A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Use of Treponema pallidum PCR in Testing of Ulcers for Diagnosis of Primary Syphilis(1.).

Treponema pallidum PCR (Tp-PCR) has been noted as a valid method for diagnosing syphilis. We compared Tp-PCR to a combination of darkfield microscopy (DFM), the reference method, and serologic testing in a cohort of 273 patients from France and Switzerland and found the diagnostic accuracy of Tp-PCR was higher than that for DFM.

South American monkeys in the development and testing of malarial vaccines - a review

South American Aoutus an d Saimiri monkeys, which are susceptible to infection with human malarias, have been used to develop models for the testing of huma malaria vaccines. Studies indicate that blood-stage and sporozoite vaccines can be tested in these monkeys using appropriate strains of parasites.

Development of an optimized methodology for tensile testing of carbon steels in hydrogen environment

The study was performed at OCAS, the Steel Research Centre of ArcelorMittal for the Industry market. The major aim of this research was to obtain an optimized tensile testing methodology with in-situ H-charging to reveal the hydrogen embrittlement in various high strength steels. The second aim of this study has been the mechanical characterization of the hydrogen effect on hight strength carbon steels with varying microstructure, i.e. ferrite-martensite and ferrite-bainite grades. The optimal parameters for H-charging - which influence the tensile test results (sample geometry type of electrolyte, charging methods effect of steel type, etc.) - were defined and applied to Slow Strain Rate testing, Incremental Step Loading and Constant Load Testing. To better understand the initiation and propagation of cracks during tensile testing with in-situ H-charging, and to make the correlation with crystallographic orientation, some materials have been analyzed in the SEM in combination with the EBSD technique. The introduction of a notch on the tensile samples permits to reach a significantly improved reproducibility of the results. Comparing the various steel grades reveals that Dual Phase (ferrite-martensite) steels are more sensitive to hydrogen induced cracking than the FB (ferritic-bainitic) ones. This higher sensitivity to hydrogen was found back in the reduced failure times, increased creep rates and enhanced crack initiation (SEM) for the Dual Phase steels in comparison with the FB steels.

Testing, not modelling, the impact of cohesion support: a theoretical framework and some preliminary results for the Spanish regions

This paper develops a simple model that can be used to estimate the effectiveness of Cohesion expenditure relative to similar but unsubsidized projects, thereby making it possible to explicitly test an important assumption that is often implicit in estimates of the impact of Cohesion policies. Some preliminary results are reported for the case of infrastructure investment in the Spanish regions.

Techniques for analytical testing of unconventional samples.

Forensic scientists have long detected the presence of drugs and their metabolites in biological materials using body fluids such as urine, blood and/or other biological liquids or tissues. For doping analysis, only urine has so far been collected. In recent years, remarkable advances in sensitive analytical techniques have encouraged the analysis of drugs in unconventional biological samples such as hair, saliva and sweat. These samples are easily collected, although drug levels are often lower than the corresponding levels in urine or blood. This chapter reviews recent studies in the detection of doping agents in hair, saliva and sweat. Sampling, analytical procedures and interpretation of the results are discussed in comparison with those obtained from urine and blood samples.

Desarrollo de un sistema de e-Learning para Nintendo DS

El proyecto trata de crear un software que dinámicamente nos proporcione exámenes o pruebas dependiendo de nuestro nivel de conocimientos actual. Estos exámenes se cargarán a través de un fichero XML configurable, lo que nos permitirá poner a prueba nuestros conocimientos en el tema que deseemos. El software se desarrollará en Nintendo DS, para aprovechar las prestaciones que nos ofrece de serie: doble pantalla, pantalla táctil, portabilidad.

Interoperability issues between learning object repositories and metadata harvesters

In this paper we describe an open learning object repository on Statistics based on DSpace which contains true learning objects, that is, exercises, equations, data sets, etc. This repository is part of a large project intended to promote the use of learning object repositories as part of the learning process in virtual learning environments. This involves the creation of a new user interface that provides users with additional services such as resource rating, commenting and so. Both aspects make traditional metadata schemes such as Dublin Core to be inadequate, as there are resources with no title or author, for instance, as those fields are not used by learners to browse and search for learning resources in the repository. Therefore, exporting OAI-PMH compliant records using OAI-DC is not possible, thus limiting the visibility of the learning objects in the repository outside the institution. We propose an architecture based on ontologies and the use of extended metadata records for both storing and refactoring such descriptions.

The Role of model uncertainty and learning in the U.S. postwar policy response to oil prices

This paper studies optimal monetary policy in a framework that explicitly accounts for policymakers' uncertainty about the channels of transmission of oil prices into the economy. More specfically, I examine the robust response to the real price of oil that US monetary authorities would have been recommended to implement in the period 1970 2009; had they used the approach proposed by Cogley and Sargent (2005b) to incorporate model uncertainty and learning into policy decisions. In this context, I investigate the extent to which regulator' changing beliefs over different models of the economy play a role in the policy selection process. The main conclusion of this work is that, in the specific environment under analysis, one of the underlying models dominates the optimal interest rate response to oil prices. This result persists even when alternative assumptions on the model's priors change the pattern of the relative posterior probabilities, and can thus be attributed to the presence of model uncertainty itself.