dataesr/scientific-paragraphs-categorization huggingface dataset:
OPIE: OpenStack Preemptible Instances Extension We have developed OPIE  [16]  as an extension for OpenStack, allowing to execute preemptible instances in an existing infrastructure. In the current implementation, preemptible instances can run within the user quota and as far as there are not preventing normal requests from being executed. Whenever the scheduler detects that a normal instance cannot be executed because of a preemptible instance, it triggers its termination, according to several filter and weight functions, configurable by the resource provider. OPIE is composed of the following modules: • A modified scheduler, that augments the OpenStack FilterScheduler with the ability to execute normal and preemptible instances, configurable and extensible via additional filter and raking functions so that each resource provider can implement its policies.
INTRODUCTION During the last decade, the term End User Development (EUD)  [1]  became popular in the field of the Web, since it is a natural environment for daily life activities nowadays. In this regard, there is a survey  [2]  conducted with 800 information workers reporting that 68% of them created some Web artefact, 65% wrote HTML pages and around 40% already coded in JavaScript. The Personal Web (PW) also arose in the last years, providing a first class support to the end-users' needs  [3]. EUD clearly benefits the PW, but currently it usually covers a single stage in the software lifecycle. End-User Software Engineering (EUSE)  [4, 5]  is a wider research aimed at providing end users with solutions to create good quality software. However, we found that the related literature in PW by end users is mainly focused in EUD.
Flavour physics observables are excellent probes of new physics up to very high energy scales. Here we present FlavBit, the dedicated flavour physics module of the global-fitting package GAMBIT. FlavBit includes custom implementations of various likelihood routines for a wide range of flavour observables, including detailed uncertainties and correlations associated with LHCb measurements of rare, leptonic and semileptonic decays of B and D mesons, kaons and pions. It provides a generalised interface to external theory codes such as SuperIso, allowing users to calculate flavour observables in and beyond the Standard Model, and then test them in detail against all relevant experimental data. We describe FlavBit and its constituent physics in some detail, then give examples from supersymmetry and effective field theory illustrating how it can be used both as a standalone library for flavour physics, and within GAMBIT.
The correlations between the different flavour observables, and the interplay between flavour measurements and direct searches at collider experiments, are key tools in the search for new physics, and its eventual understanding. Public packages exist for carrying out SM and BSM flavour fits in terms of Wilson coefficients  [1] [2] [3] , but so far no general package exists for both computing Wilson coefficients and carrying out a global fit. In this article we present FlavBit, a flavour physics library designed in the context of the Global And Modular BSM Inference Tool (GAMBIT) framework  [4] , but also usable in standalone form. FlavBit allows users to predict flavour physics observables in various models, using external programs such as SuperIso  [5] [6] [7] , and then calculate combined likelihoods for arbitrary combinations of the observables.
We include these measurements in FlavBit. This paper is organised as follows. In Sec. 2 we provide the general theoretical background of the scheme by which we compute flavour observables, before providing a brief synopsis in Sec. 3 of the broader global-fitting framework within which FlavBit sits. In Sec. 4  we discuss the predictions and measurements of individual observ-ables included in FlavBit 1. 0. 0, and highlight aspects of new physics models to which the different measurements are sensitive. Sec. 5 gives details of the likelihood calculations that FlavBit performs. Sec. 6 gives some usage examples, both in standalone mode and with GAMBIT proper. Sec. 7 summarises our conclusions, and Appendix A gives a glossary of relevant GAMBIT terminology helpful for reading this paper. The FlavBit source code is freely available from gambit.
Computational framework The GAMBIT framework defines two sorts of functions that can be used to calculate physical observables or other quantities required for computing them: module functions: functions written in C++ and contained within a GAMBIT module. backend functions: external library functions provided by a backend, such as SuperIso or FeynHiggs. For ease of reference, here we highlight and link specific GAMBIT terms to their entries in the glossary, found in Appendix A. When writing GAMBIT module functions, the author assigns each a capability, which describes what the function can calculate. This may be an observable, e. g. a particular branching fraction for a given rare B decay, or a likelihood, e. g. the combined likelihood defined using a set of rare decays. Module functions can be declared to have dependencies on the results of other module functions, which they indicate by specifying the capability of the module function that must be used to fill the dependency.
