nils-herrmann/clean_scirepeval huggingface dataset:
Menaquinone-7 supplementation improves arterial stiffness in healthy postmenopausal women. A double-blind randomised clinical trial.

Observational data suggest a link between menaquinone (MK, vitamin K2) intake and cardiovascular (CV) health. However, MK intervention trials with vascular endpoints are lacking. We investigated long-term effects of MK-7 (180 µg MenaQ7/day) supplementation on arterial stiffness in a double-blind, placebo-controlled trial. Healthy postmenopausal women (n=244) received either placebo (n=124) or MK-7 (n=120) for three years. Indices of local carotid stiffness (intima-media thickness IMT, Diameter end-diastole and Distension) were measured by echotracking. Regional aortic stiffness (carotid-femoral and carotid-radial Pulse Wave Velocity, cfPWV and crPWV, respectively) was measured using mechanotransducers. Circulating desphospho-uncarboxylated matrix Gla-protein (dp-ucMGP) as well as acute phase markers Interleukin-6 (IL-6), high-sensitive C-reactive protein (hsCRP), tumour necrosis factor-α (TNF-α) and markers for endothelial dysfunction Vascular Cell Adhesion Molecule (VCAM), E-selectin, and Advanced Glycation Endproducts (AGEs) were measured. At baseline dp-ucMGP was associated with IMT, Diameter, cfPWV and with the mean z-scores of acute phase markers (APMscore) and of markers for endothelial dysfunction (EDFscore). After three year MK-7 supplementation cfPWV and the Stiffness Index βsignificantly decreased in the total group, whereas distension, compliance, distensibility, Young's Modulus, and the local carotid PWV (cPWV) improved in women having a baseline Stiffness Index β above the median of 10.8. MK-7 decreased dp-ucMGP by 50 % compared to placebo, but did not influence the markers for acute phase and endothelial dysfunction. In conclusion, long-term use of MK-7 supplements improves arterial stiffness in healthy postmenopausal women, especially in women having a high arterial stiffness.
Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin.

We describe a fully automated system for the classification of acral volar melanomas. We used a total of 213 acral dermoscopy images (176 nevi and 37 melanomas). Our automatic tumor area extraction algorithm successfully extracted the tumor in 199 cases (169 nevi and 30 melanomas), and we developed a diagnostic classifier using these images. Our linear classifier achieved a sensitivity (SE) of 100%, a specificity (SP) of 95.9%, and an area under the receiver operating characteristic curve (AUC) of 0.993 using a leave-one-out cross-validation strategy (81.1% SE, 92.1% SP; considering 14 unsuccessful extraction cases as false classification). In addition, we developed three pattern detectors for typical dermoscopic structures such as parallel ridge, parallel furrow, and fibrillar patterns. These also achieved good detection accuracy as indicated by their AUC values: 0.985, 0.931, and 0.890, respectively. The features used in the melanoma-nevus classifier and the parallel ridge detector have significant overlap.
Deep Feature Fusion for VHR Remote Sensing Scene Classification

The rapid development of remote sensing technology allows us to get images with high and very high resolution (VHR). VHR imagery scene classification has become an important and challenging problem. In this paper, we introduce a framework for VHR scene understanding. First, the pretrained visual geometry group network (VGG-Net) model is proposed as deep feature extractors to extract informative features from the original VHR images. Second, we select the fully connected layers constructed by VGG-Net in which each layer is regarded as separated feature descriptors. And then we combine between them to construct final representation of the VHR image scenes. Third, discriminant correlation analysis (DCA) is adopted as feature fusion strategy to further refine the original features extracting from VGG-Net, which allows a more efficient fusion approach with small cost than the traditional feature fusion strategies. We apply our approach to three challenging data sets: 1) UC MERCED data set that contains 21 different areal scene categories with submeter resolution; 2) WHU-RS data set that contains 19 challenging scene categories with various resolutions; and 3) the Aerial Image data set that has a number of 10 000 images within 30 challenging scene categories with various resolutions. The experimental results demonstrate that our proposed method outperforms the state-of-the-art approaches. Using feature fusion technique achieves a higher accuracy than solely using the raw deep features. Moreover, the proposed method based on DCA fusion produces good informative features to describe the images scene with much lower dimension.
THE MEDICAL CONSTRUCTION OF GENDER : CASE MANAGEMENT OF INTERSEXED INFANTS

