Depression represents a global mental health challenge requiring efficient and reliable automated detection methods. Current Transformer- or Graph Neural Networks (GNNs)-based multimodal depression detection methods face significant challenges in modeling individual differences and cross-modal temporal dependencies across diverse behavioral contexts. Therefore, we propose P³HF (Personality-guided Public-Private Domain Disentangled Hypergraph-Former Network) with three key innovations: (1) personality-guided representation learning using LLMs to transform discrete individual features into contextual descriptions for personalized encoding; (2) Hypergraph-Former architecture modeling high-order cross-modal temporal relationships; (3) event-level domain disentanglement with contrastive learning for improved generalization across behavioral contexts. Experiments on MPDD-Young dataset show P³HF achieves around 10% improvement on accuracy and weighted F1 for binary and ternary depression classification task over existing methods. Extensive ablation studies validate the independent contribution of each architectural component, confirming that personality-guided representation learning and high-order hypergraph reasoning are both essential for generating robust, individual-aware depression-related representations.

Augmented reality (AR) can convert complex work instructions into virtual–reality fusion contents for assembly guidance. In the past, AR registration and occlusion were usually implemented separately, with low robustness and poor timeliness. This article proposes a novel deep learning scheme, named AR-CenterNet, to integrate AR registration and occlusion handling. The proposed method mainly includes two stages, i.e., the neural network prediction stage and the AR processing stage. In the first stage, AR-CenterNet is designed for keypoint detection and depth map prediction. In the second stage, the pose matrix of the physical camera is solved with the predicted keypoints and the depth map of the virtual scene is compared with the predicted depth map for occlusion handling. The experiments demonstrate that our method is robust against different conditions for assisted assembly. This article can provide a new solution method for AR virtual–reality fusion based on monocular images.

The two most common ways to activate intelligent voice assistants (IVAs) are button presses and trigger phrases. This paper describes a new way to invoke IVAs on smartwatches: simply raise your hand and speak naturally. To achieve this experience, we designed an accurate, low-power detector that works on a wide range of environments and activity scenarios with minimal impact to battery life, memory footprint, and processor utilization. The raise to speak (RTS) detector consists of four main compo- nents: an on-device gesture convolutional neural network (CNN) that uses accelerometer data to detect specific poses; an on-device speech CNN to detect proximal human speech; a policy model to combine signals from the motion and speech detector; and an off-device false trigger mitigation (FTM) system to reduce unin- tentional invocations trigged by the on-device detector. Majority of the components of the detector run on-device to preserve user privacy. The RTS detector was released in watchOS 5.0 and is running on millions of devices worldwide.

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of $p$ features across $n$ samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high- dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high- dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

MOTIVATION: Understanding how drugs and diseases are associated in the molecular level is of critical importance to unveil disease mechanisms and treatments. Until recently, few studies attempt end to discover important gene modules shared by both drugs and diseases. RESULTS: Here, we propose a novel presentation of drug-gene-disease relationship, a 'co-module', which is characterized by closely related drugs, diseases and genes. We first define a network-based gene closeness profile to relate drug to disease. Then, we develop a Bayesian partition method to identify drug-gene-disease co-modules underlying the gene closeness data. Genes share similar notable patterns with respect not only to the drugs but also the diseases within a co-module. Simulations show that our method, comCIPHER, achieves a better performance compared with a popular co-module detection method, PPA. We apply comCIPHER to a set consisting of 723 drugs, 275 diseases and 1442 genes and demonstrate that our co-module approach is able to identify new drug-disease associations and highlight their molecular basis. Disease co-morbidity emerges as well. Three co-modules are further illustrated in which new drug applications, including the anti-cancer metastasis activity of an anti-asthma drug Pranlukast, and a cardiovascular stress-testing agent Arbutamine for obesity, as well as potential side-effects, e.g. hypotension for Triamterene, are computationally identified. AVAILABILITY: The compiled version of comCIPHER can be found at http://bioinfo.au.tsinghua.edu.cn/comCIPHER/. The 86 co-modules can be downloaded from http://bioinfo.au.tsinghua.edu.cn/comCIPHER/Co_Module_Results.zip. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.