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1. Two-Phase Scheduling for Efficient Vehicle Sharing

Author: Patrick Valduriez, Lei Mo, Ji Liu, Carlyna Bondiombouy, Baidu Research, African Institute for Mathematical Sciences (AIMS), Southeast University [Jiangsu], Scientific Data Management (ZENITH), 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)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: Optimization, Mathematical optimization, Schedule, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Job shop scheduling, Scheduling, Total cost, Computer science, Mechanical Engineering, Scheduling (production processes), Context (language use), 7. Clean energy, Computer Science Applications, Path planning problem, SAFER, 11. Sustainability, Automotive Engineering, Path (graph theory), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Intelligent transportation system, Vehicle sharing
Abstract: International audience; Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.
Published: 2022

2. StreamCloud: An Elastic Parallel-Distributed Stream Processing Engine

Author: Gulisano, Vincenzo, Jiménez Peris, Ricardo, Valduriez, Patrick, Distributed Systems Laboratory (DSL), Universidad Politécnica de Madrid (UPM), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Universidad Politécnica de Madrid, Ricardo Jiménez Peris, Patrick Valduriez(Patrick.Valduriez@inria.fr), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Informática, Telecomunicaciones, Elasticidad, Scalability, Fault Tolerance, Stream Processing Engine, Data Streaming, Elasticity, Load Balancing, Sistemas de procesamiento de flujos de datos, Equilibrado de Carga, Tolerancia a fallos, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Escalabilidad, Elasticité
Abstract: In recent years, applications in domains such as telecommunications, network security or large scale sensor networks showed the limits of the traditional store-then-process paradigm. In this context, Stream Processing Engines emerged as a candidate solution for all these applications demanding for high processing capacity with low processing latency guarantees. With Stream Processing Engines, data streams are not persisted but rather processed on the fly, producing results continuously. Current Stream Processing Engines, either centralized or distributed, do not scale with the input load due to single-node bottlenecks. Moreover, they are based on static configurations that lead to either under or over-provisioning. This Ph.D. thesis discusses StreamCloud, an elastic paralleldistributed stream processing engine that enables for processing of large data stream volumes. Stream- Cloud minimizes the distribution and parallelization overhead introducing novel techniques that split queries into parallel subqueries and allocate them to independent sets of nodes. Moreover, Stream- Cloud elastic and dynamic load balancing protocols enable for effective adjustment of resources depending on the incoming load. Together with the parallelization and elasticity techniques, Stream- Cloud defines a novel fault tolerance protocol that introduces minimal overhead while providing fast recovery. StreamCloud has been fully implemented and evaluated using several real word applications such as fraud detection applications or network analysis applications. The evaluation, conducted using a cluster with more than 300 cores, demonstrates the large scalability, the elasticity and fault tolerance effectiveness of StreamCloud.; En los útimos años, aplicaciones en dominios tales como telecomunicaciones, seguridad de redes y redes de sensores de gran escala se han encontrado con múltiples limitaciones en el paradigma tradicional de bases de datos. En este contexto, los sistemas de procesamiento de flujos de datos han emergido como solución a estas aplicaciones que demandan una alta capacidad de procesamiento con una baja latencia. En los sistemas de procesamiento de flujos de datos, los datos no se persisten y luego se procesan, en su lugar los datos son procesados al vuelo en memoria produciendo resultados de forma continua. Los actuales sistemas de procesamiento de flujos de datos, tanto los centralizados, como los distribuidos, no escalan respecto a la carga de entrada del sistema debido a un cuello de botella producido por la concentración de flujos de datos completos en nodos individuales. Por otra parte, éstos están basados en configuraciones estáticas lo que conducen a un sobre o bajo aprovisionamiento. Esta tesis doctoral presenta StreamCloud, un sistema elástico paralelo-distribuido para el procesamiento de flujos de datos que es capaz de procesar grandes volúmenes de datos. StreamCloud minimiza el coste de distribución y paralelización por medio de una técnica novedosa la cual particiona las queries en subqueries paralelas repartiéndolas en subconjuntos de nodos independientes. Ademas, Stream- Cloud posee protocolos de elasticidad y equilibrado de carga que permiten una optimización de los recursos dependiendo de la carga del sistema. Unidos a los protocolos de paralelización y elasticidad, StreamCloud define un protocolo de tolerancia a fallos que introduce un coste mínimo mientras que proporciona una rápida recuperación. StreamCloud ha sido implementado y evaluado mediante varias aplicaciones del mundo real tales como aplicaciones de detección de fraude o aplicaciones de análisis del tráfico de red. La evaluación ha sido realizada en un cluster con más de 300 núcleos, demostrando la alta escalabilidad y la efectividad tanto de la elasticidad, como de la tolerancia a fallos de StreamCloud.; In recent years...
Published: 2022

3. Making the Right Move to Senior Researcher

Author: Patrick Valduriez, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Point (typography), business.industry, Computer science, Data management, Nice, 0102 computer and information sciences, 02 engineering and technology, Public relations, 01 natural sciences, Work (electrical), 010201 computation theory & mathematics, 020204 information systems, Institution (computer science), Research environment, 0202 electrical engineering, electronic engineering, information engineering, business, computer, Software, Information Systems, computer.programming_language
Abstract: International audience; I have been working on research in data management for the last 40 years. I like my job and my research institution (Inria, the French national research institute for computer science), which have offered me great opportunities to learn a lot, do good work, get to know smart and nice people and overall feel useful. However, since the early days of my mid-career, the research environment, including academia and industry, has certainly become more complex, making the move from junior (or pre-tenure) researcher to senior researcher quite challenging. Based on my experience, I review some of the main questions and challenges and give some hints on how to deal with them. I'll sometimes use stories and anecdotes to illustrate the point.
Published: 2021

4. BestNeighbor: efficient evaluation of kNN queries on large time series databases

Author: Patrick Valduriez, Djamel Edine Yagoubi, Themis Palpanas, Boyan Kolev, Florent Masseglia, Reza Akbarinia, Oleksandra Levchenko, Dennis Shasha, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), LeanXcale [Madrid], Université de Paris (UP), New York University [New York] (NYU), NYU System (NYU), Université Paris Cité (UPCité), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Distributed Querying, Computer science, Random projection, Time Series, 02 engineering and technology, computer.software_genre, Synthetic data, k-nearest neighbors algorithm, Set (abstract data type), Artificial Intelligence, 020204 information systems, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Pruning (decision trees), Fourier Coefficients, Fourier series, Database, Series (mathematics), Frequency Analysis, Human-Computer Interaction, Parallel Indexing, Hardware and Architecture, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, Filtering, computer, Software, Information Systems
Abstract: International audience; This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating kNN (k nearest neighbor) queries on large databases of time series, compares them based on various measures of quality and time performance, offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set the parameters for that algorithm. Specifically, our experiments show that: (i) iSAX and its derivatives perform best in both time and quality when the time series can be characterized by a few low frequency Fourier Coefficients , a regime where the iSAX pruning approach works well. (ii) iSAX performs significantly less well when high frequency Fourier Coefficients have much of the energy of the time series. (iii) A random projection approach based on sketches by contrast is more or less independent of the frequency power spectrum. The experiments show the close relationship between pruning ratio and time for exact iSAX as well as between pruning ratio and the quality of approximate iSAX. Our toolkit analyzes typical time series of an application (i) to determine optimal segment sizes for iSAX and (ii) when to use Parallel Sketches instead of iSAX. Our algorithms have been implemented using Spark, evaluated over a cluster of nodes, and have been applied to both real and synthetic data. The results apply to any databases of numerical sequences, whether or not they relate to time.
Published: 2020

5. Data Access With Horn Ontologies: Where Description Logics Meet Existential Rules

Author: Marie-Laure Mugnier, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), ANR-18-CE23-0003,CQFD,Requêtes Ontologiques Complexes sur des Fédérations de Données Hétérogènese(2018), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Emulation, Theoretical computer science, Computer science, French horn, Context (language use), 0102 computer and information sciences, 02 engineering and technology, Ontology language, 01 natural sciences, Existentialism, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Focus (linguistics), TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, Data access, Description logic, 010201 computation theory & mathematics, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing
Abstract: International audience; Two main families of ontology languages are considered in the context of data access, namely Horn description logics and existential rules. In this paper, we review the semantic relationships between these families in the light of the ontology-mediated query answering problem. To this end, we rely on the standard translation of description logics in first-order logic and on the notion of semantic emulation. We focus on description logics and classes of existential rules for which the con-junctive query answering problem has polynomial data complexity.
Published: 2020

6. Characterizing Boundedness in Chase Variants

Author: Marie-Laure Mugnier, Michel Leclère, Stathis Delivorias, Federico Ulliana, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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 Montpellier, LIRMM (UM, CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Chase, Computer Science - Artificial Intelligence, Computer science, Context (language use), 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Theoretical Computer Science, Set (abstract data type), Integer, Fragment (logic), Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Existential rules, Discrete mathematics, Boundedness, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], TGD, Logic in Computer Science (cs.LO), Decidability, Undecidable problem, Artificial Intelligence (cs.AI), Computational Theory and Mathematics, 010201 computation theory & mathematics, Hardware and Architecture, Bounded function, 020201 artificial intelligence & image processing, Software
Abstract: Existential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k-boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions. This paper is under consideration for publication in Theory and Practice of Logic Programming., Comment: Under consideration for publication in Theory and Practice of Logic Programming
Published: 2020

7. Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulics

Author: Renato K. Braghiere, Loïc Pagès, Frédéric Gérard, Christophe Pradal, Jochem B. Evers, Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), California Institute of Technology (CALTECH), University of California [Los Angeles] (UCLA), University of California (UC), Wageningen University and Research [Wageningen] (WUR), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Unité de recherche Plantes et Systèmes de Culture Horticoles (PSH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), contract with the National Aeronautics and Space Administration (80NM0018D0004)., European Project: 727217,ReMIX(2017), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of California, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: 0106 biological sciences, 010504 meteorology & atmospheric sciences, Biomass, GroIMP, F62 - Physiologie végétale - Croissance et développement, Déficit hydrique du sol, Plant Science, Root system, Plant Roots, 01 natural sciences, Soil, santé des plantes, Water content, media_common, Transpiration, water deficit, 2. Zero hunger, U10 - Informatique, mathématiques et statistiques, ArchiSimple, Propriété hydraulique du sol, PE&RC, soil-plant interactions, Physiologie végétale, Droughts, Crop and Weed Ecology, Plant Shoots, water uptake, Stomatal conductance, Relation plante sol, media_common.quotation_subject, Biology, Competition (biology), Culture intercalaire, functional-structural plant models, Min3P, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Absorption d'eau, 0105 earth and related environmental sciences, photosynthesis, Water, Original Articles, Interspecific competition, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Plant Leaves, Agronomy, Modélisation, Soil water, soil modelling, intercropping, 010606 plant biology & botany
Abstract: Background and AimsImproved modelling of carbon assimilation and plant growth to low soil moisture requires evaluation of underlying mechanisms in the soil, roots, and shoots. The feedback between plants and their local environment throughout the whole spectrum soil-root-shoot-environment is crucial to accurately describe and evaluate the impact of environmental changes on plant development. This study presents a 3D functional structural plant model, in which shoot and root growth are driven by radiative transfer, photosynthesis, and soil hydrodynamics through different parameterisation schemes relating soil water deficit and carbon assimilation. The new coupled model is used to evaluate the impact of soil moisture availability on plant productivity for two different groups of flowering plants under different spatial configurations.MethodsIn order to address different aspects of plant development due to limited soil water availability, a 3D FSP model including root, shoot, and soil was constructed by linking three different well-stablished models of airborne plant, root architecture, and reactive transport in the soil. Different parameterisation schemes were used in order to integrate photosynthetic rate with root water uptake within the coupled model. The behaviour of the model was assessed on how the growth of two different types of plants, i.e. monocot and dicot, is impacted by soil water deficit under different competitive conditions: isolated (no competition), intra, and interspecific competition.Key ResultsThe model proved to be capable of simulating carbon assimilation and plant development under different growing settings including isolated monocots and dicots, intra, and interspecific competition. The model predicted that (1) soil water availability has a larger impact on photosynthesis than on carbon allocation; (2) soil water deficit has an impact on root and shoot biomass production by up to 90 % for monocots and 50 % for dicots; and (3) the improved dicot biomass production in interspecific competition was highly related to root depth and plant transpiration.ConclusionsAn integrated model of 3D shoot architecture and biomass development with a 3D root system representation, including light limitation and water uptake considering soil hydraulics, was presented. Plant-plant competition and regulation on stomatal conductance to drought were able to be predicted by the model. In the cases evaluated here, water limitation impacted plant growth almost 10 times more than the light environment.
Published: 2020

8. Consumers’ Motivations Towards Environment-Friendly Dietary Changes: An Assessment of Trends Related to the Consumption of Animal Products

Author: Thomopoulos, Rallou, Salliou, Nicolas, Taillandier, Patrick, Tonda, Alberto, Thomopoulos, Rallou, Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Représentation de Connaissances et Langages à Base de Règles pour Raisonner sur les Données (BOREAL), 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)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Walter Leal Filho, Ilija Djekic, Sergiy Smetana, and Marina Kovaleva
Subjects: Machine Learning, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Levers of food diet change, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Consumer perception, Consumer Survey, Classification, [SDE.ES]Environmental Sciences/Environmental and Society, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Ecological footprint of food diets, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; In the context of global warming and environmental pressure, food chains must adapt to new production conditions while satisfying the evolving consumer demand. Livestock production is known for its negative ecological footprint, bringing forward the question of a possible transition towards more plant-based diets. Citizens' demand evolves at different speeds and integrates these new environmental concerns sometimes mixed with health or ethical issues. We carried out a survey with 1,715 respondents in France, about their food choice priorities and preferences, as well as the drivers of change. Our results indicate that 40% of respondents claim that their current diet is not what they would ideally have and 98% of them would like to reduce their animal product consumption. Classification algorithms reveals several salient variables separating classes of individuals wishing to shift their food diet towards less animal products: the willingness to change is stronger for the youngest, factors impacting the willingness to change are food pleasure, health and to a lesser extent social resistance and animal ethics. The less radical the animal product reduction is, the more environmental concerns become the main motivation.
Published: 2022

9. TSPred: A framework for nonstationary time series prediction

Author: Eduardo Bezerra, Fabio Porto, Rebecca Salles, Eduardo Ogasawara, Esther Pacitti, Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), Associated team Hpdasc, 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)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: Computer science, Process (engineering), Cognitive Neuroscience, Data transformation (statistics), 02 engineering and technology, computer.software_genre, 01 natural sciences, 010104 statistics & probability, Artificial Intelligence, Prediction methods, 0202 electrical engineering, electronic engineering, information engineering, Decomposition (computer science), Preprocessor, 0101 mathematics, Time series, preprocessing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Series (mathematics), nonstationarity, prediction, Computer Science Applications, machine learning, transform, 020201 artificial intelligence & image processing, Data mining, time series, computer
Abstract: International audience; The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differs from the mainstream frameworks since it establishes a prediction process that seamlessly integrates nonstationary time series transformations with state-of-the-art statistical and machine learning methods. It is made available as an R-package, which provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, modeling, prediction, and accuracy assessment. Besides, TSPred enables user-defined methods, which significantly expands the applicability of the framework.
Published: 2022

10. Probabilistic rule induction for transparent CBR under uncertainty

Author: Jedwabny, Martin, Bisquert, Pierre, Croitoru, Madalina, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], 02 engineering and technology
Abstract: International audience; CBR systems leverage past experiences to make decisions. Recently, the AI community has taken an interest in making CBR systems explainable. Logic-based frameworks make answers straightforward to explain. However, they struggle in the face of conflicting information, unlike probabilistic techniques. We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.
Published: 2021

11. Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution

Author: Camille Garcin, Alexis Joly, Pierre Bonnet, Jean-Christophe Lombardo, Antoine Affouard, Mathias Chouet, Maximilien Servajean, Titouan Lorieul, Joseph Salmon, Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Service Expérimentation et Développement [Nancy] (SED), Inria Nancy - Grand Est, ADVanced Analytics for data SciencE (ADVANSE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ANR-20-CHIA-0001,CAMELOT,Apprentissage automatique et optimisation coopératifs.(2020), European Project: 863463,Cos4Cloud project, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Université Paul-Valéry - Montpellier 3 (UPVM)
Subjects: [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDE.ES]Environmental Sciences/Environmental and Society, top-k, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], plant recognition, long tail, ambiguity, dataset, set-valued classification, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, image classification
Abstract: International audience; This paper presents a novel image dataset with high intrinsic ambiguity and a longtailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306,146 plant images covering 1,081 species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology: (i) the dataset has a strong class imbalance, i.e., a few species account for most of the images, and, (ii) many species are visually similar, rendering identification difficult even for the expert eye. These two characteristics make the present dataset well suited for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (macro-average top-k accuracy and macro-average average-k accuracy) and we provide baseline results established by training deep neural networks using the cross-entropy loss.
Published: 2021

12. Rule-based Languages for Reasoning on Data : Analysis, Design and Applications

Author: Ulliana, Federico, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Montpellier University, Marie-Laure Mugnier, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Règles existentielles, Rule based languages, reasoning, [INFO]Computer Science [cs], existential rules, raisonnement automatique, langages à base de règles
Published: 2021

13. Online Deep Learning Hyperparameter Tuning based on Provenance Analysis

Author: Débora B. Pina, Marta Mattoso, Aline Paes, Daniel de Oliveira, Filipe Silva, Patrick Valduriez, Liliane N. O. Kunstmann, Universidade Federal do Rio de Janeiro (UFRJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Associated team Hpdasc, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Hyperparameter, Provenance, Hyperparameter tuning, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, business.industry, Deep learning, Machine learning, computer.software_genre, Deep Learning, Artificial intelligence, business, computer
Abstract: International audience; Training Deep Learning (DL) models require adjusting a series of hyperparameters. Although there are several tools to automatically choose the best hyperparameter configuration, the user is still the main actor to take the final decision. To decide whether the training should continue or try different configurations, the user needs to analyze online the hyperparameters most adequate to the training dataset, observing metrics such as accuracy and loss values. Provenance naturally represents data derivation relationships (i.e., transformations, parameter values, etc.), which provide important support in this data analysis. Most of the existing provenance solutions define their own and proprietary data representations to support DL users in choosing the best hyperparameter configuration, which makes data analysis and interoperability difficult. We present Keras-Prov and its extension, named Keras-Prov++, which provides an analytical dashboard to support online hyperparameter fine-tuning. Different from the current mainstream solutions, Keras-Prov automatically captures the provenance data of DL applications using the W3C PROV recommendation, allowing for hyperparameter online analysis to help the user deciding on changing hyperparameters' values after observing the performance of the models on a validation set. We provide an experimental evaluation of Keras-Prov++ using AlexNet and a real case study, named DenseED, that acts as a surrogate model for solving equations. During the online analysis, the users identify scenarios that suggest reducing the number of epochs to avoid unnecessary executions and fine-tuning the learning rate to improve the model accuracy.
Published: 2021

14. Capturing Provenance to Improve the Model Training of PINNs: first handon experiences with Grid5000

Author: Silva, Rômulo, Pina, Débora, Kunstmann, Liliane, de Oliveira, Daniel, Valduriez, Patrick, Coutinho, Alvaro, Mattoso, Marta, Universidade Federal do Rio de Janeiro (UFRJ), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), IACM, Associated team Hpdasc, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Valduriez, Patrick
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Eikonal Equation, Hybrid Computing, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Provenance, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Physics-informed Neural Networks, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; The growing popularity of Neural Networks in computational science and engineering raises several challenges in configuring training parameters and validating the models. Machine learning has been used to approximate costly computational problems in computational mechanics, discover equations by coefficient estimation, and build surrogates. Those applications are outside of the common usage of neural networks. They require a different set of techniques generally encompassed by Physics-Informed Neural Networks (PINNs), which appear to be a good alternative for solving forward and inverse problems governed by PDEs in a small data regime, especially when it comes to Uncertainty Quantification. PINNs have been successfully applied for solving problems in fluid dynamics, inference of hydraulic conductivity, velocity inversion, phase separation, and many others. Nevertheless, we still need to investigate its computational aspects, especially its scalability, when running in large-scale systems. Several hyperparameter configurations have to be evaluated to reach a trained model, often requiring fine-tuning hyperparameters, despite the existence of a few setting recommendations. In PINNs, this fine-tuning requires analyzing configurations and how they relate to the loss function evaluation. We propose provenance data capture and analysis techniques to improve the model training of PINNs. We also report our first experiences on running PINNs in Grid5000 using hybrid CPU-GPU computing.
Published: 2021

