dataesr/scientific-paragraphs-categorization huggingface dataset:
Tier-1s provide access to large CPU farms and a mass storage system (MSS) with disk caches and tape libraries. They are different from Tier-2s in two main aspects. Tier-1s provide 24/7 support operation, while Tier-2s are operated during business hours and unattended in between. In addition, Tier-2s do not have tape libraries at the sites and operate purely with disk caches. The network between the sites is the backbone of the CMS computing infrastructure as it is based on transferring files between the sites for access. The network between the Tier-0 and the Tier-1s is dedicated and called the LHC Optical Private Network (LHCOPN)  [5]. The Tier-2s are connected through general purpose scientific networks (GPNs) in their host countries. The main data flows can be separated into archiving and serving.
It is followed by a reconstruction step to provide input to workflows to calculate alignment and calibration constants and for data quality monitoring. • 100% of the selected collisions are repacked into the CMS event format, and split into primary datasets according to physics content by trigger path groups (introducing an overlap of 10-25% between primary datasets). The reconstruction of all of the collisions is started 48 hours after their recording in the detector, in order to use the alignment and calibration constants calculated using the express processed collisions. Tier-1/2: Simulation Collisions are generated using theory software packages and their detector response is simulated using GEANT4  [8]. This workflow is CPU dominated and needs little or no input. Tier1: Reconstruction Collisions both from the detector and simulations are reconstructed on the Tier-1 level, in the case of detector data using updated alignment and calibration constants and/or software versions compared to the Tier-0 reconstruction.
The workflows are executed at the sites where the input is stored custodially on tape and requires prestaging of the input to disk for efficient access. For simulated collisions, additional protonproton interactions in the same or nearby bunch crossings (pileup) are taken from a simulated pileup dataset, and superimposed on the hard collision. The average number of pileup interactions can reach 140 in later stages of LHC data taking and was at average 30 in 2012, making this the most I/O intensive workflow of CMS. Tier-2: Analysis Analysis is performed by accessing both recorded and simulated collisions on the Tier-2 level. Physicists use a mixture of official and self-written code to extract physics measurements. In contrast to the production workflows, analysis involves multi-user access to files which have been pre-distributed to Tier-2s.
Every analysis job is preferentially processed at the location of its input, and the user-generated output is stored outside the CMS file catalogues. CMS operates the following services based on GRID technologies from the EGI [9], ARC [10] and OSG  [11]  GRID middlewares under the umbrella of the Worldwide LHC Computing GRID (WLCG) infrastructure  [12]  to support the execution of the CMS workflows: Transfer system: Files are organized in datasets with similar physics content. The CMS transfer system is called PhEDEx  [13]  and replicates datasets to destination CMS sites on request using GRID transfer protocols. It is based on well established and constantly monitored site-to-site PhEDEx links, using the ability of PhEDEx to mark enabled and tested network paths. Bookkeeping system: Metadata of files and datasets is catalogued in the CMS Dataset Bookkeeping System (DBS)  [13].
Constants system: Alignment and calibration constants can be accessed by every executable on every site through the Frontier  [14]  system based on a hierarchy of SQUID caches [15]. Software distribution: CMS software is available as pre-compiled releases and installed at the sites through GRID jobs  [16]  or accessed through a shared GRID based file system called CVMFS  [17] , based on SQUID caches. Submission infrastructure: Different submission infrastructures are used by CMS to submit jobs to all sites to execute production workflows and analysis. The infrastructures used are the gLite  [18] , HTCondor G  [19] , and glidein Workload Management Systems (WMS)  [20]. Site availability monitoring: The availability of the GRID services at the sites is probed periodically using the WLCG's Site Availability Monitoring (SAM) [21] system.
Site stress tests: All sites are tested periodically with complete analysis workflows submitted by the automated HammerCloud  [22]  infrastructure. Monitoring: All jobs are instrumented to report status information to the WLCG DashBoard  [23]  which is combined with site downtime information from the WLCG infrastructure as well as site readiness information compiled from the SAM and HammerCloud tests  [24] , taking into account valid PhEDEx links. Production system: All production workflows are executed through a state based production system called WMAgent  [25]. Analysis system: The CMS analysis system helps users to find the location of the input to their analyses and splits the workflows into individual jobs which are then submitted through the submission infrastructure. Unlike the production system, the analysis system, called the CMS Remote Analysis Builder (CRAB)  [26] , allows the user to move user-specific code to the GRID worker nodes of the jobs.
Both CSP and CRC shifts proved vital to the operation of the computing infrastructure during LHC run 1. One lesson learned is that the system has to be monitored constantly and its operation has to be compared to nominal operations parameters. Most CSP checks could be automated. This has not happened yet due to time and manpower constraints. CMS plans to pursue the automation of the CSP monitoring checks during LHC run 2. This process is expected to require a significant amount of time and effort. The tasks of the CRC role will not be automated, because it is important that trained experts are always available to handle critical situations. CMS computing operates many different systems as described in Section 2, many of which rely on web interfaces like the transfer system or the metadata service.
Stable operation of these systems is one of the cornerstones of successful operation of the infrastructure. But stable 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP2013) IOP Publishing operation is in contradiction to rapid development cycles and roll-out of new features caused by the evolution of data taking, especially crucial during the early phases of data taking. To enable the services to evolve while providing stable services, CMS established a specialized deployment and testing procedure for its web cluster providing access to the services called cmsweb. All services under the cmsweb platform are installed on a common load-balanced web platform. Not only web interfaces to services are provided, but also specialized functionalities like NoSQL databases.
CMS's goal for LHC run 2 is to transition all services (if possible) to this common testing and deployment scheme. The Tier-0 operation has a special role in the overall operations effort. In case of problems with the Tier-0 operation, data taking can be impacted up to a complete stop of detector operation if the buffer space at CERN fills up. If the express processing was to stop, the data quality monitoring could not be guaranteed to the fullest extent, which in turn could mean reduced data quality because of the lack of feedback to detector operation. To guarantee the flawless operation of the Tier-0, CMS designated two full-time operators who are located on either side of the Atlantic to increase the business day coverage. This turned out to be crucial especially during the startup phase of data taking.
This learned lesson is not expected to be solved completely for LHC run 2. But CMS expects to reduce the manpower needs for the operation of the production infrastructure further because workflows are expected to be more stable and experience gained in LHC run 1 will help. One of the major contributors to the completion latency of central workflows was identified in LHC run 1 to be the Tier-1 storage model. The model foresees that workflows will run at the site which holds the custodial tape replica of the input. This is not a big issue if many different workflows for inputs stored at different Tier-1s need to be processed, because the distribution of inputs across the Tier-1s would even out the CPU usage. During LHC run 1, on many occasions this was not the case. For example the reconstruction of only a few primary datasets was necessary to bring high priority analyses to a conclusion quickly.
These patterns were not foreseeable when the datasets were placed at the sites. This situation could lead to some of the Tier-1s being busy while others were idle, therefore introducing an inefficiency in CPU usage on the Tier-1 level. To remove this source of inefficiency, CMS is asking all Tier-1s to reconfigure their MSS setup to separate disk and tape into a large disk-only pool used for serving data to the CPU farm and storing the output coming from the workflows, and a small disk pool connected to the tape system to archive files. The transfer system will be used to archive files on tape and to recall them to the disk-only pool if processing is needed. Due to the disk/tape separation, input files can be recalled to multiple sites and output can be consolidated to single tape archive instances, therefore removing the inefficiency observed in LHC run 1.
This setup also enables the publication of all files on disk at the Tier-1 sites to the CMS remote access federation (AAA, for Any data, Any time, Anywhere)  [27] , something which was not possible before in most cases due to the strict coupling of disk and tape. This is expected to increase the flexibility of the CMS processing infrastructure further. In the end, this setup will also allow analysis workflows on the Tier-1s which has not yet been possible due to the possibility that random access to all samples stored on tape could overwhelm the tape systems. Physics support is concerned with the other side of CMS computing operations: the support for the analysis of the data and MC samples by the CMS physicists. Unlike the central production workflows, the analysis workflows come in a multitude of different forms and flavors with diverse requirements on resources on the worker nodes as well as non-standard code that analysts wrote themselves.
As users are in control of the definition and validation of their analysis workflows, this is a much more challenging multi-user problem than the production case, where workflows are run using the same production role. CMS has a dedicated support team of experts to help users in analyzing the data and in supporting the analysis GRID tools. Inefficiencies observed during LHC run 1 are connected to the way CMS distributes samples to the Tier-2s and how jobs are only sent to Tier-2s that provide local access to them. In the current model, the available Tier-2 disk space is logically separated into a portion managed by the transfer system holding datasets and a second portion for users to store their analysis outputs outside the metadata booking system and the transfer system. The portion managed by the transfer system is further subdivided into quotas for centrally placed samples like commonly used MC samples (typically simulations of common standard model physics processes) and data AOD samples, and quotas for the individual physics groups.
Summary & Outlook LHC run 1 provided a lot of operational challenges, sometimes more manpower-intensive than expected. Contributing to the intensity and complexity of the challenges were the rapidly changing conditions and requirements of data taking, central workflows, and analysis. These things were not unexpected and are expected to continue during LHC run 2 to some extent. During LHC run 1, CMS gained valuable operational experiences and identified areas of improvement that are currently being worked on actively: • Disk/tape separation at the Tier-1s • Dynamic data placement and automatic cache release for samples on Tier-2s • Full deployment of the global CMS remote access federation (AAA) • Global glideinWMS pool and prioritization With these improvements, CMS expects to be able to meet the challenges of LHC run 2.
We supplement the dataset with observations of the Hα line, taken with the Visible Spectrograph and Polarimeter (VEGA) on the CHARA array on Mount Wilson, California, USA. The observations are described in Sect. 2. We present the optical-to-infrared stellar and circumstellar spectra, and the most important trends and signatures found in the AMBER, CRIRES and VEGA data in Sect. 3. With baselines up to 120 m and a high spectral resolution ( = 25 km s -1 ), we are able to trace photocenter shifts at a few micro-arcseconds resolution in the line emission. In Sect. 4, the interferometric observations are compared to a geometric model, which consists of a Keplerian disk and halo. We discuss the validity of this and other possible model geometries in Sect. 5. The implications for the object's evolutionary state are also considered.
2014). All the observations were performed using the fringe-tracker FINITO  (Le Bouquin et al. 2008). The data reduction was performed following standard procedures described in  Tatulli et al. (2007b)  and  Chelli et al. (2009) , using the amdlib package, release 3. 0. 6, and the yorick interface provided by the Jean-Marie Mariotti Center (JMMC) 1. Raw spectral visibilities, differential phases, and closure phases were extracted for all the frames of each observing file. The frames of 5 to 15 consecutive observations were merged and a global selection of 80% of the highest quality frames was made to achieve higher accuracy on the visibilities and phases. The UV coverage didn't change significantly during these sequences. The transfer function was obtained by averaging the calibrator measurements (from 3 to 5 observing blocks, depending on the night), over the entire sequence of observations, after correcting for their intrinsic diameters (Uniform Disk).
VLT/X-shooter Spectroscopy Spectra of HD 50138 were obtained on 2013-02-14, UT 04:53, with X-shooter on the VLT. X-shooter covers the optical to near-infrared spectral region in three separate arms: UVB (290-590 nm), VIS (550-1010 nm) and NIR (1000-2480 nm;  Vernet et al. 2011). Narrow slits were used: 0. 5, 0. 4 and 0. 4 in the three spectrograph arms, respectively. This resulted in a spectral resolving power R ≡ λ/∆λ of 9100 in UVB, 17,400 in VIS and 11,300 in NIR. The S/N was 120 at 450 nm and 55 at 2150 nm. The frames were reduced using the X-shooter pipeline (version 1. 5. 0, Modigliani et al. 2010) , employing the standard steps of data reduction, i. e. order extraction, flat fielding, wavelength calibration and sky subtraction. The wavelength calibration was verified by fitting selected OH lines in the sky spectrum.
VLT/CRIRES Spectro-astrometry Spectra of HD 50138 were obtained in the K-band with VLT/CRIRES, for the purpose of spectro-astrometry of the Brγ line. The data were taken on 2009-11-05 at UT 06:15. A slit of 0. 2 was used, resulting in a spectral resolving power R = 100, 000. The calibrator HD 60803 was observed directly afterwards. The rotator position angles were at 15 • , 75 • and 135 • , and the respective counter-parallel angles. For a detailed description of the data reduction procedure, see  Kraus et al. (2012).
Supplementary data Additional spectroscopic and photometric data from previous studies and data archives are used in this paper. An optical highresolution (R ∼ 80, 000) spectrum was taken in March 2007 with the Narval spectropolarimeter at the telescope Bernard Lyot at the observatory of Pic du Midi, France. This spectrum was also presented in BF09. Mid-infrared spectra were obtained on 1999-12-24 and 2003-01-08 with The Aerospace Corporation's Broad-band Array Spectrograph System (BASS) at the Infrared Telescope Facility  (Sitko et al. 2004 ). This instrument covers the 3-13 µm wavelength region. BASS is described more fully in  Sitko et al. (2008). Magnitudes from the IRAS observatory are also used.
The position angle agrees with the disk major axis derived by BF11, which is perpendicular to the polarization angle. The blueshifted and red-shifted parts of the line are clearly separated between the disk hemispheres. The largest spatial offsets are seen in the lowest velocity channels. This result suggests that the Brγ emission originates in a disk at ψ ∼ 71 • with a radially decreasing rotation around an axis of ∼ 160 •. The result is consistent with the ∆φ measurements from the other AMBER datasets which were not included in the fit (Fig. A. 2, bottom) , indicating that no additional asymmetries are resolved at this angular resolution. Other rotation fields, e. g. rigid rotation or equatorial outflow, are not suggested as the velocity decreases with radius and the velocity gradient is parallel to the major disk axis.
Supporting evidence for the existence of these components is given by the spectrum of the source. -The strong variability of shell-or disk-dominated spectral line profiles indicates that significant changes take place in the system's appearance on timescales as short as months, probably inhibiting a unified "fit" to all the datasets. -The absolute offset of the photocenter in the continuum may be caused by an asymmetric disk geometry which affects the line-and continuum-emitting regions differently (e. g. , a puffed-up rim seen under an inclination angle). Alternatively, a binary companion may cause the offset. -No definitive conclusion on the evolutionary state could be reached. The system is possibly a binary, and bears much resemblance to both Herbig B[e] and post-main-sequence systems.
Acknowledgements. The authors thank  Olga Hartoog  for providing the FASTWIND models. Jerome Bouvier ,  Alex Carciofi ,  Ewine van Dishoeck ,  Carsten Dominik ,  Alex de Koter ,  Henny Lamers ,  René Oudmaijer ,  Philippe Stee  and  Rens Waters  are acknowledged for constructive discussions about the source. The  ESO  staff are acknowledged for technical support of the observations. The authors have made use of the AMBER data reduction package of the  Jean-Marie Mariotti Center 2. We also used the SearchCal service 3 co-developped by Lagrange and IPAG, and of CDS Astronomical Databases SIMBAD and VIZIER 4. The CHARA Array, operated by  Georgia State University  through the  College of Arts and Sciences  and  NSF   AST 12-11129 , was built with funding provided by the  National Science Foundation ,  Georgia State University , the  W.
Molecular microbial ecology investigations often employ large marker gene datasets, for example, ribosomal RNAs, to represent the occurrence of single-cell genomes in microbial communities. Massively parallel DNA sequencing technologies enable extensive surveys of marker gene libraries that sometimes include nearly identical sequences. Computational approaches that rely on pairwise sequence alignments for similarity assessment and de novo clustering with de facto similarity thresholds to partition high-throughput sequencing datasets constrain fine-scale resolution descriptions of microbial communities. Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into 'MED nodes', which represent homogeneous operational taxonomic units. By employing Shannon entropy, MED uses only the information-rich nucleotide positions across reads and iteratively partitions large datasets while omitting stochastic variation.
When applied to analyses of microbiomes from two deep-sea cryptic sponges Hexadella dedritifera and Hexadella cf. dedritifera, MED resolved a key Gammaproteobacteria cluster into multiple MED nodes that are specific to different sponges, and revealed that these closely related sympatric sponge species maintain distinct microbial communities. MED analysis of a previously published human oral microbiome dataset also revealed that taxa separated by less than 1% sequence variation distributed to distinct niches in the oral cavity. The information theory-guided decomposition process behind the MED algorithm enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision.
Introduction Marker gene analyses of microbial diversity require categorizing DNA sequences into ecologically meaningful units. The two major approaches for partitioning large datasets include: (i) taxonomic classification of sequences through comparison with curated databases, for example, GreenGenes  (DeSantis et al. , 2006; McDonald et al. , 2012)  or SILVA  (Pruesse et al. , 2007; Quast et al. , 2013)  and (ii) de novo clustering by sequence similarity to define operational taxonomic units (OTUs). The number of unique taxonomic assignments in reference databases limits diversity descriptions because microbiologists have not defined a unified species concept  (Gevers et al. , 2005; Doolittle and Zhaxybayeva, 2009)  and molecular databases generally lack discrete name assignments for the astonishing number of uncultured microbes  (Stewart, 2012).
In contrast, taxonomy-independent OTU clustering divides datasets into OTUs without requiring a curated taxonomic database  (Sun et al. , 2012). Database independence frees OTU clustering approaches from the apparent limitations of taxonomy and allows the detection of organisms that have not yet been characterized  (Ley et al. , 2006). FastGroup, among the first published algorithm to assign sequences to OTUs, described the three major steps employed by most contemporary OTU finding algorithms: '(1) compare all the sequences in a dataset to each other, (2) group similar sequences (4 ¼ 97% identical to each other) together, and (3) output a representative sequence from each group'  (Seguritan and Rohwer, 2001). During the last 10 years, various de novo approaches have facilitated OTU identification, including algorithms that typically use pairwise alignments to compute a distance matrix before grouping sequences into OTUs (DOTUR  (Schloss and Handelsman, 2005) , ESPRIT  (Sun et al.
, 2009) , SLP  (Huse et al. , 2010) , HPC-CLUST (Matias  Rodrigues and von Mering, 2014) ) as well as greedy but more computationally efficient heuristics that perform sequence comparison and OTU identification simultaneously (i. e. ,  ESPRIT-Tree (Cai and Sun, 2011) , CD-HIT  (Li et al. , 2001) , UCLUST  (Edgar, 2010) , DySC  (Zheng et al. , 2012) ). Various software platforms, including mothur  (Schloss et al. , 2009) , QIIME  (Caporaso et al. , 2010) , CD-HIT Suite  (Huang et al. , 2010)  and VAMPS  (Huse et al. , 2014) , have adopted most of these OTU identification strategies. Pairwise sequence identity computes the number of mismatched nucleotides between two aligned reads. However, random PCR or sequencing errors reduce the similarity between two sequences and result in false OTUs even after stringent quality filtering  (Huse et al.
, 2007; Quince et al. , 2009; Eren et al. , 2013b). To lessen the impact of sequencing errors and subsequent OTU inflation, researchers use relaxed identity thresholds for de novo clustering. For example, the de facto 97% identity often defines the diversity of clusters for 16S rRNA data. Although it reduces the number of observed OTUs, it also generates phylogenetically mixed OTUs  (Koeppel and Wu, 2013). Such OTUs aggregate distinct organisms and conceal ecologically important findings  (Eren et al. , 2013a). Not all nucleotide positions in a dataset contribute equally towards partitioning marker gene data into ecologically meaningful units. Woese  (Woese et al. , 1985)  used short, informative oligonucleotide signatures in the 16S rRNA gene to distinguish between major bacterial clades.