dataesr/scientific-paragraphs-categorization huggingface dataset:
Database structure and implementation Database records. The technology of DisProt has been updated and is now based on a document-oriented MongoDB database. Stored documents are of two types, 'protein' including general information about the protein and 'disordered region (DR)' including evidence of disorder from literature. Protein information is retrieved from UniProt and includes cleavage sites and chain/peptide boundaries for polyproteins and processed proteins. DisProt is sequence- centric and different isoforms correspond to different entries as in the previous version. Cleaved proteins are merged into a single entry as they are products of the same native sequence. DisProt accession numbers now follow a single format and all previous entries with a ' xxx' suffix were removed. DR records are evidence-centric, i.
If none of the current terms in the new ontology give a proper description of the functional mode, the curator may propose a new term to be added to the ontology. Acceptance of the new term will require approval by the IDP/IDR ontology committee. Annotation pipeline. The new DisProt data have been generated by a community effort through a web server interface accessible upon registration. The same infrastructure can be used both to create and update entries. Curators provide an annotation through a submission form where all fields are validated on the client-side and a sequence viewer allows the comparison of assigned regions with structure information (Pfam domains, MobiDB disorder). Of note, the name of the curator is clearly visible in the entry to allow proper attribution of credit. The pipeline is fully automatic and can be potentially applied to the entire UniProt database.
The DisProt public database is a snapshot of the community annotations. Entry page. The entry page features four different sections (Figure  1 ). A protein information table gives the protein name, gene, synonyms, identifiers, taxonomy and 'homologous' entries inferred from sequence similarity. An interactive feature viewer reports DisProt disorder regions separated into confident and ambiguous annotations, colored brown for intrinsically disordered regions and purple for context-dependent regions. Pfam domains along with PDB and predicted disorder derived from MobiDB are also shown. Below, a detailed feature viewer provides different visualization layers to highlight different functional aspects (ontology terms) and the strength of available disorder evidence. Each position in the sequence is colored according to the number and type of evidence.
A sortable, customizable and filterable table lists all entries by protein. Alternatively, another table listing all regions is available and accessible through the 'regions' button. Complex queries can be simulated applying different filters to different columns. Specific entries can be selected manually and customized views can be generated by adding or removing columns. Filtered and/or selected data can be downloaded both in text and JSON formats. Alternatively, the 'Search' page allows the user to search for specific words in a free-text form or to search for DisProt entries similar to a query sequence. Output for either search is a provided in a simplified form. Feedback page. DisProt users are highly encouraged to suggest additional disorder annotations or changes to existing annotations using the 'Feedback' page.
This contains a drop-down menu guiding the choice of feedback provided (e. g. website experience, novel annotations) and a message field. For feedback related to data entries, the user is asked to provide either the UniProt or DisProt ID and (where possible) a PubMed reference. All messages are reviewed by the curators and integrated in the database as time permits. Web technology. The DisProt server is implemented in Node. js (https://nodejs. org) using the REST (Representational State Transfer) architecture. The data can be accessed through the web interface or programmatically exploiting the RESTful functionality. Please refer to the 'Help' section of the website for details on using the DisProt web services. The web interface is built using Angular. js (https://angularjs. org) and Bootstrap (http://getbootstrap.
Database content: upgrades and updates Entries in DisProt 7. 0 came from three major sources: (i) from the previous version of DisProt (where conflicting cases have been re-annotated), (ii) novel cases identified as PDB entries with long regions of missing electron density and (iii) proteins identified by text-mining in PubMed abstracts for keywords 'intrinsically disordered', 'intrinsically unstructured' and 'structural disorder'. New proteins selected based on disorder content (estimated based on Mo-biDB data) were prioritized (if appropriate information was available in SwissProt) to concentrate on well-studied and most interesting cases. New proteins were also selected by curators themselves to exploit their specific previous knowledge. All entries from previous versions were re-annotated to remove inconsistencies.
