nils-herrmann/clean_scirepeval huggingface dataset:
Omnidirectional Circularly Polarized Antenna Combining Monopole and Loop Radiators

In this letter, a new omnidirectional circularly polarized (CP) antenna is proposed. By adopting a coaxial probe, in which a metal sleeve acts as a monopole, and printed spoke-like metal strips fabricated on two substrates, which act as a loop, this antenna exhibits an omnidirectional circularly polarized radiation in the azimuthal plane. Theory, design, simulation, and experiment of the antenna are presented. Measurement results show that the proposed antenna can achieve a -10-dB impedance bandwidth of 13.8% (ranges from 1.55 to 1.78 GHz) and a 3-dB axial-ratio (AR) bandwidth of 17.5% (ranges from 1.46 to 1.74 GHz) in the azimuthal plane. At the frequency of 1.575 GHz, the measured average axial ratio and right-hand CP (RHCP) gain are 1.91 dB and 0.86 dBi, respectively, in the azimuthal plane.
Visual Analysis of Cardiac 4D MRI Blood Flow Using Line Predicates

Four-dimensional MRI is an in vivo flow imaging modality that is expected to significantly enhance the understanding of cardiovascular diseases. Among other fields, 4D MRI provides valuable data for the research of cardiac blood flow and with that the development, diagnosis, and treatment of various cardiac pathologies. However, to gain insights from larger research studies or to apply 4D MRI in the clinical routine later on, analysis techniques become necessary that allow to robustly identify important flow characteristics without demanding too much time and expert knowledge. Heart muscle contractions and the particular complexity of the flow in the heart imply further challenges when analyzing cardiac blood flow. Working toward the goal of simplifying the analysis of 4D MRI heart data, we present a visual analysis method using line predicates. With line predicates precalculated integral lines are sorted into bundles with similar flow properties, such as velocity, vorticity, or flow paths. The user can combine the line predicates flexibly and by that carve out interesting flow features helping to gain overview. We applied our analysis technique to 4D MRI data of healthy and pathological hearts and present several flow aspects that could not be shown with current methods. Three 4D MRI experts gave feedback and confirmed the additional benefit of our method for their understanding of cardiac blood flow.
Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients

In this paper, we present a novel approach to deal with the problem of detecting whether the eyes in a given still face image are closed, which has wide potential applications in human–computer interface design, facial expression recognition, driver fatigue detection, and so on. The approach combines the strength of multiple feature sets to characterize the rich information of eye patches (concerning both local/global shapes and local textures) and to construct the eye state model. To further improve the model's robustness against image noise and scale changes, we propose a new feature descriptor named Multi-scale Histograms of Principal Oriented Gradients (MultiHPOG). The resulting eye closeness detector handles a much wider range of eye appearance caused by expression, lighting, individual identity, and image noise than prior ones. We test our method on real-world eye datasets including the ZJU dataset and a new Closed Eyes in the Wild (CEW) dataset with promising results. In addition, several crucial design considerations that may have significant influence on the performance of a practical eye closeness detection system, including geometric normalization, feature extraction, and classification strategies, are also studied experimentally in this work.
Requirements Traceability: a Systematic Review and Industry Case Study

Requirements traceability enables software engineers to trace a requirement from its emergence to its ful ̄llment. In this paper we examine requirements traceability de ̄nitions, challenges, tools and techniques, by the use of a systematic review performing an exhaustive search through the years 1997 2007. We present a number of common de ̄nitions, challenges, available tools and techniques (presenting empirical evidence when found), while complementing the results and analysis with a static validation in industry through a series of interviews.
Exploring Synergies between Machine Learning and Knowledge Representation to Capture Scientific Knowledge

In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including deep learning networks). From knowledge representation, we give an overview of ontological representations, qualitative reasoning, and planning. Then we discuss potential synergies that would benefit both areas.
Survival Analysis Part I: Basic concepts and first analyses

In many cancer studies, the main outcome under assessment is the time to an event of interest. The generic name for the time is survival time, although it may be applied to the time ‘survived’ from complete remission to relapse or progression as equally as to the time from diagnosis to death. If the event occurred in all individuals, many methods of analysis would be applicable. However, it is usual that at the end of follow-up some of the individuals have not had the event of interest, and thus their true time to event is unknown. Further, survival data are rarely Normally distributed, but are skewed and comprise typically of many early events and relatively few late ones. It is these features of the data that make the special methods called survival analysis necessary. This paper is the first of a series of four articles that aim to introduce and explain the basic concepts of survival analysis. Most survival analyses in cancer journals use some or all of Kaplan– Meier (KM) plots, logrank tests, and Cox (proportional hazards) regression. We will discuss the background to, and interpretation of, each of these methods but also other approaches to analysis that deserve to be used more often. In this first article, we will present the basic concepts of survival analysis, including how to produce and interpret survival curves, and how to quantify and test survival differences between two or more groups of patients. Future papers in the series cover multivariate analysis and the last paper introduces some more advanced concepts in a brief question and answer format. More detailed accounts of these methods can be found in books written specifically about survival analysis, for example, Collett (1994), Parmar and Machin (1995) and Kleinbaum (1996). In addition, individual references for the methods are presented throughout the series. Several introductory texts also describe the basis of survival analysis, for example, Altman (2003) and Piantadosi (1997).
Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates.

To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students.



A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis.



Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%).



Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.
Plasmacytoid dendritic cells sense self-DNA coupled with antimicrobial peptide

Plasmacytoid dendritic cells (pDCs) sense viral and microbial DNA through endosomal Toll-like receptors to produce type 1 interferons. pDCs do not normally respond to self-DNA, but this restriction seems to break down in human autoimmune disease by an as yet poorly understood mechanism. Here we identify the antimicrobial peptide LL37 (also known as CAMP) as the key factor that mediates pDC activation in psoriasis, a common autoimmune disease of the skin. LL37 converts inert self-DNA into a potent trigger of interferon production by binding the DNA to form aggregated and condensed structures that are delivered to and retained within early endocytic compartments in pDCs to trigger Toll-like receptor 9. Thus, our data uncover a fundamental role of an endogenous antimicrobial peptide in breaking innate tolerance to self-DNA and suggest that this pathway may drive autoimmunity in psoriasis.
Analysis and characterization of inherent application resilience for approximate computing

Approximate computing is an emerging design paradigm that enables highly efficient hardware and software implementations by exploiting the inherent resilience of applications to in-exactness in their computations. Previous work in this area has demonstrated the potential for significant energy and performance improvements, but largely consists of ad hoc techniques that have been applied to a small number of applications. Taking approximate computing closer to mainstream adoption requires (i) a deeper understanding of inherent application resilience across a broader range of applications (ii) tools that can quantitatively establish the inherent resilience of an application, and (iii) methods to quickly assess the potential of various approximate computing techniques for a given application. We make two key contributions in this direction. Our primary contribution is the analysis and characterization of inherent application resilience present in a suite of 12 widely used applications from the domains of recognition, data mining, and search. Based on this analysis, we present several new insights into the nature of resilience and its relationship to various key application characteristics. To facilitate our analysis, we propose a systematic framework for Application Resilience Characterization (ARC) that (a) partitions an application into resilient and sensitive parts and (b) characterizes the resilient parts using approximation models that abstract a wide range of approximate computing techniques. We believe that the key insights that we present can help shape further research in the area of approximate computing, while automatic resilience characterization frameworks such as ARC can greatly aid designers in the adoption approximate computing.
Forensic entomology and climatic change.

Forensic entomology establishes the postmortem interval (PMI) by studying cadaveric fauna. The PMI today is still largely based on tables of insect succession on human cadavers compiled in the late 19th- or mid-20th centuries. In the last few years, however, the gradual warming of the climate has been changing faunal communities by favouring the presence of thermophilous species. To demonstrate how globalization and climate change are overcoming geographic barriers, we present some cases of southern and allochthonous species found in north-east Italy during our entomo-forensic investigations.
Lifelong Learning with Dynamically Expandable Networks

We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets under lifelong learning scenarios, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch counterparts with substantially fewer number of parameters. Further, the obtained network fine-tuned on all tasks obtained significantly better performance over the batch models, which shows that it can be used to estimate the optimal network structure even when all tasks are available in the first place.
Field-weakening Control Algorithm for Interior Permanent Magnet Synchronous Motor Based on Space-Vector Modulation Technique

We investigate the implementation of field-weakening control for interior permanent magnet synchronous motor (IPMSM), and analyze the algorithm of field-weakening control in d-q axes. To deal with the problem that the dc-link voltage is not fully utilized when the voltage regulation is employed, we propose a new field-weakening scheme based on the space-vector modulation technique. The duty-time of zero-voltage vector is used as the feedback signal to decide the switching of fieldweakening. To avoid the regulation lag in the q-axis component, we apply the lead-angle control during the field-weakening progress. The proposed scheme is validated with Matlab/Simulink tool. Simulation results show that the scheme is feasible. It not only improves the utilization ratio of the space-vector-pulse-width-modulation (SVPWM) inverter, but also achieves a smooth transition between the constant-torque mode below the base-speed and the constant-power mode above the basespeed.
Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League.

The aim of the present study was to analyze men's football competitions, trying to identify which game-related statistics allow to discriminate winning, drawing and losing teams. The sample used corresponded to 380 games from the 2008-2009 season of the Spanish Men's Professional League. The game-related statistics gathered were: total shots, shots on goal, effectiveness, assists, crosses, offsides commited and received, corners, ball possession, crosses against, fouls committed and received, corners against, yellow and red cards, and venue. An univariate (t-test) and multivariate (discriminant) analysis of data was done. The results showed that winning teams had averages that were significantly higher for the following game statistics: total shots (p < 0.001), shots on goal (p < 0.01), effectiveness (p < 0.01), assists (p < 0.01), offsides committed (p < 0.01) and crosses against (p < 0.01). Losing teams had significantly higher averages in the variable crosses (p < 0.01), offsides received (p < 0. 01) and red cards (p < 0.01). Discriminant analysis allowed to conclude the following: the variables that discriminate between winning, drawing and losing teams were the total shots, shots on goal, crosses, crosses against, ball possession and venue. Coaches and players should be aware for these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to evaluate specificity at the time of practice and game planning.  paper increases the knowledge about soccer match analysis.Give normative values to establish practice and match objectives.Give applications ideas to connect research with coaches' practice.
Changes as First-Class Citizens: A Research Perspective on Modern Software Tooling

Software must evolve to keep up with an ever-changing context, the real world. We discuss an emergent trend in software evolution research revolving around the central notion that drives evolution: Change. By reifying change, and by modelling it as a first-class entity, researchers can now analyse the complex phenomenon known as software evolution with an unprecedented degree of accuracy. We present a Systematic Mapping Study of 86 articles to give an overview on the state of the art in this area of research and present a roadmap with open issues and future directions.
An Ultra-Low Reset Current Cross-Point Phase Change Memory With Carbon Nanotube Electrodes

Solid-state memory technology is undergoing a renaissance of new materials and novel device concepts for higher scalability as the mainstream technology, i.e., Flash, is approaching physical limits. Emerging memory technologies, which have unique characteristics not available in Flash, are leading transformations in the design of the memory hierarchy. Phase change memory (PCM) is a promising candidate for the next-generation nonvolatile-memory technology. It has been extensively studied for its electrical properties and material scalability. Yet, questions remain unanswered as to what extent a functional PCM cell can be ultimately scaled to and what properties a PCM cell has at the single-digit nanometer scale. In this paper, we demonstrated a fully functional cross-point PCM cell working close to its ultimate size-scaling limit by using carbon nanotubes (CNTs) as the memory electrode. The utilization of CNT electrode brings the lithography-independent critical dimension down to 1.2 nm and contributes to a large reduction of the reset programming current to 1.4 μA and the programming energy to 210 fJ using a 10 ns reset pulse. Measured electrical characteristics validate the advantage of further device area scaling on reducing the programming current of PCM cells and confirm the potential viability of a highly scaled ultradense PCM array down to the bottom electrode contact area that corresponds to a 1.8 nm node technology.
Power Electronic Traction Transformer—Medium Voltage Prototype

Recently, the world's first ever power electronic traction transformer (PETT) for a 15-kV 16.7-Hz railway grid has been newly developed, commissioned, and installed on the test locomotive, where the PETT is presently in use. The design and development of the PETT are described in two parts of this paper. The details related to the 1.2-MVA medium voltage (MV) PETT prototype, including the electrical design of the power circuit, the sizing and characterization of insulated-gate bipolar transistor (IGBT) devices, the design of the medium-frequency transformer, the cooling system design, insulation coordination, and protection, are presented in this part, while the preceding paper deals with the overall system requirements and control system design based on the low-voltage (LV) PETT prototype. The identical control hardware and software from the LV PETT prototype were used for the MV PETT prototype, thus reducing significantly commissioning time. The experimental results obtained during the testing are presented to demonstrate the performance of the developed MV PETT prototype.
Similarity-based Classification: Concepts and Algorithms

This paper reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and a comprehensive set of exper imental results for a rich collection of classification problems. Specifically, the generalizabili ty of using similarities as features is analyzed, design goals and methods for weighting nearest-neig hbors for similarity-based learning are proposed, and different methods for consistently converti ng s milarities into kernels are compared. Experiments on eight real data sets compare eight approache s nd their variants to similarity-based learning.
On the Kinematic Motion Primitives (kMPs) – Theory and Application

Human neuromotor capabilities guarantee a wide variety of motions. A full understanding of human motion can be beneficial for rehabilitation or performance enhancement purposes, or for its reproduction on artificial systems like robots. This work aims at describing the complexity of human motion in a reduced dimensionality, by means of kinematic Motion Primitives (kMPs). A set of five invariant kMPs are identified for periodic motions, and a set of two kMPs for discrete motions. It is shown how these two sets of kMPs can be combined to synthesize more complex motion as the simultaneous execution of the periodic and the discrete motions. The results reported are an evidence of the theory of Central Pattern Generators (CPG), showing its effects on the kinematics, and are related to what presented in the literature on the Motor Primitives extracted from EMG signals. Experimental tests with the COmpliant huMANoid (COMAN) were performed to show that the kMPs extracted from human subjects can be used to transfer the features of human locomotion to the gait of a robot.
A Multifunctional Antenna with a Small Form Factor: Designing a Novel Series-Fed Compact Triangular Microstrip Ring Resonator Antenna Array

This article examines a noval series-fed triangular microstrip ring resonator (TMRR) antenna array. Each radiating element is made up of a wavelength-long microstrip line with the two ends joined together to form an isosceles triangle. The antenna array is fed by an N-shaped microstrip feed line and excited by a coaxial feed probe. The operational mechanism of the proposed feed system in terms of its electrodynamics is examined with respect to its relative size occupancy, dispersion along the feed line, and the feed effect on the spatial orientation of the propagated radio waves. The size occupancy of the antenna array is 45 mm<sup>2</sup> x 60 mm<sup>2</sup> with a measured reflection coefficient S<sub>11</sub> of -27.5 dB at a resonant frequency of 5.76 GHz. The simulated S<sub>11</sub> is -29.2 dB at a resonant frequency of 5.8 GHz. The measured gain is roughly 10.46 dBi using the absolute gain method. The resulting antenna array is compact in size and exhibits reasonable performance in terms of gain, reflection coefficient, low mass/volume, and weight.
CIO and Business Executive Leadership Approaches to Establishing Company-wide Information Orientation

In the digital world, business executives have a heightened awareness of the strategic importance of information and information management to their companies’ value creation. This presents both leadership opportunities and challenges for CIOs. To prevent the CIO position from being marginalized and to enhance CIOs’ contribution to business value creation, they must move beyond being competent IT utility managers and play an active role in helping their companies build a strong information usage culture. The purpose of this article is to provide a better understanding of the leadership approaches that CIOs and business executives can adopt to improve their companies’ information orientation. Based on our findings from four case studies, we have created a four-quadrant leadership-positioning framework. This framework is constructed from the CIO’s perspective and indicates that a CIO may act as a leader, a follower or a nonplayer in developing the company’s information orientation to achieve its strategic focus. The article concludes with guidelines that CIOs can use to help position their leadership challenges in introducing or sustaining their companies’ information orientation initiatives and recommends specific leadership approaches depending on CIOs’ particular situations.