The probabilistic location verification (PLV) algorithm proposed in [17] uses the pmf of the hop-count from a location to a verifier, which is located at a fixed location.
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:```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:```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:```NP3L [10] is consistently outperformed by our methods, while for UPnP [8] our NPnLupC outperforms it in the noisy cases.```
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:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
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:```…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:```NP3L [10] is consistently outperformed by our methods, while for UPnP [8] our NPnLupC outperforms it in the noisy cases.```
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:```The above estimator generalizes the estimator in Sriperumbudur et al., 2017 to conditional densities.```
Sample intent: method



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:```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:```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:```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