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1. Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence.

Author

Yang Y, Huang J, and Hu D

Subjects
Intelligence, Education, Continuing, Learning, Machine Learning
Abstract

This paper explores a novel lifelong learning method with Shared and Private Latent Representations (SPLR), which are learned through synaptic intelligence. To solve a sequence of tasks, by considering the entire parameter learning trajectory, SPLR can learn task-invariant representation which changes little, and task-specific features that change greatly along the entire parameter updating trajectory. Therefore, in the lifelong learning scenarios, our model can obtain a task-invariant structure shared by all tasks and also contain some private properties that are task-specific to each task. To reduce the parameter quantity, a ℓ 1 regularization to promote sparsity is employed in the weights. We use multiple datasets under lifelong learning scenes to verify our SPLR, on these datasets it can get comparable performance compared with existing lifelong learning approaches, and learn a sparse network which means fewer parameters while requiring less model training time., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Published
2023
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2. State-of-the-Art Deep Learning Models in TensorFlow : Modern Machine Learning in the Google Colab Ecosystem

Author

David Paper and David Paper

Subjects
Machine learning, Reinforcement learning
Abstract

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will LearnTake advantage of the built-in support of the Google Colab ecosystemWork with TensorFlow data setsCreate input pipelines to feed state-of-the-art deep learning modelsCreate pipelined state-of-the-art deep learning models with clean and reliable Python codeLeverage pre-trained deep learning models to solve complex machine learning tasksCreate a simple environment to teach an intelligent agent to make automated decisionsWho This Book Is ForReaders who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab

Published
2021

3. TensorFlow 2.x in the Colaboratory Cloud : An Introduction to Deep Learning on Google’s Cloud Service

Author

David Paper and David Paper

Subjects
Machine learning, Data structures (Computer science), Artificial intelligence
Abstract

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab's built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fitWho This Book Is ForReaders who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab

Published
2021

4. Hands-on Scikit-Learn for Machine Learning Applications : Data Science Fundamentals with Python

Author

David Paper and David Paper

Subjects
Python (Computer program language), Machine learning
Abstract

Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complexmachine learning algorithms.Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.What You'll LearnWork with simple and complex datasets common to Scikit-LearnManipulate data into vectors and matrices for algorithmic processingBecome familiar with the Anaconda distribution used in data scienceApply machine learning with Classifiers, Regressors, and Dimensionality ReductionTune algorithms and find the best algorithms for each datasetLoad data from and save to CSV, JSON, Numpy, and Pandas formatsWho This Book Is ForThe aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.

Published
2020

5. Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning

Author

Philippe O. A. Navaux, Jean Luca Bez, Toni Cortes, Francieli Zanon Boito, Alberto Miranda, Ramon Nou, Instituto de Informática da UFRGS (UFRGS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Topology-Aware System-Scale Data Management for High-Performance Computing (TADAAM), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Data Aware Large Scale Computing (DATAMOVE ), 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), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), This study was financed in part by the Coordenac¸ao de Aperfeicoamento de Pessoal de Nıvel Superior - Brasil (CAPES) - Finance Code 001. It has also received support from the Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico (CNPq), Brazil, the LICIA International Laboratory, NCSA-Inria-ANL-BSC-JSC-Riken Joint-Laboratory on Extreme Scale Computing (JLESC), the Spanish Ministry of Science and Innovation under the TIN2015–65316 grant, and the Generalitat de Catalunya under contract 2014– SGR–1051. This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 800144. 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). The authors acknowledge the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) for providing HPC resources of the SDumont supercomputer, which have contributed to the research results reported within this paper., Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
I/O forwarding, Parallel I/O, I/O scheduling, Computer Networks and Communications, Computer science, Distributed computing, Auto-tuning, 02 engineering and technology, Scheduling (computing), I/O Scheduling, I/O Forwarding, Machine learning, Reinforcement learning, Aprenentatge automàtic, 0202 electrical engineering, electronic engineering, information engineering, Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC], Input/output, High performance I/O, 020206 networking & telecommunications, Workload, Reinforcement Learning, Hardware and Architecture, Superordinadors, 020201 artificial intelligence & image processing, High performance computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Software
Abstract

