bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Feature Point Matching Method for Skull
Authors: Jiabei Hu , Wen Yang , Shanghao Zhao , Shixiong Wang , Piao Wang , Xiaoning Liu 
Published: May 2019

Abstract
Skull feature points play an important role in computer-aided craniofacial restoration. An improved relative angle histogram algorithm is proposed to match the feature points of the skull, aiming at the low positioning accuracy of the existing skull feature point matching algorithm and the difference of the number of points and the difference of the distribution of the model points. First, the Iterative Closest Point (ICP) algorithm is used to register the original skull model. Then, a new spindle is established for the model after registration. The relative angle of the model points is calculated and the phase diagonal distribution of the model points is calculated. Finally, the model points which are most similar to the histogram distribution of the model feature points are selected as the matching points. Experiments show that the algorithm has achieved good results in the matching of feature points of the skull model.
A Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image
Authors: Hong Fan , Jing Yang , Cun-cun Hou , Ke-zhen Zhang , Ruo-xia Yao 
Published: May 2019

Abstract
Aiming at the characteristics of abundant Magnetic Resonance (MR) image information, uneven gray scale, fuzzy boundary, and a fine structure that is difficult to distinguish and segment, this paper proposes an algorithm for segmenting MR images by an improved density clustering algorithm. First, the superpixel clustering algorithm (SLIC) is used to divide the image into a certain number of ultra-pixel regions and subsequently search the neighborhood of each superpixel according to a pre-specified threshold. Then, the KNN-DPC algorithm, in which the value of K is adaptively determined, is used to obtain the distance information of K nearest neighbors (density) and adjacent superpixels, and image segmentation is completed for each superpixel cluster. Two sets of natural image experiments show that this algorithm has high segmentation accuracy. Experiments on clinical breast MR images showed that this algorithm achieved good results for clinical MR image segmentation.
High-accuracy Three-dimensional Measurement by Improving the Asymmetry of Dithered Patterns
Authors: Ting Wang , Bin Lin , Wanzhen Zhang , Bo Chen 
Published: May 2019

Abstract
The previously proposed dithering defocusing technology performs well for threedimensional (3D) measurement when stripes are relatively wide, yet suffers if stripes are narrow. This paper finds two asymmetries in dithered patterns generated by the Sierra Lite dithering algorithm and verifies the longitudinal fringes are more advantageous for phase-shifting technique over transverse fringes. Furthermore, this paper proposes an algorithm with a meandering scan. In each pattern, the pixels of odd lines are scanned from left to right while the even lines are scanned from right to left. The proposed method avoids the quantization errors propagating in a specific direction and greatly improves the symmetry of longitudinal fringes. Both simulation and experimental results have shown this method can effectively improve accuracy of 3D measurement especially for narrow stripes.
Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform
Authors: Lu Ziming , Xiao Jiang-Wen , Huang Zhengyi 
Published: May 2019

Abstract
Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.
Iterative Relation Reasoning for Multiple Object Recognition
Authors: Zhou Tianyu , Miao Zhenjiang 
Published: May 2019

Abstract
We present a novel model for multiple object recognition. Our model combines current deep learning recognition systems with object category relation information. This model is mainly inspired by spatial memory network [1], which treats multiple object recognition task as an iterative process, reusing some region feature as context information. We extend this work by presenting a statistical-based category relation model to measure object category semantic relevance. With the spatial memory and category relation model as relation reasoning modules, our model takes in context information. Our model achieves 4.4% per-instance and 4.9% per-class absolute improvement of average classification accuracy over plain convolutional neural networks on ADE dataset.
A Dimensionality Reduction Model for Complex Data Grouping
Authors: Yang Xiang , Xiaojun Chen , cheng Luo 
Published: May 2019

Abstract
Given the existing packet dimensionality reduction model, the simple distance hypothesis is only used as a simple assumption that there is a certain relationship between packet data. This document proposes to share information between packet data with relevant random measures as a priori. We explicitly calculated the Lévy measure of the mixed random measure and offered the inference steps of detailed parameter a posteriori. Compared with the traditional method, the grouping dimensionality reduction model can achieve faster convergence and can well maintain the original information of data. The experimental results on the public dataset show that the grouping dimensionality reduction model is an effective dimensionality reduction algorithm and can be employed to extract characteristics on big data.
Sentiment Classification of Reviews Based on BiGRU Neural Network and Fine-grained Attention
Authors: Feng Xuanzhen , Liu Xiaohong 
Published: May 2019

Abstract
Text sentiment analysis is part and parcel of natural language processing. The task of sentiment classification is actually the process of feature extraction through models. The comment text of commodities is very different from the ordinary text. The comment text has no fixed grammar and writing format and the sentiment feature information is scattered in various places of text. Due to these factors, model learning of sentiment classification is becoming increasingly complex. The paper aims at establishing a fine-grained feature extraction model based on BiGRU and attention. Firstly, the vocabulary is vectorized by means of the skip-gram model. Then, according to the pre-trained word vector, the sentiment words list can be reached and noise filtering would be conducted by Naive Bayes algorithm. Finally, the model extracts features using BiGRU and fine-grained attentions. Based on the hypothesis that a long review may lead to feature differentiation, a fine-grained attention model is proposed. In this model, the attention layer is design to focus on the feature in different level such as word level, sentence level and paragraph level. This paper validate the proposed model on two sentiment corpus JD reviews and IMDB. Empirical results show that the FGAtten-BiGRU model achieves state of the art results on sentiment analysis tasks.
A Hybrid Key Pre-distribution Scheme for Wireless Sensor Networks
Authors: Xiong Chao , Li Shuquan , Liu Lei , Li Rui , Jin Yanjiao 
Published: May 2019

Abstract
Key management in wireless sensor networks (WSNs) is the basic service for deploying security policies. In this paper, we combine q-composite scheme with polynomial scheme to propose a hybrid key pre-distribution scheme for WSNs. The characteristic of the scheme is that a partial polynomial is preloaded for each sensor node, and then a polynomial share stored by each node is used to generate a corresponding key pool. Perform the corresponding hash operation on the generated keys to hide part of the key information of the nodes. Then select the corresponding number of processed generation keys to distribute to each node. The scheme introduces a random key method, which avoids the "t-secure" problem faced by the conventional polynomial scheme and achieves the significant improvement of network security. The corresponding key is generated using the polynomial share preloaded on the sensor node, which has a higher connectivity than the polynomial scheme. Theoretical and simulation results show that the propose scheme not only enhances the network secure connectivity, but also improves the node’s anti-capture attack capability when compared to other schemes.
The Interactive Simulation of the Liquid in the Container with Spring-mass Model
Authors: Guo Peishan , Zhu Qing , Zhu Wenzhe 
Published: May 2019

Abstract
With the development of the virtual reality industry, the demand for performance of virtual simulation is increasing. In the field of virtual simulation, fluid simulation has become one of the most challenging research directions in computer graphics. High-performance fluid simulation methods are still a problem which is worth studying.This paper presents a method based on grid and Spring-mass model interactions for simulate liquid in the container. Firstly, the mass-spring model is introduced to simulate the water surface. Create a flat grid, then add physical properties to the point on the triangular grid. Grids are used to connect the water proton. Secondly, change the current height of the particle through calculating the particle density on the surface of liquid to simulate the fluctuations of the water.Finally realize the calculation method in unity, prove that the method has both performance and sense of reality and better than previous methods.
Deeper Time Delay Neural Networks for Effective Acoustic Modelling
Authors: Huang Xiaorong , Zhang Weibin , Xu Xiangmin , Yin Ruixiang , Chen Dongpeng 
Published: May 2019

Abstract
Time delay neural networks (TDNNs) have been shown to be an efficient network architecture for modelling long temporal contexts in speech recognition. Meanwhile, the training times of TDNNs are much less, compared with other long temporal contexts models based on recurrent neural networks. In this paper, we propose deeper architectures to improve the modelling power of TDNNs. At each TDNN layer that needs spliced input, we increase the number of transforms so that the lower layers can provide more salient features for upper layers. Dropout is found to be an effective way to prevent the model from overfitting once the depth of the model is substantially increased. The proposed architectures significantly improvements the recognition accuracy in Switchboard and AMI.
A Comparison of Features for Replay Attack Detection
Authors: Xie Zhifeng , Zhang Weibin , Chen Zhuxin , Xu Xiangmin 
Published: May 2019

