dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). In contrast, our method can be applied to any type of link prediction model. All these methods require entailment rules as their input. In most cases  (Wang et al. , 2015; Demeester et al. , 2016; Guo et al. , 2016) , the entailment rules are constructed manually, or selected from lexical resources such as WordNet  (Miller, 1995). Therefore, the improvement of such methods come from out-of-domain knowledge (manually built lexical resources or expert knowledge), while our entailment rules come from in-domain knowledge, i. e. , the same data which is used for  link prediction. The number of entailment rules in all the previous models is very small because of scalability issues (at most a few hundred rules in  Ding et al. (2018) ). In contrast, our method can incorporate millions of automatically discovered entailment rules.
Acknowledgements We thank  Nathanael Chambers  for many useful discussions. The authors would also like to thank the three anonymous reviewers for their valuable feedback. This work was supported in part by the  Alan Turing Institute  under the  EP-SRC  grant  EP/N510129/1. The experiments were made possible by Microsoft's donation of Azure credits to  The Alan Turing Institute. The research was supported in part by  ERC  Advanced Fellowship GA 742137 SEMANTAX, a  Google faculty award , a  Bloomberg L. P. Gift award , and a  University of Edinburgh/Huawei Technologies  award to  Steedman. Steedman  and Johnson were supported by the  Australian Research Council's Discovery Projects funding scheme  (project num-ber  DP160102156 ). Cohen was supported by an award from Bloomberg L. P.
Again, we could stop after any number of faults F , and the publicly available software  [25]  shows how to do so. We first compute the expected time between two full restarts: T ABF T = (p-f ) 2 i=p 2 µ i + D. As previously, we tolerate failures up to reaching a processor grid size of (pf ) × (p -f ), each inter-arrival time being the MTBF of the platform with the corresponding number of alive processors. Now, we define the auxiliary variable W ABF T (p 1 , p 2 ) as the parallel work when moving from a (p 1 -1) × p 2 grid with p 2 -1 spare nodes to a (p 1 -1) × p 2 grid with no spare node. There are (p 1 -1)p 2 processors working during all sub-periods, just as it was the case for GridShaped applications. A failurefree execution would imply a parallel work of 1 1+ 2 p (p1-1)p2 i=p1p2-1 µ i which is the total time of computation divided by overhead added with the checksum tiles.
Aggregate results over the 100 data sets are reported by determining the median and the robust standard deviation. The mean posterior model size is estimated by the average number of covariates which are assigned to the slab distribution. The number of true positives TP j for model M j corresponds to the number of covariates which are contained in the true model as well as in M j (as induced by the assignments to the slab). More formally, TP j is determined by TP j = V v=1 1 - k∈K v (1 -1( k = 1|M j )) , where V is the number of variables with non-zero effects (V = 5 in the linear setting and V = 6 in the nonlinear setting) and K v is the set of terms associated with variable v. For BMA and SSG-linear K v contains a single term, while for SSG-smooth K v contains two terms, the linear and the nonlinear term associated with variable v.
To determine the overall TP criterion for one data set the median number of true positives is estimated based on all models, weighted with their posterior evidence. The MAD criterion for variable inclusion is estimated by MAD PIP = 1 K K k=1 PIP k -PIP true , with PIP k the estimated PIP of variable k and K = 35 in this simulation study. In the nonlinear setting, PIP k corresponds to the estimated posterior probability of including either a linear and/or a nonlinear term. The MSE criterion to assess predictive performance is estimated by MSE y = 1 n n i=1 ( ŷi -y i ) 2 , with ŷi the predicted outcome and y i a newly sampled outcome value given the same covariates x i in order to avoid an in-sample bias. Predictions are obtained based on posterior mean estimates of the regression coefficients.
Furthermore, SSG-smooth provides a suitable approach to identify robust determinants and achieve good predictive performance when a nonlinear relationship between the covariates and the dependent variable cannot be safely a priori excluded. Also, the cost of switching to this approach is small if the linear model constitutes the correct specification. The use of SSG-smooth safeguards against obtaining completely unreliable results if the assumption of a linear relationship is violated. Smith and Kohn (1996)  used the Boston housing data set to indicate the advantages of selectively including nonlinear terms when predicting median home values from geographical and infrastructure characteristics of the property while accounting for model uncertainty. Thus, the aim in the following is to replicate the nonlinear effects reported by  Smith and Kohn (1996)  in an analysis where all variables are considered to have effects that are potentially nonlinear.
Design and specifications Before analysis the data set was standardized. This ensures that a similar amount of shrinkage in terms of standard deviations of the covariates is imposed on each of the covariates. BMA was run with 2,000,000 burn-in iterations and 2,000,000 recorded iterations, while SSG was run with 10 parallel chains using 2,000,000 burn-in iterations and 200,000 iterations recorded with a thinning of 5 for each chain. The same estimation methods and hyper-parameter values are used as in the simulation study in section III. 4 he results for BMA, SSG-linear and SSG-smooth are assessed by a detailed comparison of the estimated PIPs, PMs and PSCs for the linear terms and the estimated PIPs for the nonlinear terms. In addition the posterior model size distributions of SSG-linear and SSG-smooth are compared where the model size is determined by the number of included linear and nonlinear terms.
In the plot the 80% confidence bands and the median of their posterior distributions of the smooth effects are provided. The identified smooth effects are similar to those given in  Smith and Kohn (1996, p. 337). For the average number of rooms per dwelling (rm) a strong price increase is only detected between approximately seven and eight rooms. Regarding the percentage of lower status of the population (lstat) there is a high decrease in price if the percentage is increased at a low level. This decrease is marginally diminishing, indicating that the price decrease is smaller if the percentage is already high. With respect to the nitric oxides concentration (nox) a price decrease occurs only after a certain level is reached which seems to be at approximately 0. 6. The results for the Boston housing data indicate that some covariates might be identified as having a robust linear effect using either BMA or SSG-linear, while their posterior model inclusion probability turns out to be negligible once all covariates are included with potentially nonlinear effects.
V. Application to cross-country growth regressions Two data sets for cross-country growth empirics are investigated: the data set used by  Sala-i-Martin et al. (2004, referred  to as the SDM data set) and the data set provided by  Fernández et al. (2001, referred  to as the FLS data set). These two data sets have already been thoroughly investigated with respect to the identification of robust linear effects. The aim of the presented analyses is to uncover robust nonlinear effects on growth. The design and specification used for analysing these two data sets are the same as for the Boston housing data set with details described in section 'Design and specifications' and online Appendix B. 3.
Bulletin The SDM data set The data set by  Sala-i-Martin et al. (2004)  consists of data on 88 countries and 67 potential determinants of economic growth. 6 Sala-i-Martin et al. (  2004 ) used BMA techniques based on classical linear regression models to analyse this data set. They did not include any nonlinear terms. Table  3  reports the results for BMA, SSG-linear and SSG-smooth. Only the results for those covariates are shown where at least one estimated PIP is larger than 0. 1. Results are also shown for rather low PIPs in order to allow for a better comparison of results and assessment of the differences between the approaches, although variables with such low PIPs would not be identified as being robust determinants. The results indicate that there are some differences between the estimated PIPs for the BMA and SSG-linear approach for this data set.
However, estimating only linear effects would also give misleading results. The estimated linear effect would be heavily influenced by the outlier. For example, the robust linear effect of population density (DENS60) identified for BMA and SSG-linear is positive as indicated by the PSC values which are equal to 1. 00 and 0. 91. However, this identified positive linear effect may be due only to the high leverage point Botswana. The visualization of the smooth effect suggests that for small values of population density there is actually a negative linear relationship between population density and economic growth. Results for the SDM data set indicate that the population density variable has a low PIP for SSG-linear, but turns out to be the variable with the highest PIP when nonlinear terms are also included.
The smooth effects most frequently included in the model are visualized in Figure  4  at the bottom with their 80% confidence bands and the median of their posterior distributions. The smooth effects identified for non-equipment investment (NequipInv) and fraction Catholic (Catholic) provide a slight indication that extreme values for these covariates have a different effect than an average value. For life expectancy (LifeExp) the estimated robust smooth effect slightly indicates that the influence of the covariate shows diminishing returns for higher values. For the FLS data set SSG-smooth validates and confirms the results obtained with SSG-linear and BMA. The results indicate that more complex functional relationships are not required to identify robust determinants and similar linear effects are discerned if the assumption of a constant linear effect is relaxed.
Objectives Analysing process and outcome measures for patients suspected of or having an infection in an entire hospital requires processing large datasets and accounting for numerous patient parameters and treatment guidelines. Rapid, reproducible and adaptable analyses usually need substantial technical expertise but can yield valuable insight for infection management and antimicrobial stewardship (AMS) teams. We describe a software application (RadaR -Rapid analysis of diagnostic and antimicrobial patterns in R) for infection management allowing user-friendly, intuitive and interactive analysis of large datasets without prior in-depth statistical or software knowledge.
Methods and Results RadaR was built in R, an open source programming language, making it free to use and adaptable to different settings. Shiny, an additional open source package to implement web-application frameworks in R, was used to develop the application. RadaR was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. RadaR visualizes analytical graphs and statistical summaries in an interactive manner within seconds. Users can filter large patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results, and outcome in length of stay. Results can easily be stratified and grouped to compare individually defined patient groups. Finally, datasets of identified patients / groups can be downloaded for further analyses.
