Sentence examples for temporal discoveries from inspiring English sources

The rest of the paper is organized as follows: Section 2 briefly summarizes the related work, Section 3 describes the definitions used in spatial and temporal analysis for VANETs, and Section 4 introduces the two taxi-trace datasets and presents the spatial and temporal discoveries as well as their implications for VANET design and protocol optimization.

Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care.

Li et al. [35] examined traffic data gathered from taxicabs over 24 days by applying the Temporal Outlier Discovery (TOD) concept to detect temporal outliers.

The signal scores for this database were computed using the optimal analytic method for each outcome as follows: self-controlled case series (SCCS) method for ARF (analysis-ID 1949010), self-controlled cohort (SCC) method for ALI (analysis-ID 409002), and information component temporal pattern discovery (ICTPD) method for AMI and GIB (analysis-IDs 3016001 and 3034001) [ 19].

They describe a spatio-temporal association discovery approach to mine protein folding trajectories, to identify critical events and common pathways.

These discoveries were temporal as well as spatial: some days the courts were there, some days they weren't; what had been a tennis court had suddenly become a basketball court or a five-a-side pitch.

Although such static algorithms have demonstrated a successful identification of temporally expressed genes in some degree in the past four years and our study, any temporal dynamic analysis false discovery results in static methods can be introduced due to violation of Markovian assumptions frequently revealed in time series expression profile.

The temporal distance between the discovery of new knowledge and its practical application has been shrinking rapidly, perhaps at a geometric rate.

Discovery of temporal patterns and temporal trends from such temporal data requires new algorithms and methodologies as most of the existing algorithms do not reveal emerging, seasonal and diminishing patterns.

This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders.

After all, the temporal order of the discovery of \ e\) and of the articulation of \(h\) and \ k\) may well be an entirely accidental historical contingency.