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 effects of the waiting list on families of mentally ill patients

Thesis (M.S.)--Boston Universit

An ingestible temperature-transmitter

Pill-sized transmitter measures deep body temperature in studies of circadian rhythm and indicates general health. Ingestible device is a compromise between accuracy, circuit complexity, size and transmission range

Соціокультурні чинники підготовки вчителя до міжкультурної взаємодії

У статті проаналізовано соціокультурні чинники, що впливають на стан підготовки вчителів до організації міжкультурної взаємодії суб’єктів освітнього простору. Дослідженням доведена необхідність підвищення рівня готовності педагогів до практичної роботи в полікультурному просторі

Towards a Framework for the Discovery of Collections of Live Music Recordings and Artefacts on the Semantic Web

This paper introduces a platform for the representation and discovery of live music recordings and associated artefacts based on a dedicated data model. We demonstrate our technology by implementing a Web-based discovery tool for the Grateful Dead collection of the Internet Archive, a large collection of concert recordings annotated with editorial metadata. We represent this information using a Linked Data model complemented with data aggregated from several additional Web resources discussing and describing these events. These data include descriptions and images of physical artefacts such as tickets, posters and fan photos, as well as other information, e.g. about location and weather. The system uses signal processing techniques for the analysis and alignment of the digital recordings. During the discovery, users can juxtapose and compare different recordings of a given concert, or different performances of a given song by interactively blending between them