bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characteristics of a plasma wind tunnel for the development of thermal protection materials
Authors: Hong B. G. , Kang B. R. , Choi J. C. , Oh P. Y. 
Published: May 2017

ABSTRACTThermal plasma wind tunnels with power of 0.4 MW and 2.4 MW have been constructed at Chonbuk National University (CBNU) in Korea. This facility is capable of producing a heat flux greater than 10 MW/m2, a level that is relevant for testing thermal protection materials that are used for re-entry vehicles in space transportation. A segmented arc plasma torch was adopted as a plasma source; this was designed to have high thermal efficiency and long life, and to produce a supersonic plasma flow with enthalpy greater than 10 MJ/kg. We investigated the characteristics of the supersonic plasma flow using intrusive and non-intrusive diagnostic systems. Ablation characteristics of potential thermal protection materials such as carbon/carbon composites and graphite were investigated with the plasma wind tunnel. Cracks and pores in the materials accelerated the erosion. For carbon/carbon composites, the pores grew and the cracks which occurred at the interfaces between the carbon fibres and the matrix propagated, while for the graphite, the erosion started at the pores and peeled off the surface.
The unbearable lightness of luck: Three sources of overconfidence in the manageability of nuclear crises
Authors: Pelopidas Benoît 
Published: May 2017

AbstractOverconfidence in the controllability of nuclear weapons creates danger. The passing of the last elite witness of the most dangerous nuclear crisis, that is, the ‘Cuban Missile Crisis’, and the current Trump administration only make this more salient. In this context, this article reviews the scholarly literature about the limits of predictability and controllability of nuclear crises and investigates three failures of learning from them. Given that France displays in particularly acute form some of the sources of overconfidence in the controllability of nuclear crises that can been found in other nuclear armed states, this article offers the first study of the French experience and evolving interpretation of the Cuban missile crisis in comparative perspective, based on untapped primary material. In security studies, this article makes three contributions. First, the publication and interpretation of primary sources is a contribution in itself given the frequent misconceptions about nuclear dynamics due to theory-driven extrapolations. Second, it challenges a widespread assumption of automaticity linking a fear-induced deterrent effect and the presence of nuclear weapons. Third, empirically, this article studies part of a regime of valuation of nuclear weapons. It finally outlines a research agenda to take luck seriously in security studies.
Development of a live cell imaging method for mitochondria inTrebouxiaphotobionts
Authors: BRICKLEY Mark R. 
Published: May 2017

AbstractTheTrebouxiaphotobiont freshly isolated fromXanthoria parietina(L.) Th. Fr. was used to develop a live cell chondriome (mitochondrial DNA) labelling method. In the initial phase six candidate dyes were tested and compared for mitochondrial labelling utility as assessed by the signal to noise ratio (SNR) of the mitochondrial signal to the adjacent cellular background in standardized confocal images of 30 labelled cells. DIOC7,JC-1 and MitoTracker orange (MTO) dyes showed some labelling ability. MTO had significantly higher utility than the other dyes. In a second phase, MTO concentration was optimized. The final labelling protocol was a 30 minute incubation with 1 μM of MTO. The resultant labelling was suitable for both widefield and confocal microscopy. Both 2D thresholding and 3D volume construction are demonstrated using the resultant data. The protocol can therefore be utilized for both qualitative research and for quantitative measurement of the chondriome inTrebouxiaphotobionts. This will facilitate a wide range of mitochondrial investigations in lichenology.
Electron acceleration by whistler pulse in high-density plasma
Authors: Singh A.K. 
Published: May 2017

AbstractThe acceleration of an electron by the ponderomotive force of a Gaussian whistler pulse in a magnetized high-density quantum plasma obeying Fermi–Dirac distribution is studied using the recently developed quantum hydrodynamic model. Effective acceleration takes place when the peak whistler amplitude exceeds a threshold value, and the whistler frequency is greater than the cyclotron frequency. The threshold amplitude decreases with ratio of plasma frequency to electron cyclotron frequency. The electron is accelerated at velocities of about twice the group velocity of the whistler.
HEADROOM BEYOND THE QUALITY- ADJUSTED LIFE-YEAR: THE CASE OF COMPLEX PEDIATRIC NEUROLOGY
Authors: van Nimwegen Kirsten J.M. , Lilford Richard J. , van der Wilt Gert J. , Grutters Janneke P.C. 
Published: May 2017

Objectives: The headroom method was introduced for the very early evaluation of the potential value of new technologies. It allows for establishing a ceiling price for technologies to still be cost-effective by combining the maximum effect a technology might yield, the maximum willingness-to-pay (WTP) for this effect, and potential downstream expenses and savings. Although the headroom method is QALY-based, not all innovations are expected to result in QALY gain.Methods: This study explores the feasibility and usefulness of the headroom method in the evaluation of technologies that are unlikely to result in QALY gain. This will be illustrated with the diagnostic trajectory of complex pediatric neurology (CPN).Results: Our headroom analysis showed a large room for improvement in the current diagnostic trajectory of CPN in terms of diagnostic yield. Combining this with a maximum WTP value for an additional diagnosis and the potential downstream expenses and savings, resulted in a total headroom of €15,028. This indicates that a new technology in this particular diagnostic trajectory, might be cost-effective as long as its costs do not exceed €15,028.Conclusions: The headroom method seems a useful tool in the very early evaluation of medical technologies, also in cases when immediate QALY gain is unlikely. It allows for allocating healthcare resources to those technologies that are most promising. It should be kept in mind, however, that the headroom assumes an optimistic scenario, and for that reason cannot guarantee future cost-effectiveness. It might be most useful for ruling out those technologies that are unlikely to be cost-effective.
Intersectoral action for health: preventing psittacosis spread after one reported case
Authors: FERREIRA V. L. , SILVA M. V. , BASSETTI B. R. , PELLINI A. C. G. , RASO T. F. 
Published: May 2017

SUMMARYZoonotic diseases are a significant health threat for humans and animals. To better understand the epidemiology, etiology, and pathology of infectious agents affecting humans and animals combined approaches are needed. Here we describe an epidemiological investigation conducted by physicians and veterinarians after a reported case of psittacosis. Upon admission suffering from respiratory distress syndrome in a hospital and with a history of bird contact, a female patient was serologically diagnosed with psittacosis. After the case notification, veterinarians were able to investigate the source of infection by detecting Chlamydia psittaci in her pet cockatiel. The bird was hospitalized and successfully treated. In addition, the establishment where the pet bird was purchased was traced and through molecular techniques other birds intended to be sold as pets tested positive for C. psittaci. As a result, sanitary measures were applied and the establishment then was closed down. The birds intended for the pet commerce were treated and retested with negative molecular results for C. psittaci, thus avoiding disease propagation. Reliable data about zoonotic diseases can only be generated through the application of multidisciplinary approaches which take into account the epidemiological factors and interactions of humans, animals and their environments as an integrated system.
Family environments and leukocyte transcriptome indicators of a proinflammatory phenotype in children and parents
Authors: Robles Theodore F. , Repetti Rena L. , Reynolds Bridget M. , Chung Paul J. , Arevalo Jesusa M. G. , Cole Steven W. 
Published: May 2017

AbstractHigh conflict and low warmth in families may contribute to immune cells developing a tendency to respond to threats with exaggerated inflammation that is insensitive to inhibitory signaling. We tested associations between family environments and expression of genes bearing response elements for transcription factors that regulate inflammation: nuclear factor kappa B (NF-κB) and glucocorticoid receptor. The overall sample (47 families) completed interviews, questionnaires, and 8-week daily diary assessments of conflict and warmth, which were used to create composite family conflict and warmth scores. The diaries assessed upper respiratory infection (URI) symptoms, and URI episodes were clinically verified. Leukocyte RNA was extracted from whole blood samples provided by a subsample of 42 children (8–13 years of age) and 73 parents. In children, higher conflict and lower warmth were related to greater expression of genes bearing response elements for the proinflammatory transcription factor NF-κB, and more severe URI symptoms. In parents, higher conflict and lower warmth were also related to greater NF-κB–associated gene expression. Monocytes and dendritic cells were implicated as primary cellular sources of differential gene expression in the sample. Consistent with existing conceptual frameworks, stressful family environments were related to a proinflammatory phenotype at the level of the circulating leukocyte transcriptome.
Cardiac MRI T1 mapping in unrepaired anomalous left coronary artery from the pulmonary artery
Authors: Hanneman Kate , Wald Rachel 
Published: May 2017

AbstractA 29-year-old gravida1para1woman presented with increasing fatigue. Multi-modality imaging demonstrated the left coronary artery arising from the main pulmonary artery with large collateral vessels in the interventricular septum, in keeping with unrepaired anomalous left coronary artery from the pulmonary artery. Cardiac MRI T1 mapping demonstrated globally elevated non-contrast T1 and extracellular volume fraction values, which suggested the presence of diffuse interstitial myocardial fibrosis. Cardiac MRI T1 mapping allows for a new dimension of myocardial characterisation, providing insight into subtle, diffuse abnormalities at the tissue level.
Leveraging bilingual terminology to improve machine translation in a CAT environment
Authors: ARCAN MIHAEL , TURCHI MARCO , TONELLI SARA , BUITELAAR PAUL 
Published: May 2017

AbstractThis work focuses on the extraction and integration of automatically aligned bilingual terminology into a Statistical Machine Translation (SMT) system in a Computer Aided Translation scenario. We evaluate the proposed framework that, taking as input a small set of parallel documents, gathers domain-specific bilingual terms and injects them into an SMT system to enhance translation quality. Therefore, we investigate several strategies to extract and align terminology across languages and to integrate it in an SMT system. We compare two terminology injection methods that can be easily used at run-time without altering the normal activity of an SMT system: XML markup and cache-based model. We test the cache-based model on two different domains (information technology and medical) in English, Italian and German, showing significant improvements ranging from 2.23 to 6.78 BLEU points over a baseline SMT system and from 0.05 to 3.03 compared to the widely-used XML markup approach.
The analysis of VERITAS muon images using convolutional neural networks
Authors: Feng Qi , Lin Tony T. Y. ,   for the VERITAS Collaboration
Published: May 2017

AbstractImaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.
Galaxy Classifications with Deep Learning
Authors: Lukic Vesna , Brüggen Marcus 
Published: May 2017

AbstractMachine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.
Effects of mergers on non-parametric morphologies
Authors: Bignone Lucas A. , Tissera Patricia B. , Sillero Emanuel , Pedrosa Susana E. , Pellizza Leonardo J. , Lambas Diego G. 
Published: May 2017

AbstractWe study the effects of mergers on non-parametric morphologies of galaxies. We compute the Gini index, M20, asymmetry and concentration statistics for z = 0 galaxies in the Illustris simulation and compare non-parametric morphologies of major mergers, minor merges, close pairs, distant pairs and unperturbed galaxies. We determine the effectiveness of observational methods based on these statistics to select merging galaxies.
Challenges in Timeseries Analysis from Microlensing
Authors: Street R. A. 
Published: May 2017

AbstractDespite a flood of discoveries over the last ~ 20 years, our knowledge of the exoplanet population is incomplete owing to a gap between the sensitivities of different detection techniques. However, a census of exoplanets at all separations from their host stars is essential to fully understand planet formation mechanisms. Microlensing offers an effective way to bridge the gap around 1–10 AU and is therefore one of the major science goals of the Wide Field Infrared Survey Telescope (WFIRST) mission. WFIRST’s survey of the Galactic Bulge is expected to discover ~ 20,000 microlensing events, including ~ 3000 planets, which represents a substantial data analysis challenge with the modeling software currently available. This paper highlights areas where further work is needed. The community is encouraged to join new software development efforts aimed at making the modeling of microlensing events both more accessible and rigorous.
Starspot migration in close binaries: A fast parameters evaluation from large sky surveys
Authors: Debski B. , Zola S. 
Published: May 2017

AbstractWe developed a method that allows to classify the light curves of eclipsing binaries of the W UMa type (EW) with respect to their intrinsic variability. The algorithm measures several features of light curves, such as the amplitude of the O’Connell effect, the separation and location of maxima brightness as well as depths of the minima in subsequent orbital periods. This method is capable of distinguishing systems with presumed magnetic activity present from these without it, as well as recognizing systems with starspots migration and those with other types of intrinsic variability manifestation. The classification is done in an automatic way without a time consuming, visual inspection of light curves.
Uncertain Photometric Redshifts with Deep Learning Methods
Authors: D’Isanto A. 
Published: May 2017

AbstractThe need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.
Exploring the Parameter Space of Compact Binary Population Synthesis
Authors: Barrett Jim W. , Mandel Ilya , Neijssel Coenraad J. , Stevenson Simon , Vigna-Gómez Alejandro 
Published: May 2017

AbstractAs we enter the era of gravitational wave astronomy, we are beginning to collect observations which will enable us to explore aspects of astrophysics of massive stellar binaries which were previously beyond reach. In this paper we describe COMPAS (Compact Object Mergers: Population Astrophysics and Statistics), a new platform to allow us to deepen our understanding of isolated binary evolution and the formation of gravitational-wave sources. We describe the computational challenges associated with their exploration, and present preliminary results on overcoming them using Gaussian process regression as a simulation emulation technique.
Application of Compressive Sensing to Gravitational Microlensing Experiments
Authors: Korde-Patel Asmita , Barry Richard K. , Mohsenin Tinoosh 
Published: May 2017

AbstractCompressive Sensing is an emerging technology for data compression and simultaneous data acquisition. This is an enabling technique for significant reduction in data bandwidth, and transmission power and hence, can greatly benefit space-flight instruments. We apply this process to detect exoplanets via gravitational microlensing. We experiment with various impact parameters that describe microlensing curves to determine the effectiveness and uncertainty caused by Compressive Sensing. Finally, we describe implications for space-flight missions.
Morphology and Interaction of Galaxies using Deep Learning
Authors: Caro Fernando , Huertas-Company Marc , Cabrera Guillermo 
Published: May 2017

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.
Automatic Source Classification in Digitised First Byurakan Survey
Authors: Topinka Martin , Mickaelian Areg , Nesci Roberto , Rossi Corinne 
Published: May 2017

AbstractThe Digitised First Byurakan Survey (DFBS) provides low dispersion optical spectra for about 24 million sources. A two-step machine learning algorithm based on similarities to predefined templates is applied to select different classes of rare objects in the dataset automatically, for example late type stars, quasars and white dwarves. Identifying outliers from the groups of common astrophysical objects may lead to discovery of rare objects, such as gamma-ray burst afterglows.
An autoregressive model for irregular time series of variable stars
Authors: Eyheramendy Susana , Elorrieta Felipe , Palma Wilfredo 
Published: May 2017

AbstractThis paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.
The European perspective for LSST
Authors: Gangler Emmanuel 
Published: May 2017

AbstractLSST is a next generation telescope that will produce an unprecedented data flow. The project goal is to deliver data products such as images and catalogs thus enabling scientific analysis for a wide community of users. As a large scale survey, LSST data will be complementary with other facilities in a wide range of scientific domains, including data from ESA or ESO. European countries have invested in LSST since 2007, in the construction of the camera as well as in the computing effort. This latter will be instrumental in designing the next step: how to distribute LSST data to Europe. Astroinformatics challenges for LSST indeed includes not only the analysis of LSST big data, but also the practical efficiency of the data access.
The WFIRST Science Archive and Analysis Center
Authors: Heap Sara R. , Szalay Alexander S. ,   the WFIRST Science Archive Team
Published: May 2017

AbstractThe Wide Field Infrared Survey Telescope (WFIRST) is a 2.4 m telescope with a large field of view ( ~ 0.3 deg2) and fine angular resolution (0.11”). WFIRST’s Wide Field Instrument (WFI) will obtain images in the Z, Y, J, H, F184, W149 (wide) filter bands, and grism spectra of the same large field of view. The data volume of the WFIRST Science Archive is expected to reach a few Petabytes. We describe plans to enable users to find the data of interest and, if needed, to analyze the datain situusing sophisticated software tools provided by the archive. As preparation, we are building a mini-archive that will help us to define realistic science requirements and to design the full WFIRST Science Archive.
Classification of galaxy type from images using Microsoft R Server
Authors: de Vries Andrie 
Published: May 2017

AbstractMany astronomers working in the field of AstroInformatics write code as part of their work. Although the programming language of choice is Python, a small number (8%) use R. R has its specific strengths in the domain of statistics, and is often viewed as limited in the size of data it can handle. However, Microsoft R Server is a product that removes these limitations by being able to process much larger amounts of data. I present some highlights of R Server, by illustrating how to fit a convolutional neural network using R. The specific task is to classify galaxies, using only images extracted from the Sloan Digital Skyserver.