International Journal of Agricultural Research, Innovation and Technology (IJARIT)
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: QuickFill, QuickMixte: block approaches for reducing the number of programs in program synthesis
No full textInternational audienceRepetitive tasks are most often tedious; in order to facilitate their execution, program synthesis approaches have been developed. They consist in automatically inferring programs that satisfy the intention of a user. The best known approach in program synthesis is FlashFill integrated into the Excel spreadsheet which allows the processing of character strings. In FlashFill user intent is represented by examples i.e, pairs (input, output). FlashFill explores a very large space of programs and therefore can require a lot of execution time and infer a lot of programs some of which work on given examples but do not capture the user's intent. In this article, we propose two approaches QuickMixte and QuickFill based on blocks which aim to guide the exploration of the program space of FlashFill by enriching the specifications provided by the user. These approaches ask the user to provide associations between subparts of the output and the input to refine the specifications. Experiments carried out on a series of 12 datasets show that QuickMixte and QuickFill make it possible to considerably reduce the program space of FlashFill. We show that with these approaches, it is often possible to give fewer examples than with the original FlashFill algorithm for a larger proportion of correct programs.Keywords : Synthesis of programs, programming by example, manipulation of character strings, repetitive tasks, block approach.Les tâches répétitives sont le plus souvent fastidieuses ; afin de faciliter leur exécution, les approches de synthèse de programmes ont été développées. Elles consistent à inférer automatiquement des programmes qui satisfont l'intention d'un utilisateur. L'approche la plus connue en synthèse de programmes est FlashFill intégrée au tableur Excel qui permet le traitement des chaînes de caractères. Dans FlashFill l'intention de l'utilisateur est représentée par des exemples i.e, des couples (entrée, sortie). FlashFill explore un très grand espace de programmes et peut donc nécessiter un temps d'exécution important et inférer beaucoup de programmes dont certains fonctionnent sur des exemples donnés mais ne capturent pas l'intention de l'utilisateur. Dans cet article, nous proposons deux approches QuickMixte et QuickFill basées sur les blocs qui visent à guider l'exploration de l'espace de programmes de FlashFill en enrichissant les spécifications fournies par l'utilisateur. Ces approches demandent à l'utilisateur de fournir des associations entre les sous-parties de la sortie et de l'entrée pour affiner les spécifications. Les expérimentations menées sur une série de 12 jeux de données montrent que QuickMixte et QuickFill permettent de réduire considérablement l'espace de programmes de FlashFill. Nous montrons qu'avec ces approches, il est souvent possible de donner moins d'exemples qu'avec l'algorithme FlashFill original pour une plus grande proportion de programmes corrects
Vectorisation sur des documents orientés classe pour la recommandation d'emploi basée sur des modèles d'apprentissage automatique supervisé.
No full textInternational audienceNowadays, job recommender systems are more useful in the fight against unemployment due to their strong presence in e-recruitment platforms that are becoming very popular. Most of the recommender systems based on machine learning that recommend jobs to profiles use a vector representation of job offers based on keywords. However, these keywords are results of vectorization which is applied on a collection of documents where each one is a job offer. In this case, each keyword discriminates one job offer from another, whereas it can be preferable that each keyword discriminates one class from another. The aim of this paper is to improve the recommendation of job offers to users, by proposing to apply vectorization on a class-oriented collection of documents in order to obtain more useful keywords for the representation of job offers. In the case of job recommendation, each class-oriented document corresponds to a user profile. Experiments are done on two datasets (Nigam and Minajobs), using TF-IDF and Doc2Vec as vectorization techniques, Naive Bayes and Decision Trees as supervised machine learning models for top-N recommendation, and Precision, MRR and MAP as evaluation metrics. Our results show that, whatever the case, the best performance is always reach by a job recommender system resulting from our contribution. Compared to classic job recommender systems, the improvement rates can go up to 13% and 24% for systems based on Naive Bayes and go up to 55% and 46% for those based on Decision tree, respectively in the Nigam and Minajobs datasets.Aujourd'hui, les systèmes de recommandation d'emploi sont plus utiles dans la lutte contre le chômage en raison de leur forte présence dans les plateformes de recrutement en ligne qui deviennent très populaires. La plupart des systèmes de recommandation basés sur l'apprentissage automatique