One hundred and ninety-eight previous entries were completely removed and 469 modified. Recurring problems being fixed were wrong organism D224 Nucleic Acids Research, 2017, Vol. 45, Database issue Distribution of DisProt annotation based on experimental evidence (method) and disorder function (function). As each annotated disorder region corresponds to one piece of experimental evidence, multiple regions can map to the same sequence segment. If a protein is annotated multiple times with the same type of experiment it is counted once. The number of residues is the sum of region lengths. 1 ), and the length distribu-tion of disordered regions has significantly changed from the last release of DisProt (Figure  2 ).
CONCLUSIONS AND FUTURE WORK We have presented an updated and completely re-worked version of the DisProt database. It now features state-ofthe-art database and web technology, enabling programmatic access of interested parties. The content was expanded by defining a standardized set of experimental techniques and a novel functional ontology of disordered segments. Both allow for a richer description of disorder which may be used for further analyses. The other main improvement in DisProt is a complete re-annotation of existing entries to remove inconsistencies and an expansion of ca. 50% over the previous release, which also resulted in a significant shift in the length coverage of disordered regions in the database. This advance was made possible by a distributed annotation effort coordinated by the COST Action NGPnet (URL: ngp-net.
Thus, our experiments suggest that these three methods outperform the Monte Carlo method given our hardware and software environment. The complexity of CorLCA is conjectured to be the same as Sculli's approach. However, the implemented method performing LCA requests does not have a constant time complexity, which penalizes CorLCA. In most cases, the series-parallel reduction approach is dominated by the canonical approach both in terms of speed and in terms of precision. This dominance is sufficiently important to conclude that series-parallel reductions are not an efficient mechanism in our context. The four other approaches (excluding Monte Carlo simulations) represent each a distinct compromise in terms of speed and precision.
Last, all these approaches have been implemented in a software, Emapse, which allows a fair comparison of several approaches in a unified way. We show that series-parallel reductions do not constitute an interesting method for the considered instances as it is often less precise and slower than the canonical approach. CorLCA is the fastest heuristic among the ones for which the precision remains stable when the size of the graph increases. It is therefore a relevant choice when designing a scheduling heuristic that requires an efficient evaluation mechanism. This paper focuses mainly on the scheduling problem, but our solutions can be applied to other related fields (project management and circuit design) as the models are similar. For instance, in the digital circuit case, there are dependencies between the random variables of the set X.
Emapse In order to evaluate the quality of CorLCA and Cordyn, we have developed a software platform, Emapse (Evaluation of MAx-Plus Stochastic Expression) for evaluating the distribution of the longest path length of a directed acyclic graph with random weights. This platform implements several methods of the literature in a generic way and allows an experimental comparison of these methods on given instances. It represents 7 400 lines of Java and is available on https://gforge. inria. fr/projects/emapse/. The related code, data and analysis are available in  [8].
We present the calculation of the NLO QCD corrections to the electroweak production of top-antitop pairs at the CERN LHC in the presence of a new neutral gauge boson. The corrections are implemented in the parton shower Monte Carlo program POWHEG. Standard Model (SM) and new physics interference effects are properly taken into account. QED singularities, first appearing at this order, are consistently subtracted. Numerical results are presented for SM and Z total cross sections and distributions in invariant mass, transverse momentum, azimuthal angle and rapidity of the top-quark pair. The remaining theoretical uncertainty from scale and PDF variations is estimated, and the potential of the charge asymmetry to distinguish between new physics models is investigated for the Sequential SM and a leptophobic topcolor model.
POWHEG implementation We now turn to the implementation of our NLO corrections to electroweak top-pair production, described in the previous section, in the NLO+PS program POWHEG  [37]. We thus combine the NLO precision of our analytical calculation with the flexibility of parton shower Monte Carlo programs like PYTHIA  [69]  or HERWIG  [70]  that are indispensible tools to describe complex multi-parton final states, their hadronization, and particle decays at the LHC. Since the leading emission is generated both at NLO and with the PS, the overlap must be subtracted, which is achieved using the POWHEG method  [36]  implemented in the POWHEG BOX  [37]. In the following, we describe the required colorand spin-correlated Born amplitudes, the definition and implementation of the finite remainder of the virtual corrections, and the real corrections with a focus on the subtleties associated with the encountered QED divergences.
