Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation

Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels which only provide coarse information, with uncertainty regarding time, location or value. Using such labels often leads to considerable challenges for the learning process. Current methods for weak-label training often employ standard supervised approaches that additionally reassign or prune labels during the learning process. The information gain, however, is often limited as only the importance of labels where the network already yields reasonable results is boosted. We propose treating weak-label training as an unsupervised problem and use the labels to guide the representation learning to induce structure. To this end, we propose two autoencoder extensions: class activity penalties and structured dropout. We demonstrate the capabilities of our approach in the context of score-informed source separation of music

The Influence of Sugar-Sweetened Beverage Health Warning Labels on Parents' Choices

US states have introduced bills requiring sugar-sweetened beverages (SSBs) to display health warning labels. This study examined how such labels may influence parents and which labels are most impactful. In an online survey of 2381 demographically and educationally diverse parents, SSB health warning labels improved parents understanding of health harms associated with overconsumption of SSBs. The warning labels also lowered parents' intentions to purchase SSBs for their children

4−Equitable Tree Labelings

We assign the labels {0,1,2,3} to the vertices of a graph; each edge is assigned the absolute difference of the incident vertices’ labels. For the labeling to be 4−equitable, we require the edge labels and vertex labels to each be distributed as uniformly as possible. We study 4−equitable labelings of different trees and prove all cater-pillars, symmetric generalized n−stars (or symmetric spiders), and complete n −ary trees for all n ∈ N are 4−equitable