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Hsinchu, Taiwan

Huang C.-C.,ISA | Yang P.-C.,III | Chen K.-J.,Academia Sinica, Taiwan | Chang J.S.,CS
NAACL HLT 2012 - 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference | Year: 2012

We introduce a method for learning to predict text completion given a source text and partial translation. In our approach, predictions are offered aimed at alleviating users' burden on lexical and grammar choices, and improving productivity. The method involves learning syntax-based phraseology and translation equivalents. At run-time, the source and its translation prefix are sliced into ngrams to generate and rank completion candidates, which are then displayed to users. We present a prototype writing assistant, TransAhead, that applies the method to computer-assisted translation and language learning. The preliminary results show that the method has great potentials in CAT and CALL with significant improvement in translation quality across users. © 2012 Association for Computational Linguistics. Source

Picart G.,French National Center for Space Studies | Dosogne T.,CS | Smith M.,BART
13th International Conference on Space Operations, SpaceOps 2014 | Year: 2014

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. All the operations carried out on a spacecraft need to be logged somewhere. Such daily activity is essential to get a macroscopic view of the maintenance activities, especially for monthly or yearly reporting. Up to now the list of everyday activities carried out on CNES Earth observation spacecraft of the French Space Agency (CNES) has been filled manually by each Flight Control Team (FCT) in an Excel® file. However the accuracy of the information depends a lot on the proficiency of the FCT members involved in the process. Obviously there is always the risk of human error. For example, forgetting to log an operation or typing errors can and sometimes do lead to inaccurate overall reporting. Hence the need to improve the general process of data gathering. This paper deals with a new foolproof method which has been tested successfully on the Pleiades spacecraft (PHR1A and PHR1B) in order to automatically compile spacecraft activities from telecommand logbooks which are huge XML files (around 10 million lines per year). This kind of issue falls typically within a data mining approach and algorithms have been implemented to filter the various data inside the logbooks and extract the relevant information out of them. Output files are simple ASCII tab separated files which list the main operations performed during the period under consideration. These files may be either edited with Excel® (to benefit from its world renowned filtering capabilities) or plotted as a chronogram with PrestoPlot® which is a COTS already used in CNES for displaying telemetry parameters. Such chronograms are particularly useful for people in charge of spacecraft maintenance because they help them easily establish a potential link between spacecraft operations and telemetry behavior, especially for trend analysis. Activity files are generated every month, but they can be generated for a shorter period. The monthly files are also concatenated to build overall activity files which gather information from the beginning of life of each spacecraft. This process has been tested and tuned during PHR1B in orbit test phase beginning of 2013 and then successfully retrofitted to PHR1A. An activity data base is also a great help for the FCT because any operation performed on a spacecraft can be found again very easily. Finally this innovative system is adaptive and may be applied very easily to any existing spacecraft program. Source

Hoffman J.,University of California at Berkeley | Guadarrama S.,University of California at Berkeley | Tzeng E.,University of California at Berkeley | Hu R.,Tsinghua University | And 4 more authors.
Advances in Neural Information Processing Systems | Year: 2014

A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) have emerged as clear winners on object classification benchmarks, in part due to training with 1.2M+ labeled classification images. Unfortunately, only a small fraction of those labels are available for the detection task. It is much cheaper and easier to collect large quantities of image-level labels from search engines than it is to collect detection data and label it with precise bounding boxes. In this paper, we propose Large Scale Detection through Adaptation (LSDA), an algorithm which learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors. Our method has the potential to enable detection for the tens of thousands of categories that lack bounding box annotations, yet have plenty of classification data. Evaluation on the ImageNet LSVRC-2013 detection challenge demonstrates the efficacy of our approach. This algorithm enables us to produce a >7.6K detector by using available classification data from leaf nodes in the ImageNet tree. We additionally demonstrate how to modify our architecture to produce a fast detector (running at 2fps for the 7.6K detector). Models and software are available at lsda.berkeleyvision.org. Source

Vazquez Fernandez M.E.,CS Arturo Eyries Area Oeste | Eiros Bouza J.M.,Area de Microbiologia | Vazquez Fernandez M.J.,CS | Martin Pelayo F.,University of Valladolid | And 2 more authors.
Pediatria de Atencion Primaria | Year: 2011

Introduction: the economic crisis and its consequences are posing difficulties for the sustainability of providing a pharmaceutical and health care system. Antibiotics are the therapeutic groups with the highest consumption in children. Objective: description and analysis of the cost of prescribed antibiotics in the paediatric population of Castilla and Leon in the last decade, in the community setting. Methods: the databases of antimicrobials' expenditure financed by the NHS come from Concylia. Consume indicators: Euro (€)/DDD and €/1000 inhabitants/day. Results: there has been an antibiotic cost of € 15,750,829.26. Penicillins associated with beta lactamase inhibitors (amoxicillin with clavulanic acid) are responsible for 32.62% of spending, followed by cephalosporins and macrolides. In the last place are the broad-spectrum penicillins (amoxicillin), although they are the most prescribed antibiotics. The annual evolution reflects a sharp drop in the price of most antibiotics mainly during the last five years. Disaggregated analysis of spending by Health Areas also shows important differences. Conclusions: variations in spending are driven primarily by the frequency of use and changes in retail prices. Amoxicillin clavulanate is the antibiotic responsible for the highest expense. Macrolides are the most expensive antibiotics and amoxicillin the lowest ones. There is a downward trend in spending in most antibiotics along the decade. Source

Sengissen A.,Airbus | Giret J.-C.,CS | Coreixas C.,European Center for Research and Advanced Training in Scientific Computation | Boussuge J.-F.,European Center for Research and Advanced Training in Scientific Computation
21st AIAA/CEAS Aeroacoustics Conference | Year: 2015

This paper aims at investigating and analyzing numerical simulations of landing-gear configurations of increasing complexity using the Lattice-Boltzmann solver "LaBS". The LAGOON (LAnding-Gear nOise database for CAA validatiON) project, supported by Air- bus,1, 2 provides an accurate experimental database on simplified landing-gear configura- tions perfectly suitable for this purpose. First, an assessment of the numerical approach accuracy is carried out on LAGOON1 configuration by comparing both aerodynamic and near-field acoustic results with the LAGOON database disclosed in the frame of the NASA BANC workshop. Then, further investigations are focused on the inuence of mesh refinement, subgrid scale model and wall law parameters. Finally, the best practices obtained are applied on LAGOON2 & 3 configurations and allow to capture the impact of some geometrical components added onto LAGOON1 baseline. © 2015, American Institute of Aeronautics and Astronautics Inc, AIAA. All Rights Reserved. Source

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