Security Applications Conference (ACSAC 2020), December 7-11, 2020, Austin, USA. ACM, New York, NY, USA, 13 pages, ACM ISBN 978-1-4503-8858-0/20/12 Artifact available: https://gitlab.inria.fr/ddealmei/poc-iwd-acsac2020/-/tree/master/
Published: 2020

26. Machines and Masterpieces: Predicting Prices in the Art Auction Market

Author: Roman Kräussl, Gustavo Manso, Mathieu Aubry, Christophe Spaenjers, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Department of Economics, Tilburg University [Netherlands], HEC Research Paper Series, and Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)
Subjects: asset valuation, History, Polymers and Plastics, Computer science, Big data, Machine learning, computer.software_genre, Industrial and Manufacturing Engineering, big data, 0502 economics and business, Common value auction, Price level, auctions, 050207 economics, Business and International Management, JEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C50 - General, Valuation (finance), JEL: D - Microeconomics/D.D4 - Market Structure, Pricing, and Design/D.D4.D44 - Auctions, 050208 finance, Artificial neural network, Ex-ante, business.industry, 05 social sciences, experts, Hedonic pricing, TheoryofComputation_GENERAL, JEL: Z - Other Special Topics/Z.Z1 - Cultural Economics • Economic Sociology • Economic Anthropology/Z.Z1.Z11 - Economics of the Arts and Literature, machine learning, [SHS.GESTION]Humanities and Social Sciences/Business administration, Artificial intelligence, business, computer, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G12 - Asset Pricing • Trading Volume • Bond Interest Rates
Abstract: We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.
Published: 2020

27. A comparative study of high-productivity high-performance programming languages for parallel metaheuristics

Author: Jan Gmys, Nouredine Melab, Daniel Tuyttens, El-Ghazali Talbi, Tiago Carneiro, University of Mons [Belgium] (UMONS), Faculté polytechnique de Mons, Université de Mons (UMons), Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Université de Lille, and The experiments presented in this paper were carried out on the Grid’5000 testbed, hosted by INRIA and including several other organizations. Wethank Bradford Chamberlain, Elliot Ronaghan (Cray inc.) and Louis Jenkins (University of Rochester) for their support on Chapel’s Gitter platform.
Subjects: Parallel computing, General Computer Science, Quadratic assignment problem, Computer science, General Mathematics, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 02 engineering and technology, Thread (computing), Metaheuristics, computer.software_genre, High productivity, Chapel, 0202 electrical engineering, electronic engineering, information engineering, Metaheuristic, computer.programming_language, Programming language, 05 social sciences, 050301 education, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Python (programming language), High-productivity languages, Parallel metaheuristics, Scalability, 020201 artificial intelligence & image processing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], 0503 education, computer
Abstract: International audience; Parallel metaheuristics require programming languages that provide both, high performance and a high level of programmability. This paper aims at providing a useful data point to help practitioners gauge the difficult question of whether to invest time and effort into learning and using a new programming language. To accomplish this objective, three productivity-aware languages (Chapel, Julia, and Python) are compared in terms of performance, scalability and productivity. To the best of our knowledge, this is the first time such a comparison is performed in the context of parallel metaheuristics. As a test-case, we implement two parallel metaheuristics in three languages for solving the 3D Quadratic Assignment Problem (Q3AP), using thread-based parallelism on a multi-core shared-memory computer. We also evaluate and compare the performance of the three languages for a parallel fitness evaluation loop, using four different test-functions with different computational characteristics. Besides providing a comparative study, we give feedback on the implementation and parallelization process in each language.
Published: 2020

28. Parallel Surrogate-assisted Optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO

Author: Guillaume Briffoteaux, Romain Ragonnet, Jan Gmys, Nouredine Melab, Mohand Mezmaz, Maxime Gobert, Daniel Tuyttens, Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Mons [Belgium] (UMONS), Monash University [Melbourne], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques [Mons], Université de Mons (UMons), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), and Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).
Subjects: General Computer Science, Computer science, General Mathematics, Evolutionary algorithm, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, Surrogate model, Genetic algorithm, Surrogate-assisted Optimization, 0202 electrical engineering, electronic engineering, information engineering, Massively parallel, Global optimization, Artificial neural network, Bayesian Optimization, business.industry, 05 social sciences, Bayesian optimization, Evolutionary Algorithm, 050301 education, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Efficient Global Optimization, Benchmark (computing), 020201 artificial intelligence & image processing, Artificial intelligence, Massively Parallel Computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, 0503 education, computer, Simulation
Abstract: International audience; Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger databases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.
Published: 2020

29. Are People Willing to Pay for Reduced Inequality?

Author: Brian Hill, Thomas Lloyd, Ecole des Hautes Etudes Commerciales (HEC Paris), HEC Paris Research Paper Series, HEC Paris - Recherche - Hors Laboratoire, Centre National de la Recherche Scientifique (CNRS), Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), and Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS)
Subjects: JEL: D - Microeconomics/D.D3 - Distribution, History, Point of sale, Political spectrum, Polymers and Plastics, Inequality, media_common.quotation_subject, Population, computer.software_genre, Industrial and Manufacturing Engineering, Politics, Willingness to pay, Economic inequality, 0502 economics and business, Economics, Production (economics), inequality information provision, Income inequality, 050207 economics, Business and International Management, 10. No inequality, education, media_common, JEL: D - Microeconomics/D.D9 - Intertemporal Choice, education.field_of_study, 050208 finance, inequality attitude, 05 social sciences, [SHS.PHIL]Humanities and Social Sciences/Philosophy, 1. No poverty, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, JEL: D - Microeconomics/D.D6 - Welfare Economics/D.D6.D63 - Equity, Justice, Inequality, and Other Normative Criteria and Measurement, 8. Economic growth, inequality reporting, [SHS.GESTION]Humanities and Social Sciences/Business administration, Demographic economics, willingness to pay, computer
Abstract: In the face of rising income inequality (Acemoglu & Autor, 2011; Atkinson, Piketty, & Saez, 2011; Piketty, 2014; World Economic Forum, 2014), one recent proposal is to provide consumers with information about the income inequality across those involved in the production of each good, at the point of purchase. This has been shown to depress overall inequality (Hill, 2020), though its impact depends crucially on whether people are willing to pay more for goods whose production involves less income inequality. Here we investigate this largely unexplored empirical question through an incentivised, behavioural choice experiment on a representative sample of the English population. We find that a large majority are willing to pay significantly more for goods associated with less inequality. How much more people are willing to pay varies with political leaning and increases with the extent of the inequality reduction, but is positive across the political spectrum and for all studied inequality differences. Moreover, it is typically higher when inequality is reported in more intuitive and informa- tive formats. Our results bode well for the effectiveness of product-level inequality information provision as a tool for moderating income inequality, promising impacts even in markets where all goods involve relatively high inequality levels and potential participation across the political spectrum.
Published: 2020

30. Transfer learning of the expressivity using flow metric learning in multispeaker text-to-speech synthesis

Author: Ajinkya Kulkarni, Vincent Colotte, Denis Jouvet, Jouvet, Denis, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr)., Grid'5000, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Mean opinion score, Speech recognition, deep metric learning, Inference, Speech synthesis, 02 engineering and technology, Latent variable, 010501 environmental sciences, [INFO] Computer Science [cs], computer.software_genre, expressivity, 01 natural sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], variational autoencoder, text-to-speech, 0105 earth and related environmental sciences, Acoustic model, Speaker recognition, inverse autoregressive flow, Autoencoder, Metric (mathematics), Embedding, 020201 artificial intelligence & image processing, Transfer of learning, computer
Abstract: International audience; In this paper, we present a novel deep metric learning architecture along with variational inference incorporated in a paramet-ric multispeaker expressive text-to-speech (TTS) system. We proposed inverse autoregressive flow (IAF) as a way to perform the variational inference, thus providing flexible approximate posterior distribution. The proposed approach condition the text-to-speech system on speaker embeddings so that latent space represents the emotion as semantic characteristics. For representing the speaker, we extracted speaker em-beddings from the x-vector based speaker recognition model trained on speech data from many speakers. To predict the vocoder features, we used the acoustic model conditioned on the textual features as well as on the speaker embedding. We transferred the expressivity by using the mean of the latent variables for each emotion to generate expressive speech in different speaker's voices for which no expressive speech data is available. We compared the results obtained using flow-based variational inference with variational autoencoder as a base-line model. The performance measured by mean opinion score (MOS), speaker MOS, and expressive MOS shows that N-pair loss based deep metric learning along with IAF model improves the transfer of expressivity in the desired speaker's voice in synthesized speech.
Published: 2020

31. Deep Variational Metric Learning for Transfer of Expressivity in Multispeaker Text to Speech

Author: Vincent Colotte, Ajinkya Kulkarni, Denis Jouvet, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Experiments presented in this paper were carried out using the Grid5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. (see https://www.grid5000.fr), and Grid'5000
Subjects: Computer science, Speech recognition, deep metric learning, Contrast (statistics), 020206 networking & telecommunications, Speech synthesis, 02 engineering and technology, expressivity, computer.software_genre, Speaker recognition, Autoencoder, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Identity (music), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, variational autoencoder, 020201 artificial intelligence & image processing, Expressivity (genetics), text-to-speech, Representation (mathematics), computer
Abstract: In this paper, we propose an approach relying on multiclass N-pair loss based deep metric learning in recurrent conditional variational autoencoder (RCVAE). We used RCVAE for implementation of multispeaker expressive text-to-speech (TTS) system. The proposed approach condition text-to-speech system on speaker embeddings, and leads to clustering the latent space representation with respect to emotion. The deep metric learning helps to reduce the intra-class variance and increase the inter-class variance in latent space. Thus, we present multiclass N-pair loss to enhance the meaningful representation of the latent space. For representing the speaker, we extracted speaker embed-dings from the x-vector based speaker recognition model trained on speech data from many speakers. To predict the vocoder features, we used RCVAE for the acoustic modeling, in which the model is conditioned on the textual features as well as on the speaker embedding. We transferred the expressivity by using the mean of the latent variables for each emotion to generate expressive speech in different speaker's voices for which no expressive speech data is available. We compared the results with those of the RCVAE model without multiclass N-pair loss as baseline model. The performance measured by mean opinion score (MOS), speaker MOS, and expressive MOS shows that N-pair loss based deep metric learning significantly improves the transfer of expressivity in the target speaker's voice in synthesized speech.
Published: 2020

32. Target Search in Product Displays: A Visual Crowding Perspective

Author: Cathy L. Yang, Ana Scekic, Peter Ebbes, Selin Atalay, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series
Subjects: Visual search, Point of sale, Product Display, genetic structures, Computer science, Perspective (graphical), Eye Tracking, Information processing, computer.software_genre, Crowding, Human–computer interaction, Metric (mathematics), [SHS.GESTION]Humanities and Social Sciences/Business administration, Eye tracking, Visual Crowding, Product (category theory), computer, Target Visual Crowdedness, Visual Search
Abstract: Consumers often arrive at the point of purchase with a particular product to purchase in mind and must search for this target product in the product display. Drawing on visual crowding theory, we suggest that an individual’s ability to find a target product in a display varies according to the embeddedness of the target, which depends on the locations of the target and non-target products in the display. We refer to the visual impact of non-target products on target products that results in an inability to differentiate and recognize the target product as “target visual crowdedness (TVC).” Across five experimental studies, we manipulate TVC and show that as TVC increases the duration of the visual search increases. Eye-tracking data provide evidence of how TVC affects attention and information processing in target product search tasks. We develop a visual crowdedness metric that quantifies TVC and can be used by researchers and practitioners to estimate the visual search effort needed to find a product in a display.
Published: 2020

