Artificial intelligence has become indispensable in modern life,but its energy consumption has become a significant concern due to its huge storage and computational demands.Artificial intelligence algorithms are main...Artificial intelligence has become indispensable in modern life,but its energy consumption has become a significant concern due to its huge storage and computational demands.Artificial intelligence algorithms are mainly based on deep learning algorithms,relying on the backpropagation of convolutional neural networks or binary neural networks.While these algorithms aim to simulate the learning process of the human brain,their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption.The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power.Tunneling magnetoresistance(TMR)-based devices,namely magnetic tunnel junctions(MTJs),have great advantages in simulating the behavior of biological synapses and neurons.This is not only because MTJs can simulate biological behavior such as spike-timing dependence plasticity and leaky integrate-fire,but also because MTJs have intrinsic stochastic and oscillatory properties.These characteristics improve MTJs’bio-fidelity and reduce their power consumption.MTJs also possess advantages such as ultrafast dynamics and non-volatile properties,making them widely utilized in the field of neuromorphic computing in recent years.We conducted a comprehensive review of the development history and underlying principles of TMR,including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence.We also explored various writing methods of MTJs and their potential applications.Furthermore,we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing.TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing,particularly in the development of spiking neural networks.Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future展开更多
粒度支持向量机GSVM(Granular Support Vector Machine)在处理大规模数据集时,粒的划分对其模型的训练效能、泛化能力等有很大的影响。然而传统划分方法的随机性,严重影响着其模型的训练效果。针对这个问题提出一种结合共享最近邻法和...粒度支持向量机GSVM(Granular Support Vector Machine)在处理大规模数据集时,粒的划分对其模型的训练效能、泛化能力等有很大的影响。然而传统划分方法的随机性,严重影响着其模型的训练效果。针对这个问题提出一种结合共享最近邻法和粒度支持向量机的混合模型(GSVM-SNN)。利用共享最近邻法将样本点自动划分成若干个信息粒,从中提取出关键信息。由于支持向量点大都分布在信息粒的边缘,提出一种KNN连通度,通过计算连通度提取纯粒边缘点并融合关键信息建立最终决策模型。实验结果表明,与传统的GSVM相比,该方法在分类时间、分类准确率上都有一定的优越性。展开更多
Individuals and PCs(personal computers)can be recognized using CAPTCHAs(Completely Automated Public Turing test to distinguish Computers and Humans)which are mechanized for distinguishing them.Further,CAPTCHAs are int...Individuals and PCs(personal computers)can be recognized using CAPTCHAs(Completely Automated Public Turing test to distinguish Computers and Humans)which are mechanized for distinguishing them.Further,CAPTCHAs are intended to be solved by the people,but are unsolvable by the machines.As a result,using Convolutional Neural Networks(CNNs)these tests can similarly be unraveled.Moreover,the CNNs quality depends majorly on:the size of preparation set and the information that the classifier is found out on.Next,it is almost unmanageable to handle issue with CNNs.A new method of detecting CAPTCHA has been proposed,which simultaneously solves the challenges like preprocessing of images,proper segmentation of CAPTCHA using strokes,and the data training.The hyper parameters such as:Recall,Precision,Accuracy,Execution time,F-Measure(H-mean)and Error Rate are used for computation and comparison.In preprocessing,image enhancement and binarization are performed based on the stroke region of the CAPTCHA.The key points of these areas are based on the SURF feature.The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.展开更多
In the context of unified hydrodynamics, we discuss the pseudorapidity distributions of the charged particles produced in Au-Au and Cu-Cu collisions at the low RHIC energies of √SNN = 19.6 and 22.4 GeV, respectively....In the context of unified hydrodynamics, we discuss the pseudorapidity distributions of the charged particles produced in Au-Au and Cu-Cu collisions at the low RHIC energies of √SNN = 19.6 and 22.4 GeV, respectively. It is found that the unified hydrodynamics alone can give a good description to the experimental measurements. This is different from the collisions at the maximum RHIC energy of √SNN = 200 GeV or at LHC energy of √SNN= 2.76 TeV, in which the leading particles must be taken into account so that we can properly explain the experimental observations.展开更多
基金the National Key Research and Development Program of China(Grant Nos.2022YFB4400200 and 2022YFA1402604)the National Natural Science Foundation of China(Grant Nos.12104031 and 52121001)+2 种基金Science and Technology Major Project of Anhui Province(Grant No.202003a05020050)the International Collaboration Project B16001,the Beihang Hefei Innovation Research Institute Project BHKX-19-02,the China Postdoctoral Science Foundation No.2022M720345Outstanding Research Project of Shenyuan Honors College BUAA 230121102 for their financial support of this work.
