Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos.

Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE).

Massive symptomatic right-sided Bochdalek hernia in an adult man.

A 22-year-old man presented to a rural hospital in Australia with right-sided pleuritic chest pain, right shoulder pain and dyspnoea. The patient had been receiving chronic asthma therapy without improvement. CT of the chest was performed after an abnormal X-ray, incidentally revealing one of the largest documented right-sided diaphragmatic hernias, with left lung compression due to mediastinal shift.

Hierarchical Representation Learning for Kinship Verification.

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions.