In this paper we present PIVAJ, an Indexing and Valorization Platform for Journal Archives from the French Upper Normandy and EU funded PlaIR project. This web platform proposes sophisticated searching and browsing facilities over the transcription of the text content. The search engine is able to list the relevant articles from a textual query. The platform also integrates a collaborative OCR correction tool which was ranked as one of the most efficient in an evaluation campaign carried out by the National Library of the Netherlands during the European project IMPACT. The first real scale experiment was made to provide access to digitizations of the "Journal de Rouen" collection. Consultation statistics of the web platform and comments of users illustrate the interest for PIVAJ.

We present a complete method for article segmentation in old newspapers, which deals with complex layouts analysis of degraded documents. The designed workflow can process large amounts of documents and generates digital objects in METS/ALTO format in order to facilitate the indexing and the browsing of information in digital libraries. The analysis of the document image is performed by a two stages scheme. Pixels are labeled in a first stage with a Conditional Random Field model in order to intent to label the areas of interest with a low logical level. Then this first logical representation of the document content is analyzed in a second stage to get a higher logical representation including article segmentation and reading order. This top-level structural analysis relies on the generation of an article separation grid applied recursively on the document image, allowing analyzing any type of Manhattan page layout, even for complex structures with multiple columns and overlapping entities. This method which benefits from both a local analysis using a probabilistic model trained using machine learning procedures, and a more global structural analysis using recursive rules, is evaluated on a dataset of daily local press document images covering several time periods and different page layouts, to prove its effectiveness.

Document image binarization is still an active research area as shows the number of binarization techniques proposed since many decades. The binarization of degraded document images is still difficult and encourages the development of new algorithms. For the last decade, discrete conditional random fields have been successfully used for many domains such as automatic language analysis. In this paper, we propose a CRF based framework to explore the combination capabilities of this model by combining discrete outputs from several well known binarization algorithms. The framework uses two 1D CRF models on the horizontal and the vertical directions that are coupled for each pixel by the product of the marginal probabilities computed from the both models. Experiments are made on two datasets from the Document Image Binarization Contest (DIBCO) 2009 and 2011 and show best performances than most of the methods presented at DIBCO 2011.

Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these new images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to detect a pathological state in these images. An original approach that combines a texture-based characterization of the images and uses a boosted cascade of classifiers to detect a pathological condition is presented in this paper. We propose and compare two state-of-the-art texture descriptors: cooccurence matrices and local binary patterns (LBP). Recognition rates with LBP reach up to 86.3% and 95.1% for the non-smoking and smoking groups, respectively. Even though tests on extended databases are needed, these preliminary results are encouraging for this challenging task of image classification.

Newspapers are documents made of news item and informative articles. They are not meant to be red iteratively: the reader can pick his items in any order he fancies. Ignoring this structural property, most digitized newspaper archives only offer access by issue or at best by page to their content. We have built a digitization workflow that automatically extracts newspaper articles from images, which allows indexing and retrieval of information at the article level. Our back-end system extracts the logical structure of the page to produce the informative units: the articles. Each image is labelled at the pixel level, through a machine learning based method, then the page logical structure is constructed up from there by the detection of structuring entities such as horizontal and vertical separators, titles and text lines. This logical structure is stored in a METS wrapper associated to the ALTO file produced by the system including the OCRed text. Our front-end system provides a web high definition visualisation of images, textual indexing and retrieval facilities, searching and reading at the article level. Articles transcriptions can be collaboratively corrected, which as a consequence allows for better indexing. We are currently testing our system on the archives of the Journal de Rouen, one of France eldest local newspaper. These 250 years of publication amount to 300 000 pages of very variable image quality and layout complexity. Test year 1808 can be consulted at plair.univ-rouen.fr.

We introduce quantization feature functions to represent continuous or large range discrete data into the symbolic CRF data representation. We show that doing this convertion in a simple way allows the CRF to automaticaly select discriminative features to achieve best performance. This system is evaluated on a segmentation task of degraded newspapers archives. The results obtained show the ability of the CRF model to deal with numerical features similarly as for symbolic representation thanks to the use of quantization feature functions. The segmentation task is achieved by the definition of a horizontal CRF model dedicated to pixel labelling.