This also enables comprehensive and multidisciplinary approaches of infection management combining treatment and diagnostic perspectives  (1, 9, 11). Unfortunately, this kind of analyses still requires substantial statistical knowledge and software skills while being time consuming. Technology, data science, and application development can bring solutions to complex data handling problems like those described above. This open source approach has great advantages over traditional methods like Excel or SPSS. Hughes et al. described those in their report of an application for RNA-sequencing data analysis  (14). They highlight aspects that were also fundamental for the development or RadaR. Firstly, R allows transparent, reproducible and sustainable data analysis through scripts that can easily be shared and changed.
This can build the basis for collaboration and enforces the spirit of open science (also through the strong collaborative R community online). Secondly, R is open source and free to use, therefore, enabling use in resource-limited settings, too. Finally, Shiny empowers users to interact with the data making even very large datasets quickly interpretable. The functionality of R can be easily extended by installing additional packages. Especially the dplyr and ggplot packages are to be mentioned as they are used to transform the data and to create most plots in RadaR  (18, 19). All packages used for the development of RadaR are listed in Table  1. RadaR is developed in an open source environment and licensed under GNU General Public License v2. 0 giving options to change, modify and adapt RadaR to both personal and commercial users' needs while requiring the need to document code changes.
Application design RadaR is designed in form of a browser-based dashboard that most users are familiar with from typical websites and online tools (Fig. 1 ). The basis of RadaR's functionality is filtering datasets and producing analytical graphs according to selection criteria defined by the user. Any calculations and data aggregation is based on single observations of individual patients. To identify and analyze groups of patients, 17 different selection criteria can be found in the sidebar (Table. 2). The output of RadaR is grouped into four panels (patient, antimicrobials, diagnostics, and outcome) that each consist of three to four output boxes displaying the results. All output is based on the selection criteria defined by the user in the sidebar. By default, all available options are set to include the maximum number of patients.
Workflow RadaR was developed and tested with a dataset of all patients receiving systemic antimicrobials admitted to our institution, a 1339-bed academic tertiary referral hospital, within the years of 2009 to 2016 comprising 181,078 admissions with a total of 635,227 observations/rows. For simulation purposes and online user testing we have created a test dataset of 60,000 simulated patients that do not represent real patients. This dataset cannot be used for any meaningful results but allows testing of RadaR's functionality. A typical example workflow with RadaR comprises six steps (with examples from the test dataset): 1. Define the selection: For example, patients receiving intravenous cefuroxime for at least two days starting on the day of hospital admission from any specialty in all years in the dataset.
Customization RadaR uses common csv-files (comma-separated values) as input. The current state of RadaR doesn't provide any capabilities to be immediately connected to electronic medical records. Therefore, these data still needs to be extracted manually. However, this option shall be implemented in future versions to provide the optimal use for IT staff and users. We have documented the entire workflow of data cleaning and preparation for use with RadaR in an online accessible Rmarkdown-file ( https://github. com/ceefluz/radar/blob/master/RadaR_data_preparation. Rmd ). These steps are based on data from our institution and can be adjusted to fit the needs of different settings. The data should be structured in a dataset format where each variable is one column and each observation is one row.
This follows the concept of tidy data by Hadley Wickham  (23). Table  4  displays the final set of variables underlying RadaR's functionality. RadaR's appearance has been customized with using a CSS (Cascading Style Sheets) script ( https://github. com/ceefluz/radar/blob/master/www/radar_style. css ) that is loaded into the application upon its start. This script needs to be saved into a subdirectory of the directory of the three main files (global. R, server. R, ui. R) called "www". We recommend RStudio's project function to create a single project for RadaR and to store all information in this project directory. Users with experience in using CSS can fully alter RadaR's design by changing the underlying CSS script.
Finally, a dataset of the user-defined selection can be downloaded from the sidebar menu in a csv-file format (comma-separated value) for further analysis (e. g. retrieving a list of patient numbers of the selected patient group)Development processRadaR has been developed in close contact with infection management team at the University Medical Center Groningen, Groningen, the Netherlands, to meet the needs and requirements of this user group. Subsequently, all members of the European Society of Clinical Microbiology and Infectious Diseases Studygroup for Antimicrobial stewardshiP (ESGAP) were asked to evaluate and test the application through a running online example of RadaR and by filling out an online survey. The ESGAP comprises around 200 members coming from more than 30 countries worldwide.
Input variables for RadaR In global. R manually edit the path for the prepared data to be imported into RadaR. Only a single line of code needs to be edited. 4. Run the application in RStudio with the calling the function runApp() in the console or by clicking the green run app button. This will download and install the required R packages needed for the application if they have not been installed previously and create the final dataset for analysis. The RadaR interface will open in the RStudio viewer pane or in a new window of the standard browser of the user's operating system.
