A revision of the African genus Mesanthemum (Eriocaulaceae)

Mesanthemum is a genus comprising 16 species in the family Eriocaulaceae and is native to Africa and Madagascar. Eriocaulaceae are characterised by a basal tuft or rosette of narrow leaves and small flowers in heads. Mesanthemum can be recognised by diplostemonous flowers and fused glandular pistillate petals. While most Mesanthemum species are large perennial herbs, two small ephemeral species from West Africa, M. albidum and M. auratum differ from the rest of the genus by their shorter life cycle, smaller size, simpler floral structures and different seed surface patterning. A molecular phylogenetic study, morphological comparisons and scanning electron microscope (SEM) examination of seed coat sculpture were carried out to determine whether they should be separated as a new genus. The molecular results indicate that the two ephemeral species are nested in the Mesanthemum clade. However, they are not closely related to each other. All species of Mesanthemum are here revised, including the description of a new species M. alenicola from Equatorial Guinea. An identification key is provided, together with taxonomic descriptions, synonymy and notes. Images of the seeds as seen under SEM are provided where available. Lectotypifications are provided for Mesanthemum albidum, M. bennae, M. pilosum, M. prescottianum, M. pubescens and M. variabile. A neotype is selected for M. rutenbergianum, which is synonymised with M. pubescens

Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model

Collisionless magnetic reconnection typically requires kinetic treatments that are, in general, computationally expensive compared to fluid-based models. In this study, we use the magnetohydrodynamics with adaptively embedded particle-in-cell (MHD-AEPIC) model to study the interaction of two magnetic flux ropes. This innovative model embeds one or more adaptive PIC regions into a global MHD simulation domain such that the kinetic treatment is only applied in regions where kinetic physics is prominent. We compare the simulation results among three cases: 1) MHD with adaptively embedded PIC regions, 2) MHD with statically (or fixed) embedded PIC regions, and 3) a full PIC simulation. The comparison yields good agreement when analyzing their reconnection rates and magnetic island separations, as well as the ion pressure tensor elements and ion agyrotropy. In order to reach a good agreement among the three cases, large adaptive PIC regions are needed within the MHD domain, which indicates that the magnetic island coalescence problem is highly kinetic in nature where the coupling between the macro-scale MHD and micro-scale kinetic physics is important.Comment: 9 pages, 10 figure

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at \url{https://github.com/yxiwang/IDM}.Comment: Accepted by ICCV 202

Alzheimer’s disease and retinal neurodegeneration share a consistent stress response of the neurovascular unit

Background: The pathogenesis of Alzheimer’s disease (AD) is characterized by neuronal injury, activation of microglia and astrocytes, deposition of amyloid-beta and secondary vessel degeneration. In the polycystic kidney disease (PKD) rat model, we observed neuronal injury, microglial activation and vasoregression. We speculated that this neuroretinal degeneration shares important pathogenetic steps with AD. Therefore, we determined the activation of astrocytes and the accumulation of amyloid-beta in PKD retinae. Methods: Immunohistochemistry of PKD retinae for vimentin, carboxymethyllysin, beta-Amyloid 1-42, High-Mobility-Group-Protein B1 and amyloid protein precursor was performed. Results: Adjunct to astrocyte activation, accumulation of beta-Amyloid 1-42 and High-Mobility-Group-Protein B1 in astrocytes and around vessels of the superficial network was found in PKD retinae prior to the onset of vasoregression. Amyloid precursor protein was localized adjacent to the outer segment of photoreceptors in PKD and control rats. The parallel appearance of AD-related peptides indicates an alarmine based response to photoreceptor degeneration and secondary vasoregression. Conclusion: The model has broad overlap with AD and may be suitable to study beneficial pharmacological concepts. Copyright (c) 2012 S. Karger AG, Base

Volumetric Wireframe Parsing from Neural Attraction Fields

The primal sketch is a fundamental representation in Marr's vision theory, which allows for parsimonious image-level processing from 2D to 2.5D perception. This paper takes a further step by computing 3D primal sketch of wireframes from a set of images with known camera poses, in which we take the 2D wireframes in multi-view images as the basis to compute 3D wireframes in a volumetric rendering formulation. In our method, we first propose a NEural Attraction (NEAT) Fields that parameterizes the 3D line segments with coordinate Multi-Layer Perceptrons (MLPs), enabling us to learn the 3D line segments from 2D observation without incurring any explicit feature correspondences across views. We then present a novel Global Junction Perceiving (GJP) module to perceive meaningful 3D junctions from the NEAT Fields of 3D line segments by optimizing a randomly initialized high-dimensional latent array and a lightweight decoding MLP. Benefitting from our explicit modeling of 3D junctions, we finally compute the primal sketch of 3D wireframes by attracting the queried 3D line segments to the 3D junctions, significantly simplifying the computation paradigm of 3D wireframe parsing. In experiments, we evaluate our approach on the DTU and BlendedMVS datasets with promising performance obtained. As far as we know, our method is the first approach to achieve high-fidelity 3D wireframe parsing without requiring explicit matching.Comment: Technical report; Video can be found at https://youtu.be/qtBQYbOpVp