We therefore compute here explicitly the expression for B µν 2 , where the subscript 2 designates the photon leg (see Fig. 6 ). Applying the above procedure then leads to B µν 2 = 8π 2 α s αQ 2 t m 2 t z 2 1 y 2 1    p µ 1 p µ 2 p µ 3 A 1    p ν 1 p ν 2 p ν 3    -A 2 g µν    , (3. 10) where A 1 =    8z 2 1 2P 2 z 1 -8P 1 z 1 2P 2 z 1 4(P 1 -z 1 ) 2 z 1 6P 1 z 2 1 -4z 3 1 -2P 2 1 (2 + z 1 ) -8P 1 z 1 6P 1 z 2 1 -4z 3 1 -2P 2 1 (2 + z 1 ) 8P 2 1    , (3. 11) A 2 = m 2 t P 3 (P 1 -z 1 )z 1 , (3. 12 ) P 1 = y 1 + z 1 , (3. 13) P 2 = 2(y 1 + z 1 ) + y 2 1 , (3. 14) P 3 = y 2 1 + z 2 1 , (3. 15 ) y 1 = 1 - t m 2 t and (3. 16 ) z 1 = 1 - u m 2 t. (3. 17) As for the color-correlated squared Born matrix element, the closure relation of Eq. (3. 9) is implemented in POWHEG as a consistency check.
Numerical results In this section, we present numerical results for electroweak top-quark pair production including Z -boson contributions at LO and NLO from our new POWHEG code  [37] , which we coupled to the parton shower and hadronization procedure in PYTHIA 8  [69]. Our results pertain to pp collisions at the LHC with its current center-of-mass energy of √ S = 13 TeV. Only for total cross sections, we also study how much the reach in Z mass is extended in a future run at √ S = 14 TeV. The top quark is assigned a mass of m t = 172. 5 GeV as in the most recent ATLAS searches for Z bosons in this channel  [46]  and is assumed to be properly reconstructed from its decay products. At the top-pair production threshold, α(2m t ) = 1/126. 89. The values of sin 2 θ W = 0. 23116, m Z = 91. 1876 GeV and Γ Z = 2.
Conclusions In this paper we presented the calculation of the O(α S α 2 ) corrections to the electroweak production of top-antitop pairs through SM photons, Z and Z bosons, as predicted in the Sequential SM or in tecnicolor models. Our corrections are implemented in the NLO parton shower Monte Carlo program POWHEG. Z reconances are actively searched for by the ATLAS and CMS experiments at the LHC with its now increased center-of-mass energy of 13 TeV. We have consistently included interferences between SM and new physics contributions and have introduced a proper subtraction formalism for QED singularities. With a great variety of numerical predictions, we have demonstrated the mass dependence of the K-factor, the changing relative sizes of scale and PDF uncertainties, the large impact of new partonic channels opening up at NLO (in particular of those induced by photon PDFs in the proton), and the non-negligibility of interference effects.
Acknowledgments We thank  J. Gao  for making possible detailed numerical comparisons of our NLO Z calculations as well as  R. M. Harris  and  J. Ferrando  for help with matching their LO calculations in the topcolor model. T. J. thanks  P. Nason  and  C. Oleari  for useful discussions. This work was partially supported by the  BMBF  Verbundprojekt  05H2015  through grant  05H15PMCCA , the  DFG Graduiertenkolleg 2149 , the  CNRS/IN2P3 Theory-LHC-France initiative , and the  EU  program FP7/2007-2013 through grant  302997.
, 2016). The maximal k-truss thus yields a smaller and denser subgraph of G that better approximates its densest subgraph. Nevertheless, the finer resolution of the k-truss decomposition comes at the cost of a greater complexity, polynomial in the number of edges  (Wang and Cheng, 2012). By analogy with k-core, the truss number of an edge is the highest order of a truss the edge belongs to. By extension, we define the truss number of a node as the maximum truss number of its incident edges, like in  (Malliaros et al. , 2016). We wrote our own implementation of weighted k-core in R (  R Core Team, 2015). For unweighted k-core, we used the igraph package  (Csardi and Nepusz, 2006) , and for k-truss, the C++ implementation offered by  (Wang and Cheng, 2012).
