IPC-Net: Incomplete point cloud classification network based on data augmentation and similarity measurement

Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness.     

数据安全治理现状研究与分析

近年来,国内外数据泄露事件频发,大量企业的商业利益、声誉受损。数据安全法律法规相继颁布,监管力度不断升级,企业逐渐意识到数据安全治理的重要性与紧迫性。通过对2021年开展的企业数据安全治理能力评估现状进行整理,总结企业数据安全治理工作在组织建设、人才培养、技术工具等方面的现状与趋势,提供能力提升思路,以供业界参考。     

基于LRC与多样本扩充的指静脉识别方法北大核心CSCD

针对目前手指静脉识别由于训练样本不足引起图像识别率低的问题,提出基于线性回归分类(linear regression classification,LRC)与多样本扩充的指静脉识别方法。首先,利用矩阵变换生成原始图像的镜像,训练原始图像与镜像,增加指静脉图像中包含的有用信息;然后,基于LRC对测试和训练样本进行分类;最后,通过计算偏差得到最终分类结果,求出识别率。此外,设计了一种指静脉采集装置收集得到自建指静脉数据库。实验结果表明:所提算法在自建指静脉数据库、山东大学指静脉数据库、马来西亚理工大学指静脉数据库上的识别率分别达到98.93%、98.89%、99.67%,最低等误率为2.3888%。实验结果与其他传统和流行算法相比具有明显优势,拥有良好的实际应用价值。     

图像分类问题的几种深度学习策略

针对图像分类学习不够深入的问题,提出图像分类问题的几种深度学习策略研究。通过分析当前主流的主动深度学习图像、多标签图像和多尺度网络图像三种深度学习方法的工作原理和存在的优势与不足,探讨图像分类问题的优化学习策略。随后采用图像分类问题的几种深度学习策略实验的方式加以对比,实验结果表明,参数共享的深度学习图像分类方法不仅提高了预测速度,而且还能确保模型的准确性。     

Orientation-reversing involutions on handlebodies

The observation that the quotient orbifold of an orientation- reversing involution on a 3-dimensional handlebody has the structure of a compression body leads to a strong classification theorem, and general structure theorems. The structure theorems decompose the action along invariant discs into actions on handlebodies which preserve the -fibers of some -bundle structure. As applications, various results of R. Nelson are proved without restrictive hypotheses.

     

Universal system for liquid stationary phase classification on the basis of thermodynamical data

Summary Equations modifying Rohrschneider's relationship between I and thermodynamical characteristics values are derived. Polarity in chromatography is discussed as a possibility of stationary phases or absorbents to enter into intermolecular interactions and is determined on the basis of the partial molar free energies of solution (G) of six test substances. A simple method for the calculation of stationary phase polarity in terms of G from retention indices and McReynolds constants is given. Equations are presented for the calculation of stationary phase selectivity utilizing G data tabulated for selected test substances. The thermodynamical basis of selecting a reference phase for a unified system expressing the selectivity of stationary phases which can also be applied for adsorbents is suggested. The energy equivalent to an index unit, G_i.u., is discussed.     

磷脂酶A2家族的功能性分类研究

采用“结合强度指纹图谱分析”方法, 通过对多重分子对接得到的作用强度数据进行聚类矩阵分析对蛋白质进行功能分类. 着重研究了磷脂酶A₂家族基于抑制剂作用强度的功能分类, 并且与基于序列的聚类结果进行比较, 成功地解决了序列比对方法不能处理的远源蛋白(cPLA₂)的分类问题.     

西松烷型二萜内酯的全合成进展

按照内酯环的大小综述了西松烷二萜的合成方法。     

Protein family classification and functional annotation

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.