dataesr/scientific-paragraphs-categorization huggingface dataset:
Logger On top of the thread(s) dedicated to computations, PARTONS uses an additional thread serving as the Logger. The code sends information at four different levels: DEBUG, INFO, WARNING and ERROR. With the first three levels the code is always running, while the ERROR level forces the code to stop. Sending the information to the Logger does not slow down the computations by taking precious time to screen (or file) printing. Warning messages signal to the user that there is something to be checked carefully, e. g. slow convergence in a numerical routine. The output of the Logger can be directed either to the screen, or to a log file, or to both of them. It traces back all details of the computation that have been considered relevant by the developer.
DVCS The set of unpublished analytic expressions of Guichon and Vanderhaeghen used in Refs. [75, 107] , and the latest set of expressions  [53]  by Belitsky and Müller. Alpha Four-loop perturbative running of the strong coupling constant from PDG  [108] , and constant value. In a nutshell, the present version of PARTONS contains all the necessary tools to perform DVCS studies at leading twist and NLO. The presented set of modules is by no means limiting. Other modules will be integrated in the framework to allow systematic differential studies requiring the flexible design of PARTONS. All the previous categories should be extended by new modules, either to recover the features of existing codes, or to test brand new development in the integrated chain between models and measurements. For instance, subleading twist contributions or transversity GPDs can be studied within the framework by adding or extending the previous modules.
Examples The source code of PARTONS and the pre-configured appliances of the provided virtual machines can be downloaded for the project web page: http://partons. cea. fr. Two kinds of virtual machines are available: the light version with only the runtime environment aimed at the users, and the developer version containing both the runtime and development environments. The web page serves also as a main source of the technical information on PARTONS. On top of a detailed description of the code elements, the users may also find there many useful tutorials, such as a quick guide helping them to start their experience with PARTONS, an additional help with installation technicalities, tips on using our virtual machines, templates for adding new modules and many more. Examples on how to use specific elements of the PARTONS framework are available online, but they are also provided as one of the sub-projects called partons-example.
2 GeV 2 , Q 2 = 4 GeV 2 and beam energy E Lab = 11 GeV. This example is close to the one discussed in Sect. 4. 4, but this time the code is executed with a list of values of φ ranging between 0 and 360 degrees. This is indicated by the method computeManyKinematicOneModel. The list of kinematic configurations is provided in a file as indicated between the markups <ObservableKinematic> and </ObservableKinematic>, and is described as simple text: where we can see, on each line, from left to right: x B , t (in GeV 2 ), Q 2 (in GeV 2 ), E Lab (in GeV) and φ (in degrees). 1 <?xml version="1. 0" encoding="UTF-8" standalone="yes" ?> 2 3 <!--Scenario starts here --> 4 <!--For your convenience and for the bookkeeping you can provide creation date and a unique description --> 5 <scenario date="2017-07-18" description="DVCS observable evaluation for many kinematics example"> 6 7 <!--First task: evaluate DVCS observable for a single kinematics --> 8 <!--Indicate service and its methods to be used and indicate if the result should be stored in the database --> 9 <task service="ObservableService" method=" computeManyKinematicOneModel" storeInDB="1"> In this example the obtained values are stored in the database (storeInDB switch set to 1).
After the successful insertion the code returns such a line to the Logger (which itself outputs to the standard output and/or a file): [INFO] (ObservableService::computeTask) ObservableResultList object has been stored in database with computation_id = 1 The inserted data can be then fetched from the database by running such a scenario: Fig. 9  Beam spin asymmetry A - LU (φ) for x B = 1 /3, t = -0. 2 GeV 2 , Q 2 = 4 GeV 2 , and E Lab = 11 GeV. Compton Form Factors are evaluated at LO approximation with the GK GPD model  [97] [98] [99]  <?xml version="1. 0" encoding="UTF-8" standalone="yes" ?> <!--Scenario starts here --> <!--For your convenience and for bookkeeping provide creation date and unique description --> <scenario date="2017-07-18" description="Get observable result from database"> <!--Task: generate file with data matching indicated criteria --> <task service="ObservableService" method="generatePlotFile"> <!--Variables selected to be stored in the output file --> <task_param type="select"> <param name="xPlot" value="phi" /> <param name="yPlot" value="observable_value" /> </task_param> <!--Applied requirements --> <task_param type="where"> <param name="computation_id" value="1" /> </task_param> <!--Path to the output file --> <task_param type="output"> <param name="filePath" value="output.
Licenses One of the goals of the developers team is to allow PAR-TONS to spread through the community and for that purpose, we have adopted a license prescription in such a way that users are free to use and modify the source code of PAR-TONS and all its dependencies. More precisely, PARTONS is distributed under the GPLv3 license, and so are the subprojects numa and partons-examples. The sub-project elementary-utils is distributed under the Apache license. Users are encouraged to contribute back their own developments by joining our forge, a GitLab server with a Git repository: https://drf-gitlab. cea. fr/partons/core Concerning our external libraries, the SFML library is available under the zlib/png license while CLN and Qt are under GPLv3.
Conclusions In the last twenty years we have witnessed an intense theoretical and experimental activity in the field of exclusive processes described in terms of Generalized Parton Distributions. It is also a crucial part of the forthcoming experiments at Jefferson Lab, COMPASS and in the future at EIC or LHeC. The amount and quality of the expected data, together with the richness and versatility of theoretical approaches to its description, calls for a flexible, stable and accurate software framework that will allow for systematic phenomenological studies. In this paper we have described such a framework, called PARTONS, and how it addresses the most important tasks: automation, modularity, non-regression and data storage. We have also provided examples of simple XML scenario files illustrating automated calculations of physical observables.
Since its inception the PARTONS framework has expanded rapidly with the addition of new core developers. We intend, in the mid-to long-term future, to complement PAR-TONS with new theoretical developments, new computing techniques and other exclusive processes. To achieve this, we expect that more physicists will join the development team to integrate new modules and benefit from our integrated chain, relating theory to experimental observables. PARTONS should become the de facto software framework for the GPD analysis of the next-generation exclusive data.
int GPDKinematicDao::insert(double x, double xi, double t, double MuF2, double MuR2) const { 2 / Returned value (last inserted id if everything fine) 4 int result = -1; 5 QSqlQuery query(DatabaseManager::getInstance()->getProductionDatabase()); / Prepare the query ElemUtils::Formatter formatter; formatter << "INSERT INTO " << Database:: TABLE_NAME_GPD_KINEMATIC << " (x, xi, t, MuF2, MuR2) VALUES (:x, :xi, :t, : MuF2, :MuR2)"; query. prepare(QString(formatter. str(). c_str())); // Bind values query. bindValue(":x", x); query. bindValue(":xi", xi); query. bindValue(":t", t); query. bindValue(":MuF2", MuF2); query. bindValue(":MuR2", MuR2); // Execute query, look for errors if (query. exec()) { result = query. lastInsertId(). toInt(); } else { throw ElemUtils::CustomException(getClassName(), __func__, ElemUtils::Formatter() << query.
int GPDKinematicDaoService::insert(const List<GPDKinematic>& gpdKinematicList) const { // Returned value (last inserted id if everything fine) int gpdKinematicId = -1; // Indicate transaction mechanism QSqlDatabase::database(). transaction(); try { //Add each kinematic object for (unsigned int i = 0; i != gpdKinematicList. size(); i++) { gpdKinematicId = insertWithoutTransaction(gpdKinematicList. get(i)); } // If there is no exception we can commit all queries.
!--First task: evaluate GPD model for a single kinematics --> !--Indicate service and its methods to be used and indicate if the result should be stored in the database --> 9 <task service="GPDService" method="computeGPDModel" storeInDB=" -Second task: print results of the last computation into standard output --> 32 <task service="GPDService" method="printResults"Computation of beam spin asymmetry for many kinematic configurations with automation.
