nils-herrmann/clean_scirepeval huggingface dataset:
60-GHz Dual-Polarized Two-Dimensional Switch-Beam Wideband Antenna Array of Aperture-Coupled Magneto-Electric Dipoles

A dual-polarized aperture-coupled magneto-electric (ME) dipole antenna is proposed. Two separate substrate-integrated waveguides (SIWs) implemented in two printed circuit board (PCB) laminates are used to feed the antenna. The simulated -10-dB impedance bandwidth of the antenna is 21% together with an isolation of over 45 dB between the two input ports. Good radiation characteristics, including almost identical unidirectional radiation patterns in two orthogonal planes, frontto-back ratio larger than 20 dB, cross-polarization levels less than -23 dB, and a stable gain around 8 dBi over the operating band, are achieved. By employing the proposed radiating element, a 2 × 2 wideband antenna array working at the 60GHz band is designed, fabricated, and tested, which can generate two-dimensional (2-D) multiple beams with dual polarization. A measured -10 dB impedance bandwidth wider than 22% and a gain up to 12.5 dBi are obtained. Owing to the superiority of the ME dipole, the radiation pattern of the array is also stable over the operating frequencies and nearly identical in two orthogonal planes for both of the polarizations. With advantages of desirable performance, convenience of fabrication and integration, and low cost, the proposed antenna and array are attractive for millimeter-wave wireless communication systems.
Congestive heart failure: Diagnosis, pathophysiology, therapy, and implications for respiratory care.

Congestive heart failure (CHF) is a common clinical disorder that results in pulmonary vascular congestion and reduced cardiac output. CHF should be considered in the differential diagnosis of any adult patient who presents with dyspnea and/or respiratory failure. The diagnosis of heart failure is often determined by a careful history and physical examination and characteristic chest-radiograph findings. The measurement of serum brain natriuretic peptide and echocardiography have substantially improved the accuracy of diagnosis. Therapy for CHF is directed at restoring normal cardiopulmonary physiology and reducing the hyperadrenergic state. The cornerstone of treatment is a combination of an angiotensin-converting-enzyme inhibitor and slow titration of a beta blocker. Patients with CHF are prone to pulmonary complications, including obstructive sleep apnea, pulmonary edema, and pleural effusions. Continuous positive airway pressure and noninvasive positive-pressure ventilation benefit patients in CHF exacerbations.
Building brand loyalty in social commerce: The case of brand microblogs

Social commerce enables companies to promote their brands and products on online social platforms. Companies can, for instance, create brand pages on social networking sites to develop consumer–brand relationships. In such circumstances, how to build consumers’ brand loyalty becomes a critical concern. To address this, we draw upon the relationship quality perspective to suggest that brand loyalty is primarily determined by relationship quality, which is further influenced by self-congruence (i.e., the self factor), social norms (i.e., the social factor), information quality and interactivity (i.e., characteristics of brand pages). To test our model, we conduct an empirical survey on companies’ brand microblogs. We find that all proposed hypotheses are supported. Interestingly, the self factor rather than other factors was found to have the strongest impact in the model. In addition to its noteworthy implications for practitioners, we believe that this study provides important theoretical insights into understanding how to build brand loyalty in social commerce.
Fingerprint Restoration Using Digital Reaction-Diffusion System and Its Evaluation

This paper presents an algorithm for fingerprint image restoration using Digital Reaction-Diffusion System (DRDS). The DRDS is a model of a discrete-time discrete-space nonlinear reaction-diffusion dynamical system, which is useful for generating biological textures, patterns and structures. This paper focuses on the design of a fingerprint restoration algorithm that combines (i) a ridge orientation estimation technique using an iterative coarse-to-fine processing strategy and (ii) an adaptive DRDS having a capability of enhancing low-quality fingerprint images using the estimated ridge orientation. The phase-only image matching technique is employed for evaluating the similarity between an original fingerprint image and a restored image. The proposed algorithm may be useful for person identification applications using fingerprint images. key words: reaction-diffusion system, pattern formation, digital signal processing, digital filters, fingerprint restoration
Modern robust statistical methods: an easy way to maximize the accuracy and power of your research.

Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices.
Electric field calculation and material optimization of 765kV bushing using FEA

Transformer bushings are one of the important components in the construction of high voltage power transmission. The structure and materials used in electrical bushings play a vital role in the durability and lifespan of the bushing. Although the cost of transformer bushing is very minimal in the transmission system extensive damages are caused in case of failure. The main reason of failure in the insulation materials are caused due to moisture ingress and formation of voids, cavities. When the appropriate material is used along with an optimized structure, the electric field is distributed evenly which reduces the stress and increases the longevity of the bushing. In this paper, finite element analysis (FEA) is used to calculate The electric field distribution and thermal (temperature) analysis is done for 765 kV transformer bushing by using different insulating materials such as epoxy resin, porcelain, oil impregnated paper, resin impregnated paper and polymer. Henceforth, by using the above insulating materials the total stress level of the bushings corresponding to the net electric field distribution is in turn calculated using ANSYS 13.0 software. The results are compared and analysis is done for the different insulating materials. Based on the electric field distribution over bushing material, it is used to identify optimization of bushing design.
Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs

Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy. Due to the substantial compute and memory operations, however, they require significant execution time. The massive parallel computing capability of GPUs make them as one of the ideal platforms to accelerate CNNs and a number of GPU-based CNN libraries have been developed. While existing works mainly focus on the computational efficiency of CNNs, the memory efficiency of CNNs have been largely overlooked. Yet CNNs have intricate data structures and their memory behavior can have significant impact on the performance. In this work, we study the memory efficiency of various CNN layers and reveal the performance implication from both data layouts and memory access patterns. Experiments show the universal effect of our proposed optimizations on both single layers and various networks, with up to 27.9× for a single layer and up to 5.6× on the whole networks.
Intriguing Properties of Adversarial Examples

It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we argue that the origin of adversarial examples is primarily due to an inherent uncertainty that neural networks have about their predictions. We show that the functional form of this uncertainty is independent of architecture, dataset, and training protocol; and depends only on the statistics of the logit differences of the network, which do not change significantly during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-theart deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white and black box attacks compared to previous attempts.
Link prediction in multiplex online social networks

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Classifying the segmentation of customer value via RFM model and RS theory

Data mining is a powerful new technique to help companies mining the patterns and trends in their customers data, then to drive improved customer relationships, and it is one of well-known tools given to customer relationship management (CRM). However, there are some drawbacks for data mining tool, such as neural networks has long training times and genetic algorithm is brute computing method. This study proposes a new procedure, joining quantitative value of RFM attributes and K-means algorithm into rough set theory (RS theory), to extract meaning rules, and it can effectively improve these drawbacks. Three purposes involved in this study in the following: (1) discretize continuous attributes to enhance the rough sets algorithm; (2) cluster customer value as output (customer loyalty) that is partitioned into 3, 5 and 7 classes based on subjective view, then see which class is the best in accuracy rate; and (3) find out the characteristic of customer in order to strengthen CRM. A practical collected C-company dataset in Taiwan’s electronic industry is employed in empirical case study to illustrate the proposed procedure. Referring to [Hughes, A. M. (1994). Strategic database marketing. , this study firstly utilizes RFM model to yield quantitative value as input attributes; next, uses K-means algorithm to cluster customer value; finally, employs rough sets (the LEM2 algorithm) to mine classification rules that help enterprises driving an excellent CRM. In analysis of the empirical results, the proposed procedure outperforms the methods listed in terms of accuracy rate regardless of 3, 5 and 7 classes on output, and generates understandable decision rules.
Existence and uniqueness of fixed point for contractive mapping of integral type

Biographical notes: Vishal Gupta received his PhD degree in 2010 under the supervision of Professor Dr. R.K. Saini. He also received his degree of MEd in 2004, BEd in 2003, and Post Graduation in 2002. Presently, he works as an Associate Professor in the Department of Mathematics, Maharishi Markandeshwar University, Mullana Ambala, Haryana, India. He is an author of more than 32 research articles and one book. His fields of interest are fixed point theory, topology, linear algebra, numerical analysis, applied mathematics, differential and integral equations.
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GÉANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models. keywords Traffic Matrix, Prediction, Neural Networks, Long Short-Term Mermory
Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework

Technologies in autonomous vehicles have seen dramatic advances in recent years; however, it still lacks of robust perception systems for car detection. With the recent development in deep learning research, in this paper, we propose a LIDAR and vision fusion system for car detection through the deep learning framework. It consists of three major parts. The first part generates seed proposals for potential car locations in the image by taking LIDAR point cloud into account. The second part refines the location of the proposal boxes by exploring multi-layer information in the proposal network and the last part carries out the final detection task through a detection network which shares part of the layers with the proposal network. The evaluation shows that the proposed framework is able to generate high quality proposal boxes more efficiently (77.6% average recall) and detect the car at the state of the art accuracy (89.4% average precision). With further optimization of the framework structure, it has great potentials to be implemented onto the autonomous vehicle.
A novel segmentation algorithm for MRI brain tumor images

A novel segmentation algorithm for MRI Brain tumor images is proposed. The proposed algorithm is compared with Thresholding and Region Grow methods. Testing was performed by generating two datasets of real MRI images of brain tumors. Criteria for assessment of the quality of the segmentation results were: the Dice score, sensitivity, specificity and accuracy. Analysis of results obtained using this algorithm to solve the brain tumor MRI image segmentation task showed levels of sensitivity and specificity of 91% to 99%, which is evidence that assessment of the position and boundaries of brain pathology is highly effective.
Embedded Binarized Neural Networks

