Chiral geometry and rotational structure for 130^{130}Cs in the projected shell model

The projected shell model with configuration mixing for nuclear chirality is developed and applied to the observed rotational bands in the chiral nucleus $^{130}$Cs. For the chiral bands, the energy spectra and electromagnetic transition probabilities are well reproduced. The chiral geometry illustrated in the $K~plot$ and the $azithumal~plot$ is confirmed to be stable against the configuration mixing. The other rotational bands are also described in the same framework

Increase in soil organic carbon by agricultural intensification in northern China

Acknowledgements. This research was supported by National Natural Science Foundation of China (no. 31370527 and 31261140367) and the National Science and Technology Support Program of China (no. 2012BAD14B01-2). The authors gratefully thank the Huantai Agricultural Station for providing of the Soil Fertility Survey data. We also thank Zheng Liang from China Agricultural University for the soil sampling and analysis in 2011. Thanks are extended to Jessica Bellarby for helpful discussion and suggestions.Peer reviewedPublisher PD

Magnetic rotations in 198Pb and 199Pb within covariant density functional theory

Well-known examples of shears bands in the nuclei 198Pb and 199Pb are investigated within tilted axis cranking relativistic mean-field theory. Energy spectra, the relation between spin and rotational frequency, deformation parameters and reduced $M1$ and $E2$ transition probabilities are calculated. The results are in good agreement with available data and with calculations based on the phenomenological pairing plus-quadrupole-quadrupole tilted-axis cranking model. It is shown that covariant density functional theory provides a successful microscopic and fully self-consistent description of magnetic rotation in the Pb region showing the characteristic properties as the shears mechanism and relatively large B(M1) transitions decreasing with increasing spin.Comment: 22 pages, 8 figure

On Fine-tuned Deep Features for Unsupervised Domain Adaptation

Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptation task. In contrast, end-to-end learning based approaches optimise the pre-trained backbones and the customised adaptation modules simultaneously to learn domaininvariant features for UDA. In this work, we explore the potential of combining fine-tuned features and feature transformation based UDA methods for improved domain adaptation performance. Specifically, we integrate the prevalent progressive pseudo-labelling techniques into the fine-tuning framework to extract fine-tuned features which are subsequently used in a state-of-the-art feature transformation based domain adaptation method SPL (Selective Pseudo-Labeling). Thorough experiments with multiple deep models including ResNet-50/101 and DeiTsmall/base are conducted to demonstrate the combination of finetuned features and SPL can achieve state-of-the-art performance on several benchmark datasets

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified classifier for both the source and target domains in the high-dimensional homogeneous feature space without explicit domain alignment. To this end, we employ the effective Selective Pseudo-Labelling (SPL) technique to take advantage of the unlabelled samples in the target domain. Surprisingly, data distribution discrepancy across the source and target domains can be well handled by a computationally simple classifier (e.g., a shallow Multi-Layer Perceptron) trained in the original feature space. Besides, we propose a novel generative model norm-AE to generate synthetic features for the target domain as a data augmentation strategy to enhance the classifier training. Experimental results on several benchmark datasets demonstrate the pseudo-labelling strategy itself can lead to comparable performance to many state-of-the-art methods whilst the use of norm-AE for feature augmentation can further improve the performance in most cases. As a result, our proposed methods (i.e. naiveSPL and norm-AE-SPL) can achieve comparable performance with state-of-the-art methods with the average accuracy of 93.4% and 90.4% on Office-Caltech and ImageCLEF-DA datasets, and achieve competitive performance on Digits, Office31 and Office-Home datasets with the average accuracy of 97.2%, 87.6% and 68.6% respectively

An MHD Model For Magnetar Giant Flares

Giant flares on soft gamma-ray repeaters that are thought to take place on magnetars release enormous energy in a short time interval. Their power can be explained by catastrophic instabilities occurring in the magnetic field configuration and the subsequent magnetic reconnection. By analogy with the coronal mass ejection (CME) events on the Sun, we develop a theoretical model via an analytic approach for magnetar giant flares. In this model, the rotation and/or displacement of the crust causes the field to twist and deform, leading to flux rope formation in the magnetosphere and energy accumulation in the related configuration. When the energy and helicity stored in the configuration reach a threshold, the system loses its equilibrium, the flux rope is ejected outward in a catastrophic way, and magnetic reconnection helps the catastrophe develop to a plausible eruption. By taking SGR 1806 - 20 as an example, we calculate the free magnetic energy released in such an eruptive process and find that it is more than $10^{47}$ ergs, which is enough to power a giant flare. The released free magnetic energy is converted into radiative energy, kinetic energy and gravitational energy of the flux rope. We calculated the light curves of the eruptive processes for the giant flares of SGR 1806 - 20, SGR 0526-66 and SGR 1900+14, and compared them with the observational data. The calculated light curves are in good agreement with the observed light curves of giant flares.Comment: Accepted to Ap