dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , hold a conversation using NLP) and use stealthier assessment (gaze-behavior analysis, etc. ) to adapt itself to the needs of each individual student. If theory suggests and assumes that learning is a dynamic process that is cyclical and non-linear, the methods in which we capture and measure learning should reflect this as well as the design of future system architectures. Third, multimodal multichannel SRL CAMM data is key to understanding the dynamics of SRL during learning, problem-solving, reasoning, understanding, etc. Additional tools, methods, sensors, and techniques may be needed in the future to increase the accuracy, reliability, and validity of detecting and measuring these processes and validate the inferences researchers make about these processes to hopefully reduce the inference/increase accuracy coefficient so that intelligent systems like MetaTutor provide optimal just-in-time scaffolding.
The primary implication of this is that observed emission from a curved wall does not depend heavily on disk inclination i, whereas emission from a vertical wall depends strongly on i. McClure et al. (2013)  best fit the 2-10 μm continuum excesses of four TTSs by approximating a curved wall using two vertical walls at different radii, each with a different height, dust size distribution, and composition. The Hubble UV Legacy Library of Young Stars as Essential Standards (ULLYSES) DDT program (Roman-Duval et al. 2020), combined with the PENELLOPE Very Large Telescope (VLT) Large Program  (Manara et al. 2021a) , provides an ideal sample for exploring the variation in CTTS system properties across ranges of spectral types, ages, and masses. The Outflows and Disks around Young Stars: Synergies for the Exploration of ULLYSES Spectra (ODYSSEUS) collaboration  (Espaillat et al.
2022 ) is maximizing the scientific impact of these data by studying accretion, outflows, and disk chemistry in the largest sample of TTSs observed with the Hubble Space Telescope (HST) to date. This paper focuses on modeling the accretion and inner disks of the first sample of TTSs observed by ULLYSES. ULYSSES began observations with Orion OB1, located about 400 pc away  (Briceño et al. 2019 ). Here we study nine CTTSs in the Orion OB1b star-forming region, which has a mean age of 5. 0 Myr and a disk fraction of 12%  (Briceño et al. 2019). This is intermediate between Cloud B (2. 5 Myr, 31% CTTS) and OB1a (10. 8 Myr, 6% CTTS), indicating an intermediate level of disk evolution in the region. In this work, we model the same Orion OB1b sample of CTTSs as  Manara et al. (2021a)  to demonstrate the significance of using NUV spectra to determine the accretion and extinction properties of these targets.
Hubble Space Telescope/STIS Observations HST Space Telescope Imaging Spectrograph (STIS) and Cosmic Origins Spectrograph (COS) observations of these targets were taken as part of the ULLYSES DDT program through proposals GO16113, GO16114, and GO16115 (Roman-Duval et al. 2020; PI: Julia Roman-Duval). This paper utilizes observations of each target with the following gratings:    1  for the times of observations. Though observations were taken in the UVB, VIS, and NIR arms, we use only the NIR spectra (spectral resolution ∼11,600, plate scale 0 248 pixel -1 , using the 0 4 slit) for modeling the inner disk wall because HST data are available out to 1 μm. As described in  Manara et al. (2021a) , these flux-calibrated data are dereddened using the  Cardelli et al. (1989)  extinction law and corrected for telluric absorption using molecfit v3.
0. 3  (Smette et al. 2015; Kausch et al. 2015). Again, points with λF λ < 1 × 10 -15 erg s -1 cm -2 are removed. The X-Shooter data for the binary targets CVSO 109 and CVSO 165 are unresolved, so it is necessary to split the total flux between the two components to achieve a better representation of the resolved NIR spectra. As described in  Espaillat et al. (2022) , the X-Shooter spectrum of CVSO 109A is scaled by the J-band fluxes of the two components and their difference found by  Tokovinin et al. (2020). The J-band flux difference is not available for CVSO 165A and CVSO 165B, so these spectra are simply scaled down to align with the HST continua. This is just an approximation given that the two components have different spectral types. However, our inferred NIR flux ratio of the primary to the secondary of 2.
Transiting Exoplanet Survey Satellite Observations The Transiting Exoplanet Survey Satellite (TESS;  Ricker et al. 2014) , has observed these targets on two occasions, in Sector 6 (2018 December 11 through 2019 January 07) and in Sector 32 (2020 November 19 through December 17). The latter coincided with the observations reported here, and these data were used to estimate the stellar rotation periods of our targets (expected of order 4-9 days;  Percy et al. 2010). We note that the 27-day viewing window of TESS samples only a few of the expected periods, so this analysis only reveals gross trends in very complex light curves. We download the full-frame image data from the MAST archives using the TESScut software  (Brasseur et al. 2019). TESS images, while photometrically stable and of continuous cadence, suffer from coarse spatial resolution (21″ pixels).
TESS is a single-channel photometer with a 600-1000 nm bandpass. The temporal resolution was 10 minutes in Sector 32 and 30 minutes in Sector 6. We extract the data using aperture photometry with a 1. 5pixel radius. The background is extracted from an annulus between 5 and 10 pixels from the source. Because there are often other sources in the background annulus, we iterate on the background pixels, removing those more than 3σ from the median level until we converge on the median background level. We assume that the background is spatially flat in this region.
The best-fit model is a combination of the accretion shock model (consisting of the three columns with energy fluxes of 1 × 10 10 , 1 × 10 11 , and 1 × 10 12 erg s -1 cm -2 -cyan, sea-green, and brown dotted lines, respectively-with filling factors as indicated in the legend) and the DIAD model (consisting of the emission from the inner disk wall; lime-green dashed line). Residuals are shown in the bottom panel of each figure. Emission lines were excluded from the fit, and regions significantly affected by telluric absorption were excluded from both the observed spectra and model fitting; this produces the gaps seen in the residual plots. (a) The unscaled X-Shooter spectrum of CVSO 109 is shown in light gray. (b) The X-Shooter data for CVSO 165 are unresolved, so the NIR spectra have been scaled to align with the HST continuum of each component.
g. , D'  Alessio et al. 1998; Fischer et al. 2011). The average temperatures of the three accretion columns are 4274 K for the low flux density column, 6695 K for the medium flux density column, and 10,786 K for the high flux density column. See Table  7  in Appendix C for the best-fit temperatures for each column of each target. We are able to attain satisfactory fits to the NIR excesses of five targets, though there are a number of cases in which the photosphere goes above the data around 1 μm and thus forces the total model fit to be above the data (CVSO 58, CVSO 104, CVSO 107, CVSO 109A, CVSO 165B, and CVSO 176). In the cases of CVSO 107, CVSO 109A, CVSO 165B, and CVSO 176, there is no clear NIR excess above the photospheric and accretion shock emission, so no inner disk wall is fitted to these targets.
Uncertainties The average accretion shock model uncertainty, quantified by an MCMC nuisance parameter, is 14%. This statistical goodness of fit is a lower limit to the actual uncertainty, as it does not take the uncertainties of the input parameters, the photospheric templates, or the extinction curve into account. When accretion rates are calculated considering the uncertainties in R å and the filling factors, and assuming an uncertainty of 2 R å for the magnetospheric truncation radius R i , the median percent error increases from 4% to 32%. Uncertainties in our derived stellar radii take into account the error in the data fluxes, CTTS distance, veiling, visual extinction, and an assumed 10% error in the WTTS template fluxes. Our estimates have an average percent error of 8%. The updated luminosity measurements included in Table  3  propagate our derived error in R å , but no uncertainty is assumed for the stellar temperature.
± 254 Note. Best-fit accretion shock model parameters for each CTTS. To calculate the models, the stellar mass, distance, temperature, and veiling are adopted as given in Table  2. R i is taken to be 5 R å for all targets except CVSO 109A, which has R i = 2. 3 R å as described in Section 3. 1. For the best-fit temperatures of individual accretion columns, see Table  7  in Appendix C. (This table is available in machine-readable form. ) Once a template is chosen, it must be scaled to the data by the veiling measurement, which has its own variability and uncertainty. Three measurements of veiling, each separated by a day, were available for all targets except CVSO 90 and CVSO 104. The measured veiling for a given target varied, on average, by a factor of 1. 6 across the three epochs. By choosing the epoch closest in time to the HST observations, we minimize the influence of veiling variability, but it cannot be completely removed.
For an analysis of the effect of an enhanced ISRF on circumstellar disk evolution, see  Anderson et al. (2013). The ULLYSES survey, which will provide over 80 customcalibrated NUV spectra of CTTSs in nine star-forming regions, will allow us to examine the goodness of fit of different extinction laws in different environments. Beyond this, it would be ideal to attain extinction curves for all nearby starforming regions rather than using either a general ISM curve or the curve calculated to the specific environment of Taurus. 6. Summary 1. Accretion rates for the nine CTTSs studied here are in the range of (0. 5 -17. 2) × 10 -8 M ☉ yr -1 , relatively high for the intermediate age of Orion OB1b. 2. Our accretion rates and V-band extinctions are systematically higher than those calculated from optical data in previous works, in large part due to our wavelength coverage that extends into the NUV.
3. The NIR excesses of the five targets in which an excess is present are fit with 1200-1800 K inner disk walls located at 0. 05-0. 10 au from the host stars. 4. The choice of extinction law significantly affects the calculated accretion rate and introduces uncertainty that is difficult to quantify. Our analysis indicates that the environment of Orion OB1b is more similar to quiescent Taurus than to hot ρ Ophiuchi. Ideally, extinction curves can be calculated for each star formation region in the near future. 5. This multicolumn shock and DIAD analysis will be applied to all nine star-forming regions being observed through ULLYSES, allowing us to extend the analysis across distinct stellar populations and search for correlations between accretion and disk properties in a larger sample. Additionally, it will be applied to the four CTTSs being monitored by the ULLYSES program (GM Aur, TW Hya, BP Tau, and RU Lup).
Support for this work comes from HST  AR-16129 , as well as  NASA  through grant No. AR 16129  from the  Space Telescope Science Institute , which is operated by  AURA, Inc. , under  NASA  contract  NAS 5-26555. This work benefited from discussions with the ODYSSEUS team (https://sites. bu. edu/ odysseus/); see Espaillat et al. (2022) for an overview of the ODYSSEUS survey. J. H. acknowledges support from  CONACyT  project No. 86372  and the  UNAM-DGAPA - PAPIIT  project  IA102921. P. A. , E. F. , and Á. K. acknowledge support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 716155  (SACCRED). C. F. M. acknowledges funding by the  European Union  under the European Union's  Horizon Europe Research & Innovation Programme 101039452  (WANDA).
In recent years, software has evolved from being static, closed source, proprietary products to being dynamic, open source, ecosystems contributing to the global good. To this end, the open source software (OSS) solution and global good, Surveillance Outbreak Response Management and Analysis System (SORMAS), rapidly adjusted to the demands of the Coronavirus disease 2019 (COVID-19) outbreak by introducing a COVID-19 module. This allowed countries that were already making use of the software as part of their public health surveillance infrastructure to make use of the new module in order to respond to the pandemic. New countries in continental Europe, most notably Germany, Switzerland, Liechtenstein and France subsequently chose to adopt the software for public health surveillance purposes for the first time during 2020, requiring additional adaptations to meet local needs.
Methods From a functional perspective, SORMAS is a public health surveillance tool used to capture data on cases and contacts of over 20 types of infectious diseases. It offers a range of features and functionalities that include (at the time of writing) a dashboard with an overall system statistical overview, task management, person search, case and contact management (including follow-up), event management, sample management and immunization (including vaccination) capturing. Due to the open source nature of the project, the software is continuously evolving based on user needs, influence and feedback, with a new version of the software being published roughly every 3-6 weeks under the GPL v. 3 license. Management and documentation of the SORMAS source code repositories, backlogs and specifications happens on the open source platform GitHub (https://github.
com/hzi-braunschweig). In the SORMAS-Docker sub-project (https://github. com/hzi-braunschweig/SORMAS-Docker and https://hub. docker. com/orgs/hzibraunschweig), prefabricated container images are provided that serve as basis for a majority of SORMAS installations. Our research consists of two main parts including (1) identifying when the COVID-19 module was introduced to the SORMAS source code and (2) the analysis of the source code contributions (commits) to the SORMAS GitHub repository over the project's open source lifetime (till the end of 2021). For part 1, we identify how rapidly the COVID-19 module was introduced to the software by analyzing the GitHub user story creation date and comparing it to the release date of the version it was made available in. For part 2, we use git-bars, a utility that employs git log to render simple git commit activity bars on the terminal  [8].
SORMAS COVID-19 Module The creation of the first COVID-19 module feature request on the SORMAS GitHub repository took place on 2020-01-24  [9]. Implementation of the feature followed and introduction of the new module was on 2020-01-29 as part of the SORMAS version 1. 32. 0 release  [10]. Within a week of specification, the COVID-19 module was thus developed and ready for deployment to SORMAS instances all over the world.
SORMAS Repository Analysis We utilized the open source tool "git-bars" in January of 2022 to retrospectively analyze and visualize the SORMAS GitHub repository commit activity. For the timeframe from 2016-05 (date that SORMAS was first published open source on GitHub) to 2021-12, a total of 18534 commits to the project source code were recorded. When expressing this value in terms of years by running the command ">git-bars -p year", the result of 18534 commits, grouped over a 6 year timeframe is delivered, as is visible in Figure  1. From the results presented in Figure  1 , it is possible to see not only the most commits happening in the year 2020, but also the exponential growth during 2020 when compared to previous years. When analyzing the repository on a monthly level by running the command ">git-bars -p month", results indicate 18534 commits over 68 months as summarized in Figure  2.
As a result, by the end of 2021, an estimated 150/375 local health authorities in Germany, 8/18 regions in France, and 13/26 cantons in Switzerland, as well as Liechtenstein, were either actively using SORMAS, or were in the process of starting to use SORMAS, with at least the COVID-19 module active. Further assessments of SORMAS pertaining to management and in-country use (concerning for example user acceptance, efficiency, data quality, testing etc. ), fall outside the scope of this paper and are left to future work.
Within the survey, they provided consent to participate in the research and completed a survey on Peloton Bike use and health. Respondents were also asked to provide their Peloton username and permission for the study to access their archived usage data from the Peloton platform. Surveys were administered using the Research Electronic Data Capture (REDCap) tool  [28, 29]. REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources. Over the course of this 4-week period, 225 surveys were completed.
A total of 66 follow-up surveys were returned (response rate 45%). Peloton usage data were accessed via the "follow" function of the Peloton app. After acquiring respondents' permission, we used Peloton's application programming interface (API) to access the exercise data of our survey participants. Using each participant's Peloton username, we downloaded data from the riders' first ever cycling class through 12 weeks after the initial survey was completed, which included the date and duration of each cycling class. For each participant, we defined 2 fixed dates for our analysis: the start date (starting date of using the Peloton Bike continuously) and the initial survey date. We defined the start date as the first date after which the user had at least 4 workouts within a 4-week period. Using this definition, we were able to adjust the start date to account for participants who may have set up a Peloton account for a single trial and then entered a period of no activity until they obtained their own Peloton Bike.
Peloton Usage Data Data retrieved from the Peloton API were used to estimate exercise behavior in the 4-week period prior to the initial survey and for the 12 weeks following the initial survey. For the 4-week period prior to the survey completion, a mean number of cycling days per week was computed as an indicator of frequency of exercise. Similarly, for the 12 weeks following survey completion, frequency was expressed as mean number of cycling days per week. The duration of cycling per week (both pre-and postsurvey) was calculated as the mean number of minutes per week.
Statistical Analyses Hierarchical regression equations were used to test the association between perceived stress and exercise. Parallel analyses were conducted first using estimates of exercise participation (frequency and duration) derived from the Peloton data on cycling during the 12 weeks after the survey (frequency expressed as mean times per week and duration expressed as mean minutes per week, respectively) and second using the self-reported data on moderate to vigorous exercise reported on the 12-week follow-up survey. Step 1 of each regression analysis controlled for age and gender. Step 2 controlled for exercise behavior at the time of the first survey (either Peloton cycling data for the 4 weeks prior to the survey or self-reported moderate to vigorous exercise obtained on the initial survey).
In terms of research, detailed information that can be obtained regarding the frequency and duration of exercise can facilitate many different avenues of research. Questions about the relationship of exercise participation to different health outcomes can be more rigorously explored using exercise data that are not biased by self-report, while questions about how best to support exercise adoption and maintenance can also be addressed using metrics of duration and frequency recorded by the exercise device. Although the focus on a single mode of activity (in this case, cycling) may limit the appeal to certain subgroups of individuals and may limit the generalizability of our research findings, as a model for conducting efficacy studies, internet-connected fitness equipment is an attractive option that merits further exploration.
A high-confidence phylogeny of 155 Drosophila genomes We first used genome-scale sequence data to infer phylogenetic relationships among species in our data set. To achieve this, we annotated and generated multiple sequence alignments for 2,791 Benchmarking Universal Single-Copy Orthologs (BUSCOs; v3;  Seppey et al. , 2019; Waterhouse et al. , 2017)  across 155 independently assembled Drosophila genomes together with four outgroups (3 additional species from Drosophilidae and Anopheles gambiae;  Suvorov et al. , 2020). We used these alignments, totalling 8,187,056 nucleotide positions, and fossil calibrations to reconstruct a fossil-calibrated tree of Drosophila evolutionary history. Note that the inclusion of Anopheles as an outgroup allowed us to include a fossil of Grauvogelia, the oldest known dipteran, in our fossil calibration analysis, along with several Drosophilidae fossils and/or geological information (i.
e. , formation of Hawaiian Islands; see SI Appendix, Table  S1 ). Our phylogenetic analyses (see Materials and Methods for details) using both maximumlikelihood (ML; IQ-TREE) and gene tree coalescent-based (ASTRAL) approaches with DNA data revealed well-supported relationships among nearly all species within our dataset. Phylogenies inferred using these two approaches only differed in a single relationship, where D. DeSalle, 2018; Russo et al. , 2013; Yassin, 2013). Erroneous orthology inference as well as misalignment can impede accurate phylogenetic inference and create artificially long branches  (Mai and Mirarab, 2018). Repeating our ASTRAL analysis after removing outlier long branches via TreeShrink  (Mai and Mirarab, 2018)  resulted in an identical tree topology. Furthermore, an ML topology estimated from the dataset with more closely related outgroup species (see Materials and Methods) results in an identical topology with the aforementioned ML tree.
Genome assemblies and public data Genome sequences used by this work were obtained from concurrent projects and public databases. Genome sequencing and assembly for 84 genomes is described in  (Kim et al. , 2020). These data are available for download at NCBI BioProject PRJNA675888. For the remaining genomes: sequencing and assembly of 8 Hawaiian Drosophila were provided by E. Armstrong and D. Price, described in Armstrong et al. (in prep)  and available at NCBI BioProject PRJNA593822; sequences and/or assemblies of five nannoptera group species were provided by M. Lang and V. Courtier-Orgogozo and are available at NCBI BioProject PRJNA611543; 44 were downloaded as assembled sequences from NCBI GenBank; Z. sepsoides and D. neohypocausta were sequenced as paired-end 150bp reads on Illumina HiSeq 4000 at UNC and assembled using SPAdes v3.
11. 1 with default parameters  (Bankevich et al. , 2012) ; and 15 were generated by assembling short read sequences downloaded from NCBI SRA. For sets of unassembled short reads, we used ABySS v2. 2. 3  (Jackman et al. , 2017)  with parameters 'k=64' with paired-end reads (typically 100-150bp) to assemble the reads. Finally, outgroup genome sequences (A. gambiae, M. domestica, L. trifolii, C. hians, and E. gracilis) were obtained from NCBI GenBank. See Table  S2  for a full list of samples, strain information, accessions, and associated publications.