bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Ensemble Learning Imbalanced Data Classification Method Based on Sample Combination Optimization
Authors: Wang Yuxin 
Published: August 2019

Abstract
Imbalanced data classification is one of the hot topics in data mining and machine learning in recent years. In practice, Imbalanced data classification is very common, such as cancer detection, spam discrimination, credit card fraud detection, etc. Because of the large difference in the number of categories and Imbalanced distribution, traditional classification algorithms have poor classification effect on minority classes, and correct identification of minority classes often brings greater value. Therefore, how to effectively identify minority classes in Imbalanced data is of great importance. Practical significance. Aiming at the problems that the Bagging-based Imbalanced data classification method cannot guarantee the validity and existence of classification boundaries by adding redundant noise information and sampling, an ensemble learning GABagging method based on sample combination optimization is proposed. Firstly, the sample combination optimization algorithm uses genetic algorithm to select a subset from most classes and construct a new data set with a few classes. Subsequently, several sample combinatorial optimization algorithms are used to train and integrate several classifiers. The experimental results show that GABagging can improve the correct recognition ability of minority classes on 19 Imbalanced datasets compared with other similar methods such as TPR and AUC, without excessive loss of recognition ability of majority classes. It is proved that GABagging can compensate for the shortcomings of related Bagging-based methods such as easy loss, increasing samples and not guaranteeing the validity and existence of classification boundaries after sampling.
Research on Routing Algorithm Based on Reinforcement Learning in SDN
Authors: Fang Chuntao , Cheng Cheng , Tang Zhongyun , Li Chuanhuang 
Published: August 2019

Abstract
Reinforcement learning is an unsupervised learning method which has been used in many fields. Actually, the essence of Reinforcement learning is a decision-making problem. It constantly tries to interact with the environment. Each interaction process will get a different feedback value, and then it adjusts each trial strategy through feedback. In this paper, we apply the Reinforcement learning technology to software defined network routing algorithm, and propose the routing algorithm based on Q-learning. Through the combination of Reinforcement learning and neural network, which means the Q-table in Q-learning is replaced by neural network, we present routing algorithm based on Deep Q-learning.
Analysis of Desired Receive Signal Impact in Wireless Co-time Co-frequency Full Duplex
Authors: Zhou Juan , Chen Chao , Xue Yajuan , Shen Ying , He Meng 
Published: August 2019

Abstract
With self-interference cancellation (SIC), a wireless full duplex terminal is allowed to transmit and receive signal simultaneously in the same frequency band. This paper focuses on the analysis of desired receive signal impact on the radio frequency (RF) SIC performance. The full-duplex system model is rst given, and the principle of radio frequency self-interference cancellation is introduced. Then, from the perspective of the convex function, the in uence of the desired signal on the RF SIC is analyzed when the total power of the signal after RF SIC is taken as the objective function. It is found that when there is no desired signal, the objective function is a convex function, and the residual self-interference power can converge to the minimum value by the gradient descent method. If the desired signal exists and the channel is constant, the objective function is also a convex function, and the residual self-interference power can also converge to the minimum value. However, in the presence of the desired signal and the channel uctuates, the target is not a convex function, and the residual self-interference power cannot be converged to the minimum value by the gradient descent method.
White Light Transmitting Antenna for Four-spectrum Mobile WDM-VLC
Authors: He Donglei , Tang Yi , Chang Yuee , Li Pingan 
Published: August 2019

Abstract
In this paper, we propose a mixing white light antenna with a homogenizer and an amplification system, and demonstrated its utilization in a four-spectrum mobile VLC. The central element is the homogenizer, which is a square integrating rod that mixes four-spectrum light into white light. Behind it, there are two Fresnel lenses to amplify the light-mixing area. The system is compact with a length of 80 mm and maximal diameter of 58.5 mm. We determined the initial structural parameters of the antenna by theoretical analysis, and then we determine the best parameters by simulation. Simulation results indicate that a 0.75 m×0.75 m area can be illuminated at 1.7 m distance. The detection surface has a high uniformity (76.27%), which can meet illumination and mobile communication requirements simultaneously.
Wireless Sensor Network Deployment Optimization Using Reference-Point-Based Non-dominated Sorting Approach (NSGA-III)
Authors: Metiaf Ali , Wu Qianhong 
Published: August 2019

