Mental disorders, such as anxiety and depression, have become a global concern that affects people of all ages. Early detection and treatment are crucial to mitigate the negative effects these disorders can have on daily life. Although AI-based detection methods show promise, progress is hindered by the lack of publicly available large-scale datasets. To address this, we introduce the Multi-Modal Psychological assessment corpus (MMPsy), a large-scale dataset containing audio recordings and transcripts from Mandarin-speaking adolescents undergoing automated anxiety/depression assessment interviews. MMPsy also includes self-reported anxiety/depression evaluations using standardized psychological questionnaires. Leveraging this dataset, we propose Mental-Perceiver, a deep learning model for estimating mental disorders from audio and textual data. Extensive experiments on MMPsy and the DAIC-WOZ dataset demonstrate the effectiveness of Mental-Perceiver in anxiety and depression detection.

All engineers dislike interruptions because it takes away from the deep focus time needed to write complex code. Our goal is to reduce unnecessary interruptions at . We first describe our Workgraph platform that logs how engineers use our internal work tools at . Using these anonymized logs, we create sessions. sessions are defined in opposition to interruption and are the amount of time until the engineer is interrupted by, for example, a work chat message.

We describe a large-scale study to reduce the amount of time code review takes. Each quarter at Meta we survey developers. Combining sentiment data from a developer experience survey and telemetry data from our diff review tool, we address, “When does a diff review feel too slow?” From the sentiment data alone, we learn that 84.7% of developers are satisfied with the time their diffs spend in review. By enriching the survey results with telemetry for each respondent, we determined that sentiment is closely associated with the 75th percentile time in review for that respondent’s diffs, ie those that take more than 24 hours.

Sparsity Preserving Projections (SPP) is a well known approach for feature extraction and dimensionality reduction. Its success is mainly attributed to its high quality graph which is constructed by sparse representation. As an instance of graph embedding, SPP can be formulated as regression model. Thus we apply the idea of collaborative graph embedding, which reformulates SPP as a collaborative representation model via imposing a L2-norm constraint to projections from the perspective of linear regression, to further enhance SPP. We call this novel SPP method Collaborative Sparsity Preserving Projections (CSPP). Experiment results on four popular face datasets, namely Yale, ORL, FERET and AR, show the effectiveness in feature extraction and the improvement of CSPP over SPP.