The algorithm described in the previous section was incorporated in a tool developed at the University of Manchester, for the evaluation of different DAG scheduling algorithms [8–9].
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:```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:```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:```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:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
Sample intent: background
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:```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:```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:```…methods rely on some additional and often heuristic step to generate an initial solution, such as adversarially-trained neural networks [Conneau et al., 2018, Zhang et al., 2017a,b], robust self-learning [Artetxe et al., 2018b] or
5This can be easily verified by noting that the…```
Sample intent: background



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:```The validated Malay version of GAD-7 which was found to have good sensitivity and specificity was used in this study [27].```
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:```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:```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:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method