bluuebunny/crossref_metadata_2025_split huggingface dataset:
Infall in massive clumps harboring bright infrared sources
Authors: Yue Ying-Hua , Qin Sheng-Li , Liu Tie , Tang Meng-Yao , Wu Yuefang , Wang Ke , Zhang Chao 
Published: February 2021

Abstract
Thirty massive clumps associated with bright infrared sources were observed to detect the infall signatures and characterize infall properties in the envelope of the massive clumps by APEX telescope in CO(4–3) and C17O(3–2) lines. Eighteen objects exhibit a “blue profile” in the CO(4–3) line with virial parameters less than 2, suggesting that global collapse is taking place in these massive clumps. The CO(4–3) lines were fitted via the two-layer model in order to obtain infall velocities and mass infall rates. Derived mass infall rates range from 10−3 to 10−1
M
⊙ yr−1. A positive relationship between clump mass and infall rate appears to indicate that gravity plays a dominant role in the collapsing process. Higher luminosity clumps have a larger mass infall rate, implying that those clumps with higher mass infall rates possess a higher star formation rate.
Searching for AGN and pulsar candidates in 4FGL unassociated sources using machine learning
Authors: Zhu Ke-Rui , Kang Shi-Ju , Zheng Yong-Gang 
Published: February 2021

Abstract
In the fourth Fermi Large Area Telescope source catalog (4FGL), 5064 γ-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with blazars at low Galactic latitudes and 190 other sources. We employ two different supervised machine learning classifiers, combined with the direct observation parameters given by the 4FGL fits table, to search for sources potentially classified as AGNs and pulsars in the 1336 unassociated sources. In order to reduce the error caused by the large difference in the sizes of samples, we divide the classification process into two separate steps in order to identify the AGNs and the pulsars. First, we select the identified AGNs from all of the samples, and then select the identified pulsars from the remaining cases. Using the 4FGL sources associated or identified as AGNs, pulsars and other sources with the features selected through the K-S test and the random forest (RF) feature importance measurement, we trained, optimized and tested our classifier models. Then, the models are applied to classify the 1336 unassociated sources. According to the calculation results of the two classifiers, we report the sensitivity, specificity, accuracy in each step and the class of unassociated sources given by each classifier. The accuracy obtained in the first step is approximately 95%; in the second step, the obtained overall accuracy is approximately 80%. Combining the results of the two classifiers, we predict that there are 583 AGN-type candidates, 115 pulsar-type candidates, 154 other types of γ-ray candidates and 484 of uncertain types.
GeneticKNN: a weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars
Authors: Han Bo , Qiao Li-Na , Chen Jing-Lin , Zhang Xian-Da , Zhang Yan-Xia , Zhao Yong-Heng 
Published: February 2021

Abstract
We combine K-nearest neighbors (KNN) with a genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by a GA. This approach has two improvements compared to KNN: one is the feature weighted by GA; the other is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors, i.e. p × z
median + (1 – p) × z
mean. Based on the SDSS and SDSS-WISE quasar samples, we explore the performance of GeneticKNN for photometric redshift estimation, comparing with the other six traditional machine learning methods, i.e. the least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), multi-layer perceptrons (MLP), XGBoost, KNN and random forest. KNN and random forest show their superiority. Considering the easy implementation of KNN, we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars. Finally the performance of GeneticKNN is better than that of LASSO, SVR, MLP, XGBoost, KNN and random forest for all cases. Moreover the accuracy is better with the additional WISE magnitudes for the same method.
A hybrid Fast Multipole Method for cosmological N-body simulations
Authors: Wang Qiao 
Published: February 2021

Abstract
We investigate a hybrid numerical algorithm aimed at large-scale cosmological N-body simulation for on-going and future high precision sky surveys. It makes use of a truncated Fast Multiple Method (FMM) for short-range gravity, incorporating a Particle Mesh (PM) method for long-range potential, which is applied to deal with extremely large particle number. In this work, we present a specific strategy to modify a conventional FMM by a Gaussian shaped factor and provide quantitative expressions for the interaction kernels between multipole expansions. Moreover, a proper Multipole Acceptance Criterion for the hybrid method is introduced to solve potential precision loss induced by the truncation. Such procedures reduce the amount of computation compared to an original FMM and decouple the global communication. A simplified version of code is introduced to verify the hybrid algorithm, accuracy and parallel implementation.
Spatially-resolved stellar population properties of the M51–NGC 5195 system from multi-wavelength photometric data
Authors: Wei Peng , Zou Hu , Lin Lin , Zhou Xu , Liu Xiang , Kong Xu , Ma Lu , Ma Shu-Guo 
Published: February 2021

