Rib fracture patterns are key indicators of trauma severity. Detecting and locating these fractures is a critical yet time-consuming task, especially in 3D imaging, due to their minute size and irregular geometries. Existing voxel-based spatial methods fail to capture frequency-domain variations inherent in imaging and do not replicate the progressive refinement process used by clinicians during manual annotation, leading to suboptimal results. We propose a novel regression network, RF-DTR, incorporating a gated regressor mechanism and operating entirely in the frequency domain to address these challenges. Specifically, we present an innovative spatial-frequency transform applied to volumes and corresponding masks. Furthermore, we introduce a Mahalanobis regularization technique to enhance the model and learn high-frequency DCT components relevant to clinical tasks. Finally, a hierarchical penalty is proposed to improve the confidence of the prediction. Extensive experiments confirm our method's superiority in handling complex, sparsely annotated medical imaging datasets.

Large deep learning models have achieved significant success in various tasks. However, the performance of a model can significantly degrade if it is needed to train on datasets with noisy labels with misleading or ambiguous information. To date, there are limited investigations on how to restore performance when model degradation has been incurred by noisy label data. Inspired by the "forgetting mechanism" in neuroscience, which enables accelerating the relearning of correct knowledge by unlearning the wrong knowledge, we propose a robust model restoration and refinement (MRR) framework COLUR, namely Confidence-Oriented Learning, Unlearning and Relearning. Specifically, we implement COLUR with an efficient co-training architecture to unlearn the influence of label noise, and then refine model confidence on each label for relearning. Extensive experiments are conducted on four real datasets and all evaluation results show that COLUR consistently outperforms other SOTA methods after MRR.

User-generated content on social media platforms provides a valuable resource for developing automated computational methods to detect mental health issues online leading to suicidal thoughts automatically. Although current fully automated methods show promise, they may produce uncertain predictions, leading to flawed conclusions. To address this, we propose a novel model called DiGrI, or Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection, which reformulates suicide ideation assessment as a selective, prioritized prediction problem. The model incorporates a novel multi-classifier distorted greedy model that is optimized to operate under various levels of automation and abstains from making uncertain predictions with theoretical guarantees. Our results show that DiGrI outperforms strong comparative models including large language models in detecting mental health issues on a publicly available Reddit dataset. We discuss the empirical and practical implications, including the ethical considerations of using DiGrI for online automatic suicide ideation detection involving humans, if it were to be translated for use in clinical and public health practice.

Network architectural is indispensable for a wide-area and robust Fiber-optic time transfer (FOTT) network, however, there is currently a deficiency in effective scheme. This article proposed a reconfigurable FOTT network architecture based on software-defined time transfer units (SD-TTU), enhancing the network’s capability for flexible resource allocation, automatic protection and restoration. The architecture consists of three layers: 1) time transfer layer for time signal transmission and clock difference cancellation; 2) monitor and control layer for monitoring network states, fault detection, and dynamic reconfiguration of the SD-TTU; and 3) service and configuration layer for customizable service construction and proactive network reconfiguration. A polymorphic software-defined time transfer unit (SD-TTU) is further designed, which can operate as a master station, intermediate station, and slave station using the same hardware. The proposed architecture and the designed SD-TTU are experimentally demonstrated over chained and ring networks, respectively. Network expansion, contraction, topology reconfiguration, failure and fault recovery are validated. The changes of measured mean clock differences are below 43-ps across different scenarios, verifying the robustness and flexibility of the proposed architecture.

Graph data in real-world scenarios undergo rapid and frequent changes, making it challenging for existing graph models to effectively handle the continuous influx of new data and accommodate data withdrawal requests. The approach to frequently retraining graph models is resource intensive and impractical. To address this pressing challenge, this paper introduces a new concept of graph memory learning. Its core idea is to enable a graph model to selectively remember new knowledge but forget old knowledge. Building on this approach, the paper presents a novel graph memory learning framework - Brain-inspired Graph Memory Learning (BGML), inspired by brain network dynamics and function-structure coupling strategies. BGML incorporates a multi-granular hierarchical progressive learning mechanism rooted in feature graph grain learning to mitigate potential conflict between memorization and forgetting in graph memory learning. This mechanism allows for a comprehensive and multi-level perception of local details within evolving graphs. In addition, to tackle the issue of unreliable structures in newly added incremental information, the paper introduces an information self-assessment ownership mechanism. This mechanism not only facilitates the propagation of incremental information within the model but also effectively preserves the integrity of past experiences. We design five types of graph memory learning tasks: regular, memory, unlearning, data-incremental, and class-incremental to evaluate BGML. Its excellent performance is confirmed through extensive experiments on multiple node classification datasets.

The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this article, we organize a claim post in circulation as an ad hoc event tree, extract event elements, and convert it into bipartite ad hoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite ad hoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.