Acknowledgements We thank the  ESGAP executive committee  for supporting the evaluation of RadaR in the ESGAP study group and all its members for their valuable input, suggestions, and comments. Furthermore, we thank  Igor van der Weide ,  Jan Arends  and  Prashant Nannan Panday  for their great support in obtaining required data at our institution that built the basis for the development of RadaR. And, not to forget, the online R community that made this work possible and we are happy to contribute to the community by presenting our work on RadaR. RadaR was developed as part of a project funded by the  Marie Skłodowska-Curie Actions  (Grant Agreement number:  713660 -PRONKJEWAIL -H2020-MSCA-COFUND-2015 ).
We describe the development RadaR was developed in R, an open source programming language (16) , and RStudio and use of a software application (RadaR -Rapid analysis of diagnostics and antimicrobial ( https://www. rstudio. com ). To build this web-browser based application we used the Shiny patterns in R). It enables rapid and reproducible data analysis without extensive technical package for R (17). Shiny allows R users to build interactive web applications without expertise in a graphically appealing way while being adaptable to different settings. Analyses extensive knowledge in web design and its programming languages. They can be run and are based on datasets of individual patients. Therefore, also aggregated results can be hosted online for free ( https://www. shinyapps. io ), on local or cloud-based servers or on stripped down and additional patient features can be investigated.
With this application we personal computers. Other applications can rapidly and interactively display geographical locations of tuberculosis aim at supporting data-driven hospital insights and decision making for actors in the field of cases without the need of technical expertise improving the understanding of transmission antimicrobial stewardship. and detection (13). Data driven fields like genomics are front runners in developing new innovative software applications to handle large datasets, in close collaboration with bioinformatics (14). It is of importance to note that all of these examples have been created in an open source approach. The underlying source code can be shared, modified and freely distributed through open repositories like GitHub ( https://github. com ), taking open source software license obligations into account.
( https://github. com/ceefluz/radar ). The development will continue with more suggestions and improvements coming from its users and the R community. Further comments and suggestions for RadaR can be submitted at https://github. com/ceefluz/radar/issues. microbiological diagnostics performed at the start of antimicrobial treatments?", "What are the most frequent microorganisms found in different departments?", "Can we identify priority areas within a hospital where antimicrobial or microbiological diagnostic use has the largest room for improvement?".
Task-based parallelization Most HPC applications that support shared-memory parallelization rely on a fork-join strategy. Such a scheme uses a simple division of the work into independent operations and a global barrier that ensures all the work is done before the execution continues. The most common feature is the for-loop parallelization using pragmas as proposed by the OpenMP  (OpenMP Architecture Review Board, 1997)  standard. This model has been extended to a tasks-and-wait scheme, where operations come from different parts of the application but are still independent. The task model from OpenMP 3  (OpenMP Architecture Review Board, 2008; Ayguadé et al. , 2009)  and the task-based programming language Cilk  (Blumofe et al. , 1996)  (later extended in Cilk++  (Leiserson, 2009)  and Cilk Plus  (Intel, 2017) ) follow this idea.
, 2011) , is a RS based on the STF model that defined what later became the OpenMP tasks. It uses pragmas annotation to inform the RS about data access by the tasks. StarPU (Augonnet et al. , 2011)  is an RS that was first designed to manage heterogeneous architectures. It is a C library such that the user has to use low-level programming and function pointers. However, it is extremely flexible and it is capable of having distributed memory STF. XKaapi  (Gautier et al. , 2013)  is an RS that can be used with standard C++ or with specific annotation but which needs a given compiler. Legion (Bauer et al. , 2012)  is a data-centric programming language that allows for parallelization with a task-based approach. SuperGlue (Tillenius, 2015) is lightweight C++ task-based RS. It manages the dependencies between tasks using a data version pattern.
It is composed of two main layers. First, an extensions to the CLANG-LLVM compiler prepares the program by generating several versions of each target loop nest and several code snippets called code bones  (Martinez Caamaño et al. , 2017). During the execution, the RS orchestrates the execution of the different code versions by chunks. Our approach is different; because it is high level and not designed for a few instructions, it does not require special hardware instruction, and it is designed for applications that are already parallel at the top of a graph of tasks.
At execution During the execution, the RS has to decide if the speculation is enabled or not. It is convenient to do this when the first copy task of an STG becomes ready to be executed. In 1 The project is open-source and available at https://gitlab. inria. fr/bramas/spetabaru. fact, the decision process can then use information such as the current number of ready tasks in the scheduler. Then, the tasks have to be enabled or disabled accordingly. After any uncertain task has finished, the RS has to enable or disable tasks related to the STG. It has to iterate over the lists of the STG and, potentially, on its successor. The DAG remains unchanged, and so any RS that has a callback and activation/deactivation features can implement our mechanism.