The goal of this article is to develop a pattern recognition algorithm for detecting periods of food intake based on passively collected wearable device motion sensor data, accelerometer and gyroscope in a free-living condition. The main contributions of this work are the following. First, we use recently developed methods in topological data analysis (TDA) to create and extract key features. Second, we employ a novel Bayesian feature selection tool, BVSNLP, to reduce the dimensionality of the problem. Developing this algorithm in an efficient way, we believe it can be deployed on edge devices as well. We demonstrate the performance of our method on a dataset that contains a total of 1000 hours of accelerometer and gyroscope sensor data from 13 subjects.

MOTIVATION: The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. Although this problem has recently been addressed using penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses on the binary regression coefficient vectors. RESULTS: The resulting method, which we call iMOMLogit, provides improved performance in identifying true models and reducing estimation and prediction error in a number of simulation studies. More importantly, its application to several genomic datasets produces predictions that have high accuracy using far fewer explanatory variables than competing methods. We also describe a novel approach for setting prior hyperparameters by examining the total variation distance between the prior distributions on the regression parameters and the distribution of the maximum likelihood estimator under the null distribution. Finally, we describe a computational algorithm that can be used to implement iMOMLogit in ultrahigh-dimensional settings ([Formula: see text]) and provide diagnostics to assess the probability that this algorithm has identified the highest posterior probability model. AVAILABILITY AND IMPLEMENTATION: Software to implement this method can be downloaded at: http://www.stat.tamu.edu/∼amir/code.html CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, a novel quantization based watermarking method is proposed. For blind detection, a set of nonlinear convex functions based on geometric mean are investigated. In order to achieve minimum distortion, the optimum function set is found. The algorithm is implemented on the approximation coefficients of wavelet transform for natural images. In order to make the algorithm more robust and imperceptible, a new transform domain called Point to Point Graph (PPG), which converts a 1-D signal to a 2-D one, has been used. The error probability of the proposed scheme is analytically investigated. Simulation results show that this algorithm has great robustness against common attacks such as AWGN, JPEG and rotation in comparison with recent methods presented so far.