bluuebunny/crossref_metadata_2025_split huggingface dataset:
Medullary thymic epithelial NF–kB-inducing kinase (NIK)/IKKα pathway shapes autoimmunity and liver and lung homeostasis in mice
Authors: Shen Hong , Ji Yewei , Xiong Yi , Kim Hana , Zhong Xiao , Jin Michelle G. , Shah Yatrik M. , Omary M. Bishr , Liu Yong , Qi Ling , Rui Liangyou 
Published: September 2019

Significance
Aberrant T cell activation augments liver and lung diseases, increasing mortality and morbidity. T cell development is controlled by thymic epithelial cells (TECs); therefore, elucidation of TEC growth and differentiation holds a key for understanding of adaptive immunity and autoimmune disease. We found that NIK/IKKα pathways are highly activated in medullary TECs. TEC-specific ablation of NIK or IKKα blocked medullary TEC development, resulting in production of pathogenic autoreactive T cells due to breakdown of T cell central tolerance. Consequently, NIK- or IKKα-deficient mice displayed fatal autoimmune destruction of livers and lungs. This work unravels a regulation of autoimmunity by TEC-intrinsic NIK/IKKα pathways and identifies the NIK/IKKα pathway as a potential target for the treatment of chronic liver and/or lung disease.
Extracellular RNA in a single droplet of human serum reflects physiologic and disease states
Authors: Zhou Zixu , Wu Qiuyang , Yan Zhangming , Zheng Haizi , Chen Chien-Ju , Liu Yuan , Qi Zhijie , Calandrelli Riccardo , Chen Zhen , Chien Shu , Su H. Irene , Zhong Sheng 
Published: September 2019

Significance
The SILVER-seq technology enables sequencing extracellular RNAs (exRNAs) from a single droplet of liquid biopsy. This study revealed strong associations between serum exRNA expression levels and the donor’s sex and age. SILVER-seq detected serum exRNAs from the genes that are only expressed in brain, suggesting the possibility of monitoring brain gene expression from a blood test. Classifiers based on exRNA expression levels were able to separate breast cancer patients from control donors. The exRNA-based classifiers could also distinguish the patients with recurrent cancer from other breast cancer patients. The SILVER-seq technology can therefore lead the way to future in vitro diagnostics trials based on finger prick blood, which is more accessible for screening and frequent monitoring of human diseases.
Revealing misfit dislocations in InAsxP1−x-InP core–shell nanowires by x-ray diffraction
Authors: Lazarev Sergey , Göransson David J O , Borgström Magnus , Messing Maria E , Xu H Q , Dzhigaev Dmitry , Yefanov Oleksandr M , Bauer Sondes , Baumbach Tilo , Feidenhans’l Robert , Samuelson Lars , Vartanyants Ivan A 
Published: September 2019

Abstract
InAs
x
P1−x
nanowires are promising building blocks for future optoelectronic devices and nanoelectronics. Their structure may vary from nanowire to nanowire, which may influence their average optoelectronic properties. Therefore, it is highly important for their applications to know the average properties of an ensemble of the nanowires. Structural properties of the InAs
x
P1−x
-InP core–shell nanowires were investigated using the coplanar x-ray diffraction performed at a synchrotron facility. Studies of series of symmetric and asymmetric x-ray Bragg reflections allowed us to determine the 26% ± 3% of As chemical composition in the InAs
x
P1−x
core, core–shell relaxation, and the average tilt of the nanowires with respect to the substrate normal. Based on the x-ray diffraction, scanning, and transmission electron microscopy measurements, a model of the core–shell relaxation was proposed. Partial relaxation of the core was attributed to misfit dislocations formed at the core–shell interface and their linear density was estimated to be 3.3 ± 0.3 × 104 cm−1.
Temperature effect on the binding energy and the diamagnetic susceptibility of a magneto-donor in Cylindrical Quantum Dot (GaAs/GaAlAs)
Authors: Janati Edrissi S , Zorkani I , Rahmani K , Chrafih Y , Jorio A , Khenfouch M 
Published: September 2019

Abstract
In this work, the effect of the temperature on the binding energy and the diamagnetic susceptibility of a shallow magneto-donor confined to move in a cylindrical quantum dot made out of GaAs/GaAlAs is studied within the effective mass approximation. We describe the quantum confinement by an infinite deep potential. The results show that when the temperature or the magnetic field increases, (i) the binding energy increases and (ii) the absolute value of the diamagnetic susceptibility decreases. Its effects depend strongly on the size of the CQD.
The phase topology and bifurcation tori of the Hydrogen atom subjected to external fields
Authors: Chatar W , Benkhali M , El Fakkousy I , Kharbach J , Rezzouk A , Ouazzani-Jamil M 
Published: September 2019

Abstract
In this paper, we performed an adapted canonical transformation, and we analysed the phase space topology and the bifurcation of Liouville tori of the Hydrogen atom subjected to three static external fields: Van der Waals potential, electric and magnetic fields. In particular, for all values of the parameters of the system under consideration, the bifurcation diagrams of the momentum mapping are constructed, bifurcations of the common level sets of the first integrals are described and the all-generic bifurcations are computed for all singular points of the bifurcation diagrams. However no author has combined these three fields and studied their behavior. Numerical investigations are performed for the integrable case by means of Poincaré surfaces of section and the phase space trajectories method, and we observed the chaos-order-chaos transition
Novel approach to enhance light absorption of porphyrin/graphene oxide composites by PH and concentration modification for energy applications
Authors: Bajjou O. , Bakour A. , Khenfouch M. , Baitoul M. , Mothudi B. , Maaza M. , Faulques E. 
Published: September 2019

Abstract
Graphene oxide (GO) decorated with nanostructured porphyrin (PN) was synthesized and the interfacial interaction between these two components were investigated by using Scanning electron microscopy (SEM), X-ray, and UV-Visible absorption spectroscopy. These techniques showed good dispersion of GO and PN as well as a π-π intermolecular interaction confirming the energy/charge transfer. Moreover, X-ray diffraction study confirmed the intercalation of PN in GO and their disaggregation. The UV-Visible results showed an important interaction between GO and porphyrin nanorods under different pH values and concentrations resulting in the enhancement of optical absorption of GO-PN. The findings presented here are important to achieve the functionalization of graphene oxide with PN for various optoelectronic applications.
Research on the Development of Jinan Smart Tourism in the Age of Big Data
Authors: Wang Ainiao 
Published: September 2019

Abstract
The development of smart tourism in Jinan lags behind Yantai and Qingdao, which is inferior to other well-known tourist cities in China, such as Beijing and Hangzhou. Under the innovation of network technology and the construction of big data, the development of smart tourism in Jinan presents problems such as low level of construction, difficulty in coordination, insufficient technical support, and lack of professional talents. This paper proposes a development strategy, namely, strengthening big data construction, government leaders lead, promote technological innovation and cultivate professional talents.
Research on Author Name Disambiguation Based on Fusion Features and Semantic Fingerprints
Authors: Zhai Xiaorui , Han Hongqi , Li Zhong , Ran Yaxin 
Published: September 2019

Abstract
Author name disambiguation has been a challenging problem in many applications. In order to promote researches to solve name disambiguation, Aminer launched the Open Academic Data Challenge 2018 jointly with Chinese Association for Artificial Intelligence and China Knowledge Centre for Engineering and Technology. Aminer is a scholar-cantered academic search and mining platform covering more than 200 million papers and more than 100 million scholars in various academic fields. Our team proposed a name disambiguation method based on fusion features and semantic fingerprint technique to participate in the competition. The method identified authors with same names through organization feature and co-author feature at first, and then it solves ambiguity names by way of semantic fingerprints which are 128-bit binary vector generated from textual features of papers by Simhash algorithm. Our method scored 0.609 on the verification set and 0.879 on the test set ranking first in the final submission.
Choice of the Number of Hidden Layers for Back Propagation Neural Network Driven by Stock Price Data and Application to Price Prediction
Authors: Zhang Peipei , Shen Chuanhe 
Published: September 2019