15. Working Memory for Assessment Under Inconsistency

Author: Bisquert, Pierre, Dupin De Saint Cyr - Bannay, Florence, Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Leila Amgoud, Richard Booth, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées
Subjects: Preferred subtheories, [SCCO]Cognitive science, Bounded rationality, SAT, [INFO]Computer Science [cs], Inconsistency handling
Abstract: This is a draft version the paper has been accepted in NMR 2021; International audience; This paper proposes a new way of handling the inconsistency of a knowledge base when answering to a query about the validity of a formula. The idea is inspired by human behavior in front of inconsistency, namely, try to never encounter it. For this purpose, we encode a kind of compartmentalization of the working memory. More precisely, given a query and a potentially inconsistent knowledge base, called long term memory, our system only loads in working memory the consistent knowledge which is the most related to the query. We position this system with regard to a major reference in the field, Brewka's preferred subtheories, and study its efficiency by providing complexity and experimental results.
Published: 2021

16. Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Author: Hervé Goëau, Susan J. Mazer, Natalie Love, Alexis Joly, Pierre Bonnet, University of California [Santa Barbara] (UCSB), University of California, California Polytechnic State University [San Luis Obispo] (CAL POLY), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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 California [Santa Barbara] (UC Santa Barbara), University of California (UC), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: 0106 biological sciences, natural history collections, Plant Science, Predictor variables, Machine learning, computer.software_genre, Flowering time, 010603 evolutionary biology, 01 natural sciences, phenology, Article, phenological shift, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], visual data classification, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], regional convolutional neural network, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, 0303 health sciences, Ecology, biology, business.industry, Phenology, Botany, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], object detection, flowering time, biology.organism_classification, Specimen age, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDE.ES]Environmental Sciences/Environmental and Society, phenological stage annotation, Herbarium, climate change, QK1-989, Streptanthus tortuosus, Artificial intelligence, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, computer
Abstract: Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens, however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen, however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.
Published: 2021

17. ASAX : Segmentation adaptative basée sur la quantité d'information pour SAX

Author: Djebour, Lamia, Akbarinia, Reza, Masseglia, Florent, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
Abstract: International audience; Les approches existantes pour le calcul de similitude entre séries temporelles sont au coeur de nombreuses tâches d'analyse de données. Étant donné les volumes de données considérés, ou simplement le besoin de les traiter rapidement, ces approches s'appuient souvent sur des représentations alternatives, plus courtes, qui résument les séries d'origine avec une perte d'information acceptable. Les comparaisons de séries temporelles qui se basent sur ces représentations sont alors approximatives, ce qui fait de la précision un enjeu majeur. Nous présentons et évaluons expérimentalement ASAX, une nouvelle approche pour la segmentation de séries temporelles avant qu'elles soient transformées en représentations symboliques. ASAX réduit de manière significative la perte d'information due aux fractionnements dans les différentes étapes du calcul de la représentation. Nous fournissons des garanties théoriques sur la borne inférieure des mesures de similitude entre séries temporelles, et nos expériences illustrent l'intérêt de notre méthode sur l'état de l'art, en particulier avec un gain de précision significatif.
Published: 2021

18. Generalização de Mineração de Sequências Restritas no Espaço e no Tempo

Author: Antonio Castro, Heraldo Borges, Ricardo Campisano, Esther Pacitti, Fabio Porto, Rafaelli Coutinho, Eduardo Ogasawara, Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (Rio de Janeiro) ( CEFET/RJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Laboratorio Nacional de Computação Cientifica [Rio de Janeiro] (LNCC / MCT), SBC, Associated team Hpdasc, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract: International audience; Spatiotemporal patterns bring knowledge of sequences of events, place and time when they occur. Finding such patterns is a complex task and one of great value for different domains. However, not all patterns are frequent across an entire dataset, often occurring in restricted space and time. This work formalizes the Mining of Restricted Sequences in Space and Time, without the use of previous restrictions of time and space, allowing different sequence sizes, time intervals and space (in three dimensions) to present such patterns. It also brings validation with a tested implementation on a real seismic dataset. Resulting in a sensitivity analysis and evaluation of the use of resources that indicate the validity and feasibility of the solution. * Os autores agradecem à FAPERJ, à CAPES (código 001) e ao CNPq pelo financiamento do projeto.; Padrões espaço-temporais trazem conhecimento de sequências de eventos, local e momento em que ocorrem. Encontrar tais padrões é uma ta- refa complexa e de grande valor para diferentes domínios. No entanto, nem todos os padrões são frequentes em todo um conjunto de dados, ocorrendo com frequência em espaço e tempo restritos. Este trabalho formaliza a Mineração de Sequências Restritas no Espaço e no Tempo, sem o uso de limiares de restrição para tempo e espaço. Isso permite que diferentes tamanhos de sequências, inter- valos de tempo e espaço tridimensional apresentem tais padrões. Traz também validação com uma implementação testada sobre um conjunto de dados sísmico real. Tendo como resultado uma análise de sensibilidade e avaliação do uso de recursos que indicam a validade e viabilidade da solução.
Published: 2021

19. Massive spectral data analysis for plant breeding using parSketch-PLSDA method: Discrimination of sunflower genotypes

Author: Bruno Grezes-Besset, Maxime Metz, Maxime Ryckewaert, Daphné Héran, Jean-Michel Roger, Pierre George, Ryad Bendoula, Reza Akbarinia, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), innolea, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), ANR-16-CE04-0010,OptiPAG,Outils optiques pour l'agriculture de précision(2016), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: 0106 biological sciences, Multivariate statistics, Computer science, Calibration (statistics), Soil Science, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Context (language use), 01 natural sciences, Bottleneck, Field (computer science), Chemometrics, Spectroscopy, Digital Agriculture, 2. Zero hunger, business.industry, Massive data, 010401 analytical chemistry, Search engine indexing, Hyperspectral imaging, Pattern recognition, Precision Agriculture, 0104 chemical sciences, Control and Systems Engineering, [SDE]Environmental Sciences, Precision agriculture, Artificial intelligence, business, Agronomy and Crop Science, 010606 plant biology & botany, Food Science
Abstract: International audience; In precision agriculture and plant breeding, the amount of data tends to increase. This massive data is becoming more and more complex, leading to difficulties in managing and analysing it. Optical instruments such as NIR Spectroscopy or hyperspectral imaging are gradually expanding directly in the field, increasing the amount of spectral database. Using these tools allows access to non-destructive and rapid measurements to classify new varieties according to breeding objectives. Processing this massive amount of spectral data is challenging. In a context of genotype discrimination, we propose to apply a method called parSketch-PLSDA to analyse such a massive amount of spectral data. ParSketch-PLSDA is a combination of an indexing strategy (parSketch) and the reference method (PLSDA) for predicting classes from multivariate data. For this purpose, a spectral database was formed by collecting 1,300,000 spectra generated from hyperspectral images of leaves of four different sunflower genotypes. ParSketch-PLSDA is compared to a PLSDA. Both methods use the same set of calibration and test. The prediction model obtained by PLSDA has a classification error close to 23% on average across all genotypes. ParSketch-PLSDA method outperforms PLSDA by greatly improving prediction qualities by 10%. Indeed, the model built with ParSketch-PLSDA has the ability to take into account non-linearities among data sets. These results are encouraging and allow us to anticipate the future bottleneck related to the generation of a large amount of data from phenotyping.
Published: 2021

20. Functional–Structural Plant Modeling Highlights How Diversity in Leaf Dimensions and Tillering Capability Could Promote the Efficiency of Wheat Cultivar Mixtures

Author: Emmanuelle Blanc, Pierre Barbillon, Christian Fournier, Christophe Lecarpentier, Christophe Pradal, Jérôme Enjalbert, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ANR-11-IDEX-0003,IPS,Idex Paris-Saclay(2011), ANR-20-PCPA-0006,MoBiDiv,Mobilizing and Breeding Intra and inter-specific crop Diversity for a systemic change towards pesticide-free agriculture(2020), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro
Subjects: 0106 biological sciences, Canopy, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, F08 - Systèmes et modes de culture, [SDV]Life Sciences [q-bio], Plant Science, Agricultural engineering, Performance de culture, 01 natural sciences, F50 - Anatomie et morphologie des plantes, SB1-1110, sensitivity analysis, Cultivar, functional traits, Original Research, Mathematics, 2. Zero hunger, competition for light, Tallage, U10 - Informatique, mathématiques et statistiques, 04 agricultural and veterinary sciences, tillering plasticity, Anatomie végétale, Diversification, Trait, Interception, Assembly rules, Triticum aestivum, Crop, aerial architecture, variety mixture, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Variété, Leaf area index, Morphologie végétale, Modélisation des cultures, Plant culture, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Cropping, 010606 plant biology & botany
Abstract: International audience; Increasing the cultivated diversity has been identified as a major leverage for the agroecological transition as it can help improve the resilience of low input cropping systems. For wheat, which is the most cultivated crop worldwide in terms of harvested area, the use of cultivar mixtures is spreading in several countries, but studies have seldom focused on establishing mixing rules based on plant architecture. Yet, the aerial architecture of plants and the overall canopy structure are critical for field performance as they greatly influence light interception, plant interactions and yield. The very high number of trait combinations in wheat mixtures makes it difficult to conduct experimentations on this issue, which is why a modeling approach appears to be an appropriate solution. In this study, we used WALTer, a functional structural plant model (FSPM), to simulate wheat cultivar mixtures and try to better understand how differences between cultivars in key traits of the aerial architecture influence mixture performance. We simulated balanced binary mixtures of cultivars differing for different critical plant traits: final height, leaf dimensions, leaf insertion angle and tillering capability. Our study highlights the impact of the leaf dimensions and the tillering capability on the performance of the simulated mixtures, which suggests that traits impacting the plants' leaf area index (LAI) have more influence on the performance of the stand than traits impacting the arrangement of the leaves. Our results show that the performance of mixtures is very variable depending on the values of the explored architectural traits. In particular, the best performances were achieved by mixing cultivars with different leaf dimensions and different tillering capability, which is in agreement with numerous studies linking the diversity of functional traits in plant communities to their productivity. However, some of the worst performances were also achieved by mixing varieties differing in their aerial architecture, which suggests that diversity is not a sufficient criterion to design efficient mixtures. Overall, these results highlight the importance of simulation-based explorations for establishing assembly rules to design efficient mixtures.
Published: 2021

