dataesr/scientific-paragraphs-categorization huggingface dataset:
910 to 0. 911. For the default geocoding on the whole dataset (1360 SNEs), the recall is equal to 0. 871. Note that combining the disambiguation process and the default geocoding helps significantly improve (0. 911 vs. 0. 871) the disambiguation so that it is more accurate. We demonstrate that our disambiguation approach helps increase the accuracy of assigning relevant coordinates to the SNE that can be ambiguous in textual data. Fig. 6  illustrates the number of SNEs disambiguated per step in the process. In Fig. B. 7, we show the remaining SNEs to be disambiguated with their frequency, i. e. , the number of times they are extracted from the whole corpus. We observe that the top 3 SNEs are Africa, Asia, and Europe. In these cases, none of the integrated strategies helps disambiguate them.
Impact Addressing named entities related to the location or place name (or spatial named entities) is in demand among natural language processing tasks. The disambiguation (i. e. , giving the correct location, name & coordinates to a given SNE identified in the text) can be considered a main task goal or an intermediate task such as a prepossessing task. This tool can be utilized for many purposes: (1) textual data analysis, (2) spatial named entity extraction and geocoding, (3) enrichment of event monitoring systems based on textual data, or (4) spatiotemporal data analysis based on unstructured data. Practically, in epidemiological surveillance (human, plant, and animal) and in food safety  [25] , this tool can be integrated into such tools to improve Table  1  Evaluation of the disambiguation approach on the GeoVirus dataset.
Conclusion In this paper, we presented a spatial named entity toolkit, SNE-Toolkit, which integrates three main functionalities: (1) SNE extraction from textual documents based on spaCy, (2) SNE geocoding, which returns the coordinates of a given SNE based on the Geonames database. There are two types of geocoding: (i) default candidate geocoding, which returns one candidate that corresponds to the default result of Geonames, and (ii) multicandidate geocoding, which returns the candidates from the Geonames results. (3) Disambiguation of the ambiguous SNEs within a document. This last point is our main research contribution. Based on multiple techniques, the disambiguiser helps disambiguate ambiguous SNEs from the multicandidate geocoding results. The outputs of the disambiguation process are the ambiguous SNEs that are disambiguated (relevant candidate from multiple candidates) and the nonambiguous SNEs that are extracted from the document.
In the literature, one can find several algorithms to perform GSA based on the notion of Sobol's indices  (Tissot and Prieur, 2012; Tarantola et al. , 2006). The most popular ones are those developed by Saltelli et al. (Saltelli, 2002; Saltelli et al. , 2010). Today, they are available in classical Python libraries, together with other strategies, like the Fourier Amplitude Sensitivity Test (FAST)  (Cukier et al. , 1973). The main difference between Saltelli's algorithms and FAST method is related to the calculation of the Sobol's indices: the former relies on the standard analysis of variance (ANOVA), while the latter is based on a frequency analysis of the output. Saltelli's algorithms are more accurate than FAST method, but at the price of a higher computational effort. Consequently, in this work, the FAST algorithm is used as a compromise between accuracy, computational cost and reliability  (Vuillod et al.
Thus, the present work, which deals with open access GSA algorithms, uses another strategy to reduce the computational cost by acting directly on the numerical description of the model: the Model-Based Systems Engineering (MBSE) approach, through the Modelica language, complemented by the Functional Mock-up Interface (FMI) standard. The former allows a lighter physical 0D model and the latter a significant reduction in computational costs. The objective of this paper is to present the GSA -MBSE methodology on simple study cases to get useful information about the influence of the input variables (and on their interaction) on the output responses on the whole design space. Furthermore, the methodology can be extended to more complex systems, where Design of Experiments (DOE) cannot be conveniently applied.
Modelica and global sensitivity analysis co-simulation In the present work, the number of simulations required to obtain accurate results for the GSA respect the convergence criteria of the GSA method. Therefore, in order to minimise the global computational cost of the GSA, a modelling strategy based on the MBSE approach, via the Modelica language, is adopted. The 0D model is then built by linking different physical components. Each of these components behaves according to its own system of Differential Algebraic Equations (DAE). Finally, the software is able to build up the global DEA system before solving it. Once the Modelica model has been created and validated, a Functional Mock-up Unit (FMU) file is exported and run in co-simulation (CS) mode, defined by the FMI standard. One of the main advantages of this format, is the computational cost: a Modelica simulation of the studied cases requires about 5 s, while an FMU simulation requires about 10 -2 s.
Regarding the singular pressure loss expression, ξ = 0. 5 is the singular pressure loss coefficient in a regular pipe and ρ oil is the oil density. The last load is the added-mass force whose expression is: f a = aM 1 - ρ oil ρ steel e x , ( 8 ) where a is the external deceleration occurring in the interval t a and ρ steel the steel density composing the piston. Moreover, we assume that inside flow is equal to equivalent flow of the piston motion. Thus, it is possible to deduce that v int = A p A int v p with A int the internal section area. To model this system, the MBSE approach is used via the Modelica language. The final model is composed of four elementary blocks, corresponding to the functional scheme shown in Figure  2b  (a mass, a spring, a singular pressure loss and a support), subject to a fifth one used to apply the input force f a.