Abstract
Since the stock market is dynamic and nonlinear, we adopt the neural network to forecast the stock price. We construct the single hidden layer prediction model firstly, and analyse the effect of prediction accuracy on neurons amount and epochs. To improve the prediction accuracy and operating rate, we then construct the multiple hidden layers prediction model, and provide some theory guide on setting the number of each hidden layer for neural network with multiple hidden layers. Finally, we make a choice of the number of hidden layers by analysing the effect of stock price prediction, and the empirical results obtained demonstrate that the prediction performance of two hidden layers prediction model is better than that of the single hidden layer prediction model. Additionally, the empirical results obtained also demonstrate that the more epochs of training network, the better the results obtained with using the same number of neurons.
Design and Implementation of UAV Engine Test System
Authors: Bao Chuanmei , Zheng Xing , Cheng Jian , Xu Luoting 
Published: September 2019

Abstract
To meet the testing requirements of small UAV piston engine, a virtual instrument-based UAV engine test system is designed. This system has the capability of on-line testing on engine even without the UAV system-wide support. With virtual instruments, serial analog-to-digital conversion and other new technologies, it can replace the ground control station to control the engine; realize rapid and portable testing as well as integrated display of the engine performance parameters; and based on the testing data, perform fault analysis and provide maintenance instructions when appropriate. The system covers the shortage of existing test methods, and improves the efficiency of UAV engine system test in field operations.
Research on Eliminating Uncertain Abnormal Large Data in Network Based on Ant Colony-Genetic Algorithms
Authors: Cui Haiting 
Published: September 2019

Abstract
In order to solve problem of high miss detection rate and false detection rate in current methods of eliminating abnormal big data in network, a method under asynchronous transmission based on ant colony-genetic algorithm is proposed. Data is validated by measuring proximity and initialization parameters of ant colony are set and real-time evolution rate of offspring is counted in which pheromones are used to train ants, then chromosomes are coded to obtain fitness function of particles. The fitness function is used to calculate individual optimal value and global optimal value of particles. Then concentration of global pheromone is used to judge whether the Ant Optimization meets end condition. If it is, large uncertain data are detected and eliminated. Experiments show that the average missed detection rate is about 2.5%, and the false detection rate is low.
An Advanced Pyramid Network Technology for Optical Character Recognition
Authors: Zhang Haoruo , Cheng Zhongquan 
Published: September 2019

Abstract
Optical Character Recognition (OCR) technology can quickly convert text and digital information in pictures into text information, which has been widely used in actual scenes. However, in some complex scenes, such as different light, angle or occlusion, the existing OCR systems still cannot reach the required accuracy. This paper proposes an advanced pyramid network structure, which uses multiple different scales and parallel pyramid network structures to deal with the problem of different character scales and misalignment. Each pyramid network structure has at least four convolutional layers. At the same time, in each pyramid network connection, the proportionally discarding parameters is introduced to increase the calculation speed further. The advantage of this network structure is very robust to more differently sized characters and increases the number of valid parameters obtained through training. Experiments on open source data sets show that the method has better recognition accuracy and speed.
A Secure Authentication and Key Agreement Scheme in Smart Home
Authors: Song Dawei , Wen Fengtong 
Published: September 2019

Abstract
Secure communication between users and devices is an important aspect of IoT applications. Smart home is an emerging key element in IoT applications. Due to limited computing resources, it is difficult to deal with these communication problems. Therefore, a safe and efficient solution is needed to complete the communication between users and devices. Sometimes secure communication of information is required between devices and devices. In view of this point, this paper proposes a secure and efficient authentication and session key negotiation scheme between smart home devices.
Creating and Optimizing Learning Maps by Negative and Positive Convolutional Neural Networks
Authors: Cui Xu 
Published: September 2019

Abstract
In this paper, a model of creating and optimizing learning maps by negative and positive convolutional neural networks is established. Firstly, the ontology knowledge base, learning maps and convolutional neural network are introduced. Then after three steps of reverse link, reverse pooling, reverse convolution of negative convolutional neural networks (RCNN), three kinds of learning maps: linear learning map, tree learning map, and graphic learning map are created. Learners learn knowledge by the direction of learning maps, different learning matrices are obtained in the process of learning, and notes and ideas are recorded in electric note books. At last, positive convolutional neural networks (CNN) are used to optimize leaning maps. After three operations of convolution learning map matrix, pooling learning nodes, link knowledge domains, the results of weakening what learners are not interested in, weakening the task that learners think is difficult, reducing the repetition of public knowledge and randomly selecting according to the complexity of the learning map can reach. Improving learners’ confidence and Enriching the content of the learning maps have been done also.
Situation-based Real-time Assignment of Air Combat Command and Guidance Targets
Authors: You Hanghang , Han Qisong , Yang Haiyan 
Published: September 2019

Abstract
In order to solve the problem of lack of real-time allocation in traditional air combat command and guidance target allocation, this paper proposes a new model. Firstly, this paper establishes a target allocation model, discusses the situation under different situation conditions, and quantifies the situation of over-the-horizon air combat reasonably. On this basis, inspired by the algorithm of Traveling Salesman Problem (TSP), the 0-1 programming model is established to calculate the target allocation. Finally, a simulation scenario calculation is used to verify the correctness of the model and the algorithm, and the rationality of the results is analyzed from the qualitative analysis.
A Density-based Under-sampling Algorithm for Imbalance Classification
Authors: Hou Yun , Li Bailin , Li Li , Liu Jiajia 
Published: September 2019

AbstractImbalance classification is an interesting issue in machine learning and data mining. In recent years, many related algorithms have been proposed to solve such an issue. Among them, under-sampling is an effective and timesaving data pre-processing method, which balances the dataset by removing some examples from the majority class. However, these proposed under-sampling methods often lose some useful information or ignore noise in the datasets, which will result in the performance degradation. This paper proposes a density-based under-sampling algorithm (DBU) to solve these two problems. In feature space, similar examples are close to each other and noisy example is far from other examples belonging to the same class. Thus the similar examples have a high density while the noisy example has a low density. DBU uses the local density peaks to represent the whole majority class, so that it can retain the useful information and eliminate the noisy examples automatically. To evaluate our algorithm, experiments are conducted on 15 two-class imbalanced datasets. Experimental results show that DBU achieves the better results than other under-sampling methods.
A Novel Environment Perception Algorithm Combining Biologically Inspired Features and Brain-like Memory Pattern
Authors: Yan Xingwei , Yang Afeng , Du Chun , Liu Zhangmeng 
Published: September 2019

Abstract
In this paper, we proposed a novel framework for environment perception. The proposed model combines the physio-logical mechanisms of human vision and psychological principle of cognitive development. Our algorithm consists of two major components, a gist module and a memory module. The gist module simulates the cognition process associated with visual attention. The memory module is simulated by the Incremental Hierarchical Discriminant Regression (IHDR) tree, which ensures the memories of the world are distributed over the hierarchy. Experimental results on the USC dataset demonstrated that the proposed algorithm is robust and efficient.
The Nonlinear Filtering Feature Identification Methods for the Duffing Chaotic Fractal State
Authors: Zhang Yang , Rui Guosheng , Dong Daoguang , Zhang Haibo 
Published: September 2019