21. Clinical Characteristics, Care Trajectories and Mortality Rate of SARS-CoV-2 Infected Cancer Patients: A Multicenter Cohort Study

Author: Benderra, Marc-Antoine, Aparicio, Ainhoa, Leblanc, Judith, Wassermann, Demian, Kempf, Emmanuelle, Galula, Gilles, Bernaux, Mélodie, Canellas, Anthony, Moreau, Thomas, Bellamine, Ali, Spano, Jean-Philippe, Daniel, Christel, Champ, Julien, Canouï-Poitrine, Florence, Gligorov, Joseph, Research, on Behalf of the AP-HP/Universities/INSERM COVID, Task, Cancer AP-HP Group COVID, Initiative, AP-HP Covid CDW, CHU Tenon [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), IMRB - CEPIA/'Clinical Epidemiology And Ageing : Geriatrics, Primary Care and Public Health' [Créteil] (U955 Inserm - UPEC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), 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)-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)-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), Service d'Oncologie médicale [CHU Henri Mondor], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Henri Mondor [Créteil], Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut Universitaire de Cancérologie [Sorbonne Université] (IUC), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Service d'Oncologie médicale [CHU Tenon], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Henri Mondor, Institut Universitaire de Cancérologie [Paris] (IUC), Gestionnaire, HAL Sorbonne Université 5, 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)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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)-Service NEUROSPIN (NEUROSPIN), 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)-Université Paris-Saclay, Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Cancer Research, medicine.medical_specialty, Multivariate analysis, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), [SDV.CAN]Life Sciences [q-bio]/Cancer, mortality rate, Article, law.invention, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, law, Internal medicine, Medicine, cancer, Lung cancer, RC254-282, 030304 developmental biology, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, 0303 health sciences, business.industry, Mortality rate, SARS-CoV-2 infection, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Cancer, medicine.disease, Intensive care unit, 3. Good health, multicenter study, Oncology, 030220 oncology & carcinogenesis, Cohort, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, business, hospitals, Cohort study
Abstract: Background: COVID-19 may be more frequent and more severe in cancer patients than in other individuals. Our aims were to assess the rate of COVID-19 in hospitalized cancer patients, to describe their demographic characteristics, clinical features and care trajectories, and to assess the mortality rate. Methods: This multicenter cohort study was based on the Electronic Health Records of the Assistance Publique-Hôpitaux de Paris (AP-HP). Cancer patients with a diagnosis of COVID-19 between 3 March and 19 May 2020 were included. Main outcome was all-cause mortality within 30 days of COVID-19 diagnosis. Results: A total of 29,141 cancer patients were identified and 7791 (27%) were tested for SARS-CoV-2. Of these, 1359 (17%) were COVID-19-positive and 1148 (84%) were hospitalized, 217 (19%) were admitted to an intensive care unit. The mortality rate was 33% (383 deaths). In multivariate analysis, mortality-related factors were male sex (aHR = 1.39 [95% CI: 1.07–1.81]), advanced age (78–86 y: aHR = 2.83 [95% CI: 1.78–4.51] vs. &lt, 66 y, 86–103 y: aHR = 2.61 [95% CI: 1.56–4.35] vs. &lt, 66 y), more than two comorbidities (aHR = 2.32 [95% CI: 1.41–3.83]) and C-reactive protein &gt, 20 ng/mL (aHR = 2.20 [95% CI: 1.70–2.86]). Primary brains tumors (aHR = 2.19 [95% CI: 1.08–4.44]) and lung cancer (aHR = 1.66 [95% CI: 1.02–2.70]) were associated with higher mortality. Risk of dying was lower among patients with metabolic comorbidities (aHR = 0.65 [95% CI: 0.50–0.84]). Conclusions: In a hospital-based setting, cancer patients with COVID-19 had a high mortality rate. This mortality was mainly driven by age, sex, number of comorbidities and presence of inflammation. This is the first cohort of cancer patients in which metabolic comorbidities were associated with a better outcome.
Published: 2021

22. Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images

Author: Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, Alexis Joly, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), California Institute of Technology (CALTECH), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ADVanced Analytics for data SciencE (ADVANSE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Guglielmo Faggioli, Nicola Ferro, Alexis Joly, Maria Maistro, Florina Piroi, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Subjects: evaluation, [SCCO.COMP]Cognitive science/Computer science, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, benchmark, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, remote sensing imagery, environmental data, species distribution, LifeCLEF, presence-only data, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, species distribution models, biodiversity
Abstract: International audience; Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To advance research in this area, a large-scale machine learning competition called GeoLifeCLEF 2021 was organized. It relied on a dataset of 1.9 million observations from 31K species mainly of animals and plants. These observations were paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional lowresolution climate and soil variables. The main goal of the challenge was to better understand how to leverage remote sensing data to predict the presence of species at a given location. This paper presents an overview of the competition, synthesizes the approaches used by the participating groups, and analyzes the main results. In particular, we highlight the ability of remote sensing imagery and convolutional neural networks to improve predictive performance, complementary to traditional approaches.
Published: 2021

23. Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum

Author: Matthieu Simonin, Gabriel Antoniu, Alexis Joly, Jean-Christophe Lombardo, Daniel Rosendo, Alexandru Costan, Patrick Valduriez, Scalable Storage for Clouds and Beyond (KerData), 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 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES), Design and Implementation of Autonomous Distributed Systems (MYRIADS), HPC-BigData Inria Challenge (IPL), ANR-15-CE25-0003,OverFlow,Workflow Data Management as a Service pour des Applications Multi-Site(2015), 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 National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), 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), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Institut National des Sciences Appliquées (INSA)
Subjects: FOS: Computer and information sciences, Optimization, Computer Science - Machine Learning, Computer science, Computer Science - Artificial Intelligence, Distributed computing, Cloud computing, Computing Continuum, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Resource (project management), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], Computer Science - Performance, Continuum (topology), business.industry, Testbed, Methodology, Energy consumption, Reproducibility, Performance (cs.PF), [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Workflow, Memory management, Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Analytics, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business
Abstract: International audience; In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-to-Cloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance trade-offs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.
Published: 2021

24. Enabling Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum

Author: Rosendo, Daniel, Costan, Alexandru, Antoniu, Gabriel, Valduriez, Patrick, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Scalable Storage for Clouds and Beyond (KerData), 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 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES), Philippe Rigaux, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), 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), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut National des Sciences Appliquées (INSA)
Subjects: Networking and Internet Architecture (cs.NI), Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Networking and Internet Architecture, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Computer Science - Performance, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: National audience; Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the Digital Continuum, or the Transcontinuum). Understanding end-to-end performance in such a complex continuum is challenging. This breaks down to reconciling many, typically contradicting application requirements and constraints with low-level infrastructure design choices. One important challenge is to accurately reproduce relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. We introduce a rigorous methodology for such a process and validate it through E2Clab. It is the first platform to support the complete experimental cycle across the Computing Continuum: deployment, analysis, optimization. Preliminary results with real-life use cases show that E2Clab allows one to understand and improve performance, by correlating it to the parameter settings, the resource usage and the specifics of the underlying infrastructure.
Published: 2021

25. Pl@ntNet, ten years of automatic plant identification and monitoring

Author: Alexis Joly, Antoine Affouard, Mathias Chouet, Benjamin Deneu, Joaquim Estopinan, Hervé Goëau, Hugo Gresse, Jean-Christophe Lombardo, Titouan Lorieul, François Munoz, Maximilien Servajean, Pierre Bonnet, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ADVanced Analytics for data SciencE (ADVANSE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), IUCN, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Pl@ntNet identification tool, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
Abstract: International audience; AI-based models for IUCN conservation status prediction (see Fig 1) Digitized Herbarium analysis (phenology, traits, identification) Plant disease recognition Agro-ecological robots (weeds detection and identification, mixed seeds) Biodiversity data quality and uncertainty New AI-based services for citizen science (cos4cloud project)
Published: 2021

26. Parallelisable Existential Rules: a Story of Pieces

Author: Marie-Laure Mugnier, Michaël Thomazo, Maxime Buron, University of Oxford [Oxford], Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-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), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Value from Data (VALDA ), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), ANR-18-CE23-0003,CQFD,Requêtes Ontologiques Complexes sur des Fédérations de Données Hétérogènese(2018), Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria de Paris, University of Oxford, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL)
Subjects: FOS: Computer and information sciences, [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], Class (set theory), [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Theoretical computer science, Chase, Computer Science - Artificial Intelligence, Computer science, Formalism (philosophy), [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Databases (cs.DB), Context (language use), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Datalog, Artificial Intelligence (cs.AI), Computer Science - Databases, Bounded function, Ontology, Ontology-based data access, Rewriting, Computational aspects of knowledge representation, computer, computer.programming_language
Abstract: In this paper, we consider existential rules, an expressive formalism well adapted to the representation of ontological knowledge, as well as data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.
Published: 2021

27. Efficient Incremental Computation of Aggregations over Sliding Windows

Author: Chao Zhang, Farouk Toumani, Reza Akbarinia, Université Clermont Auvergne (UCA), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Akbarinia, Reza
Subjects: Data stream, Computer science, Computation, Parallel algorithm, Latency (audio), Boundary (topology), 02 engineering and technology, Parallel computing, Streaming Algorithm, Data Stream, 020204 information systems, Sliding window protocol, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Sliding Window Aggregation, 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Throughput (business), Streaming algorithm
Abstract: International audience; Computing aggregation over sliding windows, i.e., finite subsets of an unbounded stream, is a core operation in streaming analytics. We propose PBA (Parallel Boundary Aggregator), a novel parallel algorithm that groups continuous slices of streaming values into chunks and exploits two buffers, cumulative slice aggregations and left cumulative slice aggregations, to compute sliding window aggregations efficiently. PBA runs in (1) time, performing at most 3 merging operations per slide while consuming () space for windows with partial aggregations. Our empirical experiments demonstrate that PBA can improve throughput up to 4× while reducing latency, compared to state-of-the-art algorithms.
Published: 2021

28. Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components

Author: Midingoyi, Cyrille Ahmed, Pradal, Christophe, Enders, Andreas, Fumagalli, Davide, Raynal, Hélène, Donatelli, Marcello, Athanasiadis, Ioannis N., Porter, Cheryl, Hoogenboom, Gerrit, Holzworth, Dean, Garcia, Frédérick, Thorburn, Peter, Martre, Pierre, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria (CREA), Wageningen University and Research [Wageningen] (WUR), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Food Systems Institute [Gainesville] (UF|IFAS), CSIRO Agriculture and Food (CSIRO), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), INRAE Divisions AgroEcoSystem, NUM andDivision AgroEcoSystem, ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia agraria = Council for Agricultural Research and Economics (CREA), and Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE)
Subjects: U10 - Informatique, mathématiques et statistiques, Modélisation des cultures, Data Competence Centre, Model exchange and reuse, Modèle de simulation, PE&RC, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Science ouverte, Laboratory of Geo-information Science and Remote Sensing, Crop2ML, F01 - Culture des plantes, Modélisation, Laboratorium voor Geo-informatiekunde en Remote Sensing, Component-based software, Plante de culture, Crop model
Abstract: International audience; Process-based crop models are popular tools to analyze and simulate the response of agricultural systems to weather, agronomic, or genetic factors. They are often developed in modeling platforms to ensure their future extension and to couple different crop models with a soil model and a crop management event scheduler. The intercomparison and improvement of crop simulation models is difficult due to the lack of efficient methods for exchanging biophysical processes between modeling platforms. We developed Crop2ML, a modeling framework that enables the description and the assembly of crop model components independently of the formalism of modeling platforms and the exchange of components between platforms. Crop2ML is based on a declarative architecture of modular model representation to describe the biophysical processes and their transformation to model components that conform to crop modeling platforms. Here, we present Crop2ML framework and describe the mechanisms of import and export between Crop2ML and modeling platforms.
Published: 2021

29. Análise de Desempenho da Distribuição de Workflows Científicos em Nuvens com Restrições de Confidencialidade

Author: Yuri Frota, Daniel de Oliveira, Rodrigo A. P. Silva, Esther Pacitti, Universidade Federal Fluminense [Rio de Janeiro] (UFF), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), CSBC, Associated team Hpdasc, and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
Abstract: As nuvens fornecem um ambiente sob demanda que permite que cientistas migrem seus experimentos locais para um ambiente elástico. Os experimentos são modelados como workflows científicos, e muito deles são intensivos em computação e produção de dados. O armazenamento desses dados preocupa, uma vez que a confidencialidade pode ser comprometida. Usuários maliciosos podem inferir conhecimento dos resultados e da estrutura dos workflows. Dispersão dos dados e criptografia podem ser adotados para aumentar a confidencialidade, mas esses mecanismos não pode ser adotados de forma desacoplada ao escalonamento do workflow, sob o risco de aumentar o tempo de execução e despesas. Nesse artigo, apresentamos a SaFER (workflow Scheduling with conFidEntiality pRoblem), uma abordagem de escalonamento que considera restrições de confidencialidade dos dados.
Published: 2021

30. WeedElec: a robotic research platform for individual weed detection and selective electrical weeding

Author: Abdelghafour, Florent, Rançon, Florian, Liu, Shiyu, Champ, Julien, De Rudnicki, Vincent, Guizard, Christophe, Goëau, Hervé, Doussan, Claude, Joly, Alexis, Bonnet, Pierre, Rabatel, Gilles, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and ANR-17-ROSE-0003,WeedElec2017,' Robot de désherbage localisé par procédé électrique haute tension combiné avec une gestion prédictive par vision hyper-spectrale et post-évaluation par drone'(2017)
Subjects: robotic, [SPI]Engineering Sciences [physics], electrical weeding, selective weeding, deep learning
Abstract: International audience; The project ‘WeedElec’ aims at developing a robotic intra-row weeding solution based on a high-voltage selective electrocution. It performs the following tasks: weed detection and location, positioning of the weeding probe according to location results and electrical destruction of weeds. The detection is achieved using an RGB camera associated with the deep-learning solution of the ‘Pl@ntNet’ team. Discrimination results between crops and weeds attained 76% accuracy for maize and 62% for beans (field trials). Mortality rates weeds ranged from 75% (Polygonaceae) to 13% (Brassicaceae) for plants less than one month old (laboratory conditions). Survivors were nevertheless impacted with a severe limitation of their growth.
Published: 2021

31. Provenance Supporting Hyperparameter Analysis in Deep Neural Networks

Author: Marta Mattoso, Daniel de Oliveira, Débora B. Pina, Liliane N. O. Kunstmann, Patrick Valduriez, Universidade Federal do Rio de Janeiro (UFRJ), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), This work is funded by CNPq, FAPERJ, and Inria (HPDaSc associated team). D. Pina and L. Kunstmann are supportedby the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001., Équipes Associée Inria : SciDISC, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Hyperparameter, Flexibility (engineering), Data collection, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, business.industry, Deep learning, Interoperability, 02 engineering and technology, computer.software_genre, Machine learning, Workflow Steering, 01 natural sciences, Convolutional neural network, Software framework, Deep Learning, 020204 information systems, Provenance, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, Representation (mathematics), business, 010303 astronomy & astrophysics, computer
Abstract: International audience; The duration of the life cycle in deep neural networks (DNN) depends on the data configuration decisions that lead to success in obtaining models. Analyzing hyperparameters along the evolution of the network's execution allows for adapting the data. Provenance data derivation traces help the parameter fine-tuning by providing a global data picture with clear dependencies. Provenance can also contribute to the interpretation of models resulting from the DNN life cycle. However, there are challenges in collecting hyperparameters and in modeling the relationships between the data involved in the DNN life cycle to build a provenance database. Current approaches adopt different notions of provenance in their representation and require the execution of the DNN under a specific software framework, which limits interoperability and flexibility when choosing the DNN execution environment. This work presents a provenance data-based approach to address these challenges, proposing a collection mechanism with flexibility in the choice and representation of data to be analyzed. Experiments of the approach, using a convolutional neural network focused on image recognition, provide evidence of the flexibility, the efficiency of data collection, the analysis and the validation of network data.
Published: 2021

32. Relative Positional Encoding for Transformers with Linear Complexity

Author: Liutkus, Antoine, Cífka, Ondřej, Wu, Shih-Lun, Şimşekli, Umut, Yang, Yi-Hsuan, Richard, Gael, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Research Center for Information Technology Innovation [Taipei] (CITI), National Taiwan University [Taiwan] (NTU), Taiwan AI Labs [Taipei] (AILabs.tw), Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Statistical Machine Learning and Parsimony (SIERRA), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: MIP-Frontiers, Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Computer Science - Computation and Language, Machine Learning (stat.ML), Computer Science - Sound, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, Audio and Speech Processing (eess.AS), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], FOS: Electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity. In the meantime, relative positional encoding (RPE) was proposed as beneficial for classical Transformers and consists in exploiting lags instead of absolute positions for inference. Still, RPE is not available for the recent linear-variants of the Transformer, because it requires the explicit computation of the attention matrix, which is precisely what is avoided by such methods. In this paper, we bridge this gap and present Stochastic Positional Encoding as a way to generate PE that can be used as a replacement to the classical additive (sinusoidal) PE and provably behaves like RPE. The main theoretical contribution is to make a connection between positional encoding and cross-covariance structures of correlated Gaussian processes. We illustrate the performance of our approach on the Long-Range Arena benchmark and on music generation., Comment: ICML 2021 (long talk) camera-ready. 24 pages
Published: 2021

33. Provenance-and machine learning-based recommendation of parameter values in scientific workflows

Author: Esther Pacitti, Daniel de Oliveira, Daniel Pinheiro Da Silva Junior, Aline Paes, Universidade Federal Fluminense [Rio de Janeiro] (UFF), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: General Computer Science, Computer science, Data Mining and Machine Learning, Value (computer science), Crash, Cloud computing, 02 engineering and technology, Recommender system, Machine learning, computer.software_genre, Machine Learning, Databases, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Recommender systems, Preference Learning, Preference learning, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Data Science, QA75.5-76.95, Supercomputer, Visualization, Workflow, Electronic computers. Computer science, Scientific workflows, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract: Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturing, and storage of the data generated during execution. In some cases, they also provide components to ease the visualization and analysis of the generated data. During the workflow’s composition phase, programs must be selected to perform the activities defined in the workflow specification. These programs often require additional parameters that serve to adjust the program’s behavior according to the experiment’s goals. Consequently, workflows commonly have many parameters to be manually configured, encompassing even more than one hundred in many cases. Wrongly parameters’ values choosing can lead to crash workflows executions or provide undesired results. As the execution of data- and compute-intensive workflows is commonly performed in a high-performance computing environment e.g., (a cluster, a supercomputer, or a public cloud), an unsuccessful execution configures a waste of time and resources. In this article, we present FReeP—Feature Recommender from Preferences, a parameter value recommendation method that is designed to suggest values for workflow parameters, taking into account past user preferences. FReeP is based on Machine Learning techniques, particularly in Preference Learning. FReeP is composed of three algorithms, where two of them aim at recommending the value for one parameter at a time, and the third makes recommendations for n parameters at once. The experimental results obtained with provenance data from two broadly used workflows showed FReeP usefulness in the recommendation of values for one parameter. Furthermore, the results indicate the potential of FReeP to recommend values for n parameters in scientific workflows.
Published: 2021