Dependencies may be filled by any function within GAMBIT that has the requisite capability, whether or not it is part of the same GAMBIT module as the dependent function. Module functions may also have backend requirements, which are satisfied by functions from backend libraries. For example, in FlavBit 1. 0. 0, SuperIso supplies many of the backend requirements of the module functions that calculate observables. FlavBit notifies GAMBIT of its available module functions and their capabilities, dependencies and backend requirements. The user tells GAMBIT that they want to compute a given set of observables and likelihoods in a given scan, and the GAMBIT Core identifies the necessary module functions and runs its dependency resolution routines. These hook the module functions up to each other and run them in an order that ensures that all dependencies are computed before the functions that depend on them.
Likelihoods After calculating the observables described in Section 4, FlavBit can be used to compute likelihoods based on a comparison of the predictions with current experimental measurements. The experimental results and theoretical errors are stored in a YAML database. Taking the branching fraction of B s → µ + µ -as an example, the FlavBit database entry is -name: BR_Bs2mumu islimit: false exp_value: 3. 0e-9 exp_stat_error: 0. 6e-9 exp_sys_error: 0. 25e-9 exp_source: 1703. 05747 th_error: 0. 1 th_error_type: M th_error_source: 1208. 0934 correlation: -name: NONE The individual fields available in such entries are described in detail in Table  6. Note in particular that the theory error may be given either as a fraction, as in this example, or as an absolute value. The Flav_reader object is responsible for reading the experimental results and theoretical errors, and calculating the resulting covariance matrix.
For each q 2 bin, the FlavBit likelihood includes components arising from FL, S3, S4, S5, AFB, S7, S8 and S9. It accounts for experimental correlations between these measurements within each bin, but assumes that measurements are not correlated across q 2 bins, as the uncertainty is dominated by the statistical component. The full correlation matrices within each bin are available publicly from LHCb  [47]  and included in the FlavBit YAML database. We include theory-induced correlated uncertainties between different angular observables for the same q 2 range from Ref. [62, 103]. The branching fractions for B 0 → K * 0 µ + µ -decays are not part of the electroweak penguin likelihood in FlavBit 1. 0. 0, but are slated for inclusion in a future version, following the next update from LHCb. The isospin asymmetry of the B 0 → K * 0 µ + µ -decay is non-trivially correlated with the angular observables, so we also do not include the corresponding observables (AI_BKstarmumu and AI_BKstarmumu_zero in Table  3 ) in the likelihood function.
FlavBit standalone example GAMBIT modules can also be called directly from other codes as libraries, without actually needing to use GAM-BIT itself. To do this, the calling code must specify the physics model and parameter set to be used, the module and backend functions to be run, and any required options. The calling code is responsible for resolving the dependencies and backend requirements of each module function; this is typically done "by hand" by the author of the calling code, using simple GAMBIT utility functions to hardcode the links between the chosen module and backend functions. More details of using GAMBIT modules in this so-called 'standalone mode' can be found in Ref. [4]. An annotated driver program for calling FlavBit from outside the GAMBIT framework can be found in FlavBit/examples/FlavBit_standalone_example.
cpp. As input, this program takes an SLHA file corresponding to the output of a spectrum generator (i. e. containing pole masses, DR parameters, etc). The name of this file can be given as a command-line argument. The program then calculates the full menu of FlavBit observables using SuperIso 3. 6 and FeynHiggs 2. 11. 3, and uses them to calculate the five independent FlavBit likelihoods. Much of this short program is dedicated to resolving module function dependencies and backend requirements. This includes defining a local function that creates a GAMBIT Spectrum object from the input SLHA file, and others that fulfil the dependencies of SI_fill on the widths of the Z and W bosons. If the user does not give the name of an input SLHA file when invoking the standalone example, it will read a default file given in the line std::string infile("FlavBit/data/example.
Conclusions In this paper we have described FlavBit, the flavour physics module of the public global-fitting framework GAMBIT. FlavBit provides calculations of a wide range of observables in flavour physics, ranging from tree-level decays of B and D mesons, to electroweak penguin decays, rare purely leptonic B decays, b → sγ transitions, neutral meson oscillations, kaon and pion decays, and various isospin and forward-backward asymmetries. These are so far implemented for supersymmetric and effective field theories, with the list of available theories expected to grow rapidly. FlavBit also features detailed experimental data, uncertainties, correlations and likelihood functions for tree-level leptonic and semileptonic, electroweak penguin, rare purely leptonic and B → X s γ decays, as well as for the B 0 s -B0 s mass difference.
We gave a number of interesting examples of FlavBit in action. These include a standalone example program that runs FlavBit without GAMBIT, in order to compute flavour observables in supersymmetry from an input SLHA file. We carried out an example supersymmetric flavour fit with FlavBit in GAMBIT, illustrating the impacts of its likelihoods. Finally, we performed a fit to a number of observables in the context of an effective theory of flavour, demonstrating about a 4σ preference from combined experimental data for an approximately 25% deficit in the (muonic) C 9 Wilson coefficient, compared to the Standard Model prediction. The FlavBit source code can be freely downloaded from gambit. hepforge. org, either as part of GAMBIT, or as a standalone package.
Acknowledgements We thank our colleagues within  GAM-BIT  for many helpful discussions. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAMBIT has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ), the  European Commission Horizon   2020 Marie Skłodowska-Curie actions  ( H2020-MSCA-RISE-2015-691164 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 680-47-532 ), the  National Science Foundation (USA ;  DGE-1339067 ), the  FRQNT (Québec)  and  NSERC/The Canadian Tri-Agencies Research Councils  ( BPDF-424460-2012 ).
In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells.
The resulting data provided an atlas of promoter and enhancer activities in wide range of cell states  14  , which is a baseline of understanding complex transcriptional regulation. In the second phase, we focused on transitions of cell states by monitoring 'time course' samples, such as activations, differentiations, and developments at sequential time points  15. The monitored activities of promoters and enhancers demonstrated that enhancer activities is the earliest event during dynamic changes of transcriptome. These data sets are being utilized in many other studies inside and outside of the FANTOM5 consortium. The data production scheme was implemented based on the FANTOM5 collaboration. Sample collection was performed at individual institutes, since specific types of samples require dedicated systems with special expertise or settings, as well as through purchase from commercial sources.
RNA quality was firstly examined at the place where the samples were obtained (the first RNA quality check). The CAGE assay pipeline established in RIKEN GeNAS (Genome Network Analysis Support Facility) employed two workflows of HeliScopeCAGE, a manual workflow for samples with small amount of total RNAs 8 and a robotic workflow for samples with standard requirements  7. The assay pipeline started with checking RNA quality (the second RNA quality check), which provides a uniform quality assessment of the profiled RNA extracts. The resulting CAGE libraries were sequenced by HeliScope in RIKEN and also in Helicos Biosciences, and the obtained data were processed by the MOIRAI system  16. Quality of the resulting CAGE profiles was checked with several statistics as well as manual inspection by using the ZENBU browser  17.
Finally CAGE profiles were shared among the consortium for further analysis. In the course of the two phases focused on 'snapshot' and 'time course' samples, we profiled 1,816 human and 1,016 mouse samples in total, and obtained approximately four millions of single-molecule reads successfully aligned to the genome per sample on average. Based on frequencies of the observed 5′ends of individual capped RNA molecules at a single base-pair resolution, we identified 201,802 and 158,966 peaks for human and mouse respectively, where promoters are defined as the sequence immediately upstream of the peaks and frequencies of observed CAGE reads reflect activities of the promoters. All data generated during the course of the project were deposited to a public repository (DDBJ Read Archive, DRA) and/or provided at the FANTOM5 web resource (http://fantom.
Single molecule CAGE and data processing HeliScopeCAGE libraries were prepared, sequenced, and processed as described previously  13, 15. Most of the RNAs were subjected to the automated HeliScopeCAGE protocol  7  , except for RNAs with less than 1 μg that were subjected to the manual protocol optimized for low quantity RNAs 8. The resulting libraries were measured by OliGreen fluorescence assay kit (Life Technologies), and sequenced by following the manufacturer's instructions (LB-016_01, LB-017_01, and LB-001_04 (ref. 13). RNAs extracted from mouse whole body embryo E17. 5 (called internal control) were systematically subjected to this workflow, with one per a sequencing run. The produced data were processed as previously described  13, 15. Briefly, reads corresponding to ribosomal RNA were removed by using the program rRNAdust (http://fantom.
Identification of peaks and their annotations Non-overlapping peaks based on the all CAGE profiles were identified by using DPI (decompositionbased peak identification, https://github. com/hkawaji/dpi1/) method and annotated as previously described  13, 15. A 'robust' threshold, for which a peak must include a CTSS with more than 10 read counts and 1 TPM (tags per million) at least one sample, was employed to define a stringent subset of the CAGE peaks. The robust peaks were associated with known transcripts, such as RefSeq  26  , UCSC known gene  27  , GENCODE  28  , Ensembl  29  , and mRNAs (full-length cDNA clones), based on their 5′-end proximity to the peaks. Official gene symbols, Entrez Gene IDs, and protein (UniProt) IDs associated with the transcripts were retrieved and assigned as part of annotation.
The RLE normalization was first performed within the phase 1 samples. The naïve application of this to the entire data sets, consisting of phase 1 and phase 2 samples, might cause inconsistencies in expression levels between the two normalizations. To avoid this, we took the geometric mean of CAGE peak read counts across the phase 1 samples and used it as the reference expression for a normalization factor calculation in the same manner as RLE method. This enabled us to keep the expression levels of phase 1 as they were, and to adjust the expression levels of the phase 2 samples to be comparable  15. Two types of metadata are available at figshare and LSDB Archive (Data Citation 1, 10). One is for the samples, including their origins and extracted RNA. The other is for the CAGE assay, including the result of RNA quality check, library production, and post-processing of the CAGE tag sequences.
Data record 3: CAGE peaks Genomic coordinates, annotations and expressions of the CAGE peaks are available as '*phase1and2-combined_coord. bed. gz', '*phase1and2combined_ann. txt. gz', and '*phase1and2combined_tpm. osc. txt. gz' respectively at figshare (Data Citation 1). Genomic coordinates are formatted in BED format, and the others are formatted in OSCtable (Order Switchable Column table). The detail of the OSCtable format is available at https://sourceforge. net/projects/osctf/.
Acknowledgements FANTOM5 was made possible by a  Research Grant for RIKEN Omics Science Center  from  MEXT  to Y. H. and a grant of the Innovative Cell Biology by  Innovative Technology  ( Cell Innovation Program ) from the  MEXT, Japan  to  Y. H. It  was also supported by  Research Grants for RIKEN Preventive Medicine and Diagnosis Innovation Program  to Y. H. and  RIKEN Centre for Life Science Technologies, Division of Genomic Technologies  (from the  MEXT, Japan ).
Code availability All software used in this study are publicly available. rRNAdust, for removing ribosomal RNA, is available at http://fantom. gsc. riken. jp/5/suppl/rRNAdust/. Mapping software Delve is available at http://fantom. gsc. riken. jp/5/suppl/delve/. The program to perform DPI, decomposition-based peak identification, method is available at https://github. com/hkawaji/dpi1/.
Furthermore, oncology cannot be reduced to its human practitioners, but can be understood as a bioclinical collective that links researchers and clinicians to entities such as research tools, biological pathways, pathogenic phenomena, and new clinical techniques. To study these features, we resorted to the software platform CorText (http://www. cortext. net/) developed by IFRIS (Institut Francilien Recherche, Innovation, Societe). CorText allows for the numerical analysis of different kinds of scientific outputs and, most importantly, for the distributional and relational analysis of different types of agents (human and non-human) that characterize a given domain of scientific activities at a given time (CorText 2012). To create a database of Portuguese oncology publications we queried in parallel the databases Web of Science (WoS) of Clarivate Analytics and Medline/PubMed of the U.
S. National Library of Medicine. We searched the WoS for publications with ''Oncology'' as the subject, whose authors were affiliated with Portuguese institutions. Similarly, we searched PubMed for publications that were indexed with the MeSH descriptor ''Neoplasms'' (which captures a large number of children descriptors, such as cancer or tumour; see  Grassano et al. 2016 ) and whose authors had Portuguese affiliations. 1 Given that Medline/ PubMed does not include information about cited references, we used the PubMed ID to retrieve from WoS the publications identified in PubMed. These combined queries retrieved 5725 publications covering the 1976-2015 period. The database features different types of publications, including journal articles, meeting abstracts, review articles, editorials, proceedings papers, etc.
For each publication, we retrieved the most relevant information including title, abstract, keywords, journal, date, volume, pages, authors and coauthors, their institutional affiliation, cited references, DOI, document type, and funding. Until 2014, Medline/PubMed (contrary to WoS that listed the affiliation of all coauthors) only included the affiliation and address of the first author. Therefore, publications not listed as Portuguese in PubMed (either because Portuguese co-authors were not first authors or because PubMed did not list any authors' affiliations and addresses), and which, additionally, were not retrieved solely through the WoS query, were not included in our database. Also, major databases such as Medline/PubMed and WoS do not include publications in local journals from peripheral countries, that may nonetheless be considered relevant for our present purpose (Rafols and Molas-Gallart 2016).
Indeed, it is to be expected that, until the end of the 1990s, a share of Portuguese articles was published in journals not indexed in either WoS or PubMed. 2  We are thus aware that the database is not exhaustive. However, our aim was to capture collective patterns at higher level of aggregations and not to evaluate the performance of specific actors. The analysis of institutional affiliations presented specific challenges that deserve mention. First, we had to standardize the name of each research institution. Not only did authors spell the name of their institution differently (e. g. , using a Portuguese or English version of the name, or different abbreviations), but frequently the same institution changed its name during the period covered by our database, in which case we used its current denomination.