qui recommandent des emplois à des profils utilisent une représentation vectorielle des offres d'emploi basée sur des mots-clés. Cependant, ces mots-clés sont les résultats d'une vectorisation appliquée à une collection de documents dont chacun est une offre d'emploi. Dans ce cas, chaque mot-clé discrimine une offre d'emploi par rapport à une autre, alors qu'il peut être préférable que chaque mot-clé discrimine une classe par rapport à une autre. L'objectif de cet article est d'améliorer la recommandation d'offres d'emploi aux utilisateurs, en proposant d'appliquer la vectorisation sur une collection de documents orientée classe afin d'obtenir des mots-clés plus utiles pour la représentation des offres d'emploi. Dans le cas de la recommandation d'emploi, chaque document orienté classe correspond à un profil d'utilisateur. Les expériences sont réalisées sur deux jeux de données (Nigam et Minajobs), en utilisant TF-IDF et Doc2Vec comme techniques de vectorisation, Naive Bayes et Decision Trees comme modèles d'apprentissage automatique supervisés pour la recommandation top-N, et Precision, MRR et MAP comme métriques d'évaluation. Nos résultats montrent que, dans tous les cas, la meilleure performance est toujours atteinte par un système de recommandation d'emploi issu de notre contribution. Par rapport aux systèmes classiques de recommandation d'emploi, les taux d'amélioration peuvent aller jusqu'à 13% et 24% pour les systèmes basés sur Naive Bayes et jusqu'à 55% et 46% pour ceux basés sur Decision tree, respectivement dans les jeux de données Nigam et Minajobs
Prediction of structural alphabet protein blocks using data mining
No full textInternational audience3D protein structures determine proteins' biological functions. The 3D structure of the protein backbone can be approximated using the prototypes of local protein conformations. Sets of these prototypes are called structural alphabets (SAs). Amongst several approaches to the prediction of 3D structures from amino acid sequences, one approach is based on the prediction of SA prototypes for a given amino acid sequence. Protein Blocks (PBs) is the most known SA, and it is composed of 16 prototypes of five consecutive amino acids which were identified as optimal prototypes considering the ability to correctly approximate the local structure and the prediction accuracy of prototypes from an amino acid sequence. We developed models for PBs prediction from sequence information using different data mining approaches and machine learning algorithms. Besides the amino acid sequences, the results of the following tools were used to train the models: the Spider3 predictor of protein structure properties, several predictors of the protein's intrinsically disordered regions, and a tool for finding repeats in amino acid sequences. The highest accuracy of the constructed models is 80%, which is a significant improvement compared to the previous best available prediction, whose accuracy was 61%. Analyzing the models constructed by applying different algorithms, it was noticed that the significance of input attributes differs among the models constructed by algorithms. Using the information about amino acids belonging to intrinsically disordered regions and repeats improves the precision of prediction for some PBs using the CART classification algorithm, while this is not the case with the C5.0 classification algorithm. Improved prediction approaches can have interesting applications in protein structural model approaches or computational protein design
Interactive Dynamics of Cell Volume and Cell Death in Human Erythrocytes Exposed to α-Hemolysin from Escherichia coli
No full textInternational audienceα-hemolysin (HlyA) of E. coli binds irreversibly to human erythrocytes and induces cell swelling, ultimately leading to hemolysis. We characterized the mechanism involved in water transport induced by HlyA and analyzed how swelling and hemolysis might be coupled. Osmotic water permeability (Pf) was assessed by stopped-flow light scattering. Preincubation with HlyA strongly reduced Pf in control- and aquaporin 1-null red blood cells, although the relative Pf decrease was similar in both cell types. The dynamics of cell volume and hemolysis on RBCs was assessed by electrical impedance, light dispersion and hemoglobin release. Results show that HlyA induced erythrocyte swelling, which is enhanced by purinergic signaling, and is coupled to osmotic hemolysis. We propose a mathematical model of HlyA activity where the kinetics of cell volume and hemolysis in human erythrocytes depend on the flux of osmolytes across the membrane, and on the maximum volume that these cells can tolerate. Our results provide new insights for understanding signaling and cytotoxicity mediated by HlyA in erythrocytes
A <SUP>187</SUP>Re-<SUP>187</SUP>Os, <SUP>87</SUP>Rb-<SUP>86</SUP>Sr, highly siderophile and incompatible trace element study of some carbonaceous, ordinary and enstatite chondrite meteorites