34. Cross-European initial survey on the use of mathematical models in food industry

Author: Photis Papademas, Kamal Kansou, Alberto Tonda, Aleksandra Nikolić, Nickolas G. Kavallieratos, Ilija Djekic, Rallou Thomopoulos, Igor Tomasevic, Alen Mujčinović, Alexandrina Sirbu, Aberham Hailu Feyissa, Heiko Briesen, Alexandru Mihnea Moisescu, Anet Režek Jambrak, Urška Vrabič Brodnjak, Cristina L. M. Silva, Christos G. Athanassiou, Maria N. Charalambides, Department of Food Safety and Quality Management [Belgrade], Faculty of Agriculture [Belgrade], University of Belgrade [Belgrade]-University of Belgrade [Belgrade], Faculty of Agriculture and Food Sciences [Sarajevo], UNIVERZITET U SARAJEVU, Faculty of Food Technology & Biotechnology [Zagreb], University of Zagreb, Department of Agricultural Sciences, Biotechnology and Food Science [Limassol], Cyprus University of Technology, National Food Institute [Lyngby] (Forside), Unité de recherche sur les Biopolymères, Interactions Assemblages (BIA), Institut National de la Recherche Agronomique (INRA), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Chair Process Systems Engineering [Freising], Technische Universität München [München] (TUM), Faculty of Crop Science [Athens], Agricultural University of Athens, Department of Agriculture Crop Production and Rural Environment [Volos], University of Thessaly [Volos] (UTH), Escola Superior de Biotecnologia [Porto], Universidade Católica Portuguesa [Porto], Constantin Brancoveanu University [Râmnicu Vâlcea], University Politehnica of Bucharest [Romania] (UPB), Department of Animal Origin Products Technology [Belgrade], Faculty of Natural Sciences and Engineering [Ljubljana], University of Ljubljana, Department of Chemical Engineering [London], Imperial College London, Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, The authors would like to acknowledge the networking support by COST Action CA15118 (Mathematical and Computer Science Methods for Food Science and Industry - FoodMC) as well the help in conducting the survey provided by Prof. Dr Serafim Bakalis (Nottingham University), Dr. Efstathios Kaliviotis (Cyprus University of Technology), Mirza Uzunović (University of Sarajevo) and Erifili P. Nika (Agricultural University of Athens)., UNIVERSITY OF SARAJEVO - UNIVERZITET U SARAJEVU, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), European Project: 605414,EC:FP7:TPT,FP7-AAT-2013-RTD-1,CAPPADOCIA(2013), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), and Veritati - Repositório Institucional da Universidade Católica Portuguesa
Subjects: Micro level, Relation (database), Food industry, Knowledge in modellling, Context (language use), 03 medical and health sciences, 0404 agricultural biotechnology, 0302 clinical medicine, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Lack of knowledge, Industrial organization, 2. Zero hunger, Mathematical models, Usage of models, Mathematical model, business.industry, 04 agricultural and veterinary sciences, Limiting, 040401 food science, ddc, [STAT]Statistics [stat], Food sector, 030221 ophthalmology & optometry, Business, Knowledge in modelling, 0908 Food Sciences, Food Science
Abstract: International audience; Mathematical modelling plays an important role in food engineering having various mathematical models tailored for different food topics. However, mathematical models are followed by limited information on their application in food companies. This paper aims to discuss the extent and the conditions surrounding the usage of mathematical models in the context of European food and drinks industry. It investigates the knowledge, nature and current use of modelling approaches in relation to the industry main characteristics. A total of 203 food companies from 12 European countries were included in this research.Results reveal that the country where the company operates, and size of the company, are more important predictors on the usage of mathematical models followed by the type of food sector. The more developed countries are positioned at the higher level of knowledge and use of available models. Similar pattern was observed at the micro level showing that small or medium sized companies exhibit lack of knowledge, resources and limiting usage of models.
Published: 2019

35. Keeping track of user steering actions in dynamic workflows

Author: Vítor Silva, Alvaro L. G. A. Coutinho, Marta Mattoso, Renan Souza, José J. Camata, Patrick Valduriez, Universidade Federal do Rio de Janeiro (UFRJ), Federal University of Juiz de Fora (UFIJ), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Inria associated team SciDISC, and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: FOS: Computer and information sciences, Computer Networks and Communications, Dataflow, Computer science, Data management, Distributed computing, 02 engineering and technology, Track (rail transport), Execution time, Domain (software engineering), Computer Science - Databases, Provenance Data, 0202 electrical engineering, electronic engineering, information engineering, Overhead (computing), Computational Steering, Computational steering, Dynamic Workflows, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Databases (cs.DB), 020206 networking & telecommunications, Parameter Tuning, Workflow, Hardware and Architecture, 020201 artificial intelligence & image processing, business, Software
Abstract: International audience; In long-lasting scientific workflow executions in HPC machines, computational scientists (the users in this work) often need to fine-tune several workflow parameters. These tunings are done through user steering actions that may significantly improve performance (e.g., reduce execution time) or improve the overall results. However, in executions that last for weeks, users can lose track of what has been adapted if the tunings are not properly registered. In this work, we build on provenance data management to address the problem of tracking online parameter fine-tuning in dynamic workflows steered by users. We propose a lightweight solution to capture and manage provenance of the steering actions online with negligible overhead. The resulting provenance database relates tuning data with data for domain, dataflow provenance, execution, and performance, and is available for analysis at runtime. We show how users may get a detailed view of the execution, providing insights to determine when and how to tune. We discuss the applicability of our solution in different domains and validate its ability to allow for online capture and analyses of parameter fine-tunings in a real workflow in the Oil and Gas industry. In this experiment, the user could determine which tuned parameters influenced simulation accuracy and performance. The observed overhead for keeping track of user steering actions at runtime is less than 1% of total execution time.
Published: 2019

36. VersionClimber: Version Upgrades Without Tears

Author: Patrick Valduriez, Konrad Hinsen, Dennis Shasha, Sarah Cohen-Boulakia, Matthew J. Turk, Christophe Pradal, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Bioinformatique (LRI) (BioInfo - LRI), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Courant Institute of Mathematical Sciences [New York] (CIMS), New York University [New York] (NYU), NYU System (NYU)-NYU System (NYU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: conda, General Computer Science, Computer science, Cloud computing, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 02 engineering and technology, Logiciel, computer.software_genre, Index Terms Software Engineering, Software, 0202 electrical engineering, electronic engineering, information engineering, U10 - Informatique, mathématiques et statistiques, business.industry, Versioning, General Engineering, 020207 software engineering, Informatique, software deployment, Lexicographical order, Mac OS, Software deployment, Bounded function, Operating system, business, computer, Software versioning
Abstract: International audience; VersionClimber is an automated system to help update the package and data infrastructure of a software application based on priorities that the user has indicated (e.g. I care more about having a recent version of this package than that one). The system does a systematic and heuristically efficient exploration (using bounded upward compatibility) of a version search space in a sandbox environment (Virtual Env or conda env), finally delivering a lexicographically maximum configuration based on the user-specified priority order. It works for Linux and Mac OS on the cloud.
Published: 2019

37. Distributed Database Systems: The Case for NewSQL

Author: Valduriez, Patrick, Jiménez-Peris, Ricardo, Özsu, M. Tamer, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), LeanXcale [Madrid], University of Waterloo [Waterloo], and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: NewSQL, polystore, SQL, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], InformationSystems_DATABASEMANAGEMENT, Distributed database, NoSQL, DBMS, database system, scalable transaction management
Abstract: International audience; Until a decade ago, the database world was all SQL, distributed, sometimes replicated, and fully consistent. Then, web and cloud applications emerged that need to deal with complex big data, and NoSQL came in to address their requirements, trading consistency for scalability and availability. NewSQL has been the latest technology in the big data management landscape, combining the scalability and availability of NoSQL with the consistency and usability of SQL. By blending capabilities only available in different kinds of database systems such as fast data ingestion and SQL queries and by providing online analytics over operational data, NewSQL opens up new opportunities in many application domains where real-time decisions are critical. NewSQL may also simplify data management, by removing the traditional separation between NoSQL and SQL (ingest data fast, query it with SQL), as well as between operational database and data warehouse / data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical (HTAP) workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL). Today, only a few NewSQL systems have solved this problem. In this paper, we make the case for NewSQL, introducing their basic principles from distributed database systems and illustrating with Spanner and LeanXcale, two of the most advanced systems in terms of scalable transaction management.
Published: 2021

38. E2Clab: Reproducible Analysis of Complex Workflows on the Edge-to-Cloud Continuum

Author: Rosendo, Daniel, Costan, Alexandru, Antoniu, Gabriel, Valduriez, Patrick, Scalable Storage for Clouds and Beyond (KerData), 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 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), 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), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut National des Sciences Appliquées (INSA)
Subjects: [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], [INFO]Computer Science [cs], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract: International audience; Distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Computing Continuum, the Digital Continuum, or the Transcontinuum). Understanding end-to-end performance in such a complex continuum is challenging. This breaks down to reconciling many, typically contradicting application requirements and constraints with low-level infrastructure design choices. One important challenge is to accurately reproduce relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. We introduce a rigorous methodology for such a process and validate it through E2Clab. It is the first platform to support the complete experimental cycle across the Computing Continuum: deployment, analysis, optimization. Preliminary results with real-life use cases show that E2Clab allows one to understand and improve performance, by correlating it to the parameter settings, the resource usage and the specifics of the underlying infrastructure.
Published: 2021

39. Generating preferred plans with ethical features

Author: Martin Jedwabny, Madalina Croitoru, Pierre Bisquert, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)
Subjects: Ethical theories, ComputingMilieux_THECOMPUTINGPROFESSION, Computer science, Normative ethics, Soft goal, Management science, struct, [INFO]Computer Science [cs], State (computer science), Planning Domain Definition Language
Abstract: Normative ethics has been shown to help automated planners take ethically aware decisions. However, state- of-the-art planning technologies don’t provide a sim- ple and direct way to support ethical features. Here, we propose a new theoretical framework based on a con- struct, called ethical rule, that allows to model prefer- ences amongst ethically charged features and capture various ethical theories. We show how the framework can model and combine the strengths of these theories. Then, we demonstrate that classical planning domains extended with ethical rules can be compiled into soft goals in PDDL.
Published: 2021

