dataesr/scientific-paragraphs-categorization huggingface dataset:
II. THE BABAR DETECTOR AND DATA SET The data used in this analysis were collected with the BABAR detector at the PEP-II asymmetric-energy e + e - storage ring. The total integrated luminosity used is 468. 6 fb -1  [16] , which includes data collected at the Υ (4S) resonance (424. 7 fb -1 ) and at a c. m. energy 40 MeV below this resonance (43. 9 fb -1 ). The BABAR detector is described in detail elsewhere  [17]. Charged particles are reconstructed using the BABAR tracking system, which comprises the silicon vertex tracker (SVT) and the drift chamber (DCH) inside the 1. 5 T solenoid. Separation of pions and kaons is accomplished by means of the detector of internally reflected Cherenkov light and energy-loss measurements in the SVT and DCH. The hard ISR photon, photons from π 0 decays, and K 0 L are detected in the electromagnetic calorimeter (EMC).
Acknowledgements We are grateful for the extraordinary contributions of our PEP-II colleagues in achieving the excellent luminosity and machine conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the computing organizations that support BABAR. The collaborating institutions wish to thank  SLAC  for its support and the kind hospitality extended to them. This work is supported by the  US Department of Energy and National Science Foundation , the  Natural Sciences and Engineering Research Council (Canada) , the  Commissariat à l'Energie Atomique  and  Institut National de Physique Nucléaire et de Physique des Particules (France) , the  Bundesministerium für Bildung und Forschung and Deutsche Forschungsgemeinschaft (Germany) , the  Istituto Nazionale di Fisica Nucleare (Italy) , the  Foundation for Fundamental Research on Matter  (The Netherlands), the  Research Council of Norway , the  Ministry of Education and Science of the Russian Federation, Ministerio de Economía y Competitividad (Spain) , the  Science and Technology Facilities Council  ( United Kingdom ), and the  Binational Science Foundation (U.
Plasma Flow Direction The determination of the flow direction is computed from the CAPS singles (SNG) data and makes use of the instantaneous FOV of CAPS-IMS, the actuator position, and the spacecraft orientation. There is no mass discrimination in the SNG data, but elevation and azimuthal information allows us to reconstruct the angular distribution. During one actuator scan the instrument is able to cover a wider region which, due to its geometrical capabilities, is limited to about 2 steradian (for a fixed spacecraft orientation). The sky map seen by the instrument is a sphere where a given point can be defined with two spherical coordinates,  and. The angle between the Z KSO = 0 plane and the position r is ;  can be seen as a latitude which ranges from Figure  2a  displays the single ion fluxes measured by CAPS from 21:09 to 21:12 UTC (during flyby T40) as a function of  and  and also points out the position associated with the flow direction.
Plasma Flow Speed Determining the plasma speed requires information such as the plasma composition, the observed energy, and the spacecraft potential. Unfortunately, TOF measurements do not always allow a mass determination since the ion flux might not be intense enough. The knowledge of the ion composition is then limited to only a few time intervals. However, assuming that the different ion species are traveling at the same velocity v, it is possible to use SNG data in a complementary way. In this case, different energy spectra peaks reflect the different ion composition of the plasma under study. Most of the time, two energy peaks are observed and correspond to two ion mass groups. TOF data analyses, when sufficient counts allow a good mass determination, are used to identify more accurately the ion masses.
Local Alfvén Velocity Determination In this subsection we point out the different steps carried out to calculate the local Alfvén velocities associated with the previously selected time intervals (where the bulk velocity of the plasma was determined). The Alfvén velocity is v A = B∕ √  0  , where B is the magnetic field,  0 is the permeability of the vacuum and  = ∑ s n s m s is the total mass density of the charged plasma particles, where s goes over all plasma species of mass m s and number density n s. Considering that the electron mass m e is much smaller than the mass of any other plasma particle, we approximate  ∼ ∑ s≠e n s m s = n e ∑ s≠e n s n e m s. The electron number density n e and the magnetic field B are derived from RPWS wave measurements and MAG data. We approximate the ratio between the numerical density of the plasma species s and the electron numerical density, n s ∕n e , by the ratio between the flux of this species F s (associated with its corresponding energy peak) and the total flux F total = ∑ s≠e F s (the F s are determined from energy spectra of CAPS-IMS measurements in addition to data provided by TOF analysis).
and a well-defined minimum variance direction. As a consequence, MVA provides a good estimate for the normal vector to this plane which, in KSO coordinates, results n = (-0. 12, 0. 40, 0. 91). These results show that the variation in the magnetic field direction (in the same time interval where CAPS observations show an acceleration of the plasma flow) is mainly restricted to a plane and can be related to the existence of a one-dimensional current layer. MVAs of MAG data are also performed for flybys T17 and T19. The results are shown and discussed in the next section.
Acknowledgments The authors thank  Cassini CAPS  , RPWS, and MAG teams for providing the data used in this study. Please contact the corresponding author (nromanelli@iafe. uba. ar) for data access. N. R. is supported by a PhD fellowship from  CONICET. The research at the  University of Iowa  was supported by  NASA  through contract  1415150  with the  Jet Propulsion Laboratory. The  RPWS Langmuir Probe  is supported by the  Swedish National Space  Board (SNSB). R. M. , J-J. B. , F. L. , P. C. are indebted to program " Soleil Héliosphère and Magnétosphères" of CNES , the French space administration, for the financial support on Cassini. N. R. , R. M. and C. B. are indebted to the  ECOS-MYNCYT cooperation program  for their support.
Data collection Data concerning rowers' biography came from historians' sources as previously reported  [22]. These records were confirmed by data of the International Olympic Committee (olympic. org) and the French Federation of Rowing (http://www. avironfrance. asso. fr/). Rowers' identity information and vital statuses were collected since their first Olympic participation up to January 1st 2013 (study's endpoint) from the RNIPP via INSEE (National Institute of Statistics and Economical Studies), which registers vital status of all French people since 1891. Hence, rowers born before 1891 were not included in the study. The causes of death of each deceased subject were obtained from the CepiDc (Epidemiologic Centre on Medical Causes of Death). Currently, causes of deaths occurring on French territory are available via National registry from 1968 up to January 1 st , 2012.
Statistical Analysis We compared the mortality of French rowers with the French general population by calculating overall and disease-specific Standard Mortality Ratio (SMR) and its 95% confidence intervals (CI). SMR is the ratio between the number of deaths observed in the rowers' cohort and the number of expected deaths if the athletes had the same death rates of the French population with age and period adjustment  [23]. The source for general population death rates was the French life tables available at the Human Mortality Database (http://www. mortality. org/) via CepiDc. The number of expected deaths was therefore calculated by multiplying male French death rates by the rowers' person-years at the corresponding age and period. Person-years were computed so that each rower contributes for one record of each year during his follow-up.
Statistical Analysis Data analysis was performed using sas version 9. 2. All data are expressed as mean [standard deviation (s. d. )], median (quartile 1 and quartile 3), estimates [standard error (s. e. )] or differences with 95% confidence interval (CI) as appropriate. Missing values were imputed using the last observation carried forward method. The modified intent-to-treat population included all participants randomly assigned to treatment groups who had received at least one dose of the study drug and had at least one on-treatment assessment of any primary or secondary efficacy variable, irrespective of compliance with the study protocol and procedures. All participants randomized and treated were included in the safety population for analysis. The superiority of insulin glargine in terms of the percentage of participants reaching the HbA1c target of <7% at the end of the study was tested using a chi-squared test at 5% level; two-sided 95% CIs of the differences in success rates (insulin glargine -liraglutide) were also calculated.
Just like in the O1s experiment of Fig. 1 , distinct absorption edges were obtained for the fragments A + (AS) 3 and A + (AS) 2 (Fig. 2c  and d ). Also for this energy range the spectrum for A + (AS) 5 lacks ne structure, as seen in Fig. 2a. However, there is a jump in the detected intensity of the A + (AS) 5 fragment occurring at the point of data loss. This shi in intensity is not observed in Fig. 2c or d. Interestingly, some structure can be seen for A + (AS) 4 (from the loss of 2 AS units), in Fig. 2b. An absorption edge can be identied, likely superimposed on a signal due to a pressure/ temperature dependent process. Aligning the fragmentation curve aer the jump to the curve before the jump (shown as a  dashed blue line in Fig. 2b ) clearly reproduces the shape of the absorption edges as seen in Fig.
This work was supported by the Norwegian Research Council by the Grants no. 205512/F20, Nano-solvation in Hydrogen-Bonded Clusters; and no. 179568/V30, to the Centre of Theoretical and Computational Chemistry through their Centre of Excellence program. This work was supported by the Agence Nationale de la Recherche Scientique, France, under the project ANR-08-BLAN-0065. CM and MP acknowledge the COST action CM1204 -XUV/X-ray light and fast ions for ultrafast chemistry (XLIC).
Acknowledgements The experiments were performed at the PLEIADES beamline at  SOLEIL Synchrotron facility (France) , during beamtime allocated under proposals number 20120182 and 20130120. We are grateful to  E. Robert  for technical assistance and to the  SOLEIL  staff for stable operation of the equipment and storage ring during the experiments. T. J. , M. R. , and J. N. are grateful to the  EU  through the  CALIPSO program  for travel support, while E. U. , G. M. , K. H. , K. B. , and O. B. are grateful to  Professor Hanna.
35 vs 72. 42 μmol/L, respectively; P¼0. 067). However, no significant difference between nonresponders and responders was noted in mean baseline BUN (6. 97 vs 6. 48 mmol/L, respectively; P¼0. 067)  [7]. An analysis was also conducted on creatinine data for responders and nonresponders by treatment group (decitabine vs treatment choice). Mean baseline creatinine levels (7 standard deviation) by treatment group were 78. 81 715. 78 μmol/L for decitabine responders (n¼ 68) and 83. 06 723. 94 μmol/L for treatment choice responders (n¼ 34). Corresponding levels for nonresponders were 85. 78 726. 99 μmol/L for decitabine (n¼ 171) and 87. 62 7 27. 19 μmol/L for treatment choice (n ¼206). No significant differences were seen when treatment groups were compared within responder categories. Changes in creatinine levels over time by response to decitabine or treatment choice (Fig.
Acknowledgments The original study and this analysis were funded by  Eisai Inc ,  Woodcliff Lakes , NJ. Medical writing, editing, and graphics assistance was provided by  Peloton Advantage, LLC , and was funded by  Eisai Inc. The authors thank  Yvonne E. Yarker, PhD , CMPP, of  Peloton Advantage  for medical writing support and  Yuhan Li, MS,  of  Eisai Inc. for statistical analyses provided during the development of this manuscript.
In this process, we detected numerous seismic signals recorded within well Dh4. These signals started to occur rather suddenly on the 24 th of November 2013 at approximately 09:40h and their number increased over the following days. Fig. 2 (a-c ) displays helicorder plots for the 24 th of November, the 28 th of November and the 10 th of December. By that time, the detection of any other signals (e. g. from regional or local events) amidst these very short transient signals became noticeably difficult. The sudden onset of this activity is also clearly visible in a change of frequency content in the data (Fig. 2d ). As can be seen in Fig. 3 , these signals do not represent electronic noise (e. g. spikes), but real seismic signals. They are most visible on the vertical component and strongly decrease in amplitude from bottom to top of the well.
The propagation velocity is approximately 1. 84 km/s (measured by the difference in arrival time). Also the frequency content of the data from the different geophones varies, which might result from differences in coupling of the sensors. We currently interpret these signals as Pseudo Rayleigh waves, more precisely tube waves, originating at the bottom of the well. During a subsequent site inspection, methane gas was detected leaking out of the well (resulting in the necessity to retrieve the geophone string in May 2014, such that the well could be closed and secured). Detection of both, biogenic and thermogenic methane at the CO2 Lab wellpark is common, as methane was also observed in most other deep wells. Seismogram observations within well Dh3 also show tube waves, likely due to degassing.
Noise cancellation Waveform data from the permanent seismic network are recorded in a hut at the Longyearbyen CO2 Lab, which provides a year-round, safe, temperature-controlled environment for the acquisition equipment. However, the hut also contains electrical equipment (heaters, power supplies, installations from other scientific experiments, etc. ) that emits strong electromagnetic interference (EMI) and corrupts much of the data recorded on the installed geophones. Much of the interference consists of multiple sinusoids that are particularly troublesome for processing techniques that rely upon waveform correlation measurements. Sinusoids, of course, are highly correlated and, if persistent, can come to dominate long-term correlation measurements required in ambient noise tomography. Consequently, we desire to remove this interference by a post-processing step.
