Network Patterns of Zhongyuan Urban Agglomeration in China Based on Baidu Migration Data

As a new product of the Internet and big data era, migration data are of great significance for the revealing of the complex dynamic network patterns of urban agglomerations and for studying the relations between cities by using the “space of flows” model. Based on Baidu migration data of one week in 2021, this paper constructs a 30 × 30 rational data matrix for cities in Zhongyuan Urban Agglomeration and depicts the network pattern from static and dynamic perspectives by using social network analysis and dynamic network visualization. The results show that the network of Zhongyuan Urban Agglomeration is characterized by a circular structure with Zhengzhou as the center, a city belt around Zhengzhou as the connection, subcentral cities as the support and peripheral cities as the extension. Zhengzhou is the core city of the entire network, related to which the central and backbone networks divided in this paper account for nearly 40% of the total migration. Shangqiu, Luoyang, Zhoukou and Handan also play an important role in the structure of the migration network as subcentral cities. For a single city, the migration scale generally peaks on weekends and reaches its minimum during Tuesday to Thursday. Regarding the relations between cities, the migration variation can be divided into four types: peaking on Monday, peaking on weekends, bimodal and stable, and there are obvious phenomena of weekly commuting. In general, the links between cities outside Henan Province and other cities in the urban agglomeration are relatively weak, and the constraints of administrative regionalization on intercity migration are presumed to still exist. According to the results, the location advantage for multi-layer development and construction of Zhongyuan Urban Agglomeration should be made use of. In addition, the status as the core city and the radiation range should be strengthened, and the connections between the peripheral cities and the other cities should be improved, so as to promote the integrated and efficient development of the whole urban agglomeration.

Taxonomy and phylogeny of the novel rhytidhysteron-like collections in the Greater Mekong Subregion

During our survey into the diversity of woody litter fungi across the Greater Mekong Subregion, three rhytidhysteron-like taxa were collected from dead woody twigs in China and Thailand. These were further investigated based on morphological observations and multi-gene phylogenetic analyses of a combined DNA data matrix containing SSU, LSU, ITS, and tef1-α sequence data. A new species of Rhytidhysteron, R. xiaokongense sp. nov. is introduced with its asexual morph, and it is characterized by semi-immersed, subglobose to ampulliform conidiomata, dark brown, oblong to ellipsoidal, 1-septate, conidia, which are granular in appearance when mature. In addition to the new species, two new records from Thailand are reported viz. Rhytidhysteron tectonae on woody litter of Betula sp. (Betulaceae) and Fabaceae sp. and Rhytidhysteron neorufulum on woody litter of Tectona grandis (Lamiaceae). Morphological descriptions, illustrations, taxonomic notes and phylogenetic analyses are provided for all entries.

Phylogenetic signal in characters from Aristotle’s History of Animals

The influential Greek philosopher Aristotle (384-322 BCE) is almost unanimously acclaimed as the founder of zoology. There is a consensus that he was interested in attributes of animals, but whether or not he tried to develop a zoological taxonomy remains controversial. Fürst von Lieven and Humar compiled a data matrix from Aristotle’s Historia animalium and showed, through a parsimony analysis published in 2008, that these data produced a hierarchy that matched several taxa recognized by Aristotle. However, their analysis leaves some questions unanswered because random data can sometimes yield fairly resolved trees. In this study, we update the scores of many cells and add four new characters to the data matrix (147 taxa scored for 161 characters) and quote passages from Aristotle’s Historia animalium to justify these changes. We confirm the presence of a phylogenetic signal in these data through a test using skewness in length distribution of a million random trees, which shows that many of the characters discussed by Aristotle were systematically relevant. Our parsimony analyses on the updated matrix recover far more trees than reported by Fürst von Lieven and Humar, but their consensus includes many taxa that Aristotle recognized and apparently named for the first time, such as selachē (selachians) and dithyra (Bivalvia Linnaeus, 1758). This study suggests that even though taxonomy was obviously not Aristotle’s chief interest in Historia animalium, it was probably among his secondary interests. These results may pave the way for further taxonomic studies in Aristotle’s zoological writings in general. Despite being almost peripheral to Aristotle’s writings, his taxonomic contributions are clearly major achievements.

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part I, Reducing Reconstruction Bias

Scanning transmission electron microscopy is a crucial tool for nanoscience, achieving sub-nanometric spatial resolution in both image and spectroscopic studies. This generates large datasets that cannot be analyzed without computational assistance. The so-called machine learning procedures can exploit redundancies and find hidden correlations. Principal component analysis (PCA) is the most popular approach to denoise data by reducing data dimensionality and extracting meaningful information; however, there are many open questions on the accuracy of reconstructions. We have used experiments and simulations to analyze the effect of PCA on quantitative chemical analysis of binary alloy (AuAg) nanoparticles using energy-dispersive X-ray spectroscopy. Our results demonstrate that it is possible to obtain very good fidelity of chemical composition distribution when the signal-to-noise ratio exceeds a certain minimal level. Accurate denoising derives from a complex interplay between redundancy (data matrix size), counting noise, and noiseless data intensity variance (associated with sample chemical composition dispersion). We have suggested several quantitative bias estimators and noise evaluation procedures to help in the analysis and design of experiments. This work demonstrates the high potential of PCA denoising, but it also highlights the limitations and pitfalls that need to be avoided to minimize artifacts and perform reliable quantification.

A New Cynodont from the Late Triassic Los Colorados Formation (Argentina, South America) Reveals a Novel Paleobiogeographic Context for Mammalian Ancestors

Probainognathia is a derived lineage of cynodonts which encompass Mammalia as their crown-group. The profuse record of probainognathians from the Carnian of Argentina contrasts with their Norian representation, with only one named species. Here we describe a new probainognathian, Tessellatia bonapartei gen. et sp. nov., from the Norian Los Colorados Formation of the Ischigualasto-Villa Unión Basin of Argentina. The new taxon, represented by a partial cranium with articulated lower jaws, was analyzed through neutron and X-rays micro-tomography (µCT). The high-resolution neutron µCT data allowed the identification of a unique character state combination, including features inaccessible through traditional techniques. We constructed the largest phylogenetic data-matrix of non-mammalian cynodonts. The new species and its sister-taxon, the Brazilian Therioherpeton, are recovered as probainognathians, closely related to Mammaliamorpha. We conducted the first quantitative paleobiogeographic analysis of non-mammalian cynodonts, focusing in probainognathians. The results indicate that Probainognathia and Mammaliamorpha originated in Brazil, which was an important center of diversification during the Triassic. Finally, China is identified as the ancestral area of Mammaliaformes. These new findings, besides adding to the knowledge of the poorly represented Norian cynodonts from the Los Colorados Formation, are significant to improve our understanding of probainognathian diversity, evolution, and paleobiogeographic history.

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

Sustainable socio-economic development and Rainbow Europe Index

The issues of recognizing the rights of the LGBTQ+ community around the world and developing appropriate anti-discrimination policies and laws are one of the main topics for discussion in the global agenda. This is due to the commitment of the world community to protect human rights and meet the needs of society. The paper aims to assess the relationship between socio-economic development indicators of some European countries and the Rainbow Europe Index. To find out how discrimination against the LGBTQ+ community affects various social and economic development indicators of some European countries, a data matrix was developed and the Spearman rank correlation coefficient was calculated. The obtained results confirmed a positive relationship between the Rainbow Europe Index and GDP per capita, the Human Development Index, the Corruption Index, and the Index of Happiness. Calculations have shown that the Rainbow Europe Index had a significant impact on these indicators. The study proved the dependence of indicators and demonstrated the need to provide freedoms and rights for LGBTQ+ affiliated members in Ukraine and other European countries. AcknowledgmentThis paper is published as a part of research projects “Convergence of economic and educational transformations in the digital society: modeling the impact on regional and national security” (No. 0121U109553) and “Reforming the lifelong learning system in Ukraine for the prevention of the labor emigration: a coopetition model of institutional partnership” (No. 0120U102001).

Learning Deep Models: Critical Points and Local Openness

With the increasing popularity of nonconvex deep models, developing a unifying theory for studying the optimization problems that arise from training these models becomes very significant. Toward this end, we present in this paper a unifying landscape analysis framework that can be used when the training objective function is the composite of simple functions. Using the local openness property of the underlying training models, we provide simple sufficient conditions under which any local optimum of the resulting optimization problem is globally optimal. We first completely characterize the local openness of the symmetric and nonsymmetric matrix multiplication mapping. Then we use our characterization to (1) provide a simple proof for the classical result of Burer-Monteiro and extend it to noncontinuous loss functions; (2) show that every local optimum of two-layer linear networks is globally optimal. Unlike many existing results in the literature, our result requires no assumption on the target data matrix [Formula: see text], and input data matrix [Formula: see text]; (3) develop a complete characterization of the local/global optima equivalence of multilayer linear neural networks (we provide various counterexamples to show the necessity of each of our assumptions); and (4) show global/local optima equivalence of overparameterized nonlinear deep models having a certain pyramidal structure. In contrast to existing works, our result requires no assumption on the differentiability of the activation functions and can go beyond “full-rank” cases.