It is possible to produce a transient supersaturation or undersaturation in tissues and blood by sequentially breathing gases with different equilibration rates. If the ambient gas pressure is sufficiently high, the induced supersaturation can produce vascular bubbles. By means of the classical perfusion-dependent model of inert gas elimination, which assumes that the effects of diffusion are m...

In many pattern recognition tasks we may be interested in asking questions about the behavior of sequences of observations on a particular random vector. Given that we have observed the sequence of feature vectors x1, x2, ..., xi, at i discrete points in time, for example, how likely is it that x will take the value xi+1 at time i+1. We may be interested in asking questions about the behavior o...

Intensive Care Unit (ICU) patients are physiologically fragile and require vigilant monitoring and support. The myriad of data gathered from biosensors and clinical information systems has created a challenge for clinicians to assimilate and interpret such large volumes of data. Physiologic measurements in the ICU are inherently noisy, multidimensional, and can readily fluctuate in response to ...

Visual stimuli were constructed so that any given stimulus by itself appeared to be a random collection of dots. However, when 2 corresponding stimuli were superimposed by means of a 2-field tachistoscope, a 3-letter nonsense syllable was perceived. Temporal organization in perception was studied in Experiment I by varying the interval between the presentation of the 2 corresponding patterns ov...

Despite the importance of temporal inference in several domains especially the question answering systems it remains still in its departure compared to other languages. This article deals with the automatic co-construction of patterns of temporal relations for the question answering systems. We have implemented this approach in temporal inference called TPE: Temporal Pattern Extraction.

The rapid increase in the data available leads to the difficulty for analyzing those data and different types of frameworks are required for unearthing useful knowledge that can be extracted from such databases. The field of temporal data mining is relatively young and one expects to see many new developments in the near future. In all data mining applications, the primary constraint is the lar...

Recently, evolving networks are becoming a suitable form to model many realworld complex systems, due to their peculiarities to represent the systems and their constituting entities, the interactions between the entities and the timevariability of their structure and properties. Designing computational models able to analyze evolving networks becomes relevant in many applications [1]. The evolu...

In this paper, a unique approach to the problem of spatiotemporal pattern detection is discussed in relation to climate data; this can be understood as discovering dependent climate events that occur over space. An accurate solution in this domain will provide climate scientists with highly valuable data which can be used to improve climate models and add to the knowledge base of climate scienc...

Data mining is the process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques to extract knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspe...

A novel supervised learning-rule is derived for Spiking Neural Networks (SNNs) using the gradient descent method, which can be applied on networks with a multi-layered architecture. All existing learning-rules for SNNs limit the spiking neurons to fire only once. Our algorithm however is specially designed to cope with neurons that fire multiple spikes, taking full advantage of the capabilities...