For our experiments, we use a workload synthesized from production-cluster traces collected at Facebook, as done in [31, 9], that we label FB-dataset.
Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method
Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



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:```The studies on VA are conducted for several languages and actual datasets are acquired for different languages including English [12, 13], German [14], French [15], Spanish [16] and Chinese [17–19].```
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:```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:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```Following the experimental design proposed in Yannakakis and Hallam (2007c) and Yannakakis et al. (2008) for effectively capturing the level of entertainment, the test-bed game under investigation is played in variants.```
Sample intent: background



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



Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background



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:```To train our CNN for classification, we set initial learning rate to 0.01 and optimize cross-entropy loss with Adagrad [26].```
Sample intent: background



Sample text:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result