Chaotic activity during iron-induced `epileptiform'' discharge inrat hippocampal slices

Low-dimensional chaotic dynamics have been suggested in the rat hippocampal slice during iron-induced epileptiform activity. The dimensionality of this chaotic activity has been found to be similar in slices bathed in the same ionic extracellular medium. Some slices also displayed a drop in dimensionality prior to the onset of seizure-like activity. We suggest that techniques of nonlinear dynamical analysis are a useful reverse-engineering tool for studying the in vitro brain slice. We further conclude that neuronal circuits capable of displaying chaotic activity could exist at the level of the in vitro brain slice.     

A computational literature review of football performance analysis through probabilistic topic modeling

This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.

     

Super-resolution of images based on local correlations

An adaptive two-step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.     

Learning from Examples with Information Theoretic Criteria

This paper discusses a framework for learning based on information theoretic criteria. A novel algorithm based on Renyi's quadratic entropy is used to train, directly from a data set, linear or nonlinear mappers for entropy maximization or minimization. We provide an intriguing analogy between the computation and an information potential measuring the interactions among the data samples. We also propose two approximations to the Kulback-Leibler divergence based on quadratic distances (Cauchy-Schwartz inequality and Euclidean distance). These distances can still be computed using the information potential. We test the newly proposed distances in blind source separation (unsupervised learning) and in feature extraction for classification (supervised learning). In blind source separation our algorithm is capable of separating instantaneously mixed sources, and for classification the performance of our classifier is comparable to the support vector machines (SVMs).     

Unsuspected carcinoma of the gall bladder: case report of trocar-site metastasis following laparoscopic cholecystectomy

Cancer of the gall bladder is a rare malignant neoplasm with an unfavourable prognosis. Laparoscopic surgery has brought about the emergence of possible neoplastic diffusion along trocar tracts in cases where unrecognized carcinoma of the gall bladder is present. The authors present a case of neoplastic abdominal diffusion discovered 4 months after laparoscopic cholecystectomy in which histologic examination of the surgical specimen revealed the presence of unrecognized carcinoma of the gall bladder.     

Error whitening criterion for adaptive filtering: theory and algorithms

Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major drawback of the MSE criterion in linear filter adaptation is the parameter bias in the Wiener solution when the input data are contaminated with noise. We propose and analyze a new augmented MSE criterion called the Error Whitening Criterion (EWC). EWC is able to eliminate this bias when the noise is white. We will determine the analytical solution of the EWC, discuss some interesting properties, and develop stochastic gradient and other fast algorithms to calculate the EWC solution in an online fashion. The stochastic algorithms are locally computable and have structures and complexities similar to their MSE-based counterparts (LMS and NLMS). Convergence of the stochastic gradient algorithm is established with mild assumptions, and upper bounds on the step sizes are deduced for guaranteed convergence. We will briefly discuss an RLS-like Recursive Error Whitening (REW) algorithm and a minor components analysis (MCA) based EWC-total least squares (TLS) algorithm and further draw parallels between the REW algorithm and the Instrumental Variables (IV) method for system identification. Finally, we will demonstrate the noise-rejection capability of the EWC by comparing the performance with MSE criterion and TLS.     

Incremental backpropagation learning networks

How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains.     

On the stabilization parameter in the subgrid scale approximation of scalar convection–diffusion–reaction equations on distorted meshes

In this paper we revisit the definition of the stabilization parameter in the finite element approximation of the convection–diffusion–reaction equation. The starting point is the decomposition of the unknown into its finite element component and a subgrid scale that needs to be approximated. In order to incorporate the distortion of the mesh into this approximation, we transform the equation for the subgrid scale within each element to the shape-regular reference domain. The expression for the subgrid scale arises from an approximate Fourier analysis and the identification of the wave number direction where instabilities are most likely to occur. The final outcome is an expression for the stabilization parameter that accounts for anisotropy and the dominance of either convection or reaction terms in the equation.     

From linear adaptive filtering to nonlinear information processing - The design and analysis of information processing systems

Recent advances in computing capabilities and the interest in new challenging signal processing problems that cannot be successfully solved using traditional techniques have sparked an interest in information-theoretic signal processing techniques. Adaptive nonlinear filters that process signals based on their information content have become a major focus of interest. The design and analysis of such nonlinear information processing systems is demonstrated in this paper. Theoretical background on necessary information theoretic concepts are provided, nonparametric sample estimators for these quantities are derived and discussed, the use of these estimators for various statistical signal processing problems have been illustrated. These include data density modeling, system identification, blind source separation, dimensionality reduction, image registration, and data clustering     

Experimental upper bound for the performance of convolutive source separation methods

An important problem in the field of blind source separation (BSS) of real convolutive mixtures is the determination of the role of the demixing filter structure and the criterion/optimization method in limiting separation performance. This issue requires the knowledge of the optimal performance for a given structure, which is unknown for real mixtures. Herein, the authors introduce an experimental upper bound on the separation performance for a class of convolutive blind source separation structures, which can be used to approximate the optimal performance. As opposed to a theoretical upper bound, the experimental upper bound produces an estimate of the optimal separating parameters for each dataset in addition to specifying an upper bound on separation performance. Estimation of the upper bound involves the application of a supervised learning method to the set of observations found by recording the sources one at a time. Using the upper bound, it is demonstrated that structures other than the finite-impulse-response (FIR) structure should be considered for real (convolutive) mixtures, there is still much room for improvement in current convolutive BSS algorithms, and the separation performance of these algorithms is not necessarily limited by local minima.