Agri-food clusters: Does French policy match with real spatial dynamics?

In this paper, we attempt to ascertain to what extent the clusters identified in the agricultural and agri-food space rely on a spatial dynamic involving real agricultural and agri-food activities in the relevant geographic area. We use explanatory spatial data analysis (ESDA) to detect the spatial structure and dynamics of agri-food activities and to connect them to the competitiveness clusters’ locations. Results show that the six clusters specifically studied have different profiles because of their proximity to dynamic areas of agricultural and agri-food production and because of their collaborations with other clusters. Keywords: French Competitiveness Clusters, spatial analysis, inter cluster collaboration.

Language and Proofs for Higher-Order SMT (Work in Progress)

Satisfiability modulo theories (SMT) solvers have throughout the years been able to cope with increasingly expressive formulas, from ground logics to full first-order logic modulo theories. Nevertheless, higher-order logic within SMT is still little explored. One main goal of the Matryoshka project, which started in March 2017, is to extend the reasoning capabilities of SMT solvers and other automatic provers beyond first-order logic. In this preliminary report, we report on an extension of the SMT-LIB language, the standard input format of SMT solvers, to handle higher-order constructs. We also discuss how to augment the proof format of the SMT solver veriT to accommodate these new constructs and the solving techniques they require.Comment: In Proceedings PxTP 2017, arXiv:1712.0089

Machine Learning for Instance Selection in SMT Solving

International audienceSMT solvers are among the most suited tools for quantifier-free first-order problems, and their support for quantified formulas has been improving in recent years. To instantiate quantifiers, they rely on heuristic techniques that generate thousands of instances, most of them useless. We propose to apply state-of-the-art machine learning techniques as classifiers for instances on top of the instantiation process. We show that such techniques can indeed decrease the number of generated useless instances. We envision that this could lead to more efficient SMT solving for quantified problems. Satisfiability-modulo-theories (SMT) solvers are among the best backends for verification tools and "hammers" in proof assistants. When proof obligations contain quantified formulas, SMT solvers rely on instantiation, replacing quantified subformulas by sets of ground instances. Three main techniques have been designed: enumerative [11], trigger-based [4], and conflict-based [12] instantiation. Among these, only conflict-based instantiation computes instances that are guaranteed to be relevant, but it is incomplete and is normally used in combination with other techniques. Enumerative and trigger-based techniques are highly heuristic and generate a large number of instances, most of them useless. As a result, the search space of the solver explodes. Reducing the number of instances could improve the solver's efficiency and success rate within a given time limit. We propose to use a state-of-the-art machine learning algorithm as a predictor over the generated set of instances to filter out irrelevant instances, and thus decrease the number of instances given to the ground solver. The predictor is invoked after each instantiation round to rate the potential usefulness of each generated instance. Several strategies are then used to build a subset of potentially relevant instances that are immediately added to the ground solver. Adding the other instances is postponed. We conducted our experiment in veriT [2], an SMT solver that implements all three in-stantiation techniques described above. We chose as predictor the XGBoost gradient boosting toolkit [3] with the binary classification objective. This configuration had already been used successfully in the context of theorem proving [6, 10]. Choosing a suitable set of features is crucial for effective machine learning. The features determine how precise the representation of the problem is. Previous works already investigate features for theorem proving [1, 5, 6, 8-10]. Our features are more specifically inspired by ENIGMA [6] and RLCoP [7]. They are basically term symbols and term walks with symbol sequences projected to features using Vowpal Wabbit hashing. Term variables and Skolem constants are translated analogously to constants. The model is further enriched with abstract features such as term size, term depth, and the number of instances. To encode our problem into sparse vectors, we use three kinds of information available to the solver: the ground part of the formula (set of literals l 1 ,. .. , l m), the quantified formul

Parental Perceptions of Oral Health and School-Based Dental Sealant Programs

Introduction: Community Health Needs Assessment (University of Vermont Medical Center, 2013) Identified oral health in pediatric population as a primary concern Barriers to dental care cited: access, affordability, education School-Based Sealant Program (SBSP) Dental sealants are an evidence-based method of cavity prevention CDC strongly recommends delivery via SBSPs Few Vermont schools have such a program Vermont Medicaid State Plan amendment allows dental hygienists to bill without on-site dentist (2015)4 Unique opportunity to pilot an SBSP Pilot program implemented by the University of Vermont Medical Center Community Health Improvement Goal: sustainable model able to be replicated in Vermont schools Pilot School Selection – Milton Elementary-Middle School (MEMS) Demographics representative of Vermont schools (46% free & reduced lunch program); school administration supportive of an SBSP; no existing dental education (“Tooth Tutor”) program per Vermont Office of Oral Healthhttps://scholarworks.uvm.edu/comphp_gallery/1232/thumbnail.jp

