Deep convolutional neural networks (CNNs) leverage large-scale training dataset to
produce remarkable performance on various image classification tasks. It, however, is …

This paper proposes methods for unsupervised lexical acquisition for relative spatial
concepts using spoken user utterances. A robot with a flexible spoken dialog system must …

We analyze the diurnal cycle of maximum rainfall from∼ 300 TCs from March 2014 through
February 2017, by cross-referencing the path of tropical cyclones (TCs) and high-resolution …

Understanding when floods occur is fundamental to reducing flood risk, yet depictions of
flood seasonality rarely address two key issues: multiple seasons and the periodicity of …

Recently, spherical data (ie, normalized vectors) modeling has become a promising
research topic in various real-world applications (such as gene expression data analysis …

Preparing for and minimizing the negative impacts of flooding requires knowing when floods
are likely to occur. However, exploratory analysis of seasons may yield spurious …

The approximation properties of finite mixtures of location-scale distributions on Euclidean
space have been well studied. It has been shown that mixtures of location-scale distributions …

Due to advances in technology, there is a presence of directional data in a wide variety of
fields. Often distributions to model directional data are defined on manifold or constrained …

Modern probabilistic learning systems mainly assume symmetric distributions, however, real-
world data typically obey skewed distributions and are thus not adequately modeled through …

Floods caused by a confluence of forcing mechanisms, known as compound floods, are a
hazard of increasing importance and visibility. In many areas of the coastal zone, the …