INTRODUCTION: Recent studies have shown the feasibility of 4D PC-MRI to measure four dimensional (3D-spatial plus temporal domain) blood flow and to give a qualitative and quantitative analysis of the hemodynamics e.g. in the aorta or the carotids. This technique requires the conversion of the measured phase angle (-π to +π) to velocity vectors by the usage of a parameter that specifies the vel...

Despite representing only a small fraction of hippocampal granule cells, adult-generated newborn granule cells have been implicated in learning and memory (Aimone et al., 2011). Newborn granule cells undergo functional maturation and circuit integration over a period of weeks. However, it is difficult to assess the accompanying gene expression profiles in vivo with high spatial and temporal res...

A new nonparametric method to correct model data is proposed. At any given point in space and time the correction is determined from “analogs” in a learning dataset. The learning dataset contains model data and simultaneous observations. The method is applied to the significant wave height dataset of the 45-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40). Comparison o...

Outlier detection is very important functionality of data mining, it has enormous applications. This paper proposes a clustering based approach for outlier detection using spatio-temporal data. It uses three step approach to detect spatiotemporal outliers. In the first step of outlier detection, clustering is performed on the spatio-temporal dataset with proposed Spatio-Temporal Shared Nearest ...

We pursue two strategies for offline data collection for a temporal question answering system that uses both quantitative methods and fuzzy methods to reason about time and events. The first strategy extracts event descriptions from the structured year entries in the online encyclopedia Wikipedia, yielding clean quantitative temporal information about a range of events. The second strategy mine...

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would on...

Neurobiological research on learning assumes that temporal contiguity is essential for association formation, but what constitutes temporal contiguity has never been specified. We review evidence that learning depends, instead, on learning a temporal map. Temporal relations between events are encoded even from single experiences. The speed with which an anticipatory response emerges is proporti...

Many real-world domains, such as industrial diagnosis, require an adequate representation that combines uncertainty and time. Research in this field involves the development of new knowledge representation and inference mechanisms to deal with uncertainty and time. Current temporal probabilistic models become too complex when used for real world applications. In this paper, we propose a model, ...

Populations in resource dependent economies gain well-being from the natural environment, in highly spatially and temporally variable patterns. To collect information on this, we designed and implemented a 1586-household quantitative survey in the southwest coastal zone of Bangladesh. Data were collected on material, subjective and health dimensions of well-being in the context of natural resou...

Human pose estimation from images and videos has been a very important research field in computer vision. In this thesis, we present an end-to-end approach to human pose estimation task that based on a deep hybrid architecture that combines convolutional neural network (CNNs) and recurrent neural networks (RNNs). CNNs used to map the input image to feature space (fixed dimensionality), and then...