Let Γ?R;be a regular anisotropic fractal. We discuss the problem of the negative spectrum for the Schr?dinger operators associated with the formal expression H;=id-?+βtr;,β∈R,acting in the anisotropic Sobolev spac...Let Γ?R;be a regular anisotropic fractal. We discuss the problem of the negative spectrum for the Schr?dinger operators associated with the formal expression H;=id-?+βtr;,β∈R,acting in the anisotropic Sobolev space W;（R;）, where ? is the Dirichlet Laplanian in R;and tr;is a fractal potential（distribution） supported by Γ.展开更多
The temporal evolution of the degree of entanglement between two atoms in a system of the binomial optical field interacting with two arbitrary entangled atoms is investigated. The influence of the strength of the dip...The temporal evolution of the degree of entanglement between two atoms in a system of the binomial optical field interacting with two arbitrary entangled atoms is investigated. The influence of the strength of the dipole–dipole interaction between two atoms, probabilities of the Bernoulli trial, and particle number of the binomial optical field on the temporal evolution of the atomic entanglement are discussed. The result shows that the two atoms are always in the entanglement state. Moreover, if and only if the two atoms are initially in the maximally entangled state, the entanglement evolution is not affected by the parameters, and the degree of entanglement is always kept as 1.展开更多
Based on the quantum information theory, this paper has investigated the entanglement properties of a system which is composed of the two entangled two-level atoms interacting with the two-mode entangled coherent fiel...Based on the quantum information theory, this paper has investigated the entanglement properties of a system which is composed of the two entangled two-level atoms interacting with the two-mode entangled coherent fields. The influences of the strength of light field and the two parameters of entanglement between the two-mode fields on the field entropy and on the negative eigenvalues of partial transposition of density matrix are discussed by using numerical calculations. The result shows that the entanglement properties in a system of a pairwise entangled states can be controlled by appropriately choosing the two parameters of entanglement between the two-mode entangled coherent fields and the strength of two light fields respectively.展开更多
By the negative eigenvalues of partial transposition of density matrix, this paper investigates the time evolution of entanglement of the two entangled atoms in the system of two atoms interacting with SchrSdinger cat...By the negative eigenvalues of partial transposition of density matrix, this paper investigates the time evolution of entanglement of the two entangled atoms in the system of two atoms interacting with SchrSdinger cat state. The result shows that the two atoms are always in the entanglement state, and the degree of entanglement between the two atoms exhibits ordinary collapses and revivals at 0.2 degree of entanglement, when the light field is large enough. On the other hand, the reinforcement of three different light fields on the degree of entanglement between two atoms is not evident.展开更多
Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,there...Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic features.The advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns.This study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease classification.First,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature maps.Second,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the foreground.Finally,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava leaves.The proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and RepVGG-B3g4.To evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.展开更多
基金supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.13KJB110010)the Pre Study Foundation of Nanjing University of Finance&Economics(Grant No.YYJ2013016)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘Let Γ?R;be a regular anisotropic fractal. We discuss the problem of the negative spectrum for the Schr?dinger operators associated with the formal expression H;=id-?+βtr;,β∈R,acting in the anisotropic Sobolev space W;（R;）, where ? is the Dirichlet Laplanian in R;and tr;is a fractal potential（distribution） supported by Γ.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB922103)the National Natural Science Foundation of China(Grant Nos.11274104 and 11404108)
文摘The temporal evolution of the degree of entanglement between two atoms in a system of the binomial optical field interacting with two arbitrary entangled atoms is investigated. The influence of the strength of the dipole–dipole interaction between two atoms, probabilities of the Bernoulli trial, and particle number of the binomial optical field on the temporal evolution of the atomic entanglement are discussed. The result shows that the two atoms are always in the entanglement state. Moreover, if and only if the two atoms are initially in the maximally entangled state, the entanglement evolution is not affected by the parameters, and the degree of entanglement is always kept as 1.
基金Project supported by the Higher Education of Hubei Province of China （Grant No Z200522001） and the Natural Science Foundation of Hubei Province of China （Grant No 2006ABA055）.
文摘Based on the quantum information theory, this paper has investigated the entanglement properties of a system which is composed of the two entangled two-level atoms interacting with the two-mode entangled coherent fields. The influences of the strength of light field and the two parameters of entanglement between the two-mode fields on the field entropy and on the negative eigenvalues of partial transposition of density matrix are discussed by using numerical calculations. The result shows that the entanglement properties in a system of a pairwise entangled states can be controlled by appropriately choosing the two parameters of entanglement between the two-mode entangled coherent fields and the strength of two light fields respectively.
基金Project supported by the Higher Education of Hubei Province of China （Grant No Z200522001） and the Natural Science Foundation of Hubei Province of China （Grant No 2006ABA055）.
文摘By the negative eigenvalues of partial transposition of density matrix, this paper investigates the time evolution of entanglement of the two entangled atoms in the system of two atoms interacting with SchrSdinger cat state. The result shows that the two atoms are always in the entanglement state, and the degree of entanglement between the two atoms exhibits ordinary collapses and revivals at 0.2 degree of entanglement, when the light field is large enough. On the other hand, the reinforcement of three different light fields on the degree of entanglement between two atoms is not evident.
文摘Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic features.The advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns.This study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease classification.First,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature maps.Second,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the foreground.Finally,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava leaves.The proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and RepVGG-B3g4.To evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.