Text preprocessing The first module cleans the inputted text by (1) removing special characters, punctuation marks except the ones indicative of sentence boundary (used by the second module, see subsection 3. 2) and intra-word dashes, (2) removing numbers except dates (like "2016"), and (3) tokenizing. In addition to R built-in functions, the stringr package  (Wickham, 2015)  is used here. Also, text is split into sentences using the implementation of the Apache OpenNLP Maxent sentence detector offered by the openNLP R package  (Hornik, 2015). The list of sentences is eventually passed to the fourth module (see subsection 3. 4). Additionally, the user is provided with the following tuning parameters: Keep only nouns and adjectives? Boolean, defaults to TRUE. Uses openNLP's implementation of the Apache OpenNLP Maxent POS tagger to perform part-of-speech (POS) tagging.
This paper illustrates the algorithms and techniques supporting automated generation and scheduling of test cases for service functional tests; they are the foreground of the EU FP7 MIDAS project  [3]. The MIDAS prototype is a Software as a Service on the cloud that provides modeldriven automation of test tasks, specifically -unit and endto-end testing of services and service oriented architectures. The MIDAS prototype architecture and utilisation scheme are sketched in Fig. 2. The user, through APIs and a GUI (Graphical User Interface), supplies MIDAS with models and policies that describe the structure and the behaviours of the Distributed System Under Test (DSUT), in the specific case, of services and service architecture under test, and the test objectives, and invokes automated test generation and execution methods.
These services implement the HL7/OMG Healthcare Services Specification Program (HSSP) international standard (respectively RLUS, IXS, CTS2)  [7]. The CCN distributed application is an example of microservices architecture: patient identity management, patient record management, etc. , are implemented as loosely coupled services, each of them being equipped with specialised software running on different machines and whose data are stored in separate databases. Note that these constituent services have life cycles, in terms of software releases and managed data, that are independent from each other and also from the CCN application life cycle. The CCN back end service consumes the patient record service, the patient identity service, and other services in order to provide its service. We call these downstream services that are not consumers of other services terminal services.
The external communication module provides the means of external event exchange such as sendand invoke. The script module provides the support for ECMAScript  [37]  implementation of executable content (i. e. actions performed on transitions, entering and leaving states, emitting events, branching on conditions -if, else, elseif, updates on the data model -assignment of values, logging). The PSM data-flow transfer functions are written in ECMAscript. The data module provides the abstraction for handling named parts (e. g. arbitrary data payloads in send events, evaluation of conditional expressions on received data in events on transitions). The SBM is a collection of PSMs of the TCM actual and virtual components. the TCM -the collection of XML documents, one for each participant (component abstract type), plus a description of the test environment topology (the actual and virtual component, and the actual and virtual wires that link the components); the SIM -the collection of documents that describe the service interfaces; the SBM -the collection of PSM/SCXML documents, one for each actual and virtual component; a collection of test input templates -optionally, templates for the test inputs (stimuli) can also be supplied by the Fig.
3. Translate the state machine in TLA. Data that are declared in the PSM, but not referenced in any ECMAScript expressions are considered irrelevant, so they are ignored (i. e. treated as constants). Compilation. The model obtained in PlusCal from the translation activity is then compiled into the TLA+ core language, by using the compiler provided in the TLA+ toolkit (Compilation activity in Fig. 7 ). TLA+ is backed by the TLC model checker to exhaustively check correctness properties across all possible executions of the system. At this stage we generate, from the generation directives, one safety property. Through assertions, execution traces of the system satisfying the safety property -for instance the negation of "messages of some specific types are exchanged" -are requested to the model checker (Model checking activity in Fig.