33. Classification of Broadcast News Audio Data Employing Binary Decision Architecture

Author: Peter Feciľak, Anton Čižmár, Jozef Juhar, Jozef Vavrek, and The research in this paper was supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the project VEGA 2/0197/15 and the Slovak Research and Development Agency under the project APVV-15-0517.
Subjects: 0209 industrial biotechnology, Computational complexity theory, Computer Networks and Communications, Computer science, Binary number, 68T10, 02 engineering and technology, computer.software_genre, Multiclass classification, 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, Architecture, Support vector machine, audio classification, broadcast news data, binary decision trees, binary decision architecture, other areas of Computing and Informatics, Binary decision diagram, business.industry, 020208 electrical & electronic engineering, Pattern recognition, Support vector machine, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, Hardware and Architecture, Data mining, Artificial intelligence, Decision table, business, computer, Classifier (UML), Software
Abstract: A novel binary decision architecture (BDA) for broadcast news audio classification task is presented in this paper. The idea of developing such architecture came from the fact that the appropriate combination of multiple binary classifiers for two-class discrimination problem can reduce a miss-classification error without rapid increase in computational complexity. The core element of classification architecture is represented by a binary decision (BD) algorithm that performs discrimination between each pair of acoustic classes, utilizing two types of decision functions. The first one is represented by a simple rule-based approach in which the final decision is made according to the value of selected discrimination parameter. The main advantage of this solution is relatively low processing time needed for classification of all acoustic classes. The cost for that is low classification accuracy. The second one employs support vector machine (SVM) classifier. In this case, the overall classification accuracy is conditioned by finding the optimal parameters for decision function resulting in higher computational complexity and better classification performance. The final form of proposed BDA is created by combining four BD discriminators supplemented by decision table. The effectiveness of proposed BDA, utilizing rule-based approach and the SVM classifier, is compared with two most popular strategies for multiclass classification, namely the binary decision trees (BDT) and the One-Against-One SVM (OAOSVM). Experimental results show that the proposed classification architecture can decrease the overall classification error in comparison with the BDT architecture. On the contrary, an optimization technique for selecting the optimal set of training data is needed in order to overcome the OAOSVM.
Published: 2017

34. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning

Author: Philippe Faure, Jean-Christophe Olivo-Marin, Fabrice de Chaumont, Nicolas Torquet, Albane Imbert, Stephane Dallongeville, Anne-Marie Le Sourd, Thomas Bourgeron, Elodie Ey, Thierry Legou, Thibault Lagache, Analyse d'images biologiques - Biological Image Analysis (BIA), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Neurosciences Paris Seine (NPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche et Innovation Technologique (CITECH), Institut Pasteur [Paris], Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), This work was partially funded by the Institut Pasteur, the Bettencourt-Schueller Foundation, the Cognacq–Jay Foundation, the Conny–Maeva Foundation, the ERANET–NEURON SYNPATHY program, the Agence Nationale de la Recherche through grant number ANR-10-LABX-62-IBEID, France-BioImaging infrastructure through grant number ANR-10-INBS-04 and the INCEPTION program through grant number ANR-16-CONV-0005, the Centre National de la Recherche Scientifique, the University Paris Diderot, the BioPsy Labex, the Institut National du Cancer through grant number TABAC-16–022, the Foundation for Medical Research (Equipe DEQ20130326488), the Innovative Medicines Initiative Joint Undertaking through grant agreement number 115300, resources of which are composed of financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies in kind contribution., The authors thank Y. Archambeau and P. Ollivon at the workshop of the Institut Pasteur for building the first 12 setups and advising on hardware, W. Meiniel for the mathematical proof for decisions of head/tail probability, Microsoft France for their technical support, P. Spinicelli for optical engineering and reading of the paper, R. Marée for machine learning support, B. König for advice and reading of biological experiments, J. N. Crawley for reading and providing comments on the manuscript, A. Barmpoutis for providing us with the early Kinect 2 driver and support, N. Chenouard for driving the use of the machine learning solution, P. Dugast for drawing the mice in the different behavioural events, A. Engelberg for checking the English, S. Wagner and R. Accardi for RFID advice, M. Marim for website development, and X. Montagutelli and M. Bérard for animal facility support., Analyse d'images biologiques - BioImage Analysis (AIQ), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut Pasteur [Paris], Neuroscience Paris Seine (NPS), Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Male, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Biomedical Engineering, Video Recording, Medicine (miscellaneous), Bioengineering, Nerve Tissue Proteins, Mouse tracking, Biology, Machine learning, computer.software_genre, Machine Learning, 03 medical and health sciences, Mice, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Animals, Autistic Disorder, Real time analysis, Social Behavior, Mice, Knockout, Behavior, Animal, business.industry, Microfilament Proteins, Computer Science Applications, Disease Models, Animal, 030104 developmental biology, Phenotype, Mutation, Identification (biology), Female, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Biotechnology, Behavioral Research
Abstract: Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations—mutations that are associated with autism—on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions. A method that combines a depth-sensing camera and machine learning can track the movements of up to four mice in real time and for several days, extracting both individual and group behavioural traits.
Published: 2019

35. Expressiveness of component-based frameworks: A study of the expressiveness of BIP

Author: Simon Bliudze, Eduard Baranov, UCL - SST/ICTM/INGI - Pôle en ingénierie informatique, Inria, Université Catholique de Louvain = Catholic University of Louvain [UCL], Self-adaptation for distributed services and large software systems [SPIRALS], Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL], Université Catholique de Louvain = Catholic University of Louvain (UCL), Self-adaptation for distributed services and large software systems (SPIRALS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), The contribution of Simon Bliudze to the work presented in this paper has been partially funded by Hauts-de-France regional STaRS programme under the grant agreement № 2019.00170., Université Catholique de Louvain (UCL), Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer Networks and Communications, Semantics (computer science), Computer science, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], SOS formats, glue operators, component-based frameworks, BIP, expressiveness, computer.software_genre, 01 natural sciences, Operational semantics, Component (UML), 0202 electrical engineering, electronic engineering, information engineering, Algebraic number, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], Hierarchy (mathematics), Programming language, 020207 software engineering, 16. Peace & justice, 010201 computation theory & mathematics, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Theory of computation, computer, Software, Information Systems
Abstract: International audience; We extend our previous algebraic formalisation of the notion of component-based framework in order to formally define two forms---strong and weak---of the notion of full expressiveness and study their properties. Our earlier result shows that the BIP (Behaviour-Interaction-Priority) framework does not possess the strong full expressiveness with respect to the sub-class of GSOS rules used for the definition of its semantics. In this paper, we refine this comparison detailing the expressiveness of classical BIP, Offer BIP and a number of variations obtained either by relaxing the constraints in the definition of priority models or by introducing positive premises into the rule formats used to define the operational semantics of composition operators. The obtained results are organised into an expressiveness hierarchy.
Published: 2019

