dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank the participants and their families. Data for the clinical study was obtained from  FRONTIER clinic at Neuroscience Research Australia , which is funded by an  Australian Research Council Centre of Excellence  grant and  National Health and Medical Research Council of Australia  grants. We wish to thank the staff of FRONTIER for their assistance, in particular  David Foxe ,  Sharon Savage  and  Felicity Leslie. This work was supported by the  Australian Research Council  [ DP110104202  to M. H. ,  FF0776229  to J. R. H. ].
Synchronous programming and discrete controller synthesis Synchronous programming allows to model the dynamics (functional and/or non-functional aspects) of a system as an automaton. A system composed of several sub-systems can be modelled via a composition of automata, where each single automaton models the dynamics of each specific sub-system. Hence, the composition models the state of the system as a whole. With some controllable transitions in the automata, Discrete Controller Synthesis (DCS) tools  [6]  can compute a controller that restrains the set of reachable states (i. e. , all possible behaviours) to those satisfying a control objective (e. g. , a coordination policy). In this Section, we first briefly introduce the basics of the synchronous language Heptagon. We then describe the main features of BZR, that extends Heptagon with a new construct for expressing behavioural contracts  [3].
Automata and data-flow nodes The Heptagon language allows the programming of reactive systems by means of mixed synchronous data-flow equations and automata with parallel and hierarchical composition  [7]. The basic behaviour is that of the synchronous data-flow languages family  [8] : at each reaction step, values of the input flows are used, as well as local and memory values, in order to compute the values of the output flows for that step, and memories for the next step. Inside nodes (i. e. , block of codes defining an automaton or    2  shows the execution scheme of this manager. The CPU utilization of the machine is measured. If the CPU utilization is higher than the fixed maximum threshold, the manager increases the CPU frequency of the machine. If the CPU utilization is less than the fixed minimum threshold, the manager decreases the CPU frequency of the machine.
http://www. journalofcloudcomputing. com/content/2/1/16 a composition of automata), these computations are specified as a system of equations defining, for each output and local, the value of the flow in terms of an expression on other flows and memories. Figure  3  shows a small program in Heptagon. It expresses the control of a delayable task that can either be idle, waiting or active. When it is in the initial Idle state, the occurrence of the true value on input r requests the start of the task. Another input c (which will be controlled by an external controller) can either allow the activation, or temporarily block the request and make the automaton go to a waiting state (Wait). When in Active, the task can end and go back to the Idle state, upon the notification input e. The delayable node has two outputs, a representing activity of the task, and s being emitted on the instant when it becomes active : this latter triggers the concrete start operation in the system's API.
Such automata and data-flow reactive nodes can be composed in parallel and in a hierarchical way. They can be defined and re-used by instantiations of the nodes (see Figure  4  bellow for an illustration, with two instances of Figure  3 's node). They run in parallel, defined by synchronous composition (noted ";"): one global step corresponds to one local step for every equation, i. e. , here, for every instance of the delayable node. The compilation of an Heptagon program produces executable code in target languages such as C or Java, in the form of an initialization procedure reset, and a step procedure implementing the transition function of the resulting automaton. step takes incoming values of input flows gathered in the environment, computes the next state on internal variables, and returns values of the output flows.
It is called at relevant instants from the infrastructure where the program is used. [3]  propose BZR a that extends Heptagon with a new construct for expressing behavioural contracts. Its com-Figure  4  Mutual exclusion enforced by DCS in BZR. Figure  4  shows the design of a controller in BZR programming language. In this example we compose two instances of the program shown in Figure  3  and enforce a mutual exclusion between two instances. pilation involves discrete controller synthesis  [6]. DCS can be described as a formal operation on automata  [9] : given an automaton representing all possible behaviours of a system, its variables are partitioned into controllable and uncontrollable variables. For a given control objective (e. g. , staying permanently inside a subset of states, considered "good"), the DCS algorithm automatically computes, for each state and value of the uncontrollables, the constraint on controllable variables so that all remaining behaviours satisfy the objective.
