Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data.

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous.

Enhanced Out-of-Stock Detection in Retail Shelf Images Based on Deep Learning.

The term out-of-stock (OOS) describes a problem that occurs when shoppers come to a store and the product they are seeking is not present on its designated shelf. Missing products generate huge sales losses and may lead to a declining reputation or the loss of loyal customers. In this paper, we propose a novel deep-learning (DL)-based OOS-detection method that utilizes a two-stage training process and a post-processing technique designed for the removal of inaccurate detections.

Premature thelarche: a possible adrenal disorder.

Endocrine studies in girls with precocious thelarche were compared with those of normal girls of similar ages. Girls with precocious thelarche showed breast development and oestrogenised vaginal smears as the only signs of precocious sexual development. A few of the girls were tall and some had advanced bone ages but these two findings were not consistently present in the same patient.

A liver urocanase deficiency.

Two sisters with a rare inborn error of histidine metabolism resulting from urocanase deficiency are being presented. The more common form of familial histidinemia due to histidase deficiency is excluded. The urocanase deficiency is proven by demonstrating increased excretion of metabolites of the product of the urocanase enzyme action.