36. Non-local Social Pooling for Vehicle Trajectory Prediction

Author: Itheri Yahiaoui, Kaouther Messaoud, Fawzi Nashashibi, Anne Verroust-Blondet, Robotics & Intelligent Transportation Systems (RITS), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), Projet PIA CAMPUS (Connected Automated Mobility Platform for Urban Sustainability), and The work presented in this paper has been financially supported by PIA French project CAMPUS (Connected Automated Mobility Platform for Urban Sustainability).
Subjects: Computer science, media_common.quotation_subject, Pooling, Context (language use), 02 engineering and technology, computer.software_genre, Motion (physics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Highway environment, Originality, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], ComputingMilieux_MISCELLANEOUS, media_common, 050210 logistics & transportation, 05 social sciences, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], 020206 networking & telecommunications, Non local, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Trajectory, Data mining, Naturalistic driving, computer
Abstract: International audience; For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context.
Published: 2019

37. Polyhedral Dataflow Programming: a Case Study

Author: Laure Gonnord, Romain Fontaine, Lionel Morel, CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de l'Informatique du Parallélisme (LIP), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, CASH - Compilation and Analysis, Software and Hardware (CASH), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), CEA Tech en régions (CEA-TECH-Reg), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), We are grateful to people at Kalray for allowing us to experiment with their ΣC compiler. Some of the experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inriaand including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).This work was also partially funded by the French National Agency of Research in theCODAS Project (ANR-17-CE23-0004-01), ANR-17-CE23-0004,CODAS,Ordonnancement de programmes à structures de données complexes(2017), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL)
Subjects: parallelism, dataflow programming, Computer science, Programming language, Dataflow, Dataflow programming, 020206 networking & telecommunications, 02 engineering and technology, Load balancing (computing), runtime system, computer.software_genre, Toolchain, Runtime system, load-balancing, 0202 electrical engineering, electronic engineering, information engineering, compilation, 020201 artificial intelligence & image processing, Compiler, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], computer
Abstract: International audience; Dataflow languages expose the application's potential parallelism naturally and have thus been studied and developed for the past thirty years as a solution for harnessing the increasing hardware parallelism. However, when generating code for parallel processors, current dataflow compilers only take into consideration the overall dataflow network of the application. This leaves out the potential parallelism that could be extracted from the internals of agents, typically when those include loop nests, for instance, but also potential application of intra-agent pipelining, or task splitting and rescheduling. In this work, we study the benefits of jointly using polyhedral compilation with dataflow languages. More precisely, we propose to expend the parallelization of dataflow programs by taking into account the parallelism exposed by loop nests describing the internal behavior of the program's agents. This approach is validated through the development of a prototype toolchain based on an extended version of the ΣC language. We demonstrate the benefit of this approach and the potentiality of further improvements on relevant case studies.
Published: 2018

38. Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

Author: Jouni Paltakari, Merve Özkan, Alp Karakoç, Orlando J. Rojas, Maryam Borghei, Paper Converting and Packaging, Department of Bioproducts and Biosystems, Aalto-yliopisto, and Aalto University
Subjects: Materials science, Oxidized cellulose, lcsh:Medicine, 02 engineering and technology, 010402 general chemistry, Machine learning, computer.software_genre, 01 natural sciences, Polyvinyl alcohol, Article, chemistry.chemical_compound, Transmittance, Surface roughness, Cellulose, lcsh:Science, Multidisciplinary, business.industry, lcsh:R, 021001 nanoscience & nanotechnology, Flexible electronics, 0104 chemical sciences, chemistry, Nanofiber, lcsh:Q, Wetting, Artificial intelligence, 0210 nano-technology, business, computer
Abstract: We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the “random forest” machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.
Published: 2018

39. Towards a Safe Software Development Environment

Author: Mahmoud Hussein, Reda Nouacer, Ansgar Radermacher, Département Ingénierie Logiciels et Systèmes ( DILS ), Laboratoire d'Intégration des Systèmes et des Technologies ( LIST ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, In this paper, we have presented a number of use cases that are done in industrial and research projects. In the following, we present the funding organizations for these projects:- The SafeAdapt project was funded by the European Commission within the 7th Framework Program under the grant number '608945'.- The STANCE project was also funded by the European Commission under the ICT theme of the 7th Framework Program with the grant agreement number '317753'.- The OpenES project was funded under the CATRENE Program with the agreement number 'CA703-2013'.- The EQUITAS project was funded by Bpifrance under call FUI-AAP16 with a contract number 'F1312031-Q'.- The SESAM Grids is a 'Programme d'Investissement d'Avenir' project funded by 'FSN-Briques Génériques du Logiciel Embarqué N°3' with contract number J.- The VESSEDIA project receives funding from the European Union's Horizon 2020 Program (H2020/2014-2020) under grant agreement number '731453'., Novotny M., Kubatova H., Skavhaug A., European Project : 608945,EC:FP7:ICT,FP7-2013-ICT-GC,SAFEADAPT ( 2013 ), European Project : 317753,EC:FP7:ICT,FP7-ICT-2011-8,STANCE ( 2012 ), European Project : 731453, Département Ingénierie Logiciels et Systèmes (DILS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Novotny M., Kubatova H., Skavhaug A., European Project: 608945,EC:FP7:ICT,FP7-2013-ICT-GC,SAFEADAPT(2013), European Project: 317753,EC:FP7:ICT,FP7-ICT-2011-8,STANCE(2012), European Project: 731453, and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))
Subjects: Safety engineering, [ INFO ] Computer Science [cs], Computer science, Embedded systems, Systems analysis, Software development environment, Static code analysis, 02 engineering and technology, computer.software_genre, Software development process, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Software system, Software design, Safety analysis, Hardware and software, business.industry, Model driven development, Software development, Codes (symbols), 020207 software engineering, Open source software, Industrial research, 020202 computer hardware & architecture, Software framework, C (programming language), Engineering methodology, Systems development life cycle, Embedded system, Software construction, Package development process, Avionics software, Open source development, Software engineering, business, computer, Simulation
Abstract: Conference of 20th Euromicro Conference on Digital System Design, DSD 2017 ; Conference Date: 30 August 2017 Through 1 September 2017; Conference Code:130963; International audience; It is largely recognized that the architectures of embedded systems are becoming more and more complex both at hardware and software levels. Despite the significant advances in the development tools, developing the software of such systems while ensuring their safety is still a difficult task. In this paper, we propose an engineering methodology to ease the development of safe software systems. It consists of four main phases: system modelling and validation, code generation and integration, static code analysis, and dynamic code analysis. This methodology is realized using CEA LIST open-source development platforms: Papyrus, Frama-C, and UNISIM-VP. These platforms are results of many research and industrial projects such as FP7-SafeAdapt, FUI-EQUITAS, FP7-STANCE, CATRENE-OpenES, and FSN-SESAM Grids.
Published: 2017

