dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work is funded in France by the  Centre National de Recherches Météorologiques (CNRM) of Météo-France  and the  Centre National de Recherches Scientifiques (CNRS). The authors acknowledge the  Région Midi-Pyrénées  ( INFOAIR  project) and thank the  RTRA/STAE foundation  for their support. The authors acknowledge the strong support of the  European Commission, Airbus , and the airlines (Lufthansa, Austrian,  Air France ) who have carried the MOZAIC equipment free of charge and performed maintenance since 1994. MOZAIC is presently funded by  INSU-CNRS (France) ,  Météo-France , and  Forschungszentrum Jülich  ( FZJ, Jülich, Germany ). The MOZAIC database is supported by  ETHER , the  French atmospheric composition database (CNES  and  CNRS-INSU). The three anonymous reviewers are kindly acknowledged, especially anonymous reviewer no.
Introduction Since 2007, the Isabelle proof assistant has an architecture aimed at asynchronous proof processing. To support this architecture, the tool also gained a significant update to its user interface, in the form of Isabelle/jEdit: a plugin for jEdit that supports interaction with Isabelle. jEdit is a generic editor aimed at programmers in any language: the editor's main components are a group of text windows with syntax highlighting, and any other functionality (compilation, version control, project management,. ) can be added through the use of plugins. Isabelle's support for jEdit was added through this plugin system, by adding a 'generic' prover plugin that exchanges messages with the proof assistant in order to provide a number of functions for authoring proofs, such as: 1. prover output; 2.
Generalizing PIDE We want to use Coq with the architecture, obtaining the interaction model with as little adaption as possible: we want to reuse as much as possible from the existing PIDE and plugin implementations, by generalizing code where possible and writing Coq-specific code where necessary. With that in mind, we have a choice of where exactly to insert the desired functionality. In theory, it is enough to just implement the high-level interface with Coq-specific protocols. Such an interface is required to send textual changes to the prover. It is also required to provide an accumulation function (in a 'snapshot'): this function is called by the editor side of the plugin, and is used to determine what information the prover has on certain spans. This information is then used to provide the various displays of the proof document.
Change parsing The change parsing 'algorithm' takes a sequence of text edits (insertions and deletions) and uses these to determine what the new command spans for the prover are. To do so, the algorithm takes several steps, when a non-empty list of edits is provided. The following description is based on an analysis of the file src/Pure/Thy/thy syntax. scala of the Isabelle source code, changeset bbf4d51. This source file is available through https://bitbucket. org/Carst/pide_notes/src/bbf4d51/src/Pure/Thy/ thy_syntax. scala. The algorithm works on a previous version of the document, and creates a new version by applying the edits in a specific manner. The steps taken on a specific document are described below. 1. Determine any changes to the header of the proof document. This header is an Isabelle-specific syntax element, that declares the name of the proof document and the other documents it imports.
Determining the changes here is important there, because a dependency can define new commands, that direct the parsing in the next step. 2. The second step 'applies' the edits to the document, as well as reparsing any text that might have been affected by the header changes. We only focus on the first part here. In this procedure, the command spans that are directly affected by an edit are manipulated to reflect the new text, and following this, reparses the resulting text in order to obtain the new command spans. The first step is specific to Isabelle, but a large part of the second step is generic: it creates a new document version, and runs a language-specific parser in order to obtain the command spans. This parser is a part of the Scala side of PIDE and is used to make certain computations quicker, for example syntax highlighting.
Currently, we do not mimic the syntax highlighting described in the original architecture. Its implementation was based on the ad-hoc syntax highlighter used for the CoqIDE interface for Coq, and introduced an unwanted dependency on the GTK graphical library. The library is unwanted because it is not used to provide any graphics, and is an optional component to the Coq installation. While it is possible to eliminate the dependency, the resulting highlighting would be rather limited. As a result, it seems better to reconsider the syntax highlighting completely, and integrate it more deeply in the implementation of Coq itself, which would have the added benefit of allowing other interfaces for Coq to use the information. The adaptations made to Coq to accommodate this protocol can be found in the following branch of the Coq source tree: https://bitbucket.
Markup Finally, the PIDE protocol allows for, but does not require, reporting markup. The details of the implementation are very prover specific, and we will not go too deeply into it here. What is worth mentioning, is that we could use the messaging system to obtain hyperlinks for the Coq sources: in the jEdit interface, it is possible to click on a name of a lemma (or theorem, or definition, or. ), and be taken to the position where this entity is defined. This was achieved by exporting information in Coq, using the 'report' messages that are part of the PIDE protocol for Isabelle: the plugin is equipped to handle these messages, and transform them into source code hyperlinks.