The birth of intersexed infants, babies born with genitals that are neither clearly male nor clearly female, has been documented throughout recorded time.' In the late twentieth century, medical technology has advanced to allow scientists to determine chromosomal and hormonal gender, which is typically taken to be the real, natural, biological gender, usually referred to as "sex."2 Nevertheless, physicians who handle the cases of intersexed infants consider several factors beside biological ones in determining, assigning, and announcing the gender of a particular infant. Indeed, biological factors are often preempted in their deliberations by such cultural factors as the "correct" length of the penis and capacity of the vagina.
Crosswind kite control - A benchmark problem for advanced control and dynamic optimization

This article presents a kite control and optimization problem intended as a benchmark problem for advanced control and optimization. We provide an entry point to this exciting renewable energy system for researchers in control and optimization methods looking for a realistic test bench, and/or a useful application case for their theory. The benchmark problem in this paper can be studied in simulation, and a complete Simulink model is provided to facilitate this. The simulated scenario, which reproduces many of the challenges presented by a real system, is based on experimental studies from the literature, industrial data and the first author’s own experience in experimental kite control. In particular, an experimentally validated wind turbulence model is included, which subjects the kite to realistic disturbances. The benchmark problem is that of controlling a kite such that the average line tension is maximized. Two different models are provided: A more comprehensive one is used to simulate the ’plant’, while a simpler ’model’ is used to design and implement control and optimization strategies. This way, uncertainty is present in the form of plant-model mismatch. The outputs of the plant are corrupted by measurement noise. The maximum achievable average line tension for the plant is calculated, which should facilitate the performance comparison of different algorithms. A simple control strategy is implemented on the plant and found to be quite sub-optimal, even if the free parameters of the algorithm are well tuned. An open question is whether or not more advanced control algorithms could do better. Dr. Costello (sean.c.costello@gmail.com) was with the Laboratoire d’Automatique, EPFL, Switzerland. He is currently with Leica Geosystems, St-Gallen, Switzerland. Dr. François (gregory.francois@ed.ac.uk) was with the Laboratoire d’Automatique, EPFL, Switzerland. He is currently with the Institute of Material and Processes, School of Engineering, The University of Edinburgh, UK. Prof. Bonvin (dominique.bonvin@epfl.ch) is with the Laboratoire d’Automatique, EPFL, Switzerland.
DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering

Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computational costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multilayer complex-valued residual convolutional neural network modules. We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, which are extremely hard and impractical to solve using conventional inverse scattering methods.
Modeling and Kinematic Analysis of Industrial Robots in WebGL Interface

Web-based learning plays a vital role in the modern education system, where different technologies are being emerged to enhance this E-learning process. Therefore virtual and online laboratories are gaining popularity due to its easy implementation and accessibility worldwide. These types of virtual labs are useful where the setup of the actual laboratory is complicated due to several factors such as high machinery or hardware cost. This paper presents a very efficient method of building a model using JavaScript Web Graphics Library with HTML5 enabled and having controllable features inbuilt. This type of program is free from any web browser plug-ins or application and also server independent. Proprietary software has always been a bottleneck in the development of such platforms. This approach rules out this issue and can easily applicable. Here the framework has been discussed and neatly elaborated with an example of a simplified robot configuration.
Gateway Framework for In-Vehicle Networks Based on CAN, FlexRay, and Ethernet

This paper proposes a gateway framework for in-vehicle networks (IVNs) based on the controller area network (CAN), FlexRay, and Ethernet. The proposed gateway framework is designed to be easy to reuse and verify to reduce development costs and time. The gateway framework can be configured, and its verification environment is automatically generated by a program with a dedicated graphical user interface (GUI). The gateway framework provides state-of-the-art functionalities that include parallel reprogramming, diagnostic routing, network management (NM), dynamic routing update, multiple routing configuration, and security. The proposed gateway framework was developed, and its performance was analyzed and evaluated.
Low-Loss and Broadband Asymmetric Broadside-Coupled Balun for Mixer Design in 0.18-$\mu{\hbox {m}}$ CMOS Technology

This study presents an asymmetric broadside coupled balun with low-loss broadband characteristics for mixer designs. The correlation between balun impedance and a 3D multilayer CMOS structure are discussed and analyzed. Two asymmetric multilayer meander coupled lines are adopted to implement the baluns. Three balanced mixers that comprise three miniature asymmetric broadside coupled Marchand baluns are implemented to demonstrate the applicability to MOS technology. Both a single and dual balun occupy an area of only 0.06 mm2. The balun achieves a measured bandwidth of over 120%, an insertion loss of better than 4.1 dB (3 dB for an ideal balun) at the center frequency, an amplitude imbalance of less than 1 dB, and a phase imbalance of less than 5deg from 10 to 60 GHz. The first demonstrated circuit is a Ku-band mixer, which is implemented with a miniaturized balun to reduce the chip area by 80%. This 17-GHz mixer yields a conversion loss of better than 6.8 dB with a chip size of 0.24 mm2. The second circuit is a 15-60-GHz broadband single-balanced mixer, which achieves a conversion loss of better than 15 dB and occupies a chip area of 0.24 mm2. A three-conductor miniaturized dual balun is then developed for use in the third mixer. This star mixer incorporates two miniature dual baluns to achieve a conversion loss of better than 15 dB from 27 to 54 GHz, and occupies a chip area of 0.34 mm2.
A Dataflow Approach to Event-based Debugging

This paper describes a novel approach to event-based debugging. The approach is based on a (coarsegrained) dataflow view of events: a high-level event is recognized when an appropriate combination of lower-level events on which it depends has occurred. Event recognition is controlled using familiar programming language constructs. This approach is more flexible and powerful than current ones. It allows arbitrary debugger language commands to be executed when attempting to form higher-level events. It also allows users to specify event recognition in much the same way that they write programs. This paper also describes a prototype, Dalek, that employs the dataflow approach for debugging sequential programs. Dalek demonstrates the feasibility and attractiveness of the dataflow approach. One important motivation for this work is that current sequential debugging tools are inadequate. Dalek contributes toward remedying such inadequacies by providing events and a powerful debugging language. Generalizing the dataflow approach so that it can aid in the debugging of concurrent programs is under investigation.
Mitigating the Attraction Effect with Visualizations

Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias – the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.
Pythagorean Membership Grades in Multicriteria Decision Making

We first look at some nonstandard fuzzy sets, intuitionistic, and interval-valued fuzzy sets. We note both these allow a degree of commitment of less then one in assigning membership. We look at the formulation of the negation for these sets and show its expression in terms of the standard complement with respect to the degree of commitment. We then consider the complement operation. We describe its properties and look at alternative definitions of complement operations. We then focus on the Pythagorean complement. Using this complement, we introduce a class of nonstandard Pythagorean fuzzy subsets whose membership grades are pairs, (a, b) satisfying the requirement a2 + b2 ≤ 1. We introduce a variety of aggregation operations for these Pythagorean fuzzy subsets. We then look at multicriteria decision making in the case where the criteria satisfaction are expressed using Pythagorean membership grades. The issue of having to choose a best alternative in multicriteria decision making leads us to consider the problem of comparing Pythagorean membership grades.
Compact Tapered Slot Antenna Array for 5G Millimeter-Wave Massive MIMO Systems

A low-complexity metallic tapered slot antenna (TSA) array for millimeter-wave multibeam massive multiple-input multiple-output communication is proposed in this paper. Good beamforming performance can be achieved by the developed antenna array because the element spacing can easily meet the requirement of half-wavelength in the H-plane. The antenna element is fed by a substrate-integrated waveguide, which can be directly integrated with the millimeter-wave circuits. The proposed TSA is fabricated and measured. Measured results show that the reflection coefficient is lower than −15 dB Voltage Standing Wave Ratio ((VSWR) ≤ 1.45) within the frequency range from 22.5 to 32 GHz, which covers the 24.25–27.5-GHz band proposed by International Telecommunications Union (ITU) and the 27.5–28.35-GHz band proposed by Federal Communications Commission (FCC) for 5G. The gain of the antenna element varies from 8.2 to 9.6 dBi over the frequency range of 24–32 GHz. The simulated and measured results also illustrate good radiation patterns across the wide frequency band (24–32 GHz). A  $1\times 4$  H-plane array integrated with the multichannel millimeter-wave transceivers on one PCB is demonstrated and excellent performance is achieved.
A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe

Measurement of ground contact forces (GCFs) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases cannot be exactly distinguished by comparing sensor outputs to a threshold. This paper proposes a method by fuzzy logic for detecting the gait phases continuously and smoothly. The smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For advanced rehabilitation systems, this paper also introduces a higher level algorithm that quantitatively monitors the amount of abnormalities in a human gait. The abnormalities detected by the proposed method include an improper GCF pattern as well as an incorrect sequence of the gait phases. To realize the monitoring algorithm, the gait phases are analyzed as a vector and the abnormalities are detected by simple kinematic equations. The proposed methods are implemented by using signals from sensor-embedded shoes called smart shoes. Each smart shoe has four GCF sensors installed between the cushion pad and the sole. The GCF sensor applies an air pressure sensor connected to an air bladder. A gait monitoring system that integrates the proposed methods is shown in this paper and verified for both normal and abnormal gaits.
Possible Cognitive Benefits of Acute Physical Exercise in Children With ADHD.

Studies have suggested that even a single session of physical exercise enhances executive functions. ADHD is among the most common developmental disorders in childhood, but little is known about alternative treatments for this disorder. Therefore, we performed a systematic review of the literature to analyze articles that evaluated the executive functions of children with ADHD after an acute exercise session.



We reviewed articles indexed in the PubMed, American Psychiatric Association (APA) psychNET, Scopus, and Web of Knowledge databases between 1980 and 2013.



Of 231 articles selected, only three met the inclusion criteria.



Based on these 3 articles, we concluded that 30 min of physical exercise reportedly improved the executive functions of children with ADHD. Due to the small number of articles selected, further studies are needed to confirm these benefits.
Adsorption of CO2 on CoII3[CoIII(CN)6]2 using DRIFTS.

Adsorption of CO(2) on dehydrated Prussian blue analogue Co(II)(3)[Co(III)(CN)(6)](2) was studied using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). An infrared peak at 2340 cm(-1) assigned to adsorbed CO(2) was identified and used semi-quantitatively to construct an isotherm at 298 K that followed the Langmuir-Freundlich equation in the low-coverage Henry's law limit with CO(2) pressure below about 25 kPa. Temperature-dependence at 6.8 kPa CO(2) was used to determine DeltaH(ad)=-23+/-3 kJ mol(-1), in this limit as well. Deviation from the Langmuir-Freundlich model was significant at temperatures above 298 K and attributed primarily to a loss of reliability of the DRIFT spectra at higher CO(2) pressures, particularly at higher temperatures, and the accompanying uncertainties in the difference spectra when correcting for the presence of gaseous CO(2). Based on this work, the application of DRIFTS to study CO(2) adsorption on Prussian blue analogues and other adsorbents is promising, although the range of conditions over which it can be applied appears to be more limited than with other techniques.
Optimal Pulsewidth Modulation for Current Source Inverters

Two different pulsewidth modulation (PWM) schemes for current source inverters (CSI) are described. The first one is based on off-line optimization of individual switching angles and requires a microprocessor for implementation and the second one uses a special subharmonic modulation and could be implemented with analog and medium-scale integration (MSI) digital circuits. When CSI's are used in ac motor drives, the optimal PWM pattern depends on the performance criteria being used, which in turn depend on the drive application. In this paper four different performance criteria are considered: 1) current or torque harmonic elimination, 2) current harmonic minimization, 3) speed ripple minimization, and 4) position error minimization. As an example a self-controlled synchronous motor (SCSM) supplied by the PWM CSI is considered. The performance of the CSI-SCSM with the optimal PWM schemes proposed herein are compared with that using a conventional 120° quasi-square wave current.
Word Embeddings for Information Extraction from Tweets

This paper describes our approach on “Information Extraction from Microblogs Posted during Disasters”as an attempt in the shared task of the Microblog Track at Forum for Information Retrieval Evaluation (FIRE) 2016 . Our method uses vector space word embeddings to extract information from microblogs (tweets) related to disaster scenarios, and can be replicated across various domains. The system, which shows encouraging performance, was evaluated on the Twitter dataset provided by the FIRE 2016 shared task. CCS Concepts •Computing methodologies→Natural language processing; Information extraction;
Estimation Stability with Cross Validation (ESCV)

Cross-validation (CV) is often used to select the regularization parameter in high dimensional problems. However, when applied to the sparse modeling method Lasso, CV leads to models that are unstable in high-dimensions, and consequently not suited for reliable interpretation. In this paper, we propose a model-free criterion ESCV based on a new estimation stability (ES) metric and CV . Our proposed ESCV finds a smaller and locally ES -optimal model smaller than the CV choice so that the it fits the data and also enjoys estimation stability property. We demonstrate that ESCV is an effective alternative to CV at a similar easily parallelizable computational cost. In particular, we compare the two approaches with respect to several performance measures when applied to the Lasso on both simulated and real data sets. For dependent predictors common in practice, our main finding is that, ESCV cuts down false positive rates often by a large margin, while sacrificing little of true positive rates. ESCV usually outperforms CV in terms of parameter estimation while giving similar performance as CV in terms of prediction. For the two real data sets from neuroscience and cell biology, the models found by ESCV are less than half of the model sizes by CV , but preserves CV’s predictive performance and corroborates with subject knowledge and independent work. We also discuss some regularization parameter alignment issues that come up in both approaches.