Computing expectation values for RNA motifs using discrete convolutions

BACKGROUND: Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations. RESULTS: We introduce discrete convolutions as an accurate and fast mean to estimate score distributions of lod-score profiles. This method provides excellent score estimations for all single-strand or helical elements tested and also applies to the combination of elements into larger, complex, motifs. Further, the estimated distributions remain accurate even when pseudocounts are introduced into the lod-score profiles. Estimated score distributions are then easily converted into E-values. CONCLUSION: A good agreement was observed between computed E-values and simulations for a number of complete RNA motifs. This method is now implemented into the ERPIN software, but it can be applied as well to any search procedure based on ungapped profiles with statistically independent columns

Impact of the National Initiative for Building Community Trust and Justice on Police Administrative Outcomes

This report examines the degree to which activities associated with the National Initiative for Building Community Trust and Justice – a six-city effort to promote more equitable, just, and respectful policing practices and improve relationships and trust between law enforcement and community members – yielded their intended impacts on crime rates, departmental practices, and police-community interactions. Analyses of administrative data indicated that the impacts of the interventions varied considerably by site – as did the availability and richness of sites' data. Changes in calls for service, violent crimes, and property crimes were mixed across sites. Two of the cities observed deceases in the amount of use of force incidents, but there was no reduction in the racial disparity of those events. While rates of pedestrian and traffic stops generally declined after the start of the National Initiative's primary activities, they ultimately returned to previous levels. In addition, arrest rates declined across sites, but no differences emerged in arrest rates by racial or ethnic characteristics. Site-specific findings and their association with National Initiative activities are discussed in detail

Study of elementary micro-cutting in hardened tool steel

In order to model micro-milling cutting forces, a way is to apply a local model on discretized elements of the cutting edge and then summing on the whole edge to obtain the global cutting forces. This local model is usually obtained by numerical simulation or cutting experimentation. This paper focuses on orthogonal and oblique micro-cutting experiments of AISI 6F7 with tungsten carbide tools. Results show the influence of cutting edge sharpness on cutting forces and the existence of different mechanisms corresponding to different ranges of uncut chip thickness values. A phenomenological model has been identified to model correctly these zones. Then, by comparing experimental micro-milling forces with those deduced from these micro-cutting model and tests, a good agreement has been found. In order to complete this study, phenomenological and thermo mechanical models are being developed. The aim is to obtain an elementary cutting model that can be used for micro-milling simulation and optimization

Clinical Validation of Computer-Assisted Navigation in Total Hip Arthroplasty

A CT-based navigation system is helpful to evaluate the reamer shaft and the impactor position/orientation during unilateral total hip arthroplasty (THA). The main objective of this study is to determine the accuracy of the Navitrack system by measuring the implant's true anteversion and inclination, based on pre- and postoperative CT scans (n = 9 patients). The secondary objective is to evaluate the clinical validity of measurements based on postop anteroposterior (AP) radiographs for determining the cup orientation. Postop CT-scan reconstructions and postop planar radiographs showed no significant differences in orientation compared to peroperative angles, suggesting a clinical validity of the system. Postoperative AP radiographs normally used in clinic are acceptable to determine the cup orientation, and small angular errors may originate from the patient position on the table

Expérimentation de la micro-coupe élémentaire sur un acier dur et comparaison au micro-fraisage

Cet article présente des essais de micro-coupe orthogonale et oblique à partir de tournage sur un acier 40NiCrMo16. Les résultats obtenus démontrent l’influence du rayon d’acuité d’arête sur les efforts mesurés notamment aux faibles épaisseurs de copeau non déformé. Les efforts spécifiques de coupe déduits sont en cohérence avec ceux obtenus lors d’essais de micro-fraisage issus de travaux précédents. Pour compléter l’étude, cet article pose les bases de la modélisation phénoménologique et thermomécanique adaptée à la micro-coupe. Le but à terme est d’obtenir un modèle de coupe élémentaire utilisable dans le cas du micro-fraisage puis de comparer les résultats obtenus aux résultats expérimentaux