B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://github.com/inexhaustible419/CounterfactualultrasoundAI.

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network’s ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model’s capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://github.com/inexhaustible419/APMTLAirwaySegment.

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. We propose new data annotation strategies for certain tasks and offer a suite of data pre-processing codes, algorithm implementations, and standardized evaluation protocols to facilitate academic graph mining. Extensive experiments reveal that even advanced algorithms like large language models (LLMs) encounter difficulties in addressing key challenges in certain tasks, such as paper source tracing and scholar profiling. We also introduce the Open Academic Graph Challenge (OAG-Challenge) to encourage community input and sharing. We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field. OAG-Bench is accessible at https://www.aminer.cn/data/.

From-scratch name disambiguation is an essential task for establishing a reliable foundation for academic platforms.It involves partitioning documents authored by identically named individuals into groups representing distinct real-life experts.Canonically, the process is divided into two decoupled tasks: locally estimating the pairwise similarities between documents followed by globally grouping these documents into appropriate clusters.However, such a decoupled approach often inhibits optimal information exchange between these intertwined tasks.Therefore, we present BOND, which bootstraps the local and global informative signals to promote each other in an end-to-end regime.Specifically, BOND harnesses local pairwise similarities to drive global clustering, subsequently generating pseudo-clustering labels.These global signals further refine local pairwise characterizations.The experimental results establish BOND's superiority, outperforming other advanced baselines by a substantial margin.Moreover, an enhanced version, BOND+, incorporating ensemble and post-match techniques, rivals the top methods in the WhoIsWho competition 1 .

Name disambiguation---a fundamental problem in online academic systems--is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 authors. Such real-world challenging cases have not been effectively addressed by existing researches due to the small-scale or low-quality datasets that they have used. The development of effective algorithms is further hampered by a variety of tasks and evaluation protocols designed on top of diverse datasets. To this end, we present Who Is Who owning, a large-scale benchmark with over 1,000,000 papers built using an interactive annotation process, a regular leaderboard with comprehensive tasks, and an easy-to-use toolkit encapsulating the entire pipeline as well as the most powerful features and baseline models for tackling the tasks. Our developed strong baseline has already been deployed online in the AMiner system to enable daily arXiv paper assignments.

Abstract Aiming at the recognition of low‐probability‐of‐intercept (LPI) radar signals, a support vector machine (SVM)‐based algorithm is proposed, where the information geometry theory is utilised to optimise the kernel function of the SVM. Since signals with different modulations have different characteristics in the time‐frequency domain, the time‐frequency transformation result of the LPI radar signal is considered as an image, referred to as the time‐frequency image, and computer vision techniques are utilized to perform recognition. Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. Then, an SVM is adopted to recognise LPI radar signals due to its superiority in addressing the dimension disaster and non‐linear inseparability issue. The extracted time‐frequency features are fed into the SVM for classification and recognition. Note that the classification performance of SVM depends on the kernel function. Therefore, in the proposed algorithm, information geometry theory is exploited to improve the Gaussian kernel function, and the maximum margin between different categories of samples is further enlarged. As a consequence, the recognition accuracy for LPI radar signals with similar time‐frequency images is effectively improved. In addition, the proposed algorithm has better robustness to small samples than other deep learning‐based algorithms, since the SVM method minimises the structural risk instead of the empirical risk. Simulation results verify the effectiveness of the proposed algorithm.