40. Apple tree adaptation to shade in agroforestry: an architectural approach

Author: Pierre-Eric Lauri, Frédéric C. Do, Lydie Dufour, Christophe Pradal, Benjamin Pitchers, Agrosystèmes Biodiversifiés (UMR ABSys), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Scientific Data Management (ZENITH), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), This work was supported by Fondation de France for a Ph.D. student grant and partly funded by the French DEPHY-EXPE program ALTO (Systèmes en ArboricuLture et Transition agrOécologique) and the Hérault department program PIRAT (Programme Interdisciplinaire de Recherche développement en AgroforesTerie)., Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: 0106 biological sciences, Secondary growth, F08 - Systèmes et modes de culture, [SDV]Life Sciences [q-bio], F62 - Physiologie végétale - Croissance et développement, Plant Science, Agroforesterie, F50 - Anatomie et morphologie des plantes, 01 natural sciences, Tolérance à l'ombre, vegetative shoots, biology, Agroforestry, Phenology, floral phenology, ombre, floral initiation, Malus, Shoot, [SDE]Environmental Sciences, shade, Phénologie, Plant Shoots, plant architecture, Specific leaf area, Growing season, Apple tree, Flowers, 010603 evolutionary biology, systèmes agroforestiers, Genetics, Adaptation, Ecology, Evolution, Behavior and Systematics, ComputingMethodologies_COMPUTERGRAPHICS, Morphologie végétale, plurispecific agrosystems, shoot allometry, 15. Life on land, biology.organism_classification, Plant Leaves, Light intensity, Fruit, Malus pumila, 010606 plant biology & botany
Abstract: Correction added on 07 May 2021, after first online publication: some minor wording changes have been made in the article; International audience; PREMISE: The expression of shade adaptation traits is expected to be stronger in low light and can be detrimental to flowering and yield. Our study focused on the expression of shade adaptation traits of apple trees (Malus domestica Borkh. ‘Dalinette’) in an agroforestry system.METHODS: The architecture of 45 apple trees in their third and fourth year was extensively described and analyzed at the tree scale and compared depending on the light quantity received during the growing season. Flower cluster phenology and the relation between leaf area and floral initiation were also investigated.RESULTS: The number of growing shoots and the leaf area were reduced by shade even if specific leaf area increased with increasing shade. Shade did not modify primary growth but did decrease secondary growth, so that apple tree shoots in shade were slender, with a lower taper and reduced number and proportion of flower clusters. The correlation between floral initiation and leaf area was high both in full and moderate light but not for apple trees in low light. Shade did not impact the date of bud burst and the early phenological stages of flower clusters, but it reduced the number of days at full bloom.CONCLUSIONS: Our results suggest that while the architecture of apple trees is modified by a reduction in light intensity, it is not until a reduction of 65% that the capability to produce fruit is impeded. These results could help optimize the design of apple-tree-based agroforestry systems.
Published: 2021

41. Prototyping a Knowledge-Based System to Identify Botanical Extracts for Plant Health in Sub-Saharan Africa

Author: Pierre J. Silvie, Pierre Martin, Marianne Huchard, Priscilla Keip, Alain Gutierrez, Samira Sarter, Plant Health Institute of Montpellier (UMR PHIM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroécologie et Intensification Durables des cultures annuelles (UPR AIDA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Models And Reuse Engineering, Languages (MAREL), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Graphs for Inferences on Knowledge (GRAPHIK), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), Metaprogram Glofoods (CIRAD-INRA), CIRAD, IRD, ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE)
Subjects: H01 - Protection des végétaux - Considérations générales, biopesticides, Insecticide d'origine végétale, H02 - Pesticides, Review, Protection des plantes, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Pesticide d'origine végétale, Helicoverpa armigera, santé des plantes, pesticidal plants, natural substances, essential oils, Botany, Spodoptera frugiperda, plant-based products, knowledge management, H10 - Ravageurs des plantes, Tuta absoluta, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, Système basé sur la connaissance, Extrait d'origine végétale, QK1-989, IPM, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, crop protection
Abstract: International audience; Replacing synthetic pesticides and antimicrobials with plant-based extracts is a current alternative adopted by traditional and family farmers and many organic farming pioneers. A range of natural extracts are already being marketed for agricultural use, but many other plants are prepared and used empirically. A further range of plant species that could be effective in protecting different crops against pests and diseases in Africa could be culled from the large volume of knowledge available in the scientific literature. To meet this challenge, data on plant uses have been compiled in a knowledge base and a software prototype was developed to navigate this trove of information. The present paper introduces this so-called Knomana Knowledge-Based System, while providing outputs related to Spodoptera frugiperda and Tuta absoluta, two invasive insect species in Africa. In early October 2020, the knowledge base hosted data obtained from 342 documents. From these articles, 11,816 uses—experimental or applied by farmers—were identified in the plant health field. In total, 384 crop pest species are currently reported in the knowledge base, in addition to 1547 botanical species used for crop protection. Future prospects for applying this interdisciplinary output to applications under the One Health approach are presented.
Published: 2021

42. Coupling Agent-Based Models and Argumentation Framework to Simulate Opinion Dynamics: Application to Vegetarian Diet Diffusion

Author: Nicolas Salliou, Patrick Taillandier, Rallou Thomopoulos, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Ingénierie des Agro-polymères et Technologies Émergentes (UMR IATE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), VITAMIN ('VegetarIan Transition Argument ModellINg') project funded by INRA., Ahrweiler P., Neumann M., Institut National de la Recherche Agronomique (INRA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro
Subjects: Agent-based simulation, Computer science, Management science, Vegetarian diets, Context (language use), Opinion dynamics, Argumentation framework, GAMA platform, 02 engineering and technology, 01 natural sciences, 010305 fluids & plasmas, Argumentation theory, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Coupling (computer programming), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020201 artificial intelligence & image processing, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Realism
Abstract: International audience; Agent-based simulation has been extensively used to study opinion dynamics. However, the vast majority of the existing models have been limited to extremely abstract and simplified representations of the diffusion process, which impairs the realism of the simulations and disables the understanding of the reasons for the shift of an actor’s opinion. This paper presents a generic framework implemented in the GAMA platform allowing to explicitly represent exchanges of arguments between actors in a context of an opinion dynamic model. More precisely, we propose to formalize the inner attitude towards an opinion of each agent as an argumentation graph and give them the possibility to share arguments with other agents. We present an application of the framework to study the evolution of the vegetarian diet at a city level.
Published: 2021

43. Set-valued classification -- overview via a unified framework

Author: Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Titouan Lorieul, Statistique mathématique et apprentissage (CELESTE), 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 Mathématiques d'Orsay (LMO), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Analyse et Mathématiques Appliquées (LAMA), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Chzhen, Evgenii
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), [STAT.OT]Statistics [stat]/Other Statistics [stat.ML], [STAT.OT] Statistics [stat]/Other Statistics [stat.ML], Machine Learning (cs.LG)
Abstract: Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.
Published: 2021

44. Parallel Query Processing in a Polystore

Author: Boyan Kolev, Marta Patiño-Martínez, Pavlos Kranas, Esther Pacitti, Patrick Valduriez, Oleksandra Levchenko, Ricardo Jiménez-Peris, LeanXcale [Madrid], Distributed Systems Laboratory (DSL), Universidad Politécnica de Madrid (UPM), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: SQL, Information Systems and Management, Query processing, Computer science, Distributed computing, Big data, Joins, Heterogeneous databases, 02 engineering and technology, Query language, computer.software_genre, Native Queries, 020204 information systems, Distributed and parallel databases, Database integration, 0202 electrical engineering, electronic engineering, information engineering, computer.programming_language, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Polystores, Data structure, Parallel processing (DSP implementation), Hardware and Architecture, Query languages, business, computer, Software, Information Systems, Data integration
Abstract: International audience; The blooming of different data stores has made polystores a major topic in the cloud and big data landscape. As the amount of data grows rapidly, it becomes critical to exploit the inherent parallel processing capabilities of underlying data stores and data processing platforms. To fully achieve this, a polystore should: (i) preserve the expressivity of each data store's native query or scripting language and (ii) leverage a distributed architecture to enable parallel data integration, i.e. joins, on top of parallel retrieval of underlying partitioned datasets. In this paper, we address these points by: (i) using the polyglot approach of the CloudMdsQL query language that allows native queries to be expressed as inline scripts and combined with SQL statements for ad-hoc integration and (ii) incorporating the approach within the LeanXcale distributed query engine, thus allowing for native scripts to be processed in parallel at data store shards. In addition, (iii) efficient optimization techniques, such as bind join, can take place to improve the performance of selective joins. We evaluate the performance benefits of exploiting parallelism in combination with high expressivity and optimization through our experimental validation.
Published: 2021

45. Jointly estimating spatial sampling effort and habitat suitability for multiple species from opportunistic presence‐only data

Author: Botella, Christophe, Joly, Alexis, Bonnet, Pierre, Munoz, François, Monestiez, Pascal, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biostatistique et Processus Spatiaux (BioSP), Consortium Floris'Tic, ANR-18-CE02-0010,EcoNet,Modèles statistiques avancés pour les réseaux écologiques(2018), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: U10 - Informatique, mathématiques et statistiques, Analyse de données, [SDV]Life Sciences [q-bio], Participation communautaire, Distribution spatiale, F70 - Taxonomie végétale et phytogéographie, Distribution des populations, Botanique, Plante sauvage, Participation publique, [SDE]Environmental Sciences, Échantillonnage, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract: International audience; 1. Building reliable species distribution models (SDMs) from presence-only information requires a good understanding of the spatial variation in the sampling effort. However, in most cases, the sampling effort is unknown, leading to biases in SDMs. This study proposes a method to jointly estimate the parameters of sampling effort and species densities to avoid such biases. The method is particularly suited to the analysis of massive but highly heterogeneous presence-only data. 2. The proposed method is based on estimating the variation in sampling effort over units of a spatial mesh in parallel with the environmental density of multiple species using a marked Poisson process model. Based on simulations with realistic settings, we examined the performance and robustness of parameter estimations. We also analysed a large-scale citizen science dataset with highly heterogeneous sampling (Pl@ntNet), including around 300,000 occurrences of 150 plant species. 3. We found that sampling effort was correctly estimated when the true sampling effort was constant within the cells of a spatial mesh. Estimation bias arose when sampling effort and environmental drivers strongly covaried within cells. Otherwise, the inference was correct and robust to sampling variation within cells. Running the model on real occurrences of 150 plant species provided an estimated map of relative sampling effort for 15% of French territory. We also found that the density estimated for an exotic invasive plant was consistent with prior data. 4. This is the first method jointly estimating species densities depending on environment, and sampling effort as an explicit spatial function, from occurrence data of multiple species. An asset of the method is that a few frequently observed species greatly contribute to correctly estimate sampling effort, thereby improving density estimation of all other species. This approach can thus provide reliable SDM for This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Published: 2021

