dataesr/scientific-paragraphs-categorization huggingface dataset:
Systematic uncertainties The main sources of systematic uncertainty in the background estimate are due to the limited number of events in the photon+jets data and in the simulated samples used for the misidentification probability estimation. Two other sources are the uncertainties in the determination of f b for each of the samples used in the misidentification probability determination and the uncertainties due to differences in the composition of the non-b jets in the sample used in determining the misidentification probability compared to that in the kinematic samples. We estimate the first uncertainty by using the value of f b predicted by simulation instead of that obtained by the template fit. We estimate the second uncertainty by using the method on MC simulation. The uncertainty is estimated as the difference in the prediction when using a misidentification probability determined using an MC sample of events containing a high-p T photon and when using a misidentification probability determined using an MC sample of SM QCD multijet production.
Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract No. 675440 (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F. R. S. -FNRS and FWO (Belgium) under the "Excellence of Science -EOS"be. h project n. 30820817; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Lend ület ("Momentum") Program and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, the New National Excellence Program ÚNKP, the NKFIA research grants 123842, 123959, 124845, 124850 and 125105 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu, grant MDM-2015-0509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  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) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI ,.
Neutron -mirror-neutron oscillation, depending on the magnetic field(s) applied, may add an additional loss channel to the storage curve: N {0,B} (t s ) = N {0,B} (t s = 0) (2) × exp -R + R nn {0,B} t s. Here, N {0,B} (t s = 0) are the initial numbers of neutrons stored in the chamber and N {0,B} (t s ) denotes the UCN data points as a function of storage time t s , when the magnetic field is switched off or switched on, respectively. The value R refers to the sum of rates of known loss channels as up-scattering, absorption, and β-decay of neutrons in the chamber, which are all independent of the applied magnetic field. R nn {0,B} is the loss channel added due to nn oscillations when the magnetic field applied is zero or when it is different from zero. Note that when the applied magnetic field, B, is zero, there could still be a non-zero mirror magnetic field, B 0.
Acknowledgments The authors thank the exceptional support provided by  Michael Meier ,  Fritz Burri  and the BSQ group at PSI. LPC and LPSC groups are supported by  ANR  grant #  ANR-09-BLAN-0046. University of Sussex group  is supported by  STFC  grants #  ST/N504452/1 ,  ST/M003426/1 , and  ST/N000307/1. University of Sussex group  is also supported by their  School of Mathematical and Physical Sciences. PSI group is supported by  SNSF  grants #  200020-137664 , #  200021-117696 , #  200020-144473 , #  200021-126562 , #  200020-163413  and #  200021-157079. ETHZ is supported SNSF grant #  200020-172639. University of Fribourg group  is supported by  SNSF  grant #  200020-140421. The Polish groups are supported by  National Science Center  grant #  2015/18/M/ST2/00056. For the  KU Leuven group , this work is also partly supported by Project  GOA/2010/10  and  Fund for Scientific Research in Flanders (FWO).
As part of the Roamer project, we sought to have a picture of the available mental health research (MHR) funding, capacity-building and infrastructures resources and to establish consensus-based recommendations that would allow an increase of European MHR resources and enable better use and accessibility to them. The methods fell into three sections (i) a review of the literature, (ii) a mental health-related keywords search within the Cordis ® , On-Course ® and Meril ® databases which contain information on European research funding, training and infrastructures. These reviews provided an overview that was presented to (iii) two experts workshops with 28 participants drawn from academic which identified gaps and produced recommendations. The literature review illustrates the debates in the scientific community on funding, training and infrastructures.
The consensus method has been described in detail  (Haro et al. , 2014). This is the largest such process involving more than 1000 scientists, clinical professionals, and families including those with experience of mental ill health for the first time  (Fiorillo et al. , 2013). The process resulted in a comprehensive consensus-based Roadmap  (Belli et al. , 2015)  and a short-list of MHR priorities for the next decade  (Wykes et al. , 2015). Work package 3 (WP3) of ROAMER investigated the resources necessary to implement the research priorities. There were three "areas of knowledge" covered: capacity and infrastructure, training and capacity building, and funding. Capacity and infrastructures not only included the research units, institutes and networks necessary to perform research but also the technical infrastructures that would be essential such as databases, cohorts, biobanks, computing or biological analytical facilities.