No full textInternational audienceNew 187Re-187Os, 87Rb-87Sr, triple O-isotope isotope, bulk rock highly siderophile- (HSE: Os, Ir, Ru, Pt, Pd, Re), major- and trace-element abundance data are reported for a variety of carbonaceous, ordinary and enstatite chondrite meteorites. In addition, new mineral chemical data are reported for the Chelyabinsk LL5 ordinary chondrite fall for comparison with existing chondrite data and to investigate element sequestration into metal and mineral phases within some chondrites. The focus of the study is to link the variations observed in the HSE abundances and Re-Os isotopes with other isotopic and elemental data to explore the relative roles of sample sizes, terrestrial alteration and parent body processes more fully on chondrite meteorite compositions. Trace element variations in Chelyabinsk silicate, oxide and metal grains highlight the importance of geochemical heterogeneity imparted by mineralogical variations and mode effects, as well as sample size. Using a range of sample powder aliquot sizes, it is possible to show that this becomes significant for the HSE at 187Os/188Os of ordinary chondrites make them a close match to the composition of the bulk silicate Earth. Assuming ∼0.5% late accretion of ordinary chondrites to Earth, this would result in a long-term Rb/Sr ratio ∼0.6% higher than from late accretion of carbonaceous chondrites, indicating that ordinary chondrites are a potentially attractive source for moderately volatile enrichment
Coarsening Dynamics of 2D Subaqueous Dunes
No full textInternational audienceFluid flow over an initially flat granular bed leads to the formation of a surface-wave instability. The sediment bed profile coarsens and increases in amplitude and wavelength as disturbances develop from ripples into dunes. We perform experiments and numerical simulations to quantify both the temporal evolution of bed properties and the relationship between the initial growth rate and the friction velocity u∗. Experimentally, we study underwater bedforms originating from a thin horizontal particle layer in a narrow and counter-rotating annular flume. We investigate the role of flow speed, flow depth and initial bed thickness on dune evolution. Bedforms evolve from small, irregular disturbances on the bed surface to rapidly growing connected terraces (2D equivalent of transverse dunes) before splitting into discrete dunes. Throughout much of this process, growth is controlled by dune collisions which are observed to result in either coalescence or ejection (mass exchange). We quantify the coarsening process by tracking the temporal evolution of the bed amplitude and wavelength. Additionally, we perform Large Eddy Simulations (LES) of the fluid flow inside the flume to relate the experimental conditions to u∗. By combining the experimental observations with the LES results, we find that the initial dune growth rate scales approximately as u∗5 . These results can motivate models of finite-amplitude dune growth from thin sediment layers that are important in both natural and industrial settings
Estimations, à partir de séries d’images LANDSAT, des évolutions de stocks de carbone de différentes formations en milieu équatorial côtier - cas de Libreville au Gabon
No full textInternational audienceThe role of forests in carbon storage in a climate change context is relevance. In this paper, we will assess the carbon stock in the Libreville area using remote sensing data over a 2001-2018 period. This assessment of carbon stocks in forests is mainly based on Landsat optical imagery. A first analysis aimed at producing a land use mapping between 2001 and 2018 was carried out. These preliminary results were then used as a proxy for the assessment of carbon stocks using terrestrial and mangrove forests. The average value of carbon stock contained in each class comes from the bibliographical work carried out mainly in the same study area. The total value of the carbon stock was estimated at 70 913 018 ± 896 and 68 340 383 ± 927 tons in 2001 and 2018 respectively.Le rôle des forêts dans la séquestration du carbone est important dans un contexte de changement climatique. Dans cet article, il est question d'effectuer une évaluation du stock de carbone dans la région de Libreville à partir des données de télédétection sur une période allant de 2001 à 2018. Cette évaluation des stocks de carbone végétal est basée essentiellement sur l'imagerie optique Landsat. En amont, une première analyse visant à produire une cartographie d'occupation du sol entre 2001 et 2018 a été effectuée. Ces résultats préliminaires ont par la suite été utilisés en tant que proxy pour l'évaluation des stocks de carbone en utilisant comme base les classes végétales de forêts terrestres et forêts de mangroves. La valeur moyenne de stock de carbone contenue dans chaque classe est issue des travaux bibliographiques réalisés majoritairement dans la même zone d'étude. La valeur totale du stock de carbone a été estimée à 70 913 018 ± 896 et à 68 340 383 ± 927 tonnes respectivement en 2001 et en 2018
LiSA : An assisted literature search pipeline for detecting serious adverse drug events with deep learning
No full textDetecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year. The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness. The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results. Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection
