dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Extraction The chart review data included demographics, comorbid conditions, physical examination findings, laboratory and microbiology data, and chest radiograph reports. The PIRO (predisposition, insult, response, and organ dysfunction) score  [21]  was calculated within 24 hours of the ICU admission from 8 variables (presence of chronic obstructive pulmonary disease, immunocompromised state, multilobar opacities on chest radiograph, shock, severe hypoxemia, acute renal failure, bacteraemia and acute respiratory distress syndrome). One point was given for each feature that was present (range, 0-8 points). The 2007 American Thoracic Society/Infectious Disease Society of America (ATS/ IDSA) criteria  [3]  were defined based on the presence of major criteria. The major criteria are a requirement for mechanical ventilation and the presence of septic shock.
5 m; the third station is at a radius of 9 m (large sectors) and 10 m (small sectors). In the endcaps the first TGC station is located at |z| = 13 m and the other two at 14 m with a separation of 0. 5 m. The trigger system has three levels  [9]. The first level (L1) is a hardware-based system using information from the calorimeter and the muon detectors. The second (L2) and third (Event Filter, EF) levels are software-based systems using information from all of the ATLAS detectors. Together, L2 and EF are called the High Level Trigger (HLT). The L1 trigger uses information based on relatively coarse data from the calorimeters and the muon trigger stations. The L1 thresholds are applied to transverse energy (E T ) for calorimeter-based triggers and transverse momentum (p T ) for muon triggers.
Triggering on collision data These triggers need to meet tight bandwidth requirements for inclusion in the ATLAS trigger menu. The exclusive bandwidth allocated for the long-lived neutral particle triggers is only a few Hz. Figure  15  shows the rates as a function of instantaneous luminosity for the three triggers during a typical 2012 data-taking period and demonstrates that the rates are well within the allocated trigger bandwidth. The Muon RoI Cluster trigger shows a linear dependence on the instantaneous luminosity. A linear fit, when extrapolated to null luminosity and accounting for the different number of filled and empty bunches, predicts a rate of (0. 04 ± 0. 01) Hz. An analogous trigger only enabled on empty bunch crossings gives a rate of (0. 04 ± 0. 03) Hz in the same data-taking period as that displayed in figure  15.
The rates are found to be in excellent agreement, suggesting that the Muon RoI Cluster trigger rate increases linearly as a function of the instantaneous luminosity and  there are no other dependencies affecting its rate. The Trackless Jet and Calorimeter Ratio trigger rates show little or no dependence on the instantaneous luminosity. As discussed in the previous section, this is the result of the tight track isolation requirement included among the online selection cuts. The Muon RoI Cluster trigger uses a softer track isolation requirement and its rate is determined only by the instantaneous luminosity. More detailed studies on data are, in general, not possible since the instantaneous luminosity and pile-up are correlated quantities and the LHC running conditions do not provide different samples with the same instantaneous luminosity and different pile-up configurations.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIEN-CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW , Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
The dye laser was scanned whilst the 205 At ion current was directly measured with a Faraday cup (FC). A series of more than 30 Rydberg levels was observed in the l 3 scan range of 15,000-16,300 cm À 1 , as shown in Fig. 3a. The Rydberg atoms were ionized inside the hot cavity ion source by the residual laser light, black-body radiation or by collision processes. The Rydberg levels of lower principal quantum numbers reveal resolved multiplet structures, attributed to different fine structure components, with decreasing splitting as the ionization limit is approached. A single-Gaussian fit was applied to each resolved peak and for each unresolved multiplet. The transition energies (n n ) of each main peak are plotted against their principal quantum number n as shown in the Fig. 3b. These data are sufficient for precise determination of the ionization limit E 00 lim.
The most precise value obtained using this method was IP(At) ¼ 9. 24(  15 ) eV (see Table  1 ), in reasonable agreement with previous estimates. The uncertainty quoted indicates that still further correlations are expected to contribute to the IP. An alternative approach was based on the solution of the DC Hamiltonian  24  in combination with the coupled-cluster approach with single, double and perturbative triple excitations (CCSD(T)) for treatment of electron correlation. The IP was obtained by taking the energy difference between the calculated energies of the neutral element and its cation. The Dirac08 program package  25  was used to perform the calculations. The uncontracted Faegri basis sets  26  were used for At as well as for iodine (I) as a homologue, consisting of 26s 24p 18d 14f 7g 4h 2i orbitals for I and 28s 26p 20d 15f 7g 4h 2i orbitals for At.
