dataesr/scientific-paragraphs-categorization huggingface dataset:
Charpinet et al. : TESS first look at evolved compact pulsators ( PEA 4000119301 ) and  Stellar Astrophysics Centre (SAC) at Aarhus University. We thank the  TESS team and staff and TASC/TASOC  for their support of the present work. G. F. acknowledges the contribution of the Canada Research Chair Program. W. Z. acknowledges the support from the  National Natural Science Foundation of China (NSFC)  through the grant  11833002 , the support from the  China Postdoctoral Science Foundation  through the grant  2018M641244  and the  LAMOST  fellowship as a Youth Researcher which is supported by the  Special Funding for Advanced Users , budgeted and administrated by the  Center for Astronomical Mega-Science, Chinese Academy of Sciences (CAMS). V. V. G. is an F. R. S. -FNRS Research Associate. ZsB and ÁS acknowledge the financial support of the  GINOP-2.
3. 2-15-2016-00003 , K-115709 ,  K-113117 ,  K-119517  and  PD-123910  grants of the  Hungarian National Research, Development and Innovation Office (NKFIH) , and the  Lendület Program of the Hungarian Academy of Sciences , project No. LP2018-7/2018. DK acknowledges financial support form the  University of the Western Cape. I. P. acknowledges funding by the  Deutsche Forschungsgemeinschaft  under grant  GE2506/12-1. AP and PK-S acknowledge support from the  NCN  grant no. 2016/21/B/ST9/01126. ASB gratefully acknowledges financial support from the  Polish National Science Center  under projects No. UMO-2017/26/E/ST9/00703  and  UMO-2017/25/B ST9/02218. SJM is a DECRA fellow supported by the  Australian Research Council  (grant number  DE180101104 ). KJB is supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award AST-1903828.
Material and methods Retention and excretion data were collected from several series of wound experiments using Am alone or in presence of Pu, and in two physico-chemical forms (nitrate or MOX: mixed oxide of uranium and Pu isotopes, also containing Am-241 as decay product of Pu-241). Results of activity measurements in 189 tissues and 161 urine samples were organized in a standardized table format, which built up to 350 lines of data. Descriptive statistics analysis was carried out for each experimental group using STATBIODIS. Am tissue retention and urinary excretion of each animal were analysed as percentage of Am administered activity. To compare Am relative systemic biodistribution between the selected experimental conditions, skeleton to liver activity ratios were also computed. Statistical tests were used to evaluate the influence of the physico-chemical form and of the presence of plutonium in the contaminant on Am tissue retention and urinary excretion.
Studies and benchmark datasets for the evaluation of different SLAM techniques exist above the water  (Sturm et al. , 2012; Geiger et al. , 2013). Using such datasets, issues have been reported in the literature such as trajectory or scale drifts reporting also a metric accuracy evaluation  (Mur-Artal et al. , 2015; Mur-Artal and Tardós, 2017; Yuan et al, 2017). Under the water, because of the complexity of the environment and thus difficulty to obtain very accurate 3D measurements, comparative studies have been focused on simulated trajectories  (Duarte et al. , 2016)  or on comparing real-time results with photogrammetrically derived trajectories obtained offline, in post processing, using for example BINGO software or Agisoft Photoscan  (Drap et al, 2015; Nawaf et al. , 2018)  or Colmap structure from motion application  (Ferrera et al, 2019).
Image dataset characteristics The image dataset used in this experimentation consists of a session of 10 HZ LR stereo-rectified full HD images extracted from a session acquired with an ORUS3D 3Kv on the COMEX test-field. To reduce the risk of failures in the image orientation due to the almost featureless surface texture of the pool floor, some metallic plates with contrast random pattern were installed at the pool floor (Figure  4 b, c ). The image acquisition was carried out with the ORUS 3D set with slightly negative buoyancy, hanged from a floating platform and manoeuvred by a diver as depicted in figure (Figure  4 a ). The images were acquired at night to test the system in lighting conditions similar to those available at typical operative depths. The image sequence used in this experimentation included approximately two thirds to record the vertical walls of the pool facing each other, and another third for the rectilinear part.
Reference 3D trajectory, angles and mesh A reference trajectory was generated orienting the images in Agisoft Metashape application, using the full dataset (including HR cameras), then a bundle adjustment was run by constraining the solution using the inertial sensor and coded target coordinates as soft constraints (COMEX ORUS 3D software). A dense point cloud through dense image matching techniques and a mesh was generated (Figure  5 ). A cloud to mesh distance check was performed to verify the consistency between the reconstructed mesh and the reference coded target coordinates. The RMS of distances resulted below 1 mm. Left and right camera exterior orientations (trajectories and angles), coded targets and mesh were used as reference for comparing the real-time visual odometry and SLAM, processed offline in this experiment.
Visual odometry with windowed bundle adjustment The visual odometry method tested in this study is a mono variant of the method originally developed in  Drap et al. (2015)  and further improved in  Nawaf et al. (2018)  with the addition of a windowed bundle adjustment. The method is composed of two steps, first, a relative pose estimation is performed on each new image following multiple view geometry fundamentals, second, a structure and motion bundle adjustment approach is applied to a set of images defined by a sliding window that selects the last n images. The implementation was tested using the python scripting API of Agisoft Metashape application. A visual odometry procedure was simulated so that images are processed in sequence as if they are acquired in real-time. The influence of window size on the accuracy of the estimated trajectory was studied and window sizes of 3 and 4 are reported.
it involve? (2) Will I have less bleeding and pain? (3) Will the fibroids go away or get smaller (in size)? (4) Is it safe to get pregnant? (5)  What are the side effects? and (  6 ) What are the more serious risks? The uterine fibroid Option Grid is designed to be accessible to patients across socioeconomic and health literacy strata using plain language (readability of 6th grade or less). See Fig. 2  to view the text version of the Option Grid. There will be two versions of the uterine fibroid Option Grid available in this trial: (i) text and (ii) text with pictures (Picture Option Grid). The Picture Option Grid uses the same evidence as the text version while integrating images, thus utilizing pictorial superiority to help patients better understand and remember information  [26]. Both the text and Picture Option Grid will be available to clinicians and patients on paper pads and integrated into the EHR, leading to a website where the clinician can select the uterine fibroid Option Grid and choose any number of treatment options specific to the patient to generate the comparison table.
3  for a mockup of the paper pad stub). The research assistant at each site will refer to the numbered stubs to document the number of patients who received an Option Grid on a weekly basis. The research assistant will keep a log of the numerator and denominator in an Excel file and will report the data back to the Dartmouth project staff on a weekly basis via the Qualtrics database. For sites with EHR access to the online uterine fibroid Option Grid, we will derive weblog data from EBSCO Health including the date and time the tool was generated; this data will be incorporated into measurement of the primary outcome.
Resource utilization (ambulatory and hospital). The follow-up survey will query eligible patients to self-report the number of outpatients (or visit to primary care clinician), inpatient, or emergency visits during the 3-month follow-up period. Treatment choice. Patients will identify their intended treatment choice in the post-encounter survey. We will also ask patients in the 3-month follow-up survey whether they have seen another clinician about another treatment, whether an Option Grid tool was used in that encounter, and to identify the final treatment option they selected, or will select if they proceed with scheduling the treatment intervention. Patient demographics. We will collect the patient's email address, health insurance status, age, race, gender, and spoken language via the survey they complete on the tablet computer post-encounter.
Data analysis Patient-level data will be clustered by site and clinician. Patients will be assigned to a single phase based on the date of their first encounter with a clinician at the site during the project period. Instances in which a patient has a repeated visit in the pre-and active implementation periods will be recorded to enable sensitivity analyses accounting for repeated observations. Regression analyses will compare quantitative outcome measures (including survey scores and Option Grid use) between the pre-and active implementation phases while adjusting for patient and clinician characteristics. The extent to which clinicians involve patients in decision-making for each recorded encounter will be assessed by analyzing the Observer OPTION-5 and Option Grid fidelity checklist data from each phase of the study.
Data management and storage All patient and clinician survey data will be stored in the Dartmouth Qualtrics database, a HIPAA-compliant web-based data management system. We will assign a unique study identification number to patient and clinician participants. All audio-recordings of clinical encounters and semi-structured interviews will be loaded by site-based project staff into Dartmouth's HIPAAcompliant SharePoint system. Each recording will be labeled with a unique identification number. For statistical analysis, data will be downloaded from Qualtrics and stored on an encrypted hard drive owned by Dartmouth College. Stata software will be used for statistical analysis.
Acknowledgements We would like to thank the principal investigators, clinicians, IT personnel, and other clinical staff (nurses, administrators, receptionists, etc. ) at each site who are actively making the implementation of the uterine fibroid Option Grid possible. We would like to thank  Dr. Trudy van der Weijden  for being an advisor on our project and providing feedback on the final version of our manuscript. We would also like to express our gratitude to the members of our  Community Advisory Board , in particular, our patient advisors who provide feedback to improve the overall quality of the study.
Background: The fluorescence-based quantitative real-time PCR (qPCR) is a molecular biology technique that is routinely used in laboratories. It quantitatively monitors, in real-time, the amplification of a targeted DNA (or cDNA after reverse transcription of a target RNA) molecule during the PCR. The quantification is based on the number of cycles of PCR at which the fluorescence exceeds a given threshold (Cq). Softwares associated with qPCR apparatus provides Cq for each sample and usually propose a tool for calculation for quantification and normalization for each sample. However, the use of such specific software often requires a license and/or is restricted to a given operating system. For example, CFX Maestro ™ Software license from Bio-Rad goes from 150 euros for the Mac version to 1000 euros for the Windows version (catalog price seen on 08/02/2019).
Baseline data and variables During enrollment, numerous baseline variables were recorded through completion of an extensive questionnaire, supplemented with data from the patient's comprehensive digitalized medical record. The variables encompassed, but were not limited to: Age, gender, lifestyle, prophylactic AAM at baseline, and cardiac as well as non-cardiac comorbidity. Recorded comorbidities included hypertension, heart failure, heart valve disease, ischemic heart disease, thyroid disease, diabetes mellitus (DM) of any type, chronic obstructive pulmonary disease (COPD), and renal disease.
Follow-up data and recurrent event definition Follow-up ended on March 1st 2014, and all data regarding date and duration of hospitalizations, potential progression of AF status to PermAF, and mortality in the entire follow-up period, were obtained through the patient medical records: all written text, test results and medication were available for all patients in the entire follow-up period. The records were independently evaluated by two senior medical students and author JS. Interobserver disagreements were resolved by attainment of a consensus. Recurrent events were defined as hospitalizations directly related to a new episode of symptomatic AF, with a severity or duration of symptoms leading to hospital contact, and ensuing admittance to the cardiology ward following evaluation by the cardiologist on duty.
The AF diagnosis was confirmed by ECG, and possible electrical or pharmacological CV treatment was confirmed in the medical records. Only hospitalizations where symptomatic AF with or without CV treatment was the primary reason for admittance to the ward were classified as recurrent events. The obtained recurrent event time data served as the basis for the statistical analyses, which were performed by authors OB and TM. There was no loss to follow-up in this dataset, i. e. censoring occurred only at the end of follow up for all patients. As a competing risk, a terminal event (TE) which precluded the occurrence of further recurrent events was introduced into the model. A TE was defined as either a) progression to PermAF, defined as the date on which patient and physician agreed on accepting the presence of permanent or very frequent recurrent AF, or b) death, in which case the date of death was available in the medical record and used as the terminal date.
Statistical analyses Using the survival package in R (version 3. 2. 0), Cox models for recurrent events were used to model the impact of covariates on the incidence of AFHs  [17]. The model takes into account individual patient history using a multi-state approach with three possible states: no experience of recurrent events yet, 1 recurrent event, and 2 or more recurrent events. All transition intensities were proportional to each other, and the model included an absorbing state for the TE (PermAF or death). As advised  [18] , all probabilities including the ones for the competing event were analyzed. For details concerning use of multi-state models in recurrent event analysis, see for instance Amorim, Cai  [19]  or Andersen, Keiding  [20]. Further, the multi-state approach has very recently been applied in a clinical context by the authors  [21].
Study limitations A standardized transthoracic echocardiography was not performed at enrollment. In addition, data concerning the relevant AF risk factors body mass index (BMI) and presence of sleep apnea were not recorded at patient enrollment, explaining why these variables could regrettably not be included in the analysis. Patients treated with an ablation procedure were excluded, meaning that the results in this study only pertain to AF patients who obtained an acceptable degree of symptom control without an ablation procedure, or who had one or more contraindications to an ablation procedure. Our definition of a recurrent event as an AF episode that led to hospitalization limited us from incorporating silent or less symptomatic AF episodes not resulting in hospitalization into the statistical model.
Relationship to Design Due to the context in which the French didactics emerged, its epistemological foundations and an easy access to classrooms provided by the IREM network, classroom design has always been conceived of as an essential component of the research work. This situation is reflected by the early emergence of the concept of didactical engineering and its predominant methodological role in research for several decades  (Artigue, 2014). Didactical engineering is structured into four main phases: preliminary analyses; design and a priori analysis; realization, observation and data collection; a posteriori analysis and validation. Validation is internal, based on the contrast between a priori and a posteriori analyses, not on the comparison between experimental and control groups.
Naturally, some research sits at the border of our identified streams; for example, sometimes pupils' reasoning is studied in relation to teachers' knowledge. Nevertheless, we decided to make these distinctions for the sake of clarity. When trying to distinguish main research topics and to find relevant publications by CZ and SK authors, we scrutinised scientific journals, proceedings of international conferences and books to which we had access. We searched the database of prestigious research projects awarded by the Grant Agency of the Czech Republic (GA ČR) and its Slovak counterpart, the Scientific Grant Agency of the Ministry of Education of the Slovak Republic (VEGA). For the Czech part, we also used results of a survey conducted in 2013 at all universities educating future mathematics teachers done by the former Accreditation Committee established by the Ministry of Education, Youth and Sport.
Acknowledgements Special thanks go to the editor of this book and the series editor for their helpful and constructive comments on earlier versions of this text. The same holds for  Vince Geiger , who additionally turned my German English into understandable Australian English. Mistakes, inconsistencies and errors, which may still prevail, are my responsibility. Acknowledgements I am extremely grateful to a number of people for having provided information that made it possible for me to write this chapter. I owe big thanks to the following colleagues from  Sweden  for supplying valuable information:  Paul Andrews ,  Lisa Björklund Boistrup ,  Jorryt van Bommel ,  Bengt Johansson ,  Kristina Juter ,  Ulla Runesson Kempe ,  Cecilia Kilhamn ,  Hanna Palmér ,  F. Rønning Johan Prytz ,  Andreas Ryve ,  Jeppe Skott ,  Timo Tossavainen ,  Paola Valero  and in particular  Johan Lithner  who has also read and commented on an early version of the manuscript.
Introduction Beginning in the 1970s, it was realised that the X-ray sky is full of extended sources, among which we find emission from the Milky Way itself, other Galactic sources such as pulsar wind nebulae or supernova remnants (SNRs), and extragalactic sources such as galaxies and clusters of galaxies. The typical emission components one can see in X-rays from these types of objects are thermal emission or accelerated particles radiating through the synchrotron process. In each case, their spectral signature is distinctive and recognizable. For example, in SNRs the shock wave propagating rapidly through the interstellar medium heats it up to approximately 10 7 K, resulting in thermal emission peaking in the X-ray domain. Spectro-imaging instruments such as those aboard the current generation of X-ray satellites XMM-Newton and Chandra provide data comprising spatial and spectral information: the detectors record the position (x, y) and energy E event by event, thereby providing a data cube with two spatial dimensions and one spectral dimension.
This algorithm, the generalized morphological components analysis (GMCA), was first developed by  Bobin et al. (2015) , and has recently been applied to Planck survey data to separate the image of the Cosmic Microwave Background (CMB) from the foregrounds  (Bobin et al. 2016). The application of the GMCA method to X-ray data is nontrivial. While in Planck the data are obtained in nine fixed frequencies, the X-ray photons can be binned into an arbitrarily large number of energy bins; the X-ray photon count is drastically lower at high energies, and has higher dynamic range. In addition, the X-ray data have Poisson noise whereas the GMCA method assumes an additive Gaussian noise. Here we adapt the GMCA algorithm to the study of extended sources in X-rays, and test its implementation by applying the method to SNR data.
Motivations and current methods The telescopes XMM-Newton and Chandra have provided a major step forward in effective area and angular resolution, and have led to nearly 20 years of observations resulting in deep (Megaseconds) archival public datasets. As an example, the deep Chandra ∼2 Ms observation of SNR Cassiopeia A resulted in about a billion X-ray photons. Despite this breakthrough improvement in data quality, the analysis techniques used to extract the wealth of information contained in such datasets have stalled. The main analysis challenge lies in the fact that at each position, the different spectral components (e. g. , synchrotron and thermal emission from the shocked medium and ejecta) are projected along the line of sight, and that the observed signal is a combination of these components.
Toy model definition To test the performance of the GMCA in disentangling components in X-ray data, we designed toy models inspired by real X-ray observations of SNRs. We chose to simulate a SNR similar to Cassiopeia A, one of the best-studied SNRs and one which has benefited from deep megasecond observations. Our toy models consist of a data cube composed of the sum of individual components to which we add Poisson noise. Each component comprises an image multiplied by a spectrum (see Figure  1 ). The images were obtained by applying the GMCA to real Chandra data from Cassiopeia A (see Section 8), and smoothing the output to mitigate the noise. For now, the relevance of these images is not important: we only want to ascertain if, when the components are known, the GMCA is able to disentangle them when mixed together.
Hence we set the count rate of the synchrotron and line or thermal emission to be of the order of that observed in Cassiopeia A. We then simulated two datasets, corresponding to a 1 Ms or a 100 ks observation with the Chandra ACIS-S instrument. The ratio between the strength of the main component and that of the secondary components is also an essential factor. For Model 1, we define this as the Fe line-to-synchrotron ratio at 6. 58 keV (the peak of the Gaussian); for Model 2, it is defined as the thermal emission-to-synchrotron ratio at 0. 85 keV. We progressively decrease the contrast of the second component relative to that of the synchrotron emission following 15 ratios. Fig. 3 : Left: Spectra retrieved by GMCA for different Fe line-to-synchrotron ratios in our first toy model with a total number of counts corresponding to a 1 Ms observation.
Asymmetries of the Fe K distribution in Cassiopeia A We first applied the GMCA to the Cassiopeia A observation between 5 and 8 keV, where the prominent features are known to be the synchrotron emission and the Fe K line complex. To allow for unexpected sources to be retrieved by the algorithm, it is recommended to decompose the data into a larger number of components than expected as a first guess. By doing this, we obtained three physically meaningful components in Cassiopeia A: continuum emission and two Gaussian lines that appear to be slightly shifted with respect to one another, and with respect to the Fe K average energy. The first component is undoubtedly the synchrotron emission, for which the image is coherent with our knowledge of its spatial distribution; the corresponding spectrum can be described as a power law (Fig.