Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we de- velop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional mul- tilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrec- tional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimiza- tion search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow com- putation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Systematic study of the temperature dependence of the saturation magnetization in Fe, Fe-Ni and Co-based amorphous alloys

Magnetization versus temperature in the temperature interval 2-200 K was measured for amorphous alloys of three different compositions: Fe 81.5B14.5Si4, Fe40Ni38 Mo4B18, and Co70Fe5Ni 2Mo3B5Si15. The measurements were performed by means of a SQUID (superconducting quantum interference device) magnetometer. The aim was to extract information about the different mechanisms contributing to thermal demagnetization. A powerful data analysis technique based on successive minimization procedures has demonstrated that Stoner excitations of the strong ferromagnetic type play a significant role in the Fe-Ni alloy studied. The Fe-rich and Co-rich alloys do not show a measurable contribution from single-particle excitations.

Efficient self-synchronised blind audio watermarking system based on time domain and FFT amplitude modification

Many audio watermarking schemes divide the audio signal into several blocks such that part of the watermark is embedded into each of them. One of the key issues in these block-oriented watermarking schemes is to preserve the synchronisation, i.e. to recover the exact position of each block in the mark recovery process. In this paper, a novel time domain synchronisation technique is presented together with a new blind watermarking scheme which works in the Discrete Fourier Transform (DFT or FFT) domain. The combined scheme provides excellent imperceptibility results whilst achieving robustness against typical attacks. Furthermore, the execution of the scheme is fast enough to be used in real-time applications. The excellent transparency of the embedding algorithm makes it particularly useful for professional applications, such as the embedding of monitoring information in broadcast signals. The scheme is also compared with some recent results of the literature.

A Petri Nets-based Scheduling Methodology forMultipurpose Batch Plants.

This article presents an optimization methodology of batch production processes assembled by shared resources which rely on a mapping of state-events into time-events allowing in this way the straightforward use of a well consolidated scheduling policies developed for manufacturing systems. A technique to generate the timed Petri net representation from a continuous dynamic representation (Differential-Algebraic Equations systems (DAEs)) of the production system is presented together with the main characteristics of a Petri nets-based tool implemented for optimization purposes. This paper describes also how the implemented tool generates the coverability tree and how it can be pruned by a general purpose heuristic. An example of a distillation process with two shared batch resources is used to illustrate the optimization methodology proposed.

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Background: Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements. Results: We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer- approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shock Conclusion: Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

Short communication.Wood identification based on their common name and their transversal surface anatomy. Application to the batch from the expedition of Ruiz and Pavon

Aim of study: To identify species of wood samples based on common names and anatomical analyses of their transversal surfaces (without microscopic preparations). Area of study: Spain and South America Material and methods: The test was carried out on a batch of 15 lumber samples deposited in the Royal Botanical Garden in Madrid, from the expedition by Ruiz and Pavon (1777-1811). The first stage of the methodology is to search and to make a critical analysis of the databases which list common nomenclature along with scientific nomenclature. A geographic filter was then applied to the information resulting from the samples with a more restricted distribution. Finally an anatomical verification was carried out with a pocket microscope with a magnification of x40, equipped with a 50 micrometers resolution scale. Main results: The identification of the wood based exclusively on the common name is not useful due to the high number of alternative possibilities (14 for “naranjo”, 10 for “ébano”, etc.). The common name of one of the samples (“huachapelí mulato”) enabled the geographic origin of the samples to be accurately located to the shipyard area in Guayaquil (Ecuador). Given that Ruiz y Pavon did not travel to Ecuador, the specimens must have been obtained by Tafalla. It was possible to determine correctly 67% of the lumber samples from the batch. In 17% of the cases the methodology did not provide a reliable identification. Research highlights: It was possible to determine correctly 67% of the lumber samples from the batch and their geographic provenance. The identification of the wood based exclusively on the common name is not useful.

Social network extraction and analysis based on multimodal dyadic interaction

Social interactions are a very important component in people"s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times" Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links" weights are a measure of the"influence" a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regression

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Search and Discovery : OER's Open Loop

Open educational resources (OER) promise increased access, participation, quality, and relevance, in addition to cost reduction. These seemingly fantastic promises are based on the supposition that educators and learners will discover existing resources, improve them, and share the results, resulting in a virtuous cycle of improvement and re-use. By anecdotal metrics, existing web scale search is not working for OER. This situation impairs the cycle underlying the promise of OER, endangering long term growth and sustainability. While the scope of the problem is vast, targeted improvements in areas of curation, indexing, and data exchange can improve the situation, and create opportunities for further scale. I explore the way the system is currently inadequate, discuss areas for targeted improvement, and describe a prototype system built to test these ideas. I conclude with suggestions for further exploration and development.

Population-based incidence of myeloid malignancies: fifteen years of epidemiological data in the province of Girona, Spain

Myeloid malignancies (MMs) are a heterogeneous group of hematologic malignancies presenting different incidence, prognosis and survival.1–3 Changing classifications (FAB 1994, WHO 2001 and WHO 2008) and few available epidemiological data complicate incidence comparisons.4,5 Taking this into account, the aims of the present study were: a) to calculate the incidence rates and trends of MMs in the Province of Girona, northeastern Spain, between 1994 and 2008 according to the WHO 2001 classification; and b) to predict the number of MMs cases in Spain during 2013. Data were extracted from the population-based Girona Cancer Registry (GCR) located in the north-east of Catalonia, Spain, and covering a population of 731,864 inhabitants (2008 census). Cases were registered according to the rules of the European Network for Cancer Registries and the Manual for Coding and Reporting Haematological Malignancies (HAEMACARE project). To ensure the complete coverage of MMs in the GCR, and especially myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS), a retrospective search was performed. The ICD-O-2 (1990) codes were converted into their corresponding ICD-O-3 (2000) codes, including MDS, polycythemia vera (PV) and essential thrombocythemia (ET) as malignant diseases. Results of crude rate (CR) and European standardized incidence rate (ASRE) were expressed per 100,000 inhabitants/year

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Secure logarithmic audio watermarking scheme based on the human auditory system

Peer-reviewed