Abstract
With the wide applications of weak signal detection techniques for Duffing system, it is important to study the fast and accurate identification methods for fractal state, such as in the field of all kinds of signals detections, by fractal state discriminant of Duffing, the target detections can be achieved under the conditions of extremely low signal-to-noise ratios. Aiming at the existing problems of the algorithm, a new idea is put forward, the Duffing oscillator is converted to the state equation of nonlinear filters, respectively by CKF(Cubature Kalman Filter,) PF(Particle Filter) and EKF(Extended Kalman Filter) three nonlinear filtering algorithm for recursive estimations, the inherent rules whose filtering parameters change with the system state changing will be researched, and a discrimination method of oscillator Fractal state is proposed; the experimental results show that the three kinds of filtering methods can all achieve the Duffing oscillator state tracking, and with the help of the filtering characteristics, the identification of the oscillator state can be distinguished and feature extraction can both realize the qualitative and quantitative detections.
An Overview on Quality Evaluation Constitution in Context of Big Data Application
Authors: Li Zhe , Wu Sai , Zhou Hongwei , Zou Sheng , Dong Tingting 
Published: September 2019

AbstractBig data applications have been applied in various domain. With the development of big data in electric power, the application of smart grid is developing towards a more intelligent and coordinated direction. However, except for the functionality realization, the non-functional requirement such as data quality become an more and more important issues. For the reason that it relate to the validation of final results of function in big data area. The criterial of quality evaluation is crucial to the evaluation for data quality, and also the standardizations the scientific management of big data in enterprises can be used as the guide the construction of new information infrastructure. This paper investigates the existing data quality evaluation dimensions, model and application domain, and combines with the practice in industry, analyses the standards, dimensions and methods and then gives suggestions on promoting the data quality evaluation in specific domain.
The Pseudo-DF Approach for Learning Huge-scale Data
Authors: Deng Chang Hui , Wang Xiao Jun , Gu Jun , Wang Wei 
Published: September 2019

Abstract
As the advent of the big data era, huge-scale data continuously appears in various fields of science, commerce, industry and society. More algorithms/methods/approaches are urgently required to learn huge-scale data collected from different applications/backgrounds. Therefore, the Pseudo Data Flow (Pseudo-DF) approach with ensemble ReOS-ELMs is proposed in this paper. The Pseudo-DF approach randomly divides a huge-scale data set into K (K>1) non-overlapping data chucks, and a Pseudo-DF is constructed by these data chucks. The computation of a huge-scale data is changed into that of a Pseudo-DF with smaller-scale chucks, the computational burden will be much reduced. Then, the ensemble Regularized OS-ELMs (ReOS-ELMs) based on Different random Hidden-node Parameters (DiffHPs) is presented to learn a Pseudo-DF, which is a recursive leaning algorithm possessing the advantages of low computational burden, high accuracy, well generalization and stability, and strong robustness. Lastly, experiments are performed to validate the effectiveness of the proposed approach.
A Fraudulent Data Simulation Method Based on Generative Adversarial Networks
Authors: Wang Xiaoguo , Zhao Ran , Li Yuanxiu 
Published: September 2019

Abstract
In view of the positive and negative sample imbalance in the process of establishing anti-fraud rules and models of the third-party payment, this paper simulated the fraudulent transactions by means of generative adversarial networks. In the design of the model, the output of the discriminator is optimized where classification results are set to three categories; the loss function of the GAN model is changed to include two parts: the source loss function and the category loss function. The generated data and the real business data are mixed according to a certain ratio, to train a fraud detection model. Verify the generation effect of this simulation method by comparing the detection effects of different detection models.
Security-aware Localized Topology Control Algorithm for Mobile Ad-hoc Networks
Authors: Wang Wenjian , Feng Renjian , Zhu Min , Wu Yinfeng , Liu Yadong , Ye Yafeng 
Published: September 2019

Abstract
In this paper, we extend the topology control schemes by making security the primary concern. For this purpose, we propose a security-aware localized topology control algorithm that can maximally avoid making malicious nodes into intermediate nodes. In order to marginalize malicious nodes, we introduce a new metric for characterizing the security status of each link in the network. Each node independently builds a local spanning tree for finding a reduced neighbor set. In the meantime, we maximally avoid using insecurity links in their neighborhood for the local spanning tree construction. Simulation results show that our algorithm significantly improves the security of a network.