Compared with works [15,20,4,18], which we consider the closest to our approach, the running times show that our approach is significantly faster.
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:```We used longitudinal sequence samples for 15 patients from two independent studies [13,22].```
Sample intent: background



Sample text:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



Sample text:```On the other hand, there are research works on the design of graph-based routing algorithms [3], [16], [17], [18], [19], [20], [21], [22], [23] to deal with the void problem.```
Sample intent: method



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:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method
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:```[10] present the obstruction-free step complexity which is the maximum number of steps that an operation needs to perform if no other processes are taking steps.```
Sample intent: method



Sample text:```Our results on working memory performance are similar to those of earlier studies (e.g. Archbold et al., 2009; Cosentino et al., 2008; Naegele et al., 2006) in showing impaired working memory in the OSA group.```
Sample intent: result



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```We used longitudinal sequence samples for 15 patients from two independent studies [13,22].```
Sample intent: background



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:```Following past work, we show that such basis models can be learned from 2D annotations using techniques adapted from nonrigid structure from motion (SFM) [20, 19].```
Sample intent: method



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:```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:```Here we use the value of ten (because our previous study showed this is the number of items that can be managed efficiently in the browser‚Äôs sidebar [Moreau et al., 2002]).```
Sample intent: background