dataesr/scientific-paragraphs-categorization huggingface dataset:
The organisation of such production is the responsibility of the Monte Carlo Coordination Meeting (MCCM) team, part of the Physics Performance and Datasets coordination area. The MCCM team collects inputs from the Physics Coordination leaders and from all the level two groups, draws a plan to deliver the agreed datasets exploiting the computing and time resources available at best, and submits detailed production request to the operation team of the Computing coordination area. Three key roles take part in this process: • generator contact: collects the needs for simulated datasets from within the detector or physics group she/he represents, proposes them to the MCCM for production and composes and tests the configuration of the physics event generator to provide the desired collision type and final state; • generator convener: scrutinises and approves the event generator configurations proposed by the generator contact; • request manager: defines and configures production campaigns and submits requests to the production infrastructure.
Since mid 2014, the Monte Carlo Management (McM) web-based platform is the service used by the generator contacts, generator conveners and request managers to exchange information and submit the production requests. McM aggregates the needs for generation and processing of simulated events, holds the configuration of the processing jobs, provides their bookkeeping and prioritisation and submits the requests of events production to the computing resources. number of event to be processed, the alignment and calibration scenario, the name of the dataset to be produced. The wmAgent framework acts on the processing requests delivered to the request manager and assigns them to a pool of CMS computing resources (at Tier-1 and Tier-2 centres), determines the splitting of events across the jobs, handles recovery attempts of processing failures and and publishes completed datasets to dbs  [6] ; • statsDb: a CouchDB-based database which, for any submitted processing request (MC or data alike), dynamically aggregates the status and growth of datasets, during and after their production stage; • production Monitoring platform (pMp): pMp dynamically aggregates status of processing requests and size of the output dataset by importing information from McM and dbs/statsDb, and provides monitoring plots; its statistics which can be browsed, either for a single request or for a set of requests form the same chained campaign or the same campaign.
Requests Definition and Handling Processing requests for events simulation are split across campaigns and classified according to the simulation step they execute. Depending on the physics goal a given simulated dataset is intended to achieve, the production of events ready for analysis can comprise up to six different steps, see figure  2 : • wmLHE: simulation of the hard event by specialised event generator programs, resulting in events written in LHE format  [7] ; • generation: hadronization of the hard event, resulting in the kinematic description of the final state particles which propagate from the collision point towards the CMS detector; • simulation: interaction of the final state particles with the sensitive volumes and material budget of the CMS apparatus, resulting in a collection of time-stamped energy deposits for every sensor; • digitisation: emulation of the CMS front-end electronics, resulting in digital information formatted as if it was raw data acquired by the real experiment; • reconstruction: unpacking of the raw data and construction of physically interpretable objects such as tracks, clustered calorimetric deposits and particle flow candidates  [8] ; the sequence of algorithms of this step are the same for simulated and for real events; The capability of factoring the production steps offered by the CMSSW framework brings several key advantages, but comes with a cost of complexity.
Among the advantages the fact that the earlier steps of the chain (typically up to the simulation) can be executed ahead of time, while the subsequent steps (notably the reconstruction) are still being developed and validated; in addition, different decay channels (in the generation step) or scenarios of alignment and calibration (in the digitisation) can be simulated branching from the same events different dataset versions, with no need of repeating the previous common steps. The complexity arises from the need of defining and submitting to production multiple processing requests for any given dataset needed for analysis: as shown in figure  2  (top), when all the six production steps are necessary, grouping is possible such that only three processing requests need to be placed. The configuration of event processing requests in McM is structured around four main entities, which are at the basis of the architecture of the service and are intended to facilitate and logically organise large sets of interdependent requests: • Campaign: it is a set of requests for the same physics analysis goal, sharing software release, beam energy and event processing configuration.
Currently there are 83 campaigns defined in McM. • Flow: it connects a dataset produced in a campaign to the subsequent request which is part of the next campaign (e. g. the output of the simulation step is flown as input to the campaign executing digitisation and reconstruction); flows implement the modifications to the baseline processing configuration of a given the campaign to implement a specific processing scenario, like for instance the pile up or the event content. Currently there are 178 flows defined in McM. • Chained campaign: a sequence of campaigns connected by flows determining the succession of processing steps and campaigns which are needed to deliver datasets for analysis. Currently there are 317 chained campaign defined in McM. • Chained request: a concrete set of processing requests (currently there are about 18000 chained requests in McM), starting from a root request (e.
g. a given generated event topology) and going through the steps of a chained campaign. Currently there are about 10000 chained requests defined in McM. The request managers are in charge of constructing campaigns, flows and chained campaigns: these three constitute the back-bone of the McM design. Once they are in place, McM takes care automatically of a large set of mechanical steps needed for the submission of processing request, among which: the validation of the generator configuration (by triggering the execution of a test job on a limited number of events), the measurement of the generator efficiencies and CPU time per event and the actual submission to the request manager -see figure  2  (top) for a detailed break-down. The actual submission of processing request to the computing operations group consists of one chained request per desired analysis dataset.
In 2014 a novel kind of processing request has been made available to McM allowing the submission to production of multiple requests in the same workflow, saving labor and shortening the time needed to deliver datasets. As shown in figure  2  (bottom), the current application of such novel approach has allowed the wmLHE, generation and simulation steps to be executed in a 'task-chained' request at the same processing site. With the pile up scenarios simulated for RunII, the digitisation step can be reliably executed only at the at Tier-1 centres, which limits the scope of task-chaining to the set of simulation steps which can be executed at the same site, namely: from wmLHE to simulation (which Tier-2's can handle) and from digitisation to miniAOD (which can be processed only by Tier-1's).
Requests Monitoring: the pMp platform McM offered a suit of monitoring plots from the start, which proved very useful and created the use case for their own expansion. The interactive production of such graphs used the same database back-end and the processing resources needed by the McM service itself. To assure that the service availability would not be jeopardised by excessive traffic, the access to the monitoring had to be restricted to the production managers, generator convenors and a few other users. The production Monitoring platform (pMp) is a new service recently deployed in production at CMS to expand the capabilities of monitoring the advancement of simulated events production and the MCCM operations. pMp has been designed to expand the suit of monitoring plots and allow also the generator contacts and any CMS users to follow the progress of requests/campaigns; the service is deployed on an independent server and uses a back   These are the key functionalities offered by pMp • has access to the requests and campaign information from McM and to the historical advancement of all requests • allows the monitoring of single requests, chained campaigns or full campaigns • offers: 1) present snapshot view, where number of events/requests or processing time can be shown in a large set of configurable histograms, and 2) a historical view, where the number of expected and produced events are plotted as a function of time; see figure  3  • the present view can show static values as defined in McM or update them with live information as a request is completed • the entries displayed in either view can be filtered by variables such as the status of the processing request, the level two group which placed the request, or the priority.
The McM front end web interface is based on recent server infrastructure technology (CherryPy + AngularJS), while the back-end database uses CouchDB. As represented in figure 1, these are the services McM interacts with: • wmAgent [5]: McM submits requests for event production to the request manager infrastructure of the CMS computing project, by providing the CMSSW (the offline software framework used by CMS) job configuration and other key parameters, for instance the 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015) IOP Publishing Journal of Physics: Conference Series 664 (2015) 072018 doi:10. 1088/1742-6596/664/7/072018.
The only known scientific study was conducted near Sydney in 2007  (Divljan et al. 2009 ). Over a 140-day period, a total of 164 dead or injured flying foxes were collected and data were compiled from 136 carcasses. Eighty or so bats per week exceeded the number allowed by permits. The sex ratio was strongly skewed towards females (1:1. 73), of which 54 (65 %) were lactating at the time. Thirteen of these were shot while carrying their dependent young, while 41 pups would have been left behind in the roost to die. Hence, the total estimate of flying foxes that died due to shooting in the orchard over the two-week period was 205. Collected bats suffered from various injuries, and at least 30 % (44 % including the pups left in the camp) were alive and unattended more than 8. 5 h after shooting  (Richards et al.
Acknowledgements We are grateful to  C. Korine ,  N. Singaravelan  and  V. Tatayah  for their comments on an earlier draft; to  T. Kingston  for helpful editing; and to  N. Islam ,  A. Tsoar  and  W. Yapa  for their assistance. Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Cerebral NIRS monitoring Based on data recorded with the cerebral oximeter INVOS 5100c using the adult SomaSensor (SAFB-SM) (Covidien, Boulder, CO) on more than 400 preterm infants at the Wilhelmina Children's Hospital in Utrecht, the normal range of rStO 2 in stable very preterm infants has been defined to be from 55% to 85% during the first 72 hours of life (Petra Lemmers and Frank van Bel, Utrecht, unpublished data). This range is considered safe and the boundary thresholds for possible interventions as listed in our treatment guidelines. 18 ] [24]  We tested the suitability of devices by comparing absolute values, repeatability, and sensitivity to changes in oxygenation on the adult forearm. 25 26 The predefined criteria were absolute values and a dynamic range within 5% points of the INVOS oximeter and absolute repeatability better than 6% (within participant standard deviation) for acceptability of devices.
Randomisation and masking The web based randomisation was set up and handled by the Copenhagen Trial Unit according to a computer generated allocation sequence of 1:1 with block sizes 4 and 6 in random order concealed for the investigators. The allocation was stratified for gestational age (<26 weeks or ≥26 weeks). Singleton infants were randomised individually and twins were allocated to the same treatment group. If both twins could not be included owing to lack of equipment, we included the infant born last. Owing to a lack of NIRS devices in each unit, higher order of multiple births could not be randomised. The NIRS devices were either locked in a box, or we used a device specific research mode rStO 2 blinding. All data were sent real time to a laptop. Software developed specifically for the study could either show or conceal the current rStO 2 depending on group allocation.
Results Between 1 June 2012 and 31 December 2013 we screened 370 infants for eligibility, and 166 were randomised (fig  2⇓ ). In four instances only one twin could be included. Two infants in the control group did not have any NIRS data recorded; one had consent withdrawn and there were technical problems for the other. All other data were collected per protocol for both. In the remaining children the median monitoring time with an rStO 2 signal was 67. 7 hours (range 0. 9-71. 4 hours) with 67. 9 hours (interquartile range 64. 7-69. 0 hours) in the experimental group and 67. 4 (interquartile range 63. 9-68. 9) hours in the control group. The variability in monitoring time was mainly due to death within the first three days of life (two infants were withdrawn from the trial by parental request and in four there were technical reasons for reduced NIRS recording time).
SDO and SOHO imagers provided data from the Earth's perspective, whilst the STEREO twin instruments procured images from the orthogonal directions. This spatial configuration of spacecraft allowed optimum simultaneous observations of the AR cluster and the coronal mass ejections that originated in it. Quadrature coronal observations provided by STEREO revealed a notably large amount of ejective events compared to those detected from Earth's perspective. Furthermore, joint observations by SDO/AIA and STEREO/SECCHI EUVI of the source region indicate that all events classified by GOES as X-ray flares had an ejective coronal counterpart in quadrature observations. These results have direct impact on current space weather forecasting because of the probable missing alarms when.
The CM location makes the cluster a perfect object of study, so that a wealth of space-and Earth-borne solar instrumentation was able to register its phenomena in a variety of wavelengths and regimes. On that day, the occurrence of a number of eruptions could be deduced from low-coronal data. Based on coronagraph data from Earth's perspective, only one halo CME was reported on that day. However, images of the solar corona provided by the Solar-Terrestrial Relations Observatory (STEREO) spacecraft, located almost 180 • appart on that date, reveal a surprisingly large amount of Earth-directed CME eruptions. Joint observations of the source region procured by the Solar Dynamics Observatory (SDO) and STEREO were key to find the precise location and time of each eruption in the chromosphere and low corona, and at the same time unexpectedly disclosed that all events classified by GOES as X-ray flares had an ejective coronal counterpart in quadrature observations.
Instrumentation and Data Data captured by spacecraft on a quadrature configuration for the date of interest, covering a variety of regimes, are crucial for this study. Images from Earth's perspective were recorded by the Solar Dynamics Observatory (SDO;  Pesnell, Thompson, and Chamberlin, 2012)  and by the Solar and Heliospheric Observatory (SOHO;  Domingo, Fleck, and Poland, 1995). The quadrature views were provided by instruments on the Solar-Terrestrial Relations Observatory (STEREO;  Kaiser et al. , 2008). The STEREO mission consists of two identical spacecraft following Earth's orbit around the Sun, one ahead of it (ST-A) and the other behind it (ST-B). The spacecraft separate at a rate of ≈ 45 • per year, which implies that for 11 November 2010 the separation between them was ≈180 •.
7 to 6. 0 and from 3. 7 to 32 solar radii, respectively. Quadrature observations of the solar white-light corona were provided by the COR1 and COR2 coronagraphs of the STEREO/SECCHI investigations suite, which cover 1. 4 to 4. 0 and 2. 0 to 15 solar radii of the solar corona. A number of CME catalogs and data bases were consulted as an alternative way to obtain the number of CMEs arising from the complex of ARs facing Earth. These are the SOHO/LASCO CME Catalog (http://cdaw. gsfc. nasa. gov/CME list)  (Yashiro et al. , 2004) , the Computer Aided CME Tracking catalog (http://sidc. oma. be/cactus/) (CACTus;  Robbrecht and Berghmans, 2004) , the Solar Eruptive Event Detection System (http://spaceweather. gmu. edu/seeds/) (SEEDS;  Olmedo et al. , 2008) , the Coronal Image Processing (http: //alshamess.
, 1996) , was proposed as the origin of the pre-flare brightenings observed before the first GOES peak. This peak corresponds to a clearly visible eruptive event. The second GOES peak, with maximum at 07:35 UT, corresponds to a confined event. These authors suggested that this event was probably initiated by reconnection at the second null point; this process was forced by the destabilization of a portion of F1 (Figure  3 ) that was not observed to erupt during the previous GOES peak. Futhermore, the topology explaining the pre-flare brightness observed before FL8 is also shown in Figure  15    The activity and location of the complex of ARs implies an increased potential of production of Earth-directed events that motivates a close inspection of the ensuing ejective events. The results of investigating coronagraph data recorded from the Earth's perspective, which is the case of the SOHO/LASCO experiment, are presented in the next subsection.
STEREO/SECCHI CME Detections on 11 November 2010 Examination of SECCHI data was originally meant to aid in establishing associations between the flares attributed to the AR cluster and the CMEs detected from Earth's perspective. However, SECCHI data was revealing not only in regard to this aspect, but also to the recognition of deficiencies in the monitoring of Earth-directed events. Several ejective events not evident from Earth's perspective were discerned; these were not hinted by the ejective activity observed in SDO/AIA. The particular configuration of the STEREO spacecraft allowed us to observe the AR cluster on their respective solar limbs, i. e. on the west limb of ST-B and on the east limb of ST-A. Events listed in Table  3  are unambiguously distinguished and observed to be born in the AR cluster.
Discussion and Conclusions The fast emergence of AR 11123 within the remnants of AR11121 close to CM and the series of flares taking place on 11 November 2010 pose this cluster as an important candidate for producing Earth-directed events. Its privileged location at CM makes it a perfect subject of study, though the CMEs arising from it were poorly observed from the Earth's perspective of SOHO/LASCO. Contrary to the expected, these CMEs appeared neither as full/partial halo nor as impressive events from Earth's perspective, except for one faint, ragged, partial halo CME. The difficult task of associating the eruptive activity observed on the cluster with the CMEs detected by SOHO/LASCO from Earth's perspective triggered a deeper study involving data from the twin STEREO spacecraft. When observing the cluster from a quadrature perspective, the STEREOs revealed almost twice as many CMEs, being some of them even massive and structured.
The STEREO/SECCHI EUVI instruments allowed the identification of limb low-coronal activity associated to the eruptions. Coronal events otherwise unseen from Earth's perspective also did not leave obvious imprints on SDO/AIA disk images of the low corona, posing these events as "stealth" CMEs  (Robbrecht, Patsourakos, and Vourlidas, 2009). However, all Earth-directed coronal events and their low coronal sources detected in STEREO/SECCHI EUVI could be associated subsequently, in the worst cases, to almost imperceptible activity on the best SDO/AIA images of the AR cluster at CM (see remarks on stealth CMEs in  Howard and Harrison, 2013). Kilpua et al. (2014)  remark that out of the 16 CMEs related with their set of 20 ICMEs, 10 were stealth, i. e. with no obvious EUV on-disk activity. For the date of our case study, SDO was already operative, and the iteration between SDO/AIA and STEREO/SECCHI EUVI data was crucial to identify the precise location and time of each inconspicuous eruption.
Acknowledgements HC and CHM acknowledge financial support from the  Argentinean  grants  PICT 2012-973  ( ANPCyT)  and  PIP 2012-100413  (CONICET). HC and CHM are members of the  Carrera del Investigador Científico (CONICET). AMC acknowledges funding from the  Comisión Nacional de Energía Atómica (CNEA). The authors are thankful to  Germán Cristiani  for his help with GOES data and to the reviewer for valuable comments and suggestions. The SOHO/LASCO data are produced by an international consortium of the  NRL (USA) ,  MPI für Aeronomie (Germany), Laboratoire d'Astronomie (France) , and the  University of Birmingham (UK). SOHO is a project of international cooperation between  ESA  and  NASA. The  STEREO/SECCHI  project is an international consortium of the NRL, LMSAL and  NASA/GSFC (USA) ,  RAL  and  Univ.
Study Design This was a descriptive, prospective, epidemiological study of dengue disease in the pediatric wards of 3 sentinel regional hospitals in the Philippines (see Fig. , Supplemental Digital Content 1, http://links. lww. com/INF/C197) using data collected between November 2009 and November 2010. The 3 hospitals were the José B. Lingad Memorial Regional Hospital (JBL); Davao Medical Center (DMC; now the Southern Philippines Medical Center), both of which care for patients aged ≤15 years, and the Western Visayas Medical Center (WVMC), which cares for patients aged ≤18 years. These 3 hospitals are located on the 3 major islands of the country: Luzon, Mindanao and the Visayas. The primary objective of this study was to estimate the proportion of laboratoryconfirmed dengue cases among the clinically suspected dengue cases hospitalized in these pediatric wards.
ACKNOWLEDGMENTS The authors thank  Anamae Obcena  for study coordination and all study participants, their families and the medical and laboratory staff of the 3 sentinel hospitals. The authors also thank  Florence Coux  for study monitoring;  Céline Zocchetti  for project management;  Christine Luxemburger  for support in study conceptualization;  Laurence Pollissard  for contributions to study design and  Ruby Dizon. Medical writing services were provided by  Kurt Liittschwager  (4Clinics, Paris, France). Support for medical writing assistance and financial support for this study were provided by  Sanofi Pasteur. Sanofi Pasteur  participated in study design; the collection, analysis, and interpretation of data; writing of the report and the decision to submit the article for publication.
Many thanks to the participants at the  Li Fang-Kuei Young Scholars Symposium  in 2013, and to two anonymous reviewers, for useful comments and suggestions. I alone am responsible for remaining shortcomings. This research was financially supported by  Agence Nationale de la Recherche  as part of the projects  HimalCo ,  ANR-12-CORP-0006 , and  LabEx EFL ,  ANR-10-LABX-0083 -Investissements  d'Avenir.