Abstract
Speaker verification (ASV) systems are still vulnerable to different kinds of spoofing attacks, especially replay attack due to high-quality playback devices. Many countermeasures have been developed recently. Most of the efforts focus on the search for more salient features and many new features have been proposed. Five kinds of features, namely Mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), inverted Mel-frequency cepstral coefficients (IMFCCs), constant Q cepstral coefficients (CQCCs) and bottleneck features were compared on the public ASVspoof 2017 and BTAS 2016 datasets in this paper. Our experimental results show that MFCCs and bottleneck features yield comparable results. Both of them significantly outperform others (including the recently proposed CQCCs). However, the number of filters and cepstral bins are essential to the success of MFCCs.
An Argumentation-Based Approach for Computing Inconsistency Degree in Possibilistic Lightweight Ontologies
Authors: Boutouhami Khaoula , Musa Ibrahim Hussein 
Published: May 2019

AbstractReasoning with inconsistent ontologies plays an important role in Semantic Web applications. An important feature of argument theory is that it is able to naturally handle inconsistencies in ontologies and allows a user to represent information in the form of an argument. In argumentation, given an inconsistent knowledge base, arguments compete to decide which are the accepted consequences. In this paper, we are interested in using the argumentation for the inconsistency degree of uncertain knowledge bases expressed in possibilistic DL-Lite (the key notion in reasoning from a possibilistic DL-Lite knowledge base) without going through the negative closure. In the present work, the terminological base is assumed to be fully certain and the uncertainty is only considered on the assertion based. We proved that it is coherent and feasible to use Dung’s abstract argumentation theory to compute the inconsistency degree and how argumentation semantics relate to the state of the art of handling inconsistency.
Composition and physicochemical properties of outer mucilage from seeds of Arabidopsis natural accessions
Authors: Poulain Damien , Botran Lucy , North Helen M , Ralet Marie-Christine 
Published: May 2019

Abstract
Arabidopsis thaliana (Arabidopsis) seeds are myxospermous and release two layers of mucilage on imbibition. The outer layer can be extracted with water facilitating the analysis of its major constituent, polysaccharides. The composition and properties of outer mucilage have been determined for 306 natural accessions and six control genotypes to generate a data set comprising six traits measured in four biological replicates for each. Future exploitation of this data is possible in a range of analyses and should yield information concerning genetic diversity, underlying genetic factors and the biological function of mucilage as an adaptive trait.
Child Care and Human Development: Insights from Jewish History in Central and Eastern Europe, 1500–1930*
Authors: Botticini Maristella , Eckstein Zvi , Vaturi Anat 
Published: May 2019

AbstractEconomists increasingly highlight the role that human capital formation, institutions and cultural transmission may play in shaping health, knowledge and wealth. We study one of the most remarkable instances in which religious norms and childcare practices had a major impact: the history of the Jews in central and eastern Europe from 1500 to 1930. We show that while birth rates were about the same, infant and child mortality among Jews was much lower and accounted for the main difference in Jewish versus non-Jewish natural population growth. Jewish families routinely adopted childcare practices that recent medical research has shown as enhancing children's well-being.
Increase rate of light-induced stomatal conductance is related to stomatal size in the genus Oryza
Authors: Zhang Qiangqiang , Peng Shaobing , Li Yong 
Published: May 2019

Abstract
The rapid response of stomatal conductance (gs) to fluctuating irradiance is of great importance to maximize carbon assimilation while minimizing water loss. Smaller stomata have been proven to have a faster response rate than larger ones, but most of these studies have been conducted with forest trees. In the present study, the effects of stomatal anatomy on the kinetics of gs and photosynthesis were investigated in 16 Oryza genotypes. Light-induced stomatal opening includes an initial time lag (λ) followed by an exponential increase. Smaller stomata had a larger maximum stomatal conductance increase rate (Slmax) during the exponential increase phase, but showed a longer time lag and a lower initial stomatal conductance (gs,initial) at low light. Stomatal size was, surprisingly, negatively correlated with the time required to reach 50% of maximum gs and photosynthesis (T50%gs and T50%A), which was shown to be positively correlated with λ and negatively correlated with gs,initial. With a lower gs,initial and a larger λ, small stomata showed a faster decrease of intercellular CO2 concentration (Ci) during the induction process, which may have led to a slower apparent Rubisco activation rate. Therefore, smaller stomata do not always benefit photosynthesis as reported before; the influence of stomatal size on dynamic photosynthesis is also correlated with λ and gs,initial.
Efficacy of ruthenium coordination complex–based Rutherrin in a preclinical rat glioblastoma model
Authors: Munegowda Manjunatha Akathatti , Fisher Carl , Molehuis Daniel , Foltz Warren , Roufaiel Mark , Bassan Jay , Nitz Mark , Mandel Arkady , Lilge Lothar 
Published: May 2019

Abstract

Background
Glioblastoma is an aggressive brain cancer in adults with a grave prognosis, aggressive radio and chemotherapy provide only a 15 months median survival.

