基于局部Gabor二进制模式的高光谱伪装目标图像分类

为提高光谱伪装目标图像分类精度,提出了一种基于局部Gabor二进制模式（LGBP)的空间分类方法。LGBP作为一种多尺度算法,被用来提取高光谱图像的纹理特征。然后高光谱图像中的每一个像元可以用一个光谱特征向量及一个纹理特征向量表示。通过这种方法,增大类间距离。最后使用多核支持向量机结合光谱信息和空间纹理信息实现对高光谱伪装目标图像的分类。实验证明了该方法的有效性,分类总体精度和Kappa系数分别达到了95.6%和0.937。所提出的方法对于提高分类精度及鲁棒性具有重要意义。     

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.     

Symmetry classification of quasi-linear PDE’s containing arbitrary functions

We consider the problem of performing the preliminary “symmetry classification” of a class of quasi-linear PDE’s containing one or more arbitrary functions: we provide an easy condition involving these functions in order that nontrivial Lie point symmetries be admitted, and a “geometrical” characterization of the relevant system of equations determining these symmetries. Two detailed examples will elucidate the idea and the procedure: the first one concerns a nonlinear Laplace-type equation, the second a generalization of an equation (the Grad–Schlüter–Shafranov equation) which is used in magnetohydrodynamics.     

Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis

Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases.     

基于多角度偏振特性的云相态识别及验证

云相态识别是云参数研究的重要组成部分,为了有效识别水云和冰云,根据水云和冰云微物理性质的差别,研究了水云和冰云的单次散射特性,采用基丁倍加累加法的,人量辐射传输方程模拟了水云和冰云的多角度偏振特件.模拟结果表明,光谱的多角度偏振特件能够体现山水云粒子和冰云粒子微物理性质的差异,云在特定方向反射的偏振辐射强度对云相态非常敏感,可以用米进行云相念的识别,在模拟的基础上进行了云相态识别算法的研究,并利用多角度偏振卫星数据--POLDER 0.865μm通道数据进行了实例分析.识别结果与MODIS云相态产品及其1.38μm卷云榆测结果进行了比较.分析结果表明,基于多角度偏振特性云相态识别算法可以有效地进行云相态识别.     

ABLE: Attention based learning for enzyme classification

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.     

粉末粒度离散性对NdFeB磁性能的影响

制备了一种NdFeB永磁材料,探讨了制粉前处理方式对粉末离散性、粒径及成品NdFeB永磁体结构与性能的影响。原材料经熔炼、粗破碎后,采用高速风选和高压氢爆2种制粉前处理方式,然后采用同样的成型、烧结和回火工艺制备得到(PrNd)32.3Nb0.70Al0.60Cu0.20B1.03Fe65.17合金,并系统研究了该合金的显微结构和磁性能。结果表明:高压氢爆处理后得到的粉末粒度呈单分散性,粉末粒径较细,所制备得到的磁体样品的微观结构均匀性也较好;高压氢爆处理后制得的磁体样品剩余磁通密度比高速风选制粉方式得到的磁体样品高0.047 T,内禀矫顽力高 193.4 kAm－1,最大磁能积高24.6 kJm－３。     

基于光谱和图像特征的阔叶木材与针叶木材同时分类算法研究

木材是人们生活中必不可少的可再生资源,同时在建筑、工艺、家具、结构材料等方面有着举足轻重的地位.市场中常见的木材品种繁多,其品质和价格千差万别,使用智能化技术对木材进行正确的分类不仅可以防止不法商贩"以次充好",也可以大幅度降低木材分类人员的工作难度.通过木材的遗传信息和解剖学信息可以得到较为准确的木材分类结果,这类方...     

A central limit theorem in nonlinear filtering

It is shown that the probability law of a diffusion process conditioned on weakly corrupted observations is asymptotically Gaussian when properly scaled. The method of proof involves Fisher information matrices and a Cramér-Rao inequality.