The language-specific parser returns command spans: for Coq these are all phrases ending with a single period, including the following whitespace 2  and preceding comments: "Proof. ", "intros l. ",. The interface then sends the commands to the prover, each in a separate XML message (the empty arguments are used by Isabelle): < prover_command name = " Document. define_command " > < prover_arg > -1 </ prover_arg > < prover_arg / > < prover_arg / > < prover_arg > Proof. </ prover_arg > </ prover_command > This message triggers a function in the prover that stores the key-value pair ("-1", "Proof. ") into the lookup table for commands. The key, like any identifier coming from the prover, is a negative number: the prover uses positive numbers to report its identifiers. Updating document state After all define command messages have been sent out, the prover sends a "Document.
Conclusions Based on the original PIDE implementation for Isabelle with some minor generalizations, and by adding the protocol functions to Coq (at the time of writing, all PIDE functionality is contained in approximately 750 lines of fairly verbose OCaml code), we have obtained a functional, asynchronous editing environment for Coq. The integration was developed in a very short period: going from the STM interface in Coq, and the existing Isabelle code base, jEdit could work with Coq in two man-months of work. This time included reading the PIDE code base in order to understand how the protocol works, and what parts are essential. Going forwards, there still is more work to be done in making the PIDE architecture more accessible to other provers, but adding Coq is a first step in identifying what elements of the architecture are specific to a particular prover and what parts can be reused regardless of the proof assistant in use.
For now, a prover that wants to benefit from the PIDE architecture, needs to supply: • a parser that can recognize command spans. This parser needs to be added to the Scala code of the PIDE architecture; • a protocol implementation that can unpack the PIDE messages and provide them to the prover. This implementation is also responsible for supplying feedback and output to the interface. These recommendations are not fixed edicts, but will provide a running system with a minimum of effort, and are therefore a good starting point for adding specialized messages: this is where we started with Coq, and we are now looking forward to a more asynchronous, declarative future of interaction.
The mammograms of consented participants were retrieved from the relevant NHS screening centres and digitised on an Array 2905 laser digitizer (0-4. 0 OD, 50 micron resolution, 12 bit image). The first two control women for whom both questionnaire data and mammograms were obtained were included in the study. 76% of eligible cases and 80% of eligible controls completed the questionnaire and gave consent for access to their mammograms; images could be retrieved for 89% of consented cases and 93% of consented controls. The study complied with the Helsinki Declaration and was approved by the UK NHS South-East Multi-Centre Research Ethics Committee (05/MREC01/77). All participants provided written informed consent. Cumulus density readings. Density readings using the Cumulus software (University of Toronto, Toronto, Ontario, Canada)  (Byng et al, 1994)  were performed by three observers (IdSS, VM and ZA).
Prediction models were developed using penalised regression procedures as described in the statistical analysis section of  Li et al (2012). Separate prediction models were fitted to estimate percent density and breast size; absolute dense area was obtained from the product of percent density and breast size. The digitised images of the current UK study were of variable size and resolution, in contrast to the previously published Swedish study whose images were similar  (Li et al, 2012). Thus, before using the ImageJ-based approach in the present study, the programme was re-trained in images from two previously conducted British studies with mammogram acquisition contemporaneous to that in the current study, available Cumulus readings (conducted by two of the readers of the present study) and digitised films from a wide range of sizes, resolutions, ages, density levels and image acquisition settings (see Supplementary Data for full details on the methods and results of this training).
ACKNOWLEDGEMENTS We thank the participating  National Health Services Breast Screening Programme (NHSBSP) centres  for their help with the retrieval of the mammographic films for the study participants. This study was funded by project grants from  Breast Cancer  This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
To answer to this growing need, The Anaximen Company develops a smart and connected object named Alima  (Alima, 2013). In fact, pollutant levels are constantly changing, depending on the tenants' activities. Alima can measure every minute several physical parameters: temperature, humidity, concentrations of COV, CO2, formaldehyde and particulate matter (pm). Data are stored on the object or can be sent to a distant database, and are available for the user online via phone apps or websites. Beyond the measurement aspect, the society currently plans to embed in Alima some data analysis feature named 'predictive analytics', whose primary aim is to predict the evolution of indoor pollutants in time. Anaximen and the CRAN laboratory are associated to develop this leading-edge feature. In this article, a neural network (NN) model designed for pollutant prediction is presented.
