This is the first complete complexity classification of general-valued languages over non-Boolean domains, generalising previously obtained results in [14, 22, 43] as follows:
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:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
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:```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:```13 indicates the comparison of the results of the mesh simplification method [2] after applying different mesh enhancement techniques.```
Sample intent: background



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:```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:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
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:```have been proposed as potential alternatives to the Turing Test, specifically targeting its potential weaknesses and inadequacy [1].```
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:```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