In this paper, we propose an approach to adapt the I/O forwarding layer of HPC systems to applications’ access patterns. I/O optimization techniques can improve performance for the access patterns they were designed to target, but they often decrease performance for others. Furthermore, these techniques usually depend on the precise tune of their parameters, which commonly falls back to the users. Instead, we propose to do it dynamically at runtime based on the I/O workload observed by the system. Our approach uses a reinforcement learning technique – contextual bandits – to make the system capable of learning the best parameter value to each observed access pattern during its execution. That eliminates the need of a complicated and time-consuming previous training phase. Our case study is the TWINS scheduling algorithm, where performance improvements depend on the time window parameter, which in turn depends on the workload. We evaluate our proposal and demonstrate it can reach a precision of 88% on the parameter selection in the first hundreds of observations of an access pattern, achieving 99% of the optimal performance. We demonstrate that the system – which is expected to live for years – will be able to adapt to changes and optimize its performance after having observed an access pattern for a few (not necessarily contiguous) minutes. This study was financed in part by the Coordenaçao de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. It has also received support from the Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq), Brazil; the LICIA International Laboratory; NCSA-Inria-ANL-BSC-JSC-Riken Joint-Laboratory on Extreme Scale Computing (JLESC); the Spanish Ministry of Science and Innovation under the TIN2015–65316 grant; and the Generalitat de Catalunya under contract 2014– SGR–1051. This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 800144.

Published
2020

6. Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning

Author

Bernardini, Giulia, van Iersel, Leo, Julien, Esther, Stougie, Leen, Boucher, Christina, Rahmann, Sven, Operations Analytics, Tinbergen Institute, Amsterdam Business Research Institute, Boucher, Christina, Rahmann, Sven, Università degli studi di Trieste = University of Trieste, Centrum Wiskunde & Informatica (CWI), Delft University of Technology (TU Delft), Vrije Universiteit Amsterdam [Amsterdam] (VU), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Lyon, Institut National de Recherche en Informatique et en Automatique (Inria), This paper received funding from the Netherlands Organisation for Scientific Research (NWO) under project OCENW.GROOT.2019.015 'Optimization for and with Machine Learning (OPTIMAL)'. Giulia Bernardini: MUR-FSE REACT EU – PON R&I 2014-2020 Leen Stougie: The Netherlands Organisation for Scientific Research (NWO) under Gravitation-grant NETWORKS-024.002.003, Bernardini, Giulia, van Iersel, Leo, Julien, Esther, Stougie, Leen, and Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Subjects
Machine Learning, Phylogenetics, Phylogenetic, Cherry Picking, Machine Learning, Heuristic, [SDV]Life Sciences [q-bio], Heuristic, Hybridization, Applied computing → Computational biology, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Cherry Picking
Abstract

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. In this paper, we apply the recently-introduced theoretical framework of cherry picking to design a class of heuristics that are guaranteed to produce a network containing each of the input trees, for practical-size datasets. The main contribution of this paper is the design and training of a machine learning model that captures essential information on the structure of the input trees and guides the algorithms towards better solutions. This is one of the first applications of machine learning to phylogenetic studies, and we show its promise with a proof-of-concept experimental study conducted on both simulated and real data consisting of binary trees with no missing taxa., LIPIcs, Vol. 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022), pages 16:1-16:22

Published
2022

7. 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

8. 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

9. Compiler and optimization level recognition using graph neural networks

Author

Bardin, Sébastien, Benoit, Tristan, Marion, Jean-Yves, CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), 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), This work is supported by 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. 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), Benoit, Tristan, and ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID

Subjects
reverse engineering, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], machine learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 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, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], toolchain provenance, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
Abstract

The main objective of this workshop is to bring together researchers in the machine learning and program analysis communities and to serve as a platform for identifying cross-disciplinary problems of mutual interest.; International audience; We consider the problem of recovering the compiling chain used to generate a given bare binary code. We present a first attempt to devise a Graph Neural Network framework to solve this problem, in order 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. Feature extraction is simplified by forgetting almost everything in a CFG except transfer control instructions. While at an early stage, our experiments show that our method already recovers the compiler and the optimization level provenance with very high accuracy. We believe these are promising results that may offer new, more robust leads for compiling tool chain identification.

Published
2021

10. Scaling Human Effort in Idea Screening and Content Evaluation

Author

Cathy L. Yang, Artem Timoshenko, Pavel Kireyev, Ecole des Hautes Etudes Commerciales (HEC Paris), and HEC Paris Research Paper Series

Subjects
Winnow, 050208 finance, Information retrieval, Product design, business.industry, Information value, Computer science, 05 social sciences, Content Evaluation, Stakeholder, Crowdsourcing, Machine Learning, Ranking, 0502 economics and business, Content (measure theory), A priori and a posteriori, [SHS.GESTION]Humanities and Social Sciences/Business administration, 050207 economics, business, Innovation, Wisdom of Crowds
Abstract

