Massive Dirac surface states in topological insulator/magnetic insulator heterostructures

Topological insulators are new states of matter with a bulk gap and robust gapless surface states protected by time-reversal symmetry. When time-reversal symmetry is broken, the surface states are gapped, which induces a topological response of the system to electromagnetic field--the topological magnetoelectric effect. In this paper we study the behavior of topological surface states in heterostructures formed by a topological insulator and a magnetic insulator. Several magnetic insulators with compatible magnetic structure and relatively good lattice matching with topological insulators ${\rm Bi_2Se_3}, {\rm Bi_2Se_3}, {\rm Sb_2Te_3}$ are identified, and the best candidate material is found to be MnSe, an anti-ferromagnetic insulator. We perform first-principles calculation in ${\rm Bi_2Se_3/MnSe}$ superlattices and obtain the surface state bandstructure. The magnetic exchange coupling with MnSe induces a gap of $\sim$54 meV at the surface states. In addition we tune the distance between Mn ions and TI surface to study the distance dependence of the exchange coupling.Comment: 8 pages, 7 figure

The Effect of Training Dataset Size on SAR Automatic Target Recognition Using Deep Learning

Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy

Agriculture, Income and Conflicts

Since the 1950s, armed conflicts have become more and more recurrent. Most conflicts occur in countries where incomes are heavily dependent on the agricultural sector. This dissertation aims to systematically investigate the interconnection between agriculture, income and conflicts. The first chapter is the introduction of this dissertation. Background information about conflicts is provided in this chapter. In particular, we offer statistical evidence about the quantity of conflicts, distribution of conflicts in terms of time and location and the number of deaths as the result of these conflicts. This background information is important for understanding the severity of conflicts and the significance of reducing them. The second chapter is the overview chapter. The purpose of the overview chapter is to provide a literature review about the interrelationship between agriculture, income and conflicts. In this overview chapter, we start our discussion about how conflicts can hinder economic development emphasizing the importance of studying conflicts. We also discuss the development of conflict-related studies in the literature, estimation methods and potential issues when researchers attempt to investigate the effect of income variations on conflicts. We then end this chapter by analyzing the role of agriculture, especially the impact of agricultural productivity on conflicts. The third chapter is the main study in this dissertation and is about estimating the effect of rainfall shocks on conflicts. We thoroughly examine the results on the negative relationship between rainfall shocks and conflicts in African countries from Miguel, Satyanath and Sergenti (2004). We consider the role of data revision and cross-sectional dependence in their estimation. We find that the negative relationship between rainfall shocks and conflicts in Miguel, Satyanath and Sergenti (2004) is not valid when the revised rainfall and conflicts datasets are used in their estimation. However, we propose a new estimator that is able to take cross-sectional dependence arising from spatially-dependent weather patterns and cross-border conflict spill-overs into account to examine the link between rainfall shocks and conflicts. Using this new estimator, we find that rainfall variations are indeed a determinant of conflicts. The fourth chapter is another main study and examines the effects of productivity-enhancing technology in agriculture on conflicts. We consider the commercial legalization of Genetically-Modified (GM) soybean cultivation in Brazil in 2003 and investigate the effects of GM soybean cultivation on land conflicts in Brazil. In this chapter, we provide a theoretical model to show that the enhancement of agricultural productivity induced by GM soybean cultivation can reduce land value and then mitigate land conflicts. To assess the validity of this theoretical prediction, we employ the Difference-in-Differences estimation and find that states that have more land that is suitable for cultivating GM soybeans after the legalization in 2003 are negatively associated with land conflicts. The empirical results on the mitigating effect of GM soybean cultivation on land conflicts are reinforced by a series of robustness checks. The fifth chapter is the conclusion of this dissertation. Specifically, we summarize the contents and achievement of this dissertation.Thesis (Ph.D.) -- University of Adelaide, School of Economics, 201

Detection of Small Objects in UAV Images via an Improved Swin Transformer-based Model

Automated detection of small objects such as vehicles in images of complex urban environments taken by unmanned aerial vehicles (UAV) is one of the most challenging tasks in computer vision and remote sensing communities, with various applications ranging from traffic congestion surveillance to vision systems in intelligent transportation. Deep learning models, most of which are based on convolutional neural networks (CNNs), have been commonly used to automatically detect objects in UAV images. However, the detection accuracy is still often unsatisfactory due to the shortcomings of CNNs. For instance, CNN collects data from nearby pixels, but spatial information is lost due to the pooling operations. As such, it is difficult for CNNs to model certain long-range dependencies. In this thesis, we propose a Swin Transformer-based model that incorporates convolutions with the Swin Transformer to extract more local information, mitigating the problem of small object detection from complex backgrounds in UAV images and further improving the detection accuracy. By using the Swin Transformer, our model leverages both the local feature extraction of convolutions and the global feature modeling of transformers. The framework was designed with two main modules, a local context enhancement (LCE) module and a Residual U-Feature Pyramid Network (RSU-FPN) module. The LCE module is used to implement dilated convolution and increase the receptive field of each image pixel. By combining with the Swin Transformer block, it can efficiently encode various spatial contextual information and detect local associations and structural information within UAV images. In addition, the RSU-FPN module is designed as a two-level nested U-shaped structure with skip connections to integrate multi-scale feature maps. A loss function combining normalized Gaussian Wasserstein distance and L1 loss is also introduced, which allows the model to be trained using imbalanced data. The proposed method was compared with the state-of-the-art methods on the UAVDT dataset and Vis-Drone dataset. Our experimental results obtained on the UAVDT dataset indicated that our proposed method increased the average precision (AP) by 21.6%, 22.3% and 25.5% over Cascade R-CNN, PVT and Dynamic R-CNN detectors, respectively, demonstrating its effectiveness and reliability on small object detection from UAV images

Aortic valve tear with severe aortic regurgitation following blunt chest trauma

An aortic valve tear associated with aortic regurgitation following blunt chest trauma is seldom seen. In this case, a 55-year-old man sustained a non-penetrating chest injury caused by a sudden fall from 10 meters. This led to a sizable tear in the left coronary cusp associated with severe aortic insufficiency. The case was treated successfully by surgical replacement of the aortic valve with a mechanical prosthesis