Part 1 -Literature review We searched separately the three following domains in the PubMed and PsycINFO databases: research infrastructures (RI), capacity-building or training, and funding in relation with mental health (or psychiatric) research. This literature review had two steps, using the same research strategy. The first step was performed in 2012 covering the years 2002-2012 and aimed at building the focus groups discussion guide. The second step was performed in 2018 covering the 2012-2017 period with the aim to update the data and to capture possible evolutions in the literature covering our topics of interest. The objective was to get an overview on scientific research and opinion papers covering the human, infrastructural and financial resources of mental health research. It looked at answering to the following research question: "what are the scientific works and claims focusing on mental health research resources?" All kind of original research and opinion papers (including editorials and commentaries) published in peer-review revues were included in our review.
Part 2 -Searching of databases Three databases were searched: (i) Meril ® is an on-going mapping initiated by the European Foundation Association and supported successively by the FP7 and H2020 European Commission's research programmes. It dynamically inventories European RIs. The last time we accessed the portal (Nov. 2017), 942 European RIs were referenced. We asked the Meril ® team to send us the list of the programmes referenced as belonging to biomedical and social sciences domains (n=356), we read their description and visited their websites in order to retrieve the ones potentially useful to mental health researchers or dedicated to them. (ii) On-Course ® defines itself as "an industry and academia led course portal that is the most comprehensive and insightful biomedical postgraduate education and training resource in Europe.
", it is initiated and managed by  EMTRAIN (European Medicines Research Training Network) , part of the Innovative Medicines Initiative (IMI) and jointly funded by the European Commission and EFPIA (European Pharmaceutical Industries and Associations). Like Meril ® , it is an on-going inventory with each programme classified by one or more biomedical disciplines from a list of 40. At the date at which we accessed the database (Nov. 2017) 8 426 European courses were inventoried: 3 636 masters, 3 632 short courses and 1 158 PhD from 33 European countries (including Israel)  (Payton et al. , 2013). We selected the training for which the course disciplines included "psychiatric disorders". We then read the courses' description to classify them between "not mental health related", "partially linked to mental health" and "mental health dedicated".
We also screened the courses without any courses disciplines filled (missing data) and retrieved among them some courses related to mental health. (iii) Cordis ® is the database inventorying all the EU-funded research projects. We extracted the 25 783 research projects funded by the European Commission's Directorate for Innovation and Research between 2007 and 2013. Methods and results of this query have been published elsewhere  (Hazo et al. , 2016)  and will not be displayed in detail in the present paper. In these three databases we isolated the mental health related or dedicated elements in order to analyse them and compare them to the rest of the data.
Part 3 -Workshops Two scientific workshops were held in September 2012 and February 2013, attended by 28 mental health researchers in total (26 at the first one and 20 at the second). All the experts had demonstrated expertise through their publications or organisational positions (e. g. in the European Psychiatric Association or OECD). Geographical region, gender and age distribution were considered in their selection. The list of participants is provided in the annex. Quantitative data and preliminary findings of the literature review, organized by European and country levels, were presented at both workshops in order to provide a basis for discussion. We proceeded by area of knowledge, the presentations were followed by the reactions of each expert, then the discussions were oriented in order to enable the experts to reach consensus on gaps and advances needed in each of the areas of knowledge.
