dataesr/scientific-paragraphs-categorization huggingface dataset:
The results are stored as histograms and reference plots, in the on-line MCPlots database  [11] , which is available to the global particle-physics community. It is used by both the authors of the simulations and by their users, as a validation tool, and to guide further efforts to improve the physics models and to optimise their parameters (see e. g. [20] ). The upper part of Fig. 2  shows a time slice from the summer of 2012, of the number of new users per day signing up for the Test4Theory project. On July 4th that year, CERN announced the discovery of the Higgs boson, prompting hundreds of new users to join the project. The lower part shows one of the many thousands of plots that are available at the MCPlots site  [11]. Several state-of-theart models for particle collisions (coloured lines) are compared against an archived measurement performed in 1996 by the ALEPH experiment (black squares)  [21] , of the probability distribution for observing N charged particles (N ch on the x axis) in electron-positron collisions at the LEP collider.
A Toroidal LHC ApparatuS (ATLAS) ATLAS@Home started in 2014 as an independent project where volunteers run Geant4  [22]  MC simulation of particles passing through the ATLAS detector  [23]. These simulations are well-suited to volunteer computing for several reasons: they involve less data transfer compared against other workloads; in ATLAS they are the largest consumer of CPU resources and hence there is always a reliable source of work; many simulation campaigns run over several months, so a fast turnaround is not expected. ATLAS relies on virtualisation to allow its simulation software to run on non-Linux hosts. ATLAS software is provided to the VM through the CernVM File System (CVMFS)  [26] , a remote read-only lesystem using aggressive local caching which is mounted inside the image.
To avoid downloading the software every time the VM is started, the CVMFS cache inside the image is pre-lled with the required software, by running an example job, saving a snapshot of the image, and using that snapshot as the nal image to distribute to volunteers. One critical requirement when starting the project was that no sensitive ATLAS credentials should be distributed to volunteers. The solution was to use the model deployed in NorduGrid  [27]  and other environments such as High Performance Computing (HPC) centres which have restricted access to the outside world from the job worker nodes. The architecture of this model is shown in Fig. 3. The Advanced Resource Connector (ARC) Computing Element (ARC CE)  [28]  takes care of data staging before and after the job runs, and the ARC Control Tower (aCT)  [29]  provides the link with the ATLAS workload management system, PanDA  [30].
Jobs which are assigned to ATLAS@Home by PanDA are picked up by the aCT, and sent to an ARC CE connected to the BOINC server. ARC CE copies the required input les from Grid storage to a staging area inside the BOINC server. ARC CE supports many batch systems and a new plugin for a BOINC "batch system" was written to allow injection of jobs as work units in the BOINC server. Instead of calling batch system commands, this plugin uses the create_work command to inject jobs into the BOINC server and queries the BOINC database to nd out when jobs have completed. The BOINC client on the volunteer's PC only has access to the BOINC server data staging area and no access to Grid storage or  Grid credentials and so there is no chance of accidental or deliberate tampering with ATLAS data. Because ARC CE and aCT are services which are part of the regular ATLAS computing Grid, ATLAS@Home looks from the outside like a regular Grid site, which means no special treatment is needed when it comes to de ning tasks, monitoring, accounting etc.
ATLAS@Home is one of the most demanding volunteer computing applications in part due to its high memory usage. A job using a single core can require a virtual machine with up to 2. 5 GB of memory, and for many machines this means that it is not possible to ll all cores with ATLAS@Home tasks. However, ATLAS software can run on several cores inside a single virtual machine and can take advantage of sharing memory between processes running on each core. These multi-core jobs provide a signi cant memory saving, with an 8-core job typically using 5-6 GB of memory in total. Previously, BOINC only allowed a xed memory limit per WU no matter how many cores were used. The ATLAS@Home jobs' memory requirements are dependent on the number of cores and so the project team implemented in BOINC a way of dynamically determining the memory required based on the number of cores.
