Modified Multidimensional Scaling
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning. Yet few theoretical results characterizing its statistical performance exist, not to mention any in high dimensions. By considering a unified framework that includes low, moderate and high dimensions, we study multidimensional scaling in the setting of clustering noisy data. Our results suggest that, in order to achieve consistent estimation of the embedding scheme, the classical multidimensional scaling needs to be modified, especially when the noise level increases. To this end, we propose {\it modified multidimensional scaling} which applies a nonlinear transformation to the sample eigenvalues. The nonlinear transformation depends on the dimensionality, sample size and unknown moment. We show that modified multidimensional scaling followed by various clustering algorithms can achieve exact recovery, i.e., all the cluster labels can be recovered correctly with probability tending to one. Numerical simulations and two real data applications lend strong support to our proposed methodology. As a byproduct, we unify and improve existing results on the $\ell_{\infty}$ bound for eigenvectors under only low bounded moment conditions. This can be of independent interest. …

Locality-promoting Regularization (LOCO-REG)
This work investigates fundamental questions related to locating and defining features in convolutional neural networks (CNN). The theoretical investigations guided by the locality principle show that the relevance of locations within a representation decreases with distance from the center. This is aligned with empirical findings across multiple architectures such as VGG, ResNet, Inception, DenseNet and MobileNet. To leverage our insights, we introduce Locality-promoting Regularization (LOCO-REG). It yields accuracy gains across multiple architectures and datasets. …

Centrality
In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin.[1] They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network. …

Predictive Maintenance (PdM)
Predictive maintenance (PdM) techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. The main promise of Predicted Maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. The key is ‘the right information in the right time’. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people, etc.) and what would have been ‘unplanned stops’ are transformed to shorter and fewer ‘planned stops’, thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.
➚ “Condition Monitoring” …