We use the redshift Hubble parameter H(z) data derived from relative galaxy ages, distant type Ia supernovae (SNe Ia), the Baryonic Acoustic Oscillation (BAO) peak, and the Cosmic Microwave Background (CMB) sh...We use the redshift Hubble parameter H(z) data derived from relative galaxy ages, distant type Ia supernovae (SNe Ia), the Baryonic Acoustic Oscillation (BAO) peak, and the Cosmic Microwave Background (CMB) shift parameter data, to constrain cosmological parameters in the Undulant Universe. We marginalize the like- lihood functions over h by integrating the probability density 19 ∝ e-x^2/2. By using a Markov Chain Monte Carlo (MCMC) technique, we obtain the best fitting results and give the confidence regions in the b - Ωm0 plane. Then we compare their constraints. Our results show that the H(z) data play a similar role with the SNe Ia data in cosmological study. By presenting the independent and joint constraints, we find that the BAO and CMB data play very important roles in breaking the degeneracy compared with the H(z) and SNe Ia data alone. Combined with the BAO or CMB data, one can remarkably improve the constraints. The SNe Ia data sets constrain Ωm0 much tighter than the H(z) data sets, but the H(z) data sets constrain b much tighter than the SNe Ia data sets. All these results show that the Undulant Universe approaches the ACDM model. We expect more H(z) data to constrain cosmological parameters in the future.展开更多
胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不...胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不平衡、卷积神经网络参数量过大等问题.针对上述问题,提出了一种端到端的基于八度卷积的ResNet(octave convolution based residual network,OC-ResNet)结构.首先,利用八度卷积改进ResNet中的普通卷积,将高低频特征分离,增强对高频信息的提取,以更好地表达胸部病灶的特异性特征,降低模型计算复杂度.其次,利用渐进式迁移学习,将OC-ResNet在ImageNet数据集进行预训练,获得网络的初始参数,然后固定网络浅层参数,在ChestX-Ray14数据集上微调网络深层参数.最后,为改善样本不平衡问题,网络训练时,采用了焦点损失函数,增加样本数较少类别的权重.在ChestX-Ray14数据集上的实验结果表明,OC-ResNet对14种胸部疾病分类的平均AUC值达到0.856,与目前先进的深度学习方法相比,其中13种疾病分类的AUC值达到最优,同时,计算复杂度相比基础网络降低了44.77%.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.10473002)the Ministry of Science and Technology National Basic Science program (project 973,Grant No.2009CB24901)the Fundamental Research Funds for the Central Universities
文摘We use the redshift Hubble parameter H(z) data derived from relative galaxy ages, distant type Ia supernovae (SNe Ia), the Baryonic Acoustic Oscillation (BAO) peak, and the Cosmic Microwave Background (CMB) shift parameter data, to constrain cosmological parameters in the Undulant Universe. We marginalize the like- lihood functions over h by integrating the probability density 19 ∝ e-x^2/2. By using a Markov Chain Monte Carlo (MCMC) technique, we obtain the best fitting results and give the confidence regions in the b - Ωm0 plane. Then we compare their constraints. Our results show that the H(z) data play a similar role with the SNe Ia data in cosmological study. By presenting the independent and joint constraints, we find that the BAO and CMB data play very important roles in breaking the degeneracy compared with the H(z) and SNe Ia data alone. Combined with the BAO or CMB data, one can remarkably improve the constraints. The SNe Ia data sets constrain Ωm0 much tighter than the H(z) data sets, but the H(z) data sets constrain b much tighter than the SNe Ia data sets. All these results show that the Undulant Universe approaches the ACDM model. We expect more H(z) data to constrain cosmological parameters in the future.
文摘胸部疾病高发,且有些疾病种类的癌症转变率很高,因此基于卷积神经网络的胸部X光图像疾病自动检测分类方法是计算机辅助诊断的研究热点之一.然而,目前的自动分类方法仍面临胸部病灶的X光图像特异性特征表达不充分、不同胸部疾病发病率不平衡、卷积神经网络参数量过大等问题.针对上述问题,提出了一种端到端的基于八度卷积的ResNet(octave convolution based residual network,OC-ResNet)结构.首先,利用八度卷积改进ResNet中的普通卷积,将高低频特征分离,增强对高频信息的提取,以更好地表达胸部病灶的特异性特征,降低模型计算复杂度.其次,利用渐进式迁移学习,将OC-ResNet在ImageNet数据集进行预训练,获得网络的初始参数,然后固定网络浅层参数,在ChestX-Ray14数据集上微调网络深层参数.最后,为改善样本不平衡问题,网络训练时,采用了焦点损失函数,增加样本数较少类别的权重.在ChestX-Ray14数据集上的实验结果表明,OC-ResNet对14种胸部疾病分类的平均AUC值达到0.856,与目前先进的深度学习方法相比,其中13种疾病分类的AUC值达到最优,同时,计算复杂度相比基础网络降低了44.77%.