文摘Artificial intelligence has become indispensable in modern life,but its energy consumption has become a significant concern due to its huge storage and computational demands.Artificial intelligence algorithms are mainly based on deep learning algorithms,relying on the backpropagation of convolutional neural networks or binary neural networks.While these algorithms aim to simulate the learning process of the human brain,their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption.The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power.Tunneling magnetoresistance(TMR)-based devices,namely magnetic tunnel junctions(MTJs),have great advantages in simulating the behavior of biological synapses and neurons.This is not only because MTJs can simulate biological behavior such as spike-timing dependence plasticity and leaky integrate-fire,but also because MTJs have intrinsic stochastic and oscillatory properties.These characteristics improve MTJs’bio-fidelity and reduce their power consumption.MTJs also possess advantages such as ultrafast dynamics and non-volatile properties,making them widely utilized in the field of neuromorphic computing in recent years.We conducted a comprehensive review of the development history and underlying principles of TMR,including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence.We also explored various writing methods of MTJs and their potential applications.Furthermore,we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing.TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing,particularly in the development of spiking neural networks.Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future
文摘粒度支持向量机GSVM(Granular Support Vector Machine)在处理大规模数据集时,粒的划分对其模型的训练效能、泛化能力等有很大的影响。然而传统划分方法的随机性,严重影响着其模型的训练效果。针对这个问题提出一种结合共享最近邻法和粒度支持向量机的混合模型(GSVM-SNN)。利用共享最近邻法将样本点自动划分成若干个信息粒,从中提取出关键信息。由于支持向量点大都分布在信息粒的边缘,提出一种KNN连通度,通过计算连通度提取纯粒边缘点并融合关键信息建立最终决策模型。实验结果表明,与传统的GSVM相比,该方法在分类时间、分类准确率上都有一定的优越性。
文摘Individuals and PCs(personal computers)can be recognized using CAPTCHAs(Completely Automated Public Turing test to distinguish Computers and Humans)which are mechanized for distinguishing them.Further,CAPTCHAs are intended to be solved by the people,but are unsolvable by the machines.As a result,using Convolutional Neural Networks(CNNs)these tests can similarly be unraveled.Moreover,the CNNs quality depends majorly on:the size of preparation set and the information that the classifier is found out on.Next,it is almost unmanageable to handle issue with CNNs.A new method of detecting CAPTCHA has been proposed,which simultaneously solves the challenges like preprocessing of images,proper segmentation of CAPTCHA using strokes,and the data training.The hyper parameters such as:Recall,Precision,Accuracy,Execution time,F-Measure(H-mean)and Error Rate are used for computation and comparison.In preprocessing,image enhancement and binarization are performed based on the stroke region of the CAPTCHA.The key points of these areas are based on the SURF feature.The exploratory outcomes show that the model has a decent acknowledgment impact on CAPTCHA with foundation commotion and character grip bending.
基金Supported by the Shanghai Key Lab of Modern Optical System
文摘In the context of unified hydrodynamics, we discuss the pseudorapidity distributions of the charged particles produced in Au-Au and Cu-Cu collisions at the low RHIC energies of √SNN = 19.6 and 22.4 GeV, respectively. It is found that the unified hydrodynamics alone can give a good description to the experimental measurements. This is different from the collisions at the maximum RHIC energy of √SNN = 200 GeV or at LHC energy of √SNN= 2.76 TeV, in which the leading particles must be taken into account so that we can properly explain the experimental observations.