We report the results of laboratory experiments examining the adjustment of diapycnal mixing events, and their effect on lateral dispersion in the ocean's stratified interior. Vertically oscillated mesh grids were used to produce localized mixed patches that adjusted to form stable, geostrophically balanced eddies within a rotating, linearly stratified fluid. The effective lateral dispersion due to many such events showed a linear dependence on the frequency of mixing events, and higher order dependence on Burger number, consistent with previous theoretical and numerical results. Results also suggest, however, a revised form of the effective lateral diffusivity parameterization to extend previous results to a more general non-geostrophic form.

The first small waterplane area twin hull (SWATH) design ship in the U.S. academic research vessel fleet, the R/V Kilo Moana (Figure 1), entered service in September 2002. The ship, whose home port is in Honolulu, has been operating with diverse groups of oceanographers on board. Before the ship became operational, the University National Oceanographic Laboratory System Fleet Improvement Committee (UNOLS‐ FIC) decided that it was important to gather fair and representative impressions of the vessel's performance from the first scientists on board. Science mission requirements developed over the past year by UNOLS have all indicated that sea‐keeping is a very high priority: oceano graphers wanted to work comfortably at higher sea states. Just about the only way to achieve this is by using the SWATH design rather than the mono‐hull.

James O'Donnell, Dan Codiga, Christopher Edwards, David Ullman, David Hebert, Joseph Rice, Edward Levine, Petra Steggman, Ivar Babb, Phone: (860) 405-9208 Fax: (860) 405-9153 email: james.odonnell@uconn.edu Dept. of Marine Sciences, University of Connecticut, 1084 Shennecossett Road,Groton, CT 06340 Department of Ocean Sciences, University of California, Santa Cruz, CA Graduate School of Oceanography, University of Rhode Island, Narragansett, RI SPAWAR Systems Center, San Diego NUWC, Newport RI 02841 National Undersea Research Center, University of Connecticut, Groton, CT

BACKGROUND:Boy Scouts are an important channel to complement school-based programs to enable boys to eat more fruit, 100% juice, and vegetables (FJV) for chronic disease prevention. The "5 a Day Achievement Badge" program was presented on a pilot study basis to African-American Boy Scout troops in Houston.METHODS:Troops were the unit of recruitment and random assignment to treatment and control groups. The badge program was presented in Fall 1997 by trained dietitians and included activities to increase availability and accessibility of fruit and vegetables at scouts' homes, increase preferences for vegetables, and train in the preparation of FaSST (fast, simple, safe, and tasty) recipes. Weekly comic books demonstrated and reinforced what scouts were expected to do at home. A weekly newsletter with recipes was sent to parents. The program was revised and presented to the control group in Winter 1998. Two 24-h recalls were the primary assessment tools. Telephone interviews were conducted with parents.RESULTS:The intervention resulted in a 0.8 FJV serving difference (post values of treatment versus control groups with pre value covaried).CONCLUSIONS:The changes obtained suggest that the intervention was effective in promoting dietary change.

Family, peers and other environmental factors are likely to influence children's dietary behavior but few measures of these phenomena exist. Questionnaires to measure family and peer influences on children's fruit, juice and vegetable (FJV) consumption were developed and pilot tested with an ethnically diverse group of Grade 4-6 children. Principal components analyses revealed subscales with acceptable internal consistencies that measured parent and peer FJV modeling, normative beliefs, normative expectations, perceived peer FJV norms, supportive and permissive parenting practices, food rules, permissive eating, and child food preparation. Internal consistencies were adequate to high, but test-re-test correlations often were low. Children also completed questionnaires on FJV availability and accessibility in the home, and food records for 2 days in the classroom. Parental modeling, peer normative beliefs and FV availability were significantly correlated with FJV consumption. Further research with these scales is warranted.

Gimme 5 (Georgia) was a school-based nutrition education effectiveness trial to help fourth- and fifth-grade students eat more fruit, 100% juice, and vegetables (FJV). Process evaluation assessed fidelity of implementation, reach, and use of intervention materials and environmental mediators: teacher training, curriculum delivery, participation in family activities, attendance at evening point-of-purchase grocery store activities, and availability and accessibility of FJV at home. Approximately half of the curriculum activities were implemented in fourth and fifth grades. The lowest proportion completed were those most pertinent to behavior change. Eighty-seven percent of parents reported participating in homework activities with their fourth grader, 66% with fifth graders. Sixty-five percent of parents reported viewing a video with their child in both grades. Ten percent attended evening point-of-purchase grocery store activities. The low level of implementation and modest level of participation in family activities suggest that higher levels of behavior change may have occurred if exposure to the intervention had been higher.