The present study aimed to explore the co-occurrence between depression and smartphone addiction (SA) from the network perspective as well as the network invariance across different groups. A total of 8347 Chinese college students were included in the study. Network analysis was conducted to estimate the network structure of the co-occurrence between depression and SA symptoms. A network comparison test was utilized to explore sex, severity of depression and SA variations in the network structures and strengths. D18 ‘Sad’ was the central symptom for the estimated network. D5 ‘Mind’ may be the most important bridge symptom between depression and SA. Males and females differed in the distribution of edge weights (M = 0.103, p = 0.024). Students with or without depressive symptoms showed significant differences in the distribution of edge weights (M = 0.129, p = 0.001) and global strength (14.5 vs. 13.5, p = 0.006). In addition, there are variations in the distribution of edge weights for college students in different SA severity groups (M = 0.119, p < 0.001). Attention to these core and bridge symptoms may decrease the odds of co-occurrence of depressive symptoms and SA. Interventions to address the co-occurrence should also take into account sex and severity of depression and SA.

Few-shot semantic segmentation aims to reduce reliance on dense annotations, while enhancing generalization to unseen categories. However, most methods are constrained by static global prototypes, which fail to represent subtle defect details, resulting in pronounced support–query misalignment. To address this issue, we propose progressive adaptive network for defect-aware few-shot segmentation (PANDA), a few-shot segmentation framework that integrates representational modulation with semantic consistency constraints. Specifically, to capture subtle and scale-sensitive variations in defect patterns, we design anchored representational modulation (ARM), which overcomes the rigidity of static prototypes by dynamically adjusting representations. In addition, we develop hierarchical semantic coherence (HSC), which enforces consistency across representation hierarchies to suppress the accumulation of semantic drift as depth increases. Collectively, ARM and HSC mitigate support–query misalignment and stabilize representations in few-shot defect segmentation. PANDA achieves state-of-the-art performance on MetFS-18, with 55.1% and 56.3% mean intersection over union under the one-shot and five-shot settings. Moreover, PANDA has been integrated into a real-time industrial inspection platform, where it delivers accurate segmentation across diverse defect types, highlighting its robustness in practical application.

Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

While Large Language Models (LLMs) demonstrate impressive medical capabilities through Retrieval-Augmented Generation (RAG) and domain optimization, a critical question remains: can LLMs autonomously recognize when to seek external help rather than provide independent medical recommendations? This metacognitive capability is essential for safe healthcare deployment. To address this gap, we introduce a novel evaluation framework assessing LLMs' autonomous help-seeking behavior through three workflows: Force-RAG (mandated external retrieval), No-RAG (internal knowledge only), and Auto-RAG (autonomous decision-making). Our comprehensive evaluation of 13 LLMs configurations across six clinical departments using 954 real-world cases reveals three key insights: (1) larger models don't necessarily exhibit superior help-seeking calibration; (2) reasoning strategies significantly impact metacognitive performance across medical domains; (3) proprietary models demonstrate superior autonomy in balancing self-reliance with appropriate help-seeking. These findings challenge conventional scaling assumptions and establish help-seeking behavior as fundamental to medical AI reliability.

Natural products (NPs) and their derivatives are important resources for drug discovery. There are many in silico target prediction methods that have been reported, however, very few of them distinguish NPs from synthetic molecules. Considering the fact that NPs and synthetic molecules are very different in many characteristics, it is necessary to build specific target prediction models of NPs. Therefore, we collected the activity data of NPs and their derivatives from the public databases and constructed four datasets, including the NP dataset, the NPs and its first-class derivatives dataset, the NPs and all its derivatives and the ChEMBL26 compounds dataset. Conditions, including activity thresholds and input features, were explored to access the performance of eight machine learning methods of target prediction of NPs, including support vector machines (SVM), extreme gradient boosting, random forests, K-nearest neighbor, naive Bayes, feedforward neural networks (FNN), convolutional neural networks and recurrent neural networks. As a result, the NPs and all their derivatives datasets were selected to build the best NP-specific models. Furthermore, the consensus models, as well as the voting models, were additionally applied to improve the prediction performance. More evaluations were made on the external validation set and the results demonstrated that (1) the NP-specific model performed better on the target prediction of NPs than the traditional models training on the whole compounds of ChEMBL26. (2) The consensus model of FNN + SVM possessed the best overall performance, and the voting model can significantly improve recall and specificity.

Accurate motion capture and flexible retargeting of underwater creatures such as fish remain to be difficult due to the long-lasting challenges of marker attachment and feature description for soft bodies in the underwater environment. Despite limited new research progresses appeared in recent years, the fish motion retargeting with a desirable motion pattern in real-time remains elusive. Strongly motivated by our ambitious goal of achieving high-quality data-driven fish animation with a light-weight, mobile device, this paper develops a novel framework of motion capturing and retargeting for a fish. We capture the motion of actual fish by a monocular camera without the utility of any marker. The elliptical Fourier coefficients are then integrated into the contour-based feature extraction process to analyze the fish swimming patterns. This novel approach can obtain the motion information in a robust way, with smooth medial axis as the descriptor for a soft fish body. For motion retargeting, we propose a two-level scheme to properly transfer the captured motion into new models, such as 2D meshes (with texture) generated from pictures or 3D models designed by artists, regardless of different body geometry and fin proportions among various species. Both motion capture and retargeting processes are functioning in real time. Hence, the system can simultaneously create fish animation with variation, while obtaining video sequences of real fish by a monocular camera.

X10 language and Asynchronous Partitioned Global Address Space (APGAS) model is an emerging mechanism for programming high-performance computers and commodity clusters. However, little work exists on distributed programming framework for dynamic programming (DP) problems based on X10 and APGAS model. In this paper we present DPX10, an efficient distributed X10 framework for DP applications. DPX10 enables developers to write highly efficient DP programs without much effort. A DPX10 program is specified by a directed acyclic graph (DAG) pattern and a compute method for the vertices. DPX10 provides eight commonly used DAG patterns and a simple API to create custom patterns. The system handles all the tiresome work of implementing parallelization including DAG distribution, vertices scheduling, and vertices communication. Moreover, a new recovery method for distributed arrays is developed to provide transparent fault tolerance. We describe the design of the framework and use four DP applications with up to a billion vertices on 120 cores to demonstrate its simplicity, efficiency, and scalability.

When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.