bluuebunny/crossref_metadata_2025_split huggingface dataset:
Error analysis for filtered back projection reconstructions in Besov spaces
Authors: Beckmann M , Maass P , Nickel J 
Published: July 2020

Abstract
Filtered back projection (FBP) methods are the most widely used reconstruction algorithms in computerized tomography (CT). The ill-posedness of this inverse problem allows only an approximate reconstruction for given noisy data. Studying the resulting reconstruction error has been a most active field of research in the 1990s and has recently been revived in terms of optimal filter design and estimating the FBP approximation errors in general Sobolev spaces. However, the choice of Sobolev spaces is suboptimal for characterizing typical CT reconstructions. A widely used model are sums of characteristic functions, which are better modelled in terms of Besov spaces

B

q

α
,
p

(

R

2

)

. In particular

B

1

α
,
1

(

R

2

)

with α ≈ 1 is a preferred model in image analysis for describing natural images. In case of noisy Radon data the total FBP reconstruction error

‖
f
−

f

L

δ

‖
⩽
‖
f
−

f

L

‖
+
‖

f

L

−

f

L

δ

‖

splits into an approximation error and a data error, where L serves as regularization parameter. In this paper, we study the approximation error of FBP reconstructions for target functions

f
∈

L

1

(

R

2

)

∩

B

q

α
,
p

(

R

2

)

with positive

α
∉
N

and 1 ⩽ p, q ⩽ ∞. We prove that the L

p
-norm of the inherent FBP approximation error f − f

L
can be bounded above by

‖
f
−

f

L

‖

L

p

(

R

2

)

⩽

c

α
,
q
,
W

L

−
α

|
f

|

B

q

α
,
p

(

R

2

)

under suitable assumptions on the utilized low-pass filter’s window function W. This then extends by classical methods to estimates for the total reconstruction error.
Single-shot phase extraction for fringe projection profilometry using deep convolutional generative adversarial network
Authors: Yang Tao , Zhang Zhongzhi , Li Huanhuan , Li Xiaohan , Zhou Xiang 
Published: July 2020

Abstract
This paper presents a single-shot phase extraction approach based on a deep convolutional generative adversarial network that generates a phase map and a quality mask from an input fringe pattern image. A novel loss function is proposed, and a large-scale (28 800 samples) real fringe pattern dataset is collected to train the network. The experiments demonstrate that the proposed method achieves significantly improved phase extraction accuracy and overcomes the main limitations of Fourier transform profilometry. In addition, the proposed method presents excellent performance for real-time computing, reaching approximately 100 f s−1 with a single GPU. Moreover, the proposed learning-based approach can automatically perform denoising and phase extraction, without any manually set parameters.
Theory of the inverse Faraday effect due to the Rashba spin–oribt interactions: roles of band dispersions and Fermi surfaces
Authors: Tanaka Yasuhiro , Inoue Takashi , Mochizuki Masahito 
Published: July 2020

Abstract
We theoretically study the inverse Faraday effect, i.e., the optical induction of spin polarization with circularly polarized light, by particularly focusing on effects of band dispersions and Fermi surfaces in crystal systems with the spin–orbit interaction (SOI). By numerically solving the time-dependent Schrödinger equation of a tight-binding model with the Rashba-type SOI, we reproduce the light-induced spin polarization proportional to

E

0

2

/

ω

3

where E
0 and ω are the electric-field amplitude and the angular frequency of light, respectively. This optical spin induction is attributed to dynamical magnetoelectric coupling between the light electric field and the electron spins mediated by the SOI. We elucidate that the magnitude and sign of the induced spin polarization sensitively depend on the electron filling. To understand these results, we construct an analytical theory based on the Floquet theorem. The theory successfully explains the dependencies on E
0 and ω and ascribes the electron-filling dependence to a momentum-dependent effective magnetic field governed by the Fermi-surface geometry. Several candidate materials and experimental conditions relevant to our theory and model parameters are also discussed. Our findings will enable us to engineer the magneto-optical responses of matters via tuning the material parameters.
Capacitive effect in ultrafast laser-induced emission from low conductance diamond nanotips
Authors: Torresin Olivier , Mauchain Julien , Borz Mario , Blum Ivan , Vella Angela , Chalopin Benoit 
Published: July 2020

Abstract
Single crystal diamond nanotips reveal a new behavior for ultrafast laser-induced electron emission. Under tightly focused femtosecond laser illumination, electron yield shows a saturation with the laser intensity. When the DC bias is sufficient for dark field emission, large optical intensities can switch off the emission occurring between laser pulses during a few hundred μs, because of the low conductance of the diamond tip. We propose a macroscopic model to combine a capacitive effect with the different conduction and emission mechanisms. This study shows that non-metallic photocathodes offer different perspectives from the conventional metallic ones.
Getting around the cell: physical transport in the intracellular world
Authors: S Mogre Saurabh , Brown Aidan I , Koslover Elena F 
Published: July 2020

Abstract
Eukaryotic cells face the challenging task of transporting a variety of particles through the complex intracellular milieu in order to deliver, distribute, and mix the many components that support cell function. In this review, we explore the biological objectives and physical mechanisms of intracellular transport. Our focus is on cytoplasmic and intra-organelle transport at the whole-cell scale. We outline several key biological functions that depend on physically transporting components across the cell, including the delivery of secreted proteins, support of cell growth and repair, propagation of intracellular signals, establishment of organelle contacts, and spatial organization of metabolic gradients. We then review the three primary physical modes of transport in eukaryotic cells: diffusive motion, motor-driven transport, and advection by cytoplasmic flow. For each mechanism, we identify the main factors that determine speed and directionality. We also highlight the efficiency of each transport mode in fulfilling various key objectives of transport, such as particle mixing, directed delivery, and rapid target search. Taken together, the interplay of diffusion, molecular motors, and flows supports the intracellular transport needs that underlie a broad variety of biological phenomena.
Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions
Authors: Bolton Thomas A W , Uruñuela Eneko , Tian Ye , Zalesky Andrew , Caballero-Gaudes César , Van De Ville Dimitri 
Published: July 2020

Abstract
Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of interest is first selected and dynamic analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or states are studied, more local regional rearrangements are then not described, potentially missing important neurobiological information. Here, we propose a novel mathematical framework that jointly estimates resting-state functional networks and spatially more localised cross-regional modulations. To do so, the changes in activity of each brain region are modelled by a logistic regression including co-activation coefficients (reflective of network assignment, as they highlight simultaneous activations across areas) and causal interplays (denoting finer regional cross-talks, when one region active at time t modulates the t to t + 1 transition likelihood of another area). A two-parameter

ℓ
1

regularisation scheme is used to make these two sets of coefficients sparse: one controls overall sparsity, while the other governs the trade-off between co-activations and causal interplays, enabling to properly fit the data despite the yet unknown balance between both types of couplings. Across a range of simulation settings, we show that the framework successfully retrieves the two types of cross-regional interactions at once. Performance across noise and sample size settings was globally on par with that of other existing methods, with the potential to reveal more precise information missed by alternative approaches. Preliminary application to experimental data revealed that in the resting brain, co-activations and causal modulations co-exist with a varying balance across regions. Our methodological pipeline offers a conceptually elegant alternative for the assessment of functional brain dynamics and can be downloaded at https://c4science.ch/source/Sparse_logistic_regression.git.
Transformable Fingerprinting with Deep Metric Learning Approach for Indoor Localization
Authors: Zeng Xiangsheng , Xiao Limin , Zhao Ming , Xu Xibin , Li Yunzhou 
Published: July 2020

Abstract
Within state-of-the-art indoor localization approaches, fingerprinting based method is more applicable and easier to integrate into most of today’s commodity Wi-Fi devices such as mobile phones and IOT devices which require low cost and computation burden. However, most fingerprinting systems intrinsically depend on fixed channel propagation environment and thus suffers huge reconstruction cost and high localization error when environment changes. In this paper, we propose a novel transformable fingerprinting localization method based on deep metric learning approaches. Our fingerprinting reconstruction method only requires some fresh measurements of CSI (Channel State Information) on a few reference points (RPs) with all the outdated CSI fingerprinting. Extensive system level simulations on Quadriga show that an average of 0.2m error reduction is achieved when our reconstruction method is applied.
Knowledge Service Model of Port Supply Chain Enterprise Based on Ontology
Authors: Yang Bo , Mao Junqing 
Published: July 2020

Abstract
Under the background of the development of port industry and information age, with the help of the knowledge service theory system and ontology technology to build a smart port supply chain knowledge service model to provide a personalized, intelligent, and diversified knowledge-based service system platform solution to port supply chain enterprises, which is of great significance to realization of port supply chain transformation and upgrading and intelligent integrated operation. Based on the analysis of the demand for knowledge service and the research status of knowledge service in port supply chain during the development and operation of port supply chain, this paper uses ontology technology to build domain ontology knowledge base, which is used to dismantle and integrate the massive digital resources in port supply chain, integrate various multi-source heterogeneous data resources, and standardize the form of knowledge sharing organization. On the basis of this, the paper carries on the calculation and reasoning experiment to the port supply chain ontology. The experiment shows that the ontology technology has good effectiveness and superiority in the knowledge representation and knowledge reasoning of constructing the port supply chain knowledge service system model.
Numerical Simulation of Ice Regime Evolution of Pumped Storage Power Station in Cold Area
Authors: Zhao Haijing , Wang Jun , Zhang Yanhong , Chen Pangpang , Cheng Tiejie 
Published: July 2020

