The effect of spin-1 impurities doping on the magnetic properties of a spin-3/2 Ising nanotube is investigated using Monte Carlo simulations within the Blume-Emery-Griffiths model in the presence of an external magnet...The effect of spin-1 impurities doping on the magnetic properties of a spin-3/2 Ising nanotube is investigated using Monte Carlo simulations within the Blume-Emery-Griffiths model in the presence of an external magnetic field. The thermal behaviors of the order parameters and different macroscopic instabilities as well as the hysteretic behavior of the material are examined in great detail as a function of the dopant density. It is found that the impurities concentration affects all the system magnetic properties generating for some specific values, compensation points and multi-cycle hysteresis. Doping conditions where the saturation/remanent magnetization and coercive field of the investigated material can be modified for permanent or soft magnets synthesis purpose are discussed.展开更多
受对抗样本自身可迁移属性的影响,传统对抗样本防御方法的防御效果存在不稳定的情况,为此,提出基于深度学习的对抗样本防御方法。文章借助深度学习算法,构建了对抗样本伊辛模型,设置模型的初始状态为神经网络的输入数据,采用自旋状态表...受对抗样本自身可迁移属性的影响,传统对抗样本防御方法的防御效果存在不稳定的情况,为此,提出基于深度学习的对抗样本防御方法。文章借助深度学习算法,构建了对抗样本伊辛模型,设置模型的初始状态为神经网络的输入数据,采用自旋状态表示每一个神经元值与对抗样本伊辛模型的格点,并利用神经网络中卷积运算的特征,消解势场中预先给定的外部磁化作用,以最大限减少低对抗样本伊辛模型在能量作用下的局部自旋问题。在对抗样本防御阶段,利用对抗样本伊辛模型的通道相关性,生成重要性掩码对通道的激活进行调整,并结合对抗样本伊辛模型通道梯度累积值的实际情况设置了差异化的重要性掩码生成函数。在应用测试过程中,为验证防御效果,在快速梯度下降法(Fast Gradient Sign Method,FGSM)、Deepfool、C&W(Carlini and Wagner)攻击算法、投影梯度下降(Projected Gradient Descent,PFD)、集成对抗检测器(Energy-Aware Data-centric,EAD)共5种对抗策略下设计了对抗样本防御方法,对比不同对抗样本防御方法的性能,发现文章提出的基于深度学习的对抗样本防御方法的曲线下的面积(Area Under the Curve,AUC)值稳定在0.95以上,说明对抗样本防御方法具有较好的防御性能。展开更多
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hami...Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.展开更多
文摘The effect of spin-1 impurities doping on the magnetic properties of a spin-3/2 Ising nanotube is investigated using Monte Carlo simulations within the Blume-Emery-Griffiths model in the presence of an external magnetic field. The thermal behaviors of the order parameters and different macroscopic instabilities as well as the hysteretic behavior of the material are examined in great detail as a function of the dopant density. It is found that the impurities concentration affects all the system magnetic properties generating for some specific values, compensation points and multi-cycle hysteresis. Doping conditions where the saturation/remanent magnetization and coercive field of the investigated material can be modified for permanent or soft magnets synthesis purpose are discussed.
文摘受对抗样本自身可迁移属性的影响,传统对抗样本防御方法的防御效果存在不稳定的情况,为此,提出基于深度学习的对抗样本防御方法。文章借助深度学习算法,构建了对抗样本伊辛模型,设置模型的初始状态为神经网络的输入数据,采用自旋状态表示每一个神经元值与对抗样本伊辛模型的格点,并利用神经网络中卷积运算的特征,消解势场中预先给定的外部磁化作用,以最大限减少低对抗样本伊辛模型在能量作用下的局部自旋问题。在对抗样本防御阶段,利用对抗样本伊辛模型的通道相关性,生成重要性掩码对通道的激活进行调整,并结合对抗样本伊辛模型通道梯度累积值的实际情况设置了差异化的重要性掩码生成函数。在应用测试过程中,为验证防御效果,在快速梯度下降法(Fast Gradient Sign Method,FGSM)、Deepfool、C&W(Carlini and Wagner)攻击算法、投影梯度下降(Projected Gradient Descent,PFD)、集成对抗检测器(Energy-Aware Data-centric,EAD)共5种对抗策略下设计了对抗样本防御方法,对比不同对抗样本防御方法的性能,发现文章提出的基于深度学习的对抗样本防御方法的曲线下的面积(Area Under the Curve,AUC)值稳定在0.95以上,说明对抗样本防御方法具有较好的防御性能。
文摘Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
