but the older we get, the faster “time flies”. Yet, it is an open question why that is. One possible answer relates to age-specific differences in the neural mechanisms underlying time and event perception.
In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be light. InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train inter...
36,37,38,39,40; we specifically predicted that greater overall psychopathology would be associated with greater negative deviations (i.e., lower than normative cortical volume) in the vmPFC/mOFC, inferior temporal, daCC, and insular cortices...
Further, over time, an increase in returns is followed by a reversal. Using a cross-sectional regression model, Ying et al. (2015) document that GSV affects stock market returns positively in the Chinese stock market. By contrast, Bijl et al. (2016) investigate the impact of Google Trends...
For discriminant validity we used two criteria, cross-loadings, in which the loading of each indicator must be greater than any of the cross-loadings; and that the square root of AVE should be greater than its correlation with any other construct in the model [48] (Table 2). Henseler et...
To address our research questions, we analyzeN=126,301rumor cascades from Twitter. Our data provides a large-scale, cross-sectional sample based on a comprehensive set of cascades on Twitter during the time period from the founding of Twitter in 2006 through 2017. In particular, our sample con...
In Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach; Springer: Cham, Switzerland, 2016; pp. 149–190. [Google Scholar] Hartmann, A.K.; Weigt, M. Phase Transitions in Combinatorial Optimization Problems (Vol. 67); Wiley-VCH: Weinheim, Germany, 2005. [Google...
For optimum learning, we propose using varied training data with k-fold cross-validation for each voter, wherein for each fold model 𝑚, we create 𝑠 snapshots. The primary idea behind creating model snapshots is to train a single model while continuously lowering the learning rate to reach...
Figure 2 shows the architecture of our explanatory process and the mining of the sub-trajectory correlation, which comprises two parts: data processing and model training, and maximum explainability coverage. The first part generates the flow tensor G and the trajectory flow tensor T in Defination ...