Abstract
Wireless sensor networks (WSN) nowadays have gained more interest, pushed by the growing necessity for data collection and transmission from both civilian and military domains. WSN is constructed from interconnected sensors and limited resource (battery), which requests great importance on the deployment strategy to increase the performance metrics for WSN (lifetime, coverage, QoS connectivity). Also, the deployment is considered as a fundamental issue in (WSNs) design, and it was taken from the perspective of the multi-objective optimization problem. Many of the existing deployment strategies are based on metaheuristics algorithms such as Genetic Algorithms (GAs) to resolve the deployment problem. In this article, we use and adopt one of the most attractive approaches for wireless sensor networks deployment (WSND) optimization based on metaheuristic searching which is named Non-dominated Sorting Genetic Algorithm-III (NSGA-III) In order to reach maximum coverage and minimize the consumption of energy to maximize the network lifetime under the connectivity constraint. The comparison results have proved the NSGA-III algorithm outperformed the Constrained Pareto-based Multi-objective Evolutionary Approach (CPMEA) that taken as a benchmark for this study. Those results encourage the application of NSGA-III to real-world deployment problems, and the importance of this approach is that it can be handled many objectives.
RCS Historical Data Screening Method Based on Spatial Observation Geometric Relationship
Authors: Hu M X , Lai J Z , Xu C 
Published: August 2019

Abstract
Selecting accurate Radar Cross Section (RCS) sequence historical data has an important impact on RCS-based spatial target attitude anomaly detection. RCS is related to factors such as satellite shape size, attitude and radar observation angle, and is a sensitive function of observation angle variation. In this paper, the influence of spatial observation geometric relationship on spatial target RCS sequence is analysed. Aiming at the orbital regression characteristics unique to spatial targets, a spatial target RCS historical data screening method based on dynamic time programming (DTW) distance is proposed. According to the relationship between the azimuth and the elevation angles of the radar line of sight, the orbit data with similar spatial geometry is found from the historical data. The orbital data of the system. This method can reduce the impact of Earth’s rotation on data observation and establish a more efficient database. The variational mode decomposition (VMD) of RCS is carried out, and the fractal box dimension of each component is extracted as the data feature, and the BP neural network is used as the classifier for the anomaly detection. The simulation results prove that compared with the traditional historical data collection method, this method can improve the attitude detection rate of space targets and has better robustness.
Research of NCS Random Time Delay Prediction Based on FNN-PSO
Authors: Lei Jian-He , Lu Jing-Xue , Xue Bing-Xin , Wang Guang-Yi 
Published: August 2019

Abstract
Networked control systems have been attracting considerable attention during the last decades, not only because of their theoretical value, but also of their widespread practical applications. In practice, networked control systems are inevitably affected by the random time delays. Aiming at the problem of system performance degradation caused by random time delay in networked control systems, a method of combining particle swarm optimization with fuzzy neural network is used to establish a networked control system time delay prediction model and predicts the future time delay. According to the simulation, we verify the effectiveness and feasibility of the proposed method. The results of simulation show that the fuzzy neural network optimized by particle swarm optimization has a high prediction accuracy for random time delay and is suitable for practical applications.
Amplitude-Fluctuation Separation Based on BPM Waveform Reconstruction in MIMO Systems
Authors: Qin Xinrui , Yang Xiaoqi , Jiang Weidong 
Published: August 2019

Abstract
In Multi-input-multi-output (MIMO) systems, the orthogonality among sub-waves could be realized by binary phase modulation (BPM) encoding. Especially in the case of insufficient matched filters, Hadamard-matrix-based BPM encoding could be employed to separate the transmitted signal from echo effectively, which is verified firstly in this paper by utilizing target location, when introducing fluctuations in amplitude as well as in phase. Hence the amplitude-fluctuation separation method based on waveform reconstruction is put forward. The waveform is reconstructed on basis of the known array structure, and then the results are optimized obeying the minimum mean square error (MSE) criterion to obtain more accurate estimation. Compared with the traditional single-channel blind source separation approach, this method shows significant computation reduction by taking full use of the phase and structure information. Monte-Carlo experiments validate the method application in angle estimation improvement.
Life
Authors: Taylor C. C. W. 
Published: August 2019

Abstract
‘Life’ explains that virtually nothing is known about the first half of Socrates’ life, but details of his later life, with his wife Xanthippe and his sons, are provided. Socrates was seen as an arguer and questioner, who went about challenging people’s pretensions to expertise and revealing inconsistencies in their beliefs, but also as a religious deviant and a subverter of traditional religion and morality. In 400 or 399 he was accused of not recognizing the gods of the city, introducing new gods, and corrupting the young—a charge that probably had a political dimension. He was sentenced to death. Though there is no record of the actual trial, accounts were provided by Plato and Xenophon.
Socrates and later philosophy
Authors: Taylor C. C. W. 
Published: August 2019