Abstract
The formation, development and melt of ice in pumped storage reservoirs in cold climate are studied by numerical simulation. Based on the theory of ice hydrodynamics and the MFC module of Visual C++ 6.0, a mathematical model of ice regime of pumped storage power station in the form of dialog box was established, which includes the hydraulic calculation module, the temperature diffusion module and the ice sheet thermal fluctuation module. The model was applied in Pushihe pumped storage power stations and the results showed that the simulation results were in good agreement with the prototype observation results. The research results can provide effective technical reference for the selection of anti-ice operation mode and the reserve of frozen storage capacity of pumped storage power station in cold area in winter.
Learning Robust Scene Classification Model with Data Augmentation Based on Xception
Authors: Chen Haiyan , Yang Yu , Zhang Suning 
Published: July 2020

Abstract
Scene classification technology based on computer vision has been widely applied in many fields. However, with the increasing complexity of images, many computer vision classification models are difficult to meet requirements of current scene classification tasks, as they not only require considering the object, background, spatial layout and other information, but also many relationships in the image. Based on the analysis of existing scene classification algorithms and Xception model, an approach that adds optimization from two aspects of data set processing is proposed to solve complicated scene classification tasks. Combined with the image enhancement technology, the serialized image enhancement method is used to expand the dataset and enhance the image features, and takes advantage of the Xception model to extract the image features to obtain the scene classification model with high robustness. The experimental results showed that Xception model was able to deal with scene classification efficiently by making up for the shortcomings of traditional Convolutional Neural Networks (CNN) models in feature extraction and generalization ability.
Research on the Design of Network Ordering System for Distribution Network Operation Order
Authors: Sha Shuming , Luo Liming , Du Zhiqiang 
Published: July 2020

Abstract
The command issuing and executed feedback of dispatching operation order in distribution network is hanging from traditional telephone mode to network mode. Based on the event driven architecture, this paper implements a network ordering system for distribution network operation order, which can be flexibly configured and deployed. It is compatible with intranet site, mobile app, artificial intelligence voice and other command modes, and it supports real-time telemetry data verification of Distribution Automation System. Asynchronous and low coupling design between command channels reduces the overall logic complexity of the system, and improves the adaptability to requirements.
The Design and Implementation of an Augmented Reality Scheme Based on Netty Communication
Authors: Gao Yongping , Wang Jie , Zhang Jingzhong 
Published: July 2020

Abstract
This paper introduces a whole project of augmented reality interaction mode based on netty communication method, determines the main technical difficulties in the implementation process, and finds the solution by means of system architecture design and communication protocol development. In this paper, the netty framework is adopted to deal with network IO and business logic, and the communication between each terminal device, virtual environment and server is established through long connection, This paper adopts the netty framework to deal with network IO and business logic, establishes communication between each terminal device and virtual environment and server through long connection, so as to realize fast and natural real-time interaction between various interactive devices and other augmented reality devices and virtual environments under multiple users. It can also solve the problem of high concurrency of real-time communication between various devices and the virtual environment in augmented reality.
Vehicle and Driver Detection on Highway Based on Cascade R-CNN
Authors: Geng Yifei , Lu Xiaobo 
Published: July 2020

Abstract
Vehicle and driver detection in the highway scene has been a research hotspot in the field of object detection in recent years, and it is still a challenging problem in the research of traffic order and road safety. In this paper, we propose a novel end-to-end vehicle and driver detection method named VDDNet which is based on Cascade R-CNN and SENet. By introducing FPN structure and SENet attention mechanism in the backbone, the ability of the model to learn effective features is enhanced. It can improve the accuracy of detection in difficult scenes such as weak light, partial occlusion, and low picture resolution. The test results based on the database of highway traffic vehicle and drivers constructed by the Jiangsu Provincial Public Security Department. It shows that the detection method has the AP rate of 91.3% and the Recall rate of 92.4%, which demonstrates the effectiveness of the proposed method in complex highway environments.
Design of Employee Comment Sentiment Analysis Platform Based on AE-SVM Algorithm
Authors: Zhang Yongxiong , Wang Liangming 
Published: July 2020