control bounds Our control charts (s charts) aim to determine if a process characteristic is stable. The center line (CL) is given by the standard deviation of the considered characteristics. The Upper Control Limit (UCL) and the Lower Control Limit (LCL) are calculated for representing 99. 8% of data (NIST/SEMATECH 2012). These limits are given by: 2 4 4 2 4 4 3 1 3 1 s UCL s c c CL s s LCL s c c  = + -    =    = - -   (2) where: -s stands for the center line (CL) and correspond to the estimated standard deviation of the characteristic monitored, -n is the size of the sample, -c4 is a factor allowing to find an unbiased estimator of the standard deviation σ: 4. s c σ = (3) With c4 given by: 4 1 ! 2 2 1 1 1 ! 2 n c n n   -     = - -  -     (4) where the non-integer factorial is given by: 1 !.
Considered problem We focus here on the problem of pollutant evolution prediction. As explain previously, each Alima collects, each minute, the values of five parameters (temperature, humidity, CO 2 , COV and particulate matter (pm)). The goal is to predict 30 minutes ahead the level of each pollutant in function of the actual and past level of these pollutants. In order to improve the performance of the model, different situations (cooking, sleeping…) are detected by using a classification model. The output of this classification model is used as an input of the prediction model. The figure  3  presents the principle of the prediction of pollutants. The database collects data from the Alima. These data are use in a first step in order to detect the current situation (cooking, sleeping…) which may have an impact on the pollutant evolution.
Data sharing is becoming increasingly common, but despite encouragement and facilitation by funding agencies, journals, and some research efforts, most neuroimaging data acquired today is still not shared due to political, financial, social, and technical barriers to sharing data that remain. In particular, technical solutions are few for researchers that are not a part of larger efforts with dedicated sharing infrastructures, and social barriers such as the time commitment required to share can keep data from becoming publicly available. We present a system for sharing neuroimaging data, designed to be simple to use and to provide benefit to the data provider. The system consists of a server at the International Neuroinformatics Coordinating Facility (INCF) and user tools for uploading data to the server.
The primary design principle for the user tools is ease of use: the user identifies a directory containing Digital Imaging and Communications in Medicine (DICOM) data, provides their INCF Portal authentication, and provides identifiers for the subject and imaging session. The user tool anonymizes the data and sends it to the server. The server then runs quality control routines on the data, and the data and the quality control reports are made public. The user retains control of the data and may change the sharing policy as they need. The result is that in a few minutes of the user's time, DICOM data can be anonymized and made publicly available, and an initial quality control assessment can be performed on the data. The system is currently functional, and user tools and access to the public image database are available at http://xnat.
INTRODUCTION Data sharing is becoming increasingly common  (Biswal et al. , 2010; Di Martino et al. , 2013) , but despite encouragement and facilitation by funding agencies, journals, and some labs and larger research efforts 1   (Hall et al. , 2012; Prior et al. , 2013) , there remain political, financial, social, and technical barriers to sharing data  (Poline et al. , 2012). Excuses such as "it's too hard" and "it takes too long" are all too common, and there is anxiety about subject protection and control of data  (De Schutter, 2010). And unless one is part of a large project with dedicated sharing infrastructure, there is also a lack of open technical infrastructure and public and free archive space. There are some central, open databases for image data sharing such as The Cancer Imaging Archive 2  and the National Database for Autism Research 3  , but these are domain-specific, and contributing data requires a substantial investment of time to handle both bureaucratic and technical aspects of contributing data.
On the other end of the spectrum are image databases that can be installed locally, such as COINS 4   (Scott et al. , 2011) , the Human Imaging Database 5   (Ozyurt et al. , 2010) , LORIS 6   (Das et al. , 2012) , NIDB 7   (Book et al. , 2013) , and XNAT 8   (Marcus et al. , 2007). Using any of these to share image data requires an investment in hardware as well as initial and ongoing technical support. With the exception of XNAT Central, none of these provide a public, open instance that anyone can use to share their data. Given an open repository such as XNAT Central, other issues come into play. The actual mechanics of uploading data must then be addressed. There are tools available to facilitate data upload, but these often require somewhat involved installation, and most are then general in scope, with many options that must be understood.
XNAT Desktop 9  and DicomBrowser 10  , for instance, allow a user to manage local data and send it to XNAT Central, but the flexibility in anonymization options and subject identifier customization mean that there is a learning curve to using these tools effectively. Moreover, they often don't capture the relevant metadata simply and efficiently. We have created a system for data sharing that attempts to address many of these issues. We set up a public, open image repository within an international organization that can host and manage imaging data, and have created user tools that make data upload to this server trivial. The user software is designed to be easy to install, and once installed, data upload is initiated by a simple drag and drop. The user is then walked through the few steps necessary to anonymize and upload the data in a way that control of the data on the repository is retained.
