Predicting and Interpreting Bipartite Temporal Networks

A network, is defined as a collection of nodes interconnected by links. When this topology changes through time, we call it a temporal network. A specific class of networks, with only two types of nodes with no connections between one kind, is the bipartite network. An example is a telecommunications network, where nodes represent...

Introduction to structure learning for gaussian and pair copula bayesian networks

Due to technological breakthrough in recent decades and the rapid increase in the availability of multidimensional data, data science has become one of the most important areas of research. Within this field, modeling dependence of random variables is gaining great interest. To cope with this task, the use of graphical models is often advocated....

A systems approach for monitoring anesthesia

General anesthesia (GA) is an important medical procedure that induces unconsciousness to patients during surgery. Consciousness is a salient feature of the brain, whose neurophysiological features are difficult to be distinguished from unconsciousness. Though it can be defined as an event arising due to interactions in the nervous system, it...

Detection of Tip-Sample Interaction in Atomic Force Microscopy: Improving the Image Resolution

Currently used imaging methods in Atomic Force Microscopy (AFM) including the use of a Lock-In Amplifier or a Phase-Locked Loop, are suboptimal. In this report, the image resolution in AFM is improved by detecting the tip-sample interaction using complete measurements of the input of the cantilever and its measured deflection. Two methods are...

Stock market prediction using social media data and finding the covariance of the LASSO

Stock market prediction has been a research topic for decades; recently, efforts to increase the accuracy by including data from social media like Google and Twitter received a lot of attention. Social media can be regarded as indicator for sentiments and sentiments are known to influence the stock market. Current models lack interpretation; it...

Social Media Aided Stock Market Predictions by Sparsity Induced Regression

Prediction of the stock market has been a research topic for decades. Recently, data from social media like Google and Twitter are included in prediction models. This data serves as an indicator of sentiments that are potentially useful for prediction. Interpretation of current prediction methods is cumbersome. Beforehand it is not known which...

Underwater ultra-wideband fingerprinting-based localization

In this work a new location fingerprinting-based localization algorithm is proposed for an underwater medium by utilizing ultra-wideband (UWB) signals. In many conventional underwater systems, localization is accomplished by utilizing acoustic waves. On the other hand, electromagnetic waves haven't been employed for underwater localization due...

Efficient approximate leave-one-out cross-validation for ridge and lasso

In this thesis an approximation method is discussed that provides similar results to leave-one-out cross-validation but is less time-consuming. By means of this approximation method, estimating the optimal values of ridge and lasso parameters will take less time and carrying out (an approximated version of) double LOOCV will become practically...

Sparse multi-class prediction based on the Group Lasso in multinomial logistic regression

Continuous variable selection using shrinkage procedures have recently been considered as favorable models in a wide range of scientific research; in particular biomedical research. In some cases, it is desirable to select as few predictors as possible, to increase the interpretability of the attained prediction rule. One frequently used...