[Collection de cartes postales classées méthodiquement pour l'enseignement de la géographie, Paris, F. Nathan, s.d. (1928), offert par l'éditeur. 353 cartes postales en 15 paquets. 1. Généralités, définitions principales (1-25) 2. Ports de commerce, de pêche, militaires, fluviaux (26-50) 3. Pêches et industries diverses (51-75) 4. Relief du sol (76-95) 5. Communications en montagne (96-119) 6. Histoire d'un fleuve (120-139) 7. Phénomènes dus à l'érosion (140-159) 8. Voies de communication (160-179) 9. Paris (180-204) 10. Algérie (205-228) 11. Tunisie (229-253) 12. Maroc (254-278) 13. A.O.F. et A.E.F. (279-303) 14. Madagascar (304-328) 15. Indochine (329-353)]

Donateur : Nathan, Fernand (1858-1947)

Aptitudes communicationnelles et formations à la communication en oncologie : Discussion critique

Cet article consiste en une discussion critique de la question de la communication en matière de prise en charge du patient atteint de cancer et de l'enseignement des aptitudes communicationnelles en oncologie. Nous avons d'abord essayé de définir les contours de ce qu'est (ou devrait être) une communication adéquate en oncologie, pour ensuite aborder les concepts sous-tendant les formations à la communication dans ce domaine, le problème des recommandations d'experts et du type dominant d'évaluation se rapportant à ces formations ainsi que les contenus enseignés et ainsi véhiculés.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

The impact of communication skills training in oncology: a linguistic analysis.

This study aimed to investigate the impact of a communication skills training (CST) in oncology on clinicians' linguistic strategies. A verbal communication analysis software (Logiciel d'Analyse de la Communication Verbale) was used to compare simulated patients interviews with oncology clinicians who participated in CST (N = 57) (pre/post with a 6-month interval) with a control group of oncology clinicians who did not (N = 56) (T1/T2 with a 6-month interval). A significant improvement of linguistic strategies related to biomedical, psychological and social issues was observed. Analysis of linguistic aspects of videotaped interviews might become in the future a part of individualised feedback in CST and utilised as a marker for an evaluation of training.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.

Mathématique et statistique en science de l’information et en science de la communication: infométrie mathématique et infométrie statistique des revues scientifiques

L’application de la mathématique et de la statistique à l’étude des phénomènes informationnels a entraîné la naissance en science de l’information d’un nouvel axe de recherche et de développement, l’infométrie. Après avoir montré l’intérêt de cette application mais aussi avoir mis en garde contre certains abus et contre certains mauvais usages, nous présentons quelques exemples d’infométrie mathématique et d’infométrie statistique appliquées aux revues scientifiques. Ils illustrent l’étendue et l’efficacité des analyses qui peuvent être faites sur une ou plusieurs variables informationnelles.

E-publishing development and changes in the scholarly communication system

New and alternative scientific publishing business models is a reality driven mostly by the information and communication technologies, by the movements towards the recovery of control of the scientific communication activities by the academic community, and by the open access approaches. The hybrid business model, mixing open and toll-access is a reality and they will probably co-exist with respective trade-offs. This essay discusses the changes driven by the epublishing and the impacts on the scholarly communication system stakeholders' interrelationships (publishers-researchers, publishers-libraries and publishers-users interrelationships), and the changes on the scientific publishing business models, followed by a discussion of possible evolving business models. Whatever the model which evolves and dominates, a huge cultural change in authors' and institutions publishing practices will be necessary in order to make the open access happen and to consolidate the right business models for the traditional publishers. External changes such as policies, rewarding systems and institutions mandates should also happen in order to sustain the whole changing scenario.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.