Brands and advertisers often tap into the crowd to generate ideas for new products and ad creatives by hosting ideation contests. Content evaluators then winnow thousands of submitted ideas before a separate stakeholder, such as a manager or client, decides on a small subset to pursue. We demonstrate the information value of data generated by content evaluators in past contests and propose a proof-of-concept machine learning approach to efficiently surface the best submissions in new contests with less human effort. The approach combines ratings by different evaluators based on their correlation with the past stakeholder choices, controlling for submission characteristics and textual content features. Using field data from a crowdsourcing platform, we demonstrate that the approach improves performance by identifying nonlinear transformations and efficiently reweighting evaluator ratings. Implementing the proposed approach can affect the optimal assignment of internal experts to ideation contests. Two evaluators whose votes were a priori equally correlated with sponsor choices may provide substantially different incremental information to improve the model-based idea ranking. We provide additional support for our findings using simulations based on a product design survey.

Published
2020

11. 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

12. 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

13. The trainer, the verifier, the imitator: Three ways in which human platform workers support artificial intelligence

Author

Paola Tubaro, Antonio A. Casilli, Marion Coville, Centre National de la Recherche Scientifique (CNRS), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institutions et Dynamiques Historiques de l'Économie et de la Société (IDHES), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris Nanterre (UPN)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Sociologie Information-Communication Design (SID), Institut interdisciplinaire de l’innovation de Telecom Paris (I3 SES), Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Télécom ParisTech-Institut interdisciplinaire de l’innovation (I3), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Département Sciences Economiques et Sociales (SES), Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Institut d'Administration des Entreprises (IAE) - Poitiers (IAE Poitiers), Université de Poitiers, CEntre de REcherche en GEstion - EA 1722 (CEREGE), Université de Poitiers-Université de Poitiers-Université de Poitiers-La Rochelle Université (ULR), This paper presents results of a larger study called Digital Platform Labour (DiPLab), co-funded by Maison des Sciences de l’Homme Paris-Saclay, Force Ouvrière, a workers’ union, as part of a grant from Institut de recherches économiques et sociales (IRES), and France Stratégie, a service of the French Prime Minister’s office. The platform Foule Factory offered logistical support, and Inria provided complementary funding. For more information: http://diplab.eu, Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Nanterre (UPN)-Université Paris 8 Vincennes-Saint-Denis (UP8)-Université Paris 1 Panthéon-Sorbonne (UP1), Institut interdisciplinaire de l’innovation (I3, une unité mixte de recherche CNRS (UMR 9217)), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université de Poitiers-Université de Poitiers-Université de Poitiers-Université de La Rochelle (ULR), École polytechnique (X)-Télécom ParisTech-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Télécom ParisTech-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), and École polytechnique (X)-Télécom ParisTech-MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Information Systems and Management, Computer science, Trainer, Big data, 050801 communication & media studies, lcsh:A, Library and Information Sciences, micro-work, Digital platform labour, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0508 media and communications, datafied production processes, 0502 economics and business, 050207 economics, Focus (computing), [SHS.SOCIO]Humanities and Social Sciences/Sociology, business.industry, Communication, 05 social sciences, artificial intelligence, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Computer Science Applications, machine learning, Artificial intelligence, lcsh:General Works, business, Information Systems
Abstract

International audience; This paper sheds light on the role of digital platform labour in the development of today's artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad 'micro-workers', recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, 'artificial intelligence preparation', 'artificial intelligence verification' and 'artificial intelligence impersonation'. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes - not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.

Published
2020

14. Flood forecasting based on an artificial neural network scheme

Author

Chafiq Titouna, Abdelhak Mourad Gueroui, Ousmane Thiare, Ado Adamou Abba Ari, Francis Yongwa Dtissibe, University of Maroua (UMa), Laboratoire d'Informatique Parallélisme Réseaux Algorithmes Distribués (LI-PaRAD), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université de Paris (UP), Laboratoire d'Analyse Numérique et Informatique [Sénégal] (LANI), Université Gaston Berger de Saint-Louis Sénégal (UGB), and We like to thank the editor and the anonymous reviewers for their valuable remarks that helped us in better improving the content and presentation of the paper.

Subjects
021110 strategic, defence & security studies, Atmospheric Science, Flood forecasting, 010504 meteorology & atmospheric sciences, Artificial neural network, Flood myth, Artificial neural networks, Computer science, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Industrial engineering, Hazard, 13. Climate action, Order (exchange), Multilayer perceptron, Natural hazard, Machine learning, [SDE]Environmental Sciences, Earth and Planetary Sciences (miscellaneous), 0105 earth and related environmental sciences, Water Science and Technology, Communication channel
Abstract

International audience; Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

Published
2020

15. 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

16. 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