Methods
We evaluated the tolerability and efficacy of the Ruthenium-based photosensitizer TLD-1433 with apo-Transferrin (Rutherrin) in the rat glioma 2 (RG-2) model. The specific tumor uptake ratio and photodynamic therapy (PDT) threshold of the rat glioblastoma and normal brain were determined, survival and CD8+T-cell infiltration post-therapy were analyzed. Results were compared with those obtained for 5-aminolevulinic acid (ALA)-induced Protoporphyrin IX (PpIX)-mediated photodynamic therapy in the same animal model. As both photosensitizers have different photophysical properties, the number of absorbed photons required to achieve an equal cell kill was determined for in vitro and in vivo studies.

Results
A significantly lower absorbed energy was sufficient to achieve LD50 with Rutherrin versus PpIX-mediated PDT. Rutherrin provides a higher specific uptake ratio (SUR) &gt;20 in tumors versus normal brain, whereas the SUR for ALA-induced PpIX was 10.6. To evaluate the short-term tissue response in vivo, enhanced T2-weighted magnetic resonance imaging (MRI) provided the spatial extent of edema, post PpIX-PDT at twice the cross-section versus Rutherrin-PDT suggesting reduced nonspecific damage, typically associated with a secondary wave of neuronal damage. Following a single therapy, a significant survival increase was observed in rats bearing glioma for PDT mediated by Rutherrin versus PpIX for the selected treatment conditions. Rutherrin-PDT also demonstrated an increased CD8+T-cell infiltration in the tumors.

Conclusion
Rutherrin-PDT was well tolerated providing a safe and effective treatment of RG-2 glioma.
Evaluate Penicillin Allergy Before Deciding Against Penicillin Use
Authors: Rosenberg Karen 
Published: May 2019

According to this study:

Although many patients report having an allergy to penicillin, few have clinically significant immunoglobulin-mediated or T lymphocyte–mediated penicillin hypersensitivity.Clinicians should evaluate penicillin allergy before ruling out the use of penicillin or other beta-lactam antibiotics.
Failure to detect synergy between variants in transferrin and hemochromatosis and Alzheimer’s disease in large cohort
Authors: Vance Elizabeth , Gonzalez Josue , Miller Justin B. ,   Alzheimer’s Disease Genetics Consortium (ADGC), Staley Lindsey , Crane Paul K. , Mukherjee Shubhabrata , Kauwe John S.K. 
Published: May 2019

AbstractAlzheimer’s disease (AD) is the most common cause of dementia and, despite decades of effort, there is no effective treatment. In the last decade, many association studies have identified genetic markers that are associated with AD status. Two of these studies suggest that an epistatic interaction between variants rs1049296 in the Transferrin (TF) gene and rs1800562 in the Homeostatic Iron Regulator (HFE) gene, commonly known as “the hemochromatosis gene”, is in genetic association with AD. TF and HFE are involved in the transport and regulation of iron in the brain, and disrupting these processes exacerbates AD pathology through increased neurodegeneration and oxidative stress. However, by using a significantly larger dataset from the Alzheimer’s Disease Genetics Consortium (ADGC), we fail to detect an association between TF rs1049296 or HFE rs1800562 with AD risk (TF rs1049296 p=0.38 and HFE rs1800562 p=0.40). In addition, logistic regression with an interaction term and a Synergy Factor Analysis (SFA) both failed to detect epistasis between TF rs1049296 and HFE rs1800562 (SF=0.94; p=0.48) in AD cases. Each of these analyses had sufficient statistical power (Power>0.99), suggesting that previously-reported associations may be the result of more complex epistatic interactions, genetic heterogeneity, or were false-positive associations due to limited sample sizes.
CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies
Authors: Yang Yi , Shi Xingjie , Jiao Yuling , Huang Jian , Chen Min , Zhou Xiang , Sun Lei , Lin Xinyi , Yang Can , Liu Jin 
Published: May 2019

AbstractMotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.ResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.Contactjin.liu@duke-nus.edu.sgAvailability and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.Supplementary informationSupplementary data are available at Bioinformatics online.