nils-herrmann/clean_scirepeval huggingface dataset:
Design of tunable bandpass filter using PIN diode with constant absolute bandwidth

This paper presents a tunable bandpass filter (BPF) for cognitive radio applications. The proposed two-pole BPF is implemented using lumped elements and the frequency tuning is realized by PIN diodes. Due to the L-C parallel circuit J-inverter, constant absolute bandwidth is achieved. 4 PIN diodes in each resonator and the capacitor bank are used to cover the desired frequency tuning range from 480MHz to 687MHz with 20MHz bandwidth. The circuit is theoretically analyzed, and the complete design process is described. The fabricated filter shows wide tuning range of 42.5% with insertion loss varying from 2.7dB to 3.8dB and good linearity. The 1-dB bandwidth of the filter is kept 20±1MHz.
Electron microscopy of myelin: Structure preservation by high-pressure freezing

Electron microscopic visualization of nervous tissue morphology is crucial when aiming to understand the biogenesis and structure of myelin in healthy and pathological conditions. However, accurate interpretation of electron micrographs requires excellent tissue preservation. In this short review we discuss the recent utilization of tissue fixation by high-pressure freezing and freeze-substitution, which now supplements aldehyde fixation in the preparation of samples for electron microscopy of myelin. Cryofixation has proven well suited to yield both, improved contrast and excellent preservation of structural detail of the axon/myelin-unit in healthy and mutant mice and can also be applied to other model organisms, including aquatic species. This article is part of a Special Issue entitled SI: Myelin
Cramer-Rao Lower Bound and Information Geometry

This article focuses on an important piece of work of the world renowned Indian statistician, Calyampudi Radhakrishna Rao. In 1945, C. R. Rao (25 years old then) published a pathbreaking paper [43], which had a profound impact on subsequent statistical research. Roughly speaking, Rao obtained a lower bound to the variance of an estimator. The importance of this work can be gauged, for instance, by the fact that it has been reprinted in the volume Breakthroughs in . There have been two major impacts of this work:
Unsupervised video summarization using cluster analysis for automatic vehicles counting and recognizing

11 Automatic Vehicles Counting and Recognizing (AVCR) is a very challenging topic in transport engineering having important implications for the modern transport policies. Implementing a computer-assisted AVCR in the most vital districts of a country provides a large amount of measurements which are statistically processed and analyzed, the purpose of which is to optimize the decision-making of traffic operation, pavement design, and transportation planning. Since the advent of computer vision technology, video-based surveillance of road vehicles has become a key component in developing autonomous intelligent transportation systems. In this context, this paper proposes a Pattern Recognition system which employs an unsupervised clustering algorithm with the objective of detecting, counting and recognizing a number of dynamic objects crossing a roadway. This strategy defines a virtual sensor, whose aim is similar to that of an inductive-loop in a traditional mechanism, i.e. to extract from the traffic video streaming a number of signals containing anarchic information about the road traffic. Then, the set of signals is filtered with the aim of conserving only motion’s significant patterns. Resulted data are subsequently processed by a statistical analysis technique so as to estimate and try to recognize a number of clusters corresponding to vehicles. Finite Mixture Models fitted by the EM algorithm are used to assess such clusters, which provides ∗Corresponding author Email addresses: hana.rabbouch@gmail.com (Hana RABBOUCH), foued.saadaoui@gmail.com (Foued SAÂDAOUI), rafaa_mraihi@yahoo.fr (Rafaa MRAIHI)  2017
Impact of Users' Security Awareness on Desktop Security Behavior: A Protection Motivation Theory Perspective

This article uses the protection motivation theory to study the impact of information security awareness on desktop security behavior. It contributes to the literature by examining the roles played by awareness, an important antecedent to the cognitive processes in the protection motivation theory. The findings indicate that security awareness significantly affects perceived severity, response efficacy, self-efficacy, and response cost. Constructs in the coping appraisal process (except response cost), in turn, significantly impact recommended security behavior.
AClick SequenceModel forWeb Search

Getting a better understanding of user behavior is important for advancing information retrieval systems. Existing work focuses on modeling and predicting single interaction events, such as clicks. In this paper, we for the first time focus on modeling and predicting sequences of interaction events. And in particular, sequences of clicks. We formulate the problem of click sequence prediction and propose a click sequence model (CSM) that aims to predict the order in which a user will interact with search engine results. CSM is based on a neural network that follows the encoder-decoder architecture. The encoder computes contextual embeddings of the results. The decoder predicts the sequence of positions of the clicked results. It uses an attentionmechanism to extract necessary information about the results at each timestep. We optimize the parameters of CSM by maximizing the likelihood of observed click sequences. We test the effectiveness ofCSMon three new tasks: (i) predicting click sequences, (ii) predicting the number of clicks, and (iii) predicting whether or not a user will interact with the results in the order these results are presented on a search engine result page (SERP). Also, we show that CSM achieves state-of-the-art results on a standard click prediction task, where the goal is to predict an unordered set of results a user will click on.
Computing edit distances between an XML document and a schema and its application in document classification

In this paper, we present an algorithm to find a sequence of top-down edit operations with minimum cost that transforms an XML document such that it conforms to a schema. It is shown that the algorithm runs in O(p x log p x n), where p is the size of the schema(grammar) and n is the size of the XML document (tree). We have also shown that edit distance with restricted top-down edit operations can be computed the same way.We will also show how to use the edit distances in document classification. Experimental studies have shown that our methods are effective in structure-oriented classification for both real and synthesized data sets.
Computability Theory of Closed Timelike Curves

We ask, and answer, the question of what’s computable by Turing machines equipped with time travel into the past: that is, closed timelike curves or CTCs (with no bound on their size). We focus on a model for CTCs due to Deutsch, which imposes a probabilistic consistency condition to avoid grandfather paradoxes. Our main result is that computers with CTCs can solve exactly the problems that are Turing-reducible to the halting problem, and that this is true whether we consider classical or quantum computers. Previous work, by Aaronson and Watrous, studied CTC computers with a polynomial size restriction, and showed that they solve exactly the problems in PSPACE, again in both the classical and quantum cases. Compared to the complexity setting, the main novelty of the computability setting is that not all CTCs have fixed-points, even probabilistically. Despite this, we show that the CTCs that do have fixed-points suffice to solve the halting problem, by considering fixed-point distributions involving infinite geometric series. The tricky part is to show that even quantum computers with CTCs can be simulated using a Halt oracle. For that, we need the Riesz representation theorem from functional analysis, among other tools. We also study an alternative model of CTCs, due to Lloyd et al., which uses postselection to “simulate” a consistency condition, and which yields BPPpath in the classical case or PP in the quantum case when subject to a polynomial size restriction. With no size limit, we show that postselected CTCs yield only the computable languages if we impose a certain finiteness condition, or all languages nonadaptively reducible to the halting problem if we don’t.
Treatment of Unstable Thoracolumbar Burst Fractures by Indirect Reduction and Posterior Stabilization: Short-Segment Versus Long-Segment Stabilization

In order to compare short-segment stabilization with long-segment stabilization for treating unstable thoracolumbar fractures, we studied fifty patients suffered from unstable thoracolumbar burst fractures. Thirty of them were managed with long-segment posterior transpedicular instrumentation and twenty patients with short-segment stabilization. The mean follow up period was 5.2 years. Pre-operative and post-operative radiological parameters, like the Cobb angle, the kyphotic deformation and the Beck index were evaluated. A statistically significant difference between the two under study groups was noted for the Cobb angle and the kyphotic deformation, while, as far as the Beck index is concerned, no significant difference was noted. In conclusion, either the long-segment or the short-segment stabilization is able for reducing the segmental kyphosis and the vertebral body deformation postoperatively. However, as time goes by, the long-segment stabilization is associated with better results as far as the radiological parameters, the indexes and the patient's satisfaction are concerned.
Big data in tourism industry

As an infrastructural and productive industry, tourism is very important in modern economy and includes different scopes and functions. If it is developed appropriately, cultural relations and economic development of countries will be extended and provided. Web development as an applied tool in the internet plays a very determining role in tourism success and proper exploitation of it can pave the way for more development and success of this industry. On the other hand, the amount of data in the current world has been increased and analysis of large sets of data that is referred to as big data has been converted into a strategic approach to enhance competition and establish new methods for development, growth, innovation, and enhancement of the number of customers. Today, big data is one of the important issues of information management in digital age and one of the main opportunities in tourism industry for optimal exploitation of maximum information. Big data can shape experiences of smart travel. Remarkable growth of these data sources has inspired new Strategies to understand the socio-economic phenomenon in different fields. The analytical approach of big data emphasizes the capacity of data collection and analysis with an unprecedented extent, depth and scale for solving the problems of real life and uses it. Indeed, big data analyses open the doors to various opportunities for developing the modern knowledge or changing our understanding of this scope and support decision-making in tourism industry. The purpose of this study is to show helpfulness of big data analysis to discover behavioral patterns in tourism industry and propose a model for employing data in tourism.
Schinzel-Giedion syndrome in two Brazilian patients: Report of a novel mutation in SETBP1 and literature review of the clinical features.

Schinzel-Giedion syndrome is a rare autosomal dominant disorder comprising postnatal growth failure, profound developmental delay, seizures, facial dysmorphisms, genitourinary, skeletal, neurological, and cardiac defects. It was recently revealed that Schinzel-Giedion syndrome is caused by de novo mutations in SETBP1, but there are few reports of this syndrome with molecular confirmation. We describe two unrelated Brazilian patients with Schinzel-Giedion syndrome, one of them carrying a novel mutation. We also present a review of clinical manifestations of the syndrome, comparing our cases to patients reported in literature emphasizing the importance of the facial gestalt associated with neurological involvement for diagnostic suspicion of this syndrome.
Smart Grid — Safe , Secure , Self-Healing Challenges and Opportunities in Power System Security , Resiliency , and Privacy

By S. Massoud Amin and Anthony M. Giacomoni T THE EXISTING POWER DELIVERY system is vulnerable to both natural disasters and intentional attack. A successful terrorist attempt to disrupt the power delivery system could have adverse effects on national security, the economy, and the lives of every citizen. Secure and reliable operation of the electric system is fundamental to national and international economic systems, security, and quality of life.  The report concluded: “Terrorists could emulate acts of sabotage in several other countries and destroy critical [power system] components, incapacitating large © B R A N D X P IC T U R E S
Gate and Base Drivers for Silicon Carbide Power Transistors: An Overview

Silicon carbide (SiC) power transistors have started gaining significant importance in various application areas of power electronics. During the last decade, SiC power transistors were counted not only as a potential, but also more importantly as an alternative to silicon counterparts in applications where high efficiency, high switching frequencies, and operation at elevated temperatures are targeted. Various SiC device designs have been proposed and excessive investigations in terms of simulation and experimental studies have shown their advantageous performance compared to silicon technology. On a system-level, however, the design of gate and base drivers for SiC power transistors is very challenging. In particular, a sophisticated driver design is not only associated with properly switching the transistor and decreasing the switching power losses, but also it must incorporate protection features, as well as comply with the electromagnetic compatibility. This paper shows an overview of the gate and base drivers for SiC power transistors which have been proposed by several highly qualified scientists. In particular, the basic operating principle of each driver along with their applicability and drawbacks are presented. For this overview, the three most successful SiC power transistors are considered: junction-field-effect transistors, bipolar-junction transistors, and metal-oxide-semiconductor field-effect transistors. Last but not least, future challenges on gate and base drivers design are also presented.
The Neurological Examination in Aging , Dementia and Cerebrovascular Disease Part 4 : Reflexes and Sensory Examination

This four-part series of articles provides an overview of the neurological examination of the elderly patient, particularly as it applies to patients with cognitive impairment, dementia or cerebrovascular disease.The focus is on the method and interpretation of the bedside physical examination; the mental state and cognitive examinations are not covered in this review.Part 1 (featured in the September issue) began with an approach to the neurological examination in normal aging and in disease, and reviewed components of the general physical,head and neck,neurovascular and cranial nerve examinations relevant to aging and dementia.Part 2 (featured in the October issue) covered the motor examination with an emphasis on upper motor neuron signs and movement disorders. Part 3(featured in the November issue) reviewed the assessment of coordination,balance and gait,and Part 4, featured here, discusses the muscle stretch reflexes, pathological and primitive reflexes, and sensory examination, and offers concluding remarks.Throughout this series, special emphasis is placed on the evaluation and interpretation of neurological signs in light of findings considered normal in the elderly.
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Salimans et al. (2016), we achieve state-of-the-art results for GAN-based semisupervised learning on the CIFAR-10 dataset, with a method that is significantly easier to implement than competing methods.
Thiocapsa halophila sp. nov., a new halophilic phototrophic purple sulfur bacterium

A new phototrophic sulfur bacterium has been isolated from a red layer in a laminated mat occurring underneath a gypsum crust in the mediterranean salterns of Salin-de-Giraud (Camargue, France). Single cells were coccus-shaped, non motile, without gas vacuoles and contained sulfur globules. Bacteriochlorophyll a and okenone were present as major photosynthetic pigments. These properties and the G+C content of DNA (65.9–66.6 mol% G+C) are typical characteristics of the genus Thiocapsa. However, the new isolate differs from known species in the genus, particularly in NaCl requirement (optimum, 7% NaCl; range, 3–20% NaCl) and some physiological characteristics. Therefore, a new species is proposed, Thiocapsa halophila, sp. nov.
Age of Menarche and Knowledge about Menstrual Hygiene Management among Adolescent School Girls in Amhara Province, Ethiopia: Implication to Health Care Workers & School Teachers

Effective menstrual hygiene has direct and indirect effect on achieving millennium development goals two (universal education), three (gender equality and women empowerment) and, five (improving maternal health). However, in Ethiopiait is an issue which is insufficiently acknowledged in the reproductive health sector. The objective of this study therefore, is to assess the age of menarche and knowledge of adolescents about menstrual hygiene management in Amhara province.



School based cross sectional study was conducted from November 2012 to June 2013. Multistage stage sampling technique was used. The school was first clustered in to grades & sections and thenparticipants were selected by lottery method. A pretested &structured questionnaire was used. Data were entered, cleaned and analyzed using SPSS version 16.0. Finally, multivariate analysis was used to assess independent effect of predictors.



In this study, 492 students were included, making a response rate of 100%. Mean age at menarche was 14.1±1.4 years. The main sources of information about menstrual hygiene management were teachers for 212 (43.1%). Four hundred forty six (90.7%) respondents had high level knowledge about menstrual hygiene management. Most of the respondents 457 (92.9%) and 475 (96.5%) had access for water and toilet facility respectively. Place of residence (AOR = 1.8, 95%CI: [1.42-1.52]) and educational status of their mothers' (AOR = 95%CI: [1.15-13.95]) were independent predictors of knowledge about menstrual hygiene management.



Knowledge of respondents about menstrual hygiene management was very high. School teachers were the primary source of information. Place of residence and their mother's educational status were independent predictors of menstrual hygiene management. Thus, the government of Ethiopia in collaboration with its stalk holders should develop and disseminatereproductive health programmes on menstrual hygiene management targeting both parents and their adolescents. Moreover, parents should be made aware about the need to support their children with appropriate sanitary materials.
Distributed Machine Learning: Foundations, Trends, and Practices

In recent years, artificial intelligence has achieved great success in many important applications. Both novel machine learning algorithms (e.g., deep neural networks), and their distributed implementations play very critical roles in the success. In this tutorial, we will first review popular machine learning algorithms and the optimization techniques they use. Second, we will introduce widely used ways of parallelizing machine learning algorithms (including both data parallelism and model parallelism, both synchronous and asynchronous parallelization), and discuss their theoretical properties, strengths, and weakness. Third, we will present some recent works that try to improve standard parallelization mechanisms. Last, we will provide some practical examples of parallelizing given machine learning algorithms in online application (e.g. Recommendation and Ranking) by using popular distributed platforms, such as Spark MlLib, DMTK, and Tensorflow. By listening to this tutorial, the audience can form a clear knowledge framework about distributed machine learning, and gain some hands-on experiences on parallelizing a given machine learning algorithm using popular distributed systems.
Indoor location based services challenges, requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge.
Isolated Bidirectional Full-Bridge DC – DC Converter with a Flyback

An isolated bidirectional full-bridge dc–dc converter with high conversion ratio, high output power, and soft start-up capability is proposed in this paper. The use of a capacitor, a diode, and a flyback converter can clamp the voltage spike caused by the current difference between the current-fed inductor and leakage inductance of the isolation transformer, and can reduce the current flowing through the active switches at the current-fed side. Operational principle of the proposed converter is first described, and then, the design equation is derived. A 1.5kW prototype with low-side voltage of 48 V and high-side voltage of 360 V has been implemented, from which experimental results have verified its feasibility. IndexTerms— Flybackconverter, isolated fullbridge bidirectional converter, soft start-up.
Am I Done? Predicting Action Progress in Videos

In this paper we introduce the problem of predicting action progress in videos. We argue that this is an extremely important task because, on the one hand, it can be valuable for a wide range of applications and, on the other hand, it facilitates better action detection results. To solve this problem we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make framewise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets. Additionally, we show that exploiting action progress it is also possible to improve spatio-temporal localization.