ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative e...
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With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and ...
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ISBN:
(数字)9781728170053
ISBN:
(纸本)9781728170060
With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.
Cancer is one of the top leading causes of death in the world according to the World Health Organization (WHO). Despite the continuous efforts, drug discovery often takes 10-15 years if done traditionally, and it cost...
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Cancer is one of the top leading causes of death in the world according to the World Health Organization (WHO). Despite the continuous efforts, drug discovery often takes 10-15 years if done traditionally, and it costs over $2.6 billion to finally bring a single drug to market. The integration of deep learning (DL) with these traditional methods, however, is transforming the process of drug design and prediction, evolving at high speeds, often relying on the molecular data for reference. This paper explores and compares various deep learning models, presenting a multi-model for anticancer small molecule design and bioactivity (GI50%) prediction. A fine-tuned Variation Autoencoder (VAE) model is trained on a set of anticancer molecules to generate new molecules that mimic the drug. These molecules are later fed to a meta-model based on two ensemble methods: averaging and stacking, to predict their activity against different cancer cell lines; leveraging the strengths of different Graph Neural Networks (GNNs), namely: Graph Attention Networks (GATs), Graph Convolutional Networks (GCNs), and Message Passing Neural Networks (MPNNs), based on chemical structure and a pre-trained ChemBERTa model based on the attention mechanism. The experiments were conducted on a dataset of multiple compounds across the breast cancer tumour with 6 cancer cell lines, demonstrating our model's superiority against the literature, outperforming most models; the Pearson's correlation coefficients reached up to 83% using the stacking ensemble method.
Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been onl...
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ISBN:
(数字)9781728186351
ISBN:
(纸本)9781728186368
Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.
This study investigates a Cloud–Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configurati...
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Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed t...
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Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaus...
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Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and ...
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Sparse bundle adjustment(SBA) is a key but time-and memory-consuming step in three-dimensional(3 D) reconstruction. In this paper, we propose a 3 D point-based distributed SBA algorithm(DSBA) to improve the speed and scalability of SBA. The algorithm uses an asynchronously distributed sparse bundle adjustment(A-DSBA)to overlap data communication with equation computation. Compared with the synchronous DSBA mechanism(SDSBA), A-DSBA reduces the running time by 46%. The experimental results on several 3 D reconstruction datasets reveal that our distributed algorithm running on eight nodes is up to five times faster than that of the stand-alone parallel SBA. Furthermore, the speedup of the proposed algorithm(running on eight nodes with 48 cores) is up to41 times that of the serial SBA(running on a single node).
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenge...
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Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framew...
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Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.
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