OVERVIEW The system has two components: the image repository and the user tools. The repository is an XNAT installation, and while some XNAT customizations were necessary, most of the innovation lies with the user tools. An overview of the design of the system can be found in Figure  1. Since the overarching goal of this system is to make data sharing simple, we describe the components of the system in the order they are encountered by the data as it moves from a local disk to the server. The ultimate effect is that given a few minutes of a researcher's attention, data is anonymized, archived, and shared, and the researcher gets feedback on the quality of the data. The server itself can be found at http://xnat. incf. org, and the user tools can be downloaded from this location as well. Source code for the user tools and custom code for the repository can be found on GitHub at http://github.
USER UPLOAD TOOL The driving design principle for the user tool is ease of use. Our goal is to remove the barriers to data sharing, and the more difficult it is to install or successfully run any tool, the more likely it is that the user will give up. We provide two user tools, a command line script and a graphical user interface (GUI). The two options provide the same functionality, but in different ways: the command line script is useful for users comfortable at the command line, while the GUI uploader is useful for users accustomed to a more interactive experience. The only requirement for these tools is that data is prepared in a certain well-defined way before being sent to the archive (see below). The current user tools are written in Python, released under the BSD license, and can be installed on Linux or Mac OS machines.
Dependencies are pydicom 11  , httplib2 12  , and DCMTK 13. The user tools can be downloaded directly from the International Neuroinformatics Coordinating Facility (INCF) web site 14. The command line tool requires manual installation of the dependencies, although it is packaged and released through NeuroDebian 15   (Halchenko and Hanke, 2012)  which simplifies installation and dependency handling on Debian systems. The Linux GUI tool also requires PyQt 16. All of the dependencies are bundled for the Mac OS GUI. The custom code for the archive server is also made available on line via GitHub 17  and released under the BSD license. Although we plan to support the ability to push to alternate archives, focus so far has been on the user tools and user experience, with one archive sufficient for testing.
Data selection, validation, and annotation The first step is selecting the Digital Imaging and Communications in Medicine (DICOM) data to share. This can be invocation of the command line script that takes the containing directories of the data as arguments or dragging and dropping a folder containing data onto the GUI tool (Figure  2 ). The selected data is then validated and sorted into subjects and imaging sessions: the user tool scans the specified directories for DICOM data using pydicom and groups the data by subject (by the DICOM Patient ID field) and imaging session (by Study Instance UID). If valid data is found, the user is asked to consent to a simple usage agreement before proceeding (Figure  3 ). This agreement is intentionally broad and simple; waiting to implement this upload system until all of the legal aspects of sharing have been perfected is a recipe for failure.
The user is then prompted for a user name and password that identify the user on the INCF portal 18. The  user name allows the archive to assign the data to the user so they retain control of the data, and links to the e-mail address to which reports are sent. Since the archive shares the users and passwords of the INCF portal, the password allows the user tool to query the archive for existing data under the user's control to avoid collisions of new subject or session identifiers (Figure  4 ). This all takes a few short minutes of the user's time and attention. There is some coordination with the archive required at this stage. The archive server is running XNAT, which provides a set of REST 19  services that allow these queries. XNAT structures data hierarchically into projects, subjects, and sessions.
Anonymization and upload The data is anonymized locally before it leaves the user's computer and is then sent to the archive. At this stage, all necessary information has been collected from the user, and the data must be prepared and sent to the archive. One benefit of using DICOM data in our initial test case is that the DICOM standard includes a network communication protocol for transferring data, a protocol which XNAT handles natively on the receiving end. But the user tool must first anonymize the data so no identifiable information leaves the user's machine and then annotate the data with the user information and the specified data identifiers. Depending on the amount of data and the quality of the network connection, this may take an hour or more, but it does not require the user's attention.
Anonymization is a challenge because of the various levels and interpretation of anonymization that can be applied. DICOM defines concepts such as patient name and study date that it stores in fields, and there are several different conflicting DICOM anonymization schemes that specify what information should be protected (meaning, in our case, removed from the data). We can illustrate this challenge by examining three different examples of existing anonymization protocols: DICOM Supplement 55 20 (developed primarily with clinical uses in mind), the National Clearly, no consensus is to be found: the level of anonymization depends on the application context and the specifics of the data. In addition, the DICOM specification defines fields that must be present in valid data sets  23  , and programs at both the sending and receiving ends of the network transfer have their own quirks regarding what fields they require to be present.