S i S H i S T i Reference In this paper, a GSA is used to study two configurations of a device reproducing the behaviour of an airbag: a classical piston-spring-mass device and another including a porous material. A MBSE approach, implemented via the Modelica language and exported in FMU format, is adopted as modelling strategy. The objective is to study and to understand the influence of different input variables on the device behaviour, and to point out the importance of this modelling approach when addressing new design solutions. The results show that, for the classical configuration, the centre hole radius is the variable with the strongest influence on the system output. Indeed, its total Sobol's index value is about 90 %, more than 8 times greater than the second influential variable, the piston length.
• 2P: 50% of wells produce more than 2P predictions (probable). • 3P: 10% of wells produce more than 3P predictions (possible). These rules are to be disclosed to security investors for publicly traded oil and gas companies and aim to provide investors with consistent information and associated value assessments. Many Machine Learning algorithms have shown their efficiency in estimating the median 2P but failed to estimate 1P and 3P. Thus, the objective of this study is to build a proper estimation of the quantiles p 0. 90 and p 0. 10 by applying the RPIE method described in section 4. Our dataset, field data, is derived from unconventional wells localized in the Utica shale oil reservoir, located in the north-east of the United States. It contains approximately n = 1850 wells and d = 12 variables, including localization, Cumulative Production over 12 months, completion design and exploitation conditions.
The raw dataset can be found at the Ohio Oil & Gas well locator of the Ohio Department of Natural Resources [?]. We standardized the data (X, y) and we divided into a 60% -20% -20% partition of three datasets : a training set and two validation sets. The response y (Cumulative Production over 12 months) is noisy due to the uncertainty of the reservoir parameters in the field. The nugget effect σ 2 ε is unknown but estimated to σ 2 ε = 0. 16 using the method of  [25]. Table  12  shows the performances of the GP models trained by MLE and MSE CV methods compared with two other statistical models : Random Forest and Gradient Boosting whose Prediction Intervals are built using the Bootstrap method. Here we consider the Prediction Intervals of level 1 -α = 80%: the lower bound is the 10% quantile (p 0.
Acknowledgements The author (1) would like to thank  Achraf Ourir ,  Zinyat Agharzayeva  ( TotalEnergies SE -La Défense, France ),  Daniel Busby  ( TotalEnergies SE, CSTJF -Pau, France ) and the  SINCLAIR Lab (IA Commun Lab -Saclay, France)  for useful discussions and helpful suggestions.
CPACS CPACS is a data definition for the air transportation system. It is therefore a driver for multi-disciplinary and multi-fidelity design in distributed environments  [14]. CPACS MDO Case Studies Implementation describes the characteristics of aircraft, rotorcraft, engines, climate impact, fleets and mission in a structured, hierarchical manner. Not only product but also process information is stored in CPACS, which is fundamental for our purpose. Every tool needs of a CPACS input and returns a CPACS output, which preserves all the previous information and adds the analysis results of its domain. The tools integration is necessary to execute the MDO studies presented in the previous chapter. In fact, the tools need to exchange information to run. For instance, the TF which is an output of the manufacturing tools is needed to OpenAD to perform the aircraft performance estimation.
We have put together a matrix of questions structured around four subjects, enabling us to understand the journals' financial situation and their view of a direct funding system: the journal's financial configuration, the publishing acts, the relationship with funders plus their ability to report certain information, and their opinion regarding a model of direct financial support. Most of the questions are closed and provide a quantifiable view of the financial realities confronting the Diamond journals. Some open questions were handled separately using qualitative software. After cleaning the dataset and deleting duplicates, we have gathered a total of 260 usable responses. As in the OA Diamond Journals survey, we received responses coming from a wide range of journals because 55 countries were represented, with the UK, Italy, and France dominating.
Providing support for publishing infrastructures The first model therefore concerns supporting infrastructures -whether they be software or distribution platforms. ID 12747276117 : « What we need funding for to keep going is the software and platform ». ID 12784421591 : « For overlay journals like ours, the fundings should go to the hosting platform to enable them to offer editorial services ». We would like to point out that tools of this type already exist, such as software that exhibits OJS functionality, or platforms such as the Amelica/Redalyc, OpenEdition, Episciences, or OLH, which integrate software. ID 12747678560 : « Funding should be directed towards infrastructure (central archiving and metadata collection, managed OJS instances, free service providers for copyediting etc. ) such as the épisciences platform that provide their services free to scholarly diamond OA journals ».
