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Using a large multi-country multi-industry sample of over 158,000 companies, the earlystage company sector is documented to have sizable destruction of revenues and jobs and as well as sizable gross creation of revenues and jobs. The creation aspect has captured the dominant attention of researchers, commentators, and policy makers. Destruction, despite its large magnitude, has long been a backwater of research and most commentary on this sector. Destruction is not simply non-growth but rather prior growth that is subsequently reversed. This paper analyzes creation and destruction evidence across 10 different countries and across eight different major industry groups. Yearly growth/decline rates using revenues and headcount for Years 2 to 5 are analyzed. In each of the three growth years examined there are large amounts of destruction as well as creation simultaneously occurring. For example, in Year 5 gross revenue destruction is 34% of gross revenue creation whilst gross job destruction is 65% of gross job

Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaudal and mediolateral oblique. We use EfficientNet as the basis of our model. We "end-to-end" train the entire system using CBIS-DDSM dataset. To ensure statistical robustness, we test our system twice using: (a) 5-fold cross validation; and (b) the original training/test division of the dataset. Our technique reached an AUC of 0.934 using 5-fold cross validation (accuracy, sensitivity and specificity are 85.13% at the equal error rate point of ROC). Using the original dataset division, our technique achieved an AUC of 0.8483, as far as we know the highest reported AUC for this problem, although the subtle differences in the testing conditions of each work do not allow for an accurate comparison. The inference code and model are available at https://github.com/dpetrini/two-views-classifier.INDEX TERMS Breast cancer diagnosis, deep learning, convolutional neural network, mammogram, transfer learning.

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