Dijkstra’s shortest-path algorithm with a Fibonacci heap was used as the supporting shortest-path algorithm in the modified Yen’s Kshortest-paths algorithm [24, 25].
Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result
Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:```Although the time-constants of the learning window used in this study were much shorter than those reported for hippocampal neurons by Bi and Poo [27] (viz.```
Sample intent: background



Sample text:```The D-HOTM system is based on the Text Mining Infrast ucture (TMI) developed by the parallel and distributed text mining lab at Lehigh University [20].```
Sample intent: background



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method
Sample text:```This seems to be the main drawback of the current implementation in comparison to the method of Black Jepson (“refined” in Table 1).```
Sample intent: result



Sample text:```A rook pivoting scheme as in [3] and [11] can be implemented for tridiagonal matrices, but a full rook scheme for general banded matrices would probably lead to a ”chasing” scheme as in [5] or [15] .```
Sample intent: background



Sample text:```Although the time-constants of the learning window used in this study were much shorter than those reported for hippocampal neurons by Bi and Poo [27] (viz.```
Sample intent: background



Sample text:```Finding optimal paths in dynamic environments has attracted significant attention in the robotics community (See Latombe (1990); LaValle (1999); Lu et al (2015); Jur (2007) and the references therein).```
Sample intent: method



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:```For seeking the optimal value of Wh of the sub-problems which corresponds to the h-th level, we can solve the problem by the smoothing proximal gradient (SPG) method [6] fortunately.```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```The present observations support and extend earlier contentions that Parkinson patients prefer to adopt a closedloop control mode, adjusting their movements continuously on the basis of available visual information (Flowers 1976).```
Sample intent: result



Sample text:```In-between methods can be subdivided into linear approximations [12,24] and quadratic interpolation methods [22,23,25–27] based on the degree of the polynomial approximation employed.```
Sample intent: method