Nonuniform, non-Cartesian k-space trajectories enable fast scanning with reduced motion and flow artifacts. In such cases, the data are usually convolved with a kernel and resampled onto a Cartesian grid before reconstruction. For trajectories such as undersampled variable-density spirals, the mainlobe width of the kernel for undersampled high spatial frequencies has to be larger to limit the a...

We propose two simple evaluation methods for time varying density forecasts of continuous higher dimensional random variables. Both methods are based on the probability integral transformation for unidimensional forecasts. The first method tests multinormal densities and relies on the rotation of the coordinate system. The advantage of the second method is not only its applicability to any cont...

The challenges of network data display are among the most longstanding in visualization. Current approaches typically rely on some form of network abstraction to reduce network density, or “clutter”, to reveal structural elements salient for the task at hand. Any reduction of complexity must consider the competing goals of providing visual representations that reveal overall, global relations, ...

A new method of kernel density estimation with a varying adaptive window size is proposed. It is based on the socalled intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations. 2002 Elsevier Science B.V. All rights reserved.

Purpose: As in-line process analytics are becoming more widely accepted, it is important to illustrate that a measurement of interest will provide the expected outcome if that control point has reached a desired level. This methodology is especially true for novel applications. The purpose of this work was to investigate the role of densification during high shear granulation and the resulting ...

This paper analyzes the large sample of a varying kernel density estimator of the marginal density of a nonnegative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator.

Many exist studies always use Markov decision processes (MDPs) in modeling optimal route choice in stochastic, time-varying networks. However, taking many variable traffic data and transforming them into optimal route decision is a computational challenge by employing MDPs in real transportation networks. In this paper we model finite horizon MDPs using directed hypergraphs. It is shown that th...

In this thesis we contribute to the understanding of the pivotal role of the temporal dimension in networked social systems, previously neglected and now uncovered by the data revolution recently blossomed in this field. To this aim, we first introduce the time-varying networks formalism and analyze some empirical data of social dynamics, extensively used in the rest of the thesis. We discuss t...

| The random-code capacity region of the multiple-access arbitrarily varying channel subject to both state and input constraints is determined. Consideration of a simple erasure channel shows that the capacity region is not convex in general.

The spread of an infection on a real-world social network is determined by the interplay of two processes – the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but d...