dataesr/scientific-paragraphs-categorization huggingface dataset:
We relied on the posterior estimates of the episodic birth-death of the conifer fossil record and calculated the times of speciation and of extinction as the mean of the posterior samples from each replicate. Thus, we obtained 10 posterior estimates of the times of speciation and extinction for all genera and used them as input data in all subsequent analyses. These analyses focused therefore exclusively on the estimation of birth-death parameters (i. e. , without remodeling preservation or reestimating times of speciation and extinction). This procedure reduced drastically the computational burden while allowing us to account for the preservation process and the uncertainties associated with the fossil ages. We used the estimated times of speciation and extinction of all taxa to test whether speciation and extinction dynamics correlate with abiotic factors using the global temperature or instead, correlate with biotic factors via competition and/or positive interaction between species through the rise of angiosperms.
We ran the PDDM model using default gamma priors on the baseline speciation and extinction rates and normal priors on the correlation parameters α and β for speciation and extinction, respectively  (6). We ran 10 million MCMC iterations with sampling frequency of 1,000 and combined the posterior samples of the parameters after excluding the first 20% of the samples as burn-in. We monitored chain mixing and effective sample sizes by examining the log files. Posterior samples of the parameters were summarized over all replicates as mean values and 95% credibility interval. We considered the correlation to be statistically significant when zero was not included within the 95% credibility interval of α and β. Data Availability. Phylogenetic trees and fossil occurrence data have been made freely available in a publicly accessible database (https://doi.
ACKNOWLEDGMENTS. We thank three anonymous reviewers who provided insightful comments to improve the study. We are grateful to  Andrew Leslie  and  Michael Donoghue  for discussions. We thank  John Alroy ,  Allister Rees ,  Bruce Tiffney ,  Hallie Sims , and  Carlos Jaramillo  for their contributions of paleobotanical data to the Paleobiology Database. This study benefited from  Marie Curie International Outgoing Fellowship 627684  (to F. L. C. ), Carl Tryggers Stiftelse Grant  12 : 24  (to F. L. C. ),  Swiss National Science Foundation   PCEFP3_187012 ,  FN-1749  (to D. S. ),  Swedish Research Council  Grants  2019-04739  (to D. S. ) and  2019-05191  (to A. A. ), the  Swedish Foundation for Strategic Research  (A. A. ), the  Biodiversity and Ecosystems in a Changing Climate Programme  (A. A.
We present a description of the Australian Dark Energy Survey (OzDES) and summarise the results from its six years of operations. Using the 2dF fibre positioner and AAOmega spectrograph on the 3. 9-m Anglo-Australian Telescope, OzDES has monitored 771 AGN, classified hundreds of supernovae, and obtained redshifts for thousands of galaxies that hosted a transient within the 10 deep fields of the Dark Energy Survey. We also present the second OzDES data release, containing the redshifts of almost 30,000 sources, some as faint as r AB = 24 mag, and 375,000 individual spectra. These data, in combination with the time-series photometry from the Dark Energy Survey, will be used to measure the expansion history of the Universe out to z ∼ 1. 2 and the masses of hundreds of black holes out to z ∼ 4.
Additionally, OzDES has obtained spectra for a number of ancillary projects, such as improving and quantifying the accuracy of redshifts obtained from broad-band photometry. In total, about 40% of the fibre hours not used for active supernovae, host galaxies and AGN were used for these projects. This paper presents results from the full six years of OzDES, and describes the second OzDES data release 2. After giving a brief summary of the principal scientific aims of OzDES in Sec. 2, we provide an overview of OzDES operations for the last three years of the survey. Throughout the paper we will use Y4, Y5, and Y6 to denote the fourth, fifth and sixth years of OzDES. The observing strategy for the first three years of OzDES (Y1, Y2 and Y3) and the precursor survey are discussed in two papers:  Yuan et al.
(2015)  gives an overview up the end of Y1 (2013), including the OzDES precursor survey, and  Childress et al. (2017)  gives an overview up until the end of  Y3 (2015). We will refer to these papers as Yu15 and Ch17 henceforth. The reliability and completeness of OzDES redshifts are analysed in Sec. 4, and the data release is described in Sec. 5. In the sections that follow, we examine various aspects of the survey, such as the frequency at which we were able to target AGN, and how the signal-to-noise ratio behaves with time for objects that have the longest integration times: the host galaxies of transients. Before giving the summary of the paper in the final section, we compare OzDES to TiDES, a OzDES-like survey using the 4MOST facility  (Swann et al. 2019). All magnitudes listed in this paper are measured using 2 diameter apertures and are on the AB magnitude system.