Acknowledgements We thank the  ISOLDE Collaboration  for flexible allocation of beam time. We acknowledge the support from  TRIUMF , which receives federal funding via a contribution agreement with the  National Research Council of Canada  and support through an  NSERC  discovery grant, as well as beam time allocation for experiment S1237 at TRIUMF. We thank the  GSI Target Group  for manufacturing the carbon foils. We acknowledge support by the  Wolfgang-Gentner-Programme of the Bundesministerium fu ¨r Bildung und Forschung  ( BMBF, Germany ), by  FWO-Vlaanderen (Belgium) , by  GOA/2010/010  ( BOF-KU Leuven ), by the  IUAP-Belgian Science Policy Office (BriX network   P7/12 ), by a grant from the  European Research Council  ( ERC-2011-AdG-291561-HELIOS ), by the  United Kingdom Science and Technology Facilities Council (STFC) , by the  European Union Seventh Framework through ENSAR  (contract no.
Detector description and event samples The data used in the analysis were recorded by the ATLAS detector in proton-proton (pp) collisions at a centre-of-mass energy of √ s = 7 TeV during the 2011 data-taking period of the Large Hadron Collider (LHC)  [21]. Events were required to pass a high-transverse momentum (p T ) single-lepton (e/µ) trigger, and to have been recorded when all detector systems critical to muon, electron, and jet identification were operational. The lepton triggers required in the different data taking periods had varying p T thresholds: 20-22 GeV for the electron trigger and 18 GeV for the muon trigger. The resulting dataset corresponds to an integrated luminosity of 4. 7 fb - 1   [22, 23]. The ATLAS detector  [24]  consists of an inner tracking system immersed in a 2 T axial magnetic field provided by a thin solenoid; electromagnetic and hadronic calorimeters; and a muon spectrometer (MS) embedded in a toroidal magnet system.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COL-CIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF ,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW , Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
In this paper, we present a new approach relevant to the discovery of correlated patterns, based on the use of multicore architectures. Our work rests on a full KDD system and allows one to extract Decision Correlation Rules based on the Chi-squared criterion that include a target column from any database. To achieve this objective, we use a levelwise algorithm as well as contingency vectors, an alternate and more powerful representation of contingency tables, in order to prune the search space. The goal is to parallelize the processing associated with the extraction of relevant rules. The parallelization invokes the PPL (Parallel Patterns Library), which allows a simultaneous access to the whole available cores / processors on modern computers. We finally present first results on the reached performance gains.
Independently of this framework, we soon developed a KDD system based on the discovery of Decision Correlation Rules with large and specialized databases  (Casali and Ernst, 1). These rules are functional in semiconductor fabrication capabilities. The goal is to discover the parameters that have the most impact on a specific parameter, the yield of a given product. Decision Correlation Rules (DCRs) are similar to Association Rules. But, as it will be shown, there are huge technical differences, and the overall computation times of DCRs are in the end much better. Furthermore, after implementing DCRs using "conventional sequential algorithms", we decided to adapt this approach to multicore implementation possibilities. This paper is organized as follows. In Section 2, we recall current features of multicore programming.
Former and more organization oriented research efforts include the Codeplay Sieve System, Cray's Chapel, Sun's Fortress, and IBM's X10. A comparison of some OpenXXX approaches can be found in  (Hamidouche and al. , 3). But the majority of these models roughly bases on the intelligent transformation of general code into multithreaded code. A new, even if simple idea, has been proposed by the OpenMP consortium in FORTRAN in 1997, based on the fact that looping functions are the key area where splitting parts of a loop across all available hardware resources may increase application performance. The Open Multiprocessing Architecture Review Board (or OpenMP ARB) became an API that supports shared memory multiprocessing programming in C++. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.
Association Rules Mining using Multicore Support Current research can be divided into three main categories: (i) adaptation of the A-Priori algorithm, (ii) vertical mining, and (iii) pattern-growth method. A-Priori based algorithms: Most of the parallel ARM algorithms are based on parallelization of A-Priori  (Agrawal and Srikant, 6 ) that iteratively generates and tests candidate itemsets from length 1 to k until no more frequent itemsets are found. These algorithms can be categorized into Count Distribution, Data Distribution and Candidate Distribution methods (Agrawal and Shafer, 7),  ( Han and Kamber, 8). The Count Distribution method partitions the database into horizontal partitions, that are independently scanned, in order to obtain the local counts of all candidate on each process.
At the end of each iteration, the local counts are summed up a into the global counts so that frequent itemsets can be found. The Data Distribution method utilizes the main memory of parallel machines by partitioning both the database and the candidate itemsets. Since each candidate is counted by only one process, all processes have to exchange database partitions during each iteration in order, for each process, to obtain the global counts of the assigned candidate itemsets. The Candidate Distribution method also partitions candidate itemsets but replicates, instead of partition and exchanging, the database transactions. Thus, each process can proceed independently. Vertical mining: To better utilize the aggregate computing resources of parallel machines, a localized algorithm  (Zaki et. al, 9)  based on parallelization of Eclat was proposed and exhibited excellent scalability.
