nils-herrmann/clean_scirepeval huggingface dataset:
The QCD running coupling

We review the present theoretical and empirical knowledge for a s the fundamental coupling underlying the interactions of quarks and gluons in Quantum Chromodynamics (QCD) The dependence of a s Q 2 on momentum transfer Q encodes the underlying dynamics of hadron physics from color confinement in the infrared domain to asymptotic freedom at short distances. We review constraints on a s Q 2 at high Q 2 as predicted by perturbative QCD, and its analytic behavior at small Q 2 based on models of nonperturbative dynamics. In the introductory part of this review, we explain the phenomenological meaning of the coupling, the reason for its running, and the challenges facing a complete understanding of its analytic behavior in the infrared domain. In the second, more technical, part of the review, we discuss the behavior of a s Q 2 in the high momentum transfer domain of QCD. We review how a s is defined, including its renormalization scheme dependence, the definition of its renormalization scale, the utility of effective charges, as well as "Commensurate Scale Relations" which connect the various definitions of the QCD coupling without renormalization scale ambiguity. We also report recent significant measurements and advanced theoretical analyses which have led to precise QCD predictions at high energy. As an example of an important optimization procedure, we discuss the "Principle of Maximum Conformality" which enhances QCD's predictive power by removing the dependence of the predictions for physical observables on the choice of theoretical conventions such as the renormalization scheme. In the last part of the review, we discuss the challenge of understanding the analytic behavior a s Q 2 in the low momentum transfer domain. We survey various theoretical models for the nonperturbative strongly coupled regime, such as the light front holographic approach to QCD. This new framework predicts the form of the quark confinement potential underlying hadron spectroscopy and dynamics, and it gives a remarkable connection between the perturbative QCD scale L and hadron masses. One can also identify a specific scale Q 0 which demarcates the division between perturbative and nonperturbative QCD. We also review other important methods for computing the QCD coupling, including lattice QCD, the Schwinger Dyson equations and the Gribov Zwanziger analysis. After describing these approaches and enumerating their conflicting predictions, we discuss the origin of these discrepancies and how to remedy them. Our aim is not only to review the advances in this difficult area, but also to suggest what could be an optimal definition of a s Q 2 in order to bring better unity to the subject.
Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?
Venue: Proceedings of the IEEE

Space based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line of sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs) barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image based, terrain based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever growing numbers of users, the hybridization of a suite of different PNT solutions is required.
Real time magnetic resonance guided ablation of typical right atrial flutter using a combination of active catheter tracking and passive catheter visualization in man: initial results from a consecutive patient series.
Venue: Europace European pacing, arrhythmias, and cardiac electrophysiology journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

Recently cardiac magnetic resonance (CMR) imaging has been found feasible for the visualization of the underlying substrate for cardiac arrhythmias as well as for the visualization of cardiac catheters for diagnostic and ablation procedures. Real time CMR guided cavotricuspid isthmus ablation was performed in a series of six patients using a combination of active catheter tracking and catheter visualization using real time MR imaging.  Cardiac magnetic resonance utilizing a 1.5 T system was performed in patients under deep propofol sedation. A three dimensional whole heart sequence with navigator technique and a fast automated segmentation algorithm was used for online segmentation of all cardiac chambers, which were thereafter displayed on a dedicated image guidance platform. In three out of six patients complete isthmus block could be achieved in the MR scanner, two of these patients did not need any additional fluoroscopy. In the first patient technical issues called for a completion of the procedure in a conventional laboratory, in another two patients the isthmus was partially blocked by magnetic resonance imaging (MRI) guided ablation. The mean procedural time for the MR procedure was 109 58 min. The intubation of the CS was performed within a mean time of 2.75 2.21 min. Total fluoroscopy time for completion of the isthmus block ranged from 0 to 7.5 min.  The combination of active catheter tracking and passive real time visualization in CMR guided electrophysiologic (EP) studies using advanced interventional hardware and software was safe and enabled efficient navigation, mapping, and ablation. These cases demonstrate significant progress in the development of MR guided EP procedures.
Elevated cortisol content in dog hair with atopic dermatitis.
Venue: The Japanese journal of veterinary research

Canine atopic dermatitis (CAD) is a chronic relapsing inflammatory skin disease occurring in 10% of the canine population. Although most studies have focused on the pathophysiological mechanism involved in CAD, the detrimental impact of CAD on quality of life has received only little attention. Hair cortisol analysis is becoming a valuable tool in monitoring chronic stress. To further validate this approach in CAD, we compared the hair cortisol concentration of atopic dogs with that of healthy conditioned dogs. The extent and severity of cutaneous lesions of atopic dermatitis were assessed according to modified CADESI 03 scores. In addition, skin barrier function was evaluated by measuring transepidermal water loss (TEWL) and stratum corneum conductance. The correlation between CAD severity and hair cortisol concentration was evaluated. The level of hair cortisol evaluated by ELISA assay showed that the atopic dermatitis group had significantly increased cortisol levels compared to that of the healthy control group. A significant positive correlation was identified between hair cortisol level and the CADESI score in CAD patients. The TEWL value of the cubital flexor of the forelimb in the atopic group was significantly higher compared to the healthy controls. These findings imply that the hair cortisol analysis can be an effective and objective biomarker in assessment of long term stress of CAD patients.
Identification of muscle and subcutaneous and intermuscular adipose tissue on thigh MRI of muscular dystrophy
Venue: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Muscular dystrophies can affect the muscle distribution within the legs. In order to effectively study and track disease progression, it is important to quantify both muscle and fat volumes, and distinguish between subcutaneous (SAT) and intermuscular adipose tissue (IMAT) While several techniques have been previously described that perform such classification, they rely heavily on the muscle location, and so may not be suitable for differentiating SAT and IMAT in severe cases of dystrophy. We propose a method that utilizes muscle location if available, but also identifies the fascia lata to serve as the boundary between SAT and IMAT. Our method achieved DICE coefficients of 0.93, 0.88, and 0.68 for muscle, SAT, and IMAT, respectively, in mild cases, of 0.94, 0.91, and 0.85 in moderate cases, and of 0.79, 0.90, and 0.91 in severe cases.
Satellite Imagery Classification Based on Deep Convolution Network

Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold First, to cope with the large scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.  Satellite imagery classification, deep convolution network, genetic algorithm, hyper parameter optimization.
Forensic investigation framework for the document store NoSQL DBMS: MongoDB as a case study
Venue: Digit. Investig.

The NoSQL DBMS provides an efficient means of storing and accessing big data because its servers are more easily horizontally scalable and replicable than relational DBMSs. Its data model lacks a fixed schema, so that users can easily dynamically change the data model of applications. These characteristics of the NoSQL DBMS mean that it is increasingly used in real time analysis, web services such as SNS, mobile apps and the storage of machine generated data such as logs and IoT (Internet of Things) data. Although the increased usage of the NoSQL DBMS increases the possibility of it becoming a target of crime, there are few papers about forensic investigation of NoSQL DBMS.In this paper, we propose a forensic investigation framework for the document store NoSQL DBMS. It is difficult to cover all of the NoSQL DBMS, as 'NoSQL' includes several distinct architectures; our forensic investigation framework, however, is focused on the document store NoSQL DBMS. In order to conduct an evaluative case study, we need to apply it to MongoDB, which is, a widely used document store NoSQL DBMS. For this case study, a crime scenario is created in an experimental environment, and then we propose in detail a forensic procedure and technical methods for MongoDB. We suggested many substantial technical investigation methods for MongoDB, including identifying real servers storing evidences in a distributed environment and transaction reconstruction method, using log analysis and recovering deleted data from the MongoDB data file structure.
Executing SPARQL queries over Mapped Document Store with SparqlMap M
Venue: SEMANTICS

With the increasing adoption of NoSQL data base systems like MongoDB or CouchDB more and more applications store structured data according to a non relational, document oriented model. Exposing this structured data as Linked Data is currently inhibited by a lack of standards as well as tools and requires the implementation of custom solutions. While recent efforts aim at expressing transformations of such data models into RDF in a standardized manner, there is a lack of approaches which facilitate SPARQL execution over mapped non relational data sources. With SparqlMap M we show how dynamic SPARQL access to non relational data can be achieved. SparqlMap M is an extension to our SPARQL to SQL rewriter SparqlMap that performs a (partial) transformation of SPARQL queries by using a relational abstraction over a document store. Further, duplicate data in the document store is used to reduce the number of joins and custom optimizations are introduced. Our showcase scenario employs the Berlin SPARQL Benchmark (BSBM) with different adaptions to a document data model. We use this scenario to demonstrate the viability of our approach and compare it to different MongoDB setups and native SQL.
Design of Ultra Low Noise and Low Temperature Usable Power System for High Precision Detectors
Venue: IEEE Transactions on Nuclear Science

In high precision detector systems, the power supply usually need to be ultra low noise and stabilized. Design and implementation of an ultra low noise power supply is described in this paper. We implemented and tested the power system actually in a scientific CCD (Charge coupled device) detector system, but the design structure could be used in many high precision detector systems as infrared detector or high energy particles detectors. The power system uses DC DC switching regulators and LDO regulators for power generating, uses multi stage filters for noise reducing. Multi output power supply with noise about 40 mVrms is achieved, the overall energy efficiency is 68.5% Besides, the electronic system takes the low temperature as an important consideration, and the system has been tested in an extreme environment as low as 193 K.
Design of ultra low noise power system for high precision detectors
Venue: 2016 IEEE NPSS Real Time Conference (RT)

In this paper, we introduce an ultra low noise power system designed for high precision detectors, like CCD detector. We build low noise power circuit for the front end electronics, whose noise generally needs to be under 1 mVrms. Because the system may be used in low temperature environment as in Antarctica, the influence of electronic equipment working at 213K 193K is fully considered in the beginning of the design. As a result, the power system passed noise and long time low temperature test.
Drop Impact on a Solid Surface

A drop hitting a solid surface can deposit, bounce, or splash. Splashing arises from the breakup of a fine liquid sheet that is ejected radially along the substrate. Bouncing and deposition depend crucially on the wetting properties of the substrate. In this review, we focus on recent experimental and theoretical studies, which aim at unraveling the underlying physics, characterized by the delicate interplay of not only liquid inertia, viscosity, and surface tension, but also the surrounding gas. The gas cushions the initial contact; it is entrapped in a central microbubble on the substrate; and it promotes the so called corona splash, by lifting the lamella away from the solid. Particular attention is paid to the influence of surface roughness, natural or engineered to enhance repellency, relevant in many applications.
Frugal Innovation: How to Do Better with Less
Venue: J. Sci. Res.

Frugal innovation takes the bottom up approach involving the geeks' next door, or people habituated with Do ItYourself (DIY) stuff for co creating innovative products, solutions, or services for the start ups as well as big corporations. In a globalized knowledge economy, frugal innovation is projected as a new model for the business success, where entrepreneurs are encouraging co production by consumers, who have become 'prosumers' As the sub title of the book suggests, the frugal innovation is a result of the ability to do more and better with fewer resources. In the book, the authors suggest a measurement of the intensity of frugal innovation. Intensity can be measured in terms of a simple formula: "Greater Value (for customers, shareholders, and society) Fewer Resources (natural resources, capital, time)" The communitydriven innovation ecosystem primarily addresses the societal needs, sustainability, and cost efficiency. Other business considerations come much later. Innovation is made a key constituent in the 2030 Global Development Agenda, while Sustainable Development Goals (SDGs) are set to achieve by the year 2030 as envisaged the member countries of the United Nations. Goal 9 (SDG9) is set to build resilient infrastructure, promote sustainable industrialization and foster innovation. In this context, frugal innovation helps in achieving the mandate of fostering innovation in developing countries. Post 2008 (post recession or the time of austerity) the corporations were pushed to achieve cost efficiency without compromising the products' quality, affordability, and comfortability of the end users. Thus, the key consideration of the book is that how resources constrained corporations could turn around ensuring the bottom line innovations are taking place in their backyards. At a central stage, the community participation is really essential for engaging its members in collaborative innovation development processes. The knowledge economy nurtures the innovation frameworks across the board while encouraging the collaborative development of the innovation ecosystem in the country by establishing an array of the innovation clusters, national systems of innovation, and sectoral systems of innovation across the states. The ecosystem is put in place with special concessions are given to the actors in an innovation system, such as the start ups, entrepreneurs, and intrapreneurs in terms of extending the ease of doing business.
Pap smear image classification using convolutional neural network
Venue: ICVGIP '16

This article presents the result of a comprehensive study on deep learning based Computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre cancerous and cancerous lesion of cervix. The proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.
Intelligent Coordination of Demand Response and Secondary Frequency Control in Multi area Power Systems

Frequency control is among most important issues in a power system due to increasing size, changing structure and the complexity of interconnected power systems. Increasing economic constraints for power system quality and reliability and high operational costs of generation side controllers have inclined researchers to consider demand response as an alternative for preserving system frequency during off normal conditions. However, the main obstacle is calculating the accurate amount of load related to the value of disturbances to be manipulated, specifically in a multi area power system. Dealing with this challenge, this paper makes an attempt to find a solution via monitoring the deviations of tie line flows. The proposed solution calculates the magnitude of disturbances and simultaneously determines the area where disturbances occurred, to apply demand response exactly to the involved area. To address communication limitations, the impact of demand response delay on the frequency stability is investigated. Furthermore, this paper introduces a fuzzy PI based supervisory controller as a coordinator between the demand response and secondary frequency control avoiding large frequency overshoots/undershoots caused by the communication delays. To evaluate the proposed control scheme, simulation studies are carried out on the 10 machine New England test power system.
Early Design Research at the RCA: The Royal College of Art in the 1960s and 1970

This online exhibition introduces a project to document and evaluate the early history of Design Research in the UK, focusing on the Department of Design Research and its predecessors at the Royal College of Art, London. The exhibits illustrate a number of themes, many of them concerned with the varied roles of the leader of the Department, Professor L Bruce Archer. They are organised around a series of images, highlighting important themes, and not necessarily presented in chronological order. The exhibition has been put together by Stephen Boyd Davis, Professor of Design Research at the RCA, and Dr. Simone Gristwood of Middlesex and Lancaster universities.
Monitoring neo adjuvant chemotherapy: comparison of contrast enhanced spectral mammography (CESM) and MRI versus breast cancer characteristics

"ECR 2016 B 1062 Monitoring neo adjuvant chemotherapy: comparison of contrast enhanced spectral mammography (CESM) and MRI versus breast cancer characteristics" by: "V. Iotti1, S. Ravaioli1, R. Sghedoni1, C. Coriani1, R. Vacondio1, S. Caffarri2, A. Nitrosi1, G. Tondelli3, P. Pattacini1; 1Reggio Emilia/IT, 2Guastalla/IT, 3Scandiano/IT"
Intuitionistic Fuzzy Petri Nets for Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are an ideal modeling tool for knowledge based systems, which are based on fuzzy production rules. FPNs are widely used in knowledge representation and reasoning, assessment, fault diagnosis, exception handling, and other fields, but they have the defects of single membership degree. To solve this problem, intuitionistic fuzzy Petri nets (IFPNs) were presented for knowledge representation and reasoning. First, the IFPN model was constructed for knowledge representation by combining intuitionistic fuzzy sets theory with Petri nets theory. Second, an algorithm based on IFPN was proposed, and the matrix operation was introduced into the reasoning process to make full use of the parallel computing capability of Petri nets. Finally, an example was illustrated to prove the feasibility and advantages of the proposed IFPN model and reasoning algorithm. Moreover, the reasoning result was analyzed and discussed. Compared with FPN, IFPN can describe three states, namely, the support state, the opposite state, and the neutral state. Thus, IFPN can overcome the single membership degree of FPN and describe the reasoning result more comprehensively and precisely than FPN. Moreover, IFPN is an effective extension and development of FPN and will become a promising method for knowledge representation and reasoning. Subject Categories and Descriptors I.2.4 [Artificial Intelligence] Knowledge Representation Formalisms and MethodsRepresentations(procedural and rule based) General Terms: Algorithm, Performance