Field-Assisted and Thermionic Contributions to Conductance in SnO2 Thick-Films

A deep analysis of conductance in nanostructured SnO2 thick films has been performed. A model for field-assisted thermionic barrier crossing is being proposed to explain the film conductivity. Themodel has been applied to explain the behavior of resistance in vacuum of two sets of nanostructured thick-films with grains having two well-distinct characteristic radii (R = 25nm and R = 125 nm). In the first case the grain radius is shorter than the depletion region width, a limit at which overlapping of barriers takes place, and in the second case it is longer. The behavior of resistance in the presence of dry air has been explained through the mechanism of barrier modulation through gas chemisorption.Fil: Malagú, C.. Universita Di Ferrara; ItaliaFil: Carotta, M. Cristina. Universita Di Ferrara; ItaliaFil: Martinelli, Giuliano. Universita Di Ferrara; ItaliaFil: Ponce, Miguel Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Castro, Miriam Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Aldao, Celso Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; Argentin

Information Hiding Using Convolutional Encoding

We consider two functions f1(r) and f2(r), for r 2 Rn and the problem of ‘Diffusing’ these functions together, followed by the application of an encryption process we call ‘Stochastic Diffusion’ and then hiding the output of this process in to one or other of the same functions. The coupling of these two processes (i.e., data diffusion and stochastic diffusion) is considered using a form of conditioning that generates a wellposed and data consistent inverse solution for the purpose of decrypting the output. After presenting the basic encryption method and (encrypted) information hiding model, coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management, for example

Phase-only Digital Encryption using a Three-pass Protocol

Abstract—This paper considers an application of phase-only digital encryption to the three-pass protocol leading to a new ‘nokey- exchange algorithm’. After providing a study on the theoretical background to the method, an algorithm is presented on a step-by-step basis together with three examples of cryptanalysis. A prototype MATLAB function is provided for validation of the approach and for further development by interested readers

Cleavage of the C-C bond in the ethanol oxidation reaction on platinum. Insight from experiments and calculations

"This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry C, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/acs.jpcc.6b03117, see http://pubs.acs.org/page/policy/articlesonrequest/index.html".[EN] Using a combination of experimental and computational methods, mainly FTIR and DFT calculations, new insights are provided here in order to better understand the cleavage of the C–C bond taking place during the complete oxidation of ethanol on platinum stepped surfaces. First, new experimental results pointing out that platinum stepped surfaces having (111) terraces promote the C–C bond breaking are presented. Second, it is computationally shown that the special adsorption properties of the atoms in the step are able to promote the C–C scission, provided that no other adsorbed species are present on the step, which is in agreement with the experimental results. In comparison with the (111) terrace, the cleavage of the C–C bond on the step has a significantly lower activation energy, which would provide an explanation for the observed experimental results. Finally, reactivity differences under acidic and alkaline conditions are discussed using the new experimental and theoretical evidence.This work has been financially supported by the MINECO (Spain) (project CTQ2013-44083-P) and Generalitat Valenciana (project PROMETEOII/2014/013).Ferre Vilaplana, A.; Buso-Rogero, C.; Feliu, JM.; Herrero, E. (2016). Cleavage of the C-C bond in the ethanol oxidation reaction on platinum. Insight from experiments and calculations. Journal of Physical Chemistry C. 120(21):11590-11597. https://doi.org/10.1021/acs.jpcc.6b03117S11590115971202

Increasing social complexity, climate change, and why societies might fail to cope

The PEOPLE 3000 working group focuses on integrating archaeological and paleoecological case studies with mathematical modeling. We seek to understand how coevolving human societies and ecosystems can successfully cope with the interrelated forces of population growth, increasing social complexity and climate change, and the diversity of trajectories of reorganization that social-ecological systems follow.Fil: Byers, David A.. State University of Utah; Estados UnidosFil: Lima, M.. State University of Utah; Estados UnidosFil: Gil, Adolfo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina. Universidad Tecnologica Nacional. Facultad Regional San Rafael; ArgentinaFil: Gayo, E.. Pontificia Universidad Católica de Chile; ChileFil: Latorre, C.. Pontificia Universidad Católica de Chile; ChileFil: Robinson, Erick. University Of Wyoming; Estados UnidosFil: Villalba, R.. Universidad Tecnologica Nacional. Facultad Regional San Rafael; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin

A Bayesian methodology for estimating uncertainty of decisions in safety-critical systems

Published as chapter in Frontiers in Artificial Intelligence and Applications. Volume 149, IOS Press Book, 2006. Integrated Intelligent Systems for Engineering Design. Edited by Xuan F. Zha, R.J. Howlett. ISBN 978-1-58603-675-1, pp. 82-96. This version deposited in arxiv.orghttp://arxiv.org/abs/1012.0322Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available. Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes. Unfortunately, a priori information on favored structure of DTs is not always available. For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also suggest a new procedure of selecting a single DT and describe an application scenario. In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.Supported by a grant from the EPSRC under the Critical Systems Program, grant GR/R24357/0

Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique

Comparison of the Bayesian and Randomised Decision Tree Ensembles within an Uncertainty Envelope Technique

Copyright © 2006 Springer. The final publication is available at link.springer.comMultiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models for experts. The required diversity of MCSs exploiting such classification models can be achieved by using two techniques, the Bayesian model averaging and the randomised DT ensemble. Both techniques have revealed promising results when applied to real-world problems. In this paper we experimentally compare the classification uncertainty of the Bayesian model averaging with a restarting strategy and the randomised DT ensemble on a synthetic dataset and some domain problems commonly used in the machine learning community. To make the Bayesian DT averaging feasible, we use a Markov Chain Monte Carlo technique. The classification uncertainty is evaluated within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. Exploring a full posterior distribution, this technique produces realistic estimates which can be easily interpreted in statistical terms. In our experiments we found out that the Bayesian DTs are superior to the randomised DT ensembles within the Uncertainty Envelope technique

Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data

Copyright © 2007 Springer. The final publication is available at link.springer.comBook title: Perception-based Data Mining and Decision Making in Economics and FinanceSummary Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble and the required performance. The interpretability of classification models can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models. The required diversity of the DT ensemble can be achieved by using the Bayesian model averaging all possible DTs. In practice, the Bayesian approach can be implemented on the base of a Markov Chain Monte Carlo (MCMC) technique of random sampling from the posterior distribution. For sampling large DTs, the MCMC method is extended by Reversible Jump technique which allows inducing DTs under given priors. For the case when the prior information on the DT size is unavailable, the sweeping technique defining the prior implicitly reveals a better performance. Within this chapter we explore the classification uncertainty of the Bayesian MCMC techniques on some datasets from the StatLog Repository and real financial data. The classification uncertainty is compared within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. This technique provides realistic estimates of the classification uncertainty which can be easily interpreted in statistical terms with the aim of risk evaluation

A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems

In: Integrated Intelligent Systems for Engineering Design (editors: Zha, X.F. and Howlett, R.J.)Frontiers in Artificial Intelligence and Applications vol. 14