Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks...
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Background: In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. data privacy within the...
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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limit...
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Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued data using real-valued features. Compl...
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In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acu...
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In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85.47\pm 0.73\%$ . In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.
Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition betw...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.
Object Storage Systems (OSS) inside a cloud promise scalability, durability, availability, and concurrency. However, open-source OSS does not have a specific approach to letting users and administrators search based o...
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Traffic loads have dramatically grown as a result of the small-cell / Internet of Everything devices' increasing popularity. The present network has undergone a revolution with this expansion, transforming it into...
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ISBN:
(数字)9798331541583
ISBN:
(纸本)9798331541590
Traffic loads have dramatically grown as a result of the small-cell / Internet of Everything devices' increasing popularity. The present network has undergone a revolution with this expansion, transforming it into 5G wireless technology, which requires ultra-low latency, high data rates, and higher capacity. Maximizing the use of its bands and investigating the spectrum resource are two study topics that are prioritized to satisfy these needs. Nevertheless, obtaining an effective management plan is severely hampered by the spectrum resource's scarcity. The goal of this effort is to provide a thorough assessment of current 5G-enabling technologies and spectrum sharing technologies in relation to 5G development. SS surveys and associated research on SS methods pertinent to 5G networks will are evaluated, and SS techniques are categorized. Surveys and studies are categorized into one of the primary spectrum allocation methodologies based on the network architecture, spectrum access method, and spectrum allocation behavior. Additionally, a thorough analysis of cognitive radio technology in SS in relation to the deployment of 5G is carried out. To ensure a thorough analysis, talks are held about the problems and difficulties with the way SS and CR are currently being implemented, and resources for effective 5G development are offered.
Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate th...
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Audio processing has become an inseparable part of modern applications in domains ranging from health care to speech-controlled devices. In automated audio segmentation, deep learning plays a vital role. In this artic...
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