Abstract
Using multi-band photometric images of M51 and its companion NGC 5195 from ultraviolet to optical and infrared, we investigate spatially resolved stellar population properties of this interacting system with stellar population synthesis models. The observed infrared excess (IRX) is used to constrain dust extinction. Stellar mass is also inferred from the model fitting. By fitting observed spectral energy distributions (SEDs) with synthetical ones, we derive two-dimensional distributions of stellar age, metallicity, dust extinction and stellar mass. In M51, two grand-designed spiral arms extending from the bulge show young age, rich metallicity and abundant dust. The inter-arm regions are filled with older, metal-poorer and less dusty stellar populations. Except for the spiral arm extending from M51 into NGC 5195, the stellar population properties of NGC 5195 are quite featureless. NGC 5195 is much older than M51, and its core is very dusty with AV
up to 1.67 mag and dense in stellar mass surface density. The close encounters might drive the dust in the spiral arm of M51 into the center of NGC 5195.
An explanation for 13 consecutive day activities of Mrk 421
Authors: Zheng Yong-Gang , Yang Chu-Yuan , Kang Shi-Ju , Bai Jin-Ming 
Published: February 2021

Abstract
It is surprising to find an instance of migration in the peak positions of synchrotron spectral energy distribution components during the activity epochs of Markarian 421 (Mrk 421), accompanying an orphan flare at the X-ray and GeV-TeV γ-ray bands. A geometric interpretation and standard shock or stochastic acceleration models of blazar emission have difficulty reproducing these observed behaviors. The present paper introduces a linear acceleration by integrating the reconnection electric field into the particle transport model for the observed behaviors of Mrk 421. We note that strong evidence for evolution in characteristic of multi-wavelength spectral energy distribution including shifting the peak frequency, accompanying an orphan flare at the X-ray and GeV-TeV γ-ray bands provides an important electrostatic acceleration diagnostic in a blazar jet. Assuming suitable model parameters, we apply the results of the simulation to the 13-day flaring event in March 2010 of Mrk 421, concentrating on the evolution of multi-wavelength spectral energy distribution characteristic by shifting the peak frequency. It is clear that the ratio of the electric field and magnetic field strength plays an important role in temporal evolution of the peak frequency of synchrotron spectral energy distribution component. We suggest it is reasonable that the electrostatic acceleration is responsible for the evolution of multi-wavelength spectral energy distribution characteristic by shifting the peak frequency. Based on the model results, we assert that the peak frequency of the synchrotron spectral energy distribution component may signify a temporary characteristic of blazars, rather than a permanent one.
Electrolyte-gated transistors for neuromorphic applications
Authors: Huang Heyi , Ge Chen , Liu Zhuohui , Zhong Hai , Guo Erjia , He Meng , Wang Can , Yang Guozhen , Jin Kuijuan 
Published: February 2021

Abstract
Von Neumann computers are currently failing to follow Moore’s law and are limited by the von Neumann bottleneck. To enhance computing performance, neuromorphic computing systems that can simulate the function of the human brain are being developed. Artificial synapses are essential electronic devices for neuromorphic architectures, which have the ability to perform signal processing and storage between neighboring artificial neurons. In recent years, electrolyte-gated transistors (EGTs) have been seen as promising devices in imitating synaptic dynamic plasticity and neuromorphic applications. Among the various electronic devices, EGT-based artificial synapses offer the benefits of good stability, ultra-high linearity and repeated cyclic symmetry, and can be constructed from a variety of materials. They also spatially separate “read” and “write” operations. In this article, we provide a review of the recent progress and major trends in the field of electrolyte-gated transistors for neuromorphic applications. We introduce the operation mechanisms of electric-double-layer and the structure of EGT-based artificial synapses. Then, we review different types of channels and electrolyte materials for EGT-based artificial synapses. Finally, we review the potential applications in biological functions.
Multiply accumulate operations in memristor crossbar arrays for analog computing
Authors: Chen Jia , Li Jiancong , Li Yi , Miao Xiangshui 
Published: February 2021

Abstract
Memristors are now becoming a prominent candidate to serve as the building blocks of non-von Neumann in-memory computing architectures. By mapping analog numerical matrices into memristor crossbar arrays, efficient multiply accumulate operations can be performed in a massively parallel fashion using the physics mechanisms of Ohm’s law and Kirchhoff’s law. In this brief review, we present the recent progress in two niche applications: neural network accelerators and numerical computing units, mainly focusing on the advances in hardware demonstrations. The former one is regarded as soft computing since it can tolerant some degree of the device and array imperfections. The acceleration of multiple layer perceptrons, convolutional neural networks, generative adversarial networks, and long short-term memory neural networks are described. The latter one is hard computing because the solving of numerical problems requires high-precision devices. Several breakthroughs in memristive equation solvers with improved computation accuracies are highlighted. Besides, other nonvolatile devices with the capability of analog computing are also briefly introduced. Finally, we conclude the review with discussions on the challenges and opportunities for future research toward realizing memristive analog computing machines.
Neuromorphic vision sensors: Principle, progress and perspectives
Authors: Liao Fuyou , Zhou Feichi , Chai Yang 
Published: February 2021

