Depression has become one of the most serious mental illnesses, leading to a substantial decline in quality of life, an elevated risk of suicide, and significant societal challenges. Despite significant progress in the application of deep learning for depression diagnosis, most prevalent methods rely on correlative rather than causal features, limiting their accuracy and interpretability. Here, we propose a causal graph learning (CGL) method for the hierarchical diagnosis of depression. Specifically, we first construct a novel depression facial graph (DFGraph) structure based on a prior knowledge, which collects information about subjects’ facial cues. Our CGL model leverages the DFGraph structure and incorporates a built-in masking mechanism, which is designed to effectively differentiate causal features from confounding ones. It employs backdoor adjustment techniques, which control for confounding variables by blocking noncausal paths, to identify and select pertinent causal features, thereby enhancing the accuracy of the hierarchical diagnosis of depression. We conducted extensive experiments on the collected depression dataset. Our results show that the proposed method provides better results and interpretability is further improved compared to the publicly available baseline.

Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GC-Net uses vertical multi-convolutions and horizontal multi-paths for training, which are reparameterized into a single convolution for inference, optimizing both performance and speed. This design allows GCNet to self-enlarge during training and self-contract during inference, effectively becoming a "teacher model" without needing external ones. Experimental results show that GCNet outperforms existing state-of-the-art models in terms of performance and speed on the Cityscapes, CamVid, and Pascal VOC 2012 datasets. The code is available at https://github.com/gyyang23/GCNet.

Static binary analysis is a widely used approach for ensuring the security of closed-source software. However, the absence of type information in stripped binaries, particularly for composite data types, poses significant challenges for both static analyzers and reverse engineering experts in achieving efficient and accurate analysis. Existing methods often struggle with inaccuracies and scalability limitations when dealing with such data types. To address these problems, we present Typeforge, a novel approach inspired by the workflow of reverse engineering experts, which uses a two-stage synthesis-selection strategy to automate the recovery of composite data types from stripped binaries. We design a new graph structure, the Type Flow Graph (TFG) to represent type information within stripped binaries. In the first stage, TFG-based Type Synthesis focuses on efficiently and accurately building constraints and synthesizing possible composite type declarations from the stripped binaries. In the second stage, we propose an LLM-assisted double-elimination framework to select the best-fit type declaration from the candidates by assessing the readability of the decompiled code. Our comparison with state-of-the-art approaches demonstrates that TYPEFORGE achieves F1 scores of 81.7% and 88.2% in Composite Data Type Identification and Layout Recovery, respectively, substantially outperforming existing methods. Additionally, TYPEFORGE achieves an F1 score of 72.1% in Relationship Recovery, a particularly challenging task for previous approaches. Furthermore, TYPEFORGE has significantly lower time overhead, requiring only about 3.8% of the time taken by OSPREY, the best-performing existing approach, making it a promising solution for various real-world reverse engineering tasks.

OBJECTIVE: This study aims to conduct a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) using speech samples in depression. MATERIALS AND METHODS: This review included studies reporting diagnostic results of DL algorithms in depression using speech data, published from inception to January 31, 2024, on PubMed, Medline, Embase, PsycINFO, Scopus, IEEE, and Web of Science databases. Pooled accuracy, sensitivity, and specificity were obtained by random-effect models. The diagnostic Precision Study Quality Assessment Tool (QUADAS-2) was used to assess the risk of bias. RESULTS: A total of 25 studies met the inclusion criteria and 8 of them were used in the meta-analysis. The pooled estimates of accuracy, specificity, and sensitivity for depression detection models were 0.87 (95% CI, 0.81-0.93), 0.85 (95% CI, 0.78-0.91), and 0.82 (95% CI, 0.71-0.94), respectively. When stratified by model structure, the highest pooled diagnostic accuracy was 0.89 (95% CI, 0.81-0.97) in the handcrafted group. DISCUSSION: To our knowledge, our study is the first meta-analysis on the diagnostic performance of DL for depression detection from speech samples. All studies included in the meta-analysis used convolutional neural network (CNN) models, posing problems in deciphering the performance of other DL algorithms. The handcrafted model performed better than the end-to-end model in speech depression detection. CONCLUSIONS: The application of DL in speech provided a useful tool for depression detection. CNN models with handcrafted acoustic features could help to improve the diagnostic performance. PROTOCOL REGISTRATION: The study protocol was registered on PROSPERO (CRD42023423603).

MOTIVATION: Accurate subcategorization of tumour types through gene-expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting to address this problem. RESULTS: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion. Using simulated data, we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using freely available tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturing biologically relevant structure.