40. Analyzing the exhaustiveness of the synapse protocol

Author: Vincenzo Ciancaglini, Zoran Ognjanović, Bojan Marinković, Paola Glavan, Luigi Liquori, Petar Maksimovic, Mathematical Institute of the Serbian Academy of Sciences and Arts, Serbian Academy of Sciences and Arts (SASA), Logical Networks: Self-organizing Overlay Networks and Programmable Overlay Computing Systems (LOGNET), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Zagreb, and The work presented in this paper was supported by the Serbian Ministry of Education, Science and Technological Development, projects ON174026 and III44006, through Matematicki Institut SANU and by Ministarstvo znanosti, obrazovanja i sporta republike Hrvatske.
Subjects: Theoretical computer science, Computer Networks and Communications, Computer science, Distributed computing, Overlay network, 0102 computer and information sciences, 02 engineering and technology, Peer-to-peer, computer.software_genre, 01 natural sciences, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Synapse, ACM: F.: Theory of Computation/F.3: LOGICS AND MEANINGS OF PROGRAMS, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], 0202 electrical engineering, electronic engineering, information engineering, Abstract State Machines, Protocol (object-oriented programming), Computer communication networks, Retrieval probability, Formal description, Quantitative Biology::Neurons and Cognition, DASM, Peer-to-Peer Protocols, ACM: F.: Theory of Computation/F.3: LOGICS AND MEANINGS OF PROGRAMS/F.3.1: Specifying and Verifying and Reasoning about Programs, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS, [MATH.MATH-LO]Mathematics [math]/Logic [math.LO], DHT-based overlay networks, 010201 computation theory & mathematics, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.2: Network Protocols, Scalability, Abstract state machines, 020201 artificial intelligence & image processing, computer, Software
Abstract: International audience; The Synapse protocol is a scalable protocol designed for information retrieval over inter-connected heterogeneous overlay networks. In this paper, we give a formal description of Synapse using the Abstract State Machines framework. The formal description pertains to Synapse actions that manipulate distributed keys. Based on this formal description, we present results concerning the expected exhaustiveness for a number of scenarios and systems maintained by the Synapse protocol, and provide comparisons to the results of the corresponding simulations and experiments. We show that the predicted theoretical results match the obtained experimental results, and give recommendations on the design of systems using Synapse.
Published: 2015

41. ROS-based online robot programming for remote education and training

Author: Enric Cervera, Gustavo A. Casañ, Jaime Alemany, Amine Abou Moughlbay, Philippe Martinet, Robotic Intelligence Lab, Universitat Jaume I, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), and This paper describes research done at the Robotic Intelligence Laboratory, with support in part by Ministerio de Economia y Competitividad (DPI2011-27846), by Generalitat Valenciana (PROMETEOII/2014/028), by Universitat Jaume I (P1-1B2014-52), and by IEEE RAS under a CEMRA grant (Creation of Educational Materials for Robotics and Automation).
Subjects: Flexibility (engineering), Engineering, Multimedia, business.industry, Process (computing), Context (language use), computer.software_genre, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Human–computer interaction, Server, Information system, Code (cryptography), Robot, The Internet, business, computer
Abstract: International audience; RPN (Robotic Programming Network) is an initiative to bring existing remote robot laboratories to a new dimension, by adding the flexibility and power of writing ROS code in an Internet browser and running it in the remote robot with a single click. The code is executed in the robot server at full speed, i.e. without any communication delay, and the output of the process is returned back. Built upon Robot Web Tools, RPN works out-of-the-box in any ROS-based robot or simulator. This paper presents the core functionality of RPN in the context of a web-enabled ROS system, its possibilities for remote education and training, and some experimentation with simulators and real robots in which we have integrated the tool in a Moodle environment, creating some programming courses and make it open to researchers and students (http: //robotprogramming.uji.es).
Published: 2015

42. Automatic Distributed Code Generation from Formal Models of Asynchronous Concurrent Processes

Author: Frédéric Lang, Hugues Evrard, Construction of verified concurrent systems (CONVECS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), This work was partly funded by the French Fonds national pour la Société Numérique (FSN), Pôles Minalogic, Systematic and SCS (project OpenCloudware).Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr)., Grid'5000, and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Programming language, Semantics (computer science), Computer science, Distributed computing, Concurrency, Process (computing), 02 engineering and technology, computer.software_genre, Asynchronous communication, 020204 information systems, Synchronization (computer science), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Code generation, Compiler, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], computer, Formal verification
Abstract: International audience; Formal process languages inheriting the concurrency and communication features of process algebras are convenient formalisms to model distributed applications, especially when they are equipped with formal verification tools (e.g., model-checkers) to help hunting for bugs early in the development process. However, even starting from a fully verified formal model, bugs are likely to be introduced while translating (generally by hand) the concurrent model —which relies on high-level and expressive communication primitives— into the distributed implementation —which often relies on low-level communication primitives. In this paper, we present DLC, a compiler that enables distributed code to be generated from models written in a formal process language called LNT, which is equipped with a rich verification toolbox named CADP. The generated code can be either executed in an autonomous way (i.e., without requiring additional code to be defined by the user), or connected to external software through user-modifiable C functions. We present an experiment where DLC generates a distributed implementation from the LNT model of the Raft consensus algorithm.
Published: 2015