The latter provide high level programming languages and discrete controller synthesis techniques for the automated synthesis http://www. journalofcloudcomputing. com/content/2/1/16 of the controller capable to ensure the coordination. [22]  propose an approach for synchronizing multiple Control-Loops to ensure stability of their behaviours based on a binary linear program. It introduces an Actions Synchronization Module (ASM) that selects, whenever a set of actions needs to be synchronized, the best subset allowed to execute which maximizes a set of QoS metrics. Our approach is similar to the latters since it allows, among a set of actions, a subset to execute. However, contrary to a binary linear program, in our approach the decision is based on the invariants on Control-Loops behaviours.
INTELLIGENT AGILE METHOD FRAMEWORK The main idea of the approach that we call Intelligent Agile Method Framework (in further text iAMF) is to provide methods and tools to (a) help companies capture the software processes they are de-facto using (i. e. the real ways of working when developing software), (b) help them follow these processes consistently and finally (c) help them to continuously improve and document what they do on IT projects. While there are other approaches striving to the same goal, the peculiarity of our approach is that it will require only insignificant effort from the developers and other stakeholders of the project. The latter is one of the main reasons why similar initiatives and approaches have not succeeded to penetrate in real practice  (Mirbel and Ralyté, 2006) ,  (Karlsson and Ågerfalk, 2012).
The first step in iAMF is to capture how projects are typically performed in a particular company. Our hypothesis is that if prescribed methods are not just some off-the-shelf methods (such as RUP, SSADM etc. ) but rather based on the knowledge and experiences of the company employees, and take into account the real ways of working in the company, then it is much more likely that developers will follow them rigorously. Instead of involving developers to describe how they do projects, iAMF will learn from the observation on what developers and other stakeholders actually do on the projects, how they perform and act, and based on that create (document) base methods. With a base method we denote a semiformal description of how a certain company is performing its projects. Note that a particular company Figure  1 : High-level architecture of iAMF supporting toolset might employ different software processes for different projects.
In this sense, the base method represents an overall method that comprises all the different processes that a company is using when performing its projects. With the first step of the iAMF the respected software process maturity rises to level 3 (documented). Once base methods are captured, iAMF can be used to supervise project performance and guide developers reusing the knowledge and past experience. Any declination from base methods would be detected and either prevented or treated as a new knowledge and used to supplement the base method. By this, iAMF facilitates continuous process improvement, which helps to raise the maturity of the process to level 4 (managed) or even 5 (optimized).
Documenting base methods and metamodels To support the enactment of software process following some prescribed SDM, the first step is to capture and document the prescribed method, i. e. the base method. Even though it is generally accepted that elaboration of SDM have a positive influence on the software development process (e. g. for the sake of knowledge reuse and quality assurance), companies very much differ in the level of details they believe is useful to capture and document  (García et al. , 2011). Furthermore, keeping ways of working documented and up-to-date is "expensive" and time-consuming. One of the goals of the iAMF toolset is to enable efficient and user friendly capturing of base methods and metamodels that underpin them. Companies can select from metamodels that support known methods, such as RUP, and adapt them to suit their purposes and specifics, or to create new metamodels from scratch  (Bajec and Vavpotic, 2008).
Phase 3: Testing. The iAMF will be first tested within the university environment (at both universities). A group of students will use the framework as a part of their seminar work. The purpose of these tests will be to detect and eliminate software bugs, as well as to get general opinion on the usability of the framework. iAMF will be then improved based on the testing results. Phase 4: Evaluation. In the evaluation, our goal is to empirically confirm the usability of the approach. This is not an easy task, as it cannot be performed within university environment. To get reliable feedback, several companies will be invited to evaluate the framework. With the evaluation we will try to confirm the main hypotheses: (H1) If prescribed methods were based on real ways of working and up to date then developers would follow them much more rigorously.
