Infrared-visible image fusion (IVF) aims to integrate complementary information from infrared and visible sensors into a single, more informative representation. However, achieving both visual clarity and semantic consistency in the fused results remains a critical challenge, particularly for real-world applications like scene understanding. To address this, we propose DICFusion, a deep integrated and semantic-coordinated network, tailored for perceptually and semantically enriched infrared-visible fusion tasks. Firstly, the DICFusion employs a novel modality-aware fusion strategy to integrate infrared and visible modalities into a cohesive feature embedding. Secondly, the framework incorporates hybrid mamba-convolution blocks, which leverage the combined strengths of mamba and convolution neural networks to accurately capture both global context and localized details while maintaining computational efficiency. To mitigate the feature heterogeneity between fusion and downstream tasks, DICFusion adopts a comprehensive framework of deep integration and collaborative optimization. This design utilizes a unified multi-scale encoder to harmonize feature representations, followed by parallel fusion and segmentation branches to enhance both visual quality and task performance. Moreover, a semantic guidance module leveraging cross-attention mechanism is incorporated to refine the semantic consistency of the fused outcomes. Comprehensive experimental evaluations validate the performance and efficiency of DICFusion, demonstrating its superiority over contemporary state-of-the-art methods, both in terms of fusion visual quality and downstream task precision. The code is available at https://github.com/fd-qhwang/DICFusion.

Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still poses a substantial challenge. To address this, our study introduces the Spatio Temporal Attention-based methodology for Target Vehicle Trajectory Prediction (STATVTPred). This approach integrates Global Positioning System(GPS) localization technology to track target movement and dynamically predict the vehicle’s future path using comprehensive spatio-temporal trajectory data. We map the vehicle trajectory onto a directed graph, after which spatial attributes are extracted via a Graph Attention Networks(GATs). The Transformer technology is employed to yield temporal features from the sequence. These elements are then amalgamated with local road network structure maps to filter and deliver a smooth trajectory sequence, resulting in precise vehicle trajectory prediction.This study validates our proposed STATVTPred method on T-Drive and Chengdu taxi-trajectory datasets. STATVTPred achieves an AMR of 73.07% on the Beijing dataset, surpassing the Transformer by 6.38% and the LSTM Encoder-Decoder by 37.45%, while also reducing Distance Error (DE) by 26.93% and 20.95% in Beijing and Chengdu, respectively, also much lower than the baseline results. This is expected to establish STATVTPred as a new approach for handling trajectory prediction of targets in logistics and transportation scenarios, thereby enhancing prediction accuracy.

Foundation models like the Segment Anything Model (SAM) have shown promising performance in general image segmentation tasks. However, their effectiveness is limited when applied to pathology images due to the inherent multi-scale structural complexity and staining heterogeneity. To address these challenges, we introduce SegAnyPath, a foundational model specifically designed for pathology image segmentation. SegAnyPath is trained on an extensive public pathology dataset comprising over 1.5 million images and 3.5 million masks. We propose a multi-scale proxy task to handle the diverse resolutions in pathology images, complementing the reconstruction objective in the supervised learning stage. To enhance segmentation performance across stain variations, we introduce a novel self-distillation scheme based on stain augmentations. Furthermore, we propose an innovative task-guided Mixture of Experts (MoE) architecture in the decoder of SegAnyPath for efficient management of distinct pathology segmentation tasks, including cell, tissue, and tumor segmentation. Experimental results demonstrate SegAnyPath's zero-shot generalization capability, achieving a Dice score of 0.6797 across multiple datasets and organs while maintaining consistent performance across varying staining styles and resolutions. In comparison, the fine-tuned SAM achieves a Dice score of only 0.5258 on the same external test sets, indicating a substantial 29.27% improvement by SegAnyPath. SegAnyPath has the potential to advance the field of pathology analysis and improve diagnostic accuracy in clinical settings. The code is available at https://github.com/wagnchogn/SegAnyPath.

In this paper, we qualitatively and quantitatively discuss the design choices, production experience, and lessons in building the Elastic Block Storage ( EBS ) at Alibaba Cloud over the past decade. To cope with hardware advancement and users’ demands, we shift our focus from design simplicity in EBS1 to high performance and space efficiency in EBS2 , and finally reducing network traffic amplification in EBS3 . In addition to the architectural evolutions, we also summarize development lessons and experiences as four topics, including: (i) achieving high elasticity in latency, throughput, IOPS, and capacity; (ii) improving availability by minimizing the blast radius of individual, regional, and global failure events; (iii) identifying the motivations and key tradeoffs in various hardware offloading solutions; and (iv) identifying the pros/cons of alternative solutions and explaining why seemingly promising ideas would not work in practice.

MOTIVATION: Anatomical therapeutic chemical (ATC) classification system is very important for drug utilization and studies. Correct prediction of the 14 classes in the first level for given drugs is an essential problem for the study on such system. Several multi-label classifiers have been proposed in this regard. However, only two of them provided the web servers and their performance was not very high. On the other hand, although some rest classifiers can provide better performance, they were built based on some prior knowledge on drugs, such as information of chemical-chemical interaction and chemical ontology, leading to limited applications. Furthermore, provided codes of these classifiers are almost inaccessible for pharmacologists. RESULTS: In this study, we built a simple web server, namely iATC-FRAKEL. This web server only required the SMILES format of drugs as input and extracted their fingerprints for making prediction. The performance of the iATC-FRAKEL was much higher than all existing web servers and was comparable to the best multi-label classifier but had much wider applications. Such web server can be visited at http://cie.shmtu.edu.cn/iatc/index. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://cie.shmtu.edu.cn/iatc/index. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.