Factors Influencing Extravasation of Newborn Intravenous Infusions: A Review

Huang, Fang; Huang, Lixuan; Liao, Fengzhen; Huang, Jiangping; Wei, Jiaojiao; Liang, Qun; Huang, Zhenpeng

doi:10.14218/erhm.2023.00030

Cited by 3 publications

References 42 publications

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Forward Learning of Large Language Models by Consumer Devices

Pau,

Aymone

2024

Electronics

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Large Language Models achieve state of art performances on a broad variety of Natural Language Processing tasks. In the pervasive IoT era, their deployment on edge devices is more compelling than ever. However, their gigantic model footprint has hindered on-device learning applications which enable AI models to continuously learn and adapt to changes over time. Back-propagation, in use by the majority of deep learning frameworks, is computationally intensive and requires storing intermediate activations into memory to cope with the model’s weights update. Recently, “Forward-only algorithms” have been proposed since they are biologically plausible alternatives. By applying more “forward” passes, this class of algorithms can achieve memory reductions with respect to more naive forward-only approaches and by removing the need to store intermediate activations. This comes at the expense of increased computational complexity. This paper considered three Large Language Model: DistilBERT, GPT-3 Small and AlexaTM. It investigated quantitatively any improvements about memory usage and computational complexity brought by known approaches named PEPITA and MEMPEPITA with respect to backpropagation. For low number of tokens in context, and depending on the model, PEPITA increases marginally or reduces substantially arithmetic operations. On the other hand, for large number of tokens in context, PEPITA reduces computational complexity by 30% to 50%. MEMPEPITA increases PEPITA’s complexity by one third. About memory, PEPITA and backpropagation, require a comparable amount of memory to store activations, while MEMPEPITA reduces it by 50% to 94% with the benefits being more evident for architectures with a long sequence of blocks. In various real case scenarios, MEMPEPITA’s memory reduction was essential for meeting the tight memory requirements of 128 MB equipped edge consumer devices, which are commonly available as smartphone and industrial application multi processors.

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Forward Learning of Large Language Models by Consumer Devices

Pau,

Aymone

2024

Electronics

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Improving Single-Image Super-Resolution with Dilated Attention

Zhang,

Cheng,

Yang

et al. 2024

Electronics

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Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality.

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Beyond Surface Linguistics

Šprogar

2023

Principles and Applications of Adaptive Artificial Intelligence

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Contemporary artificial intelligence has advanced markedly toward mimicking human intelligence. Despite linguistic proficiency, machines remain bereft of genuine text comprehension, leading to perceived, albeit superficial, intelligence. This chapter introduces a straightforward test that highlights the non-human-like cognition of machines such as ChatGPT. Eschewing the prevalent approach of progressively complex testing, the long memory test highlights ChatGPT's inability to function at a human level. Central to this assessment, the test mandates reliable information retention, a feat the transformer architecture of GPT fails to achieve.

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Factors Influencing Extravasation of Newborn Intravenous Infusions: A Review

Cited by 3 publications

References 42 publications

Forward Learning of Large Language Models by Consumer Devices

Forward Learning of Large Language Models by Consumer Devices

Improving Single-Image Super-Resolution with Dilated Attention

Beyond Surface Linguistics

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