Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand

The study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. However, due to practical constraints, long-term data displaying all beach states are rare. Additionally, when the dataset is available, the accuracy of the classification is not entirely objective since it depends on the operator. To address this problem, we collected hourly coastal images and corresponding tidal data for more than 20 years (November 1998–August 2019). We classified the images into eight categories according to the classic beach state classification, defined as (1) reflective, (2) incident scaled bar, (3) non-rhythmic, attached bar, (4) attached rhythmic bar, (5) offshore rhythmic bar, (6) non-rhythmic, 3-D bar, (7) infragravity scaled 2-D bar, (8) dissipative. We developed a classification model based on convolutional neural networks (CNN). After image pre-processing with data enhancement, we compared different CNN models. The improved ResNext obtained the best and most stable classification with F1-score of 90.41% and good generalization ability. The classification results of the whole dataset were transformed into time series data. MDLats algorithms were used to find frequent temporal patterns in morphology changes. Combining the pattern of coastal morphology change and the corresponding tidal data, we also analyzed the characteristics of beach morphology and the changes in morphodynamic states.

An updated tribal classification of Lamiaceae based on plastome phylogenomics

Background A robust molecular phylogeny is fundamental for developing a stable classification and providing a solid framework to understand patterns of diversification, historical biogeography, and character evolution. As the sixth largest angiosperm family, Lamiaceae, or the mint family, consitutes a major source of aromatic oil, wood, ornamentals, and culinary and medicinal herbs, making it an exceptionally important group ecologically, ethnobotanically, and floristically. The lack of a reliable phylogenetic framework for this family has thus far hindered broad-scale biogeographic studies and our comprehension of diversification. Although significant progress has been made towards clarifying Lamiaceae relationships during the past three decades, the resolution of a phylogenetic backbone at the tribal level has remained one of the greatest challenges due to limited availability of genetic data. Results We performed phylogenetic analyses of Lamiaceae to infer relationships at the tribal level using 79 protein-coding plastid genes from 175 accessions representing 170 taxa, 79 genera, and all 12 subfamilies. Both maximum likelihood and Bayesian analyses yielded a more robust phylogenetic hypothesis relative to previous studies and supported the monophyly of all 12 subfamilies, and a classification for 22 tribes, three of which are newly recognized in this study. As a consequence, we propose an updated phylogenetically informed tribal classification for Lamiaceae that is supplemented with a detailed summary of taxonomic history, generic and species diversity, morphology, synapomorphies, and distribution for each subfamily and tribe. Conclusions Increased taxon sampling conjoined with phylogenetic analyses based on plastome sequences has provided robust support at both deep and shallow nodes and offers new insights into the phylogenetic relationships among tribes and subfamilies of Lamiaceae. This robust phylogenetic backbone of Lamiaceae will serve as a framework for future studies on mint classification, biogeography, character evolution, and diversification. Graphical abstract

The Hidden Paths of Category Research: Climbing new heights and slippery slopes

Category research has flourished over the last decade. While this body of work has prioritized the behavioral and economic consequences of stable classification systems, the papers in this special issue challenge this orientation by highlighting the importance of category dynamics for improving our understanding of markets and fields. We show how these papers support the emergence of category maintenance, the recategorization of mature categories, and the consolidation of new categories as understudied phenomena and as the next research challenges to pursue. After connecting the main findings of the papers in this special issue into a unified process model, we discuss various alternative pathways to further explore those challenges. We also point to how this theoretical endeavor runs on slippery slopes and might lead to cul-de-sacs such as terminological balkanization. We conclude by highlighting the need for developing a more comprehensive understanding of category dynamics.

A new proof of Kirchberg's 𝒪2-stable classification

I present a new proof of Kirchberg’s \mathcal{O}_{2}-stable classification theorem: two separable, nuclear, stable/unital, \mathcal{O}_{2}-stable C^{\ast}-algebras are isomorphic if and only if their ideal lattices are order isomorphic, or equivalently, their primitive ideal spaces are homeomorphic. Many intermediate results do not depend on pure infiniteness of any sort.

Emitter Individual Identification Based on Nonlinearity Analysis of Oscillators

Background: According to the characteristics of phase noise, phase noise power spectrum feature was used for emitter individual identification. Methods: For different emitter individuals, we established a phase noise model with the influence of both transmitter and receiver based on the research of its characteristics. Using power spectrum of phase noise, the corresponding scattered information entropy was proposed. The same type of communication equipments can be identified by Minimum Error Minimax Probability Machine (MEMPM) classifier through extracting this feature at a different frequency offset. Results: Simulation results show that the new features can be effectively used to classify emitter individuals with stable classification performance. Conclusion: According to the simulation, when the SNR was higher than 10dB, the accuracy rate was higher than 90%. It proved that the method is useful and effective. In addition, the recognition performance of the proposed method is very stable, showing the stability of the device phase noise. Therefore, it can be used in practice.

A comprehensive decision support approach for credit scoring

Purpose The purpose of this paper is to provide a comprehensive decision support approach in credit risk assessment. Design/methodology/approach A comprehensive decision support approach is proposed for credit scoring and prediction. The predictive performance of the new approach has been investigated by using data including number and text. Findings The results demonstrate that the proposed approach achieves better and more stable classification accuracy than the single classifiers in most cases. Meanwhile, the prediction accuracy of individual classifiers is also improved by the proposed approach. Originality/value This study provides a comprehensive model for credit risk scoring and provides valuable information to the existing literature on credit scoring by using artificial intelligence.

Systematics, Phylogeny, and Evolution of Braconid Wasps: 30 Years of Progress

The parasitoid wasp family Braconidae is likely the second-most species-rich family in the animal kingdom. Braconid wasps are widely distributed and often encountered. They constitute one of the principal groups of natural enemies of phytophagous insects, of which many are serious pest species. The enormous biological diversification of braconid wasps has led to many homoplasies, which contributed widely to instabilities in historical classifications. Recent studies using combinations of genetic markers or total mitochondrial genomes allow for better founded groupings and will ultimately lead to a stable classification. We present the current status of the phylogenetics of the Braconidae in a historical perspective and our understanding of the effects on higher classification.