Abstract
In order to solve the high-dimensional data set of employees’ sentiment analysis on enterprise comment, in this paper, we presented three model algorithms: SVM algorithm, PCA-SVM algorithm, and AE-SVM algorithm. And we made a comparative analysis of three index parameters such as the precision, recall and F1 in three different algorithm model scenarios. According to the experimental results, the AE-SVM algorithm is superior to other two algorithms in this kind of Chinese text sentiment analysis. Based on the AE-SVM Algorithm, we also design a solution of sentiment analysis platform of employee comment. This solution has the characteristics of high availability and high concurrency. It can also collect and analyse the sentiment text information of employees’ comment in time.
Comments Prediction Model on Emotional Analysis Based on Bayes Classification
Authors: Chen Xuegang , Duan Sheng , Wang Luda 
Published: July 2020

Abstract
The comments of e-commerce platform are the information feedback of users on the purchase of goods, which shows the concerns and emotional tendencies of the commodity. The research has important research value for enterprises. In this paper, we constructed the analysis model of e-commerce comments. Firstly, reptile obtained data to segment word segmentation, and then use the characteristics of the initial selection model to review data and transfer matrix conversion, using Bayes probability model to prediction work, finally through the prediction error to select the optimal characteristics of the model. Through the empirical study of the commentary data of BOE, the results show the method is effective, and has better performance and higher classification accuracy.
Application of Computer Technology in English Translation
Authors: Yang Ting , Fan Hongbin 
Published: July 2020

Abstract
With the continuous development of economy, communication is increasing between China and overseas, and business activities are becoming more and more frequent. In this process, foreign trade English plays an important role. Foreign trade English is unique, professional and practical. Under different scenes of foreign trade, English words can express different meanings. In the course of trade, foreign trade English can help both of parties to communicate, and can achieve the translation of language, the interpretation of contract, the understanding of letter and so on. In the process of language communication and reading important documents, we need a solid foundation of English language and profound foreign cultural background, and have a strong ability to deal with the professional knowledge of international trade and the use of English vocabulary. The characteristics of foreign trade English language are analyzed in terms of vocabulary, style and grammar. At the same time, in the process of language understanding and translation, we need to meet the requirements of real-time. With the frequent exchange of foreign trade between our country and other countries, a large number of English documents will be produced, the relevant departments have put forward higher requirements for the accuracy of these documents and real time of these documents, the traditional manual translation can not meet the growing demand for foreign trade translation. With the help of computer aided-translation software memoQ, we help people do auxiliary translation, therefore, it can reduce translator’s burden and improve the accuracy of translation and efficiency of translation. We select a large number of texts and adopt memoQ software to translate, so as to analyze the role of computer-aided translation software in the translation process of English documents.
Rotary Kiln Combustion State Recognition Based on Convolutional Neural Network
Authors: Li Tao , Peng Tao , Chen Hua 
Published: July 2020

AbstractThe accurate recognition of combustion state is an important part of combustion control system. The rotary kiln is nonlinear, large lag, multi-disturbance and multivariable in the combustion process, so it is difficult to realize the intelligent control of the rotary kiln. Moreover, the traditional image recognition method is easily affected by the external environment, and the accuracy of recognition depends on the extraction of artificial features, so the satisfactory results can not be obtained. In order to solve these problems, a method of flame image recognition of combustion state in rotary kiln based on convolution neural network is proposed in this paper. In this method, convolution neural network vgg-16 model is used for feature migration, and the flame images of different combustion states in rotary kiln are trained and tested by network in order to achieve the purpose of automatic recognition of combustion state. The experimental results show that the combustion control system based on convolution neural network image recognition is effective, robust and has high accuracy. Compared with the traditional image recognition method, this method can ahieve more effective accurate and more reliable automatic classification effect.
Design and Implementation of A Website Record Management System Supporting Automatic Verification in IPv6 Transition Phase
Authors: Zhou Jingjing , Deng Gengsheng , Fu Aiying , Lu Zhe 
Published: July 2020

Abstract
Website information registration and filing is the basis of website information security management. In view of the current situation of various websites and increasingly severe network security in Colleges and universities, in order to better manage the websites in Colleges and universities, this paper takes the network environment in the transition stage of IPv6 as an example to design and implement the website filing management system architecture supporting automatic verification. This paper analyzes the methods and shortcomings of the improvement of the traditional website filing management system in the domestic universities, and puts forward the automatic verification mechanism which can actively distinguish the content and status of the website, and actively realize the verification of the filing information by means of scanning detection, page collection, access control, etc., designs and introduces the process of the automatic verification in detail Lists the main code for page collection and reverse proxy services. The actual application shows that the filing management system supporting automatic verification can realize the automatic verification and recording of the information of the completed filing website, detect the website services existing in the managed network, and check out the sites that have not been record, greatly reducing the workload of the management personnel to check the website filing information, and improving the timeliness of the website filing management. Statistics show that IPv6 link access support has become a problem that must be considered by colleges and universities in the transition phase of IPv6. The architecture is based on open source software, and can be used in other universities or similar network environment with only a few configuration and code modification.