For instance, the user of the test cases generator might be looking for test cases where the message type input of the operation createIden-tityFromEntity exposed by the service POCDPatientMQService of the actual component mpi. ixs. component in the DSUT standardportal. saut can be observed. This example has been used for testing the Healthcare Pilot described in Sect. 4. All these test generation directives are specified in a simple XML configuration file alongside the files of the TCM, SIM, and SBM. Driven by the test generation directives and the templates for the payloads provided by the tester, the test case generator can focus on interesting specific ranges of values for the test case payloads. Discrete data domains (numbers, enumerations, booleans) from the definition (WSDL file) of SOAP message fields are handled well by the TLC model checker.
In our approach, it is seamlessly handled by the SCXML execution engine that supports executable content in ECMAScript expressed in the PSM, such as actions on transitions, in entering and leaving states, emitting events, branching on conditions, testing incoming data, creating outgoing data -possibly by precisely modifying specific fields in the SOAP messages skeletons or directly on incoming data (transfer functions), and logging. The additional requirement of automation is fully met and tool support is partially met in our approach: the specification is unambiguous, machine-readable and executable, and the test logic can be as sophisticated as the test developer wishes. However, the creation of the test specification is not yet supported by wizards that will help in checking structural consistency across the different types of input models, or checking behavioural consistency across the PSMs, automatically.
Discussion on the modelling approach for the TCM and the PSMs. It is clear that TCM and SBM building is a modelling effort that requires the test designer to have knowledge of: (i) the topology of the DSUT; (ii) the external behaviour of the services under test; (iii) the capability to express this knowledge in terms of test configuration model, protocol state machines and data flow transfer functions. Conversely, this activity requires neither knowledge of the implementations of the service components nor advanced testing skills. There is no size limitation for the SBM models. Moreover, the main SCXML constructs supported in the current implementation of the MIDAS prototype are the following: - Effects are transition sub-elements that contain expressions that are evaluated when the transition is fired.
Allowed effects in the MIDAS prototype are: -TRANSITION/ASSIGN which modifies the data model through its location attribute which designates the location to be filled with the result of the evaluation of a functional expression hold in its expr attribute. The value of the location can pinpoint a specific field in the SOAP message referenced by the data model. -TRANSITION/SEND which sends an event through its eventexpr attribute, and the associated data through its namelist attribute, to another PSM. A send operation thus creates an event specified by the eventexpr attribute. That event is used as a trigger by the receiving PSM transition in its event attribute. For example, the sent event POCDPatientMQReference:: findIdentitiesByTraits::input matches the expected event POCDPatientMQService:: findIdentitiesByTraits::input.
Rees, Wooff and colleagues  [75] [85] present a seminal work about the use of probabilistic inference based on a BN framework to support input partitioning test methods that are aimed at understanding which kind of stimuli are more appropriate to reveal software failures. Regression testing is a target of choice for prioritisation of test cases on the basis of specific criteria. Mirarab and Tahvildari  [58]  present an approach based on probability to prioritising test cases in order to enhance the fault detection rate. They utilise Bayesian Networks to incorporate source code changes, software fault-proneness, and test coverage data into a unified model. The dynamic scheduler algorithm, referred in Fig. 10  as inf4sat, builds a Bayesian Network model from (i) the Test Configuration Model (TCM), (ii) the Test Scenario Definition (TSD) and the Test Suite (TS), and (iii) the (optional) user initial beliefs.
Otherwise, the session is terminated and the user shall upgrade the SBM in order to run new testing sessions with specific coverage objectives on the basis of the previous test session coverage reports. The evidence-based planning approach proposes a solution of the test coverage problem for a complex services architecture on the basis of troubleshooting heuristics driven by probabilistic reasoning. It pushes the test automation very far, but is still in an experimental phase. We need experience feedback on real-world case studies in order to tune, refine and improve the approach. The general sequence diagram of the implementation of the evidence-based reactive planning is shown in Fig. 15. It shows four main phases. The initial request from the front-end service follows an invocation by the end user through the MIDAS Web portal, or through direct program invocation using the front-end service API.