We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently on small embedded devices. We focus on minimizing the required memory footprint, given that these devices often have memory as small as tens of kilobytes (KB). Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference. To accomplish this, eBNN reorders the computation of inference while preserving the original BNN structure, and uses just a single floating-point temporary for the entire neural network. All intermediate results from a layer are stored as binary values, as opposed to floating-points used in current BNN implementations, leading to a 32x reduction in required temporary space. We provide empirical evidence that our proposed eBNN approach allows efficient inference (10s of ms) on devices with severely limited memory (10s of KB). For example, eBNN achieves 95% accuracy on the MNIST dataset running on an Intel Curie with only 15 KB of usable memory with an inference runtime of under 50 ms per sample. To ease the development of applications in embedded contexts, we make our source code available that allows users to train and discover eBNN models for a learning task at hand, which fit within the memory constraint of the target device.
Comparisons for Effectiveness of Aromatherapy and Acupressure Massage on Quality of Life in Career Women: A Randomized Controlled Trial.

This study was aimed to compare the effectiveness of aromatherapy and acupressure massage intervention strategies on the sleep quality and quality of life (QOL) in career women.



The randomized controlled trial experimental design was used in the present study. One hundred and thirty-two career women (24-55 years) voluntarily participated in this study and they were randomly assigned to (1) placebo (distilled water), (2) lavender essential oil (Lavandula angustifolia), (3) blended essential oil (1:1:1 ratio of L. angustifolia, Salvia sclarea, and Origanum majorana), and (4) acupressure massage groups for a 4-week treatment. The Pittsburgh Sleep Quality Index and Short Form 36 Health Survey were used to evaluate the intervention effects at pre- and postintervention.



After a 4-week treatment, all experimental groups (blended essential oil, lavender essential oil, and acupressure massage) showed significant improvements in sleep quality and QOL (p < 0.05). Significantly greater improvement in QOL was observed in the participants with blended essential oil treatment compared with those with lavender essential oil (p < 0.05), and a significantly greater improvement in sleep quality was observed in the acupressure massage and blended essential oil groups compared with the lavender essential oil group (p < 0.05).



The blended essential oil exhibited greater dual benefits on improving both QOL and sleep quality compared with the interventions of lavender essential oil and acupressure massage in career women. These results suggest that aromatherapy and acupressure massage improve the sleep and QOL and may serve as the optimal means for career women to improve their sleep and QOL.
The relations of trait anxiety, anxiety sensitivity, and sensation seeking to adolescents' motivations for alcohol, cigarette, and marijuana use.

The present study investigated relations of anxiety sensitivity and other theoretically relevant personality factors to Copper's [ (1994) 117.] four categories of substance use motivations as applied to teens' use of alcohol, cigarettes, and marijuana. A sample of 508 adolescents (238 females, 270 males; mean age = 15.1 years) completed the Trait subscale of the State-Trait Anxiety Inventory for Children, the Childhood Anxiety Sensitivity Index (CASI), and the Intensity and Novelty subscales of the Arnett Inventory of Sensation Seeking. Users of each substance also completed the Drinking Motives Questionnaire-Revised (DMQ-R) and/or author-compiled measures for assessing motives for cigarette smoking and marijuana use, respectively. Multiple regression analyses revealed that, in the case of each drug, the block of personality variables predicted "risky" substance use motives (i.e., coping, enhancement, and/or conformity motives) over-and-above demographics. High intensity seeking and low anxiety sensitivity predicted enhancement motives for alcohol use, high anxiety sensitivity predicted conformity motives for alcohol and marijuana use, and high trait anxiety predicted coping motives for alcohol and cigarette use. Moreover, anxiety sensitivity moderated the relation between trait anxiety and coping motives for alcohol and cigarette use: the trait anxiety-coping motives relation was stronger for high, than for low, anxiety sensitive individuals. Implications of the findings for improving substance abuse prevention efforts for youth will be discussed.
Dynamical Systems : Some Computational Problems

We present several topics involving the computation of dynamical systems. The emphasis is on work in progress and the presentation is informal – there are many technical details which are not fully discussed. The topics are chosen to demonstrate the various interactions between numerical computation and mathematical theory in the area of dynamical systems. We present an algorithm for the computation of stable manifolds of equilibrium points, describe the computation of Hopf bifurcations for equilibria in parametrized families of vector fields, survey the results of studies of codimension two global bifurcations, discuss a numerical analysis of the Hodgkin and Huxley equations, and describe some of the effects of symmetry on local bifurcation.
Design of digital beamforming subarrays for a multifunction radar

Digital beamforming (DBF) in phased array radars is an enabling architecture for advanced signal processing algorithms such as adaptive beamforming and high resolution angle estimation. At the current level of technology, DBF can only be practically implemented at a subarray level for large arrays operating at high frequencies. The choice of subarray layout has a significant impact on the performance of array signal processing algorithms. In this paper, an approach to optimizing the subarray layout with respect to select performance metrics is presented.