Anderson Localization from Berry-Curvature Interchange in Quantum Anomalous Hall System

We theoretically investigate the localization mechanism of the quantum anomalous Hall effect (QAHE) in the presence of spin-flip disorders. We show that the QAHE keeps quantized at weak disorders, then enters a Berry-curvature mediated metallic phase at moderate disorders, and finally goes into the Anderson insulating phase at strong disorders. From the phase diagram, we find that at the charge neutrality point although the QAHE is most robust against disorders, the corresponding metallic phase is much easier to be localized into the Anderson insulating phase due to the \textit{interchange} of Berry curvatures carried respectively by the conduction and valence bands. At the end, we provide a phenomenological picture related to the topological charges to better understand the underlying physical origin of the QAHE Anderson localization.Comment: 6 pages, 4 figure

Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation

A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them. Under this self-training paradigm, some competitive methods have sought to the latent-space information, which establishes the feature centroids (a.k.a prototypes) of the semantic classes and determines the pseudo label candidates by their distances from these centroids. In this paper, we argue that the latent space contains more information to be exploited thus taking one step further to capitalize on it. Firstly, instead of merely using the source-domain prototypes to determine the target pseudo labels as most of the traditional methods do, we bidirectionally produce the target-domain prototypes to degrade those source features which might be too hard or disturbed for the adaptation. Secondly, existing attempts simply model each category as a single and isotropic prototype while ignoring the variance of the feature distribution, which could lead to the confusion of similar categories. To cope with this issue, we propose to represent each category with multiple and anisotropic prototypes via Gaussian Mixture Model, in order to fit the de facto distribution of source domain and estimate the likelihood of target samples based on the probability density. We apply our method on GTA5->Cityscapes and Synthia->Cityscapes tasks and achieve 61.2 and 62.8 respectively in terms of mean IoU, substantially outperforming other competitive self-training methods. Noticeably, in some categories which severely suffer from the categorical confusion such as "truck" and "bus", our method achieves 56.4 and 68.8 respectively, which further demonstrates the effectiveness of our design

One-Stage Cascade Refinement Networks for Infrared Small Target Detection

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.Comment: Submitted to TGR

Spatial-Temporal ARX Modeling and Optimization for Polymer Flooding

A new polymer flooding model based on spatial-temporal decomposition and autoregressive model with external input (ARX) (STDARX model) is proposed. Karhunen-Loeve (K-L) decomposition is used to model the two-dimensional state parameters of reservoir (such as water saturation, pressure, and grid concentration). The polymer injection concentration and time coefficient got from the decomposition are taken as the input and output information. After being identified by least square method, the time iterative ARX models of all state variables are obtained, we build the ARX model among pressure, water saturation, grid concentration, and moisture content of production well, and identify it with recursive least-squares (RLS) method. After combining the above two models, we get the STDARX model of polymer flooding. The accuracy is proved by model with four injection wells and nine production wells through data which is obtained from mechanism model. In order to enhance the polymer flooding oil recovery when oil price is changing, iterative dynamic programming (IDP) is applied to optimize the STDARX model, to get the optimal injection of production scheme

Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping

Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods