Sequence-to-graph alignment is a critical component of pan-genome-based read alignment and represents the most computationally intensive step in this process. To address this challenge, we have introduced a sequence-t...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Sequence-to-graph alignment is a critical component of pan-genome-based read alignment and represents the most computationally intensive step in this process. To address this challenge, we have introduced a sequence-to-graph alignment algorithm based on graph folding, which minimizes the size of the graph structure and enhances alignment efficiency while identifying the optimal path. Experiments on both simulated and real datasets demonstrate that our algorithm significantly improves the speed of sequence-to-graph alignment. The source code is available at Github: https://***/zzzerd/fgpoa
The theory of rough sets provides a valuable approach in artificial intelligence and data mining. Optimal scale selection and attribute reduction are meaningful problems in rough set theory. Many related studies have ...
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Interacting with real-world cluttered scenes poses several challenges to robotic agents that need to understand complex spatial dependencies among the observed objects to determine optimal pick sequences or efficient ...
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Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm ...
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Soft robots offer promising solutions for rehabilitation due to their inherent attributes such as suppleness, lightweight construction, and safety features. However, prevalent soft actuators face limitations in output...
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Due to the influence of objective factors such as scene noise and hardware equipment, compared to CAD modeling point clouds, the real point clouds collected by LiDAR often have strong non-uniformity and irregularity, ...
Due to the influence of objective factors such as scene noise and hardware equipment, compared to CAD modeling point clouds, the real point clouds collected by LiDAR often have strong non-uniformity and irregularity, which leads to the low ability of traditional point cloud classification models to extract point cloud features in real scenes and poor classification robustness. To address this issue, we have added a local feature fusion module and a dense connected feature fusion method to PointMLP, thereby encoding broader point cloud information into local features. The simple stacking of multiple MLP and Mixer blocks constitutes our network model-PointMM. The shallow features of the point cloud are used as input and enter the local feature fusion module to perform feature fusion within and between point sets. Finally, by maximizing the pooling layer and selecting points with stronger semantic features as inputs to the second stage feature extraction module. This method has been tested on the ScanObjectNN dataset and shows that it can approach SOTA with a classification accuracy of 0.8%, surpassing other mainstream models.
This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach th...
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This paper proposes a dynamic information fusion interactive system with a transparent display applied in the vehicle. The dynamic information fusion interactive system integrates three leading technologies: relative ...
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The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small...
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Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computation...
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