121 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
Copyright © 2014 Elsevier. NOTICE: this is the authorâs version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts
Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
Abstract not availableH.R. Maier, Z. Kapelan, Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha,
G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic,
D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour,
G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Ree
The effect of di-(-2-ethyl hexyl) phthalate (Dehp) as plasticizer on the thermal and mechanical properties of pvc/pmma blends
A Survey of Bayesian Statistical Approaches for Big Data
The modern era is characterised as an era of information or Big Data. This
has motivated a huge literature on new methods for extracting information and
insights from these data. A natural question is how these approaches differ
from those that were available prior to the advent of Big Data. We present a
review of published studies that present Bayesian statistical approaches
specifically for Big Data and discuss the reported and perceived benefits of
these approaches. We conclude by addressing the question of whether focusing
only on improving computational algorithms and infrastructure will be enough to
face the challenges of Big Data
V/Ti-oxide Catalysts in Toluene Partial Oxidation: What one can learn from transient kinetics coupled with spectroscopic methods?
Transient Kinetics for Determination of Active Sites on V/Ti-oxide catalysts in Toluene Partial Oxidation
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