Two new parameters were added to the plan class, which describes the characteristics of the virtual machine. A base memory and memory per core can be speci ed and the memory of the virtual machine is calculated as base memory + (memory per core × number of cores). This feature was passed upstream and is now part of the standard BOINC software. At the time of writing, ATLAS volunteers have simulated almost 170 million ATLAS events (one event typically takes around 5 minutes of CPU time to simulate) and the combined resources add up to around 2% of overall ATLAS computing resources.
Compact Muon Solenoid (CMS) CMS  [31]  is one of two general-purpose detectors at the LHC project, alongside ATLAS. Development began on a CMS@Home project in 2015 using a modi ed CMS Remote Analysis Builder v3 (CRAB3)  [32]  server VM submitting jobs running CMS standard software (CMSSW)  [33]  to a dedicated HTCondor  [34]  server VM rather than the normal submission to the Worldwide LHC Computing Grid (WLCG)  [35]. The VMs were run at Rutherford Appleton Laboratory (RAL), UK. Care was taken to match the type of jobs being run to the limitations of the volunteer environment. Of particular concern was the amount of data to be transferred, since many users still have ADSL connections which may have upload speeds as low as 1 Mbps. This obviously ruled out analysis of CMS data, but still allowed the generation of MC simulations of collision events.
The project has now turned to the use of the workow management system (WMAgent)  [37]  for job submission. WMAgent gives the ability to specify a destination site within the CMS infrastructure to which results are automatically replicated using the transport software PhEDEx  [38]. Thus fully end-to-end running of CMS MC production jobs has been demonstrated and the project will be able to contribute a signi cant computing resource to the CMS Collaboration. At the time of writing, volunteers are providing around 800 job slots to production, a gure that is expected to rise in the future.
Large Hadron Collider beauty experiment (LHCb) The LHCb  [39]  experiment detector has been designed to lter out from the di erent particles generated by LHC those containing beauty and anti-beauty quarks (Bmesons) and the products of their decay. Unlike the other LHC experiments that surround the entire collision point with layers of sub-detectors, the LHCb detector extends along the beam pipe, with its sub-detectors piled behind each other. This is because the B-mesons do not travel in all directions, but rather stay close to the line of the beam pipe. Considering the growing need of computing power, the LHCb computing group has created a rst prototype of the Beauty@Home project in 2013 to pro t from volunteer computing resources. The project uses the CernVM Virtual Software Appliance  [40] , the BOINC framework, and the Distributed Infrastructure with Remote Agent Control (DIRAC) system for distributed computing  [41, 42].
SixTrack performance with BOINC The processing time of SixTrack studies submitted to BOINC over the month of September 2017, those that were not yet purged from the result database, have been analysed. Data extraction has been performed in order to select all results that completed with error-free processing. For the sake of the time analysis, SixTrack tasks in BOINC can be divided in two categories, based on the total number of turns used in the beam dynamic simulations, which translate directly into CPU time. A sample of , runs with turns and , runs with turns have been prepared, the latter set represents studies including beam-beam e ects (note that these e ects are particularly expensive in terms of CPU-time). In Fig. 8  the actual processing time on volunteers' computers is shown. The distribution of the SixTrack computing time on volunteers' computer is determined by the properties of the initial conditions.
This approach will make it easier for scientists to submit work to di erent resources allowing the IT team to direct workloads to the appropriate ones. In this respect, further attention is needed to evolve the BOINC middleware and to improve the integration with HTCondor. Development at CERN on the HTCondor-BOINC remote submission gateway is on-going, and will be brought into the BOINC code base. It should be mentioned that also other BOINC projects have posed questions on BOINC software governance and the inclusion of contributed code. Hence an e ort to evolve the BOINC community software with contributions from major BOINC projects and stakeholders is required to ensure a long-term future for BOINC and the current volunteer computing community  [57]. More speci cally for the case of SixTrack, the computational problem in accelerator physics is largely one of throughput and the number of processors available is more important than the per processor performance.
Therefore, by providing support for ARM processors with Android (tablets and smart phones) and for Raspberry Pi, an even larger number of processors can be made available for at least the SixTrack application. We are also working on the porting of the SixTrack application to use GPU resources. In fact, since most computers used by volunteers have graphics processors, usage of GPUs might generate an estimated ve-to ten-fold increase of the throughput of SixTrack jobs running on same number of volunteers' computers. It is worth stressing that SixTrack is undergoing major development e orts to open up new domains of accelerator physics, needed for a better understanding of current and future circular particle colliders. LHC@Home is the ideal environment to exploit at best the new code capabilities in view of massive numerical simulations.
Acknowledgement: Our warm thanks go to all people that supported and continue to support us by donating CPU capacity, which is a huge contribution to our studies!We hope that even more volunteers will join LHC @Home  to help us pushing further the detail of the studies that we can perform. We gratefully acknowledge the contributions made to the  CMS@Home  development by the  CMS Collaboration  in providing the many software packages used, especially CMSSW, CRAB3, WMAgent and PhEDEx. We gratefully acknowledge nancial support from the  Science and Technology Facilities Council, UK , under grant  ST/N001273/1. We also gratefully acknowledge the support by the  European Circular Energy-Frontier Collider Study ,  H2020  under grant agreement no. 654305  and by the  Swiss State Secretariat for Education, Research and Innovation SERI.
Law of Large Numbers in RCTs To illustrate how the properties of the Law of Large Number may ensure the covariate balance and impact the results of RCTs, we re-analyzed two published studies. The IST, including 19,435 adult patients suspected of acute ischaemic stroke from 36 countries, evaluated the effect of Aspirin and Heparin on death or dependency at 6 months, by a two-bytwo factorial design  [11]. The database of this trial has been made available by the investigators  [13]. The CORONARY study, including 4,752 senior patients undergoing a CABG from 19 countries, compared the procedure off-pump versus on-pump regarding a composite primary outcome of death, nonfatal stroke, nonfatal myocardial infarction, or new renal failure requiring dialysis at 30 days  [12]. For each RCT, we generated random subsamples with replacement from the original data set, by varying the sample size (n from 50 to 5,000 subjects, stepwise 50), which replicated RCTs with different enrollments.
Number of trial participants in medical literature To describe the actual enrollment in clinical trials, we extracted all of the studies indexed in PubMed as "clinical trials" and published on November 15 th , 2015, in one of the top five highest-impact journals in Medicine (the Annals of Internal Medicine, the British Medicine Journal, the Journal of the American Medical Association, The Lancet, and the New England Journal of Medicine). Of these studies, we kept those reporting a link to ClinicalTrials. gov. After merging the two databases (PubMed and ClinicalTrials. gov) on the NCT registration number, we automatically extracted the number of enrolled participants in the interventional studies, in which the treatment was randomly allocated in a parallel or factorial design. Though some of these could use alternatives to simple randomization, such as blocked randomization or minimization on measured covariates, imbalance could remain in unmeasured covariates.
Califf et al. reported that 3. 1% of studies registered on ClinicalTrials. gov between 2007 and 2010 included more than 1,000 subjects  [9]. According to Hopewell et al. [10] , the median sample sizes of RCTs indexed in PubMed database in 2000 and 2006, were 32 and 36 in each treatment group. In the top five highest-impact journals, we report a larger proportion of RCTs enrolling more than 1,000 participants in phase 3. However, more than a half should still be considered too small to provide fair evidences. This finding is quite alarming, as these studies were designed to provide evidences of effectiveness of new treatments. "Studies of lesser quality and smaller scope should not carry equal weight to large-scale comprehensive trials just because they are randomized" said Gregg Stone and Stuart Pocock  [37].
M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT considered as the highest-quality studies. As discussed before, the prospective registration of such database may reduce publication bias  [43, 44]. In our case, regarding the number of trial participants, only a few discrepancies are expected between the registry and publications  [43, 45]. To conclude, simple randomization does not necessarily protect against bias without respect to the Law of Large Numbers. Insufficiently sized trials may exhibit substantial bias and wrong conclusions may be drawn.