43. Classification Models Via Tabu Search: An Application to Early Stage Venture Classification

Author: Canan Akdemir, Thomas B. Astebro, Samir Elhedhli, Department of Management Sciences, University of Waterloo [Waterloo], Joseph L. Rotman School of Management, University of Toronto, HEC Research Paper Series, Haldemann, Antoine, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), and Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS)
Subjects: JEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods, Mixed integer program, Computer science, media_common.quotation_subject, Benders' decomposition, Machine learning, computer.software_genre, decision heuristic, JEL: C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics/C.C4.C45 - Neural Networks and Related Topics, JEL: C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C63 - Computational Techniques • Simulation Modeling, Artificial Intelligence, Classification models, tabu search, Quality (business), Decision-making, Selection (genetic algorithm), media_common, Mathematics, business.industry, General Engineering, early stage venture forecast, large-scale mixed integer program, Tabu search, Computer Science Applications, Data set, classification, [SHS.GESTION.STRAT]Humanities and Social Sciences/Business administration/domain_shs.gestion.strat, [SHS.GESTION]Humanities and Social Sciences/Business administration, Stage (hydrology), Data mining, Artificial intelligence, [SHS.GESTION] Humanities and Social Sciences/Business administration, business, computer, Integer (computer science)
Abstract: We model the decision making process used by Experts at the Canadian Innovation Centre to classify early stage venture proposals based on potential commercial success. The decision is based on thirty-seven attributes that take values in { - 1 , 0 , 1 } . We adopt a conjunctive decision framework due to Astebro and Elhedhli (2005) that selects a subset of attributes and determines two threshold values: one for the maximum allowed negatives (n) and one for minimum required positives (p). A proposal is classified as a success if the number of positives is greater than or equal to p and the number of negatives is less than or equal to n over the selected attributes. Based on a data set of 561 observations, the selection of attributes and the determination of the threshold values is modeled as a large-scale mixed integer program. Two solution approaches are explored: Benders decomposition and Tabu search. The first, was very slow to converge, while the second provided high quality solutions quickly. Tabu search provides excellent classification accuracy for predicting commercial successes as well as replicating Experts’ forecasts, opening the venue for the use of Tabu search in scoring and classification problems.
Published: 2015

44. Dislocation detection in field environments: A belief functions contribution

Author: Carl T. Haas, Saiedeh Razavi, Emmanuel Duflos, Philippe Vanheeghe, Department of Civil and Environmental Engineering [Waterloo], University of Waterloo [Waterloo], Sequential Learning (SEQUEL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), The research work was sponsored by a CNRS International Scientific Collaboration Program (PICS)., This paper results from the collaboration between the Laboratoire d'Automatique Génie Informatique et Signal (UMR CNRS 8219, Lille, France) and the Department of Civil and Environmental Engineering of the University of Waterloo (Canada)., Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), and Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)
Subjects: GPS, 0211 other engineering and technologies, Construction materials, 02 engineering and technology, Machine learning, computer.software_genre, Field (computer science), Dislocation detection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Position (vector), 021105 building & construction, 0202 electrical engineering, electronic engineering, information engineering, Radio-frequency identification, Mathematics, RFID, business.industry, Frame (networking), General Engineering, Function (mathematics), belief functions, Sensors network, Computer Science Applications, Discrete time and continuous time, Global Positioning System, 020201 artificial intelligence & image processing, Artificial intelligence, business, Focus (optics), Algorithm, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: Highlights? The greedy acceptance criterion for the glowworms updating positions is proposed. ? The new formulas for the glowworms movement are proposed. ? Uniform design experiments were investigated the effect of parameters. ? The proposed improvement algorithms were effective than the classical algorithm. Dislocation is defined as the change between discrete sequential locations of critical items in field environments such as large construction projects. Dislocations on large sites of materials and critical items for which discrete time position estimates are available represent critical state changes. The ability to detect dislocations automatically for tens of thousands of items can ultimately improve project performance significantly. Detecting these dislocations in a noisy information environment where low cost radio frequency identification tags are attached to each piece of material, and the material is moved sometimes only a few meters, is the main focus of this study. We propose in this paper a method developed in the frame of belief functions to detect dislocations. The belief function framework is well-suited for such a problem where both uncertainty and imprecision are inherent to the problem. We also show how to deal with the calculations. This method has been implemented in a controlled experimental setting. The results of these experiments show the ability of the proposed method to detect materials dislocation over the site reliably. Broader application of this approach to both animate and inanimate objects is possible.
Published: 2012

45. Subgraph Sampling Methods for Social Networks: The Good, the Bad, and the Ugly

Author: Peter Ebbes, Zan Huang, Arvind Rangaswamy, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series
Subjects: social networks, sampling, Dynamic network analysis, Social network, business.industry, Sampling (statistics), Network science, Network theory, Organizational network analysis, computer.software_genre, subgraph sampling, social network structure, Betweenness centrality, [SHS.GESTION]Humanities and Social Sciences/Business administration, Data mining, Centrality, business, computer, Mathematics
Abstract: The trajectories of social processes (e.g., peer pressure, imitation, and assimilation) that take place on social networks depend on the structure of those networks. Thus, to understand a social process or to predict the associated outcomes accurately, marketers would need good knowledge of the social network structure. However, many social networks of relevance to marketers are large, complex, or hidden, making it prohibitively expensive to map out an entire social network. Instead, marketers often need to work with a sample (i.e., a subgraph) of a social network. In this paper we evaluate the efficacy of nine different sampling methods for generating subgraphs that recover four structural characteristics of importance to marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, which are important for understanding how social network structure influences outcomes of processes that take place on the network. Via extensive simulations, we find that sampling methods differ substantially in their ability to recover network characteristics. Traditional sampling procedures, such as random node sampling, result in poor subgraphs. When the focus is on understanding local network effects (e.g., peer influence) then forest fire sampling with a medium burn rate performs the best, i.e., it is most effective for recovering the distributions of degree and clustering coefficient. When the focus is on global network effects (e.g., speed of diffusion, identifying influential nodes, or the “multiplier” effects of network seeding), then random-walk sampling (i.e., forest-fire sampling with a low burn rate) performs the best, and it is most effective for recovering the distributions of betweenness and closeness centrality. Further, we show that accurate recovery of social network structure in a sample is important for inferring the properties of a network process, when one observes only the process in the sampled network. We validate our findings on four different real-world networks, including a Facebook network and a co-authorship network, and conclude with recommendations for practice.
Published: 2012

46. Cost Reduction Through SLA-driven Self-Management

Author: Jean-Louis Pazat, Nikos Parlavantzas, André Lage Freitas, Design and Implementation of Autonomous Distributed Systems (MYRIADS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the European Community's Seventh Framework Programme [FP7/2007-2013] under grant greement 215483 (S-CUBE). Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr)., European Project: 215483,EC:FP7:ICT,FP7-ICT-2007-1,S-CUBE(2008), Inria, GRID'5000, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, computer.internet_protocol, Distributed computing, QoS, Cloud computing, 02 engineering and technology, computer.software_genre, grid, self-sadptation, Profit (economics), 020204 information systems, self-adaptation, 0202 electrical engineering, electronic engineering, information engineering, cloud, service-oriented computing, business.industry, Quality of service, Service-oriented architecture, Service provider, Grid, Cost reduction, web services, Risk analysis (engineering), 020201 artificial intelligence & image processing, Web service, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, computer
Abstract: International audience; A main challenge for service providers is managing service-level agreements (SLAs) with their customers while satisfying their business objectives, such as maximizing profits. Most current systems fail to consider business objectives and thus to provide a complete SLA management solution. This work proposes an SLA-driven management solution that aims to maximize the provider's profit by reducing resource costs as well as fines owning to SLA violations. Specifically, this work proposes a framework that comprises multiple, configurable control loops and supports automatically adjusting service configurations and resource usage in order to maintain SLAs in the most cost-effective way. The framework targets services implemented on top of large-scale distributed infrastructures, such as clouds. Experimental results demonstrate its effectiveness in maintaining SLAs while reducing provider costs.
Published: 2011

47. A computational framework for gene regulatory network inference that combines multiple methods and datasets

Author: Philipp Antczak, Anna Stincone, Sarah Durant, Francesco Falciani, Andreas Bikfalvi, Rita Gupta, Roy Bicknell, School of Biosciences, University of Birmingham [Birmingham], Institute of Biomedical Research, Mecanismes Moleculaires de l'Angiogenese, Université Sciences et Technologies - Bordeaux 1-Institut National de la Santé et de la Recherche Médicale (INSERM), The work described in this paper was funded by the CRUK grant C8504/A9488 and partially funded by the BBSRC grant BBC5151041. AS is a recipient of a Darwin Trust PhD fellowship and PA is a recipient of a BBSRC PhD studentship., BMC, Ed., and Université Sciences et Technologies - Bordeaux 1 (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: MESH: Hydrogen-Ion Concentration, Time Factors, Gene regulatory network, Inference, Multiple methods, computer.software_genre, 0302 clinical medicine, Structural Biology, Gene regulatory network inference, Neoplasms, MESH: Gene Silencing, MESH: Neoplasms, Gene Regulatory Networks, MESH: Stress, Physiological, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], MESH: Gene Regulatory Networks, Genetics, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Escherichia coli, Applied Mathematics, Systems Biology, Methodology Article, Hydrogen-Ion Concentration, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Modeling and Simulation, MESH: Systems Biology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Reverse engineering, MESH: Cell Line, Tumor, Systems biology, Biology, Machine learning, Models, Biological, 03 medical and health sciences, Stress, Physiological, Modelling and Simulation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Cell Line, Tumor, Escherichia coli, Humans, Gene Silencing, Molecular Biology, 030304 developmental biology, MESH: Humans, business.industry, MESH: Time Factors, Ode, MESH: Models, Biological, lcsh:Biology (General), Time course, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery
Abstract: Background Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. Results This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies. Conclusions The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.
Published: 2011

48. Attribute-Level Heterogeneity

Author: Peter Ebbes, John Liechty, Rajdeep Grewal, Matthew Tibbits, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research paper Series
Subjects: Bayes estimator, Computer science, Mixture Models, Regression analysis, Markov chain Monte Carlo, Reversible-jump Markov chain Monte Carlo, computer.software_genre, Mixture model, Conjoint analysis, Reversible Jump MCMC, symbols.namesake, Segmentation, Linear regression, symbols, Hierarchical Bayes, [SHS.GESTION]Humanities and Social Sciences/Business administration, Data mining, Conjoint Analysis, Random variable, computer
Abstract: Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture, that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneity lies in its estimation. A proposed Bayesian estimation approach, based on recent advances in reversible jump Markov Chain Monte Carlo (MCMC) methods, can estimate parameters for the attribute-based finite mixture model, assuming that the number of components for each finite mixture is a discrete random variable. An attribute specification has several advantages over traditional, vector-based, finite mixture specifications; specifically, the attribute mixture model offers a more appropriate aggregation of information than the vector specification facilitating estimation. In an extensive simulation study and an empirical application, we show that the attribute model can recover complex heterogeneity structures, making it dominant over traditional (vector) finite mixture regression models and a strong contender compared with mixture-of-normals models for modeling heterogeneity.The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2370423
Published: 2010

49. Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

Author: Luis A. Hernández Gómez, José Luis Blanco Murillo, José Alcázar Ramírez, Eduardo López Gonzalo, Rubén Fernández Pozo, Doroteo Torre Toledano, [Fernández Pozo,R, Blanco Murillo,JL, Hernández Gómez,L, López Gonzalo,E] Signal, Systems and Radiocommunications Departament, Universidad Politécnica de Madrid, Madrid, Spain. [Alcázar Ramírez,J] Respiratory Departament, Hospital Torrecárdenas, Almería, Spain. [Toledano,DT] ATVS Biometric Recognition group, Universidad Autónoma de Madrid, Madrid, Spain., The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project., UAM. Departamento de Ingeniería Informática, and Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002)
Subjects: Sustained speech, Artificial intelligence, Information Science::Information Science::Computing Methodologies::Software::Speech Recognition Software [Medical Subject Headings], Computer science, Speech recognition, 0206 medical engineering, lcsh:TK7800-8360, 02 engineering and technology, computer.software_genre, Voice analysis, Nasalization, lcsh:Telecommunication, Vowel, lcsh:TK5101-6720, 0202 electrical engineering, electronic engineering, information engineering, Distribución normal, Diseases::Respiratory Tract Diseases::Respiration Disorders::Apnea::Sleep Apnea Syndromes::Sleep Apnea, Obstructive [Medical Subject Headings], Audio signal processing, Telecomunicaciones, Phenomena and Processes::Mathematical Concepts::Statistical Distributions::Normal Distribution [Medical Subject Headings], lcsh:Electronics, 020206 networking & telecommunications, Phonetics, Speaker recognition, Speech processing, Diseases::Otorhinolaryngologic Diseases::Laryngeal Diseases::Voice Disorders [Medical Subject Headings], 020601 biomedical engineering, Continuous speech, Obstructive sleep apnea, respiratory tract diseases, Apnea del sueño obstructiva, Pattern recognition (psychology), Speech dynamics, Gaussian mixture models, computer, Programa informático para el reconocimiento del lenguaje hablado, Trastornos de la voz, Classification and regression tree (CART)
Abstract: The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531, This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry., The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project.
Published: 2009