It makes use of a vertical data layout by transforming the horizontal database transactions into vertical tidlists of itemsets. By 1-item, the tid-list of an itemset is a sorted list of IDs for all transactions which contain the 1-itemset. Frequent k-itemsets are organized into disjoint equivalence classes by common (k -1)-prefixes, so that candidate (k +1)-itemsets can be generated by joining pairs of frequent k-itemsets from the same classes. The support of a candidate itemset can then be computed simply by intersecting the tid-lists of the two component subsets. Task parallelism is employed by dividing the mining tasks for different classes of itemsets among the available processes. The equivalence classes of all frequent 2-itemsets are assigned to processes and the associated tid-lists are distributed accordingly.
Each process then mines frequent itemsets generated from its assigned equivalence classes independently, by scanning and intersecting the local tid-lists. Pattern-Growth Method: The pattern-growth method derives frequent itemsets directly from the database without the costly generation and test of a large number of candidate itemsets. The detailed design is explained in the FP-growth algorithm  (Han et al. , 10). Basically, it makes use of a frequent-pattern tree structure (FP-tree) where the repetitive transactions are compacted. Transaction itemsets are organized in that frequency-ordered prefix tree such that they share common prefix part as much as possible, and re-occurrences of items/itemsets are automatically counted. Then the FP-tree is pruned to mine all frequent patterns (itemsets).
A partitioning-based, divide and conquer strategy is used to decompose the mining task into a set of smaller sub-tasks for mining confined patterns in the so-called conditional pattern bases. The conditional pattern base for each item is simply a small database of counted patterns that co-occur with the item. That small database is transformed into a conditional FP-tree that can be processed recursively (Zaiane et al. , 11, Pramudiono and Kitsuregawa 12, Li and Liu 13).
Assessment Patient data were recorded in a single study visit, in a face-to-face interview conducted by a trained interviewer. The description of the study, as well as the variables assessed, has been presented previously  [11] : The main variable referred to the current therapeutic management of patients with opioid dependence  [11] , such as current replacement therapy, treatment phase, dosage, time in the current replacement therapy, frequency of visits to the center, etc. For the current study, we have analyzed the following variables: Usual driving of vehicles/use of machinery: No/Yes; when Yes: Work/Leisure. Criminal justice status: i) No pending charges ii) Imprisoned iii) Bail/probation, iv) Other: then, specify. The information on legal issues provided by the EuropASI legal subscale, regarding driving and legal statuses.
Measures and analyses Descriptive statistics were obtained for all analyzed variables on i) EuropASI legal subscale, ii) driving and iii) criminal justice status. In a subsequent analysis, the EuropASI legal subscale variables (as well as the criminal justice status) were analyzed, depending on whether the patients were a usual or non-usual driver. Chi square and T tests were used to compare the characteristics of the sample when required. All analyses were performed using the number of valid cases (N) for each variable. Statistical analyses were performed using the SAS program (Statistical Analysis System), version 9. 1. 3. For all comparisons, a statistical significance level of 0. 05 was considered.
Observations & Data Reduction Data from five outbursts of GRO J1008-57 are analyzed. The earliest outburst studied here occurred in 2005 February and 1 Table  1. Log of observations. The table includes the satellite used (Sat. ), the observation ID, the Modified Julian Date of the start of observation, and the Exposure (Exp. ) in seconds. The last column (E) marks data epochs, for which spectra are combined for spectral analysis. Horizontal lines separate different outbursts or campaigns. was well covered by pointed RXTE observations (Fig. 1 ). In 2007 December, GRO J1008-57 underwent its strongest outburst during the lifetime of the RXTE-All Sky Monitor. The 2-10 keV peak flux was 84(5) mCrab (Fig. 1 ). Except for the rise, the whole outburst was observed regularly by RXTE. Swift recorded the source around maximum intensity and Suzaku during its decay.
During 2011 April we triggered RXTE observations to cover the start of the third outburst analyzed here. Later outbursts in 2011 December and 2012 were covered by a few Swift observations each. In addition, to measure the diffuse emission from the region, data from an RXTE observation campaign in 1996/1997, performed while the source was off, exists. In the following the instruments used in the analysis of these outbursts are described. All spectral and timing modeling was performed with the Interactive Spectral Interpretation System (ISIS,  Houck & Denicola 2000, version 1. 6. 2-7). Unless otherwise stated, all error bars are given at the 90% level for each parameter of interest.
RXTE The Proportional Counter Array (PCA,  Jahoda et al. 2006 ) was one of two pointed instruments onboard the Rossi X-ray Timing Explorer (RXTE) and was sensitive for X-rays between 2 and 90 keV. The PCA consisted of five Proportional Counter Units (PCUs), collimated to a field of view of ∼1 •. Unless stated otherwise, only data from PCU2 were used, which was the best calibrated PCU  (Jahoda et al. 2006 ). Since the source can be weak away from the peak of the outbursts, only data from the top layer of PCU2 were used. Data were reduced using heasoft 6. 11 and using standard data reduction pipelines  (Wilms et al. 2006). Light curves for the timing analysis were extracted using barycentered GoodXenon data with 1 s time resolution. For spectral analysis, 4. 5 keV-50 keV PCA data were used to avoid calibration issues around the Xenon L-edge.
The rocking of cluster B was switched off in 2010 April, thus no HEXTE data was used for analysis during the 2011 April outburst. 20-100 keV HEXTE data were used for spectral analysis and rebinned by a factor of 2, 3, 4, and 10 in the energy intervals 20-30 keV, 30-45 keV, 45-60 keV, and >60 keV, respectively. The analysis of the GRO J1008-57 data is complicated due to the low Galactic latitude of the source (b = -1. • 827). At such low latitudes, the cumulative effect of the Galactic ridge emission in the ∼1 • radius field of view of the PCA is strong enough to lead to visible features in the X-ray spectrum. When comparing simultaneous PCA measurements with those from imaging instruments, these features mainly show up as excess emission in the Fe Kα band around 6. 4 keV and a slight difference in the continuum shape (e.
Swift For the analysis of the Swift observations data from its Xray Telescope's CCD detector, XRT, were used  (Gehrels et al. 2004 ). The 1. 5-9 keV spectra were extracted in the "Windowed Timing mode", a fast mode that prevents pileup for sources up to 600 mCrab. Only the observations after the 2011 December outburst and obs. 00031030013 before the giant outburst of 2012 October/November were extracted in the "Photon Counting mode". Source spectra were obtained from a 1 radius circle centered on the source, or an annular circular region if pileup was present. Background spectra were accumulated from two circular regions off the source position with radii up to ∼1. 5. Unless stated otherwise, spectral channels are added up until a minimum signal-to-noise ratio of 20 is achieved. The light curve was extracted with 1 s time resolution.
The Orbit of GRO J1008-57 To determine the orbit of GRO J1008-57 all data available from the 2005 and 2007 outbursts were used. Template pulse pro- Due to a comparable sensitive energy range the profiles as detected by Swift-XRT (0. 2-10 keV) and Suzaku-XIS3 (0. 2-12 keV) are similar in shape, while the secondary peak is much more prominent at higher energies as seen in RXTE-PCA (2-60 keV). In order to show the good agreement between the Swift and Suzaku data, profiles are shown with 64 bins instead of the 32 bins used for the arrival time analysis. files have 32 phase bins. During the 2005 outburst a period of P obs = 93. 698(7) s was found, while for the 2007 one the period is P obs = 93. 7369(12) s. The reference time needed to determine the time shifts was set to the first time bin of the Suzakulight curve (MJD 54434.
4819) for data taken in 2007, and to the first time bin of RXTE-observation 90089-03-02-00 (MJD 53427. 6609) for the 2005 outburst. See Fig. 5  for example pulse profiles. Figure  4  shows the difference between the measured pulse arrival times and a pulse ephemeris which assumes constant pulse periods with values given above. Calculating the arrival time using the ephemeris of  Coe et al. (2007,  see Table  2 ) did not result in a good description of the data (Fig. 4 , gray lines). In principle, the remaining difference could be explained by a very large spin-up and spin-down. If the difference is explained in this way, the resulting orbital parameters are, however, inconsistent with the phasing of the outbursts seen with all sky monitors (see Sect. 3. 3 below). The only orbital parameters that can result in the difference between the gray line in Fig.
ASM-and BAT-analysis The orbital period of 249. 48(4) d found by the combined pulse arrival times and outburst times analysis in the previous section differs by more than 4σ from the value found by  Coe et al. (2007 , P orb = 247. 8(4) d) and by slightly more than 1σ from the orbital period found by  Levine & Corbet (2006 , P orb = 248. 9(5) d) from the RXTE-ASM light curves. Since outbursts occur during each periastron passage, as a consistency check P orb can also be determined by measuring the time of peak flux from the available RXTE-ASM and Swift-BAT data only. The average distance between outburst peaks is 249. 7(4) d, which is in agreement with the results of the arrival times analysis of Sect. 3. 2 and  Levine & Corbet (2006). Having confirmed the updated orbital period, it is possible to compare the time of maximum source flux with the periastron passages predicted by the revised orbital solution.