This work was supported in part by the  Commissariat à l'Energie Atomique et aux Energies Alternatives , by the  French National Research Agency (ANR)  grant  ANR-12-MONU-0008-01 , by the Grant No. 2017/26/M/ST2/01074  of the  National Science Centre, Poland , and by the  DOE  grant  DE-FG-04-ER41309. distributed under the terms of the Creative Commons.
1 3 / 6 // Initialize QSqlQuery object 7 8 / 3678 int GPDKinematicDao::insert(double x, double xi, double t, double MuF2, double MuR2) const { 2 / Returned value (last inserted id if everything fine) 4 int result = -1; 5 QSqlQuery query(DatabaseManager::getInstance()->getProductionDatabase()); / Prepare the query ElemUtils::Formatter formatter; formatter << "INSERT INTO " << Database:: TABLE_NAME_GPD_KINEMATIC << " (x, xi, t, MuF2, MuR2) VALUES (:x, :xi, :t, : MuF2, :MuR2)"; query. prepare(QString(formatter. str(). c_str())); // Bind values query. bindValue(":x", x); query. bindValue(":xi", xi); query. bindValue(":t", t); query. bindValue(":MuF2", MuF2); query. bindValue(":MuR2", MuR2); // Execute query, look for errors if (query. exec()) { result = query. lastInsertId(). toInt(); } else { throw ElemUtils::CustomException(getClassName(), __func__, ElemUtils::Formatter() << query.
6 7< 8 < 4 684 !--First task: evaluate GPD model for a single kinematics --> !--Indicate service and its methods to be used and indicate if the result should be stored in the database --> 9 <task service="GPDService" method="computeGPDModel" storeInDB=" -Second task: print results of the last computation into standard output --> 32 <task service="GPDService" method="printResults"Computation of beam spin asymmetry for many kinematic configurations with automation.
METHOD: RADIO + MACHINE-LEARNING IDENTIFICATIONS To apply supervised machine-learning classification we require a list of observed properties for a training sample made up of submillimeter-detected and submillimeterundetected galaxies. Therefore, firstly, we need to select those features of SMGs which best separate them from field galaxies ("non-SMGs"). Given the power of radio-identification to locate the counterparts we adopt a two-pronged approach, where we combine likelihood test to select probable radio counterparts, along with a machine-learning method to increase the completeness of the resulting SMG sample. As we will show, we apply these two tests separately in part because of the requirements in terms of multi-wavelength detections needed for the machine-learning analysis and in part because of differences in the coverage of the field in the radio and optical and near-infrared imaging datasets.
The machine-learning method Having selected five properties that are likely to have diagnostic power to differentiate SMGs from the non-SMGs, we first use the SVM model to build a non-linear classifier for optimally separating these two populations. This is implemented by using the algorithm coded in the scikit-learn 1 Python package  (Pedregosa et al. 2011). The SVC takes a labelled training set (in this case "SMG" versus "non-SMG") and associated set of feature vectors (e. g. , observable colors) and attempts to build hyperplanes that maximizes the separation between the two classes in the n-dimensional ( in this case n = 5) feature space. Having established the hyperplane(s), new, unlabelled test data can be presented to the trained classifier to determine which class it belongs to according to its relative position in this five-dimensional parameter space.
0, γ = 1/n features (here n = 5), d = 3 and c 0 = 0. 0, are optimized for performing the classification in our work by SVM classifier with a polynomial kernel. The feature selection module in the scikit-learn package can also select the best features for classification based on the univariate statistical tests  (Pedregosa et al. 2011). The univariate score is derived by U score = -log(p), where p is the p-value of corresponded univariate feature  (Pedregosa et al. 2011 ). Among the five features we selected, the best one for separating SMGs from non-SMGs is (J -K) color with a U score = 891, followed by  ([3. 6 ]-[4. 5]) color with a score of 707 and then (K -[3. 6]) with a score of 695 and the absolute H-band magnitude has a U score = 579. The photometric redshift has a relatively lower univariate score of 324, however, as we described above, including photometric redshift and absolute H-band magnitude as the input features for the machine-learning doesn't affect the completeness of our analyses but increases the recovery rate of the SVM classifier by about 6 percent.
′′ 6. Therefore, half of the 334 ALMA SMGs are recovered by radio identification alone. Combining the machine-learning classification with the radio identification, 285/334 (85 percent) of the ALMA SMGs are recovered with a precision > 62 percent. This proves that our combined radio and machine-learning method can efficiently recover SMGs from the general population of K-band-selected galaxies. To increase the completeness of the self-test sample, we also include the K-band detected galaxies that lack a secure detection at J-band or at 4. 5µm and adopt the XGBoost machine-learning module to perform the classification. The sample size is increased to 2305 with 366 of them being ALMA-detected SMGs. The XGBoost model identifies 409 "SMGs" from this enlarged test sample. For the ALMA SMGs, 270/366 (74 percent) are recovered with an precision of > 66 percent.
This fraction increases to 71 percent for SMGs brighter than S 850µm ≥ 4. 5 mJy. The completeness of recovered SMGs increases to 64 percent if we adopt a second machine-learning module, XGBoost. This machine-learning algorithm has a very similar performance as SVM in classifying SMGs from K-band detected field galaxies but can deal with missing features and so can employ a slightly large test sample by including objects with only limits on their J-band or 4. 5 µm fluxes. 6. By stacking the emission in ALMA maps at the position of the machine-learning classified, but individually ALMA-undetected, K-band galaxies we show that on average there is faint submillimeter emission from these galaxies. Moreover, a stack of the far-infrared Herschel SPIRE maps at the position of the "blank-ALMA" maps and a stack of "blank-ALMA" maps at the position of machine-learning classified "SMGs" within these maps demonstrate that the majority of SCUBA-2 sources are real, although the submillimeter galaxies responsible for these sources are either too faint and/or diffuse to be detected by ALMA.
Because of the relative maturity of online transcription platforms, the inaugural WeDigBio event focused on the transcription step of the digitization process. Note: For the purposes of this research, we have defined "on-site" as "at the physical location of the data source" and generally include field sites outdoors, as well as indoor venues such as museums and universities. Values provided are most current available data. www. notesfromnature. org), and the Symbiota crowdsourcing module (www. symbiota. org). The goal of each of these platforms is the same: to facilitate volunteer transcription of biodiversity specimen information from digital images that include specimen labels. However, their approaches vary (reviewed by  Ellwood et al. 2015)  in ways that became relevant to interpreting WeDigBio event statistics.
For example, DigiVol, Les Herbonautes, SITC, and Symbiota employ an approach in which one volunteer transcribes and then a second validates the transcription  (Rouhan et al. 2014). In contrast, Notes from Nature asks three different volunteers to transcribe a specimen, and then the transcriptions are reconciled using Notes from Nature tools to derive a final output  (Matsunaga et al. 2016 ; see also www. github. com/ juliema/label_reconciliations). Online citizen-science projects, such as volunteer transcription platforms, have several known strengths and weaknesses  (Lukyanenko et al. 2011 , Newman et al. 2012). A tremendous strength of online projects is the ability to reach an Internet-scale audience. Anyone with a computer and an Internet connection can participate. This is invaluable to researchers who require assistance in collecting or processing large amounts of data.
A content management system, Drupal 7. x, deployed on a Linux/Apache/MySQL/PHP host, served this content to site visitors. A custom theme and dashboard elements were developed using different visualization components, including the D3 JavaScript plotting library and the web-mapping tools from Carto (www. carto. com). Materials and resources working group. This working group produced content for the on-site event hosts. The group created planning documents, example games, a lesson plan for undergraduate classes, and a press kit that on-site public event organizers could use to recruit and advertise in newsletters, organizational communications, and local media. The first three items relied heavily on the group's experience with single-institution on-site events held at Florida State University, the Smithsonian Institution, Valdosta State University, and the Australian Museum.
Across platforms, there was an increase in the number of countries represented during the event compared with those of the before and after time periods (figure  5 ). Before the event, www. wedigbio. org had 66 sessions from 15 countries. During the event, there were 1843 sessions from 61 countries, and after the event, there were 212 sessions from 12 countries. Surveys. At the end of on-site events, the volunteers were provided an anonymous link to a survey. To preserve anonymity, the volunteers were not asked to provide names, and no other identifiable information (e. g. , email address) was collected. Qualtrics Survey Software was used for the surveys prepared by the evaluation working group. One hundred thirty-nine participants at 12 on-site events completed the survey (appendix S1). A majority of the respondents reported a "higher" or "much higher" level of awareness about the number (67%), kinds (62%), and value (70%) of biodiversity specimens held in the collections.
In 2016, we expanded this concept using Sococo online software (www. sococo. com), which allowed us to simultaneously host numerous video-conference rooms in the same virtual space. A quarter of the on-site events experimented with this in 2016, and we are eager to provide more research talks, improved scheduling of video participation, and intersite transcription games for a richer online experience in future years. The individual transcription projects at each platform were somewhat heterogeneous: One could have transcribed specimen labels of insects, spiders, plants, and marine invertebrates. We welcome this heterogeneity, because it caters to the diversity of hosts and citizen-scientist interests and broadens the impact of the event. However, we plan to experiment in the future with rotating research themes that might highlight the relatedness of multiple digitization projects for citizen scientists and (especially) the media.
Researchers have made considerable use of data produced by other international citizen-science projects, such as the National Audubon Society's Christmas Count  (Dunn et al. 2005  and references therein) and eBird  (Sullivan et al. 2009). A specimen-to-publication model made possible through WeDigBio (similar to the rapid publication that resulted from a BioBlitz and work of international taxonomic experts;  Telfer et al. 2015)  would go far to demonstrate the immediate need for biocollections data and the whole of the scientific process. We would also like to help the biocollections community build its capacity to sustain high rates of digitization beyond the WeDigBio event by supporting the establishment of interest (dubbed WeDigInterest) groups. These would represent partnerships with potentially large organizations whose memberships are motivated to digitize subsets of biocollection specimens.
The Smithsonian Transcription Center team would like to thank the National Museum of Natural History Departments of Botany and Entomology for opening their collaboration and the Office of the Chief Information Officer and the Libraries and Archives Support Services Branch team for their ongoing support. A special thank you and acknowledgment to the digital volunteers who share their time, energy, and discoveries with us all as we work together to unlock and connect biodiversity specimen data #BeeByBee and beyond. The Les.
While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.
The model from  (Lebret et al. , 2016)  takes the Wikipedia infobox as an input, while  (Chisholm et al. , 2017)  uses a sequence of slot-value pairs extracted from Wikidata. Both models are only able to generate single-subject relationships. In our model the input triples go beyond the single-subject relationships of a Wikipedia infobox or a Wikidata page about a specific item (Section 2). Similarly to our approach, the model proposed by  (Vougiouklis et al. , 2017)  accepts a set of triples as input, however, it leverages instance-type-related information from DBpedia in order to generate text that addresses rare or unseen entities. Our solution is much broader since it does not rely on the assumption that unseen triples will adopt the same pattern of properties and entities' instance types pairs as the ones that have been used for training.
To this end, we use copy actions over the labels of entities in the input triples. This relates to previous works in machine translation which deals with rare or unseen word problem for translating names and numbers in text. (Luong et al. , 2015)   propose a model that generates positional placeholders pointing to some words in source sentence and copy it to target sentence (copy actions). (Gulcehre et al. , 2016)  introduce separate trainable modules for copy actions to adapt to highly variable input sequences, for text summarisation. For text generation from tables,  (Lebret et al. , 2016)  extend positional copy actions to copy values from fields in the given table. For Question Generation,  (Serban et al. , 2016)  use a placeholder for the subject entity in the question to generalise to unseen entities.