Acknowledgements The work has been financed by  Norwegian Research Council  grants no. 189994  ( SafeCO2  project) and  224880  ( SafeCO2-II  project). The projects have been sponsored additionally by our industry partners Lundin, Octio, READ and  Statoil  ( SafeCO2 ) as well as  Lundin ,  RWE DEA Norge and Statoil  ( SafeCO2-II ). Further, we would like to thank the project staff not co-authoring this publication:  S. Sikora  (UNIS),  I. Lecomte  (NORSAR),  M. Ohrnberger  ( University of Potsdam ),  F. Brenguier  (ISTerre),  B. Bohloli  ( NGI ),  C. Krawzcyk  ( LIAG ) and  U. Polom  (LIAG). Fig. 1 has been prepared using QGIS (version 2. 0. 1. "Dufour"). G. Hillers acknowledges support from a  Heisenberg fellowship  from the  German Research Foundation.
PATIENTS AND METHODS We retrieved the medical records of all AICTDs patients for which skin biopsy showed significant neutrophilic infiltrate; records were examined from Saint Louis Hospital (Paris, France), between 2003 and 2013. Among 7028 AICTDs patients, a skin biopsy was available in 932 cases; there was mainly neutrophilic infiltrate on histology in 9 cases (Figure  1 ). Patients were included if they met the following criteria: 1. Diagnosis of AICTD, according to the standard criteria of AICTDs, which is based on the revised 1997 American College of Rheumatology (ACR) criteria for SLE. 23 The ACR 2012 criteria was used for Sjo ¨gren syndrome;  24   Biopsies, medical records, and photographs of patients included were reviewed and analyzed. We also searched the National Library of Medicine's MEDLINE database (Bethesda, MD) for relevant literature using the keywords ''neutrophil,'' ''neutrophilic dermatosis,'' '' Sweet syndrome,'' ''neutrophilic urticaria,'' ''pyoderma gangrenosum,'' ''annular erythema,'' ''palisaded neutrophilic granulomatous dermatitis,'' or ''amicrobial pustulosis of the folds,'' together with ''connective tissue disease,'' ''discoid lupus erythematosus,'' ''systemic lupus erythematosus,'' ''Sjogren syndrome,'' ''rheumatoid arthritis,'' and ''dermatomyositis.
Assumptions, Components and Roles The network is supposed to be divided into zones, each containing one or more sensor clusters. Zones and clusters have different criticalities, security levels and security policies. It is also assumed that the sensors in one cluster can communicate with each other. In addition, we assume that the awake time is negligible compared to the sleep time. Finally, we exclude any form of synchronization between nodes. The security system we present in this paper is made up of the following elements:  Threat Detection Client, which identifies threats and notifies the TD server.  Threat Detection Server, which chooses which sensor(s) will be in monitoring mode for each cluster by taking into account status parameters such as batteries level and available resources, updates the global network database, receives the alarms from TD clients and transfers them to the SA Server.
 Security Adaptation Server, which decides upon threat identification which is the best policy to apply and stores the new policy in the security policy mailbox.  Security Adaptation Client, which regularly prompts the SA Server for new policies, and either applies them or generates new affordable security policies.  Inference engine, which deals with reasoning and allows for easy rule changes.  Security Policy mailbox, which stores the generated policies and delivers them at nodes request. The use of this module is required since the nodes are not synchronized. It also reduces the overall bandwidth consumption.  Global Network Database, which contains a global view of the network and the threats detected in the past.
It then sends an ACK if the received policy was applied without change, or sends a descriptor of the locally generated policy. 7. The SA Server receives the ACK or locally generated policy descriptor and updates the global network database accordingly. The Normal Mode is the default mode for a bootstrapped sensor that has not been designated as a monitoring node. In this mode, the sensors alternate between active and sleeping states. Upon leaving sleeping state, the node interrogates the SA server about an eventual new policy to enforce. It then performs the task(s) for which it has left the sleeping state. An alarm may be raised by a node in Normal Mode only if one of the run tasks detects a threat and notifies the TD Client through an API call. The overall process of our solution, depicting its state machines and internal/network message exchanges, is depicted in Figure  6.
Acknowledgements We are grateful to  Prof. Robert H Anderson  for the invaluable help and discussions during this study and to  Mrs Marianna AS Capuani  for her precious secretarial assistance and translation support. Figures 3, 4, 5, 6 and 8 have been reproduced from The  Society of Thoracic Surgeons , Annals of Thoracic Surgery 1995; 59: 352-360 by permission of the Authors. Figures 7 and 9, 10, 11 have been reproduced from "The Heart Structure in Health and Disease" 1992 Gower Medical Publishing by permission of  Prof. Robert H Anderson. The convention of sliding is used in similar fashion throughout the illustrations: heavy stipples TSM, fine stipples IS, wavy lines VIF.
Funding This project was funded by the  Swedish Science Council , the  Brain Research Foundation ,  Mr B Hållsten 's  Brain Research Foundation , The  Ulla-Carin Lindquist's Fundation for ALS Research , the  Knut and Alice Wallenberg Foundation ,  Swedish Brain Power , the  European Community 's  Health Seventh Framework Programme  ( FP7/2007-2013 ) (grant agreement no. 259867 ), The  Belgian Science Policy Office Interuniversity Attraction Poles (IAP) programme , the  Flemish Government supported Europe Initiative on Centers of Excellence in Neurodegeneration (CoEN) , the  Flemish Government  initiated Methusalem excellence research programme,  Alzheimer Research Foundation , the  Medical Foundation Queen Elisabeth , the  Research Foundation Flanders (FWO)  and the  FWO  provided a postdoctoral scientist fellowship to JvdZ,  University of Antwerp Research Fund , the  Swiss ALS Foundation , the  Italian Ministry of Health  ( RF-2009-1473856 ),  Grant-in-Aid for the Research Committee  of  CNS Degenerative Diseases and Comprehensive Research on Disability Health  and Welfare from the  Ministry of Health, Labour and Welfare in Japan  and  Dr Van Blitterswijk  is supported by the  Milton Safenowitz Post-Doctoral Fellowship for ALS research  from the  ALS Association.
Sentence Connectivity is a textual characteristic that may be incorporated intelligently for the selection of sentences of a well meaning summary. However, the existing summarization methods do not utilize its potential fully. The present paper introduces a novel method for singledocument text summarization. It poses the text summarization task as an optimization problem, and attempts to solve it using Weighted Minimum Vertex Cover (WMVC), a graph-based algorithm. Textual entailment, an established indicator of semantic relationships between text units, is used to measure sentence connectivity and construct the graph on which WMVC operates. Experiments on a standard summarization dataset show that the suggested algorithm outperforms related methods.
S9 Apartheid is South Africa's policy of racial separation. Summary "There are very strong rumors in South Africa today that on Nov. 15 Nelson Mandela will pe released," said Yusef Saloojee, chief representative in Canada for the ANC, which is fighting to end white-minority rule in South Africa. He was transferred from prison to a hospital in August for treatment of tuberculosis. A South African government source last week indicated recent rumours of Mandela's impending release were orchestrated by members of the anti-apartheid movement to pressure the government into taking some action. Apartheid is South Africa's policy of racial separation. Table  1 : The sentence array of article AP881113-0007 of cluster do106 in the DUC'02 dataset. Figure  1 : Outline of the proposed method. D 1×N. An example article is shown in Table  1.
Experimental settings We have conducted experiments on the singledocument summarization task of the DUC 2002 dataset 2  , using a random sample that contains 60 news articles picked from each of the 60 clusters available in the dataset. The target summary length limit has been set to 100 words. We used version 2. 1. 1 of BIUTEE  (Stern and Dagan, 2012) , a transformation-based TE system to compute textual entailment score between pairs of sentences. 3 BIUTEE was trained with 600 texthypothesis pairs of the RTE-5 dataset  (Bentivogli et al. , 2009).
Baselines We have compared our method's performance with the following re-implemented methods: 1. Sentence selection with tf-idf: In this baseline, sentences are ranked based on the sum of the tf-idf scores of all the words except stopwords they contain, where idf figures are computed from the dataset of 60 documents. Top ranking sentences are added to the summary one by one, until the word limit is reached. 2. LTT: (see Section 2) 3. ATESC : (see Section 2).