dataesr/scientific-paragraphs-categorization huggingface dataset:
Legislative dates and sample selection Our analysis focuses on the equity reaction (risk and return) of U. S. financial firms around key legislative events leading to the passage of the DFA. We extract the list of major congressional actions on the DFA from the THOMAS database of the Library of Congress. 11 The date the Conference Committee began reconciling the House and Senate versions of the Bill (June 25, 2010), as well as the date Senator Scott Brown backed the Act  (July 12, 2010) , are also included, as these events might have conveyed information to the market regarding the details of the Act, and/or partially removed uncertainty regarding its passage. Table  1  presents a list of the events. Using Bloomberg, the financial institutions traded on the New York Stock Exchange (NYSE), the American Stock Exchange (ASE) or NASDAQ, are grouped on the basis of the standard industrial classification (SIC) system.
The limit of control input u max (v) can also be identified as u max (v) = c 1c 2 tanh(c 3 (vc 4 )), where the constants are c 1 = 1. 523, c 2 = 1. 491, c 3 = 0. 08751, and c 4 = 15. 6 with 99. 74% coefficient of determination. The maximum hybrid brake system control input is chosen to be constant, u min (v) = -5 + d 1 v (N/Kg) (d 1 = 0)  [10]. A closed test track located at Colmar-Berg, Luxembourg, is chosen to model the road geometry with traffic information (Fig. 2 )  [17]. The test track has a total length of 1. 255 km and The preceding vehicle motion prediction based on 85 th percentile speed concept with test track geometry and speed limit zone information is shown in Fig. 3. The measured data include seven different rounds of human drivers velocity profiles on the test track. It can be shown that the physicalstatistical motion model is capable of foreseen an expected velocity profile.
The problem we are interested in this paper is to find original methods for discovering the implicit structure of electrical diagrams using the PDF description. In this context, we are working with a software company, Algo'Tech Informatique, that provides a set of specialized tools for the design of electrical diagrams. These tools are able to provide a synthetic view of any electrical diagram and to produce the corresponding booklet. To do so, whenever Algo'Tech informatique has a new customer, it gets existing electrical diagrams of the customer, to analyse them and adapt the database of their softwares. Furthemore, if customers want to find back their concepts and vocabulary into the new electrical diagrams once they are generated by Algo'Tech informatique's softwares, customers are also mostly interested to find their "footprints", their way of drawing and to dispose electrical components inside a circuit.
Another field that didn't receive a lot of attention (compared to symbol recognition by computer vision for example) is the field of diagram interpretation. The identification of the structure in a diagram, the semantics of its constituents and their relationship, is almost always domain-specific. Recently,  [3]  focuses on the problem of diagram interpretation and reasoning, also defined as syntactic parsing (detection of the constituents and their syntaxic relationships in a diagram) and semantic interpretation (tasks of mapping constituents and their relationships to semantic entities or real-word concepts) in order to propose a global approach of the diagrams, regardless of the domain. However, the approach benefits and actually exploits a huge dataset. More precisely, it exploits a deep learning algorithm that learns on a basis of 5,000 diagrams, a size one order of magnitude bigger than our 160 PDF booklets that we are currently analysing plus the 500 ones that we would like to test.
The PDF files are important : it is by using the folios indications, the folios titles and the folios number, that we realise the first approach described in the current section. Electrical diagrams on DXFs files DXF, for Drawing Interchange Format, or Drawing Exchange Format, is a CAD data file that enables data interoperability between many CAD softwares. It is considered an open access format because it is basicaly an ASCII file that can be read by text editors and that is organized by sections (HEADER, BLOCKS, ENTITIES, etc. ). A DXF file is thus an ASCII translation of a drawing file : by analysing the blocks and their attributes, it is possible to extract data and meta-data (coordinates, attributes, block name, etc. ) related to electrical components. If the extracted text from a folio of a PDF allows us to get the name of an electrical component for example, the DXF processing allows us to get its x, y coordinates inside that folio.
Acknowledgments: the simulations were performed using the Froggy platform of the CIMENT infrastructure, which is supported by the  Rhône-Alpes region  ( GRANT CPER07_13 CIRA ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the program Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. The authors would like to thank  Pierre Girard.
and Antoine Fortuné for technical support. This work used the platforms of the Grenoble Instruct-ERIC Centre (ISBG; UMS 3518 CNRS-CEA-UJF-EMBL) with support from FRISBI (ANR-10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology (PSB). We thank Isabelle Bally and Jean-Baptiste Reiser for assistance and access to the  SPR facility. We thank  Elisabetta Boeri Erba  and  Luca Signor  for the technical support to acquire MS spectra. We are indebted to  Xavier Brazzoloto  who provided the drosophila expression system. We gratefully acknowledge our colleagues of  LFB SA Toufik Abache ,  Alexandre Fontayne  and  Jean-Luc Plantier  for fruitful discussions. Funding: the project was funded by the  Agence Nationale pour la Recherche   13-RPIB-0011 Correspondence: marie-claire.
Material and Methods The study analysis was based on comments of 546 Internet users who expressed their relationship to the legislative acts on websites of the most popular Polish newspapers, TV and radio stations, Internet portals and blogs between July 2015 and September 2016. Using advanced software for combined qualitative and quantitative anlaysis (Atlas. ti 7. 0), attitudes of supporters and opponents of the acts were compared in two categories: 1/ types of arguments and 2/ semantic relations between arguments.
Material and Methods We used data from the Global Adult Tobacco Survey (GATS) -designed to produce national estimates among all non-institutionalized men and women 15 years of age or older -from the 13 countries where almost 2/3 of smokers live. Those smokers who reported having tried to quit at least once were included in the present analysis: Bangladesh (n = 1,058); Brazil (n = 2,928); China (n = 489); Egypt (n = 1,577); India (n = 3,499); Indonesia (n = 821); Mexico (n = 839); Philippines (n = 1,288); Russia (n = 1,403); Thailand (n = 1,503); Turkey (n = 1,028); Ukraine (n = 832); Vietnam (n = 1,168). We carried out weighted regression models for VUAQ including sociodemographic, smoking, treatment, and media/perceptions as dependent variables. Results VUAQ varied from 1. 0% in the Philippines to 13.
Material and Methods Analysis of data from a multi-site randomized controlled trial that enrolled 1357 hospitalized smokers planning to quit, offered two intensities of conventional cessation treatment after discharge, and reached 1100 participants at one month post-discharge. We assessed patterns of e-cigarette use before, during and after hospitalization, reasons for use, and factors associated with use. Results E-cigarettes were used by 21. 4% of smokers in the month before hospitalization but were used intermittently (median=4/30 days). E-cigarettes were used by 3. 1% of smokers in the hospital and by 18. 3% during the month after discharge, primarily as quit aids. At 1 month, 10. 6% reported past 7-day e-cigarette use (median=4/7 days), including 4. 6% who used e-cigarettes exclusively and 6.
Material and Methods Considering the human capital approach, labor productivity was approximated through remuneration in the labor market using the average gross wage. Employment data were obtained from the INE's Labor Force Survey. Deaths were obtained from the Register of Deaths. Relative risks associated to death rate with active cigarette smoking and smoking cessation was applied. The future values obtained were applied an annual discount rate of 3% and an annual rate of growth of labor productivity of 1%. Univariate deterministic sensitivity analysis was performed on discount rates and labor productivity growth rates. Potential Years of Lost Work Life (APVLP) were also estimated.
Results The data was collected from 103 smoker patients with the mean age of 58. 73. Medical chart review showed that about 96% of medical records included patients' smoking status (Ask). Almost 88% received smoking cessation advice from the physician (Advice). Only one patient received smoking cessation assistance (self-help material) (Assist). We could not document the performance of the "Assess" component of the model at the hospital. No follow-up arrangements were done by the physicians (Arrange). Almost all patients attempted to quit after MI, but only 54. 37% maintained non-smoking status at 6-12 months.
Material and Methods The study was conducted in Crete, Greece. Data (5,057 LC cases) were obtained from the Cancer Registry of Crete (CRC). Age-Standardized Incidence and Mortality Rates (ASIR, ASMR/100,000/year), adjusted Charlson's Comorbidity Index (CCI%), deprivation index (HPI-2) and exposure to Outdoor Air Pollution (OAP) were estimated. The analysis was performed for two time periods  (Period A: 1992 (Period A: -2008;; Period B: 2009 Period B: -2013)). Results ASIR presented a significant increase during the economic crisis, while even higher increase was observed in ASMR (Period A: ASMR=30. 5/100,000/year; Period B: ASMR=43. 8/100,000/year; p<0. 001). A significant increase was also observed in smokers (Variation rate=7. 4; p=0. 02) during the austerity period. After 2009, a significant increase of the LC hot spots was observed in several subregions of Crete (p=0.
Material and Methods Smoking prevalence data were extracted from the databases from the (bio) behavioral surveillance surveys among populations most at risk for HIV and those living with HIV. Self-administered or interviewer administered questionnaires were used in  2008 (n=2,818), 2010 (n=2,305), 2012 (n=1,823) and 2013 (n=3,299). Different sampling methodologies are applied across the groups. Vulnerable groups included: men who have sex with men, (snow-ball sampling (SB) in 2008-2012 and respondent driven sampling (RDS) in 2013); sex workers (SB); Roma youth (RDS); children without parental care living in institutions (cluster sampling); prisoners (cluster sampling); people living with HIV (convenient sampling).
The surveys were conducted with the contribution of the Italian Ministry of Health. This work was partially funded by the European Union's Horizon 2020 Research and Innovation Programme (The TackSHS Project; grant agreement: 681040) and by the Italian Ministry of Health (MADES project, chapter 4100/22). The work of SG and AL was partially supported by the Italian League Against Cancer (Milan). The authors declare that there are no conflicts of interest. Tob. Prev. Cessation 2017;3(May Supplement):54 DOI: 10. 18332/tpc/70217 Introduction Examine the association between social context and smoking status among non-institutionalized adults.
TackSHS is a four-year project that started in 2015. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 681040 Tob. Prev. Cessation 2017;3(May Supplement):21 http://dx. doi. org/10. 18332/tpc/70598 Introduction Between 2013-2015 the Maria Sklodowska-Curie Institute -Oncology Center in Warsaw, Poland was responsible for the coordination of a nationwide educational program «School promoting the European Code Against Cancer». Over 770,000 students aged 11-15 years old have participated in the program. The aim of this work is to present results of the pilot survey on schoolchildren's knowledge on cancer prevention.
Funding Instituto de Salud Carlos III, Government of Spain  and the  European Regional Development Fund, ERDF  (RTICC Funding This document was developed under the Project TackSHS that received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  681040. Funding This work is supported by a  George D. Behrakis Foundation  Grant. Funding This work was supported by a grant of the Romanian Funding This study was funded by the  Italian Ministry of Health  ( MADES  project, chapter  4100/22 ). Funding Russia's tobacco control program was supported by the Funding The current manuscript was supported by  Deanship of Scientific Research at the University of Science and Technology  with fund No. MA/388/2015  and  NA-MA/90/2015. Funding Global Bridges Healthcare Alliance for Tobacco Dependence Treatment , hosted by  Mayo Clinic  and  Pfizer Independent Grants for Learning and Change.
Funding This work was supported by  Global Bridges: Healthcare Alliance for Tobacco Dependence Treatment and Pfizer Independent Grants for Learning and Change  ( GB-13522581 ). Tob. Prev. Cessation 2017;3( May Supplement ):83 Funding This study has been funded by  Global Bridges Mayo Tobacco Prevention & Cessation  Funding The study was supported by the  Global Bridges Healthcare Alliance for Tobacco Dependence Treatment , hosted by  Mayo Clinic  and  Pfizer Independent Grants for Learning and Change. Funding This project was funded by the  Spanish Ministry of Economy and Competitiveness  under the  "Programa Estatal de Investigaciόn, Desarrollo e Innovaciόn Orientada a los Retos de la Sociedad ,  Plan Estatal de Investigaciόn Cientίfica Técnica y de Innovaciόn 2013-2016 " programme. Research project references:  ECO2013-48217-C2-1 & ECON2013-48217-C2-2-R  (http:// invesfeps.
ulpgc. es/en). The funder had no influence over the conducting of this study or the drafting of this manuscript. Funding Data collection was funded by  Life NGO, Kiev , Ukraine Funding Funding was provided by  Global Bridges, Mayo Clinic, U. S. Tob. Prev. Cessation 2017;3( May Supplement ):58 Funding Funding was provided by  Global Bridges, Mayo Clinic, U. S. Funding Funding was provided by  Global Bridges, Mayo Clinic, U. S. Funding Funding was provided by  Global Bridges, Mayo Clinic, U. S. Funding Study was funded by the  American University of Beirut , Funding The Deanship of Scientific Research at Jordan University Funding Thai Health Foundation Funding Thai Health Foundation Funding Supported by grant N o  15-06-10839  of the Russian State Funding The study was funded by the  Armenian Medical Fund.
Funding TackSHS is a four-year project that started in 2015. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 681040 Tob. Prev. Cessation 2017;3(May Supplement):21 http://dx. doi. org/10. 18332/tpc/70598 Introduction Between 2013-2015 the Maria Sklodowska-Curie Institute -Oncology Center in Warsaw, Poland was responsible for the coordination of a nationwide educational program «School promoting the European Code Against Cancer». Over 770,000 students aged 11-15 years old have participated in the program. The aim of this work is to present results of the pilot survey on schoolchildren's knowledge on cancer prevention.
ABSTRACT 15 New issues and age-old challenges: a review of young people's relationship with tobacco Conclusions STPs' impact can be influenced by adequate implementation and embedding them in continuous monitoring and adaptation cycles, so that schools can proactively deal with suboptimal responses. Funding This review is part of the SILNE-R project, which is supported by the European Union's Horizon 2020 research and innovation programme, under grant agreement 635056. Tob. Prev. Cessation 2017;3(May Supplement):102 DOI: 10. 18332/tpc/70595 Stacey Williams1 1Cut Films, London, UK.
Images were acquired by using a CCD camera (Cooled SNAP HQ, Photometrics). Synchronization of the monochromator and CCD camera was performed through a control unit run by a Fluorescence Ratio Imaging software (Meta-Fluor). Image analysis was performed with an ImageJ software. For time course experiments, fluorescence intensity in guard cells was determined over regions of interest, at 530 nm after excitation at 438 nm or 475 nm (E i 438 and E i 475). Background fluorescence signals were measured in regions outside the cell, using similar excitation and emission wavelengths (E b 438 and E b 475), and subtracted from corresponding fluorescence values measured in guard cells. A fluorescence ratio R was calculated as R = (E i 475-E b 475)/(E i 438-E b 438). Changes in fluorescence over time were expressed with respect to the initial ratio R 0 as R/ R 0.
ACKNOWLEDGMENTS. We thank the  Plant Growth Facility personnel of the Biochemistry and Plant Molecular Physiology department. O. R. and A. G. were both supported by doctoral fellowships from the  French Ministry of Higher Education and Research  while G. R. was supported by an  Erasmus Mundus EUROEAST  grant from the  European Commission. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.
This paper presents the nuclear analysis of the European DEMO baseline 2015 with HCLL blanket carried out with the TRIPOLI-4 ® Monte Carlo code and the JEFF-3. 2 nuclear data library. The TRIPOLI-4 ® model was imported from CAD using the McCad tool. A procedure that generates the detailed 3D model describing all the HCLL blanket internal structures was developed. This procedure allows parametrization of the blanket internal structures such as the number of cooling plates, manifolds, etc. and the thickness of the stiffening grid for instance. Different design variants were studied to improve the tritium production. From this previous study a complete nuclear analysis was carried out on a promising design which is a compromise between tritium production and mechanical robustness. All criteria (TBR, nuclear heating in coils and displacement damage in vacuum vessel) are met using this new reference design.
HCLL DEMO model The TRIPOLI-4 ® EU DEMO1 2015 HCLL model is based on a generic CAD model with empty blanket developed at KIT  [12]. The parameters of the studied tokamak are presented in Table  1. Compared to the previous baseline breeding blanket coverage is now bigger because the divertor is smaller in the 2015 DEMO baseline. The segmentation CAD model developed by the HCLL design team has been implemented in the generic model using the SALOME platform  [13]. The TRIPOLI-4 ® model was generated using the CAD import tool McCad [14]. To ease CAD import only empty modules are considered. An automated procedure, written in python, fills the empty blanket cells with the internal structures (FW, Caps, BPs, CPs, hSP, vSP, manifolds). This automated procedure allows parametrization of the BU to study different designs.
DATA REDUCTION The central galaxy in Abell 1795 was observed with the ALMA 12 m array using the Band 6 receiver for a total time on source of 72 min divided equally between 11 and 14 June 2016. The single pointing was centred on the Hα and FUV emission peaks close to the galaxy nucleus with a field of view of 28. 5 arcsec. One spectral window covered the CO(2-1) emission line at 216. 822 GHz and three additional windows were used to image the sub-mm continuum emission at 218. 791  GHz, 232. 491 GHz and 234. 491 GHz. The baselines ranged from 15 -704 m and thirty-eight 12 m antennas were available for each observation. J1229+0203 and Titan were observed for bandpass and flux calibration. Observations of the bright quasar J1333+2725 were interspersed with the target observations for phase calibration.
The frequency division correlator mode was used for the spectral line observation with a 1. 875 GHz bandwidth and frequency resolution of 977 kHz. The velocity channels were later binned to a resolution of 10 km s -1 for the analysis. The datasets were calibrated in CASA version 4. 5. 3 (McMullin et al. 2007 ) with the ALMA pipeline reduction scripts. The continuum emission was determined from line-free channels and subtracted from visibilities using the task UVCONTSUB. The calibrated continuum-subtracted visibilities were then imaged and deconvolved with the CLEAN algorithm. Self-calibration was not found to produce a significant reduction in the image rms noise. Stellar absorption measurements for the central galaxy give a redshift z = 0. 06326, which is consistent with the ionized gas velocities around the nucleus  (Hill et al.
2 arcsec and 1. 8 arcsec spatial resolution, respectively  (Salomé & Combes 2004). Fig. 2 (top)  shows the continuum-subtracted total CO(2-1) spectrum extracted from a 4 arcsec × 14 arcsec region that covers the central peak and the full extent of both filaments. The emission line has two separate velocity components spanning roughly 500 km s -1. The total spectrum was fitted with two Gaussian components using the spectral fitting package MPFIT  (Markwardt 2009). The brightest velocity component is blueshifted to -272 ± 5 km s -1 and has a broader FWHM of 220 ± 10 km s -1. The fainter component is narrower with a FWHM of 150 ± 20 km s -1 and a velocity centre at 1 ± 7 km s -1 , which is consistent with the central galaxy's systemic velocity. The best-fit results are corrected for instrumental broadening and the primary beam attenuation and are detailed in Table  1.
Multiple velocity components Additional narrow velocity components (Fig. 2 ) were located by fitting multi-component models to spectra extracted in synthesized beam-sized regions centred on each spatial pixel in the ALMA cube. Each extracted spectrum was fitted with one, two or three Gaussian components using the spectral fitting code MPFIT. At least 3σ significance, based on 5000 Monte Carlo simulations of the spectrum, was required for the detection of a velocity component in each region. Fig. 6  shows the resulting maps of the best-fit velocity centre, FWHM and integrated intensity for each component. As discussed in section 3. 2, the molecular filaments have smooth velocity gradients along their lengths. The velocity dispersion is less than 100 km s -1 for the bulk of the molecular gas, which is considerably below the stellar velocity dispersion of 297 ± 12 km s -1  (Oegerle & Hoessel 1991; Blakeslee & Tonry 1992).
ACKNOWLEDGEMENTS HRR and ACF acknowledge support from  ERC  Advanced Grant Feedback 340442. BRM acknowledges support from the  Natural Sciences and Engineering Council of Canada  and the  Canadian Space Agency Space Science Enhancement Program. ACE acknowledges support from  STFC  grant  ST/P000541/1. PS acknowledges support from the  ANR  grant  LYRICS  ( ANR-16-CE31-0011 ). We thank the reviewer for their helpful and constructive comments. This paper makes use of the following ALMA data:  ADS/JAO. ALMA 2015. 1. 00623. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ.