Using a pump with a multi-line spectrum to broaden the Brillouin gain bandwidth is an effective way to achieve lowdistortion amplification with high gain. Here, we theoretically and experimentally investigate the gene...Using a pump with a multi-line spectrum to broaden the Brillouin gain bandwidth is an effective way to achieve lowdistortion amplification with high gain. Here, we theoretically and experimentally investigate the generation of a broadband Brillouin gain spectrum based on multi-frequency intensity modulation in an optical fiber. The arbitrary bandwidth of the Brillouin gain spectrum of stimulated Brillouin scattering(SBS) can be obtained as expected. In our experiment, a broadband Brillouin gain spectrum with a bandwidth of about 200 MHz is demonstrated. We also achieve a low-distortion amplification of a weak signal, whose maximum magnification is 65 d B for a-68-dBm input power signal.展开更多
Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD)...Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.展开更多
Propagation stability of non-paraxial beam in nonlinear Kerr media is investigated with a linear stability method. Both theoretical analysis and numerical simulation show that modulation instability (MI) gain spectrum...Propagation stability of non-paraxial beam in nonlinear Kerr media is investigated with a linear stability method. Both theoretical analysis and numerical simulation show that modulation instability (MI) gain spectrum has three different distribution features determined by the times of incident power p0 and the non-paraxial parameter a. Furthermore, the corresponding criterion is put forward to distinguish the three different distributions. Key words non-paraxial beam - modulation instability(MI) - gain spectrum - Kerr media PASC 2001 42.65-k Project supported by the National Natural Science Foundation of China(Grant No. 60177020)展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.61605034)
文摘Using a pump with a multi-line spectrum to broaden the Brillouin gain bandwidth is an effective way to achieve lowdistortion amplification with high gain. Here, we theoretically and experimentally investigate the generation of a broadband Brillouin gain spectrum based on multi-frequency intensity modulation in an optical fiber. The arbitrary bandwidth of the Brillouin gain spectrum of stimulated Brillouin scattering(SBS) can be obtained as expected. In our experiment, a broadband Brillouin gain spectrum with a bandwidth of about 200 MHz is demonstrated. We also achieve a low-distortion amplification of a weak signal, whose maximum magnification is 65 d B for a-68-dBm input power signal.
基金This work was supported by the National Natural Science Foundation of China(No.61906006).
文摘Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.
文摘Propagation stability of non-paraxial beam in nonlinear Kerr media is investigated with a linear stability method. Both theoretical analysis and numerical simulation show that modulation instability (MI) gain spectrum has three different distribution features determined by the times of incident power p0 and the non-paraxial parameter a. Furthermore, the corresponding criterion is put forward to distinguish the three different distributions. Key words non-paraxial beam - modulation instability(MI) - gain spectrum - Kerr media PASC 2001 42.65-k Project supported by the National Natural Science Foundation of China(Grant No. 60177020)