50. BMC Genomics

Author: Grace C. Davey, Penelope K. Lindeque, Richard Reinhardt, Christophe Klopp, Pascal Favrel, Dario Moraga, Pierre Boudry, Sylvie Lapegue, Patrick Prunet, Arnaud Huvet, Julien de Lorgeril, Jeanne Moal, Viviane Boulo, Elodie Fleury, Christophe Lelong, Christopher Sauvage, Patrick Wincker, François Moreews, Michel Mathieu, Frédérick Gavory, Arnaud Tanguy, Caroline Fabioux, Charlotte Corporeau, Evelyne Bachère, Jenny P. Shaw, Yannick Gueguen, Laboratoire Environnement Ressources Morbihan Pays de Loire (LERMPL), LITTORAL (LITTORAL), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Physiologie et Ecophysiologie des Mollusques Marins (PE2M), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Ecosystèmes lagunaires : organisation biologique et fonctionnement (ECOLAG), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO), Plymouth Marine Laboratory (PML), Max-Planck-Institut für Molekulare Genetik (MPIMG), Max-Planck-Gesellschaft, Station commune de Recherches en Ichtyophysiologie, Biodiversité et Environnement (SCRIBE), Institut National de la Recherche Agronomique (INRA), Génétique fonctionnelle, agronomie et santé [IFR 140] (GFAS), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), National Diagnostics Centre (NDC), National University of Ireland [Galway] (NUI Galway), Laboratoire de Génétique et Pathologie (LGP), Amélioration génétique, du contrôle des performances et de la santé des mollusques marins (AGSAE), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Systèmes d'Elevage, Nutrition Animale et Humaine (SENAH), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Rennes, Système d'Information des GENomes des Animaux d'Elevage (SIGENAE), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Laboratoire de Physiologie des Invertébrés (LPI), Physiologie Fonctionnelle des Organismes Marins (PFOM), The research presented in this paper was performed within the framework of several research projects funded by: Genoscope (11/AP2006-2007), Marine Genomics Network of Excellence (GOCE-CT-2004-505403), the European project 'Aquafirst' (513692) in the Sixth Framework Program, ANR 'CgPhysiogène' (ANR-06-GANI-0009) and 'Gametogenes' (ANR-08-GENM-041), ANR-06-GANI-0009,CgPhysiogene,Bases moléculaires des fonctions physiologiques de l'huître Crassostrea gigas : interactions hôte/pathogène/milieu(2006), ANR-08-GENM-0041,Gametogenes,Génomiques de la gamétogénèse chez l'huître creuse Crassostrea gigas(2008), Laboratoire Laboratoire Environnement Ressources Morbihan Pays de Loire (LER/MPL), Institut de Recherche pour le Développement (IRD)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS), Adaptation et Biologie des Invertébrés en Conditions Extrêmes (ABICE), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory, Institut National de la Recherche Agronomique (INRA)-IFR140, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Ecole Nationale Supérieure Agronomique de Rennes-Institut National de la Recherche Agronomique (INRA), Unité de Biométrie et Intelligence Artificielle de Toulouse [Castanet-Tolosan] (UBIA), Institut National de la Recherche Agronomique (INRA)-Plateforme bioinformatique du GIS GENOTOUL - Génopole Toulouse Midi-Pyrénées, Laboratoire de Physiologie des Invertébrés [Plouzané] (LPI), Adaptation et diversité en milieu marin (AD2M), Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Station biologique de Roscoff (SBR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Oyster, genome annotation, génomique fonctionnelle, computer.software_genre, Genome, User-Computer Interface, single nucleotide polymorphisms, Databases, Genetic, crassostrea gigas, structure du génome, Expressed Sequence Tags, base de données, 0303 health sciences, Expressed sequence tag, biology, Database, 04 agricultural and veterinary sciences, Genomics, crustacea, Pacific oyster, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], energy-balance, factor-beta superfamily, DNA microarray, expression des gènes, Biotechnology, lcsh:QH426-470, Sequence analysis, lcsh:Biotechnology, kappa-b, Polymorphism, Single Nucleotide, génomique, 03 medical and health sciences, biology.animal, lcsh:TP248.13-248.65, Genetics, summer mortality, Animals, Crassostrea, 030304 developmental biology, Gene Library, Whole genome sequencing, [SDV.GEN]Life Sciences [q-bio]/Genetics, huître, cell-development, génome, Gene Expression Profiling, linkage maps, Sequence Analysis, DNA, biology.organism_classification, lcsh:Genetics, coquillage, 040102 fisheries, 0401 agriculture, forestry, and fisheries, identification, marine genomics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Microsatellite Repeats
Abstract: Background Although bivalves are among the most-studied marine organisms because of their ecological role and economic importance, very little information is available on the genome sequences of oyster species. This report documents three large-scale cDNA sequencing projects for the Pacific oyster Crassostrea gigas initiated to provide a large number of expressed sequence tags that were subsequently compiled in a publicly accessible database. This resource allowed for the identification of a large number of transcripts and provides valuable information for ongoing investigations of tissue-specific and stimulus-dependant gene expression patterns. These data are crucial for constructing comprehensive DNA microarrays, identifying single nucleotide polymorphisms and microsatellites in coding regions, and for identifying genes when the entire genome sequence of C. gigas becomes available. Description In the present paper, we report the production of 40,845 high-quality ESTs that identify 29,745 unique transcribed sequences consisting of 7,940 contigs and 21,805 singletons. All of these new sequences, together with existing public sequence data, have been compiled into a publicly-available Website http://public-contigbrowser.sigenae.org:9090/Crassostrea_gigas/index.html. Approximately 43% of the unique ESTs had significant matches against the SwissProt database and 27% were annotated using Gene Ontology terms. In addition, we identified a total of 208 in silico microsatellites from the ESTs, with 173 having sufficient flanking sequence for primer design. We also identified a total of 7,530 putative in silico, single-nucleotide polymorphisms using existing and newly-generated EST resources for the Pacific oyster. Conclusion A publicly-available database has been populated with 29,745 unique sequences for the Pacific oyster Crassostrea gigas. The database provides many tools to search cleaned and assembled ESTs. The user may input and submit several filters, such as protein or nucleotide hits, to select and download relevant elements. This database constitutes one of the most developed genomic resources accessible among Lophotrochozoans, an orphan clade of bilateral animals. These data will accelerate the development of both genomics and genetics in a commercially-important species with the highest annual, commercial production of any aquatic organism.
Published: 2009

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Journal

Database

Publisher

50 results

Search Results

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources