Inverse problems and data assimilation research is widely spread among various disciplines. Today, there are are several journals particularly dedicated to the inversion of data and to the assimilation of data into dynamical models.
Jan 7, 2014. Call for Papers
Image fusion is the image processing technique through which multiple images from the same or complementary modalities are combined into a single image. Examples include the fusion of X-ray CT and PET images, the combination of Landsat and Panchromatic images, and the creation of spectral optical images. A current research frontier is the integration of multiple modalities to create a hybrid imaging system, such as the PET/CT, PET/MRI, DOT/MRI and DOT/CT systems. This development provides not only an improvement of imaging performance but also the opportunity for image fusion with higher performance. More importantly, it calls for advanced image reconstruction methods that take advantage of the coupled multi-physics underlying the hybrid imaging processes. With synergies among different modalities, image quality can be enhanced by combining the reconstruction algorithms for individual modalities such as with appropriate regularization terms.
This topical issue is focused on but not limited to the following topics: • Design and implementation of hybrid imaging techniques and systems • Image reconstruction methods for hybrid imaging systems • Image fusion methods for hybrid imaging systems
We invite submissions of full papers and short correspondences as related to theoretical analysis, algorithm design, system development, and performance assessment. Papers on feasibility of futuristic imaging modalities are also welcome.
Authors should submit their manuscripts through the online Manuscript Tracking System at http://www.editorialmanager.com/ssta, indicate that they are for this special issue, and choose one of the guest editors to handle their manuscripts. Authors are encouraged to discuss with a guest editor to determine the suitability of their intended contributions.
Guest Editors Ming Jiang, Peking University, China email@example.com Simon Arridge, University College London, UK S.Arridge@cs.ucl.ac.uk Shutao Li, Hunan University, China firstname.lastname@example.org Ge Wang, Rensselaer Polytechnic Institute, USA email@example.com
Submission Guidelines For author guidelines and submission details please see http://www.springer.com/journal/11220
Submission Deadline: March 31 2014.
Invisible in the Storm is the first book to recount the history, personalities, and ideas behind one of the greatest scientific successes of modern times–the use of mathematics in weather prediction. Although humans have tried to forecast weather for millennia, mathematical principles were used in meteorology only after the turn of the twentieth century. From the first proposal for using mathematics to predict weather, to the supercomputers that now process meteorological information gathered from satellites and weather stations, Ian Roulstone and John Norbury narrate the groundbreaking evolution of modern forecasting.
The authors begin with Vilhelm Bjerknes, a Norwegian physicist and meteorologist who in 1904 came up with a method now known as numerical weather prediction. Although his proposed calculations could not be implemented without computers, his early attempts, along with those of Lewis Fry Richardson, marked a turning point in atmospheric science. Roulstone and Norbury describe the discovery of chaos theory's butterfly effect, in which tiny variations in initial conditions produce large variations in the long-term behavior of a system–dashing the hopes of perfect predictability for weather patterns. They explore how weather forecasters today formulate their ideas through state-of-the-art mathematics, taking into account limitations to predictability. Millions of variables–known, unknown, and approximate–as well as billions of calculations, are involved in every forecast, producing informative and fascinating modern computer simulations of the Earth system.
Accessible and timely, Invisible in the Storm explains the crucial role of mathematics in understanding the ever-changing weather.
Ian Roulstone is professor of mathematics at the University of Surrey. John Norbury is a fellow in applied mathematics at Lincoln College, University of Oxford. They are the coeditors of Large-Scale Atmosphere-Ocean Dynamics.
Jan 3, 2014. Reminder: Call for Papers, Advances in Econometrics, Volume 34 Bayesian Model Comparison
Model uncertainty is a key component of statistical data analysis and an integral part in the inferential process. Because theory typically implies a range of possible competing empirical specifications, accounting for model uncertainty is crucial for understanding the processes under investigation, and is a necessary step in interpreting model parameters and performing predictions. Bayesian model comparison and model averaging is an active research area that continues to generate new ideas and innovative approaches.
The aim of this call for papers is to produce a research volume that examines key aspects of modern Bayesian research on model comparison and model averaging. The volume will address important challenges in this area with the goal of improving theoretical foundations and practical implementation. Possible topics include, but are not limited to:
Approaches for evaluating marginal likelihoods and Bayes factors Comparative studies of alternative methods Computational issues in model comparison Variable selection methods, Bayesian LASSO Comparisons of semi-parametric and non-parametric models Approximate methods, asymptotic behavior, information criteria The importance of prior assumptions Forecasting under model uncertainty, model averaging Applications in economics, statistics, and the social sciences Selected papers will appear in Advances in Econometrics, Volume 34. The volume will be edited by Dale Poirier and Ivan Jeliazkov. Please e-mail extended abstracts or complete papers no later than January 10, 2014 to: firstname.lastname@example.org and email@example.com.
A research conference for contributors will be held at the University of California, Irvine, February 22-23, 2014. Review of manuscripts will commence soon after the conference and accepted articles will appear in print in Fall 2014/Winter 2015.
Advances in Econometrics is a research annual whose editorial policy is to publish original articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Authors should be able to provide, upon request, computer programs and data used in their articles. For more information on the Advances in Econometrics series and the contents of previous volumes, see http://faculty.smu.edu/millimet/AiE.html.
Jan 4, 2014. Dear Colleague, As a global publisher, we know that getting research papers published can be a challenge. Nearly a third of visitors to www.elsevier.com/authors are trying to decide to which journal they ought to submit their paper. To help authors find the perfect journal match for their paper, we have developed a new tool, called Journal Finder. This is designed to:
help less experienced researchers to select suitable journals for their papers enable researchers working across multidisciplinary fields to identify appropriate journals highlight journals that offer open access options and provide information on publication speeds and impact factors
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You enter the paper title, abstract and/or keywords and the tool creates a list of Elsevier journals which match the topic of the article. You can then order the results based on their priorities, such as highest Impact Factor or shortest editorial time. The selection contains links to each journals homepage and Elsevier Editorial Submission (EES) page.
As we continue to develop Journal Finder, your feedback is extremely valuable to us. Over the past couple of months we have made a number of improvements. We are now reaching out to you to ask for your feedback. If you have already used Journal Finder, we invite you to check out the new improved version. If you have not yet tried Journal Finder, we would like to take this opportunity to invite you to do so via this link.