Decoupled Local Aggregation for Point Cloud Learning

The unstructured nature of point clouds demands that local aggregation be adaptive to different local structures. Previous methods meet this by explicitly embedding spatial relations into each aggregation process. Although this coupled approach has been shown effective in generating clear semantics, aggregation can be greatly slowed down due to repeated relation learning and redundant computation to mix directional and point features. In this work, we propose to decouple the explicit modelling of spatial relations from local aggregation. We theoretically prove that basic neighbor pooling operations can too function without loss of clarity in feature fusion, so long as essential spatial information has been encoded in point features. As an instantiation of decoupled local aggregation, we present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed, and only pointwise convolutions plus edge max-pooling are used for local aggregation then. Further, a regularization term is employed to reduce potential ambiguity through the prediction of relative coordinates. Conceptually simple though, experimental results on five classic benchmarks demonstrate that DeLA achieves state-of-the-art performance with reduced or comparable latency. Specifically, DeLA achieves over 90\% overall accuracy on ScanObjectNN and 74\% mIoU on S3DIS Area 5. Our code is available at https://github.com/Matrix-ASC/DeLA

Extremely large magnetoresistance in topologically trivial semimetal α\alpha-WP2_2

Extremely large magnetoresistance (XMR) was recently discovered in many non-magnetic materials, while its underlying mechanism remains poorly understood due to the complex electronic structure of these materials. Here, we report an investigation of the $\alpha$-phase WP$_2$, a topologically trivial semimetal with monoclinic crystal structure (C2/m), which contrasts to the recently discovered robust type-II Weyl semimetal phase in $\beta$-WP$_2$. We found that $\alpha$-WP$_2$ exhibits almost all the characteristics of XMR materials: the near-quadratic field dependence of MR, a field-induced up-turn in resistivity following by a plateau at low temperature, which can be understood by the compensation effect, and high mobility of carriers confirmed by our Hall effect measurements. It was also found that the normalized MRs under different magnetic fields has the same temperature dependence in $\alpha$-WP$_2$, the Kohler scaling law can describe the MR data in a wide temperature range, and there is no obvious change in the anisotropic parameter $\gamma$ value with temperature. The resistance polar diagram has a peanut shape when field is rotated in $\textit{ac}$ plane, which can be understood by the anisotropy of Fermi surface. These results indicate that both field-induced-gap and temperature-induced Lifshitz transition are not the origin of up-turn in resistivity in the $\alpha$-WP$_2$ semimetal. Our findings establish $\alpha$-WP$_2$ as a new reference material for exploring the XMR phenomena.Comment: 18 pages, 12 figure

A new Monte Carlo sampling method based on Gaussian Mixture Model for imbalanced data classification

Imbalanced data classification has been a major topic in the machine learning community. Different approaches can be taken to solve the issue in recent years, and researchers have given a lot of attention to data level techniques and algorithm level. However, existing methods often generate samples in specific regions without considering the complexity of imbalanced distributions. This can lead to learning models overemphasizing certain difficult factors in the minority data. In this paper, a Monte Carlo sampling algorithm based on Gaussian Mixture Model (MCS-GMM) is proposed. In MCS-GMM, we utilize the Gaussian mixed model to fit the distribution of the imbalanced data and apply the Monte Carlo algorithm to generate new data. Then, in order to reduce the impact of data overlap, the three sigma rule is used to divide data into four types, and the weight of each minority class instance based on its neighbor and probability density function. Based on experiments conducted on Knowledge Extraction based on Evolutionary Learning datasets, our method has been proven to be effective and outperforms existing approaches such as Synthetic Minority Over-sampling TEchnique

Hermansky-Pudlak syndrome type 2: A rare cause of severe periodontitis in adolescents—A case study

Background and aimsHermansky-Pudlak syndrome (HPS) is an autosomal recessive disorder characterized by oculocutaneous albinism (OCA) and platelet storage pool deficiency. The HPS-2 subtype is distinguished by neutropenia, and little is known about its periodontal phenotype in adolescents. AP3B1 is the causative gene for HPS-2. A 13-year-old Chinese girl presented to our department suffering from gingival bleeding and tooth mobility. Her dental history was otherwise unremarkable. Suspecting some systemic diseases as the underlying cause, the patient was referred for medical consultation, a series of blood tests, and genetic tests. In this case study, periodontal status and mutation screening of one HPS-2 case are presented.MethodsBlood analysis including a complete blood count (CBC) and glycated hemoglobin levels were measured. Platelet transmission electron microscopy (PTEM) was performed to observe the dense granules in platelets. Whole-exome sequencing (WES) and Sanger sequencing were performed to confirm the pathogenic variants.ResultsA medical diagnosis of HPS-2 was assigned to the patient. Following the medical diagnosis, a periodontal diagnosis of “periodontitis as a manifestation of systemic disease” was assigned to the patient. We identified novel compound heterozygous variants in AP3B1 (NM_003664.4: exon7: c.763C>T: p.Q255*) and (NM_003664.4: exon1: c.53_56dup: p.E19Dfs*21) in this Chinese pedigree with HPS-2.ConclusionThis case study indicates the importance of periodontitis as a possible indicator of underlying systemic disease. Systemic disease screening is needed when a young patient presents with unusual, severe periodontitis, as the oral condition may be the first of a systemic abnormality. Our work also expands the spectrum of AP3B1 mutations and further provides additional genetic testing information for other HPS-2 patients

The role of 1-octyl-3-methylimidazolium hexafluorophosphate in anticorrosion coating formula development

A hydrophobic ionic liquid (1-octyl-3-methylimidazolium hexafluorophosphate, C8mimPF6) with a function of inhibiting corrosion was encapsulated at different concentrations in the copolymer of poly (methyl methacrylate) and poly (butyl acrylate) through miniemulsion polymerization. These latexes were coated on steel samples whose corrosion properties were evaluated by electrochemical techniques. It was found that increasing the C8mimPF6 concentration from 0 wt% to 30 wt%, the corrosion inhibition efficiency was remarkably improved from 41% to 89% based on the charge transfer resistance and from 64% to 87% based on the corrosion current density, respectively. The ionic liquid did not attend the reaction during latex preparation but behaved as corrosion inhibitors on the steel surface. Such an anticorrosion effect could be ascribed to the physical adsorption of the C8mim+ cation on the reaction sites and the hydrophobicity enhancement resulting from the hydrophobic PF6− anion

Chinese herb medicine in augmented reality

Augmented reality becomes popular in education gradually, which provides a contextual and adaptive learning experience. Here, we develop a Chinese herb medicine AR platform based the 3dsMax and the Unity that allows users to visualize and interact with the herb model and learn the related information. The users use their mobile camera to scan the 2D herb picture to trigger the presentation of 3D AR model and corresponding text information on the screen in real-time. The system shows good performance and has high accuracy for the identification of herbal medicine after interference test and occlusion test. Users can interact with the herb AR model by rotating, scaling, and viewing transformation, which effectively enhances learners' interest in Chinese herb medicine

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure