Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding technique...
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With the rapid development of mobile Internet technology, various public events appear on social media platforms and attract a lot of attention. Since netizens can express their opinions freely on the Internet, some e...
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For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set sele...
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With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-fr...
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Population aging is one of the biggest demographic challenges facing China in the 21st century. Its impact on the economy cannot be ignored. This study employs empirical research to illustrate how population aging aff...
Population aging is one of the biggest demographic challenges facing China in the 21st century. Its impact on the economy cannot be ignored. This study employs empirical research to illustrate how population aging affects China's economic development. It is mainly explained from the perspectives of the labor force, savings rate, GDP, and pension insurance *** to the relevant data and the definition standard of the United Nations on aging society,the relevant data from 2002 to 2020 are selected for empirical analysis. The conclusion is obtained through the ADF test, determination of optimal lag order, the AR characteristic root test, and impulse response analysis. The results display that from the perspective of GDP and savings rate, population aging has an inhibitory effect on the economy, and has a lasting negative impact on the labor *** results suggest that some measures should be taken to deal with the effects of aging in order to counteract its negative impacts on the economy and society.
In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently de...
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Adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence *** is the most common adaptive algorithm among ***,it has revealed that Adam has a poor generalization *** i...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
Adaptive algorithms are extensively employed in the field of deep learning owing to their rapid convergence *** is the most common adaptive algorithm among ***,it has revealed that Adam has a poor generalization *** is an algorithm based on Adam with exact stepsize *** introduces a new second-order momentum that corrects Adam's second-order *** algorithms still fail to converge during training due to the presence of instability and extreme learning *** this paper,we propose a new adaptive and momental bounded algorithm,called AdaBMod,which can effectively mitigate the sudden large learning rate problem and is especially suitable for training deep neural *** setting an adaptive finite learning rate in AdaBelief algorithm,the obtained AdaBMod can effectively eliminate the problem of high learning rate in the late training of neural networks,so as to make the training process more *** simulation experiments on deep neural network tasks also show that our proposed AdaBMod algorithm eliminates the large learning rate during the training *** results are also better than other current state-of-the-art optimizers.
Linearizability is a commonly accepted correctness criterion for concurrent data structures. Concurrent queues are among the most fundamental concurrent data structures. In this paper, we present necessary and suffici...
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Although deep neural networks have made significant advancements in machine vision, detecting small defects on thin films using deep neural networks remains a challenging task. The shape and size of the target defect ...
Although deep neural networks have made significant advancements in machine vision, detecting small defects on thin films using deep neural networks remains a challenging task. The shape and size of the target defect are often uncertain, while environmental conditions such as illumination and camera vibration can affect image quality. Additionally, the defect detection model needs to process images at high speed, further adding to the complexity of the task. To address these challenges, this paper proposes a novel approach by utilizing the “one-stage” object detection method as the backbone structure, while also enhancing global representation and multilevel feature fusion for improved defect detection. Evaluation results demonstrate that the proposed network structure not only performs well on our datasets but also outperforms other state-of-the-art object detection methods when tested on public datasets.
data selection for fine-tuning Large Language Models (LLMs) aims to select a high-quality subset from a given candidate dataset to train a Pending Fine-tune Model (PFM) into a Selective-Enhanced Model (SEM). It can im...
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