JASPAR (http://jaspar. genereg. net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth major release greatly expands the heart of JASPAR-the JASPAR CORE subcollection, which contains curated, non-redundant profiles-with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting.
INTRODUCTION Transcription factors (TFs) influence gene expression by binding to specific cis-acting elements in a genomic sequence. Thus, accurate models for describing the binding properties of TFs are essential in modeling transcription. From a set of known transcription factor binding sites (TFBSs) for a given TF, the binding preference is generally represented in the form of a position weight matrix (PWM) (also called position-specific scoring matrix) derived from a position frequency matrix (PFM). A PFM is essentially an occurrence table, summarizing the number of each nucleotide observed at each position of a set of aligned TFBSs  (1, 2). Compared with simpler models like consensus sequences, PWMs allow for an additive probabilistic description of binding preferences  (3). The JASPAR database holds collections of PFM nucleotide profiles based on published experiments from diverse sources, and has grown gradually from its inception  (4) (5) (6) (7).
The most widely used JASPAR collection is JASPAR CORE, which is a curated non-redundant set of TFBS profiles for multicellular eukaryotes, based on experimental evidence. The JASPAR database aims to provide the best canonical DNA binding profile per TF, as assessed by expert curators. Non-redundancy of TFBS profiles (i. e. one profile per TF) is intended with the exception of cases in which curators observe a clear difference in the sequence (e. g Nkx2-5) or length (e. g. JUND) at the core of a profile. Other JASPAR motif collections, with different characteristics than the CORE database, are available  (7). Over the years, JASPAR has been equipped with functions aimed at casual and power users. The web-based graphical user interface functionality includes browsing, searching, subsetting and downloading, as well as basic sequence searching tools, dynamic clustering of matrices and generation of random PFMs by sampling selected profiles (4-7).
EXTENSIVE EXPANSION AND IMPROVEMENT OF JASPAR CORE The JASPAR CORE database has been substantially expanded. In total, 135 new PFMs have been added (a 30% increase), and 43 older PFMs (9% of last release) have been updated with new data, from vertebrate, insect, nematode and plant species (Table  1 ). These additions are described in more details later. We compiled published sequence-specific DNA binding TF chromatin immunoprecipitation (ChIP)-seq data collections into the PAZAR database  (11, 12)  along with TF ChIP-seq datasets from the ENCODE (13-15) and modENCODE  (16, 17)  consortia for Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. From these studies, we extracted the bound regions, identified over-represented motifs close to the ChIP-seq peak max position (corresponding to the region where the maximum number of ChIP-seq reads are mapped) using the MEME suite  (18)  and constructed PFMs describing the binding preferences of the TFs (see Supplementary Text for details).
As in previous JASPAR CORE additions, we manually curated the profiles. To confirm the putative binding patterns, we identified independent publications with TFBSs or profiles consistent with the candidates, as described in  (7). To gain additional profiles, we considered bound regions derived from ChIP-chip experiments from modENCODE and (19) for D. melanogaster. A similar strategy as for ChIP-seq datasets was used to derive PFMs from ChIP-chip data (see Supplementary Text for details). In total, we obtained 45, 28, 8 and 10 high-quality PFMs in H. sapiens, M. musculus, D. melanogaster and C. elegans, respectively, for TFs that have never been described previously in JASPAR (see Supplementary Table  S1 ). It represents a 57, 6 and 200% increase when compared with the previous release for vertebrates, insects and nematodes, respectively.