Abstract
Conventional frame-based image sensors suffer greatly from high energy consumption and latency. Mimicking neurobiological structures and functionalities of the retina provides a promising way to build a neuromorphic vision sensor with highly efficient image processing. In this review article, we will start with a brief introduction to explain the working mechanism and the challenges of conventional frame-based image sensors, and introduce the structure and functions of biological retina. In the main section, we will overview recent developments in neuromorphic vision sensors, including the silicon retina based on conventional Si CMOS digital technologies, and the neuromorphic vision sensors with the implementation of emerging devices. Finally, we will provide a brief outline of the prospects and outlook for the development of this field.
Study of short-term synaptic plasticity in Ion-Gel gated graphene electric-double-layer synaptic transistors
Authors: Gong Chenrong , Chen Lin , Liu Weihua , Zhang Guohe 
Published: February 2021

Abstract
Multi-terminal electric-double-layer transistors have recently attracted extensive interest in terms of mimicking synaptic and neural functions. In this work, an Ion-Gel gated graphene synaptic transistor was proposed to mimic the essential synaptic behaviors by exploiting the bipolar property of graphene and the ionic conductivity of Ion-Gel. The Ion-Gel dielectrics were deposited onto the graphene film by the spin coating process. We consider the top gate and graphene channel as a presynaptic and postsynaptic terminal, respectively. Basic synaptic functions were successfully mimicked, including the excitatory postsynaptic current (EPSC), the effect of spike amplitude and duration on EPSC, and paired-pulse facilitation (PPF). This work may facilitate the application of graphene synaptic transistors in flexible electronics.
Self-consistent renormalization group approach to continuous phase transitions in alloys: application to ordering in β-brass
Authors: Tokar V I 
Published: February 2021

Abstract
A self-consistent (SC) renormalization group approach of the effective medium kind has been developed and applied to the solution of the Ising model (IM). A renormalization group equation in the local potential approximation (LPA) derived previously for spatially homogeneous systems has been extended to the lattice case and supplemented with a self-consistency condition on the pair correlation function. To validate the approach it has been applied to the simple cubic IM and good agreement of the spontaneous magnetization calculated with the use of the SC–LPA equation with the available exact Monte Carlo simulations data has been established. Next the approach has been applied to the bcc IM corresponding to β-brass. With the use of the effective pair interaction parameters from available ab initio calculations the critical temperature, the correlation length and the long range order parameter in the vicinity of the critical point have been calculated in excellent agreement with experimental data. Qualitative and quantitative arguments have been given in support of the suggestion that the experimentally observed decrease of the effective critical exponent of the order parameter in comparison with the universal value is enhanced by the positive value of the second neighbour pair interaction found in the ab initio calculations.
Assessing natural resources using knowledge-based information processing tools
Authors: Nosov V V , Tindova M G , Ramazanov I A , Poletaeva L P , Avdotin V P 
Published: February 2021

Abstract
To date, other than the land market, the Russian Federation still does not have an established natural resources market due to restrictions on transactions in the natural resources segment; therefore, the market value of natural resources cannot be calculated using classical evaluation methods, and only cadastral appraisal can be conducted. We propose to assess the resources as a whole, using the evaluation of nature reserves, assessment of the resource’s ecological functions and the environmental damage. However, the existing cadastral appraisal methods take no account of resources functions and consider them separately. Moreover, the information on natural systems used for the assessment of resources is incomplete and inaccurate. Such uncertainty makes it difficult to model using classical statistical tools. Therefore, there is a need to use information processing tools based on data mining. We propose to use the fuzzy modelling techniques in natural resources assessment, as they will allow for the evaluation of nature reserves and environmental monitoring at the same time. This paper contains fuzzy models for a land plot evaluation that include linguistic variables - factors that affect the land plot value, and fuzzy rule bases that define relationships between linguistic variables. The results show that the cadastral value of land plots is 2.5 times lower than the one calculated using the fuzzy model, with regard to the ecological factor. The error between the market value of those land plots and the one calculated using the fuzzy model is 11%. Therefore, the use of proposed tools allows for the calculation of the market value of resources based on their cadastral value with an account of various factors using the comparative approach, and also for adjustments to the market value in order to increase the efficiency of natural resources use.
Mutual recognition mechanism of legally significant e-documents and data in cross-border document flow
Authors: Kustov V N , Silanteva E S 
Published: February 2021

Abstract
In this article we will observe mutual recognition mechanism that can provide transparent and reliable environment for data and documents in electronic form exchange across borders. Implementation of the Mutual recognition mechanism concept will help to facilitate interaction and ensure trust between parties taking part in cross-border legally significant e-documents exchange.
Mutual recognition mechanism is a set of legal, organizational, semantic and technical conditions for reciprocal recognition of the validity of data and documents in electronic form exchanged across borders among two or more countries. Mutual recognition mechanism allows public authorities, physical and legal entities to interact in the framework of relations arising during generation, sending, transmission, reception, storage and use of legally significant data and documents in electronic form exchange across borders. Cross-border exchange of legally significant data and documents in electronic form between countries can be exercised by the trust ensured by the Trust services providers. Trust in paperless transboundary interaction is a set of objective signs that are used in electronic interaction. For example, electronic signatures are recognized as electronic signatures and their signed electronic documents are validated as documents signed by subjects of legal relations (owners of electronic signatures), the identity of which is confirmed. In this article we will observe Transboundary trust environment (TTE). TTE is a set of normative and organizational-technical conditions which implementation provides the mode of trust in electronic interaction with the participation of a limited or unlimited number of members. TTE as an integral structure operates based on a set of trusted services and may include a trusted third party. Trusted Third Party (TTP) is the organization responsible for the relevant trust that should be provided to complex information technologies and organizational-legal measures to ensure legally significant electronic interaction. Also, the article describes conceptual model of the mutual recognition mechanism.
Multi-level hybrid recommender decision support system with verbal output
Authors: Novikova S V , Tunakova Y A , Shagidullin A R , Kremleva E S 
Published: February 2021

Abstract
The article describes decision-making methods based on intelligent learning algorithms, for the construction of which verbal elements are used. Such algorithms and methods operate in calculations with strictly quantitative data, however, taking into account the human way of perceiving information in verbal form. A person does not directly participate in the process of constructing a model, that is, its structure does not depend on expert or other human opinions, however, qualitative verbal information (for example, elements of regulatory acts, documents, orders, etc.) is embedded in the algorithm in encoded form.
Unlike the classical methods of fuzzy logic and fuzzy inference, the proposed models consist of several interconnected classical and author’s models, using author’s numerical methods. This increases their accuracy and adequacy. The great advantage is the automatic way to build these models, only on the basis of sets of initial quantitative and qualitative data, which will allow you to quickly and efficiently create recommender systems for solving a wide range of decision-making problems.
The system is built in three stages:
First, the fuzzy logical inference Takagi – Sugeno – Kang system (TSK) is automatically built on the sounding of the available quantitative and qualitative data. The advantage of such a system is that it can be retrained (adjust its parameters).
Then, after the system is built according to the available data, at the second stage its accuracy is increased using a fuzzy neural network. The drawback of the constructed system is that it does not give out a verbal answer.
At the third stage, based on the adjusted parameters of the TSK system, a Mamdani type system is constructed using a special algorithm. As a result, it can produce a verbal answer from a recommender system, and at the same time gets all the advantages of a neural network refinement.
Computational experiments are presented.
A fuzzy model for selecting travel services to produce personalized offers to Internet users
Authors: Shpolianskaya I , Dolzhenko A , Stepanov N , Potapov L 
Published: February 2021

Abstract
With the spread of information technologies in the field of tourism and the emergence of a specific sector of e-tourism, an increasingly urgent problem is not the traditional search for relevant information on the Internet, but the search for personally oriented, personalized information adapted to the individual needs of specific users. The main issue in e-tourism is to determine the most suitable tourist resources from the many available for use by a particular user. Recommender systems in e-tourism provide the mechanism of satisfying customized users’ requirements by supplying them with adequate information about travel resources and services. The implementation of tourist goals can be carried out by a variety of different combinations of travel services. The optimal selection of services is a complex multi-alternative task due to the lack of data about the quality of services and the complexity of the description of user preferences. The paper considers the possibility of using fuzzy logic to the development of the e-tourism support system. Based on user profile data, web services that provide information about travel services, and ratings of services by other users, the system evaluates various alternatives of service’s composition and selects the recommended objects (hotels, flights, restaurants, car rental, museums, etc.). The service-oriented paradigm has been proposed to model the composition of tourism services, and fuzzy logic is used to evaluate the quality and compliance of tourism services with user requests and preferences. The proposed fuzzy logic-based approach analyzes the quality of travel services for personal recommendations to the tourist. The proposed model can provide decision support to users to select the most appropriate travel services and to plan a trip.