A Generalized Construction of OFDM M-QAM Sequences With Low Peak-to-Average Power Ratio

A construction of $2^{2n}$-QAM sequences is given and an upper bound of the peak-to-mean envelope power ratio (PMEPR) is determined. Some former works can be viewed as special cases of this construction.Comment: published by Advances in Mathematics of Communication

Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models

Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time

Learning from interpreting transitions in explainable deep learning for biometrics

Máster Universitario en Métodos Formales en Ingeniería InformáticaWith the rapid development of machine learning algorithms, it has been applied to almost every aspect of tasks, such as natural language processing, marketing prediction. The usage of machine learning algorithms is also growing in human resources departments like the hiring pipeline. However, typical machine learning algorithms learn from the data collected from society, and therefore the model learned may inherently reflect the current and historical biases, and there are relevant machine learning algorithms that have been shown to make decisions largely influenced by gender or ethnicity. How to reason about the bias of decisions made by machine learning algorithms has attracted more and more attention. Neural structures, such as deep learning ones (the most successful machine learning based on statistical learning) lack the ability of explaining their decisions. The domain depicted in this point is just one example in which explanations are needed. Situations like this are in the origin of explainable AI. It is the domain of interest for this project. The nature of explanations is rather declarative instead of numerical. The hypothesis of this project is that declarative approaches to machine learning could be crucial in explainable A