The newly introduced vertebrate profiles are derived from 34 and 40 ChIP-seq experiments collected from PAZAR and ENCODE, respectively. The fact that almost 50% of the new PFMs are from individual studies collected in PAZAR highlights the importance of our manual retrieval of published ChIPseq data. From ChIP-seq data sets of the vertebrate sequence-specific TFs not previously described in JASPAR, we obtained 71 ($60%) canonical motifs satisfying our literature-based manual curation (see Supplementary Table  S2 ). The rich data from ChIP experiments allowed replacement of 39 existing profiles for TFs in mammals (36 PFMs updated) and in D. melanogaster (3 PFMs updated). As part of the curation of ChIP-seq data, and as introduced earlier, we computed a centrality score as described in  (20) , based on our expectation that the positions where the maximum number of ChIP-seq reads map on the genome of reference will be strongly enriched for binding sites corresponding to the ChIPed TF  (21).
We provide the centrality plot and log(P-value) for each newly introduced PFMs in vertebrates (see Figure  1 ), showing the propensity of the motif to be found close to the peak-max position in the corresponding peaks of the ChIP-seq dataset used to generate the profile (see Supplementary Figure  S1 ). The high quality of the vertebrates PFMs and the ChIP-seq datasets used to construct them is reflected by the low centrality log(P-values), which are all below À200, with the exception of the Bach1::Mafk, ESRRA, FOXP1, FOXP2, Hoxa9, Sox6, SP2, SREBF1, SREBF2, and THAP1 binding profiles (see Supplementary Table  S1 ). Moreover, we expanded the collection of PFMs for Arabidopsis thaliana TFs in JASPAR, with the first targeted JASPAR curation effort for plant TFs. We have included 43 new DNA-binding profiles for A.
MODELS FOR DUAL BINDING BY THE SAME TF In this release, in extremely select cases, we introduce multiple binding profiles for a same TF, as motivated by the fact that some TFs display diverse target specificity that cannot be represented using single PFM model. For instance, JUND has been previously shown to bind the DNA with motifs of flexible lengths  (22)  with a core composed of either TGACGTCA or TGAC/GTCA, where C/G stands for C or G. The two new profiles introduced for JUND (see Figure  2A ) are derived from the same ChIP-seq dataset, confirming the binding to the two subclasses. Similarly, we introduce two profiles for JUN (see Figure  2B ), displaying equivalent characteristics to the JUND profiles. A new profile for Nkx2-5 (see Figure  2C ) derived from ChIP-seq data has been introduced.
ENHANCED WEB INTERFACE AND NEW RESOURCES FOR POWER USERS For casual users, we have enhanced the web search interface to the JASPAR database. Fuzzy searching is now enabled to search one or multiple profiles by gene name, species official or common name, protein accession ID, DNA-binding domain family or class, experiment type (e. g. ChIP-seq) and any other keyword associated to the profile(s) in the underlying database. This fuzzy searching performs approximate string matching in case-insensitive mode and offers suggestions below the search box while typing. It also includes the gene name aliases from HGNC (PMID:23161694) for searching gene synonyms. Furthermore, for each TF profile, we have now included links to the Transcription Factor Encyclopedia  (25)  and to the protein structures from the Protein Data Bank when available  (26).
Each binding profile links to the corresponding TFBSshape profile of DNA structural analysis  (27). For power users, we have developed an open source Python package (freely available at https://github. com/ biopython/) within the extensively used tools of the BioPython Project  (28). We implemented the jaspar package as part of the 'motifs' BioPython package, which provides functions such as reading profiles, writing profiles, scanning sequences for motif instances and more. The specific jaspar 'motif' class allows to store all the metadata information related to the profiles in JASPAR, and specific functions allow the user to retrieve profiles from the database. We also developed an R/Bioconductor (PMID: 15461798) software package TFBSTools, available at http://www. bioconductor. org/ packages/2.
13/bioc/html/TFBSTools. html under the General Public License-2 (GPL-2), to provide developers handy tools to generate, read and convert the JASPAR template, an internal data format to describe each motif instance and its meta information. An R/Bioconductor (29) data package JASPAR2014Data is freely available at http://www. bioconductor. org/packages/devel/data/experiment/html/JASPAR2014. html to provide the users with tools for data analysis using the JASPAR profiles. In addition, a web-based curator interface was developed for JASPAR, focusing on giving the super-users the ability to edit and update the database: this capacity is released for users wishing to produce custom PFM databases using the JASPAR framework.
CONCLUSIONS AND FUTURE DEVELOPMENTS In this release of JASPAR, we have focused on the CORE database and expanded it primarily with new ChIP-based data. Although these types of expansions are important and will continue, the increasing availability of rich data sources highlights important questions for the future development of JASPAR, which need to be discussed with its user base. Two such larger questions are as follows. Non-redundancy versus species-specific matrix models? JASPAR CORE was originally designed with the clear goal of finding the 'best' PFM for a certain TF, unlike other databases that can hold several models for the same factor. Although many users have appreciated the clarity, it is not established how to resolve cases where the same factor has been characterized in-depth in two or more species.
FUNDING The AS lab was supported by from the  Novo Nordisk Foundation , the  Lundbeck Foundation  and The  European Research Council  under the  EU   7th Framework Programme  [FP7/2007-2013]/ ERC  grant agreement  204135. The WWW lab was supported by the  Canadian Institutes for Health Research  [to W. W. W. ]; the  National Sciences and Engineering Research Council of Canada  (to W. W. W. and C. Y. C. ); the  National Institute of General Medical Sciences  [ R01GM084875  to W. W. W. ];  Michael Smith Foundation for Health Research  (C. R. W. H. ); GenomeCanada ( ABC4DE  Project) and  Genome British Columbia  ( ABC4DE  and  CanEuCre Projects) ; the  Rhoˆnes-Alpes region CMIRA  fellowship and CNRS support (to F. P. ); Supported by the  EU   FP7 large-scale  integrated project  ZF HEALTH [HEALTH-F4-2010-242048  to G.
IV. S5R5 POST-PROCESSING As said earlier, roughly 10 11 candidates from the S5R5 run were returned to the Einstein@Home server. They were then transferred to the 6720-CPU-core Atlas Computing Cluster  [44]  at the Albert Einstein Institute in Hannover, and post-processed. The goal is to filter the set of 10 11 candidates, excluding false candidate events. The post-processing strategy consists of the following steps: • selection of 100 most significant candidates in 0. 5 Hz frequency bands; • removal of known instrumental noise artifacts; • removal of unknown data artifacts through the Fstatistic consistency veto; • follow-up of the most significant candidates with S5R3 data; • fully-coherent follow-up of the surviving candidates. The items outlined above are described in the next subsections.
B. Removing known data artifacts As a first step of the post-processing pipeline, we eliminated from the list of top candidates any candidate whose frequency was too close to that of either a known artifact or to the cleaned noise bands described in section III A. Specifically, we discarded those candidates whose detection statistic could have been constructed with contributions either from: • data polluted in either of the two instruments by spurious disturbances; details of such detector disturbances are given in Appendix C and, in particular, a list of known spectral disturbances for the H and L instruments are listed in Tables VI and VII. • from fake noise that had been inserted by the cleaning process and hence could not host a CW signal (see Table  I  in Sec. III A). After this veto, about 25% of the candidates were eliminated from the original set of 228 000 loudest S5R5 candidates.
C. The F-statistic consistency veto We have thus far considered only known instrumental disturbances for vetoing candidates. However, we expect there to be more such disturbances present in the data that have not yet been explicitly identified. The idea is to discriminate between disturbances in a single detector and signals, which should produce consistent values of the F-statistic in both detectors  [45]. We refer to this method as the F-statistic consistency veto. For each of the 172 038 S5R5 surviving candidates, the single-detector and multi-detector F-statistic was computed for each of the 121 data segments and then averaged over the segments. We refer to these averaged 2F values as 2F H and 2F L for the H and L detectors respectively, and 2F HL for the coherent combination of the data from the two detectors.