Decoding Happiness from Neural and Video Recordings for Better Insight Into Emotional Processing in the Brain.

Gaining a better understanding of which brain regions are responsible for emotional processing is crucial for the development of novel treatments for neuropsychiatric disorders. Current approaches rely on sparse assessments of subjects' emotional states, rarely reaching more than a hundred per patient. Additionally, data are usually obtained in a task solving scenario, possibly influencing their emotions by study design.

Compact representation of temporal processes in echosounder time series via matrix decomposition.

The recent explosion in the availability of echosounder data from diverse ocean platforms has created unprecedented opportunities to observe the marine ecosystems at broad scales. However, the critical lack of methods capable of automatically discovering and summarizing prominent spatio-temporal echogram structures has limited the effective and wider use of these rich datasets. To address this challenge, a data-driven methodology is developed based on matrix decomposition that builds compact representation of long-term echosounder time series using intrinsic features in the data.

Detecting reports of unsafe foods in consumer product reviews.

Objectives: Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem.