dataesr/scientific-paragraphs-categorization huggingface dataset:
7 ± 0. 1). The integrated spectrum does not show a spectral break. This indicates that we average over regions of different ages having different intrinsic brightness. The sum of those spectra, each having spectral breaks at different frequencies, produces the remarkably straight integrated spectrum (see the discussion below for details). High-redshift radio sources that will be detected in future radio surveys will be (in most cases) unresolved. Therefore, it is   3  (circles) and its central region (stars). The LOFAR data points are in red. The green line represents a power law fit, while the blue dashed line gives the synchrotron aging model fit for region C. important to benchmark the information obtained from the integrated flux density spectral studies against the one for the spatially resolved studies.
To expand on the different phases of radio-loud activity in this object, we used our LOFAR and WSRT observations, as well as archival VLA images to distinguish between the spectral index of the inner regions of 4C 35. 06, and the outer regions of extended radio emission. The resulting spectral index images are shown in Fig. 5. The maximum UV range of the LOFAR data set is 4 kλ, while that of the WSRT is 7 kλ × 13 kλ. The UV coverage within the range specified for the WSRT is uniform, while the LOFAR data set covers all of the spacings even though it has gaps due to the observation length. We have smoothed the WSRT image to highlight the shorter spacing's. The VLA archival images were obtained from data sets with matching UV ranges and uniform UV coverage. We see that the inner region where the core and the two brighter lobes are located shows a typical spectral index in the range of α ∼ -0.
Conclusions Studying the field centered on the nearby Compact Steep Spectrum radio source B2 0258+35A (observed during the commissioning of LOFAR), we have made a 61 MHz map of 4C 35. 06 (B2 0258+35B), a radio source hosted by the core of the galaxy cluster Abell 407. This source has a complex morphology on several spatial scales. We used our LOFAR image in combination with archival VLA imaging as well as VLSS, WENSS and other survey data to argue that the most extended emission can be interpreted as a remnant of a past episode of AGN activity. The integrated spectral index is moderately steep and shows no signs of spectral curvature. We confirm the existence of an extended emission region and derive spectral index maps of the inner parts of the source closer to the AGN. We discuss two possible scenarios regarding the nature of the source and conclude that the best explanation is that we observe two distinct episodes of AGN activity in 4C 35.
Acknowledgements. LOFAR, the  Low Frequency Array designed  and constructed by  ASTRON , has facilities in several countries that are owned by various parties (each with their own funding sources), and that are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy. Data taken with the Westerbork Synthesis Radio Telescope were part of this work. It is operated by  ASTRON  ( Netherlands Institute for Radio Astronomy ) with support from the  Netherlands Foundation for Scientific Research (NWO). This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. The VLA images used in this work were produced as part of the  NRAO VLA Archive Survey, (c) AUI/NRAO.
This research has made use of APLpy, an open-source plotting package for Python hosted at http:// aplpy. github. com. R. M. gratefully acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Advanced Grant  RADIOLIFE-320745. R. Jv. W. acknowledges the support by  NASA  through the  Einstein Postdoctoral  grant number  PF2-130104  awarded by the Chandra X-ray Center, which is operated by the  Smithsonian Astrophysical Observatory  for  NASA  under contract  NAS8-03060.
A search for neutral Higgs bosons decaying into a bb quark pair and produced in association with at least one additional b quark is presented. This signature is sensitive to the Higgs sector of the minimal supersymmetric standard model (MSSM) with large values of the parameter tan β. The analysis is based on data from protonproton collisions at a center-of-mass energy of 8 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 19. 7 fb -1. The results are combined with a previous analysis based on 7 TeV data. No signal is observed. Stringent upper limits on the cross section times branching fraction are derived for Higgs bosons with masses up to 900 GeV, and the results are interpreted within different MSSM benchmark scenarios, m max h , m mod+ h , m mod- h , light-stau and light-stop.
At the trigger level, b jets are identified using an algorithm that requires at least two tracks with high 3D impact parameter significance to be associated with the jet. At least two jets within the event must meet the online b tagging criteria to be accepted by the trigger. The efficiency of the jet-p T requirements in the trigger are derived from the data with zero-bias triggered events. The online b tagging efficiencies relative to the offline b tagging selection are obtained from simulations of QCD events generated with PYTHIA and scaled to account for the different b tagging efficiencies between data and simulation. The total trigger efficiency for events satisfying the offline selection requirements detailed below ranges from 46-62% over the Higgs boson mass range of 100-900 GeV. The offline selection requires events to have the two leading jets within |η| ≤ 1.
Background model The main background for this analysis originates from QCD multijet production, with at least two energetic jets actually containing b hadrons, and a third jet that passes the b tagging selection but possibly as a result of a mistag. Since this type of background cannot be accurately predicted by MC simulation, it is estimated from the data using control samples. The chosen method is similar to the one used in Ref. [42]. The background is modeled by a combination of templates, which are constructed from the double-b-tag sample. Only the shape of these background templates is relevant, since the normalization will be determined by the fit to the data. Three categories of events are distinguished in the double-b-tag sample, which are denoted as xbb, bxb and bbx depending on whether the jet with the highest, second-highest or thirdhighest p T is exempt from the b-tag requirement.
For the two b-tagged jets, the secondary vertex mass sum information is taken as measured. For the untagged jet, each of the four possible values of the secondary vertex mass sum index is taken into account with a weight according to the probability that a jet with given flavor, p T and η will assume this value. These probabilities, parametrized as a function of the jet p T and η, were determined from simulated events, and validated in control data samples. Two additional corrections are applied to the templates. The first correction addresses a contamination in the double-b-tag sample from non-bb events at the level of a few percent. This contamination is estimated directly from the data using a negative b tagging discriminator  [34]  constructed with a track counting algorithm based on the negative impact parameter of the tracks, ordered from the most negative impact parameter significance upward.
Beyond QCD multijet production, top-quark pair (tt) events pose the largest potential background to the signal topology. The requirement of three b-tagged jets reduces this background substantially, since only two highly energetic b-tagged jets are expected from the decays of the top quarks. However, one of the W bosons can decay into a cs pair, and the c jet can be mistagged as b jet. Using the tt Monte Carlo sample, the tt contribution is found to be relatively small; the number of tt events passing the selections of the double-and triple-b-tag datasets it estimated to be about a factor of 70 smaller than the total amount of data in these samples. The invariant mass spectrum from tt is very similar to the one from the QCD multijet background, and does not show any narrow peaks. Since the tt events contribute to the double-b-tag sample, they are also taken into account in the background model.
Signal templates A signal template is obtained for each MSSM Higgs boson mass considered by applying the full selection to the corresponding simulated signal data set, for nominal masses in the range of 100-900 GeV. The sensitivity of this analysis does not extend down to cross sections as low as that of the SM Higgs boson. Thus, a signal model with a single mass peak is sufficient, in contrast to the φ → ττ analysis  [14] , where the signal model comprises the three neutral Higgs bosons of the MSSM, one of which is SM-like. The projections for the M 12 and X 123 distributions [GeV].
Signal efficiency The signal efficiency for each MSSM Higgs mass point is obtained from the simulated data sets. The efficiency of the kinematic trigger selection has been derived with data from control triggers and is applied by weighting. Scale factors to account for the different b tagging efficiencies in data and MC  [34]  are also applied. The efficiency ranges between 0. 17 and 6. 38 per mille and peaks around 300 GeV. The detailed mass dependence is shown in Appendix A. The decrease of the efficiency for masses beyond 300 GeV is due to the degradation of the b tagging efficiency at high jet p T. For masses around 300 GeV the kinematic selections give rise to an efficiency of approximately 0. 12, which is reduced to approximately 0. 0065 when triple b tagging is required.
Upper limits on cross sections times branching fractions Cross sections are obtained from the fractions determined by the fit multiplied by the total number of data events after the selection in the signal region, and divided by the corresponding signal efficiencies (Section 6. 2) and the integrated luminosity of 19. 7 fb -1. In the absence of any significant signal, the results are translated into upper limits on the cross section times the branching fraction, σ(pp → bφ + X) B(φ → bb), of a generic Higgs-like state in the mass range 100-900 GeV. For calculations of exclusion limits, the modified frequentist construction CL s  [44, 45]  is adopted using the ROOSTATS package  [46]. The chosen test statistic, used to determine how signal-and background-like the data are, is based on the profile likelihood ratio.
The observed upper limits range from tan β about 20 in the low-m A region to about 50 at m A = 500 GeV, and extend the existing measurement at 7 TeV  [26]  into the hitherto unexplored m A region beyond 350 GeV. The model interpretation is not extended to higher masses above 500 GeV because the theoretical predictions are not reliable for tan β much higher than 60. While the cross section limits obtained from the 2011 and 2012 data cannot be combined directly due to the different center-of-mass energies, such a combination is possible for the modeldependent interpretation. The resulting upper limits on tan β versus m A from both data periods benchmark scenario with µ = +200 GeV. The excluded parameter space (observed limit) is indicated by the shaded area. Regions where the mass of neither of the CP-even MSSM Higgs bosons h or H is compatible with the discovered Higgs boson of 125 GeV within a range of 3 GeV are marked by the hatched areas.
acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Experiments To assess the range of instances that can be solved by enumeration-based algorithms, we performed experiments on biological and synthetic data. We implemented and evaluated both algorithms. Experimental Setup and Implementation Details. Our algorithms were implemented in Python, Version 2. 7. 3; source code and data are publicly available  [28]. We used the NetworkX package for the graph data structures and basic graph algorithms, such as computing the shortest path. The test machine is a 4-core 3. 6-GHz Intel Xeon E5-1620 (Sandy Bridge-E) with 10 MB L3 cache and 64 GB main memory, running under Debian GNU/Linux 7. 0. We report wall-clock times. In all experiments, a timeout threshold of 30 seconds was used. The reported running times do not include the time for the first pass of Rules 1 to 3 (as it was performed only once in advance for the biological instance), but they include the time needed for the exhaustive application of all other data reduction rules.
If |S| = k+1 and v ∈ S, then the algorithm checks whether D[S] has a Hamiltonian path from u to v. Otherwise, it recursively extends S by considering each neighbor of a vertex in S. In this procedure, we use the two following observations for early termination of the subgraph enumeration. The first observation identifies sets that are too far from v. The correctness stems from the fact that the desired path has k arcs and that every deletion of a vertex can remove at most two path arcs from the graph. Unfortunately, the second observation yields a reduction only if |S| > k/2. Nevertheless, it proved to be crucial for this algorithm, as without the corresponding early termination rule, the number of enumerated subgraphs is too large. Biological Data. We downloaded the biochemical pathways information from the Saccharomyces Genome Database (SGD)  [29] , which contains for each enzyme of S.
cerevisiae a mapping from its EC (enzyme catalog) number to its gene identifier. Then, also from SGD, we obtained the protein interactions for the proteins encoded by these genes. To obtain the reactions from the EC numbers, we used the Explore Enz database  [30] : For each pair of EC numbers x and y, we checked whether there is at least one product of x that is a substrate of y. If this were the case, then the arc (g x , g y ) was added to D, where g x and g y are the gene identifiers associated with the EC numbers x and y, respectively. To restrict the product/substrate relationships in the directed graph to meaningful ones, we excluded five frequent metabolites ("ATP", "ADP", "AMP", "H 2 O" and "H + ") that have high abundance in the cell. For the resulting pair of directed graph D and undirected graph G, we randomly sampled 500 vertex pairs and tried to determine for each pair (u, v) whether there is a (D, G)-supported path from u to v.
Thus, for constant values of p D and p G , the probability that a vertex pair (u, v) has a (D, G)-supported path in n-vertex graph is one in the limit. Biological Examples. A short (D, G)-supported path that was found in the biological dataset is ENO2 → ARO1 → BNA4 → ALD6. Interestingly, ENO2 and ALD6 encode enzymes of the glucose fermentation pathway, whereas the enzymes encoded by ARO1 and BNA4 are not annotated as parts of this pathway. Ideally, the existence of an alternative short (D, G)-supported path between ENO2 and ALD6 could either point to alternative (sub)pathways for glucose fermentation or to missing data in the yeast protein interaction network. The longest (D, G)-supported path that was found is the length 13 path HOM6 → HOM2 → BNA4 → ARO1 → ARO2 → TRP3 → GDH3 → AAT2 → GDH2 → SER1 → ALT1 → ARO9 → AGX1 → GCV2.
For the 3,502 specimens, the collection, sequence analysis, taxonomic assignment, data release and manuscript submission by 100+ co-authors all occurred in less than one week. This demonstrates the speed at which barcode-assisted inventories can be completed and the utility that barcoding provides in minimizing and guiding valuable taxonomic specialist time. The final product is more than a comprehensive biotic inventory -it is also a rich dataset of finescale occurrence and sequence data, all archived and cross-linked in the major biodiversity data repositories. This model of rapid generation and dissemination of essential biodiversity data could be followed to conduct regional assessments of biodiversity status and change, and potentially be employed for evaluating progress towards the Aichi Targets of the Strategic Plan for Biodiversity 2011-2020.
Barcode Index Numbers and Taxonomic Assignment For the sequences derived from animal specimens, the records were assigned operational taxonomic units (OTUs) called Barcode Index Numbers (BINs) by the Refined Single Linkage (RESL) algorithm implemented on BOLD (Ratnasingham and Hebert 2013). For the sequences that have at least 500 bp coverage of the barcode region, < 1% ambiguous bases, and no stop codon or contamination flags, the RESL algorithm calculates the number of clusters and their membership (see Ratnasingham and Hebert 2013). The RESL algorithm runs weekly on all qualifying barcode sequences in BOLD, which as of August 2015, includes 5M specimens and 420K BINs. The BIN system is accessible through public, individual 'BIN pages' and permits rapid diversity assessments, even in the absence of taxonomic information.
Data resources Collection data, taxonomic assignment, sequence, electropherograms and primer details for each specimen record, and often a high resolution image, are available on BOLD in the public dataset, "rare BioBlitz 2015 [DS-RBB15]" (http://dx. doi. org/10. 5883/DS-RBB15 or http://boldsystems. org/index. php/MAS_Management_OpenDataSet?datasetcode=DS-RBB15). The sequence data for each successfully barcoded specimen were deposited to GenBank by using the 'Submit to GenBank' function in the BOLD workbench (see Suppl. material 4 for accession numbers). With the 'Data Spreadsheets' function in the BOLD workbench, the complete dataset was downloaded and reformatted into a Darwin Core Archive (Suppl. material 5) for upload to the Canadensys repository (http://www. canadensys. net), Canada's national node for the Global Biodiversity Information Facility (http://www.
The most abundant BINs were BOLD:ACC0651 (Thripidae: Taeniothrips inconsequens), BOLD:AAD5253 (Chironomidae: Thienemanniella xena), and BOLD:AAP5920 (Chironomidae: Cricotopus triannulatus) with 349, 636, and 1619 specimens collected. For these three BINs, and many similarly abundant BINs, the presence of closely allied and morphologically similar taxa makes oversampling of these exceptionally common species unavoidable. Combining all existing data results in a final tally of 3,348 species (Table  1 ), 1,102 of them new records for the reserve (Suppl. material 10). An incidence-based rarefaction analysis (Fig. 2  Suppl. material 11) computed in EstimateS (Colwell and Elsensohn 2014) approximates that 6,744 BINs of invertebrate animals are present at the reserve, indicating that our inventory is at most 30% complete.
Resource Highlights The biotic inventory of rare Charitable Research Reserve performed between May and August 2015 was noteworthy for a number of reasons. The taxonomic scope of the surveys, which covered fourteen phyla over several kingdoms, was only made possible by the integration of DNA barcoding and by assembling a diverse group of experts. Taxa that appeared under-represented on the prior species inventory were targeted where possible, and in many cases, increased significantly. Spiders (Araneae) for instance were completely absent from the prior inventory. Using the expertise of single specialist, focused collecting efforts, and a comprehensive barcode reference library for Canadian spiders  (Blagoev et al. 2015)  our survey resulted in the addition of 181 species (12. 3% of Canadian fauna), two of which were newly recorded for BOLD (Grammonota inornata, Philodromus praelustris) and three representing new provincial records (Cicurina pallida, Neoantistea gosiuta, Xysticus winnipegensis) (Suppl.
Refinement of the Resource Many BINs are presently identified only to family, subfamily, or at most, genus level. It's important to note that the inventoried taxa that lack species-level determination, including intractable groups such as mites and midges, will be refined over time. Not only does DNA barcoding, empowered by the BIN system, roughly sort the material to direct and minimize the efforts of taxonomic experts  (deWaard et al. 2009 ), but it also facilitates crowd-sourcing of taxonomic refinement. For example, if two specimens are collected in unrelated projects and locations, but assigned to the same BIN, any taxonomic determination on one specimen will be applicable to the other, and the pertinent data (such as identifier and locality) is shared on the public BIN page. For the specimens of this study, their taxonomy could be honed by the efforts of completely unrelated (but now linked) projects.
g. , tree files and image libraries: Suppl. materials 8, 9). There will however be taxa for which species names will be problematic, due to a lack of active specialists, keys, and/or revisionary work, and where a large proportion of that taxon awaits description. For example, the number of gall midges (Diptera: Cecidomyiidae) inventoried for rare (584) is nearly six times the number of Canadian species described to date (ca. 100, following Gagne and Jaschhof 2014). When a significant number of species and other taxon categories become available, the refined dataset can be updated in the various data repositories and potentially in a second, updated version of the paper in BDJ. Any subsequent versions -each with a separate Digital Object Identifier (DOI) -would be linked to the original paper, allowing for continuing improvement and additions to the species list for the reserve.
2014 , Kress et al. 2015). Moreover, these studies and others are increasingly using next generation sequencing (NGS) technologies, where validated reference libraries linked to voucher specimens are of critical importance, since this link is generally lost in NGS studies (Cristescu 2014). This reference library created can also be amalgamated with external barcode records, local inventories, and taxon-specific efforts to create regional and national libraries; this study for instance has enhanced the reference library for animals by providing 468 unique BINs to BOLD. Finally, as mentioned above, it is crucial that we begin to assess biodiversity change and disseminate these data widely --the resource created here can form the baseline for accessible, repeated assessments, to gauge trends in an important temperate reserve.