46. Materializing Knowledge Bases via Trigger Graphs

Author: Efthymia Tsamoura, Jacopo Urbani, Enrico Malizia, David Carral, Samsung AI Center [Cambridge], Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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 Bologna, Vrije Universiteit Amsterdam [Amsterdam] (VU), Computer Systems, Network Institute, High Performance Distributed Computing, TSAMOURA EFTHYMIA, CARRAL DAVID, MALIZIA E, URBANI JACOPO, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and University of Bologna/Università di Bologna
Subjects: FOS: Computer and information sciences, Theoretical computer science, Chase, Computer science, Commodity hardware, Computer Science - Artificial Intelligence, Computation, General problem, Ontologie, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Knowledge base, Trigger Graphs, Empirical research, Computer Science - Databases, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], General Engineering, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Databases (cs.DB), Reasoning, Artificial Intelligence (cs.AI), Knowledge graph, 020201 artificial intelligence & image processing, Query answering
Abstract: The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs) for tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion of Trigger Graphs (TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, called GLog, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize Knowledge Graphs with 17B facts in less than 40 min using a single machine with commodity hardware.
Published: 2021

47. Conditional Multi-Task learning for Plant Disease Identification

Author: Pierre Bonnet, Alexis Joly, Sue Han Lee, Hervé Goëau, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), The International Association for Pattern Recognition (IAPR), ICPR2020 Organizing committee, and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: Computer science, Multi-task learning, 02 engineering and technology, Machine learning, computer.software_genre, Field (computer science), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, business.industry, Deep learning, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDE.ES]Environmental Sciences/Environmental and Society, Plant disease, Constraint (information theory), Identification (information), Scalability, Pattern recognition (psychology), plant disease identification, 020201 artificial intelligence & image processing, Artificial intelligence, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, computer
Abstract: International audience; Several recent studies have proposed an automatic plant disease identification system based on deep learning. However, these approaches are generally based on learned classification models with target classes of joint host species-disease pairs that may not allow optimal use of the available information. This is because these approaches require distinguishing between similar host species or diseases, and more importantly, they have limited scalability due to the size of a network gradually increases as new classes are added, despite the fact that information on host species or diseases is already available. This constraint is all the more important as it can be difficult to collect/establish a specific list of all diseases for each host plant species in an actual application. In this paper, we address the problems by proposing a new conditional multi-task learning (CMTL) approach which allows the distribution of host species and disease characteristics learned simultaneously with a conditional link between them. This conditioning is formed in such a way that the knowledge to infer the prediction of one concept (the diseases) depends on the other concept (the host species), which corresponds to the way plant pathologists used to infer the diseases of the host species. We show that our approach can improve the performance of plant disease identification compared to the usual species-disease pair modeling in the previous studies. Meanwhile, we also compose a new dataset on plant disease identification that could serve as an important benchmark in this field
Published: 2021

48. Intensive Data and Knowledge-Driven Approach for Sustainability Analysis: Application to Lignocellulosic Waste Valorization Processes

Author: Jean-Pierre Belaud, Sébastien Destercke, Abdellatif Barakat, Nancy Prioux, Patrice Buche, Caroline Sablayrolles, Claire Vialle, Laboratoire de Génie Chimique (LGC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Chimie Agro-Industrielle (CAI), Ecole nationale supérieure des ingénieurs en arts chimiques et technologiques-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-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), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), and This project was funded by the 3BCAR French Carnot network (http://www.3bcar.fr), which brings together seven French research units (500 researchers) involved in the design of processes for transforming biomass into bioenergy, biomaterials and biomolecules.
Subjects: Environmental Engineering, Circular economy, Computer science, [SDV]Life Sciences [q-bio], Knowledge engineering, Big data, Context (language use), 010501 environmental sciences, 01 natural sciences, 12. Responsible consumption, Life cycle assessment, 11. Sustainability, Waste Management and Disposal, Life-cycle assessment, 0505 law, 0105 earth and related environmental sciences, Agricultural waste, 2. Zero hunger, Scope (project management), Renewable Energy, Sustainability and the Environment, business.industry, 05 social sciences, Environmental economics, Lignocellulosic biomass, Ranking, 13. Climate action, Sustainability, 050501 criminology, business
Abstract: International audience; The use of circular economy is becoming more and more important, particularly in the field of agriculture, a major provider of waste. In particular, a lot of researches are being done to transform the lignocellulosic waste from agriculture through desired "sustainable" processes. Sustainable processes mean economically viable, socially accepted, and environmentally responsible processes. Thanks to the "life cycle thinking", it is possible to assess such potential environmental impacts. However, these environmental analyzes require a lot of specific data, whose collection can be long and tedious, or simply impossible in practice. On the other hand, the huge amount of scientific articles describing the processes of valorization of co-products of agriculture constitutes a great, largely under-exploited source of data. Knowledge engineering (KE) tools can be used to compile processes and analyze them. In this paper, we propose an innovative approach, based on intensive data and KE methods, to help a decision maker to choose between different pretreatment processes and different biomasses. The main goal is to develop an intensive, semi-automated data collection approach and an associated tool for assistance with choices in a circular economy context. It is defined by five steps: (1) goal and scope, (2) intensive data and knowledge and the allocation of flows and releasesstructuration and integration, (3) life cycle inventory (LCI), (4) sustainability assessment and (5) analysis and ranking. The study of 13 pretreatment processes of rice straw and corn stover validate our proposal.
Published: 2021

49. AI-based Identification of Plant Photographs from Herbarium Specimens

Author: Hervé Goëau, Pierre Bonnet, Alexis Joly, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: 0106 biological sciences, Computer science, plant, Biodiversity informatics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, Plant identification, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Mean reciprocal rank, herbarium, 030304 developmental biology, Domain adaptation, 0303 health sciences, Photos, Information retrieval, domain adaptation, business.industry, Deep learning, General Medicine, identification, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, 15. Life on land, Identification (information), Herbarium, Test set, Pl@ntNet, Metric (mathematics), Artificial intelligence, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, photos, business, 010606 plant biology & botany
Abstract: Automated plant identification has recently improved significantly due to advances in deep learning and the availability of large amounts of field photos. As an illustration, the classification accuracy of 10K species measured in the LifeCLEF challenge (Goëau et al. 2018) reached 90%, very close to that of human experts. However, the profusion of field images only concerns a few tens of thousands of species, mainly located in North America and Western Europe. Conversely, the richest regions in terms of biodiversity, such as tropical countries, suffer from a shortage of training data (Pitman 2021). Consequently, the identification performance of the most advanced models on the flora of these regions is much lower (Goëau et al. 2019). Nevertheless, for several centuries, botanists have systematically collected, catalogued, and stored plant specimens in herbaria. Considerable recent efforts by the biodiversity informatics community, such as DiSSCo (Addink et al. 2018) and iDigBio (Matsunaga et al. 2013), have made millions of digitized specimens from these collections available online. A key question is therefore whether these digitized specimens could be used to improve the identification performance of species for which we have very few (if any) photos. However, this is a very difficult problem from a machine learning point of view. The visual appearance of a herbarium specimen is actually very different from a field photograph because the specimens are dried and crushed on a herbarium sheet before being digitized (Fig. 1). To advance research on this topic, we built a large dataset that we shared as one of the challenges of the LifeCLEF 2020 (Goëau et al. 2020) and 2021 evaluation campaigns (Goëau et al. 2021). It includes more than 320K herbarium specimens collected mostly from the Guiana Shield and the Northern Amazon Rainforest, focusing on about 1K plant species of the French Guiana flora. A valuable asset of this collection is that some of the specimens are accompanied by a few photos of the same specimen, allowing for more precise machine learning. In addition to this training data, we also built a test set for model evaluation, composed of 3,186 field photos collected by two of the best experts on Guyanese flora. Based on this dataset, about ten research teams have developed deep learning methods to address the challenge (including the authors of this abstract as the organizing team). A detailed description of these methods can be found in the technical notes written by the participating teams (Goëau et al. 2020, Goëau et al. 2021). The methods can be divided into two categories: those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. those based on classical convolutional neural networks (CNN) trained simply by mixing digitized specimens and photos and those based on advanced domain adaptation techniques with the objective of learning a joint representation space between field and herbarium representations. The domain adaptation methods themselves were of two types, those based on adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space adversarial regularization (Motiian et al. 2017) to force herbarium specimens and photos to have the same representations, metric learning to maximize inter-species distances and minimize intra-species distances in the representation space In Table 1, we report the results achieved by the different methods evaluated during the 2020 edition of the challenge. The evaluation metric used is the mean reciprocal rank (MRR), i.e., the average of the inverse of the rank of the correct species in the list of the predicted species. In addition to this main score, a second MRR score is computed on a subset of the test set composed of the most difficult species, i.e., the ones that are the least frequently photographed in the field. The main outcomes we can derive from these results are the following: Classical deep learning models fail to identify plant photos from digitized herbarium specimens. The best classical CNN trained on the provided data resulted in a very low MRR score (0.011). Even with the of use additional training data (e.g. photos and digitized herbarium from GBIF) the MRR score remains very low (0.039). Domain adaptation methods provide significant improvement but the task remains challenging. The best MRR score (0.180) was achieved by using adversarial regularization (FSDA Motiian et al. 2017). This is much better than the classical CNN models but there is still a lot of progress to be made to reach the performance of a truly functional identification system (the MRR score on classical plant identification tasks can be up to 0.9). No method fits all. As shown in Table 1, the metric learning method has a significantly better MRR score on the most difficult species (0.107). However, the performance of this method on the species with more photos is much lower than the adversarial technique. In 2021, the challenge was run again but with additional information provided to train the models, i.e., species traits (plant life form, woodiness and plant growth form). The use of the species traits allowed slight performance improvement of the best adversarial adaptation method (with a MRR equal to 0.198). In conclusion, the results of the experiments conducted are promising and demonstrate the potential interest of digitized herbarium data for automated plant identification. However, progress is still needed before integrating this type of approach into production applications.
Published: 2021

50. Cache-aware scheduling of scientific workflows in a multisite cloud

Author: Esther Pacitti, François Tardieu, Christophe Pradal, Gaëtan Heidsieck, Patrick Valduriez, Daniel de Oliveira, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), Universidade Federal Fluminense [Rio de Janeiro] (UFF), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
Subjects: Multisite cloud, Exploit, Computer Networks and Communications, Computer science, Distributed computing, Workflow System, Cloud computing, 02 engineering and technology, Reuse, Scheduling (computing), Distributed Caching, processus, Workflow Scheduling, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Workflow scheduling, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [INFO]Computer Science [cs], business.industry, U10 - Informatique, mathématiques et statistiques, Scientific Workflow, 020206 networking & telecommunications, Informatique, Workflow, Hardware and Architecture, 020201 artificial intelligence & image processing, Cache, business, Software
Abstract: International audience; Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution.
Published: 2021

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