nils-herrmann/clean_scirepeval huggingface dataset:
A Compact Ultra-Wideband Bandpass Filter Based on Split-Mode Resonator

A compact ultra-wideband (UWB) bandpass filter is proposed based on the coplanar-waveguide (CPW) split-mode resonator. By suitably introducing a short-circuited stub to implement the shunt inductance between two quarter wavelength CPW stepped-impedance resonators, a strong magnetic coupling may be realized so that a CPW split-mode resonator may be constructed. Moreover, by properly designing the dual-metal-plane structure, one may accomplish a microstrip-to-CPW feeding mechanism to provide strong enough capacitive coupling for bandwidth enhancement and also introduce an extra electric coupling between input and output ports so that two transmission zeros may be created for selectivity improvement. The implemented UWB filter shows a fractional bandwidth of 116% and two transmission zeros at 1.705 and 11.39 GHz. Good agreement between simulated and measured responses is observed.
A theory of social media dependence: Evidence from microblog users

Anecdotal evidence suggests that problematic use of social media has become prevalent among a large proportion of users and led to significant behavioral and psychological problems. Nevertheless, theory-driven investigation into this issue is still relatively scarce, and the few existing studies tend to adopt only a conceptual or descriptive approach. This study uses a theory-guided approach and seeks to clarify the development of psychological dependence in the context of social media, with a particular focus on microblogging. Building on the theory of rational addiction, this study hypothesizes that dependence is initially developed from habit. Furthermore, the study draws on the cognitive-affective-behavioral modelling paradigm to hypothesize that maladaptive cognition and affect tend to distort habit into psychological dependence. We conduct a longitudinal empirical test to validate the underlying mechanism of social media dependence as theorized in our study. The study concludes with a discussion of theoretical and practical implications.
Applying ISO / IEC 9126-1 Quality Model to Quality Requirements Engineering on Critical Software

In order to develop a software product for a critical system, specifying quality requirements is vitally important. Quality requirements should be defined based on various stakeholders’ needs. Software quality impacts the information system’s behavior, and the behavior impacts the behavior of the External-System that contains the information system. Safety is an issue of the External-System. A software product alone is harmless, because it can do nothing without computer hardware. However, any software quality characteristic, such as security and reliability, impacts the External-System’s safety. In this paper, conceptual models for quality requirements are presented. Then needs, requirements, and quality requirements are defined. Requirements for a Quality Requirements Engineering method are also stated. Then a method for Quality Requirements Engineering and associated specification is provided with a simple example.
Comparing Performances of Big Data Stream Processing Platforms with RAM3S

Nowadays, Big Data platforms allow the analysis of massive data streams in an efficient way. However, the services they provide are often too raw, thus the implementation of advanced real-world applications requires a non-negligible effort for interfacing with such services. This also complicates the task of choosing which one of the many available alternatives is the most appropriate for the application at hand. In this paper, we present a comparative study of the three major opensource Big Data platforms for stream processing, as performed by using our novel RAMS framework. Although the results we present are specific for our use case (recognition of suspect people from massive video streams), the generality of the RAMS framework allows both considering such results as valid for similar applications and implementing different use cases on top of Big Data platforms with very limited effort.
Transcranial Magnetic Stimulation: A Primer

Transcranial magnetic stimulation (TMS) is a technique for noninvasive stimulation of the human brain. Stimulation is produced by generating a brief, high-intensity magnetic field by passing a brief electric current through a magnetic coil. The field can excite or inhibit a small area of brain below the coil. All parts of the brain just beneath the skull can be influenced, but most studies have been of the motor cortex where a focal muscle twitch can be produced, called the motor-evoked potential. The technique can be used to map brain function and explore the excitability of different regions. Brief interference has allowed mapping of many sensory, motor, and cognitive functions. TMS has some clinical utility, and, because it can influence brain function if delivered repetitively, it is being developed for various therapeutic purposes.
Question Dependent Recurrent Entity Network for Question Answering

Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of Memory Network, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named Question Dependent Recurrent Entity Network and extends Recurrent Entity Network by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the bAbI question answering dataset and the CNN & Daily News reading comprehension dataset. In our experiments, the models achieved a State-ofThe-Art in the former and competitive results in the latter.
Application of MEMS Technology in Automotive Sensors and Actuators

Sensors and actuators are the critical system components that collect and act on information in the analog environment and link it to the world of digital electronics. The functional groups of sensors, software, controller hardware, and actuators form the backbone of present and future automotive systems. Unit volumes for sensors and actuators in the automotive industry are measured in millions per year and at a unit cost of a few dollars. The design of sensors and actuators has increasingly made use of microelectromechanical systems (MEMS) technology. This technology is well suited to producing a class of micromachined sensors and actuators that combines signal processing and communications on a single silicon chip or contained within the same package. This paper contains a discussion of the issues in producing MEMS sensors and actuators from the concept selection stage to the manufacturing platform. Examples of commercial and emerging automotive sensors and actuators are given, which illustrate the various aspects of device development. Future trends in MEMS technology as applied to automotive components are also discussed.
Intentional systemic mindfulness: an integrative model for self-regulation and health.

Self-regulation is the process whereby systems maintain stability of functioning and adaptability to change. Self-regulation is based on feedback loops which can be enhanced through attention. All self-regulation techniques, therefore, involve the cultivation of attention. However, the intention with which attention is directed may be crucial. In this paper, we explore intentional systemic mindfulness a model that explicitly introduces intention into self-regulation theory and practice. Intention as defined by this model is composed of the context of attention-systemic perspectives - and the quality of attention - mindfulness qualities. Intentional systemic mindfulness addresses both "why" (systemic perspectives) and "how" (mindfulness qualities) one directs attention, which may promote healing on multiple levels. Directions for research and implications for multiple levels of integrative health are considered.
Advances in the application of information technology to sport performance.

This paper overviews the diverse information technologies that are used to provide athletes with relevant feedback. Examples taken from various sports are used to illustrate selected applications of technology-based feedback. Several feedback systems are discussed, including vision, audition and proprioception. Each technology described here is based on the assumption that feedback would eventually enhance skill acquisition and sport performance and, as such, its usefulness to athletes and coaches in training is critically evaluated.
Continuities and discontinuities in children and scholarship.

This article introduces a collection of essays on continuity and discontinuity in cognitive development. In his lead essay, J. Kagan (2008) argues that limitations in past research (e.g., on number concepts, physical solidarity, and object permanence) render conclusions about continuity premature. Commentaries respectively (1) argue that longitudinal contexts are essential for interpreting developmental data, (2) illustrate the value of converging measures, (3) identify qualitative change via dynamical systems theory, (4) redirect the focus from states to process, and (5) review epistemological premises of alternative research traditions. Following an overview of the essays, this introductory article discusses how the search for developmental structures, continuity, and process differs between mechanistic-contextualist and organismic-contextualist metatheoretical frameworks, and closes by highlighting continuities in Kagan's scholarship over the past half century.
Improved Point-Line Feature Based Visual SLAM Method for Indoor Scenes

In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features-point and line segments-have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.
Analysis and Observations From the First Amazon Picking Challenge

This paper presents an overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team’s background, mechanism design, perception apparatus, planning, and control approach. We identify trends in this data, correlate it with each team’s success in the competition, and discuss observations and lessons learned based on survey results and the authors’ personal experiences during the challenge.Note to Practitioners—Perception, motion planning, grasping, and robotic system engineering have reached a level of maturity that makes it possible to explore automating simple warehouse tasks in semistructured environments that involve high-mix, low-volume picking applications. This survey summarizes lessons learned from the first Amazon Picking Challenge, highlighting mechanism design, perception, and motion planning algorithms, as well as software engineering practices that were most successful in solving a simplified order fulfillment task. While the choice of mechanism mostly affects execution speed, the competition demonstrated the systems challenges of robotics and illustrated the importance of combining reactive control with deliberative planning.
A comprehensive review of knowledge mapping techniques

Knowledge mapping is one of the most popular techniques used to identify knowledge in organizations. Using knowledge mapping techniques; a large and complex set of knowledge resources can be acquired and navigated more easily. Knowledge mapping has attracted the senior managers' attention as an assessment tool in recent years and is expected to measure deep conceptual understanding and allow experts in organizations to characterize relationships between concepts within a domain visually. Here the very critical issue is how to identify and choose an appropriate knowledge mapping technique. This paper aims to explore the different types of knowledge mapping techniques and give a general idea of their target contexts to have the way for choosing the appropriate map. It attempts to illustrate which techniques are appropriate, why and where they can be applied, and how these mapping techniques can be managed. The paper is based on the comprehensive review of papers on knowledge mapping techniques. In addition, this paper attempts to further clarify the differences among these knowledge mapping techniques and the main purpose for using each. Eventually, it is recommended that experts must understand the purpose for which the map is being developed before proceeding to activities related to any knowledge management dimensions; in order to the appropriate knowledge mapping technique .
How to feed Apache HBase with Petabytes of RDF Data: An Extremely Scalable RDF Store Based on Eclipse RDF4J Framework and Apache HBase Database

Majority of the systems designed to handle big RDF data rely on a single high-end computer dedicated to a certain RDF dataset and do not easily scale out, at the same time several clustered solution were tested and both the features and the benchmark results were unsatisfying. In this paper we describe a system designed to tackle such issues, a system that connects RDF4J and Apache HBase in order to receive an extremely scalable RDF store.
A psychologically-based taxonomy of misdirection

Magicians use misdirection to prevent you from realizing the methods used to create a magical effect, thereby allowing you to experience an apparently impossible event. Magicians have acquired much knowledge about misdirection, and have suggested several taxonomies of misdirection. These describe many of the fundamental principles in misdirection, focusing on how misdirection is achieved by magicians. In this article we review the strengths and weaknesses of past taxonomies, and argue that a more natural way of making sense of misdirection is to focus on the perceptual and cognitive mechanisms involved. Our psychologically-based taxonomy has three basic categories, corresponding to the types of psychological mechanisms affected: perception, memory, and reasoning. Each of these categories is then divided into subcategories based on the mechanisms that control these effects. This new taxonomy can help organize magicians' knowledge of misdirection in a meaningful way, and facilitate the dialog between magicians and scientists.
5G mm-Wave front-end-module design with advanced SOI process

In this paper, we first introduce the RF performance of Globalfoundries 45RFSOI process. NFET Ft > 290GHz and Fmax >380GHz. Then we present several mm-Wave circuit block designs, i.e., Switch, Power Amplifier, and LNA, based on 45RFSOI process for 5G Front End Module (FEM) applications. For the SPDT switch, insertion loss (IL) < 1dB at 30GHz with 32dBm P1dB and > 25dBm Pmax. For the PA, with a 2.9V power supply, the PA achieves 13.1dB power gain and a saturated output power (Psat) of 16.2dBm with maximum power-added efficiency (PAE) of 41.5% at 24Ghz continuous-wave (CW). With 960Mb/s 64QAM signal, 22.5% average PAE, −29.6dB EVM, and −30.5dBc ACLR are achieved with 9.5dBm average output power.
Anti-hyperglycemic Effects of Vaccinium arctostaphylos L. Fruit and Leaf Extracts in Alloxan-Induced Diabetic Rats

Vaccinium arctostaphylos L. (Caucasian whortleberry) fruit is used as an antihyperglycemic agent for treatment of diabetes mellitus.  The effects of whortleberry fruit and leaf extracts on the blood levels of fasting glucose, HbA1c (glycosylated hemoglobin), insulin, creatinine and liver enzymes SGOT and SGPT in alloxan-diabetic rats as well as LD50s of the extracts in rats were studied.  The effects of 2 months daily gavage of each extract at the doses of 250 mg/kg, 500 mg/kg and 1000 mg/kg on the parameters after single alloxan intraperitoneal injection at a dose of 125 mg/kg in the rats were evaluated. To calculate LD50 (median lethal dose), each extract was gavaged to groups of 30 healthy male and female Wistar rats at various doses once and the number of dead animals in each group within 72 hours was determined.  Alloxan injection resulted in significant increase of fasting glucose and HbA1c levels but decreased insulin levels significantly. Oral administration of whortleberry fruit and leaf extracts (each at the doses of 250, 500 and 1000 mg/kg) significantly reduced the fasting glucose and HbA1c levels but significantly increased the insulin levels without any significant effects on the SGOT, SGPT and creatinine levels in the diabetic rats compared with the control diabetic rats. The LD50s of the extracts were more than 15 g/kg.  Whortleberry fruits and leaves may have anti-hyperglycemic and blood insulin level elevating effects without hepatic and renal toxicities in the alloxan-diabetic rats and are relatively nontoxic in rats.
Taxonomies for Reasoning About Cyber-physical Attacks in IoT-based Manufacturing Systems

— The Internet of Things (IoT) has transformed many aspects of modern manufacturing, from design to production to quality control. In particular, IoT and digital manufacturing technologies have substantially accelerated product development cycles and manufacturers can now create products of a complexity and precision not heretofore possible. New threats to supply chain security have arisen from connecting machines to the Internet and introducing complex IoT-based systems controlling manufacturing processes. By attacking these IoT-based manufacturing systems and tampering with digital files, attackers can manipulate physical characteristics of parts and change the dimensions, shapes, or mechanical properties of the parts, which can result in parts that fail in the field. These defects increase manufacturing costs and allow silent problems to occur only under certain loads that can threaten safety and/or lives. To understand potential dangers and protect manufacturing system safety, this paper presents two taxonomies: one for classifying cyber-physical attacks against manufacturing processes and another for quality control measures for counteracting these attacks. We systematically identify and classify possible cyber-physical attacks and connect the attacks with variations in manufacturing processes and quality control measures. Our tax-onomies also provide a scheme for linking emerging IoT-based manufacturing system vulnerabilities to possible attacks and quality control measures.
Why are depressed patients inflamed? A reflection on 20 years of research on depression, glucocorticoid resistance and inflammation

Studies over the last 20 years have demonstrated that increased inflammation and hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis are two of the most consistent biological findings in major depression and are often associated: but the molecular and clinical mechanisms underlying these abnormalities are still unclear. These findings are particularly enigmatic, especially considering the accepted notion that high levels of cortisol have an anti-inflammatory action, and therefore the coexistence of inflammation and hypercortisolemia in the same diagnostic group appears counter-intuitive. To celebrate the 2015 Anna-Monika Foundation Award to our laboratory, this review will discuss our own 20 years of research on the clinical and molecular evidence underlying the increased inflammation in depression, especially in the context of a hyperactive HPA axis, and discuss its implications for the pathogenesis and treatment of this disorder.
A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function

The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this note, we consider constructive approximation on any finite interval of by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of at any reasonable point of the real axis.
Attitude estimation and control of a quadrocopter

The research interest in unmanned aerial vehicles (UAV) has grown rapidly over the past decade. UAV applications range from purely scientific over civil to military. Technical advances in sensor and signal processing technologies enable the design of light weight and economic airborne platforms. This paper presents a complete mechatronic design process of a quadrotor UAV, including mechanical design, modeling of quadrotor and actuator dynamics and attitude stabilization control. Robust attitude estimation is achieved by fusion of low-cost MEMS accelerometer and gyroscope signals with a Kalman filter. Experiments with a gimbal mounted quadrotor testbed allow a quantitative analysis and comparision of the PID and Integral-Backstepping (IB) controller design for attitude stabilization with respect to reference signal tracking, disturbance rejection and robustness.
A Multi-Pass Sieve for Coreference Resolution

Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features. This approach can lead to incorrect decisions as lower precision features often overwhelm the smaller number of high precision ones. To overcome this problem, we propose a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision. Each tier builds on the previous tier’s entity cluster output. Further, our model propagates global information by sharing attributes (e.g., gender and number) across mentions in the same cluster. This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available at the time. The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. In spite of its simplicity, our approach outperforms many state-of-the-art supervised and unsupervised models on several standard corpora. This suggests that sievebased approaches could be applied to other NLP tasks.