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It is challenging to scale parallel applications to modern supercomputers because of load imbalance, resource contention, and communications between processes. Profiling and tracing are two main performance analysis approaches for detecting these ...

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large ...

• A simulation model that replicates the real conditions of a wheat field was proposed.
• Four wheat head data sets (GWHD2021, SDAU2021, SDAU2022, SDAU2023) were collected as the test data.
• Nine deep learning models, including Faster-...

Numerous studies have reported a significant positive correlation between wheat yield and the quantity of wheat heads. However, collecting data on wheat heads in the field poses a challenge for several reasons, including the uncontrollable nature ...

This paper examines the interactions between supplier encroachment formats (centralized encroachment or decentralized encroachment) and pricing schemes (specific pricing or uniform pricing) in a two-tier supply chain consisting of one supplier ...

• Interactions between supplier encroachment formats and pricing schemes are examined.
• The players’ preference for encroachment formats is derived.
• The players’ preference for pricing schemes is derived.

The reptile search algorithm (RSA) is a well-known swarm-based metaheuristic algorithm inspired by the hunting behaviors of crocodiles. To overcome the problems of falling into local optima and premature convergence, this paper proposes a multi-...

With the continuous development of Graph Neural Network (GNN) technologies, securing their robustness is crucial for their broad adoption in practical applications. Although various verification methods for training GNNs have been proposed, ...

Satellite video object tracking involves tracking a specified tiny object within a wide scene. The insufficient appearance features of these tiny objects pose significant challenges to appearance-based object trackers, particularly in situations involving ...

Despite the extensive research on RGBT object tracking, there are still several challenges and issues in practical applications, such as modality differences, lighting variations and disappearance of the target, and changes in viewpoint. Existing methods ...

Multi-modal tracking has garnered significant attention due to its wide range of potential applications. Existing multi-modal tracking approaches typically merge data from different visual modalities on top of RGB tracking. However, focusing ...

• Visual-Language Interaction Prompt Manager is proposed.
• Multi-modal Image Description Co-Generation Module is introduced.
• Multi-modal Visual Tracking Based on Textual Generation method is designed.

There are usually a large number of concurrent graph queries (CGQs) requirements in streaming graphs. However, existing graph processing systems mainly optimize a single graph query in streaming graphs or CGQs in static graphs. They have a large number of ...

Networks are error-prone due to misconfigurations, and it is hard to identify the root causes in the configuration and find a repair due to the size and complexity of networks running distributed routing protocols. Thus, we advocate Automatic ...

Low-resource language translation remains a significant challenge in natural language processing, particularly for the Mongolian-Chinese language pair under the “Belt and Road” initiative. Existing translation systems struggle with this pair due ...

This study aims to investigate the impact of emotion regulation strategies on adolescent internalizing problems, incorporating artificial intelligence (AI) techniques for data preprocessing to explore the parallel mediating roles of psychological capital ...

The curriculum is designed around the foundational principles of "AI empowerment, the integration of learning and teaching, and the leadership of educator spirit." It delves into three critical "pain points" encountered during the curriculum development ...

Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task characteristics. These methods, however, fall short of modeling intrinsic ...

Domain generalization 3D segmentation aims to learn the point clouds with unknown distributions. Feature augmentation has been proven to be effective for domain generalization. However, each point of the 3D segmentation scene contains uncertainty in the ...

Spatio-temporal graph neural networks have proven efficacy in capturing complex dependencies for urban computing tasks such as forecasting and kriging. Yet, their performance is constrained by the reliance on extensive data for training on a specific ...

With the development of the power system, the number of substation equipment continues to increase, and the use of intelligent mobile robots to replace traditional manual inspection methods is a trend, which can improve inspection efficiency and ...

Accurate prediction of road traffic conditions is essential for the effectiveness of Intelligent Transportation Systems (ITS) and the advancement of smart cities. While existing methodologies mainly focus on the complex structures of road ...

Solving the complex challenges of sophisticated terrain and multi-scale targets in remote sensing (RS) images requires a synergistic combination of Transformer and convolutional neural network (CNN). However, crafting effective CNN architectures remains a ...