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Accurate runoff forecasting results can not only provide an important basis for flood control scheduling, but also provide scientific support for water resources optimization, which promotes the maximization of the overall benefits of the basin. ...

MPI tracing tools is essential to collect the communication events and performance metrics of large-scale programs for further performance analysis and optimization. However, towards the exascale era, the performance and storage overhead for tracing ...

With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there ...

Bearings are key support components in rotating machinery, and their stability is crucial to the reliability of the entire mechanical system. To address the limitations of existing Transformer architectures in edge-side optimization and ...

Autonomous exploration in dynamic and unknown environments poses severe challenges for multirobot systems, requiring the consideration of key factors such as task allocation, robot coordination, and dynamic obstacle avoidance. To address these ...

• Voronoi partitioning and multi-objective cost function combinatorial exploration.
• DDPG algorithms for dynamic obstacle avoidance.
• Human experience and transfer learning enhance training efficiency.
• Simulations and experiments ...

In nighttime traffic scenarios, achieving real-time visible and thermal (RGB-Thermal) images semantic segmentation is a pivotal and intricate challenge within autonomous driving systems, demanding a delicate balance between precision and ...

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• Asymmetric network for real-time RGB-Thermal semantic segmentation.
• Multi-scale cross-select enhancement module reduces network computing burden.
• Cross rank selection sub-module avoids chaotic spatial information.
• Guidance ...

Modern optimizing compilers are able to exploit memory access or computation patterns to generate vectorized codes. However, such patterns in irregular programs are unknown until runtime due to the input dependence. Thus, either compiler’s static ...

This paper examines how AI-driven matching technology impacts influencer competition and platform profitability, revealing nuanced effects on campaign pricing and platform revenue.

This paper explores the impact of using advanced technology such as artificial intelligence (AI) to match marketers with social media influencers. We develop a theoretical model to examine how matching accuracy affects the competition between influencers ...

Graph convolution networks (GCNs) have achieved remarkable performance in skeleton-based action recognition by exploiting the adjacency topology of body representation. However, the adaptive strategy adopted by the previous methods to construct the ...

Personalized federated learning (pFL) is a promising approach to train customized models for multiple clients over heterogeneous data distributions. However, existing works on pFL often rely on the optimization of model parameters and ignore the ...

Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting.

Utilize a deep learning approach to measure ureter length on CT urography (CTU) images and compare the obtained results with those ...

• A deep learning model based on CTU images was used to measure ureteral length and performed superiorly to other methods.
• Bland-Altman analysis found a -1.3 mm bias between the model and reference, not significant (P = 0.057).
• The ...

In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types ...

In the enhanced Loran (eLoran) system, the separation of skywave and groundwave is a crucial step to ensure the accuracy of signal processing, especially when facing challenges such as strong skywave interference and low signal-to-noise ratio (SNR). This ...

The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. However, this convenience has raised significant privacy concerns, as these ...

Multimodal intent detection integrates various types of information to identify user intent, which is crucial for developing dialog systems that function effectively in complex, real-world environments. Current methods show potential for improving ...

Traditional information theory-based feature selection methods are designed for discrete features, which require additional discretization steps when working with continuous features. In contrast, fuzzy information theory-based feature selection ...

The automatic medical report generation is a challenging task because it requires accurate capture and description of abnormal regions, especially for those discrepancies between patient and normal. In most cases, normal region descriptions ...

• Focuses more on the lesion area by comparing with normal images.
• Uses feature difference to generate more accurate descriptions of lesion areas.
• Uses contrastive learning to enhance visual representation of feature difference.
• ...

Multimodal image segmentation utilizes a variety of modality images with RGB, infrared, polarization, etc. Unfortunately, the mainstream focus on digital modality fusion leads to the cost of computing abundant information and increased model size. ...

The emergence of ViT demonstrates the viability of Transformer in the field of computer vision, rivaling or even surpassing the performance of some CNN-based models. However, ViT’s substantial computation and memory demands present deployment ...