DES aims to place constraints on the dark energy equation of state parameter and deviations from General Relativity using four astronomical probes: weak gravitational lensing, galaxy clusters, the large scale distribution of galaxies, and type Ia supernovae. Results from small subset of the DES data set have been published in a number of papers, the most recent of which are constraints that combine supernovae, gravitational lensing, and galaxy clustering  (Dark Energy Survey 2019a). DES data have been used in many studies covering a diverse range of fields  (Dark Energy Survey 2016). Notable examples include studies of bodies in the outer solar system  (Khain et al. 2018 , for example), AGN accretion disk sizes  (Mudd et al. 2018; Yu et al. 2020) , dwarf galaxies and stellar streams in the halo of the Milky Way  (Marshall et al.
Using an initial sample of 207 spectroscopically confirmed type Ia supernovae from the first three years of data in combination with 122 low redshift supernova from the literature and constraints from the Cosmic Microwave Background (CMB), the Dark Energy Survey (2019b) obtained a 7% constraint on a constant dark energy equation-of-state parameter. Analysis of the full DES sample is ongoing, and the final cosmological results will include approximately an order of magnitude more supernovae, the majority of which will be classified photometrically. At the same time, OzDES has monitored a sample of 771 AGN up to z ∼ 4 with the goal of measuring black hole masses using the time lag between variations in the continuum, measured from the broad-band photometry obtained by DES, and the broad lines, measured from the spectra obtained by OzDES.
Instrumental Setup Apart from the final OzDES run in Y6 (run 041), the instrumental setup used in Y4, Y5 and Y6 was identical to the set up used in Y3. The set up in Y1 and Y2 and the precursor survey were slightly different. During these years, the central wavelength of the blue grating was set 20 Å to the blue. Details of the setup and the reasons for the change between Y2 and Y3 can be found in Ch17. The final OzDES run was not officially scheduled as an OzDES run. At the beginning of 2019, an opportunity arose to take additional data in the C3 and X3 fields during time that was allocated to the HSC Transient Survey, which has similar aims to the SN programme in OzDES, but is targeting fainter, more distant host galaxies. For this run, and this run only, we used the setup used by the HSC Transient Survey, which uses the x6700 dichroic (OzDES uses the x5700 dichroic) and a wavelength setting that results in a wavelength coverage that is shifted about 1000 Å to the red, from 3700 Å-8800 Å to 4700 Å-9800 Å.
Observing Strategy, Calibration and Data Reduction During each run, we aimed to get at least two 2400 second exposures per field. This was not always possible due to the length of some runs and weather conditions. Of the 31 runs that were scheduled for OzDES, we were able to observe all 10 fields on 16 occasions. There were only two, relatively short runs where we did not observe any fields because of poor weather. Overall, ∼ 25% of time was lost to poor observing conditions. Another ∼ 5% of the time was sub-optimal (thick cirrus and/or poor seeing), and was used for the back up programme. Observing logs for the final three years of OzDES, including the amount of time lost for poor weather, are included in the appendixes. The two extra deep fields, C3 and X3, had the highest priority, so these fields were the most frequently observed, as can be seen in Fig.
These artifacts are described in section C3. All data were processed at the telescope, and combined with data taken during earlier observing runs. Once processed, all raw and reduced files were then archived on a server in Canberra. Typically, this was done daily, after the night had ended, but before the start of the next night. After each field was processed, the data was made available for OzDES team members 4  dedicated to the task of measuring redshifts within 24 to 48 hours of the data being taken. With the exception of transients and AGN, sources with secure redshifts (see the next section) were removed from the target catalogues and not re-observed during subsequent nights. During some of the longer OzDES runs, we observed  a number of the DES fields on multiple occasions. If this occurred, then all objects that were observed on the first occasion would be given higher priority in subsequent observations, unless they had a secure redshift and had been removed from the target catalogue.
REDSHIFTS As noted in Ch17, redshifts have been measured using MARZ  (Hinton et al. 2016 ) since the start of Y3. Earlier than this RUNZ, which was developed by Will Sutherland, had been used. Measuring redshifts is a task that is performed by two redshifters, whose results are collated, scrutinised and then merged by the chief redshift whip. Merging is also done using MARZ. The merged results then enter the OzDES database, from which all other products are derived. OzDES uses a redshift flag to quantify the confidence that the reported redshift is correct. As reported in Yu15 and Ch17, the flag can take on five main values. • Q = 4, redshift based on multiple strong spectroscopic features matched, > 99% confidence. • Q = 3, redshift based typically on a single strong spectroscopic feature or multiple weak features, > 95 % confidence.
A number of the fainter SN hosts have been observed over several years and 20 or more observing runs. As more data were obtained and added to earlier data, they would have been re-redshifted, sometimes twice or more per run, which can occur if they were observed over multiple times during a run. Some sources have dozens of redshift measurements. Every time a source was redshifted, there is a chance of an erroneous redshift being assigned to that source. We can get a rough estimate of how often this happens by searching for objects that obtained a Q = 3 quality redshift during one run, but did not subsequently obtain a Q = 3 or Q = 4 quality redshift with more data. We search the database for examples where a Q = 3 quality redshift was assigned once and not assigned again, even if the exposure time had increased by a factor of two or three by the end of Y6.
ACKNOWLEDGMENTS Parts of this research were supported by the Australian Research Council under grants DP160100930, FL180100168, and FT140101270. Based in part on data acquired at the Anglo-Australian Telescope, under programme A/2013B/012. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past, present, and emerging. We acknowledge the support of James The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium.
Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universi-ties for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants  AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016 -0588, SEV-2016 -0597, and MDM-2015-0509, some   This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U. S. Department of Energy, Office of Science, Office of High Energy Physics. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
Tocknell and Simon O'Toole from Data Central in helping us prepare OzDES-DR2. L. G. was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No. 839090. F. H. P. was supported by an Australian Government Research Training Programme Scholarship. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey.
of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA programme of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe (CNPq grant 465376/2014-2).
This enables the team to identify large numbers of MDR patients but misses others because such tests are not always ordered. Patients who had a positive test before admission might also be missed. Care units complete initial screening for MDR and inform the IC team using alert systems that recall available data on current and prior history of MDR colonization or infection. Different expert groups have proposed specific screening rules for hospital patients, e. g. the alert system described for MRSA by Evans et al. [2]. Data mining techniques can also be used to generate alert rules automatically. There has been a large volume of work dedicated to medical data mining  [3] , including identification of patients at risk of contagious infection  [4]  or risks factors for MDR or MRSA harbouring  [5] [6] [7] [8].
In our case, we used MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data), a rule-based classification algorithm adapted to specificities of medical data  [9]. First, MOCA-I is able to process binary or qualitative data (ordered or not) with more than 15,000 variables, when the previously presented approaches deal with a small number of variables (n≤50)  [4, [6] [7] [8]. This eliminates the need for data filtering and the risk of setting aside useful information. Secondly, an interesting feature of MOCA-I is its capacity to manage highly imbalanced data sets. According to the MDR 2014 report from RAISIN (French Nosocomial Infection Warning and Surveillance Investigation Network), the incidence density of MRSA is 0. 27 per 1000 hospitalization days. This explains why many classical data mining algorithms fail  [10].
Data set 2. 1. 1. Data set elaboration The data used for this retrospective study was obtained from the annual activity records of the 750-bed Lille Catholic Hospitals (St-Philibert and St-Vincent-de-Paul hospitals, Lille -France) in 2013, which represents 48,945 hospital stays, all units combined. During this period, the IC (Infection Control) team identified 340 stays concerning patients who were tested positive for MDR before or during their stay, including 128 for MRSA. Our preliminary work focused on MRSA, which has long been used in France as a marker for nosocomial diseases. Seeing the promising results, we have extended to MDR requiring additional precautionary measures as recommended by the French Hospital Hygiene Society  [13] : MRSA, enterobacteria BLSE, pseudomonas aeruginosa and acinetobacter baumannii.
When one of the antibiotics was mentioned for a patient using the drug's proprietary name or the international non-proprietary name, or a close approximation thereof (e. g. amoxicilin instead of amoxicillin), it was added to the patient's data set. If an antibiotic was found in a report or letter up to 6 months before the start of the stay, the number of days between this report and the first day of the stay was also added. Such reports were also searched to find elements indicating a patient's poor nutritional status including specific words (undernutrition, poor nutritional status) or blood test results (albumin, CRP, pre-albumin). We also used the patient's address to identify nursing home residents. Specific terms using the name of specific nursing or retirement homes were taken to indicate the patient resided in a nursing home.
Dividing the dataset We followed the same protocol as Tandan et al. [17] , we divided the data set into training data and test data to detect overfitting. Table  2  gives insights about the data set repartition. Two-thirds of the set, i. e. 32,192 stays included 235 stays involving known MDR patients of which 85 were MRSA. The other third of the data set, i. e. 16,753 stays included 105 stays concerning MDR patients (43 MRSA patients).
The rules also identify metabolic diseases as a risk factor. These diseases group together factors such as diabetes mellitus for MRSA  [8, 15]  or other factors such as poor nutritional status or obesity, for which we have not found a literature reference. Certain risk factors are related to coding practices. Several rules do not specifically contain the code for MRSA (ICD-10 B956) but have neighboring codes (ICD-10 B95 to B98) that correspond to other bacteria. MOCA-I focused more on the neighboring codes because they produced more pertinent results. This would suggest that the coding process is not necessarily straightforward for practitioners who may often choose a code for bacteria other than MRSA. It is also noteworthy that certain candidate risk factors are not present in the rules, e.
Even languages like Smalltalk that have extensive reflective features present many possibilities for improvement  [45]. In the case of Smalltalk, structural reflection stops at the method level: methods are objects, but the structure inside of methods is not modeled. From a behavioral point of view, neither message sending nor access to variables are reflectively accessible. The Reflectivity subsystem of Pharo  [3]  is a practical approach to improve the reflective capabilities of Pharo from both a structural and behavioral point of view. Behavioral reflection using Reflectivity, paired with the live programming paradigm provided by Smalltalk, provides a stable and robust way of dealing with unanticipated behavioral adaptation. Reflectivity has been put to the use in many contexts, either in Pharo itself, where it provides key mechanisms for the system, or in connected works.
PBR provides fine-grained spatial and temporal selection over controlled behavior and reified run-time entities. Reflex pioneers the concept of metalinks. But here, the link is installed on a hook set. A hook set defines a set of operations at the Java bytecode level. Hooksets can only be defined at load time and thus have to be anticipated by the programmer, while the (un)installation of links happens dynamically at run time. As Reflex was working with bytecode level abstractions, it was realized using bytecode transformation. We discuss related work in section 2. 2. Partial Behavioral Reflection can serve as an implementation substrate for Aspect Oriented Programming  [27]. A discussion of the relationship between AOP and Partial Behavioral Reflection is out of the scope of this paper and has been discussed by Tanter  [62].
However, it seems not possible yet to fully replace an original node by another node (the instead of Reflex and Reflectivity), nor to query specific part of the execution context (the reifications). Object-centric instrumentation, although it seems possible through the conditioning of the instrumentation, is not covered by the Truffle Instrumentation Framework. However, the possibilities of efficient and flexible instrumentation offered by the framework are appealing for experimenting new and high-performance implementations of Reflectivity in Truffle based languages.
Reflectivity: Overview Reflectivity improves the reflective abilities concerning both structure and behavior  [16]. The Reflectivity model therefore consists of two related parts: Sub-method reflection  [17]  and Partial Behavioral Reflection  [61]. Sub-method Reflection. It provides a fine-grained model of method structure down to the AST level, by making the AST available inside the language  [17]. Partial Behavioral Reflection. On top of the structure provided by sub-method reflection, Reflectivity implements behavioral reflection by annotating the structural model with metalinks. To that end, a metalink annotates an AST node, that is expanded (with instrumentation), compiled and executed on-the-fly. A metalink can be seen as being installed on the base-level of a program, leading to execution of code on a meta-level.
Pharo Implementation Aspects Reflectivity achieves sub-method and partial behavioral reflection by the instantiation, the configuration and the installation of metalinks on AST nodes of a program. The current Pharo implementation of Reflectivity takes advantage of the Pharo live programming environment, and makes possible to define and modify reflective operations in an unanticipated manner while the program is running. Sub-method reflection. Reflectivity reimplements sub-method reflection by providing on-demand surrogates of instrumented methods named reflective-methods as sketched in figure  2. Each time a metalink is installed on a method, the AST of this original method is annotated. A reflective-method is generated by recompiling the code from the original method with instrumentation defined by metalinks installed on its AST.