Literature review In total, 992 papers were retrieved, 121 duplicates (from different databases and strategies) were removed, and 746 papers were not meeting inclusions criteria. 125 papers were retained as relevant and analysed. 25 of them were explicitly the writers' opinions, these papers were varied and perspective pieces from every area of knowledge, including editorials planning the future of psychiatric research, comments based on real-life experience in training young researchers and cultural competencies and debates on last MHR evolutions. There were arguments for common strategies, users involvement, more funding (especially from private not-for-profit sources), equitably shared between neurosciences and traditional psychiatric approaches, or demands for more capacity-building, multidisciplinary approaches, collaborations with the pharma industry, and for a public health paradigm in mental health research as well as bridging the psychotherapeutic and pharmacological research  (Nutt, 2005 , Erickson and Erickson, 2007 , Atkins and Frazier, 2011 , Drake, 2013 , Owen, 2014 , Schachar and Ickowicz, 2014 , Okkels et al.
Databases Within the 8 627 biomedical courses registered in On-Course ® , 43. 6% were located in the UK and 272 were classified as "psychiatric disorders". Among these, 61 were excluded because they were not related to the mental health fields included in Roamer. Of the 211 after exclusions, 194 were dedicated to mental health and 17 were only partially related to mental health. We added six unclassified courses found manually giving a total of 217 mental health related courses. Courses retrieved as mental health related were classified by degree, by broad discipline (psychology, psychiatry or general mental health and/or mental disorders related), and by disorders when they were disorders specific. Results of these classifications can be found in Table  1. -  ----------------------------TABLE 1 ABOUT HERE ------------------------------------- Within the 356 European RI belonging to biomedical and social sciences "domains" in the Meril ® database and located in Europe, we excluded 106 RI that were not useful to MHR.
20% were either bibliographic tools (2) or data archives (48); 14% were dedicated to imagery services (36); 10% were dedicated to data computing (24) and 6% were cohorts (  14 ). The remaining services provided (networks and clinical trials services) accounted for 1 to 2% of the RI inventoried in Meril ®. The Cordis ® database search and results has been described in detail in a dedicated paper that describes the amount of research funding from European Commission between 2007 and 2013 (7 th Framework Program) dedicated to MHR and by beneficiaries' country  (Hazo et al. , 2016). The main results of the study were that among the 25 783 research projects funded during the period, 215 (0. 8%) were specifically dedicated to mental health and 170 (0. 7%) were partially related to mental health.
Resources and infrastructures Goal 1: Optimise European research strengths Available European research infrastructures (RI), and among them European research databases, seem to be neither sufficiently known nor used by researchers. This leads to a waste of resources and loss of research opportunities. Due to differences in the collection, reporting, classification and languages, databases can be difficult to understand, use and compare across Europe. To improve the accessibility and the (e-)sharing of European clinical and research data, it is suggested to create an infrastructure which maps the available databases of population, clinical and research data in mental health (recommendation #1), and which promotes mental health disorders registries, mental health status surveys and the use of common indicators across countries (recommendation #2).
Providing harmonised information about these databases (including owner, objectives, variables, method and language) would facilitate their dissemination and usage in a way that matches their potential. This common tool would facilitate collaboration, single-and multi-national projects, and would attract young researchers into the field. The European network of RI is sufficiently developed so that an online database of health databases with free access for the research community could be established. Specific funding should be dedicated to the creation, maintenance and translations related to this database of mental health databases. The most important prerequisite of such infrastructure is to implement open-access for publicly-funded MHR data (recommendation #4). Indeed, another identified gap is the waste of resources resulting from lack of access to data from publicly-funded research projects.
The open data philosophy is making inroads in the scientific community, and it would be possible to condition public funding on open access to data (e. g. clinical trial or cohort data) unless these data are particularly sensitive or not anonymised. The open-access policy in H2020 program conditions funding to open access to publicly-funded MHR publications (recommendation #3); since the 1st January 2017 EU funded projects are by default part of the Open Data Pilot, i. e. should share their research data through a dedicated data repository  (Guedj and Ramjoué, 2015, OpenAIRE Consortium, 2017) , hopefully most national funders will follow this lead. Even when they are known, rules of accessibility of RI, such as databases, are not homogeneous and sometimes not clearly communicated. Even though Europe has an increasing network of RI, there is still a need to better communicate in order to facilitate access by a maximum of users.
The networks could ensure the storage, sustainability and protection of open-access databases, develop the use of common research languages, procedures and standards throughout Europe, limiting biases and increasing the reproducibility of studies. Such networks could be formal or informal but require a minimum level of funding which could be included in any funding proposal as 'networking activities'. To be competitive with the rest of the world, the critical mass could easily be reached at the European level through "scientific networks" especially when researchersled. They would also foster informal meetings and knowledge exchange activities. Such small events where researchers could meet and develop ideas together could be initiated by public authorities, but they likely would be more efficient if they were initiated by the researchers themselves, therefore, a specific budget/cost for "knowledge exchange activities" could be included in all funding, as a routine part of researchers' activities.
Finally, researchers should be helped to cross both countries and the scientific boundaries of disciplines. These recommendations and conclusions must be taken with regards to the limitations of this paper. First, the workshop attendance rate was 38%, this low to intermediate participation rate entails the risk of biases. However, given the selection process and professional background of the ROAMER group and experts who participated, research priorities and recommendations reflect both scientific evidence and leading opinions in MHR. In addition, previous ROAMER publications yielded similar rates, and outcomes were further supported in two online Delphi-based survey  (Elfeddali et al. , 2014 , van der Feltz-Cornelis et al. , 2014). Second, two databases searched were not exhaustive, for example important RI such as the British 1958 National Child Development Study, which is still of great interest and importance for MHR  (Takizawa et al.
, 2014) , were not included when we searched the Meril ® database. The On-Course ® database was significantly biased toward British biomedical courses, extensively inventoried while we could not find any masters in psychology of other European countries for instance. This does not reflect reality. However, these database queries should be used as indicators to spot the share of MHR resources in these mappings, and their relative absence could be interpreted in two ways. Firstly: either mental health related training and RI are less prone to be included in these mappings than other disciplines, which might indicate a lack of resources, information or awareness. Alternatively it may simply be that these courses are relatively rare compared to other biomedical fields. For the RI mapping, we should also consider the possibility that MHR infrastructures such as cohorts and data registries do not yet consider themselves as infrastructures open to the research community.
Also, MHR might be less resources-consuming in terms of infrastructures. Finally, some specific areas of mental health research, such as children psychiatry or addiction, which can be treated with dedicated specific research resources, were not investigated during our work. To address these potential biases, several data sources (literature, databases and workshops) were used. This triangulation is known to increase the validity of research findings. Finally, the validity and generalizability were increased by the workshop participation of 14 mental health experts from the Roamer consortium who created a bi-directional linkage with the recommendations formulated in other thematic WPs  (Haro et al. , 2014). Our results and recommendations are also consistent with what can be found in the literature published after our workshop.
Materials and Methods This section discusses the hardware implementation. In order to perform the automated translation of reaction systems to synchronous circuits a compiler RStoVerilog was written that allows to generate hardware description of the circuit using Verilog language. The syntax of the input file for RStoVerilog is the same as for brsim/WEBRSIM reaction systems simulator  [56, 57]  and the description of the command-line options can be found in  [36]. As a result it produces a Verilog description of the circuit, a Verilog test bench testing the circuit with the supplied input and the graph describing the corresponding Moore machine in GraphViz format  [58]. The program can be found at  [36]  and can be freely used. The compiler performed the following steps: 1. Parse the input file.
2. Identify input and output symbols. 3. Duplicate input symbols that are at the same time output symbols. 4. Transform the reaction system into a Boolean circuit in DNF. 5. Write Verilog output (module and the test bench). 6. Optionally, construct Moore automaton of the obtained system. Step 1 was performed using standard compiling techniques. Now in order to apply the algorithm described in Section 3 one needs to identify input and output symbols. Since the initial concept of reaction systems does not possess this information, we used the following algorithm to automatically identify them. So during Step 2, the program identified symbols that never appeared in the products of a reaction as input symbols, while the others were identified as output symbols. This behavior can be overridden by special annotations in the source file that give the explicit list of input and output symbols (the remaining ones are considered as internal states).