Le SGBD relationnel pour microordinateur Pepin:un outil pour l'evaluation de performance

Voir rappor

Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening

Fractional anisotropy distributions in 2- to 6-year-old children with autism

Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterisation of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories

Multiversion views: Constructing views in a multiversion database

Commercial DBMS offer mechanisms for views and for versions. Research and development efforts in these directions are, however, characterized by concentration on either the one or the other mechanism, very seldom trying to take advantage of their complementary properties. This paper presents the multiversion view mechanism, which allows these orthogonal concepts to be managed together, taking advantage of their combined characteristics. Unlike previous efforts to combine views and versions, multiversion views create views over versions of data, thereby offering users coherent logical units of the versioned world. They allow a wide range of (virtual) data reorganization possibilities, which encompass, among others, operations found in temporal databases and CLAP. Multiversion views are illustrated and motivated by needs from a real life large case study of complex configuration management, described at the end of the paper. (C) 2000 Elsevier Science B.V. All rights reserved.33327730

A system for change documentation based on a spatiotemporal database

The evolution of geographic phenomena has been one of the concerns of spatiotemporal database research. However, in a large spectrum of geographical applications, users need more than a mere representation of data evolution. For instance, in urban management applications-e.g., cadastral evolution users often need to know why, how, and by whom certain changes have been performed as well as their possible impact on the environment. Answers to such queries are not possible unless supplementary information concerning real world events is associated with the corresponding changes in the database and is managed efficiently. This paper proposes a solution to this problem, which is based on extending a spatiotemporal database with a mechanism for managing documentation on the evolution of geographic information. This solution has been implemented in a GIS-based prototype, which is also discussed in the paper.8217320

Managing Sensor Data On Urban Traffic

Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embarked) sensors, generating large and complex spatio-temporal series. Research efforts in handling these data range from pattern matching and data mining techniques (for forecasting and trend analysis) to work on database queries (e.g., to construct scenarios). Work on embarked sensors also considers issues on trajectories and moving objects. This paper presents a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and database procedures to query these data. The first component is geared towards supporting pattern matching, whereas the second deals with spatio-temporal database issues. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with test conducted on 1000 sensors, during 3 years, in a large French city. © 2008 Springer Berlin Heidelberg.5232 LNCS385394(2007) TheCADDYWebsite, , http://norma.mas.ecp.fr/wikimas/Caddy, CADDYScemama, G., Carles, O., Claire-SITI, Public road Transport Network Management Control: A Unified Approach (2004) 12th IEEE Int. Conf. on Road Transport Information and Control (RTICJoliveau, M., (2008) Reduction of Urban Traffic Time Series from Georeferenced Sensors, and extraction of Spatio-temporal series -in French, , Ph.D thesis, Ecole Centrale Des Arts Et Manufactures Ecole Centrale de ParisJolliffe, I., (1986) Principal Component Analysis, , Springer, New YorkJoliveau, M., Vuyst, F.D., Space-time summarization of multisensor time series. case of missing data (2007) Int. Workshop on Spatial and Spatio-temporal data mining, IEEE SSTDMDempster, A., Laird, N., Rubin, D., Maximum likelihood for incomplete data via the em algorithm (1977) Journal of the Royal Statistical Society series B, 39, pp. 1-38Hugueney, B., Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures (2006) Proc. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 542-552Hugueney, B., Joliveau, M., Jomier, G., Manouvrier, M., Naja, Y., Scemama, G., Steffan, L., Towards a data warehouse for urban traffic (in french) (2006) Revue des Nouvelles Technologies de L'Information RNTI (B2), pp. 119-137Yi, B.K., Faloutsos, C., Fast time sequence indexing for arbitrary Lp norm (2000) Proc. of the 26th VLBD Conference, pp. 385-394Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S., (2000) Dimensionality reduction for fast similarity search in large time series databases, , Journal of Knowledge and Information SystemsMariotte, L., Medeiros, C.B., Torres, R., Diagnosing Similarity of Oscillation Trends in Time Series (2007) International Workshop on spatial and spatio-temporal data mining -SSTDM, pp. 243-248Mautora, T., Naudin, E., Arcs-states models for the vehicle routing problem with time windows and related problems (2007) Computers and Operations Research, 34, pp. 1061-1084Kriegel, H.P., Kröger, P., Kunath, P., Renz, M., Schmidt, T., Proximity queries in large traffic networks (2007) Proc. ACM GIS, pp. 1-8Kim, K., Lopez, M., Leutenegger, S., Li, K., A Network-based Indexing Method for Trajectories of Moving Objects (2006) LNCS, 4243, pp. 344-353. , Yakhno, T, Neuhold, E.J, eds, ADVIS 2006, Springer, HeidelbergGuting, R., Bohlen, M., Erwig, E., Jensen, C., Lorentzos, N., Schneider, M., Vazirgianis, M., A Foundation for Representing and Querying Moving Objects (2000) ACM Transactions on Database Systems, 25 (2), pp. 1-42Spaccapietra, S., Parent, C., Damiani, M.L., Macedo, J.A., Porto, F., Vangenot, C., A conceptual view on trajectories (2008) Knowledge and Data Engineering, 65 (1), pp. 126-14

Managing sensor traffic data and forecasting unusual behaviour propagation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.143SI279305Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)French Research ProgramConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq