Publication Year Range: Last 50 years / Topic: software - Searchworks@Jio Institute Digital Library Search Results

161 results

1. Protein ORIGAMI: A program for the creation of 3D paper models of folded peptides.

Author: Reißer S, Prock S, Heinzmann H, and Ulrich AS
Subjects: Comprehension, Humans, Learning, Models, Molecular, Paper, Peptides chemistry, Protein Folding, Software, Teaching
Abstract: Protein ORIGAMI (http://ibg.kit.edu/protein_origami) is a browser-based web application that allows the user to create straightforward 3D paper models of folded peptides for research, teaching and presentations. An amino acid sequence can be turned into α-helices, β-strands and random coils that can be printed out and folded into properly scaled models, with a color code denoting the biophysical characteristics of each amino acid residue (hydrophobicity, charge, etc.). These models provide an intuitive visual and tactile understanding of peptide interactions with other partners, such as helix-helix assembly, oligomerization, membrane binding, or pore formation. Helices can also be displayed as a helical wheel or helical mesh in 2D graphics, to be used in publications or presentations. The highly versatile programme Protein ORIGAMI is also suited to create less conventional helices with arbitrary pitch (e.g., 3 10 -helix, π-helix, or left-handed helices). Noncanonical amino acids, labels and different terminal modifications can be defined and displayed at will, and different protonation states can be shown. In addition to the web application, the program source code can be downloaded and installed locally on a PC. The printed paper models can be readily used for daily research and discussions, just as for educational purposes and teaching. © 2018 by The International Union of Biochemistry and Molecular Biology, 46:403-409, 2018., (© 2018 The International Union of Biochemistry and Molecular Biology.)
Published: 2018
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2. Experiences in digitizing and digitally measuring a paper-based ECG archive.

Author: Holkeri A, Eranti A, Kenttä TV, Noponen K, Haukilahti MAE, Seppänen T, Junttila MJ, Kerola T, Rissanen H, Heliövaara M, Knekt P, Aro AL, and Huikuri HV
Subjects: Finland, Health Surveys, Humans, Information Storage and Retrieval, Paper, Electrocardiography, Medical Records Systems, Computerized, Signal Processing, Computer-Assisted, Software
Abstract: Background: No established method for digitizing and digital measuring of paper electrocardiograms (ECG) exists. We describe a paper ECG digitizing and digital measuring process, and report comparability to manual measurements., Methods: A paper ECG was recorded from 7203 health survey participants in 1978-1980. With specific software, the ECGs were digitized (ECG Trace Tool), and measured digitally (EASE). A sub-sample of 100 ECGs was selected for manual measurements., Results: The measurement methods showed good agreement. The mean global (EASE)-(manual) differences were 1.4ms (95% CI 0.5-2.2) for PR interval, -1.0ms (95% CI -1.5-[-0.5]) for QRS duration, and 11.6ms (95% CI 10.5-12.7) for QT interval. The mean inter-method amplitude differences of RampV5, RampV6, SampV1, TampII and TampV5 ranged from -0.03mV to 0.01mV., Conclusions: The presented paper-to-digital conversion and digital measurement process is an accurate and reliable method, enabling efficient storing and analysis of paper ECGs., (Copyright © 2017 Elsevier Inc. All rights reserved.)
Published: 2018
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3. Disks back from the dead.

Author: Baker M
Subjects: Archives, Computer Storage Devices economics, Datasets as Topic history, History, 19th Century, History, 20th Century, History, 21st Century, Information Storage and Retrieval economics, Libraries, Paper, Picea, Research economics, Research history, Computer Storage Devices history, Information Storage and Retrieval methods, Software economics
Published: 2017
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4. Cost analysis for computer supported multiple-choice paper examinations.

Author: Mandel A, Hörnlein A, Ifland M, Lüneburg E, Deckert J, and Puppe F
Subjects: Costs and Cost Analysis, Efficiency, Germany, Humans, Word Processing economics, Education, Medical economics, Educational Measurement economics, Internet economics, Paper, Self-Evaluation Programs economics, Software economics
Abstract: Introduction: Multiple-choice-examinations are still fundamental for assessment in medical degree programs. In addition to content related research, the optimization of the technical procedure is an important question. Medical examiners face three options: paper-based examinations with or without computer support or completely electronic examinations. Critical aspects are the effort for formatting, the logistic effort during the actual examination, quality, promptness and effort of the correction, the time for making the documents available for inspection by the students, and the statistical analysis of the examination results., Methods: Since three semesters a computer program for input and formatting of MC-questions in medical and other paper-based examinations is used and continuously improved at Wuerzburg University. In the winter semester (WS) 2009/10 eleven, in the summer semester (SS) 2010 twelve and in WS 2010/11 thirteen medical examinations were accomplished with the program and automatically evaluated. For the last two semesters the remaining manual workload was recorded., Results: The cost of the formatting and the subsequent analysis including adjustments of the analysis of an average examination with about 140 participants and about 35 questions was 5-7 hours for exams without complications in the winter semester 2009/2010, about 2 hours in SS 2010 and about 1.5 hours in the winter semester 2010/11. Including exams with complications, the average time was about 3 hours per exam in SS 2010 and 2.67 hours for the WS 10/11., Discussion: For conventional multiple-choice exams the computer-based formatting and evaluation of paper-based exams offers a significant time reduction for lecturers in comparison with the manual correction of paper-based exams and compared to purely electronically conducted exams it needs a much simpler technological infrastructure and fewer staff during the exam.
Published: 2011
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5. ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files.

Author: Badilini F, Erdem T, Zareba W, and Moss AJ
Subjects: Artificial Intelligence, Database Management Systems, Humans, Pattern Recognition, Automated methods, Databases, Factual, Electrocardiography methods, Information Storage and Retrieval methods, Medical Records Systems, Computerized, Paper, Signal Processing, Computer-Assisted, Software
Abstract: Background: Measurements of parameters from electrocardiograms (ECGs) are still largely performed from paper ECG records. Recent guidelines from regulatory agencies and, in particular, the requirement of the Food and Drug Administration to enforce the digital submission of annotated ECGs have triggered significant efforts in the pharmaceutical industry, which, to comply with the new guidelines, is adopting digital ECG technology. At the same time, the new requirements justify the need for tools to convert existing paper ECG records into digital format, particularly for retrospective studies., Methods: This article presents ECGScan, a computer application developed for the conversion of paper ECG records to digital ECG files. An image processing engine is used to first detect the underlying grid and, subsequently, to extrapolate the ECG waveforms using a technique based on active contour modeling., Results: ECGScan was validated using a set of 60 ECGs for which both the original digital waveform and paper printouts were available. Sample-by-sample comparisons provided evidence of a robust wave reconstruction (root mean square value from 169 PQRST complexes was 16.8+/-11.8 microV). Semiautomatic measurements of QT intervals performed on 144 complexes also indicated a strong agreement between original and derived ECGs (DeltaQT=0.577+/-5.41 milliseconds)., Conclusions: ECGScan provides a robust reconstruction of a digital ECG, both in waveform reconstruction and in QT measurements performed on original (digital) ECGs and on digitized ECGs from paper printouts.
Published: 2005
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6. Electrocardiogram digitization: a practical perspective on the usefulness of a new tool to convert paper electrocardiograms into digital waveform.

Author: Baeza R
Subjects: Artificial Intelligence, Database Management Systems, Humans, Pattern Recognition, Automated methods, Databases, Factual, Electrocardiography methods, Information Storage and Retrieval methods, Medical Records Systems, Computerized, Paper, Signal Processing, Computer-Assisted, Software
Published: 2005
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7. Points to consider in electrocardiogram waveform extraction.

Author: Stockbridge N
Subjects: Artificial Intelligence, Database Management Systems, Humans, Pattern Recognition, Automated methods, Databases, Factual, Electrocardiography methods, Information Storage and Retrieval methods, Medical Records Systems, Computerized, Paper, Signal Processing, Computer-Assisted, Software
Published: 2005
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8. Structuring Clinical Guidelines through the Recognition of Deontic Operators.

Author: Georg G, Colombet I, and Jaulent MC
Subjects: Humans, Orthotic Devices, Paper, Practice Guidelines as Topic, Linguistics, Software
Abstract: In this paper, we present a novel approach to structure Clinical Guidelines through the automatic recognition of syntactic expressions called deontic operators. We defined a grammar and a set of Finite-State Transition Networks (FSTN) to automatically recognize deontic operators in Clinical Guidelines. We then implemented a dedicated FSTN parser that identifies deontic operators and marks up their occurrences in the document, thus producing a structured version of the Guideline. We evaluated our approach on a corpus (not used to define the grammar) of 5 Clinical Guidelines. As a result, 95.5% of the occurrences of deontic expressions are correctly marked up. The automatic detection of deontic operators can be a useful step to support Clinical Guidelines encoding.
Published: 2005

9. Analysis of neuro-behavioral Discovery data on the Macintosh computer.

Author: Akhavein R, Weiss C, Larson CR, and Disterhoft JF
Subjects: Analysis of Variance, Blinking physiology, Hippocampus cytology, Hippocampus physiology, Neurons physiology, Neurophysiology instrumentation, Paper, Printing, Conditioning, Psychological physiology, Microcomputers, Neurophysiology methods, Software
Abstract: This paper describes a suite of routines using IgorPro, a powerful analysis and graphing software package for the Macintosh computer, to enhance the ability to analyze, manipulate, and display data recorded with the Discovery acquisition software marketed by DataWave Technologies. The routines are able to time-align fast and slow data channels, and are especially useful for analyses that involve both neural and behavioral data. The software was designed for eyeblink conditioning and vocalization experiments, but it can easily be used for analyzing other types of neurobehavioral data. The data are first prepared on the PC with routines that inspect the header of the data file and translate the data file into a compact binary format that can be read by IgorPro. An option is also available to splice out data from unnecessary portions of an intertrial interval. The new file is then put on the Macintosh computer for display and analysis by IgorPro. These routines enable both neural and behavioral data to be quickly and easily reduced, manipulated, and statistically and graphically summarized.
Published: 1996
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10. Software Citation in the Digital Humanities

Author: Jettka, Daniel, Henny-Krahmer, Ulrike, Ferger, Anne, Alvares Freire, Fernanda, Scholger, Walter, Vogeler, Georg, Tasovac, Toma, Baillot, Anne, Raunig, Elisabeth, Scholger, Martina, Steiner, Elisabeth, Centre for Information Modelling, and Helling, Patrick
Subjects: Paper, and methods, Long Presentation, sustainable procedures, Informatics, citation, software, analysis and methods, annotation structures, sustainability, Humanities computing, software development, systems, RSE
Abstract: In contemporary humanities research, software plays a central role in the acquisition, enrichment, analysis, and publication of digital data. This paper aims at measuring the current practice of citation of software in DH conference abstracts and to foster the sustainable and scholarly use of software.
Published: 2023
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11. Collaboration and Professionalization. The role of software and Research Software Engineers in the Digital Humanities

Author: Czmiel, Alexander, Henny-Krahmer, Ulrike, Jettka, Daniel, Scholger, Walter, Vogeler, Georg, Tasovac, Toma, Baillot, Anne, Raunig, Elisabeth, Scholger, Martina, Steiner, Elisabeth, Centre for Information Modelling, and Helling, Patrick
Subjects: Paper, Informatics, software, analysis and methods, organization, Computer science, Humanities computing, professionalization, software development, systems, Poster, project design, management, software engineering
Abstract: The poster presents the results of a workshop to be held during the annual DHd conference in March 2023. The workshop addresses the question how to strengthen the role of software and research software engineers in DH and how openness and collaboration can lead to more professionalization.
Published: 2023
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12. EVALUATION OF VGC ANALYZER BY COMPARISON WITH GOLD STANDARD ROC SOFTWARE AND ANALYSIS OF SIMULATED VISUAL GRADING DATA

Author: Lars Gunnar Månsson, Jonny Hansson, and Magnus Båth
Subjects: Paper, Spectrum analyzer, Computer science, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Software, Resampling, Radiology, Nuclear Medicine and imaging, Paired Data, AcademicSubjects/SCI00180, Radiation, Radiological and Ultrasound Technology, Receiver operating characteristic, business.industry, Public Health, Environmental and Occupational Health, Pattern recognition, General Medicine, Gold standard (test), Rejection rate, Confidence interval, ROC Curve, 030220 oncology & carcinogenesis, Artificial intelligence, business
Abstract: The purpose of the present work was to evaluate the use of resampling statistical methods for analysis of visual grading data—implemented in the software VGC Analyzer—by comparing the reanalyzed results from previously performed visual grading studies with the results calculated by gold standard receiver operating characteristic (ROC) methodology, Obuchowski-Rockette (OR)-Dorfman–Berbaum–Metz (DBM) multiple-readers and multiple-case (MRMC) and by analysis of simulated visual grading data where the true distribution was presumed to be known. The reanalysis was performed on two multiple-reader studies with non-paired data and paired data, respectively. The simulation study was performed by simulating a large number of visual grading characteristics (VGC) studies and by analyzing the statistical distribution of null hypothesis (H0) rejection rate. The comparison with OR-DBM MRMC showed good agreement when analyzing non-paired data for both fixed-reader and random-reader settings for the calculated area under the curve values and the confidence intervals (CIs). For paired data analysis, VGC Analyzer showed significantly lower CIs compared with the ROC software. This effect was also illustrated by the simulation study, where the VGC Analyzer, in general, showed good accuracy for simulated studies with stable statistical basis. For simulated studies with unstable statistics, the accuracy in the H0 rejection rate decreased. The present study has shown that resampling methodology can be used to accurately perform the statistical analysis of a VGC study, although the resampling technique used makes the method sensitive to small data sets.
Published: 2021

13. Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

Author: Peshawa Jammal Muhammad Ali, Abdolreza Roshani, Maryam Ghasemi, Ehsan Nazemi, Farhad F. Nia, and Gholam Hossein Roshani
Subjects: Paper, Technology, Computer science, Science, Markov model, upgraded Markov model, Radial basis functions, Software, Multilayer perceptron, Scissors game, MATLAB, General Environmental Science, computer.programming_language, Graphical user interface, Computer. Automation, Artificial neural network, Markov chain, business.industry, Agriculture, Rock, General Earth and Planetary Sciences, Robot, business, Engineering sciences. Technology, computer, Algorithm
Abstract: In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.
Published: 2021

14. Design of tourism package with paper and the detection and recognition of surface defects – taking the paper package of red wine as an example

Author: Congrui Gao
Subjects: Surface (mathematics), Wine, Computer science, paper, Science, 020208 electrical & electronic engineering, 010401 analytical chemistry, detection, QA75.5-76.95, 02 engineering and technology, 01 natural sciences, surface defects, Manufacturing engineering, 0104 chemical sciences, Artificial Intelligence, tourism package, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, Software, Tourism, Information Systems
Abstract: In the tourism industry, the sales of local specialties is an important part, and the package design and integrity of the specialties are very important. This paper first introduced the support vector machine (SVM) algorithm that was used for detecting defects on the surface of paper packages. Then, the design of red wind packages was briefly described, and the simulation experiment was carried out on SVM algorithm using red wine packages with different degrees of surface defects. Proper parameters were tested using the k-fold cross-validation method. The results demonstrated that the properties of paper improved the value of packages and the SVM algorithm had better accuracy than artificial recognition in recognizing different degrees of defects on the surface of packages. In conclusion, this paper describes the application of paper in packages and provides an effective method for the defection of defects on the surface of packages. This study provides an effective references to the improvement of package values and the enhancement of package integrity.
Published: 2021

15. Likelihood Ratios for Deep Neural Networks in Face Comparison

Author: Andrea Macarulla Rodriguez, Zeno Geradts, Marcel Worring, Intelligent Sensory Information Systems (IVI, FNWI), and Operations Management (ABS, FEB)
Subjects: Paper, Computer science, Automated Facial Recognition, Kernel density estimation, ENFSI proficiency test, Sensitivity and Specificity, 01 natural sciences, Convolutional neural network, Pathology and Forensic Medicine, 03 medical and health sciences, 0302 clinical medicine, Software, Genetics, face verification likelihood ratio, Humans, 030216 legal & forensic medicine, Parametric statistics, Likelihood Functions, Commercial software, Models, Statistical, business.industry, Deep learning, Forensic Sciences, 010401 analytical chemistry, Nonparametric statistics, deep learning, Confusion matrix, Pattern recognition, 0104 chemical sciences, digital forensic science, Papers, Digital & Multimedia Sciences, Neural Networks, Computer, Artificial intelligence, business, face recognition
Abstract: In this study, we aim to compare the performance of systems and forensic facial comparison experts in terms of likelihood ratio computation to assess the potential of the machine to support the human expert in the courtroom. In forensics, transparency in the methods is essential. Consequently, state‐of‐the‐art free software was preferred over commercial software. Three different open‐source automated systems chosen for their availability and clarity were as follows: OpenFace, SeetaFace, and FaceNet; all three based on convolutional neural networks that return a distance (OpenFace, FaceNet) or similarity (SeetaFace). The returned distance or similarity is converted to a likelihood ratio using three different distribution fits: parametric fit Weibull distribution, nonparametric fit kernel density estimation, and isotonic regression with pool adjacent violators algorithm. The results show that with low‐quality frontal images, automated systems have better performance to detect nonmatches than investigators: 100% of precision and specificity in confusion matrix against 89% and 86% obtained by investigators, but with good quality images forensic experts have better results. The rank correlation between investigators and software is around 80%. We conclude that the software can assist in reporting officers as it can do faster and more reliable comparisons with full‐frontal images, which can help the forensic expert in casework.
Published: 2020

16. A paper-based whole-cell screening assay for directed evolution-driven enzyme engineering

Author: Xian Yang, Tadesse Fantaye Bogale, Ijaz Gul, Yong Chen, Ruiqin Fang, Hui Gao, Lixia Tang, and Juan Feng
Subjects: Paper, Hydrolases, Computer science, Halohydrin dehalogenase, Applied Microbiology and Biotechnology, Catalysis, 03 medical and health sciences, Digital image, Software, Escherichia coli, Instrumentation (computer programming), Gene Library, 030304 developmental biology, 0303 health sciences, 030306 microbiology, business.industry, Reproducibility of Results, Screening assay, General Medicine, Protein engineering, Paper based, Directed evolution, High-Throughput Screening Assays, Mutagenesis, Mutation, Colorimetry, Smartphone, Biochemical engineering, Directed Molecular Evolution, business, Biotechnology
Abstract: Directed evolution has become an important method to unleash the latent potential of enzymes to make them uniquely suited for human purposes. However, the need for a large reagent volume and sophisticated instrumentation hampers its broad implementation. In an attempt to address this problem, here we report a paper-based high-throughput screening approach that should find broad application in generating desired enzymes. As an example case, the dehalogenation reaction of the halohydrin dehalogenase was adopted for assay development. In addition to visual detection, quantitative measurements were performed by measuring the color intensity of an image that was photographed by a smartphone and processed using ImageJ free software. The proposed method was first validated using a gold standard method and then applied to mutagenesis library screening with reduced consumption of reagents (i.e., ≤ 10 μl per assay) and a shorter assay time. We identified two active mutants (P135A and G137A) with improved activities toward four tested substrates. The assay not only consumes less reagents but also eliminates the need for expensive instrumentation. The proposed method demonstrates the potential of paper-based whole-cell screening coupled with digital image colorimetry as a promising approach for the discovery of industrially important enzymes.Key Points• A frugal method was developed for directed enzyme evolution.• Mutagenesis libraries were successfully screened on a paper platform.• Smartphone imaging was efficiently used to measure enzyme activities.
Published: 2020

17. Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks

Author: J. Griffiths, Antonin Vacheret, Steven Kleinegesse, Robert A. Taylor, D. Saunders, Vacheret, A [0000-0001-7792-4349], and Apollo - University of Cambridge Repository
Subjects: Paper, Photomultiplier, Physics - Instrumentation and Detectors, Computer science, FOS: Physical sciences, Scintillator, 01 natural sciences, Convolutional neural network, 6Li F:ZnS(Ag), pulse shape discrimination, Silicon photomultiplier, Artificial Intelligence, 0103 physical sciences, convolutional neural networks, 010306 general physics, Continuous wavelet transform, Scintillation, Artificial neural network, 010308 nuclear & particles physics, business.industry, Detector, Li-6 F:ZnS(Ag), Pattern recognition, Instrumentation and Detectors (physics.ins-det), t-SNE, Human-Computer Interaction, machine learning, Artificial intelligence, business, Software
Abstract: We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised SiPM signals from a dual scintillator detector element made of 6LiF:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilsing a separate PMT was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and is found to provide superior levels of discrimination, achieving an AUC of 0.995 +/- 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods., 13 pages, 8 figures, submitted to Nucl. Instr. Meth. A
Published: 2022
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18. MCX Cloud—a modern, scalable, high-performance and in-browser Monte Carlo simulation platform with cloud computing

Author: Qianqian, Fang and Shijie, Yan
Subjects: Paper, Monte Carlo method, Biomaterials, optical imaging, Computers, Special Section Celebrating 30 Years of Open Source Monte Carlo Codes in Biomedical Optics, cloud computing, Biomedical Engineering, Computer Simulation, light transport, Software, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials
Abstract: Significance: Despite the ample progress made toward faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers to widespread use among the broad user community. Aim: An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization. Approach: We have developed a configuration-free, in-browser 3D MC simulation platform—Monte Carlo eXtreme (MCX) Cloud—built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas. Results: The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using MCX over a dynamically expandable virtual cluster. Conclusion: MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.
Published: 2022

19. Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model

Author: Babak Haghighat, Mohammadreza Noormandipour, Youran Sun, Noormandipour, Mohammadreza [0000-0001-9294-2035], and Apollo - University of Cambridge Repository
Subjects: High Energy Physics - Theory, Paper, Focus on Machine Learning for Quantum Physics, conformal blocks, Lattice (group), Boltzmann machine, FOS: Physical sciences, Topological quantum computer, Theoretical physics, Condensed Matter - Strongly Correlated Electrons, topological field theory, 46 Information and Computing Sciences, Artificial Intelligence, 4611 Machine Learning, Periodic boundary conditions, 4601 Applied Computing, Quantum computer, Physics, Kitaev honeycomb model, Strongly Correlated Electrons (cond-mat.str-el), Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Human-Computer Interaction, machine learning, High Energy Physics - Theory (hep-th), Excited state, Ising model, Ground state, Software, restricted Boltzmann machine
Abstract: In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate (GS) energy of the system is compared and, for small lattice sizes (e.g. $3 \times 3$ with $18$ spinors), shown to agree with the analytically derived value of the energy up to a deviation of $0.09\%$. Moreover, the wave-functions we find have $99.89\%$ overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build these anyonic excitations and braid them for possible future applications in quantum computation. Using the correspondence between topological field theories in (2+1)d and 2d CFTs, we propose an identification between our RBM states with the Moore-Read state and conformal blocks of the $2$d Ising model., Comment: (15+5) pages, 11 figures, 5 tables: Minor journal revisions and additions. + Journal ref
Published: 2022
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20. Atom cloud detection and segmentation using a deep neural network

Author: Milan Krstajić, Péter Juhász, Robert Smith, L. R. Hofer, A. L. Marchant, Hofer, Lucas R [0000-0002-5526-587X], Juhász, Péter [0000-0002-5187-730X], Marchant, Anna L [0000-0002-6350-4842], and Apollo - University of Cambridge Repository
Subjects: Physics, Paper, Artificial neural network, Cloud detection, Atom (order theory), object detection, Molecular physics, image processing, Human-Computer Interaction, machine learning, deep neural networks, 46 Information and Computing Sciences, Artificial Intelligence, instance segmentation, 4611 Machine Learning, Segmentation, ultracold quantum matter, 4601 Applied Computing, Software, Bayesian optimization
Abstract: Funder: Royal Society; doi: http://dx.doi.org/10.13039/501100000288, Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727, Funder: John Fell Fund, University of Oxford; doi: http://dx.doi.org/10.13039/501100004789, We use a deep neural network (NN) to detect and place region-of-interest (ROI) boxes around ultracold atom clouds in absorption and fluorescence images—with the ability to identify and bound multiple clouds within a single image. The NN also outputs segmentation masks that identify the size, shape and orientation of each cloud from which we extract the clouds’ Gaussian parameters. This allows 2D Gaussian fits to be reliably seeded thereby enabling fully automatic image processing. The method developed performs significantly better than a more conventional method based on a standardized image analysis library (Scikit-image) both for identifying ROI and extracting Gaussian parameters.
Published: 2021

21. Predicting polarizabilities of silicon clusters using local chemical environments

Author: Andrew P. Horsfield, Stefano Dal Forno, Mario G. Zauchner, Johannes Lischner, Gábor Cśanyi, Zauchner, Mario G [0000-0002-0901-5642], Dal Forno, Stefano [0000-0002-9869-7306], and Apollo - University of Cambridge Repository
Subjects: Paper, Silicon, chemistry.chemical_element, FOS: Physical sciences, 02 engineering and technology, 01 natural sciences, Cross-validation, machine learning polarizabilities, Artificial Intelligence, 0103 physical sciences, Cluster (physics), Physics::Atomic and Molecular Clusters, Limit (mathematics), Statistical physics, Physics::Atomic Physics, 010306 general physics, Scaling, Physics, Condensed Matter - Materials Science, Silicon clusters, RPA polarizabilities of silicon clusters, Materials Science (cond-mat.mtrl-sci), predicting polarizabilities of nanoparticles, 021001 nanoscience & nanotechnology, Human-Computer Interaction, chemistry, Cluster size, silicon cluster polarizabilities, 0210 nano-technology, Software
Abstract: Calculating polarizabilities of large clusters with first-principles techniques is challenging because of the unfavorable scaling of computational cost with cluster size. To address this challenge, we demonstrate that polarizabilities of large hydrogenated silicon clusters containing thousands of atoms can be efficiently calculated with machine learning methods. Specifically, we construct machine learning models based on the smooth overlap of atomic positions (SOAP) descriptor and train the models using a database of calculated random-phase approximation polarizabilities for clusters containing up to 110 silicon atoms. We first demonstrate the ability of the machine learning models to fit the data and then assess their ability to predict cluster polarizabilities using k-fold cross validation. Finally, we study the machine learning predictions for clusters that are too large for explicit first-principles calculations and find that they accurately describe the dependence of the polarizabilities on the ratio of hydrogen to silicon atoms and also predict a bulk limit that is in good agreement with previous studies.
Published: 2021
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22. The Paperless Music Classroom.

Author: Giebelhausen, Robin
Subjects: *PAPER, *MUSIC education, *WASTE paper, *TECHNOLOGY & society, *DIGITAL audio
Abstract: In an age where the world is becoming ever more aware of paper consumption, educators are turning toward technology to cut back on paper waste. Besides the environmental reasons, a paperless music classroom helps students develop their musicianship in new and exciting ways. This article will look at the considerations for setting up a paperless music classroom by examining hardware, software, and hybrid models for the music classroom. [ABSTRACT FROM AUTHOR]
Published: 2016
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23. Improving sample and feature selection with principal covariates regression

Author: Cersonsky, Rose K, Helfrecht, Benjamin A, Engel, Edgar A, Kliavinek, Sergei, Ceriotti, Michele, Cersonsky, Rose K [0000-0003-4515-3441], Helfrecht, Benjamin A [0000-0002-2260-7183], Engel, Edgar A [0000-0003-2944-9445], Kliavinek, Sergei [0000-0001-8326-325X], Ceriotti, Michele [0000-0003-2571-2832], and Apollo - University of Cambridge Repository
Subjects: Sample selection, Paper, semi-supervised learning, farthest point sampling, materials science, input, Feature selection, Sample (statistics), 02 engineering and technology, Semi-supervised learning, sample selection, 01 natural sciences, feature selection, 46 Information and Computing Sciences, rank-one modification, Artificial Intelligence, 4611 Machine Learning, 0103 physical sciences, Statistics, Singular value decomposition, Covariate, physical chemistry, 010306 general physics, Principal (computer security), singular value decomposition, 021001 nanoscience & nanotechnology, Regression, Human-Computer Interaction, machine learning, networks, Generic health relevance, 0210 nano-technology, Software
Abstract: Funder: Trinity College, University of Cambridge; doi: http://dx.doi.org/10.13039/501100000727, Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.
Published: 2021

24. Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

Author: Vincent A. Magnotta, Samuel H. Forbes, John P. Spencer, Adam T. Eggebrecht, and Sobanawartiny Wijeakumar
Subjects: Paper, Channel (digital image), Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Neuroscience (miscellaneous), Image processing, Iterative reconstruction, functional near infra-red spectroscopy, 01 natural sciences, Data modeling, 010309 optics, 03 medical and health sciences, 0302 clinical medicine, Software, 0103 physical sciences, Radiology, Nuclear Medicine and imaging, Image restoration, Radiological and Ultrasound Technology, business.industry, Anatomy, Image segmentation, image reconstruction, Research Papers, Pipeline (software), Radiology Nuclear Medicine and imaging, diffuse optical tomography, individual anatomy, business, 030217 neurology & neurosurgery
Abstract: Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy. Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space. Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis. Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.
Published: 2021

25. Validating TrueAllele® Interpretation of DNA Mixtures Containing up to Ten Unknown Contributors*

Author: Mark W. Perlin, Nasir Butt, David W Bauer, and Jennifer M Hornyak
Subjects: Paper, Validation study, likelihood ratio, low‐template DNA, Dna evidence, Genotype, forensic science, Bayesian probability, Bayesian analysis, Real-Time Polymerase Chain Reaction, Sensitivity and Specificity, probabilistic genotyping, Pathology and Forensic Medicine, Interpretation (model theory), genotype separation, chemistry.chemical_compound, genotype deconvolution, Statistics, Genetics, Humans, Low template dna, Reliability (statistics), Alleles, Mathematics, DNA mixture, Likelihood Functions, Models, Genetic, Reproducibility of Results, DNA, DNA Fingerprinting, chemistry, validation study, Papers, TrueAllele® system, Criminalistics, Software, Microsatellite Repeats
Abstract: Most DNA evidence is a mixture of two or more people. Cybergenetics TrueAllele® system uses Bayesian computing to separate genotypes from mixture data and compare genotypes to calculate likelihood ratio (LR) match statistics. This validation study examined the reliability of TrueAllele computing on laboratory‐generated DNA mixtures containing up to ten unknown contributors. Using log(LR) match information, the study measured sensitivity, specificity, and reproducibility. These reliability metrics were assessed under different conditions, including varying the number of assumed contributors, statistical sampling duration, and setting known genotypes. The main determiner of match information and variability was how much DNA a person contributed to a mixture. Observed contributor number based on data peaks gave better results than the number known from experimental design. The study found that TrueAllele is a reliable method for analyzing DNA mixtures containing up to ten unknown contributors.
Published: 2019

26. Application of molecular simulation in transformer oil–paper insulation

Author: Xiao Xia, Li Linduo, Zhong Tingting, Wenyan Yang, and Xiaolin Zhang
Subjects: ComputingMethodologies_SIMULATIONANDMODELING, Computer science, Transformer oil, power transformer insulation, plant insulating oil, Electrical insulation paper, Energy Engineering and Power Technology, Mechanical engineering, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Molecular simulation, 010402 general chemistry, 01 natural sciences, law.invention, law, Insulation system, 0103 physical sciences, Transformer, Research method, transformer oil, insulating paper, molecular simulation technology, 010304 chemical physics, paper, General Engineering, transformer insulating oil, 0104 chemical sciences, computer technology, nanomodified insulating oil, lcsh:TA1-2040, transformer oil–paper insulation, microscopic models, lcsh:Engineering (General). Civil engineering (General), Software, Computer technology
Abstract: Molecular simulation technology is a new technology emerging with the rapid development of computer technology. It has the advantages of clear microscopic models, in-depth and precise calculation etc., it has been widely used in the oil–paper insulation system of transformer in recent years. Based on the application of molecular simulation technology in the oil–paper insulation system of transformers in this study, the current research status and progress of molecular simulation in these area are elaborated in detail from the aspects of transformer insulating oil and insulating paper according to the domestic and foreign literature, and the application of molecular simulation technology in plant insulating oil and nano-modified insulating oil is emphatically analysed. The complementing research method of molecular simulation and experiment is pointed out, which provides certain reference for solving many problems existing in the field of oil–paper insulation system of transformers.
Published: 2018

27. Atomic Permutationally Invariant Polynomials for Fitting Molecular Force Fields

Author: Allen, Alice E A, Dusson, Genevi��ve, Ortner, Christoph, Cs��nyi, G��bor, Department of Engineering [Cambridge], University of Cambridge [UK] (CAM), Laboratoire de Mathématiques de Besançon (UMR 6623) (LMB), Université de Bourgogne (UB)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), Leverhulme Trust under Grant RPG-2017-191, ANR-15-IDEX-0003,BFC,ISITE ' BFC(2015), Allen, Alice E A [0000-0002-8727-8333], Dusson, Geneviève [0000-0002-7160-6064], Ortner, Christoph [0000-0003-1498-8120], Csányi, Gábor [0000-0002-8180-2034], and Apollo - University of Cambridge Repository
Subjects: Paper, Computer science, Extrapolation, FOS: Physical sciences, 010402 general chemistry, 01 natural sciences, Force field (chemistry), Data-driven, Organic molecules, molecular potentials, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], symbols.namesake, Artificial Intelligence, Physics - Chemical Physics, 0103 physical sciences, Statistical physics, force field parametrization, Chemical Physics (physics.chem-ph), Training set, 010304 chemical physics, Computational Physics (physics.comp-ph), Invariant (physics), molecular mechanics, 0104 chemical sciences, Human-Computer Interaction, small molecules, Mach number, symbols, [PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph], Physics - Computational Physics, Software, Curse of dimensionality
Abstract: We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high degree of transferability on the one hand, and the machine learned potentials that are systematically improvable and can converge to very high accuracy, on the other. Our framework extends the atomic permutationally invariant polynomials (aPIP) developed for elemental materials in (2019 Mach. Learn.: Sci. Technol. 1 015004) to molecular systems. The body order decomposition allows us to keep the dimensionality of each term low, while the use of an iterative fitting scheme as well as regularisation procedures improve the extrapolation outside the training set. We investigate aPIP force fields with up to generalised 4-body terms, and examine the performance on a set of small organic molecules. We achieve a high level of accuracy when fitting individual molecules, comparable to those of the many-body machine learned force fields. Fitted to a combined training set of short linear alkanes, the accuracy of the aPIP force field still significantly exceeds what can be expected from classical empirical force fields, while retaining reasonable transferability to both configurations far from the training set and to new molecules.
Published: 2021

28. The ATLAS fast TracKer system

Author: Aad, Georges, Abbott, Braden Keim, Abreu, Henso, Arcangeletti, Chiara, Gutierrez Zagazeta, Luis Felipe, Gutschow, Christian, Guyot, Claude, Gwenlan, Claire, Gwilliam, Carl, Haaland, Even Simonsen, Haas, Andy, Haas, Andrew, Haber, Carl, Hadavand, Haleh, Arce, Ayana Tamu, Hadef, Asma, Haleem, Mahsana, Haley, Joseph, Hall, Jack Joseph, Halladjian, Garabed, Hallewell, Gregory, Hamano, Kenji, Hamdaoui, Hassane, Hamer, Matthias, Hamity, Guillermo Nicolas, Arguin, Jean-francois, Han, Kunlin, Han, Liangliang, Han, Liang, Han, Shuo, Han, Yi Fei, Hanagaki, Kazunori, Hance, Mike, Hank, Michael Donald, Hankache, Robert, Hansen, Eva Brottmann, Argyropoulos, Spyros, Hansen, Jorgen Beck, Hansen, Dines, Hansen, Maike Christina, Hansen, Peter, Hanson, Emily Claire, Hara, Kazuhiko, Harenberg, Torsten, Harkusha, Siarhei, Harrison, Paul Fraser, Hartman, Nicole Michelle, Arling, Jan-hendrik, Hartmann, Nikolai, Hasegawa, Yoji, Hasib, Ahmed, Hasley, Lloyd Alan, Hassani, Samira, Haug, Sigve, Hauser, Reiner, Havranek, Miroslav, Hawkes, Chris, Hawkings, Richard, Armbruster, Aaron James, Hayashida, Shota, Hayden, Daniel, Hayes, Christopher Robyn, Hayes, Robin, Hays, Chris, Hays, Jonathan, Hayward, Helen, Haywood, Stephen, He, Fudong, He, Yunjian, Armstrong, Alexander, Heath, Matthew Peter, Hedberg, Vincent, Heggelund, Andreas Lokken, Hehir, Natasha, Heidegger, Constantin, Heidegger, Kim Katrin, Heidorn, William Dale, Heilman, Jesse Alan, Heim, Sarah, Heim, Timon, Arnaez, Olivier, Heinemann, Beate, Heinlein, James Geddy, Heinrich, Jochen Jens, Heinrich, Lukas Alexander, Hejbal, Jiri, Helary, Louis, Held, Alexander, Hellesund, Simen, Helling, Cole Michael, Hellman, Sten, Arnold, Hannah, Helsens, Clement, Henderson, Robert, Henkelmann, Lars, Henriques Correia, Ana Maria, Herde, Hannah Elizabeth, Hernandez Jimenez, Yesenia, Herr, Holger Arnold, Herrmann, Maximilian Georg, Herrmann, Tim, Herten, Gregor, Arrubarrena Tame, Zulit Paola, Hertenberger, Ralf, Hervas, Luis, Hessey, Nigel, Hibi, Hiroaki, Higashino, Satoshi, Higon-rodriguez, Emilio, Hildebrand, Kevin, Hill, John, Hill, Kurt Keys, Hiller, Karl Heinz, Abulaiti, Yiming, Artoni, Giacomo, Hu, Yi Fan, Hiller, Karlheinz, Hillier, Stephen, Hils, Maximilian, Hinchliffe, Ian, Hinterkeuser, Florian, Hirose, Minoru, Hirose, Shigeki, Hirschbuehl, Dominic, Hiti, Bojan, Asada, Haruka, Hladik, Ondrej, Hobbs, John David, Hobincu, Radu, Tal Hod, Noam, Hodgkinson, Mark, Hoecker, Andreas, Hohn, David, Hohov, Dmytro, Holm, Tanja, Holmes, Tova, Asai, Kanae, Holzbock, Michael, Hommels, Bart, Hong, Tae Min, Honig, Jan Cedric, Honle, Andreas, Hooberman, Benjamin Henry, Hopkins, Walter, Horii, Yasuyuki, Horn, Philipp, Horyn, Lesya Anna, Asai, Shoji, Hou, Suen, Howarth, James William, Hoya, Joaquin, Hrabovsky, Miroslav, Hrynevich, Aliaksei, Hrynova, Tetiana, Hsu, Pai-hsien, Hsu, Shih-chieh, Hu, Qipeng, Hu, Shuyang, Asawatavonvanich, Thanawat, Iakovidis, Georgios, Hu, Yifan, Huang, Danping, Huang, Xiaozhong, Huang, Yicong, Huang, Yanping, Hubacek, Zdenek, Hubaut, Fabrice, Hubner, Michael, Huegging, Fabian, Asbah, Nedaa Alexandra, Huffman, Todd Brian, Huhtinen, Mika, Hulsken, Raphael, Hunter, Robert Francis, Husejko, Michal, Huseynov, Nazim, Huston, Joey, Huth, John, Hyneman, Rachel Jordan, Hyrych, Sofiia, Asimakopoulou, Eleni Myrto, Iacobucci, Giuseppe, Iakovidis, George, Ibragimov, Iskander, Iconomidou-fayard, Lydia, Iengo, Paolo, Ignazzi, Rosanna, Iguchi, Ryunosuke, Iizawa, Tomoya, Ikegami, Yoichi, Ilic, Nikolina, Asquith, Lily, Imam, Hajar, Introzzi, Gianluca, Iodice, Mauro, Iordanidou, Kalliopi, Ippolito, Valerio, Isacson, Max Fredrik, Ishino, Masaya, Islam, Wasikul, Issever, Cigdem, Istin, Serhat, Assahsah, Jihad, Ito, Hiroki, Iturbe, Julia, Iuppa, Roberto, Ivina, Anna, Izen, Joseph Michael, Izzo, Vincenzo, Jacka, Petr, Jackson, Paul, Jacobs, Ruth Magdalena, Jaeger, Benjamin Paul, Assamagan, Ketevi Adikle, Jakel, Gunnar, Jakobi, Katharina Bianca, Jakobs, Karl, Jakoubek, Tomas, Jamieson, Jonathan, Janas, Krzysztof, Jansky, Roland, Janus, Piotr Andrzej, Jarlskog, Goran, Jaspan, Adam Elliott, Acharya, Bobby Samir, Astalos, Robert, Javadov, Namig, Javurek, Tomas, Javurkova, Martina, Jeanneau, Fabien, Jeanty, Laura, Jejelava, Juansher, Jenni, Peter, Jezequel, Stephane, Jia, Jiangyong, Jia, Zihang, Atkin, Ryan Justin, Jiang, Yi, Jiggins, Stephen, Jimenez Morales, Fabricio Andres, Jimenez Pena, Javier, Jin, Shan, Jinaru, Adam, Jinnouchi, Osamu, Jivan, Harshna, Johansson, Per Daniel Conny, Johns, Kenneth, Atkinson, Markus Julian, Johnson, Christian, Jones, Eleanor, Jones, Roger, Jones, Samuel David, Jones, Tim, Joos, Markus, Jovicevic, Jelena, Ju, Xiangyang, Junggeburth, Johannes Josef, Juste Rozas, Aurelio, Atlay, Naim Bora, Kaczmarska, Anna, Kado, Marumi, Kagan, Harris, Kagan, Michael Aaron, Kahn, Alan Mathew, Kahra, Christian, Kaji, Toshiaki, Kajomovitz Must, Enrique, Kalderon, Charles William, Kartvelishvili, Vakhtang, Atmani, Hicham, Kalderon, Will, Kaluza, Adam, Kamenshchikov, Andrey, Kaneda, Michiru, Kang, Nathan Jihoon, Kang, Shuaiyan, Kano, Yuya, Kanzaki, Junichi, Kar, Deepak, Karava, Kla, Atmasiddha, Prachi, Kareem, Mohammad, Karkanias, Ioannis, Karpov, Sergey, Karpova, Zoya, Kaya, Fikriye Idil, Kartvelishvili, Vato, Karyukhin, Andrei, Kasimi, Eirini, Kastanas, Alex, Kasten, Michael Scott, Augsten, Kamil, Kato, Chikuma, Katzy, Judith, Kawade, Kentaro, Kawagoe, Kiyotomo, Kawaguchi, Tomomi, Kawaguchi, Yoshimasa, Kawamoto, Tatsuo, Kawamura, Gen, Kay, Ellis, Kaya, Colette, Austrup, Volker Andreas, Kazakos, Stergios, Kazanin, Vassili, Keaveney, James Michael, Keeler, Richard, Keller, John Stakely, Kelsey, Daniel Christopher, Kempster, Jacob Julian, Kendrick, James Andrew, Kennedy, Kiley Elizabeth, Kepka, Oldrich, Avolio, Giuseppe, Kersten, Susanne, Kersevan, Borut Paul, Ketabchi, Sana, Khalil-zada, Farhad, Khandoga, Mykola, Khanov, Alexander, Kharlamov, Alexey, Kharlamova, Tatyana, Khoda, Elham E, Khoo, Teng Jian, Ayoub, Mohamad Kassem, Khoriauli, Gia, Khramov, Evgeny, Khubua, Djemal, Kido, Shogo, Kiehn, Moritz, Kilgallon, Aaron Joseph, Kim, Eunchong, Kim, Young-kee, Kimura, Naoki, Kirchhoff, Andreas, Achkar, Baida, Azuelos, Georges, Kirchmeier, David, Kirk, Julie, Kiryunin, Andrei, Kishimoto, Tomoe, Kisliuk, Dylan Perry, Kitali, Vincent, Kitsaki, Chara, Kivernyk, Oleh, Klapdor-kleingrothaus, Thorwald, Klassen, Martin, Babal, Dominik, Klein, Christoph Thomas, Klein, Lucas, Klein, Matthew Henry, Klein, Max, Klein, Uta, Klimek, Pawel Jan, Klimentov, Alexei, Klimpel, Fabian, Klingl, Tobias, Klioutchnikova, Tatiana, Bachacou, Henri, Klitzner, Felix Fidelio, Kluit, Peter, Kluth, Stefan, Kneringer, Emmerich, Knoops, Edith, Knue, Andrea Helen, Kobayashi, Dai, Kobel, Michael, Kocian, Martin, Kodama, Takafumi, Bachas, Dinos, Kodys, Peter, Kock, Daniela, Konig, Philipp, Koffas, Thomas, Koehler, Nicolas, Kolb, Mathis, Koletsou, Iro, Komarek, Tomas, Koeneke, Karsten, Kong, Albert, Backman, Karl Filip, Kono, Takanori, Konstantinides, Vasilis, Konstantinidis, Nikolaos, Konya, Balazs, Kopeliansky, Revital, Koperny, Stefan Zenon, Korcyl, Krzysztof Marian, Kordas, Konstantinos, Kowalewski, Robert Victor, Kordas, Kostas, Bagnaia, Paolo, Koren, Guy, Korn, Andreas, Korolkov, Ilya, Korolkova, Elena, Korotkova, Natalia, Kortner, Oliver, Kortner, Sandra, Kostyukhin, Vadim, Kotsokechagia, Anastasia, Kotwal, Ashutosh, Bahrasemani, Sina, Koulouris, Aimilianos, Kourkoumeli-charalampidi, Athina, Kourkoumelis, Christine, Kourlitis, Evangelos, Kowalewski, Bob, Kozanecki, Witold, Kozhin, Anatoli, Kramarenko, Viktor, Kramberger, Gregor, Krasnopevtsev, Dimitrii, Bailey, Adam, Krasny, Mieczyslaw, Krasznahorkay, Attila, Kremer, Jakub, Kretzschmar, Jan, Kreul, Ken Matthias, Krieger, Peter, Krieter, Ferdinand, Krishnamurthy, Samyukta, Krishnan, Anjali, Krivos, Martin, Bailey, Virginia, Krizka, Karol, Kroeninger, Kevin Alexander, Kroha, Hubert, Kroll, Jiri, Kroll, Joe, Kulchitsky, Yuri, Kuleshov, Sergey, Kroll, Joseph Ira, Krowpman, Kyle Stuart, Kruchonak, Uladzimir, Baines, John, Krueger, Hans, Krumnack, Nils Erik, Kruse, Mark, Krzysiak, Janina Anna, Kubota, Arisa, Kuchinskaia, Olesia, Kuday, Sinan, Kuchler, Daniela, Kuechler, Jan, Kuehn, Susanne, Adam, Lennart, Bakalis, Christos, Kuhl, Thorsten, Kukhtin, Victor, Koultchitski, Yuri, Kuleshov, Serguei, Kulinich, Yakov Petrovich, Kumar, Mukesh, Kuna, Marine, Kupco, Alexander, Kupfer, Tobias, Kuprash, Oleg, Baker, Keith, Kurashige, Hisaya, Kurchaninov, Leonid, Kurochkin, Yurii, Kurova, Anastasia, Kurth, Matthew Glenn, Kuwertz, Emma Sian, Kuze, Masahiro, Kvam, Audrey Katherine, Kvita, Jiri, Kwan, Tony, Bakker, Pepijn Johannes, Lacasta Llacer, Carlos, Lacava, Francesco, Lack, David Philip John, Lacker, Heiko Markus, Lacour, Didier, Ladygin, Evgueni, Lafaye, Remi, Laforge, Bertrand, Lagouri, Theodota, Lai, Stanley, Bakos, Evelin, Lai, Stan, Lakomiec, Inga Katarzyna, Lambert, Joseph Earl, Lammers, Sabine Wedam, Lampl, Walter, Lampoudis, Christos, Lancon, Eric Christian, Landgraf, Ulrich, Landon, Murrough, Lang, Valerie, Bakshi Gupta, Debottam, Lange, Joern, Langenberg, Robert Johannes, Lankford, Andrew James, Lanni, Francesco, Lantzsch, Kerstin, Lanza, Agostino, Lapertosa, Alessandro, Laporte, Jean-francois, Lari, Tommaso, Lasagni Manghi, Federico, Balaji, Shyam, Lassnig, Mario, Latonova, Vera, Lau, Tak Shun, Laudrain, Antoine, Laurier, Alexandre, Lavorgna, Marco, Lawlor, Sean Dean, Lazzaroni, Massimo, Le, Brian, Le Guirriec, Emmanuel, Balasubramanian, Rahul, Lebedev, Alexandre, Leblanc, Matthew Edgar, LeCompte, Thomas, Leblanc, Matt, Le Compte, Tom, Ledroit, Fabienne, Lee, Ava Chloe Audrey, Lee, Claire, Lee, Graham Richard, Lee Jr, Lawrence, Baldin, Evgenii, Lee, Shih-chang, Lee, Songkyo, Lefebvre, Benoit, Lefebvre, Helena, Lefebvre, Michel, Leggett, Charles, Lehmann, Konstantin, Lehmann, Niklaus, Lehmann Miotto, Giovanna, Leight, William Axel, Balek, Petr, Leisos, Antonios, Lisboa Leite, Marco, Leitgeb, Clara Elisabeth, Leitner, Rupert, Leney, Katharine, Lenz, Tatjana, Leone, Sandra, Leonidopoulos, Christos, Leopold, Alexander, Leroy, Claude, Balli, Fabrice, Les, Robert, Lester, Christopher, Levchenko, Mikhail, Leveque, Jessica, Levin, Daniel, Levin, Dan, Levinson, Lorne, Lewis, Daniel James, Li, Boyang, Li, Bing, Adam Bourdarios, Claire, Balunas, William Keaton, Li, Changqiao, Li, Fan, Li, Heng, Li, Haifeng, Li, Jing, Li, Ke, Li, Liang, Li, Mengran, Li, Quanyin, Li, Shu, Balz, Johannes, Li, Xingguo, Li, Yichen, Li, Zhi, Li, Zhiying, Li, Zhelun, Li, Zhiyuan, Liang, Zhijun, Liberali, Valentino, Liberatore, Marianna, Liberti, Barbara, Banas, Elzbieta, Lie, Ki, Lim, Sanghoon, Lin, Chiao-ying, Lin, Kuan-yu, Linck, Rebecca, Lindley, Rachel Elizabeth, Lindon, Jack, Linss, Arthur, Lionti, Anthony, Lipeles, Elliot, Bandieramonte, Marilena, Lipniacka, Anna, Lisovyi, Mykhailo, Liss, Tony, Liss, Anthony Michael, Lister, Alison, Little, Jared, Liu, Bo, Liu, Bingxuan, Liu, Jianbei, Liu, Jesse, Bandyopadhyay, Anjishnu, Liu, Kun, Liu, Minghui, Liu, Mingyi, Liu, Peilian, Liu, Xiaotian, Liu, Yi, Liu, Yang, Liu, Yanlin, Liu, Yanwen, Livan, Michele, Barak, Liron, Lleres, Annick, Llorente Merino, Javier, Lloyd, Stephen, Lloyd, Steve, Lobodzinska, Ewelina Maria, Loch, Peter, Loffredo, Salvatore, Lohse, Thomas, Lohwasser, Kristin, Lokajicek, Milos, Barbe, William Mickael, Long, Jonathan, Long, Robin Eamonn, Longarini, Iacopo, Longo, Luigi, Longo, Riccardo, Lopez Paz, Ivan, Lopez Solis, Alvaro, Lorenz, Jeanette Miriam, Lorenzo Martinez, Narei, Lory, Alexander, Barberio, Elisabetta, Loesle, Alena, Lou, Xuanhong, Lou, Xinchou, Lounis, Abdenour, Love, Jeremy Robert, Love, Peter, Lozano Bahilo, Julio, Lu, Miaoran, Lu, Sicong, Lu, Yun-ju, Barberis, Dario, Lubatti, Henry, Luci, Claudio, Lucio Alves, Fabio Lucio, Lucotte, Arnaud, Luehring, Fred, Luise, Ilaria, Luminari, Lamberto, Lund-jensen, Bengt, Luongo, Nicholas, Lutz, Margaret Susan, Barbero, Marlon Benoit, Lynn, David, Lyons, Harry John, Lysak, Roman, Lytken, Else, Lyu, Feng, Lyubushkin, Vladimir, Lyubushkina, Tatiana, Ma, Hong, Ma, Lianliang, Ma, Yanhui, Adamczyk, Leszek, Barbour, Gregory, Macdonell, Danika Marina, Maccarrone, Giovanni, Macdonald, Calum Michael, Macdonald, Jack, Machado Miguens, Joana, Madar, Romain, Mader, Wolfgang, Madugoda Ralalage Don, Madhuranga, Madysa, Nico, Maeda, Junpei, Barillari, Teresa, Maeno, Tadashi, Maerker, Max, Magerl, Veronika, Magro, Jacopo, Mahon, Devin, Maidantchik, Carmen, Maio, Amelia, Maj, Klaudia, Majersky, Oliver, Majewski, Stephanie, Barisits, Martin, Makovec, Nikola, Malaescu, Bogdan, Malecki, Pawel, Maleev, Victor, Malek, Fairouz, Malito, Davide, Mallik, Usha, Malone, Claire, Maltezos, Stavros, Malyukov, Sergey, Barkeloo, Jason Tyler Colt, Mamuzic, Judita, Mancini, Giada, Mandalia, Jesal, Mandic, Igor, Manhaes De Andrade Filho, Luciano, Maniatis, Ioannis Michail, Manjarres, Joany, Mankinen, Katja Hannele, Mann, Alexander, Manousos, Athanasios, Barklow, Tim, McCarthy, Tom, McDonald, Emily, Manousos, Thanos, Mansoulie, Bruno, Manthos, Ioannis, Manzoni, Stefano, Marantis, Alexandros, Marchese, Luigi, Marchiori, Giovanni, Marcisovsky, Michal, Barnett, Bruce M, Marcoccia, Lorenzo, Marcon, Caterina, Marjanovic, Marija, Marshall, Zach, Martensson, Mikael, Marti I Garcia, Salvador, Martin, Tim, Martin, Victoria, Martin Dit Latour, Bertrand, Martinelli, Luca, Barnett, Michael, Martinez-perez, Mario, Martinez Agullo, Pablo, Martinez Outschoorn, Verena Ingrid, Martin-haugh, Stewart, Martoiu, Sorin, Martyniuk, Alex Christopher, Marzin, Antoine, Maschek, Stefan Raimund, Masetti, Lucia, Mashimo, Tetsuro, Blenessy, Zuzana, Mashinistov, Ruslan, Masik, Jiri, Maslennikov, Alexei, Massa, Lorenzo, Massarotti, Paolo, Mastrandrea, Paolo, Mastroberardino, Anna, Masubuchi, Tatsuya, Matakias, Dimitrios, Mathisen, Thomas, Baroncelli, Toni, Matic, Andrea, Matsuzawa, Nobuo, Maurer, Julien, Macek, Bostjan, Maximov, Dmitriy, Mazini, Rachid, Maznas, Ioannis, Mazza, Simone Michele, Mc Ginn, Christopher, Mc Gowan, John Patrick, Barone, Gaetano, Mc Kee, Shawn, Mcfayden, Joshua Angus, McPherson, Robert, Mikenberg, Giora, Mccarthy, Thomas, Mccormack, William Patrick, Milke, Christopher Don, Mina, Robert Arthur, Mcdonald, Millie, Mcdougall, Ashley Ellen, Adamek, Lukas, Barr, Alan, Mcfayden, Josh, Mchedlidze, Gvantsa, Mc Kay, Maddie, Mclean, Kayla, Mcmahon, Stephen, Mcnamara, Peter Charles, Mcnicol, Christopher John, Mcpherson, Rob, Mdhluli, Joyful Elma, Meadows, Zachary Alden, Barranco Navarro, Laura, Meehan, Samuel Ross, Megy, Theo, Mehlhase, Sascha, Mehta, Andrew, Meirose, Bernhard, Melini, Davide, Mellado Garcia, Bruce, Meloni, Federico, Melzer, Alexander, Gouveia, Emanuel, Barreiro Alonso, Fernando, Mendes Jacques Da Costa, Antonio Manuel, Meng, Huan Yu, Meng, Lingxin, Menke, Sven, Meoni, Evelin, Mergelmeyer, Sebastian, Merkt, Sebastian Andreas, Merlassino, Claudia, Mermod, Philippe, Merola, Leonardo, Barreiro Guimaraes Da Costa, Joao, Meroni, Chiara, Merz, Garrett William, Meshkov, Oleg, Meshreki, John Kamal Rizk, Metcalfe, Jessica, Mete, Alaettin Serhan, Meyer, Christopher John, Meyer, Jean-pierre, Michetti, Michele, Middleton, Robin, Barron, Uriel, Mijovic, Liza, Mikenberg, George, Mikestikova, Marcela, Mikuz, Marko, Mildner, Hannes, Milic, Adriana, Milke, Chris, Miller, David, Miller, Laura Stephanie, Milov, Alexander, Barsov, Sergey, Milstead, David Anthony, Mina, Rob, Minaenko, Andrei, Minashvili, Irakli, Mince, Laurynas, Mincer, Allen Irving, Mindur, Bartosz, Mineev, Mikhail, Minegishi, Yuji, Mino, Yuya, Bartels, Falk, Mir Martinez, Lluisa Maria, Mironova, Maria, Mitani, Takashi, Mitrevski, Jovan, Mitsou, Vasiliki, Mittal, Monika, Miu, Ovidiu, Miucci, Antonio, Miyagawa, Paul, Mizukami, Atsushi, Bartoldus, Rainer, Mjoernmark, Jan-ulf, Mkrtchyan, Tigran, Mlynarikova, Michaela, Moa, Torbjorn, Mobius, Silke, Mochizuki, Kazuya, Moder, Paul, Mogg, Philipp, Mohapatra, Soumya, Mokgatitswane, Gaogalalwe, Bartolini, Giovanni, Momiyama, Keisuke, Mondal, Buddhadeb, Mondal, Santu, Monig, Klaus, Monnier, Emmanuel, Montalbano, Alyssa Rae, Montejo Berlingen, Javier, Montella, Marco, Monticelli, Fernando, Morange, Nicolas, Barton, Adam Edward, Moreira De Carvalho, Ana Luisa, Moreno Llacer, Maria, Moreno Martinez, Carlos, Morettini, Paolo, Morgenstern, Marcus Matthias, Morgenstern, Stefanie, Mori, Daniel, Morii, Masahiro, Morinaga, Masahiro, Morisbak, Vanja, Adelman, Jahred, Bartos, Pavol, Morley, Anthony, Mornacchi, Giuseppe, Morris, Alice, Morvaj, Ljiljana, Moschovakos, Paraschos, Moss, Josh, Nachman, Benjamin Philip, Moschovakos, Paris, Moser, Brian, Mosidze, Maia, Basalaev, Artem, Moskalets, Tetiana, Moskvitina, Polina, Naryshkin, Iouri, Moss, Joshua, Moyse, Edward, Muanza, Steve, Mueller, James Alfred, Muenstermann, Daniel, Mullier, Geoffrey, Mullin, Joseph, Basan, Alexander, Mungo, Davide Pietro, Munoz Martinez, Jose Luis, Munoz Sanchez, Francisca, Murin, Pavel, Murray, Bill, Murrone, Alessia, Muse, Joseph M, Muskinja, Miha, Mwewa, Chilufya, Myagkov, Alexei, Bassalat, Ahmed, Myers, Ava Anne, Myers, Greg, Myers, John, Myska, Miroslav, Neep, Thomas James, Ng, Yan Wing, Nachman, Ben, Nackenhorst, Olaf, Nag, Abhishek, Nagai, Koichi, Basso, Matthew Joseph, Nagano, Kunihiro, Nagle, James Lawrence, Nagy, Elemer, Nairz, Armin, Higuchi, Yu Nakahama, Nakamura, Koji, Nanjo, Hajime, Napolitano, Fabrizio, Naranjo, Roger, Narayan, Rohin Thampilali, Basson, Candice Ruth, Naryshkin, Iurii, Naseri, Mohsen, Naumann, Thomas, Navarro, Gabriela Alejandra, Navarro Gonzalez, Josep, Nechaeva, Polina, Nechansky, Filip, Neep, Tom, Negri, Andrea, Negrini, Matteo, Bates, Richard, Nellist, Clara, Nelson, Christina, Nelson, Michael Edward, Nemecek, Stanislav, Nessi, Marzio, Neubauer, Mark, Neuhaus, Friedemann, Neumann, Manuel, Newhouse, Robin, Newman, Paul Richard, Batlamous, Souad, Ng, Chi Wing, Ng, Sam Yanwing, Ng, Ying Wun Yvonne, Ngair, Badr-eddine, Nguyen, Hoang Dai Nghia, Nguyen Manh, Tuan, Nibigira, Emery, Nickerson, Richard, Nikolaidou, Rosy, Nielsen, Daniel, Batley, Richard, Nielsen, Jason, Niemeyer, Marcel, Nikiforou, Nikiforos, Nikolaenko, Vladimir, Nikolic, Irena, Nikolopoulos, Konstantinos, Nilsson, Paul, Nindhito, Herjuno Rah, Nisati, Aleandro, Nishu, Nishu, Batool, Binish, Nisius, Richard, Nitsche, Isabel, Nitta, Tatsumi, Nobe, Takuya, Noel, Daniel Louis, Noguchi, Yohei, Nomidis, Ioannis, Nomura, Marcelo Ayumu, Nordberg, Markus, Bin Norisam, Raif Rafideen, Adiguzel, Aytul, Battaglia, Marco, Novak, Jakob, Novak, Tadej, Novgorodova, Olga, Novotny, Radek, Nozka, Libor, Ntekas, Konstantinos, Ntekas, Kostas, Nurse, Emily Laura, Oakham, Gerald, Oberling, Michael B, Bauce, Matteo, Ocariz, Jose Humberto, Ochi, Atsuhiko, Ochoa, Ines, Ochoa, Jean-pierre, O'connor, Kelsey Shea, Oda, Susumu, Odaka, Shigeru, Ordek, Serhat, Ogrodnik, Agnieszka Ewa, Oh, Alexander, Bauer, Florian, Ohm, Christian, Oide, Hideyuki, Oishi, Reiyo, Ojeda, Martina Laura, Okazaki, Yuta, O'keefe, Michael William, Okumura, Kento, Okumura, Yasuyuki, Olariu, Albert, Seabra, Luis, Bauer, Patrick, Olivares, Sebastian, Oliveira Damazio, Denis, Oliver, Jason, Olsson, Mats Joakim Robert, Olszewski, Andrzej, Olszowska, Jolanta, Oncel, Omer Ogul, O'neil, Dugan, O'neill, Aaron Paul, Onofre, Antonio, Bawa, Harinder Singh, Onyisi, Peter, Oppen, Henrik, Oreamuno Madriz, Rafael Guillermo, Oreglia, Mark, Orellana, Gonzalo Enrique, Orestano, Domizia, Orlando, Nicola, Orr, Robert, O'shea, Val, Ospanov, Rustem, Bayirli, Arif, Otero Y Garzon, Gustavo, Otono, Hidetoshi, Ott, Philipp Sebastian, Ottino, Gregory James, Ouchrif, Mohamed, Ouellette, Jeff, Ould-saada, Farid, Ouraou, Ahmimed, Ouyang, Qun, Owen, Mark Andrew, Beacham, James, Owen, Rhys, Ozcan, Erkcan, Ozturk, Nurcan, Pacalt, Josef, Pacey, Holly, Pachal, Katherine, Pacheco Pages, Andreu, Padilla Aranda, Cristobal, Pagan Griso, Simone, Palacino, Gabriel, Beau, Tristan, Palazzo, Serena, Palestini, Sandro, Palka, Marek, Palni, Prabhakar, Panchal, Devanshu Kiran, Panduro Vazquez, Jose Guillermo, Panchal, Dev, Pandini, Carlo Enrico, Panduro Vazquez, William, Pani, Priscilla, Beauchemin, Pierre-hugues, Panizzo, Giancarlo, Paolozzi, Lorenzo, Papadatos, Constantine, Papageorgiou, Konstantinos, Parajuli, Santosh, Paramonov, Alexander, Paraskevopoulos, Christos, Paredes Hernandez, Daniela Katherinne, Paredes Saenz, Santiago Rafael, Parida, Bibhuti, Becherer, Fabian, Park, Tae Hyoun, Parker, Adam Jackson, Parker, Andy, Parodi, Fabrizio, Parrish, Elliot, Parsons, John, Parzefall, Ulrich, Pascual Dominguez, Luis, Pascuzzi, Vincent, Pasner, Jacob Martin, Abbott, Dale, Adorni Braccesi Chiassi, Sofia, Bechtle, Philip, Pasquali, Federica, Pasqualucci, Enrico, Passaggio, Stefano, Pastore, Francesca, Pasuwan, Patrawan, Pater, Joleen, Pater, Jo, Pathak, Atanu, Patton, Joseph, Pauly, Thilo, Beck, Hans Peter, Pearkes, Jannicke Andree, Pedersen, Maiken, Pedraza Diaz, Lucia, Costa Batalha Pedro, Rute, Peiffer, Thomas, Peleganchuk, Sergey, Penc, Ondrej, Pham, Thu, Peng, Chen, Peng, Haiping, Becker, Kathrin, Sotto-maior Peralva, Bernardo, Perego, Marta Maria, Peixoto, Ana, Pereira Sanchez, Laura, Perepelitsa, Dennis, Perez Codina, Estel, Perini, Laura, Pernegger, Heinz, Perrella, Sabrina, Perrevoort, Ann-kathrin, Becot, Cyril Pascal, Peters, Krisztian, Peters, Reinhild, Petersen, Brian, Petersen, Troels, Petit, Elisabeth, Petousis, Vlasios, Petridou, Chariclia, Petroff, Pierre, Petrucci, Fabrizio, Pettee, Mariel, Beddall, Ayda, Pettersson, Nora Emilia, Petukhova, Krystsina, Qin, Yang, Raine, John Andrew, Rajagopalan, Srinivasan, Mihule, Kristina, Peyaud, Alan, Pezoa Rivera, Raquel, Pezzotti, Lorenzo, Pezzullo, Gianantonio, Beddall, Andrew, Pham, Joni, Phillips, Peter, Phipps, Michael William, Piacquadio, Giacinto, Pianori, Elisabetta, Picazio, Attilio, Pickles, Rebecca Hayley, Piegaia, Ricardo, Piendibene, Marco, Pietreanu, Dorel, Bednyakov, Vadim, Pilcher, James, Pilcher, Jim, Pilkington, Andrew, Pinamonti, Michele, Pinfold, James, Pitman Donaldson, Charlie Bruno, Pizzimento, Luca, Pizzini, Alessio, Pleier, 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Serbanp, Proudfoot, James, Przybycien, Mariusz, Pudzha, Dennis, Puri, Akshat, Puzo, Patrick Michel, Behr, Janna Katharina, Pyatiizbyantseva, Diana, Qian, Jianming, Qin, Quake, Quadt, Arnulf, Queitsch-maitland, Michaela, Rabanal Bolanos, Gabriel, Racko, Michal, Ragusa, Francesco, Rahal, Ghita, Raine, Johnny, Beisiegel, Florian, Rajagopalan, Srini, Ramirez Morales, Andres, Ran, Kunlin, Rassloff, Damir Fabrice, Rauch, Daniel, Rauscher, Felix, Rave, Stefan, Ravina, Baptiste, Ravinovich, Ilia, Raymond, Michel, Belfkir, Mohamed, Read, Alexander Lincoln, Readioff, Nathan Peter, Reale, Marilea, Rebuzzi, Daniela, Redlinger, George, Reeves, Kendall, Reikher, David, Reiss, Andreas Dominik, Rej, Amartya, Rembser, Christoph, Bella, Gideon, Renardi, Alessia, Renda, Michele, Rendel, Marian Benedikt, Rennie, Adam, Resconi, Silvia, Resseguie, Elodie Deborah, Rettie, Sebastien, Reynolds, Bryan, Reynolds, Elliot, Rezanova, Olga, Bellagamba, Lorenzo, Reznicek, Pavel, Ricci, Ester, Richter, Robert, Richter, Stefan, Richter-was, Elzbieta, Ridel, Melissa, Rieck, Patrick, Rifki, Othmane, Rijssenbeek, Michael, Rimoldi, Adele, Bellerive, Alain, Rimoldi, Marco, Rinaldi, Lorenzo, Rinn, Timothy Thomas, Ripellino, Giulia, Riu, Imma, Rivadeneira Bracho, Pablo Andres, Rivera Vergara, Juan Cristobal, Rizatdinova, Flera, Rizvi, Eram Syed, Rizzi, Chiara, Bellos, Panagiotis, Robertson, Steven, Robin, Matthieu, Robinson, Dave, Robles Gajardo, Carolina Michel, Robles Manzano, Maria, Robson, Aidan, Rocchi, Alessandro, Roda, Chiara, Rodriguez Bosca, Sergi, Rodriguez Rodriguez, Arturo, Beloborodov, Konstantin, Rodriguez Vera, Ana Maria, Roe, Shaun, Roggel, Jens, Rohne, Ole, Rojas Caballero, Rimsky Alejandro, Roland, Benoit, Roland, Christophe Pol A, Roloff, Jennifer Kathryn, Romaniouk, Anatoli, Romano, Emanuele, Affolder, Tony, Belotskiy, Konstantin, Romano, Marino, Rompotis, Nikolaos, Ronzani, Manfredi, Roos, Lydia, Rosati, Stefano, Rosin, Guy, Rosser, Benjamin John, Rossi, Edoardo, Rossi, Eleonora, Rossi, Elvira, Belyaev, Nikita, Rossi, Leonardo, Rossini, Lorenzo, Rosten, Rachel Christine, Rotaru, Marina, Rottler, Benjamin, Rousseau, David, Rovelli, Giulia, Roy, Avik, Rozanov, Alexandre, Rozen, Yoram, Benchekroun, Driss, Ruan, Xifeng, Ruiz-Martinez, Aranzazu, Salnikov, Andrei, Sampsonidis, Dimitrios, Sankey, Dave, Ruan, Ruanxf, Ruby, Adam James, Ruggeri, Tristan Andrew, Ruehr, Frederik, Ruiz Martinez, Arantxa, Benekos, Nectarios, Rummler, Andre, Rurikova, Zuzana, Rusakovich, Nikolai, Russell, Heather, Rustige, Lennart, Rutherfoord, John P, Ruttinger, Elias, Rybar, Martin, Rye, Eli Baverfjord, Ryzhov, Andrey, Benhammou, Yan, Sabater Iglesias, Jorge Andres, Sabatini, Paolo, Sabetta, Luigi, Sacerdoti, Sabrina, Sadrozinski, Hartmut, Sadykov, Renat, Safai Tehrani, Francesco, Safarzadeh Samani, Batool, Safdari, Murtaza, Saha, Puja, Benjamin, Doug, Saha, Shreya, Sahinsoy, Merve, Sahu, Arunika, Saimpert, Matthias, Saito, Masahiko, Saito, Tomoyuki, Salamani, Dalila, Salamanna, Giuseppe, Schaffer, Arthur, Schegelsky, Valery, Benoit, Mathieu, Salnikov, Andy, Salt, Jose, Salvador Salas, Adrian, Salvatore, Daniela, Salvatore, Fabrizio, Salzburger, Andreas, Sammel, Dirk, Sampsonidis, Dimos, Sampsonidou, Despoina, Sanchez Martinez, Francisco Javier, Bensinger, Jim, Sanchez Pineda, Arturos, Sandaker, Heidi, Sander, Christian, Sanderswood, Izaac, Sandhoff, Marisa, Sandoval Usme, Carlos, Sankey, David, Sannino, Mario, Sano, Yuta, Sansoni, Andrea, Bentvelsen, Stan, Santoni, Claudio, Santos, Helena, Santpur, Sai Neha, Santra, Arka, Saoucha, Kamal, Sapronov, Andrey, Mendes Saraiva, Joao Gentil, Sasaki, Osamu, Sato, Koji, Sauerburger, Frank, Beresford, Lydia Audrey, Sauvan, Emmanuel, Savard, Pierre, Sawada, Ryu, Sawyer, Craig Anthony, Sawyer, Lee, Sayago Galvan, Ivan, Sbarra, Carla, Sbrizzi, Antonio, Scanlon, Timothy Paul, Schaarschmidt, Jana, Afik, Yoav, Beretta, Matteo Mario, Schacht, Peter, Schaefer, Douglas Michael, Schaefer, Leigh Catherine, Schaefer, Uli, Schaffer, R. D., Schaile, Dorothee, Schamberger Jr, Robert Dean, Schanet, Eric, Scharf, Christian, Scharmberg, Nicolas, Berge, David, Chtcheguelski, Valery, Scheirich, Daniel, Schenck, Ferdinand, Schernau, Michael, Schiavi, Carlo, Schildgen, Lara Katharina, Schillaci, Zachary Michael, Schioppa, Enrico Junior, Schioppa, Marco, Schleicher, Katharina, Bergeaas Kuutmann, Elin, Schlenker, Stefan, Schmidt-sommerfeld, Korbinian Ralf, Schmieden, Kristof, Schmitt, Christian, Schmitt, Stefan, Schoeffel, Laurent Olivier, Schoening, Andre, Scholer, Patrick, Schopf, Elisabeth, Schott, Matthias, Berger, Nicolas, Schouwenberg, Jeroen, Schovancova, Jaroslava, Schramm, Steven, Schroeder, Frederic, Schulte, Alexandra, Schultz-coulon, Hans-christian, Schumacher, Markus, Schumm, Bruce Andrew, Schune, Philippe, Schwartzman, Ariel Gustavo, Bergmann, Benedikt Ludwig, Schwarz, Thomas Andrew, Schwemling, Philippe, Schwienhorst, Reinhard, Sciandra, Andrea, Sciolla, Gabriella, Scuri, Fabrizio, Scutti, Federico, Scyboz, Ludovic Michel, Sebastiani, Cristiano, Sedlaczek, Kevin, Bergsten, Laura Jean, Seema, Pienpen, Seidel, Sally, Seiden, Abraham, Seidlitz, Blair Daniel, Seiss, Todd Michael, Seitz, Claudia, Seixas, Jose, Sekhniaidze, Givi, Sekula, Stephen Jacob, Semprini Cesari, Nicola, Beringer, Juerg, Sen, Sourav, Serfon, Cedric, Serin, Laurent, Serkin, Leonid, Sessa, Marco, Severini, Horst, Sevova, Stanislava, Sforza, Federico, Sfyrla, Anna, Shabalina, Elizaveta, Berlendis, Simon, Shahinian, Jeffrey David, Shaikh, Nabila Wahab, Shatalov, Pavel, Shojaii, Jafar, Sijacki, Djordje, Soukharev, Andrey, Shahinian, Jeff, Ahlgren, Nabila Wahab, Shaked Renous, Dan, Shan, Lianyou, Bernardi, Gregorio, Shang, Ruo-yu, Shapiro, Marjorie, Sharma, Abhishek, Shatalov, Sppavel, Shaw, Kate, Shaw, Savanna, Shehade, Mahran, Shen, Yu-ting, Sherwood, Peter, Bernius, Catrin, Shi, Liaoshan, Shimmin, Chase Owen, Shimogama, Yoshihiro, Shimojima, Makoto, Shinner, James David, Shipsey, Ian, Shirabe, Shohei, Shiyakova, Mariya, Shlomi, Jonathan, Shmeleva, Alevtina, Agapopoulou, Christina, Bernlochner, Florian Urs, Shochet, Mel, Shojaii, Seyed Ruhollah, Shope, David Richard, Shrestha, Suyog, Shrif, Esra Mohammed, Shroff, Maheyer Jamshed, Shulga, Evgeny, Sicho, Petr, Sickles, Anne Marie, Sideras Haddad, Elias, Berry, Tracey, Sidiropoulou, Ourania, Sidoti, Antonio, Siegert, Frank, Solodkov, Alexander, Sijacki, Dorde, Silva Oliveira, Marcos Vinicius, Silverstein, Samuel, Simion, Stefan, Simoniello, Rosa, Simpson-allsop, Cameron James, Berta, Peter, Simsek, Sinem, Sinervo, Pekka, Sinetckii, Viktor, Singh, Sundeep, Sinha, Sukanya, Sioli, Maximiliano, Siral, Ismet, Sivoklokov, Serguei, Sjoelin, Joergen, Skaf, Ali, Berthold, Anne-sophie, Skorda, Eleni, Skubic, Patrick, Slawinska, Magdalena, Sliwa, Krzysztof Jan, Smakhtin, Vladimir, Smart, Ben Harry, Smiesko, Juraj, Smirnov, Nikita, Smirnov, Serge, Smirnov, Yury, Bertram, Iain, Smirnova, Lidia, Smirnova, Oxana, Smith, Emily Ann, Smith, Hayden Alexander, Smizanska, Maria, Smolek, Karel, Smykiewicz, Andrzej, Snesarev, Andrei, Snoek, Hella, Snyder, Ian Michael, Bessidskaia Bylund, Olga, Snyder, Scott, Sobie, Randall, Soffer, Abner, Soloshenko, Alexei, Sopio, Alexander Linus, Sobie, Randy, Soffer, Abi, Sogaard, Andreas, Sohns, Fabian, Solans Sanchez, Carlos, Bethke, Siegfried, Soldatov, Evgeny, Soldevila Serrano, Urmila, Solodkov, Sanya, Soloshenko, Aleksey, Solovyanov, Oleg, Solovyev, Victor, Sommer, Philip, Son, Hyungsuk, Sonay, Anil, Song, Wen Yi, Betti, Alessandra, Sopczak, Andre, Sopio, Alex, Sopkova, Filomena, Sotiropoulou, Calliope-louisa, Sottocornola, Simone, Soualah, Rachik, Sukharev, Andrei, South, David, Stavropoulos, Georgios, Spagnolo, Stefania, Bevan, Adrian, Spalla, Margherita, Spangenberg, Martin, Spano, Francesco, Sperlich, Dennis, Spieker, Thomas Malte, Spigo, Giancarlo, Spina, Mario, Spiteri, Dwayne Patrick, Spousta, Martin, Strauss, Michael, Bhatta, Somadutta, Stabile, Alberto, Stamas, Brianna, Stamen, Rainer, Stamenkovic, Marko, Stampekis, Alexios, Stanecka, Ewa, Stanislaus, Beojan, Stanitzki, Marcel, Stankaityte, Migle, Stapf, Birgit Sylvia, Agaras, Merve Nazlim, Bhattacharya, Deb Sankar, Starchenko, Jenya, Stark, Giordon Holtsberg, Stark, Jan, Staroba, Pavel, Starovoitov, Pavel, Staerz, Steffen, Staszewski, Rafal, Stavropoulos, George, Steinberg, Peter Alan, Steinhebel, Amanda, Bhattarai, Prajita, Stelzer, Bernd, Stelzer, Joerg, Stelzer-chilton, Oliver, Stenzel, Hasko, Stevenson, Thomas James, Stewart, Graeme A, Stockton, Mark, Stoicea, Gabriel, Stolarski, Marcin, Stonjek, Stefan, Bhopatkar, Vallary Shashikant, Straessner, Arno, Strandberg, Jonas, Strandberg, Sara, Strauss, Mike, Strebler, Thomas, Strizenec, Pavol, Strohmer, Raimund, Strom, David, Stroynowski, Ryszard, Strubig, Antonia, Bi, Runyu, Stucci, Stefania Antonia, Stugu, Bjarne, Stupak, John, Styles, Nicholas, Su, Dong, Su, Wanyun, Su, Xiaowen, Suarez, Nicholas Bruno, Sulin, Vladimir, Sullivan, Matthew James, Bianchi, Riccardo Maria, Sultan, D. M. S., Sultanov, Salekh, Sumida, Toshi, Sun, Siyuan, Sun, Xiaohu, Suster, Carl Joseph Edmund, Sutton, Mark, Svatos, Michal, Swiatlowski, Maximilian J, Swift, Stewart Patrick, Biebel, Otmar, Swirski, Thorben, Sydorenko, Alexander, Sykora, Ivan, Sykora, Martin, Sykora, Tomas, Ta, Duc Bao, Tackmann, Kerstin, Kinghorn-taenzer, Joseph Peter, Taffard, Anyes, Tafirout, Reda, Biedermann, Dustin, Tagiev, Emin, Taibah, Reem Hani M, Takashima, Ryuichi, Takeda, Kosuke, Takeshita, Toru, Takeva, Emily Petrova, Takubo, Yosuke, Talby, Mossadek, Talyshev, Alexei, Tam, Kai Chung, Bielski, Rafal, Tamir, Nadav Michael, Tanaka, Junichi, Tanaka, Reisaburo, Tokar, Stanislav, Tsukerman, Ilya, Tsybychev, Dmitri, Tuts, Michael, Vacek, Vaclav, Tanaka, Rei, Tang, Fukun, Bierwagen, Katharina, Tapia Araya, Sebastian, Tapprogge, Stefan, Tarek, Ahmed, Tarem, Shlomit, Tariq, Khuram, Tarna, Grigore, Vandewall, Evan Richard, Vermeulen, Jos, Tartarelli, Francesco, Tas, Petr, Biesuz, Nicolo Vladi, Tasevsky, Marek, Tassi, 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Gasiorowski, Sean Joseph, Do Nascimento Gaspar, Philipp, Gatta, Maurizio, Gaudio, Gabriella, Annovi, Alberto, Gauzzi, Paolo, Gavrilenko, Igor, Gavrilyuk, Alexander, Gavriliuk, Aleksandr, Gay, Colin Warren, Gaycken, Goetz, Gazis, Evangelos, Geanta, Andrei Alexandru, Gee, Carolyn, Gee, Norman, Antel, Claire, Geisen, Jannik, Geisen, Marc, Gemme, Claudia, Genest, Marie-helene, Geng, Cong, Gentile, Simonetta, Gentsos, Christos, George, Simon, Geralis, Theodoros, Gerlach, Lino Oscar, Anthony, Matthew Thomas, Gessinger-befurt, Paul, Gessner, Gregor, Ghasemi Bostanabad, Meisam, Ghneimat, Mazuza, Ghosh, Aishik, Ghosh, Anindya, Giacobbe, Benedetto, Giagu, Stefano, Giangiacomi, Nico, Giannetti, Paola, Antipov, Egor, Giannini, Antonio, Giannini, Giulia, Gibson, Stephen, Gignac, Matthew, Gil, Damian Tomasz, Gilbert, Benjamin Jacob, Gillberg, Dag, Gilles, Geoffrey, Gillwald, Nils, Gingrich, Douglas, Abramowicz, Halina, Antonelli, Mario, Giordani, MarioPaolo, Gingrich, Doug, Giordani, Mapo, Giraud, Pierre-francois, Giugliarelli, Gilberto, Giugni, Danilo, Giuli, Francesco, Gkaitatzis, Stamatios, Gkialas, Ioannis, Gkougkousis, Evangelos, Antrim, Daniel Joseph, Gkougkousis, Vagelis, Gkountoumis, Panagiotis, Gladilin, Leonid, Glasman, Claudia, Glaysher, Paul, Gledhill, Galen Rhodes, Gnesi, Ivan, Goblirsch-kolb, Maximilian Emanuel, Godin, Dominique, Goldfarb, Steven, Anulli, Fabio, Golling, Tobias, Golubkov, Dmitry, Da Silva Gomes, Agostinho, Goncalves Gama, Rafael, Morais Silva Goncalo, Ricardo Jose, Gonella, Giulia, Gonella, Laura, Gongadze, Alexi, Gonnella, Francesco, Gonski, Julia Lynne, Aoki, Masato, Gonzalez De La Hoz, Santiago, Gonzalez Fernandez, Sergio, Gonzalez Lopez, Ricardo, Gonzalez Renteria, Cesar, Gonzalez Suarez, Rebeca, Gonzalez Sevilla, Sergio, Gonzalvo Rodriguez, Galo Rafael, Goossens, Luc, Gorasia, Nandish Arjan, Gorbounov, Petr, Aparisi Pozo, Javier Alberto, Gordon, Howard, Gorini, Benedetto, Gorini, Edoardo, Gorisek, Andrej, Goshaw, Alfred, Grillo, Alexander, Goshaw, Al, Gostkin, Mikhail, Gottardo, Carlo Alberto, Gouighri, Mohamed, Aparo, Marco, Goussiou, Anna, Govender, Nicolin, Goy, Corinne, Grabowska-bold, Iwona, Gramstad, Eirik, Grancagnolo, Sergio, Grandi, Mario, Grachev, Vadim, Gravila, Paul, Gravili, Francesco Giuseppe, Aperio Bella, Ludovica, Gray, Chloe, Gray, Heather, Green, Barry, Grefe, Christian, Gregor, Ingrid, Grenier, Philippe, Grevtsov, Kirill, Grieco, Chiara, Grieser, Nathan Allen, Grillo, Alex, Aranzabal Barrio, Nordin, Grimm, Kathryn, Grinstein, Sebastian, Grivaz, Jean-francois, Groh, Sabrina, Gross, Eilam, Grosse-knetter, Joern, Grout, Zara Jane, Grud, Christopher Ryan, Grummer, Aidan, Grundy, James Cameron, Araujo Ferraz, Victor, Guan, Liang, Guan, Wen, Gubbels, Chris, Guenther, Jaroslav, Gubbels, Christopher, Guerrero Rojas, Jesus, Guescini, Francesco, Guest, Dan, Guest, Daniel, Gugel, Ralf, Pereira, Rodrigo, Guida, Alessandro, Guillemin, Thibault, Guindon, Stefan, Guo, Jun, Guo, Ziyu, Gupta, Ruchi, Gurbuz, Saime, Gustavino, Giuliano, Guth, Manuel, Gutierrez, Phillip, Aad, G, Bachas, K, Chiodini, G, Gorini, E, Gravili, F, Longo, L, Primavera, M, Reale, M, Schioppa, E, Spagnolo, S, Ventura, A, ATLAS Collaboration, And, Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de Clermont (LPC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), ATLAS, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), De Castro , S., Fabbri , L., Gabrielli , A., Rinaldi , L., Semprini Cesari , N., Sioli , M., Villa , M., Zoccoli , A., Bellagamba , L., Boscherini , D., Bruni , A., Bruni , G., Giacobbe , B., Polini , A., Sbarra , C., Sidoti , A., Franchini , M., Romano , M., Valentinetti , S., Alberghi , G. L., Lasagni Manghi , F, Massa , L., Vittori , C., Alfonsi , F, Cabras , G., Cervelli , A., Negrini , M., Todome , K., Carratta , G., Cavalli , N., Clissa , L., ATLAS collaboration, Aad, G., Abbott, B., Abbott, D. C., Abud, A. A., Abeling, K., Abhayasinghe, D. K., Abidi, S. H., Abouzeid, O. S., Abraham, N. L., Abramowicz, H., Abreu, H., Abulaiti, Y., Acharya, B. S., Achkar, B., Adam, L., Bourdarios, C. A., Adamczyk, L., Adamek, L., Adelman, J., Adiguzel, A., Adorni, S., Adye, T., Affolder, A. A., Afik, Y., Agapopoulou, C., Agaras, M. N., Aggarwal, A., Agheorghiesei, C., Aguilar-Saavedra, J. A., Ahmad, A., Ahmadov, F., Ahmed, W. S., Ai, X., Aielli, G., Akatsuka, S., Akbiyik, M., Akesson, T. P. A., Akilli, E., Akimov, A. V., Al Khoury, K., Alberghi, G. L., Albert, J., Albicocco, P., Verzini, M. J. A., Alderweireldt, S., Aleksa, M., Aleksandrov, I. N., Alexa, C., Alexopoulos, T., Alfonsi, A., Alfonsi, F., Alhroob, M., Ali, B., Ali, S., Aliev, M., Alimonti, G., Alison, J., Allaire, C., Allbrooke, B. M. M., Allport, P. P., Aloisio, A., Alonso, F., Alpigiani, C., Camelia, E. A., Estevez, M. A., Alviggi, M. G., Coutinho, Y. A., Ambler, A., Ambroz, L., Amelung, C., Amidei, D., Dos Santos, S. P. A., Amoroso, S., Amrouche, C. S., Anastopoulos, C., Andari, N., Andeen, T., Anders, J. K., Anderson, J. T., Andrean, S. Y., Andreazza, A., Andrei, V., Anelli, C. R., Angelidakis, S., Angerami, A., Anisenkov, A. V., Annovi, A., Antel, C., Anthony, M. T., Antipov, E., Antonelli, M., Antrim, D. J. A., Anulli, F., Aoki, M., Pozo, J. A. A., Aparo, M. A., Bella, L. A., Aranzabal, N., Ferraz, V. A., Pereira, R. A., Arcangeletti, C., Arce, A. T. H., Arguin, J. -F., Argyropoulos, S., Arling, J. -H., Armbruster, A. J., Armstrong, A., Arnaez, O., Arnold, H., Tame, Z. P. A., Artoni, G., Asada, H., Asai, K., Asai, S., Asawatavonvanich, T., Asbah, N., Asimakopoulou, E. M., Asquith, L., Assahsah, J., Assamagan, K., Astalos, R., Atkin, R. J., Atkinson, M., Atlay, N. B., Atmani, H., Atmasiddha, P. A., Augsten, K., Austrup, V. A., Avolio, G., Ayoub, M. K., Azuelos, G., Babal, D., Bachacou, H., Bachas, K., Backman, F., Bagnaia, P., Bahmani, M., Bahrasemani, H., Bailey, A. J., Bailey, V. R., Baines, J. T., Bakalis, C., Baker, O. K., Bakker, P. J., Bakos, E., Gupta, D. B., Balaji, S., Balasubramanian, R., Baldin, E. M., Balek, P., Balli, F., Balunas, W. K., Balz, J., Banas, E., Bandieramonte, M., Bandyopadhyay, A., Barak, L., Barbe, W. M., Barberio, E. L., Barberis, D., Barbero, M., Barbour, G., Barillari, T., Barisits, M. -S., Barkeloo, J., Barklow, T., Barnett, B. M., Barnett, R. M., Barnovska-Blenessy, Z., Baroncelli, A., Barone, G., Barr, A. J., Navarro, L. B., Barreiro, F., da Costa, J. B. G., Barron, U., Barsov, S., Bartels, F., Bartoldus, R., Bartolini, G., Barton, A. E., Bartos, P., Basalaev, A., Basan, A., Bassalat, A., Basso, M. J., Basson, C. R., Bates, R. L., Batlamous, S., Batley, J. R., Batool, B., Battaglia, M., Bauce, M., Bauer, F., Bauer, P., Bawa, H. S., Bayirli, A., Beacham, J. B., Beau, T., Beauchemin, P. H., Becherer, F., Bechtle, P., Beck, H. P., Becker, K., Becot, C., Beddall, A., Beddall, A. J., Bednyakov, V. A., Bee, C. P., Beermann, T. A., Begalli, M., Begel, M., Behera, A., Behr, J. K., Beisiegel, F., Belfkir, M., Bella, G., Bellagamba, L., Bellerive, A., Bellos, P., Beloborodov, K., Belotskiy, K., Belyaev, N. L., Benchekroun, D., Benekos, N., Benhammou, Y., Benjamin, D. P., Benoit, M., Bensinger, J. R., Bentvelsen, S., Beresford, L., Beretta, M., Berge, D., Kuutmann, E. B., Berger, N., Bergmann, B., Bergsten, L. J., Beringer, J., Berlendis, S., Bernardi, G., Bernius, C., Bernlochner, F. U., Berry, T., Berta, P., Berthold, A., Bertram, I. A., Bylund, O. B., Bethke, S., Betti, A., Bevan, A. J., Bhatta, S., Bhattacharya, D. S., Bhattarai, P., Bhopatkar, V. S., Bi, R., Bianchi, R. M., Biebel, O., Biedermann, D., Bielski, R., Bierwagen, K., Biesuz, N. V., Biglietti, M., Billoud, T. R. V., Bindi, M., Bingul, A., Bini, C., Biondi, S., Birch-Sykes, C. J., Birman, M., Bisanz, T., Biswal, J. P., Biswas, D., Bitadze, A., Bittrich, C., Bjorke, K., Blair, R. E., Blazek, T., Bloch, I., Blocker, C., Blue, A., Blumenschein, U., Bobbink, G. J., Bobrovnikov, V. S., Bogavac, D., Bogdan, M., Bogdanchikov, A. G., Bohm, C., Boisvert, V., Bokan, P., Bold, T., Bomben, M., Bona, M., Bonilla, J. S., Boonekamp, M., Booth, C. D., Borbely, A. G., Borecka-Bielska, H. M., Borgna, L. S., Borisov, A., Borissov, G., Bortoletto, D., Boscherini, D., Bosman, M., Sola, J. D. B., Bouaouda, K., Boudreau, J., Bouhova-Thacker, E. V., Boumediene, D., Bouquet, R., Bourlis, G., Boveia, A., Boyd, J., Boye, D., Boyko, I. R., Bozson, A. J., Bracinik, J., Brahimi, N., Brandt, G., Brandt, O., Braren, F., Brau, B., Brau, J. E., Madden, W. D. B., Brendlinger, K., Brener, R., Brenner, L., Brenner, R., Bressler, S., Brickwedde, B., Briglin, D. L., Britton, D., Britzger, D., Brock, I., Brock, R., Brooijmans, G., Brooks, W. K., Brost, E., de Renstrom, P. A. B., Bruers, B., Bruncko, D., Bruni, A., Bruni, G., Bruschi, M., Bruscino, N., Bryant, P., Bryngemark, L., Buanes, T., Buat, Q., Buchholz, P., Buckley, A. G., Budagov, I. A., Bugge, M. K., Bulekov, O., Bullard, B. A., Burch, T. J., Burdin, S., Burgard, C. D., Burger, A. M., Burghgrave, B., Burr, J. T. P., Burton, C. D., Burzynski, J. C., Buscher, V., Buschmann, E., Bussey, P. J., Butler, J. M., Buttar, C. M., Butterworth, J. M., Buttinger, W., Vazquez, C. J. B., Buzykaev, A. R., Cabras, G., Urban, S. C., Caforio, D., Cai, H., Cairo, V. M. M., Cakir, O., Calace, N., Calafiura, P., Calderini, G., Calfayan, P., Callea, G., Caloba, L. P., Caltabiano, A., Lopez, S. C., Calvet, D., Calvet, S., Calvet, T. P., Calvetti, M., Toro, R. C., Camarda, S., Munoz, D. C., Camarri, P., Camerlingo, M. T., Cameron, D., Camincher, C., Campana, S., Campanelli, M., Camplani, A., Canale, V., Canesse, A., Bret, M. C., Cantero, J., Cao, Y., Capua, M., Cardarelli, R., Cardillo, F., Carducci, G., Carli, T., Carlino, G., Carlson, B. T., Carlson, E. M., Carminati, L., Carney, R. M. D., Caron, S., Carquin, E., Carra, S., Carratta, G., Carter, J. W. S., Carter, T. M., Casado, M. P., Casha, A. F., Castiglia, E. G., Castillo, F. L., Garcia, L. C., Gimenez, V. C., Castro, N. F., Catinaccio, A., Catmore, J. R., Cattai, A., Cavaliere, V., Cavasinni, V., Celebi, E., Celli, F., Cerny, K., Cerqueira, A. S., Cerri, A., Cerrito, L., Cerutti, F., Cervelli, A., Cetin, S. A., Chadi, Z., Chakraborty, D., Chan, J., Chan, W. S., Chan, W. Y., Chapman, J. D., Chargeishvili, B., Charlton, D. G., Charman, T. P., Chatterjee, M., Chau, C. C., Chekanov, S., Chekulaev, S. V., Chelkov, G. A., Chen, B., Chen, C., Chen, C. H., Chen, H., Chen, J., Chen, S., Chen, S. J., Chen, X., Chen, Y., Chen, Y. -H., Cheng, H. C., Cheng, H. J., Cheplakov, A., Cheremushkina, E., El Moursli, R. C., Cheu, E., Cheung, K., Chevalerias, T. J. A., Chevalier, L., Chiarella, V., Chiarelli, G., Chiodini, G., Chisholm, A. S., Chitan, A., Chiu, I., Chiu, Y. H., Chizhov, M. V., Choi, K., Chomont, A. R., Chou, Y., Chow, Y. S., Christopher, L. D., Chu, M. C., Chu, X., Chudoba, J., Chwastowski, J. J., Cieri, D., Ciesla, K. M., Cindro, V., Cioara, I. A., Ciocio, A., Cirotto, F., Citraro, S., Citron, Z. H., Citterio, M., Ciubotaru, D. A., Ciungu, B. M., Clark, A., Clark, P. J., Clawson, S. E., Clement, C., Clissa, L., Coadou, Y., Cobal, M., Coccaro, A., Cochran, J., de Sa, R. C. L., Cohen, H., Coimbra, A. E. C., Cole, B., Colijn, A. P., Collot, J., Muino, P. C., Connell, S. H., Connelly, I. A., Conventi, F., Cooper-Sarkar, A. M., Cormier, F., Corpe, L. D., Corradi, M., Corrigan, E. 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M., Del Peso, J., Delabat Diaz, Y., Delgove, D., Deliot, F., Delitzsch, C. M., della Pietra, M., della Volpe, D., Dell'Acqua, A., Dell'Asta, L., Delmastro, M., Delporte, C., Delsart, P. A., Demers, S., Demichev, M., Demontigny, G., Denisov, S. P., D'Eramo, L., Derendarz, D., Derkaoui, J. E., Derue, F., Dervan, P., Desch, K., Dette, K., Deutsch, C., Deviveiros, P. O., Di Bello, F. A., Di Ciaccio, A., Di Ciaccio, L., Di Donato, C., Di Girolamo, A., Di Gregorio, G., Di Luca, A., Di Micco, B., Di Nardo, R., Di Sipio, R., Diaconu, C., Dias, F. A., Do Vale, T. D., Diaz, M. A., Capriles, F. G. D., Dickinson, J., Didenko, M., Diehl, E. B., Dietrich, J., Cornell, S. D., Pardos, C. D., Dimitrievska, A., Ding, W., Dingfelder, J., Dittmeier, S. J., Dittus, F., Djama, F., Djobava, T., Djuvsland, J. I., Do Vale, M. A. B., Dobre, M., Dodsworth, D., Doglioni, C., Dolejsi, J., Dolezal, Z., Donadelli, M., Dong, B., Donini, J., D'Onofrio, A., D'Onofrio, M., Dopke, J., Doria, A., Dova, M. T., Doyle, A. T., Drake, G., Drechsler, E., Dreyer, E., Dreyer, T., Drobac, A. S., Du, D., du Pree, T. A., Duan, Y., Dubinin, F., Dubovsky, M., Dubreuil, A., Duchovni, E., Duckeck, G., Ducu, O. A., Duda, D., Dudarev, A., Dudder, A. C., D'Uffizi, M., Duflot, L., Duhrssen, M., Dulsen, C., Dumancic, M., Dumitriu, A. E., Dunford, M., Dungs, S., Duperrin, A., Yildiz, H. D., Duren, M., Durglishvili, A., Dutta, B., Duvnjak, D., Dyckes, G. I., Dyndal, M., Dysch, S., Dziedzic, B. S., Eggleston, M. G., Eifert, T., Eigen, G., Einsweiler, K., Ekelof, T., El Jarrari, H., Ellajosyula, V., Ellert, M., Ellinghaus, F., Elliot, A. A., Ellis, N., Elmsheuser, J., Elsing, M., Emeliyanov, D., Emerman, A., Enari, Y., Erdmann, J., Ereditato, A., Erland, P. A., Errenst, M., Escalier, M., Escobar, C., Pastor, O. E., Etzion, E., Evans, G., Evans, H., Evans, M. O., Ezhilov, A., Fabbri, F., Fabbri, L., Fabiani, V., Facini, G., Fakhrutdinov, R. M., Falciano, S., Falke, P. 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[0000-0002-8487-8480], Wang, H. [0000-0003-3952-8139], Wang, J. [0000-0002-5246-5497], Wang, P. [0000-0002-6730-1524], Wang, R.-J. [0000-0002-5059-8456], Wang, R. [0000-0001-9839-608X], Wang, R. [0000-0001-8530-6487], Wang, S.M. [0000-0002-5821-4875], Wang, T. [0000-0002-1152-2221], Wang, W.T. [0000-0002-7184-9891], Wang, W.X. [0000-0002-1444-6260], Wang, Y. [0000-0003-2693-3442], Wang, Z. [0000-0002-0928-2070], Wanotayaroj, C. [0000-0002-8178-5705], Warburton, A. [0000-0002-2298-7315], Ward, C.P. [0000-0002-5162-533X], Ward, R.J. [0000-0001-5530-9919], Warrack, N. [0000-0002-8268-8325], Watson, A.T. [0000-0001-7052-7973], Watson, M.F. [0000-0002-9724-2684], Watts, G. [0000-0002-0753-7308], Waugh, B.M. [0000-0003-0872-8920], Webb, A.F. [0000-0002-6700-7608], Weber, C. [0000-0002-8659-5767], Weber, M.S. [0000-0002-2770-9031], Weber, S.A. [0000-0003-1710-4298], Weber, S.M. [0000-0002-2841-1616], Wei, Y. [0000-0001-9725-2316], Weidberg, A.R. [0000-0002-5158-307X], Weingarten, J. [0000-0003-2165-871X], Weirich, M. [0000-0002-5129-872X], Weiser, C. [0000-0002-6456-6834], Wells, P.S. [0000-0003-4999-896X], Wenaus, T. [0000-0002-8678-893X], Wendland, B. [0000-0003-1623-3899], Wengler, T. [0000-0002-4375-5265], Wenig, S. [0000-0002-4770-377X], Wermes, N. [0000-0001-9971-0077], Wessels, M. [0000-0002-8192-8999], Whalen, K. [0000-0002-9383-8763], Wharton, A.M. [0000-0002-9507-1869], White, A.S. [0000-0003-0714-1466], White, A. [0000-0001-8315-9778], White, M.J. [0000-0001-5474-4580], Whiteson, D. [0000-0002-2005-3113], Whitmore, B.W. [0000-0001-9130-6731], Wiedenmann, W. [0000-0003-3605-3633], Wiel, C. [0000-0003-1995-9185], Wielers, M. [0000-0001-9232-4827], Wiglesworth, C. [0000-0001-6219-8946], Wiik-Fuchs, L.A.M. [0000-0002-5035-8102], Wilkens, H.G. [0000-0002-8483-9502], Wilkins, L.J. [0000-0002-7092-3500], Williams, D.M. [0000-0002-5646-1856], Williams, S. [0000-0001-6174-401X], Willocq, S. [0000-0002-4120-1453], Windischhofer, P.J. [0000-0001-5038-1399], Wingerter-Seez, I. [0000-0001-9473-7836], Winkels, E. [0000-0003-0156-3801], Winklmeier, F. [0000-0001-8290-3200], Winter, B.T. [0000-0001-9606-7688], Wobisch, M. [0000-0002-0688-3380], Wolf, A. [0000-0002-4368-9202], Wölker, R. [0000-0002-7402-369X], Wolter, M.W. [0000-0001-9184-2921], Wolters, H. [0000-0002-9588-1773], Wong, V.W.S. [0000-0001-5975-8164], Wongel, A.F. [0000-0002-6620-6277], Woods, N.L. [0000-0002-8993-3063], Worm, S.D. [0000-0002-3865-4996], Wosiek, B.K. [0000-0003-4273-6334], Woźniak, K.W. [0000-0003-1171-0887], Wraight, K. [0000-0002-3298-4900], Wu, S.L. [0000-0001-5866-1504], Wu, X. [0000-0001-7655-389X], Wu, Y. [0000-0002-1528-4865], Wuerzinger, J. [0000-0002-4055-218X], Wyatt, T.R. [0000-0001-9690-2997], Wynne, B.M. [0000-0001-9895-4475], Xella, S. [0000-0002-0988-1655], Xiao, X. [0000-0002-1344-8723], Xie, X. [0000-0001-6473-7886], Xu, D. [0000-0001-6355-2767], Xu, H. [0000-0001-6110-2172], Xu, L. [0000-0001-8997-3199], Xu, R. [0000-0002-1928-1717], Xu, T. [0000-0002-0215-6151], Xu, W. [0000-0001-5661-1917], Xu, Y. [0000-0001-9563-4804], Xu, Z. [0000-0001-9571-3131], Xu, Z. [0000-0001-9602-4901], Yabsley, B. [0000-0002-2680-0474], Yacoob, S. [0000-0001-6977-3456], Yallup, D.P. [0000-0003-4716-5817], Yamaguchi, N. [0000-0002-6885-282X], Yamaguchi, Y. [0000-0002-3725-4800], Yamazaki, T. [0000-0003-0411-3590], Yamazaki, Y. [0000-0003-3710-6995], Yan, Z. [0000-0002-2483-4937], Yang, H.J. [0000-0001-7367-1380], Yang, H.T. [0000-0003-3554-7113], Yang, S. [0000-0002-0204-984X], Yang, T. [0000-0002-4996-1924], Yang, X. [0000-0002-1452-9824], Yang, X. [0000-0002-9201-0972], Yang, Y. [0000-0001-8524-1855], Yang, Z. [0000-0002-7374-2334], Yao, W-M. [0000-0002-3335-1988], Yap, Y.C. [0000-0001-8939-666X], Ye, H. [0000-0002-4886-9851], Ye, J. [0000-0001-9274-707X], Ye, S. [0000-0002-7864-4282], Yeletskikh, I. [0000-0003-0586-7052], Yexley, M.R. [0000-0002-1827-9201], Yin, P. [0000-0003-2174-807X], Yorita, K. [0000-0003-1988-8401], Yoshihara, K. [0000-0002-3656-2326], Young, C.J.S. [0000-0001-5858-6639], Young, C. [0000-0003-3268-3486], Yuan, R. [0000-0002-8452-0315], Yue, X. [0000-0001-6956-3205], Zaazoua, M. [0000-0002-4105-2988], Zabinski, B. [0000-0001-5626-0993], Zacharis, G. [0000-0002-3156-4453], Zaffaroni, E. [0000-0003-1714-9218], Zahreddine, J. [0000-0002-6932-2804], Zaitsev, A.M. [0000-0002-4961-8368], Zakareishvili, T. [0000-0001-7909-4772], Zakharchuk, N. [0000-0002-4963-8836], Zambito, S. [0000-0002-4499-2545], Zanzi, D. [0000-0002-1222-7937], Zeißner, S.V. [0000-0002-9037-2152], Zeitnitz, C. [0000-0003-2280-8636], Zemaityte, G. [0000-0001-6331-3272], Zeng, J.C. [0000-0002-2029-2659], Zenin, O. [0000-0002-5447-1989], Ženiš, T. [0000-0001-8265-6916], Zenz, S. [0000-0002-9720-1794], Zerradi, S. [0000-0001-9101-3226], Zerwas, D. [0000-0002-4198-3029], Zgubič, M. [0000-0002-5110-5959], Zhang, B. [0000-0002-9726-6707], Zhang, D.F. [0000-0001-7335-4983], Zhang, G. [0000-0002-5706-7180], Zhang, J. [0000-0002-9907-838X], Zhang, K. [0000-0002-9778-9209], Zhang, L. [0000-0002-9336-9338], Zhang, L. [0000-0001-5241-6559], Zhang, M. [0000-0001-8659-5727], Zhang, R. [0000-0002-8265-474X], Zhang, X. [0000-0003-4731-0754], Zhang, X. [0000-0003-4341-1603], Zhang, Y. [0000-0002-4554-2554], Zhang, Z. [0000-0002-7853-9079], Zhao, P. [0000-0003-0054-8749], Zhao, Y. [0000-0003-0494-6728], Zhao, Z. [0000-0001-6758-3974], Zhemchugov, A. [0000-0002-3360-4965], Zheng, Z. [0000-0002-8323-7753], Zhong, D. [0000-0001-9377-650X], Zhou, C. [0000-0001-5904-7258], Zhou, H. [0000-0002-7986-9045], Zhou, M. [0000-0001-7223-8403], Zhou, N. [0000-0002-1775-2511], Zhu, C.G. [0000-0001-8015-3901], Zhu, C. [0000-0002-5918-9050], Zhu, H.L. [0000-0001-8479-1345], Zhu, H. [0000-0001-8066-7048], Zhu, J. [0000-0002-5278-2855], Zhu, Y. [0000-0002-7306-1053], Zhuang, X. [0000-0003-0996-3279], Zhukov, K. [0000-0003-2468-9634], Zhulanov, V. [0000-0002-0306-9199], Zieminska, D. [0000-0002-6311-7420], Zimine, N.I. [0000-0003-0277-4870], Zimmermann, S. [0000-0002-1529-8925], Živković, L. [0000-0003-4236-8930], Zoccoli, A. [0000-0002-0993-6185], Zoch, K. [0000-0003-2138-6187], Zorbas, T.G. [0000-0003-2073-4901], Zou, R. [0000-0002-0542-1264], Zwalinski, L. [0000-0002-9397-2313], Apollo - University of Cambridge Repository, Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2890, Aloisio, Alberto, Alviggi, Mariagrazia, Canale, Vincenzo, Della Pietra, Massimo, Merola, Leonardo, Massarotti, Paolo, Conventi, Francesco, Rossi, Elvira, Carlino, Giampaolo, de Asmundis, Riccardo, Di Donato, Camilla, Doria, Alessandra, Iengo, Paolo, Izzo, Vincenzo, Perrella, Sabrina, Sekhniaidze, Givi, Cirotto, Francesco, Lavorgna, Marco, Giannini, Antonio, and Atlas, Collaboration
Subjects: Kjerne- og elementærpartikkelfysikk: 431 [VDP], Physics - Instrumentation and Detectors, Computer science, data acquisition, Tracking (particle physics), 01 natural sciences, Online farms and online filtering, High Energy Physics - Experiment, Subatomär fysik, High Energy Physics - Experiment (hep-ex), Data acquisition, Software, cluster finding, Subatomic Physics, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], hardware, Calibration and fitting methods, Cluster finding, Modular electronics, Pattern recognition, Trigger concepts and systems (hardware and software), Detectors and Experimental Techniques, Instrumentation, Mathematical Physics, FPGA, Settore FIS/01, Large Hadron Collider, Pattern recognition, cluster finding, calibration and fitting methods, track data analysis, Detector, integrated circuit, microstrip [semiconductor detector], Modular electronic, Instrumentation and Detectors (physics.ins-det), calibration and fitting methods, ATLAS, medicine.anatomical_structure, Nuclear and elementary particle physics: 431 [VDP], Pattern recognition (psychology), design [electronics], semiconductor detector: microstrip, LHC, Computer hardware, performance, Particle Physics - Experiment, 530 Physics, FOS: Physical sciences, LHC, ATLAS, High Energy Physics, Accelerator Physics and Instrumentation, programming, Calibration and fitting method, semiconductor detector: pixel, Atlas (anatomy), 0103 physical sciences, medicine, High Energy Physics, ddc:610, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], 010306 general physics, Field-programmable gate array, numerical calculations, pixel [semiconductor detector], 010308 nuclear & particles physics, business.industry, paper, bibliography, Acceleratorfysik och instrumentering, trigger, LHC, Particle Tracking, High-level Trigger, Particle Tracking, efficiency, High-level Trigger, data management, business, Hadron-hadron collisions, electronics: design
Abstract: We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerba¼an; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada; Be¼ing Municipal Science & Technology Commission, China; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO TEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF(Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (U.K.) and BNL (U.S.A.), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in ref. [52]., The ATLAS Fast TracKer (FTK) was designed to provide full tracking for the ATLAS high-level trigger by using pattern recognition based on Associative Memory (AM) chips and fitting in high-speed field programmable gate arrays. The tracks found by the FTK are based on inputs from all modules of the pixel and silicon microstrip trackers. The as-built FTK system and components are described, as is the online software used to control them while running in the ATLAS data acquisition system. Also described is the simulation of the FTK hardware and the optimization of the AM pattern banks. An optimization for long-lived particles with large impact parameter values is included. A test of the FTK system with the data playback facility that allowed the FTK to be commissioned during the shutdown between Run 2 and Run 3 of the LHC is reported. The resulting tracks from part of the FTK system covering a limited η-ϕ region of the detector are compared with the output from the FTK simulation. It is shown that FTK performance is in good agreement with the simulation., ANPCyT, Argentina, YerPhI, Armenia, ARC, Australia, BMWFW and FWF, Austria, ANAS, Azerbaijan, SSTC, Belarus, CNPq and FAPESP, Brazil, NSERC, NRC and CFI, Canada, CERN, ANID, Chile, CAS, MOST and NSFC, China, COLCIENCIAS, Colombia, MSMT CR, MPO CR and VSC CR, Czech Republic, DNRF and DNSRC, Denmark, IN2P3-CNRS and CEA-DRF/IRFU, France, SRNSFG, Georgia, BMBF, HGF and MPG, Germany, GSRT, Greece, RGC and Hong Kong SAR, China, ISF and Benoziyo Center, Israel, INFN, Italy, MEXT and JSPS, Japan, CNRST, Morocco, NWO, Netherlands, RCN, Norway, MNiSW and NCN, Poland, FCT, Portugal, MNE/IFA, Romania, JINR, MES of Russia and NRC KI, Russian Federation, MESTD, Serbia, MSSR, Slovakia, ARRS and MIZŠ, Slovenia, DST/NRF, South Africa, MICINN, Spain, SRC and Wallenberg Foundation, Sweden, SERI, SNSF and Cantons of Bern and Geneva, Switzerland, MOST, Taiwan, TAEK, Turkey, STFC, United Kingdom, DOE and NSF, United States of America, BCKDF, CANARIE, Compute Canada, CRC and IVADO, Canada, Bejiing Municipal Science & Technology Commission, China, COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union, Investissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France, DFG and AvH Foundation, Germany, Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece, BSF-NSF and GIF, Israel, La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain, Göran Gustafssons Stiftelse, Sweden, The Royal Society and Leverhulme Trust, United Kingdom
Published: 2021

29. Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials

Author: Nicholas Marshall, Johan Coolen, Hilde Bosmans, Sunay Rodríguez Pérez, Charlotte Biebaû, Jeroen Desmet, Lesley Cockmartin, Walter De Wever, and Lara Struelens
Subjects: Paper, Radiography, Image processing, Imaging phantom, 030218 nuclear medicine & medical imaging, COVID-19 imaging, 03 medical and health sciences, 0302 clinical medicine, Software, computed tomography segmentation, voxel phantoms, Medicine, computer simulations, Radiology, Nuclear Medicine and imaging, Computer vision, Polygon mesh, mesh modeling, Physics of Medical Imaging, Computational model, business.industry, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Ray tracing (graphics), Artificial intelligence, Noise (video), COVID-19 pathologies, business
Abstract: Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5 mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases. ispartof: JOURNAL OF MEDICAL IMAGING vol:8 issue:Suppl 1 ispartof: location:United States status: published
Published: 2021
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30. Faster dense deformable image registration by utilizing both CPU and GPU

Author: Simon Ekström, Håkan Ahlström, Filip Malmberg, Joel Kullberg, Martino Pilia, and Robin Strand
Subjects: Paper, brain MRI, Image quality, Image Processing, Graphics processing unit, Image registration, graphics processing unit, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Software, Medicine, Radiology, Nuclear Medicine and imaging, Computer vision, deformable image registration, Image fusion, business.industry, Medicinsk bildbehandling, Image segmentation, Pipeline (software), Atlas-based segmentation, Medical Image Processing, 030220 oncology & carcinogenesis, Artificial intelligence, Central processing unit, business
Abstract: Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants. Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software. Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively. Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.
Published: 2021

31. NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis

Author: Zheng Li, Zong Zhang, Chen Zhao, Lian Duan, Chaozhe Zhu, Yilong Gong, and Xin Hou
Subjects: Paper, Visual analytics, Computer science, data analysis, Neuroscience (miscellaneous), Tutorials, Machine learning, computer.software_genre, 01 natural sciences, Task (project management), 010309 optics, 03 medical and health sciences, 0302 clinical medicine, Software, resting-state, 0103 physical sciences, toolbox, Preprocessor, functional near-infrared spectroscopy, Radiology, Nuclear Medicine and imaging, Graphical user interface, near-infrared spectroscopy-KIT, Radiological and Ultrasound Technology, business.industry, task activation, Toolbox, Visualization, Functional near-infrared spectroscopy, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract: Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.
Published: 2021

32. Naked Eye Y Amelogenin Gene Fragment Detection Using DNAzymes on a Paper-based Device

Author: Marian M. de Pancorbo, Lourdes Basabe-Desmonts, Enrique Azuaje-Hualde, Gaizka Garai-Ibabe, Fernando Benito-Lopez, Susana Arroyo-Jimenez, and European Commission
Subjects: Paper, Analyte, paper substrate, DNAzyme, Microfluidics, Deoxyribozyme, Nanotechnology, 02 engineering and technology, Biosensing Techniques, DNAzyme, amelogenin, paper substrate, gene detection, colorimetric, paper based microfluidics, 01 natural sciences, Biochemistry, DNA sequencing, colorimetric, Analytical Chemistry, Lab-On-A-Chip Devices, Environmental Chemistry, Humans, Spectroscopy, Fragment (computer graphics), Chemistry, 010401 analytical chemistry, gene detection, paper based microfluidics, Paper based, DNA, Catalytic, 021001 nanoscience & nanotechnology, amelogenin, 0104 chemical sciences, Point-of-Care Testing, Naked eye, Amelogenin, 0210 nano-technology, Software
Abstract: Nowadays, there are strong efforts in developing new technology for rapid detection of specific DNA sequences for environmental monitoring, forensic analysis and rapid biomedical diagnosis applications. That is where microfluidic paper-based analytical devices are positioned as suitable platforms for the development of point of care analytical devices, due to their simple fabrication protocols, ease of use and low cost. Herein, a methodology for in situ single strand DNA detection by using a colorimetric assay based on the formation of a DNAzyme within a paper substrate was developed. A DNAzyme that could only be formed in the presence of a specific sequence of the Y human amelogenin gene was designed. The performance of the DNAzyme was followed colorimetrically first in solution and then in paper substrates. The reaction was found to be specific to the Y fragment selected as analyte. The DNAzyme reaction on paper enabled the unequivocal colorimetric identification of the Y single strand DNA fragment both qualitatively, with the naked eye (143 ng), and quantitatively by image analysis (45.7 ng). As a proof of concept, a microfluidic paper-based device, pre-loaded with all DNAzyme reagents, was characterized and implemented for the simultaneous detection of X and Y single strand DNA fragments. This work was supported by Gobierno de España, Ministerio de Economía y Competitividad, with Grant No. BIO2016-80417-P; funding from Basque Government (Grant: IT1271-19) and European Union funds: DNASURF (H2020-MSCA-RISE-778001). E.A.-H. acknowledges funding from the Basque Government, Department of Education, for predoctoral fellowship 2016.
Published: 2020

33. A meta-analytic review of measurement equivalence study findings of the SF-36® and SF-12® Health Surveys across electronic modes compared to paper administration

Author: Avery A. Rizio, Michelle K. White, Stephen Maher, and Jakob B. Bjorner
Subjects: PRO, Male, Paper, SF-36, English language, Equivalence, Article, ePRO, 03 medical and health sciences, 0302 clinical medicine, Surveys and Questionnaires, Statistics, Electronic, Humans, 030212 general & internal medicine, Patient Reported Outcome Measures, Equivalence (measure theory), Language, Measurement, Patient-reported outcomes, 030503 health policy & services, Public Health, Environmental and Occupational Health, Moderation, Health Surveys, Meta-analysis, Search terms, Research studies, Quality of Life, Health survey, Female, 0305 other medical science, Psychology, Software
Abstract: Purpose Patient-reported outcome (PRO) measures originally developed for paper administration are increasingly being administered electronically in clinical trials and other health research studies. Three published meta-analyses of measurement equivalence among paper and electronic modes aggregated findings across hundreds of PROs, but there has not been a similar meta-analysis that addresses a single PRO, partly because there are not enough published measurement equivalence studies using the same PRO. Because the SF-36(R) Health Survey (SF-36) is a widely used PRO, the aim of this study was to conduct a meta-analysis of measurement equivalence studies of this survey. Methods A literature search of several medical databases used search terms for variations of “SF-36” or “SF-12” and “equivalence” in the title or abstract of English language publications. The eight scale scores and two summary measures of the SF-36 and SF-12 were transformed to norm-based scores (NBS) using developer guidelines. A threshold of within ± 2 NBS points was set as the margin of equivalence. Comprehensive meta-analysis software was used. Results Twenty-five studies were included in the meta-analysis. Results indicated that mean differences across domains and summary scores ranged from 0.01 to 0.39 while estimates of agreement ranged from 0.76 to 0.91, all well within the equivalence threshold. Moderator analyses showed that time between administration, survey language, and type of electronic device did not influence equivalence. Conclusions The results of the meta-analysis support equivalence of paper-based and electronic versions of the SF-36 and SF-12 across a variety of disease populations, countries, and electronic modes.
Published: 2018

34. Программное обеспечение для фильтрации неровностей профиля поверхности бумаги

Author: Kyrychok, Tetiana Yuriivna and Klymenko, Tetiana Yevhenivna
Subjects: Surface (mathematics), Computer science, 655.531, Surface finish, filtration of surface profile roughness, структура поверхні паперу, шероховатость бумаги, symbols.namesake, Software, погрешности и помехи устройства, Interference (communication), фільтрація нерівностей профілю поверхні, inaccuracies and interference of the device, roughness parameters of the paper, структура поверхности бумаги, профілограма поверхні паперу, похибки та перешкоди приладу, Basis (linear algebra), business.industry, paper, Gaussian filter, профилограмма поверхности бумаги, бумага, фільтр Гауса, Vibration, шорсткість паперу, quality of the prints, папір, фильтрация неровностей профиля поверхности, symbols, качество оттисков, якість відбитків, Profilometer, фильтр Гаусса, business, paper surface profiligram, Algorithm, structure of the paper surface
Abstract: Фільтрація профілю є одним з важливих етапів оцінки профілю поверхні паперу. Під фільтрацією розуміють методи (електричні, оптичні, механічні, математичні) для вибору необхідної інформації із загального профілю поверхні. Для визначення впливу мікрогеометрії на експлуатаційні властивості необхідно спочатку виключити з профілю такі фактори, як перешкоди та похибки приладу. Вплив перешкод, особливо з боку навколишнього середовища, неминучий при записі профілів, до того ж для повного нівелювання похибки приладу досліджуваного зразка необхідна велика кількість часу. Отже, необхідно розробити математичну модель для фільтрації нерівностей профілю поверхні паперу. Мета дослідження: розробити методику цифрової обробки профілограм поверхні паперу для подальшого встановлення взаємозв’язку параметрів паперу з якістю відбитків. Використано математичний підхід на основі фільтру Гауса та аналіз профілограм, отриманих контактним та оптичним методами профілометрії. Розроблено програмне забезпечення на основі програмного пакету Free Pascal. Для проведення експериментальних досліджень використані профілограми поверхонь різних видів паперу, які були отримані в попередніх дослідженнях за допомогою профілометра з індуктивним перетворювачем, модель 296. Розроблено програмне забезпечення для фільтрації нерівностей профілю поверхні незадрукованого або задрукованого паперу з використанням фільтру Гаусса, що дозволяє отримати профілограми, виключивши з профілю такі фактори, як перешкоди та похибки, викликані випадковими вібраціями, розраховувати відповідні показники шорсткості, та встановити в подальшому взаємозв’язок з якістю відбитків на цьому папері. Filtering profile is one of the important stages of evaluation of the surface profile of the paper. The term ‘filtration’ should be understood as the methods (electrical, optical, mechanical, mathematical) to select the necessary information from the general profile of the surface. The impact of interference, especially emanating from the environment is inevitable when recording profiles. Above that, to level completely the device error of the test sample it is necessary to spend a large amount of time. Therefore, it is necessary to develop a mathematical model for the filtration of roughness of the surface paper profile. Goal of the research: to develop a technique for digital processing of paper surface profilographs to the further identifying the relation between the paper parameters and quality of the prints. The mathematical approach based on Gaussian filter and the analysis of profilograms obtained by the contact and optical methods of profilometry were used. It was developed software on the basis of Free Pascal software package as well. In order to provide the experimental investigations, profilograms of surfaces of various types of paper obtained in previous studies using a profilometer with an inductive converter, model 296 were used. Software for filtering the roughness of the surface profile of unprinted or printed paper using Gaussian filter, which allows to receive profilograms, eliminating in the profile the factors such as obstacles and errors caused by random vibrations, as well as to calculate the relevant roughness indexes, and to identify in the future the interaction with the quality of the prints on this paper.
Published: 2018

35. THE GERMAN DOSE RATE MONITORING NETWORK AND IMPLEMENTED DATA HARMONIZATION TECHNIQUES

Author: W. Harms, I. Hellmann, F. Weiler, M. Bleher, H. Dombrowski, R. Luff, H. Doll, U. Stöhlker, S Seifert, and B. Prommer
Subjects: Paper, Quality management, Computer science, Population, Radiation Dosage, 030218 nuclear medicine & medical imaging, German, 03 medical and health sciences, 0302 clinical medicine, Radiation Protection, Radiation Monitoring, Humans, Radiology, Nuclear Medicine and imaging, education, education.field_of_study, Radiation, Radiological and Ultrasound Technology, Warning system, business.industry, Public Health, Environmental and Occupational Health, General Medicine, language.human_language, Europe, Data exchange, 030220 oncology & carcinogenesis, language, Systems engineering, Radiation monitoring, Radiation protection, business, Quality assurance, Software, Radioactive Pollutants
Abstract: Environmental radiation monitoring networks have been established in Europe and world-wide for the purpose of protecting population and environment against ionizing radiation. Some of these networks had been established during the cold war period and were improved after the Chernobyl accident in 1986. Today, the German Federal Office for Radiation Protection (BfS) operates an early warning network with roughly 1800 ambient dose equivalent rate (ADER) stations equally distributed over the German territory. The hardware and software of all network components are developed in-house allowing the continuous optimization of all relevant components. A probe characterization and quality assurance and control program are in place. Operational and technical aspects of the network and data harmonization techniques are described. The latter allows for calculating of the terrestrial and net ADER combined with uncertainties mainly from site specific effects. Harmonized data are finally used as input to the German emergency management system and the European radiological data exchange platform.
Published: 2018

36. Microfluidic Paper-based Analytical Devices for Determination of Creatinine in Urine Samples

Author: Suphanan Sununta, Narong Praphairaksit, Orawon Chailapakul, and Poomrat Rattanarat
Subjects: Paper, Time Factors, Picrate, Microfluidics, Picric acid, 02 engineering and technology, Urine, Urinalysis, 01 natural sciences, Analytical Chemistry, chemistry.chemical_compound, Limit of Detection, Lab-On-A-Chip Devices, Humans, Detection limit, Creatinine, Chromatography, 010401 analytical chemistry, Paper based, 021001 nanoscience & nanotechnology, 0104 chemical sciences, chemistry, Linear range, 0210 nano-technology, Software
Abstract: Simple, low-cost and portable microfluidic paper-based analytical devices (μPADs) for determination of creatinine in urine samples were developed. The methodology was based on Jaffé reaction between the creatinine and picric acid in alkaline conditions, generating a colorimetric creatinine-alkaline picrate complex. The product exhibits an orange color that is clearly visible on the μPADs. The color intensity of the complex, which is indicative of the concentration of creatinine, is then quantitatively determined using ImageJ software. Various experimental parameters were optimized to achieve the best performance of the μPADs. Under the optimum conditions, a wide linear range was obtained in the range of 0.2 - 1 mM with a limit of detection and limit of quantitation of 0.08 and 0.26 mM, respectively. The accuracy of the proposed method was in good agreement with the standard Jaffé method. Finally, the developed devices were successfully applied for the determination of creatinine in urine samples.
Published: 2018

37. A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

Author: Tommi Välikangas, Tomi Suomi, and Laura L. Elo
Subjects: Paper, Proteomics, 0301 basic medicine, Proteome, Computer science, imputation, computer.software_genre, ta3111, differential expression, Mass Spectrometry, 03 medical and health sciences, Software, Imputation (statistics), Molecular Biology, Data processing, evaluation, business.industry, ta1182, logarithmic fold change, Missing data, Regression, 030104 developmental biology, Workflow, software workflow, Data mining, business, computer, Algorithms, Chromatography, Liquid, Information Systems
Abstract: Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.
Published: 2018

38. Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation

Author: Alexander A. Aldrick, Vidhi Lalchand, Miguel Garcia-Ortegon, Alpha A. Lee, Ryan-Rhys Griffiths, Griffiths, Ryan-Rhys [0000-0003-3117-4559], Apollo - University of Cambridge Repository, and Griffiths, RR [0000-0003-3117-4559]
Subjects: FOS: Computer and information sciences, Paper, Computer Science - Machine Learning, Mathematical optimization, Heteroscedasticity, Computer science, Heuristic, Bayesian probability, Gaussian processes, Machine Learning (stat.ML), Bayesian optimisation, Machine Learning (cs.LG), heteroscedasticity, Human-Computer Interaction, Noise, symbols.namesake, Surrogate model, Statistics - Machine Learning, Artificial Intelligence, Robustness (computer science), symbols, Aleatoric music, Gaussian process, Software
Abstract: Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation strategies however, is the representation of heteroscedastic aleatoric uncertainty. In many practical applications it is desirable to identify inputs with low aleatoric noise, an example of which might be a material composition which consistently displays robust properties in response to a noisy fabrication process. In this paper, we propose a heteroscedastic Bayesian optimisation scheme capable of representing and minimising aleatoric noise across the input space. Our scheme employs a heteroscedastic Gaussian process (GP) surrogate model in conjunction with two straightforward adaptations of existing acquisition functions. First, we extend the augmented expected improvement (AEI) heuristic to the heteroscedastic setting and second, we introduce the aleatoric noise-penalised expected improvement (ANPEI) heuristic. Both methodologies are capable of penalising aleatoric noise in the suggestions and yield improved performance relative to homoscedastic Bayesian optimisation and random sampling on toy problems as well as on two real-world scientific datasets. Code is available at: \url{https://github.com/Ryan-Rhys/Heteroscedastic-BO}, Published in Machine Learning: Science and Technology 2021 (https://iopscience.iop.org/article/10.1088/2632-2153/ac298c) Earlier version accepted to the 2019 NeurIPS Workshop on Safety and Robustness in Decision Making
Published: 2021

39. Comparative analysis of differential gene expression tools for RNA sequencing time course data

Author: Tobias A. Beyer, Daniel Spies, Peter F. Renz, and Constance Ciaudo
Subjects: Paper, Time Factors, Computer science, 0206 medical engineering, RNA-Seq, 02 engineering and technology, Computational biology, Signal-To-Noise Ratio, Bioinformatics, differential expression, time course RNA-seq, tools comparison, simulated and biological data sets, Massively parallel signature sequencing, 03 medical and health sciences, Bayes' theorem, Robustness (computer science), False positive paradox, Humans, Computer Simulation, Molecular Biology, 030304 developmental biology, 0303 health sciences, Models, Statistical, Markov chain, Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, Bayes Theorem, Molecular Sequence Annotation, Markov Chains, Data set, Pairwise comparison, Databases, Nucleic Acid, Software, 020602 bioinformatics, Information Systems
Abstract: RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (, Briefings in Bioinformatics, 20 (1), ISSN:1467-5463, ISSN:1477-4054
Published: 2017

40. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization

Author: Kazunori D. Yamada, Kazutaka Katoh, and John Rozewicki
Subjects: Paper, Service (systems architecture), sequence analysis, Sequence analysis, Interface (Java), Computer science, 0206 medical engineering, 02 engineering and technology, World Wide Web, User-Computer Interface, 03 medical and health sciences, Data sequences, Databases, Genetic, phylogenetic tree, Molecular Biology, 030304 developmental biology, Internet, 0303 health sciences, Sequence, Multiple sequence alignment, Information retrieval, Computational Biology, Visualization, multiple sequence alignment, Sequence Alignment, Relevant information, Algorithms, Software, 020602 bioinformatics, Information Systems
Abstract: This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.
Published: 2017

41. MicroScope—an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data

Author: Johan Rollin, Mathieu Gachet, Adrien Josso, Rémi Planel, Guillaume Gautreau, Hugo Pereira, David Roche, Alexandra Calteau, Jordan Langlois, Zoé Rouy, Claudine Médigue, David Vallenet, Stéphane Cruveiller, Aurélie Lajus, Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Génomique métabolique (UMR 8030), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Analyse Bio-Informatique pour la Génomique et le Métabolisme (LABGeM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Service d'Ecologie Sociale, CP 231, Universite´ Libre de Bruxelles, Boulevard du Triomphe, 1050 Brussels, Belgium, Université libre de Bruxelles (ULB), Représenter, Inventer la Réalité, du Romantisme au XXIe siècle (RIRRA 21), Université Paul-Valéry - Montpellier 3 (UPVM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)-Genoscope - Centre national de séquençage [Evry] (GENOSCOPE)
Subjects: 0301 basic medicine, Paper, Computer science, Data management, Context (language use), Bacterial genome size, Computational biology, comparative genomics, Genome, 03 medical and health sciences, Annotation, transcriptomics, User-Computer Interface, microbial genome annotation system, Computer Graphics, Relevance (information retrieval), Molecular Biology, ComputingMilieux_MISCELLANEOUS, variant detection, Comparative genomics, Microbiological Phenomena, Internet, 030102 biochemistry & molecular biology, business.industry, Computational Biology, Molecular Sequence Annotation, Genome project, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Genome, Microbial, 030104 developmental biology, ComputingMethodologies_PATTERNRECOGNITION, metabolic networks, Database Management Systems, business, gene function curation, Databases, Chemical, Metabolic Networks and Pathways, Software, Information Systems
Abstract: The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.
Published: 2017

42. Federation in genomics pipelines: techniques and challenges

Author: Folker Meyer, Somali Chaterji, Ananth Grama, Saurabh Bagchi, Jin-Kyu Koo, and Ninghui Li
Subjects: Paper, Big Data, Computer science, 0206 medical engineering, Genomics, 02 engineering and technology, 03 medical and health sciences, Resource (project management), Open research, Databases, Genetic, Humans, Genetic Privacy, Molecular Biology, 030304 developmental biology, Gene expression omnibus, 0303 health sciences, Information Dissemination, Management science, Scale (chemistry), Computational Biology, Data science, Pipeline (software), United States, Pipeline transport, Metagenomics, National laboratory, Confidentiality, Software, 020602 bioinformatics, Information Systems
Abstract: Federation is a popular concept in building distributed cyberinfrastructures, whereby computational resources are provided by multiple organizations through a unified portal, decreasing the complexity of moving data back and forth among multiple organizations. Federation has been used in bioinformatics only to a limited extent, namely, federation of datastores, e.g. SBGrid Consortium for structural biology and Gene Expression Omnibus (GEO) for functional genomics. Here, we posit that it is important to federate both computational resources (CPU, GPU, FPGA, etc.) and datastores to support popular bioinformatics portals, with fast-increasing data volumes and increasing processing requirements. A prime example, and one that we discuss here, is in genomics and metagenomics. It is critical that the processing of the data be done without having to transport the data across large network distances. We exemplify our design and development through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline. Currently, it is hosted completely at Argonne National Laboratory. However, through a recently started collaborative National Institutes of Health project, we are taking steps toward federating this infrastructure. Being a widely used resource, we have to move toward federation without disrupting 50 K annual users. In this article, we describe the computational tools that will be useful for federating a bioinformatics infrastructure and the open research challenges that we see in federating such infrastructures. It is hoped that our manuscript can serve to spur greater federation of bioinformatics infrastructures by showing the steps involved, and thus, allow them to scale to support larger user bases.
Published: 2017

43. The BioCyc collection of microbial genomes and metabolic pathways

Author: Wai Kit Ong, Pallavi Subhraveti, Markus Krummenacker, Peter D. Karp, Suzanne M. Paley, Peter E. Midford, Quang Ong, Carol A. Fulcher, Ingrid M. Keseler, Mario Latendresse, Ron Caspi, Richard Billington, and Anamika Kothari
Subjects: Paper, 0301 basic medicine, Internet, 030102 biochemistry & molecular biology, Microbial Genomes, Computer science, Computational Biology, Genomics, Computational biology, Models, Biological, Genome, Search Engine, Genome, Microbial, 03 medical and health sciences, ComputingMethodologies_PATTERNRECOGNITION, 030104 developmental biology, Databases, Genetic, Escherichia coli, Analysis software, Microbial genome, Molecular Biology, Metabolic Networks and Pathways, Software, Information Systems
Abstract: BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of query tools, visualization services and analysis software. Recent advances in BioCyc include an expansion in the content of BioCyc in terms of both the number of genomes and the types of information available for each genome; an expansion in the amount of curated content within BioCyc; and new developments in the BioCyc software tools including redesigned gene/protein pages and metabolite pages; new search tools; a new sequence-alignment tool; a new tool for visualizing groups of related metabolic pathways; and a facility called SmartTables, which enables biologists to perform analyses that previously would have required a programmer’s assistance.
Published: 2017

44. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes

Author: Todd J. Treangen, Sergey Koren, Christopher M. Hill, Victoria Cepeda-Espinoza, Nathan D. Olson, Mihai Pop, and Jay Ghurye
Subjects: Paper, 0303 health sciences, Assembly algorithm, Computer science, Microbiota, 0206 medical engineering, Computational Biology, High-Throughput Nucleotide Sequencing, 02 engineering and technology, Computational biology, Multiple strain, Genome, DNA sequencing, 03 medical and health sciences, Metagenomics, Databases, Genetic, Metagenome, Molecular Biology, Algorithms, Software, 020602 bioinformatics, 030304 developmental biology, Information Systems, Synteny
Abstract: Metagenomic samples are snapshots of complex ecosystems at work. They comprise hundreds of known and unknown species, contain multiple strain variants and vary greatly within and across environments. Many microbes found in microbial communities are not easily grown in culture making their DNA sequence our only clue into their evolutionary history and biological function. Metagenomic assembly is a computational process aimed at reconstructing genes and genomes from metagenomic mixtures. Current methods have made significant strides in reconstructing DNA segments comprising operons, tandem gene arrays and syntenic blocks. Shorter, higher-throughput sequencing technologies have become the de facto standard in the field. Sequencers are now able to generate billions of short reads in only a few days. Multiple metagenomic assembly strategies, pipelines and assemblers have appeared in recent years. Owing to the inherent complexity of metagenome assembly, regardless of the assembly algorithm and sequencing method, metagenome assemblies contain errors. Recent developments in assembly validation tools have played a pivotal role in improving metagenomics assemblers. Here, we survey recent progress in the field of metagenomic assembly, provide an overview of key approaches for genomic and metagenomic assembly validation and demonstrate the insights that can be derived from assemblies through the use of assembly validation strategies. We also discuss the potential for impact of long-read technologies in metagenomics. We conclude with a discussion of future challenges and opportunities in the field of metagenomic assembly and validation.
Published: 2017

45. ‘Multi-omic’ data analysis using O-miner

Author: Sangaralingam, Ajanthah, Dayem Ullah, Abu Z, Marzec, Jacek, Gadaleta, Emanuela, Nagano, Ai, Ross-Adams, Helen, Wang, Jun, Lemoine, Nicholas R, and Chelala, Claude
Subjects: Paper, Data Analysis, O-miner, Internet, Whole Genome Sequencing, Sequence Analysis, RNA, Gene Expression Profiling, Gene Dosage, Computational Biology, sequencing, Genomics, multi-omics, DNA Methylation, Software Design, Neoplasms, Databases, Genetic, Humans, data integration, Software
Abstract: Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.
Published: 2017

46. A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications

Author: Xue Li, Guanghua Xiao, and Xinlei Wang
Subjects: Paper, Lung Neoplasms, Computer science, Process (engineering), 0206 medical engineering, MEDLINE, 02 engineering and technology, Aggregation methods, 03 medical and health sciences, Bayes' theorem, Carcinoma, Non-Small-Cell Lung, Databases, Genetic, Humans, Computer Simulation, Molecular Biology, 030304 developmental biology, 0303 health sciences, Information retrieval, Models, Statistical, Markov chain, partial list, full list, Rank (computer programming), Computational Biology, Bayes Theorem, Genomics, Markov Chains, performance evaluation, meta-analysis, top ranked list, Ranking, Meta-analysis, Data Interpretation, Statistical, coverage rate, 020602 bioinformatics, Software, Information Systems
Abstract: Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.
Published: 2017

47. Picture books: custom-printed photo books really show off your pictures, and they're easier than ever to design and make. We tried out 10 of the biggest photo book services to find the best, and gathered our most useful tips and tricks, all to help you create the most impressive volume possible

Author: Grossman, Debbie
Subjects: Photographic industry, Software, Paper, Software quality
Published: 2010

48. Top considerations for creating bioinformatics software documentation

Author: Michael M. Hoffman and Mehran Karimzadeh
Subjects: Paper, 0301 basic medicine, software, business.industry, Computer science, perspective, Computational Biology, Guidelines as Topic, Documentation, World Wide Web, 03 medical and health sciences, 030104 developmental biology, Software, Bioinformatics software, Humans, business, Molecular Biology, Information Systems
Abstract: Investing in documenting your bioinformatics software well can increase its impact and save your time. To maximize the effectiveness of your documentation, we suggest following a few guidelines we propose here. We recommend providing multiple avenues for users to use your research software, including a navigable HTML interface with a quick start, useful help messages with detailed explanation and thorough examples for each feature of your software. By following these guidelines, you can assure that your hard work maximally benefits yourself and others.
Published: 2017

49. Graphics processing unit-accelerated mesh-based Monte Carlo photon transport simulations

Author: Shijie Yan and Qianqian Fang
Subjects: Paper, Optics and Photonics, Speedup, Computer science, Monte Carlo method, Biomedical Engineering, Graphics processing unit, Symmetric multiprocessor system, 01 natural sciences, Computational science, 010309 optics, Biomaterials, Software, Imaging, Three-Dimensional, 0103 physical sciences, Computer Graphics, Image Processing, Computer-Assisted, Computer Simulation, Graphics, light transport, General, Photons, business.industry, Computers, Optical Imaging, Reproducibility of Results, 3D modeling, heterogeneous computing, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Benchmark (computing), business, Algorithms
Abstract: The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC)-based methods. Compared to multilayered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. We report a highly efficient MMC—MMCL—using the OpenCL heterogeneous computing framework and demonstrate a speedup ratio up to 420× compared to state-of-the-art single-threaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay, and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc.
Published: 2019

50. Colorimetric determination of cysteine by a paper-based assay system using aspartic acid modified gold nanoparticles

Author: Xuejiao Zhang, Chunye Liu, Xiaojun Zhao, Shuli Zhang, and Yanqing Miao
Subjects: Paper, Absorption spectroscopy, Nanochemistry, Metal Nanoparticles, 02 engineering and technology, 01 natural sciences, Analytical Chemistry, Limit of Detection, Aspartic acid, Zeta potential, Humans, Cysteine, Detection limit, Aspartic Acid, Chemistry, 010401 analytical chemistry, Paper based, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Carbohydrate Sequence, Colloidal gold, Colorimetry, Gold, 0210 nano-technology, Cell Phone, Software, Nuclear chemistry
Abstract: A method is described for cysteine (Cys) determination on paper-based analytical devices using aspartic acid modified gold nanoparticles (Asp-AuNPs). The Asp-AuNPs were characterized by their size, zeta potential, and UV-visible absorption spectrum. After the addition of Cys, it will interact with Asp-AuNPs selectively and leads to the aggregation of Asp-AuNPs. A color change from red to blue can be observed on the paper-based analytical devices. The results were recorded using a cell phone and subsequently analyzed using the Photoshop software. The ratiometric color intensity at red channel and blue channel (Red/Blue) increased linearly in the range 99.9-998.7 μM for Cys (R = 0.9984), and the limit of detection was 1.0 μM. The effects of assay conditions have been investigated and are discussed. The Cys concentration was determined as (0.27 ± 0.02 mM) in human plasma, and the recovery was from 99.2 to 101.1%. Graphical abstract Schematic representation of the paper-based assay system using aspartic acid modified gold nanoparticles (Asp-AuNPs). The ratiometric color intensity method was used for the cysteine (Cys) determination.
Published: 2019

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