ACKNOWLEDGEMENTS We are thankful to  Professor Peter A. G. Sandercock  and the  International Stroke Trial collaborative group  for releasing the IST database in open-access. This trial was principally funded by the  UK Medical Research Council , the  UK Stroke Association , and the  European Union   BIOMED-1 program. Limited support for collaborators' meetings and travel was provided by  Eli Lilly, Sterling Winthrop (now Bayer USA) ,  Sanofi , and  Bayer UK. Follow-up in Australia was supported by a grant from the  National Heart Foundation  and in Canada by a  Nova Scotia Heart and Stroke Foundation  grant. Czech Republic IST was supported by a grant from the  IGA Ministry of Health. India IST  was supported by the  McMaster INCLEN program  and the  All India Institute of Medical Sciences.
The IST in New Zealand was funded by the  Julius Brendel Trust  and the  Lottery Grants Board. In Norway, the  IST  was supported by the  Norwegian Council on Cardiovascular Disease and Nycomed  (for insurance). We are thankful to the  CORONARY collaborative group. All funding was provided by the  Canadian Institutes of Health Research. No manufacturers of off-pump or on-pump CABG supplies or devices had any role in the study. The data were gathered and analyzed by the  Population Health Research Institute at McMaster University and Hamilton Health Sciences.
The main concern of this paper is the development of a fully three dimensional viscoelastic model at finite strain to describe nonfactorizable behavior of rubber-like materials within the framework of rational thermodynamics and internal state variable approach such that the second law of thermodynamics in the form of Clausius-Duhem inequality is satisfied. The nonfactorizable aspect of the behavior is introduced via a strain dependent relaxation times. The model is applied to describe the response of the isotropic Pipkin multi-integral viscoelastic model and the BIIR material, several parameters involved are then identified using quasi-static and dynamic experiments thanks to a least-square minimization procedure performed with Matlab software. The proposed model is able to reproduce quasi-static experimental response and show an ability to predict the dynamic response of nonfactorizable rubber-like materials in a wide range of strain.
An alternative useful representation of equation 34 with respect to the identification procedure is used. Let c t = {c 01 ,. , c 0j , c 10 ,. , c 1j ,. , c i0 ,. , c ij } be the vector of material parameters, Φ be a matrix representation of the function φ (i, j, λ) and Π e the discrete vector of nominal stress. Equation  34 became Π e = Φ c (37) The identification of the material parameters c ij is performed using data for simple extension and pure shear simultaneously. Therefore, a modification of the objective function 29 is adopted  [28]. The new objective function reads as follow min c∈R ij Φ se c -Πse 2 2 + Φ ps c -Πps 2 2 (38) The superscript se and ps refers to the simple extension and pure shear respectively. Π denotes the recorded experimental nominal stress vector. A least square minimization procedure is then employed under conditions 33 using Matlab software to reach the numerical values of c ij.
Application of the identification procedure to experimental data In this section, the identification procedure outlined in section 4 is used to identify the parameters of the proposed model using experimental data for a bromobutyl (BIIR) rubber material. Experimental data used here are those from  [13] , in which a complete experimental characterization was performed to obtain the response of the material for several strain history configuration and several temperatures. In what follows, results of the identification of the model' parameters are highlighted and discussed. The identification of the free energy density coefficients of relation 38 is performed under stability conditions of the relation 33 using Matlab software. A second order Mooney-Rivlin potential was found to be satisfactory with respect to the value of the total relative error of the stress for simple extension and pure shear experiments.
From shear relaxation experiment The shear relaxation experiment is performed in simple shear deformation at a strain level of 10 %. Despite that the value of the strain is quite big, it's considered, however, in the linear range of the behavior since the material is highly deformable. From figure 1 data for shear relaxation experiment at 10 % are extracted and used in the identification of the Prony series parameters. These parameters are reported in table 2. The average relative error between the experimental and the identified viscoelastic kernel is in the order of 0. 1 %. The identified and the experimental normalized shear relaxation modulus are plotted versus time in figure  10.
• Application of the Tikhonov method to dynamic data from  [13] : The dynamic experiment are represented by the storage and loss moduli G and G as functions of the frequency for the experimental frequency window of [10 -5 , 10 The reduced time is identified using the discretization formula 50 and    Our main object was to develop a constitutive equation compatible with the second law of thermodynamics and suitable with the Finite-elements theory. The implementation of the model within Abaqus software and the simulation of real component with complex industrial application is the goal of future work.
In some cases we also considered very large values of n in order to illustrate the asymptotic framework. We denote by p n the empirical estimator and by p k n , respectively p * k n , the least-squares estimator of p on the set of k-monotone probabilities, respectively sequences. For each simulation configuration, 1000 random samples were generated. The simulations were carried out with R (www. r-project. org), the code being available at the following web address: http://www. maiage. jouy. inra. fr/jgiguelay λ 1 = 1, λ 2 = 2- √ 2.
Numerical Results A computational experiment was conducted where the SRAS algorithm was compared to Gurobi 7. 0 using data derived from historical stock prices from two stock market indices. All computing was done using Matlab R2016a on a Windows 10 64-bit, AMD A8-7410 processor with 8 GB of RAM. Six problems were used for testing. Historical stock prices of the Dow Jones Industrial Average and the S&P/TSX 60 were used to calculate the expectation and covariance of instrument returns. For each index, the past year, 2 years and 5 years were used for estimation. This data was generated using the website InvestSpy  [10]. Results are presented in Table  1  below. We observe that the mean computing time of SRAS is over an order of magnitude faster when compared to Gurobi. Another positive aspect of the SRAS algorithm is its relative simplicity compared to the quadratic programming reformulation, which is generally solved by using an interior point method.
Sharing scientific data with the objective of making it discoverable, accessible, reusable, and interoperable requires work and presents challenges being faced at the disciplinary level to define in particular how the data should be formatted and described. This paper represents the Proceedings of a session held at SciDataCon 2016 (Denver, 12-13 September 2016). It explores the way a range of disciplines, namely materials science, crystallography, astronomy, earth sciences, humanities and linguistics, get organized at the international level to address those challenges. The disciplinary culture with respect to data sharing, science drivers, organization, lessons learnt and the elements of the data infrastructure which are or could be shared with others are briefly described. Commonalities and differences are assessed.
Thus, we considered that an excellent opportunity to examine this question -of how a number of different disciplines in practice build their international disciplinary data sharing frameworks -was the SciDataCon 2016 conference, organized by CODATA (the ICSU Committee on Data) and WDS (the World Data System, another organisation affiliated to ICSU). This meeting was part of International Data Week (12-17 September 2016, Denver, CO, USA), which also included the 8 th Plenary meeting of the Research Data Alliance. The present paper arose from a session designed to gather data specialists from different disciplines to exchange perspectives on their data sharing practices. This session, called 'Building a disciplinary, world-wide data infrastructure', took the form of a number of brief presentations and a panel discussion around a specific set of questions.
a. Humanities It is difficult, if not impossible, to provide a comprehensive answer to the proposed questions for the entirety of the humanities, as practices within the sub-disciplines of the humanities can be as diverse as those of the individual sciences. Nonetheless, some general observations can be made. The culture of data sharing in the humanities follows a variety of (often ad hoc) approaches: via linked data URIs, the Application Programming Interfaces (APIs) of Content Management Systems and custom websites, GitHub repositories, Google drive, Dropbox, etc. More formalized approaches, such as institutional repositories, shared infrastructure services, Dataverses, etc. are used when they are available and accessible, but there is not a single common practice across the entire domain of the humanities and availability of such services is not universal  (Padilla 2016).