In this paper,we give a survey of our recent results on extension theorems on Kähler manifolds for holomorphic sections or cohomology classes of(pluri)canonical line bundles twisted with holomorphic line bundles ...In this paper,we give a survey of our recent results on extension theorems on Kähler manifolds for holomorphic sections or cohomology classes of(pluri)canonical line bundles twisted with holomorphic line bundles equipped with singular metrics,and also discuss their applications and the ideas contained in the proofs.展开更多
In this paper, we obtain Chen’s inequalities in (k,?μ)-contact space form with a semi-symmetric non-metric connection. Also we obtain the inequalites for Ricci and K-Ricci curvatures.
Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Netw...Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments.展开更多
We prove that, under a semi-ampleness type assumption on the twisted canonical line bundle, the conical Khler-Ricci flow on a minimal elliptic Khler surface converges in the sense of currents to a generalized coni...We prove that, under a semi-ampleness type assumption on the twisted canonical line bundle, the conical Khler-Ricci flow on a minimal elliptic Khler surface converges in the sense of currents to a generalized conical Khler-Einstein on its canonical model. Moreover,the convergence takes place smoothly outside the singular fibers and the chosen divisor.展开更多
WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用...WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用固定的K值,其定位精度在实际使用时具有局限性。尽管动态K的方案被提出,但是由于引入了新的不确定性参数,因此,并未真正解决问题。针对这个问题,提出了一种自适应动态K的WKNN室内定位方法。提出的算法的K值自适应调整仅依赖于离线和在线数据,即可以不引入新的不确定参数。在这个前提下,提出的算法采用"多雷达搜索策略"的方式自适应选择近邻数K值进行在线位置估计。在真实环境中采样了大量数据进行了试验。试验结果表明,提出的算法可根据在线情况自适应调整K值,获得了较好的定位结果。展开更多
基金the National Natural Science Foundation of China(11688101 and 11431013)the National Natural Science Foundation of China(12022110,11201347 and 11671306).
文摘In this paper,we give a survey of our recent results on extension theorems on Kähler manifolds for holomorphic sections or cohomology classes of(pluri)canonical line bundles twisted with holomorphic line bundles equipped with singular metrics,and also discuss their applications and the ideas contained in the proofs.
文摘In this paper, we obtain Chen’s inequalities in (k,?μ)-contact space form with a semi-symmetric non-metric connection. Also we obtain the inequalites for Ricci and K-Ricci curvatures.
文摘Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks(CNNs)for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample,the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition,training with binary crossentropy loss sometimes leads to poor margins.In this paper,a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition(HCR)tasks.The novelties of the proposed network are in.1)Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2)Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6%and 82%in few-and zero-shot learning respectively.In addition,a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments.
基金supported by the Science and Technology Development Fund(Macao S.A.R.),Grant FDCT/016/2013/A1the Project MYRG2015-00235-FST of the University of Macao
文摘We prove that, under a semi-ampleness type assumption on the twisted canonical line bundle, the conical Khler-Ricci flow on a minimal elliptic Khler surface converges in the sense of currents to a generalized conical Khler-Einstein on its canonical model. Moreover,the convergence takes place smoothly outside the singular fibers and the chosen divisor.
文摘WiFi室内定位已被广泛研究,并且提出了许多解决方案,其中以接收信号强度(received signal strength,RSS)作为位置指纹的加权K-最近邻(weighted K-Nearest neighbor,WKNN)算法是目前使用最广泛的位置指纹算法之一。由于WKNN算法通常采用固定的K值,其定位精度在实际使用时具有局限性。尽管动态K的方案被提出,但是由于引入了新的不确定性参数,因此,并未真正解决问题。针对这个问题,提出了一种自适应动态K的WKNN室内定位方法。提出的算法的K值自适应调整仅依赖于离线和在线数据,即可以不引入新的不确定参数。在这个前提下,提出的算法采用"多雷达搜索策略"的方式自适应选择近邻数K值进行在线位置估计。在真实环境中采样了大量数据进行了试验。试验结果表明,提出的算法可根据在线情况自适应调整K值,获得了较好的定位结果。
