GPS scintillations associated with cusp dynamics and polar cap patches

This paper investigates the relative scintillation level associated with cusp dynamics (including precipitation, flow shears, etc.) with and without the formation of polar cap patches around the cusp inflow region by the EISCAT Svalbard radar (ESR) and two GPS scintillation receivers. A series of polar cap patches were observed by the ESR between 8:40 and 10:20 UT on December 3, 2011. The polar cap patches combined with the auroral dynamics were associated with a significantly higher GPS phase scintillation level (up to 0.6 rad) than those observed for the other two alternatives, i.e., cusp dynamics without polar cap patches, and polar cap patches without cusp aurora. The cusp auroral dynamics without plasma patches were indeed related to GPS phase scintillations at a moderate level (up to 0.3 rad). The polar cap patches away from the active cusp were associated with sporadic and moderate GPS phase scintillations (up to 0.2 rad). The main conclusion is that the worst global navigation satellite system space weather events on the dayside occur when polar cap patches enter the polar cap and are subject to particle precipitation and flow shears, which is analogous to the nightside when polar cap patches exit the polar cap and enter the auroral oval

TBFormer: Two-Branch Transformer for Image Forgery Localization

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/pu-flow

Ionospheric Flow Vortex Induced by the Sudden Decrease in the Solar Wind Dynamic Pressure

Abrupt changes in the solar wind dynamic pressure can greatly affect the Earth's magnetosphere-ionosphere system. We present an ionospheric flow vortex in the morning sector during the sudden decrease in the solar wind dynamic pressure. The flow vortex was clearly observed by both the Hankasalmi radar and the azimuthal scan mode of the European Incoherent Scatter (EISCAT) Svalbard Radar (ESR). The flow vortex was first seen in the eastern field of view (FOV) of the Hankasalmi radar, and then propagated poleward and westward into the FOV of the ESR. During the passage of the flow vortex, a gradual decrease of electron density was observed by the field-aligned ESR 42 m antenna. When the equatorward directed ionospheric flow reached the ESR site, weak and visible increases in the electron density and electron temperature were observed. This impact was likely caused by soft electron precipitation associated with the clockwise flow vortex and upward field-aligned current. The azimuthal scan mode of the ESR 32 m radar at low elevation angle (30°) allowed us to measure key ionospheric parameters over a larger area (6° in latitude and 120° in azimuthal angle). The latitudinal scan of the electron temperature was used to proxy the equatorward auroral boundary, which shows that the flow vortex was located in the subauroral region. We further demonstrated that it is possible to study the weak increase of electron density by using GPS total electron content (TEC) data. A minor TEC increase was observed near the center of the flow vortex

Large-scale Point Cloud Registration Based on Graph Matching Optimization

Point Clouds Registration is a fundamental and challenging problem in 3D computer vision. It has been shown that the isometric transformation is an essential property in rigid point cloud registration, but the existing methods only utilize it in the outlier rejection stage. In this paper, we emphasize that the isometric transformation is also important in the feature learning stage for improving registration quality. We propose a \underline{G}raph \underline{M}atching \underline{O}ptimization based \underline{Net}work (denoted as GMONet for short), which utilizes the graph matching method to explicitly exert the isometry preserving constraints in the point feature learning stage to improve %refine the point representation. Specifically, we %use exploit the partial graph matching constraint to enhance the overlap region detection abilities of super points ($i.e.,$ down-sampled key points) and full graph matching to refine the registration accuracy at the fine-level overlap region. Meanwhile, we leverage the mini-batch sampling to improve the efficiency of the full graph matching optimization. Given high discriminative point features in the evaluation stage, we utilize the RANSAC approach to estimate the transformation between the scanned pairs. The proposed method has been evaluated on the 3DMatch/3DLoMatch benchmarks and the KITTI benchmark. The experimental results show that our method achieves competitive performance compared with the existing state-of-the-art baselines

Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation

Automatic segmentation of fluid in Optical Coherence Tomography (OCT) images is beneficial for ophthalmologists to make an accurate diagnosis. Although semi-supervised OCT fluid segmentation networks enhance their performance by introducing additional unlabeled data, the performance enhancement is limited. To address this, we propose Superpixel and Confident Learning Guide Point Annotations Network (SCLGPA-Net) based on the teacher-student architecture, which can learn OCT fluid segmentation from limited fully-annotated data and abundant point-annotated data. Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations. The label trust maps provide an indication of the reliability of the pseudo-labels. Furthermore, we propose the Confident Learning Guided Label Refinement (CLGLR) module identifies error information in the pseudo-labels and leads to further refinement. Experiments on the RETOUCH dataset show that we are able to reduce the need for fully-annotated data by 94.22\%, closing the gap with the best fully supervised baselines to a mean IoU of only 2\%. Furthermore, We constructed a private 2D OCT fluid segmentation dataset for evaluation. Compared with other methods, comprehensive experimental results demonstrate that the proposed method can achieve excellent performance in OCT fluid segmentation.Comment: Submission to BSP

GNSS Scintillations in the Cusp, and the Role of Precipitating Particle Energy Fluxes

Using a large data set of ground-based GNSS scintillation observations coupled with in situ particle detector data, we perform a statistical analysis of both the input energy flux from precipitating particles, and the observed occurrence of density irregularities in the northern hemisphere cusp. By examining trends in the two data sets relating to geomagnetic activity, we conclude that observations of irregularities in the cusp grows increasingly likely during storm-time, whereas the precipitating particle energy flux does not. We thus find a weak or nonexistent statistical link between geomagnetic activity and precipitating particle energy flux in the cusp. This is a result of a previously documented tendency for the cusp energy flux to maximize during northward IMF, when density irregularities tend not to be widespread, as we demonstrate. At any rate, even though ionization and subsequent density gradients directly caused by soft electron precipitation in the cusp are not to be ignored for the trigger of irregularities, our results point to the need to scrutinize additional physical processes for the creation of irregularities causing scintillations in and around the cusp. While numerous phenomena known to cause density irregularities have been identified and described, there is a need for a systematic evaluation of the conditions under which the various destabilizing mechanisms become important and how they sculpt the observed ionospheric “irregularity landscape.” As such, we call for a quantitative assessment of the role of particle precipitation in the cusp, given that other factors contribute to the production of irregularities in a major way

Effects of bilirubin on the development and electrical activity of neural circuits

In the past several decades, bilirubin has attracted great attention for central nervous system (CNS) toxicity in some pathological conditions with severely elevated bilirubin levels. CNS function relies on the structural and functional integrity of neural circuits, which are large and complex electrochemical networks. Neural circuits develop from the proliferation and differentiation of neural stem cells, followed by dendritic and axonal arborization, myelination, and synapse formation. The circuits are immature, but robustly developing, during the neonatal period. It is at the same time that physiological or pathological jaundice occurs. The present review comprehensively discusses the effects of bilirubin on the development and electrical activity of neural circuits to provide a systematic understanding of the underlying mechanisms of bilirubin-induced acute neurotoxicity and chronic neurodevelopmental disorders

Steepening Plasma Density Spectra in the Ionosphere: The Crucial Role Played by a Strong E-Region

Based on the Swarm 16 Hz Advanced Plasma Density data set, and using the Swarm A satellite, we apply automatic detection of spectral breaks in seven million sampled plasma density power spectra in the high-latitude F-region ionosphere. This way, we survey the presence of plasma irregularity dissipation due to an enhanced E-region conductance, caused both by solar photoionization and particle precipitation. We introduce a new quantity named the steepening slope index (SSI) which we use to estimate the occurrence rate of break-points in sampled plasma densities. We provide an interpretation of SSI in the context of solar photoionization-induced conductance enhancements of the E-region. We present a comprehensive climatology of the SSI occurrence rate, along with statistics documenting characteristic high-latitude plasma density spectra. In the absence of steepening, the typical spectral index is 2.1. When density spectra steepen, the index is typically 1.6 at large scales, and 2.7 at small scales. We discuss the impact of high-energy deeply penetrating electron precipitation in the diffuse aurora, and precipitating electrons in the aurora at large. Here, a key finding is that near the cusp, where the F-region conductance is enhanced, spectra tend not to steepen. We find that both the diffuse and discrete aurora are modulating F-region plasma irregularity dissipation through an enhancement of E-region conductance, highlighting the role played by factors other than solar zenith angle in high-latitude plasma dynamics. The influence of E-region conductance on spectral shapes indicates the need for a new discussion of how particle precipitation can structure the local winter high-latitude F-region ionosphere