Analysis of Urban Impervious Surface in Coastal Cities: A Case Study in Lianyungang, China

Impervious surface is an important indicator of the level of urbanization. It is of great significance to study the impervious surface to promote the sustainable development of the city. In the process of urban development, the increase of impervious surface cities is bound to be accompanied by a reduction of one or more types of land use in the city. This paper, taking Lianyungang as an example, introduces the methods of extracting urban impervious surface based on VIS model, NDVI (normalized vegetation index), MNDWI (modified normalized water body index), and unsupervised classification, analyzes the changes of impervious surface in Lianyungang from 1987 to 2014, and on this basis, analyzes the trend and driving forces of land use types in Lianyungang city in depth. The results show that the impervious surface of Lianyungang increased by a total of 29.70% between 1987 and 2014. While the impervious surface continues to increase, the area of cultivated land and coastal areas (including salt works and tidal flats) has been greatly reduced, and the types of land use have undergone significant changes

Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

Robust autonomous driving requires agents to accurately identify unexpected areas in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Previous effort usually resorts to uncertainty estimation and sample synthesis from classification tasks, which ignore the context information and sometimes requires auxiliary datasets with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing the likelihood of self-generated anomaly pixels. To this end, we design two estimators for anomaly likelihood estimation, one is a simple task-agnostic binary estimator and the other depicts anomaly likelihood as residual of task-oriented energy model. Based on proposed estimators, we further incorporate our framework with likelihood-guided mask refinement process to extract informative anomaly pixels for model training. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves competitive performance to other SOTA schemes

Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods

First molecular phylogenetic insights into the evolution of Eriocaulon (Eriocaulaceae, Poales)

Eriocaulon is a genus of c. 470 aquatic and wetland species of the monocot plant family Eriocaulaceae. It is widely distributed in Africa, Asia and America, with centres of species richness in the tropics. Most species of Eriocaulon grow in wetlands although some inhabit shallow rivers and streams with an apparent adaptive morphology of elongated submerged stems. In a previous molecular phylogenetic hypothesis, Eriocaulon was recovered as sister of the African endemic genus Mesanthemum. Several regional infrageneric classifications have been proposed for Eriocaulon. This study aims to critically assess the existing infrageneric classifications through phylogenetic reconstruction of infrageneric relationships, based on DNA sequence data of four chloroplast markers and one nuclear marker. There is little congruence between our molecular results and previous morphology-based infrageneric classifications. However, some similarities can be found, including Fyson’s sect. Leucantherae and Zhang’s sect. Apoda. Further phylogenetic studies, particularly focusing on less well sampled regions such as the Neotropics, will help provide a more global overview of the relationships in Eriocaulon and may enable suggesting the first global infrageneric classification

Virtual Reality Research: Design Virtual Education System for Epidemic (COVID-19) Knowledge to Public

open access articleAdvances in information and communication technologies have created a range of new products and services for the well-being of society. Virtual Reality (VR) technology has shown enormous potential in educational, commercial, and medical fields. The recent COVID-19 outbreak highlights a poor global performance in communicating epidemic knowledge to the public. Considering the potential of VR, the research starts from analyzing how to use VR technology to improve public education in COVID-19. The research uses Virtual Storytelling Technology (VST) to promote enthusiasm in user participation. A Plot-based VR education system is proposed in order to provide an immersive, explorative, educational experiences. The system includes three primary modules: the Tutorial Module, the Preparation Module, and the Investigation Module. To remove any potential confusion in the user, the research aims to avoid extremely complicated medical professional content and uses interactive, entertainment methods to improve user participation. In order to evaluate the performance efficiency of the system, we conducted performance evaluations and a user study with 80 participants. Compared with traditional education, the experimental results show that the VR education system can used as an effective educational tool for epidemic (COVID-19) fundamental knowledge. The VR technology can assist government agencies and public organizations to increase public understanding of the spread the epidemic (COVID-19