Abstract
‘Socrates and later philosophy’ examines the legacy of Socrates, the most important aspect of which was his influence on Plato. Antisthenes, another personal associate, adhered to some of Socrates’ ethical doctrines and his austere lifestyle. The Stoics accepted the cardinal doctrines of Socratic ethics—that virtue is knowledge and that virtue is sufficient for eudaimonia—while the Epicureans were consistently hostile to his ideas. The major medieval philosophers showed little interest in Socrates, but the revival of Platonism in the late 15th century changed that. The tradition of adapting the figure of Socrates to fit the general preconceptions of the writer is discernible in his treatment by three 19th-century philosophers: Hegel, Kierkegaard, and Nietzsche.
Does Artistic Value Pose a Special Problem for Time Travel Theories?
Authors: McAllister James W 
Published: August 2019

Abstract
Michael Dummett and Storrs McCall have claimed that time travel scenarios in which an artist copies an artwork from a reproduction of it that has been sent from the future introduce a causal loop of a new kind: one involving artistic value. They have suggested that this poses a hitherto unacknowledged challenge to time travel theories. I argue that their conclusion depends on some unstated essentialist assumptions about metaphysics of art and the status of representations. By relaxing these assumptions, I show that Dummett and McCall’s scenarios contain no causal loop involving artistic value, and thus pose no new problem for time travel theories.
Apolipoprotein E ε4 Allele Is Associated with Reduced Retention of the “Where” Memory Component in Cognitively Intact Older Adults
Authors: Chi Chia-Hsing , Chiu Yen-Shiang , Chang Yu-Ling 
Published: August 2019

AbstractObjectiveThe present study investigated the effect of the apolipoprotein E (ApoE) ε4 allele on the four memory components (i.e., who, when, where, and what) among cognitively intact older adults.MethodsParticipants comprised 47 cognitively intact older adults, who were classified into 2 groups based on the presence or absence of at least 1 ApoE ε4 allele. All participants completed standardized neuropsychological tests, including the Logical Memory subtest of the Wechsler Memory Scale-III with a revised scoring method.ResultsThe results revealed that recollection for each component followed a pattern of who &gt; what &gt; when = where. Furthermore, a significant group-by-component-by-condition interaction indicated that the presence of the ApoE ε4 allele resulted in a disproportionately detrimental effect on the where component retention in the verbal episodic memory task; this finding was significantly correlated with hippocampal volumes.ConclusionThese results highlighted the importance of evaluating the subcomponents of verbal episodic memory to detect subtle cognitive differences related to ApoE ε4 status, which could help elucidate the mechanism behind the cascades caused by ApoE ε4 in the trajectories of cognitive aging.
Management of Asymptomatic Patients With Textured Surface Breast Implants
Authors: McGuire Patricia A , Deva Anand K , Glicksman Caroline A , Adams William P , Haws Melinda J 
Published: August 2019

Abstract
With the recent voluntary recall by Allergan of their Biocell textured implants, many plastic surgeons are left with questions of how to best manage asymptomatic patients who have concerns about having these devices. We realized that there is no clear, published recommendations or scientific data to guide surgeons on how to discuss options with their patients and recommendations for surgical management in this uncharted territory. Using available literature and personal experience, we answer the most common questions we are hearing from our plastic surgery colleagues.
Multi-scale structural analysis of proteins by deep semantic segmentation
Authors: Eguchi Raphael R , Huang Po-Ssu 
Published: August 2019

Abstract

Motivation
Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment.

Results
We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.

Availability and implementation
The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information.

Supplementary information
Supplementary data are available at Bioinformatics online.
Automatic local resolution-based sharpening of cryo-EM maps
Authors: Ramírez-Aportela Erney , Vilas Jose Luis , Glukhova Alisa , Melero Roberto , Conesa Pablo , Martínez Marta , Maluenda David , Mota Javier , Jiménez Amaya , Vargas Javier , Marabini Roberto , Sexton Patrick M , Carazo Jose Maria , Sorzano Carlos Oscar S 
Published: August 2019

Abstract

Motivation
Recent technological advances and computational developments have allowed the reconstruction of Cryo-Electron Microscopy (cryo-EM) maps at near-atomic resolution. On a typical workflow and once the cryo-EM map has been calculated, a sharpening process is usually performed to enhance map visualization, a step that has proven very important in the key task of structural modeling. However, sharpening approaches, in general, neglects the local quality of the map, which is clearly suboptimal.

Results
Here, a new method for local sharpening of cryo-EM density maps is proposed. The algorithm, named LocalDeblur, is based on a local resolution-guided Wiener restoration approach of the original map. The method is fully automatic and, from the user point of view, virtually parameter-free, without requiring either a starting model or introducing any additional structure factor correction or boosting. Results clearly show a significant impact on map interpretability, greatly helping modeling. In particular, this local sharpening approach is especially suitable for maps that present a broad resolution range, as is often the case for membrane proteins or macromolecules with high flexibility, all of them otherwise very suitable and interesting specimens for cryo-EM. To our knowledge, and leaving out the use of local filters, it represents the first application of local resolution in cryo-EM sharpening.

Availability and implementation
The source code (LocalDeblur) can be found at https://github.com/I2PC/xmipp and can be run using Scipion (http://scipion.cnb.csic.es) (release numbers greater than or equal 1.2.1).

Supplementary information
Supplementary data are available at Bioinformatics online.
Obesogenic diet exposure alters uterine natural killer cell biology and impairs vasculature remodeling in mice†
Authors: Baltayeva Jennet , Konwar Chaini , Castellana Barbara , Mara Danielle L , Christians Julian K , Beristain Alexander G 
Published: August 2019

Abstract
Prepregnancy obesity associates with adverse reproductive outcomes that impact maternal and fetal health. While obesity-driven mechanisms underlying adverse pregnancy outcomes remain unclear, local uterine immune cells are strong but poorly studied candidates. Uterine immune cells, particularly uterine natural killer cells (uNKs), play central roles in orchestrating developmental events in pregnancy. However, the effect of obesity on uNK biology is poorly understood. Using an obesogenic high-fat/high-sugar diet (HFD) mouse model, we set out to examine the effects of maternal obesity on uNK composition and establishment of the maternal–fetal interface. HFD exposure resulted in weight gain-dependent increases in systemic inflammation and rates of fetal resorption. While HFD did not affect total uNK frequencies, HFD exposure did lead to an increase in natural cytotoxicity receptor-1 expressing uNKs as well as overall uNK activity. Importantly, HFD-associated changes in uNK coincided with impairments in uterine artery remodeling in mid but not late pregnancy. Comparison of uNK mRNA transcripts from control and HFD mice identified HFD-directed changes in genes that play roles in promoting activity/cytotoxicity and vascular biology. Together, this work provides new insight into how obesity may impact uNK processes central to the establishment of the maternal–fetal interface in early and mid pregnancy. Moreover, these findings shed light on the cellular processes affected by maternal obesity that may relate to overall pregnancy health.
Differential sensitivity of inbred mouse strains to ovarian damage in response to low-dose total body irradiation†
Authors: Quan Natalie , Harris Lacey R , Halder Ritika , Trinidad Camille V , Johnson Brian W , Horton Shulamit , Kimler Bruce F , Pritchard Michele T , Duncan Francesca E 
Published: August 2019

AbstractRadiation induces ovarian damage and accelerates reproductive aging. Inbred mouse strains exhibit differential sensitivity to lethality induced by total body irradiation (TBI), with the BALB/cAnNCrl (BALB/c) strain being more sensitive than the 129S2/SvPasCrl (129) strain. However, whether TBI-induced ovarian damage follows a similar pattern of strain sensitivity is unknown. To examine this possibility, female BALB/c and 129 mice were exposed to a single dose of 1 Gy (cesium-137 γ) TBI at 5 weeks of age, and ovarian tissue was harvested for histological and gene expression analyses 2 weeks post exposure. Sham-treated mice served as controls. 1 Gy radiation nearly eradicated the primordial follicles and dramatically decreased the primary follicles in both strains. In contrast, larger growing follicles were less affected in the 129 relative to BALB/c strain. Although this TBI paradigm did not induce detectable ovarian fibrosis in either of the strains, we did observe strain-dependent changes in osteopontin (Spp1) expression, a gene involved in wound healing, inflammation, and fibrosis. Ovaries from BALB/c mice exhibited higher baseline Spp1 expression that underwent a significant decrease in response to radiation relative to ovaries from the 129 strain. A correspondingly greater change in the ovarian matrix, as evidenced by reduced ovarian hyaluronan content, was also observed following TBI in BALB/c mice relative to 129 mice. These early changes in the ovary may predispose BALB/c mice to more pronounced late effects of TBI. Taken together, our results demonstrate that aspects of ovarian damage mirror other organ systems with respect to overall strain-dependent radiation sensitivity.