Finally, acts relating to distribution (acts 19 to 26) show an even more diversified breakdown. While rights management is dominated by the sub-editor for image rights (37%), both the editorsin-chief (who perform the licence management and rights/contracts acts respectively in 41% and 39% of cases) and the service providers dominate in the remuneration for acts relating to metadata (assigning metadata) and dissemination (between 28% and 37%). Funding publishing acts in an ideal world This study's aim is to explore possible means of funding for Diamond journals, following on from the recommendation of the first OA Diamond Journals survey carried out in 2020 within the OPERAS consortium. In this light, we would like to clarify the funding needs of Diamond journals. We are going one act further compared to the previous section.
The advantages of direct funding models for journals One general notion that emerges is that this funding would provide support for journals and would ensure their continuity, whether carried out by funding software such as OJS, a "capacity centre" to access services, or by subcontracting certain acts such as copy editing, proofreading, etc. This notion of complementary support is consistent with what many of the journals say, stating that they are not in need of a great deal of money to operate, even if there is a wide difference in the sums of money mentioned (between 750 and 20,000 dollars a year). ID 12796318042 : « Find me $500-$750 per annum to distribute as I see fit to reviewers who are willing to turn substantive revision reports around in four weeks ». ID 12749362499 : « Annual funding of US$20,000 ».
Qualitative processing There were several open questions within the questionnaire which required free answers and which cannot be compiled and tallied. We have differentiated between two kinds of open questions which we have processed in different ways. The first kind involves questions with a large number of answers (at least 200). This is the case for the last two questions (5. 1 and 5. 2) which probe the respondents' opinion on ways of directly funding Diamond journals, as well as for the free questions associated with publishing acts (3. 4. 1, 3. 4. 2, 3. 4. 3). In order process this data, we exported the responses to a text file and used qualitative data analysis software called ATLAS. ti. ATLAS. ti makes it possible to perform inductive coding while reading, and ultimately organise the material into several different categories and subcategories.
" Learned Publishing 33. 1 (2020): 14-27. 8 Grossmann, Alexander, and Björn Brembs. "Current market rates for scholarly publishing services. " F1000Research 10 (2021). See also Antoine Blanchard, Diane Thierry, Maurits van der Graaf. Retrospective and prospective study of the evolution of APC costs and electronic subscriptions for French institutions. Comité pour la science ouverte. 2022. doi. org/10. 52949/26 9 Bosman, Jeroen, Jan Erik Frantsvåg, Bianca Kramer, Pierre-Carl Langlais, and Vanessa Proudman. "The OA diamond journals study. Part 1: Findings. " (2021). Becerril, Arianna, Lars Bjørnshauge, Jeroen Bosman, Jan Erik Frantsvåg, Bianca Kramer, Pierre-Carl Langlais, Pierre Mounier, Vanessa Proudman, Claire Redhead, and Didier Torny. "The OA Diamond Journals Study. Part 2: Recommendations.
However, in medium CPI countries, no literature exists on dishonesty. Hence, we explore this new issue on an exploratory basis and do not propose a specific hypothesis. incentives. We collected data from 6704 participants in 33 countries across six continents. We deleted one country (Italy) because researchers removed the dishonesty measure and retained a sample of 6500 managers. Our 32/203 ratio (32-country/6500/32-person/country) exceeded the 30/30 rule in cross-level studies, achieving higher levels of power (1β)  (Aguinis et al. , 2013). We carefully employed a six-page survey and collected demographic variables (age, education), job tenure (in years), gender (%male), marital status (married/not-married), income (in USD$), industry (manufacturing/service), type of organization (public/private), domicile (urban/rural), our major variables, and many filler items (see the section on measures below).
Please note that we did not mention CPI in our survey. Therefore, most managers were unaware of their country's CPI score and the nature of our 32-country cross-cultural study. We applied a complex cross-level model and group-mean centered measures (within each country). Managers could not imagine our theoretical expectations and completed the survey accordingly. Table  1  illustrates the results of significant variables for the whole sample (N = 6500). Table  2  shows the demographic variables, CPI, and response rate for all 32 countries across three CPI levels. There were missing variables in some countries. Our data were reasonable. There were no reasons to believe that our data were atypical. We controlled for age, education, gender, and income in our data analysis.
In the last few years, the natural language processing community witnessed advances in neural representations of free-form text with transformer-based language models (LMs). Given the importance of knowledge available in relational tables, recent research efforts extend LMs by developing neural representations for tabular data. In this tutorial 1 , we present these proposals with three main goals. First, we aim at introducing the potentials and limitations of current models to a database audience. Second, we want the attendees to see the benefit of such line of work in a large variety of data applications. Third, we would like to empower the audience with a new set of tools and to inspire them to tackle some of the important directions for neural table representations, including model and system design, evaluation, application and deployment.
INTRODUCTION Several efforts are researching how to represent tabular data with neural models for natural language processing (NLP) and database (DB) applications. These models enable effective solutions that go beyond the limits of traditional declarative specifications built around first order logic and SQL. Examples include answering queries expressed in natural language  [19, 23, 36] , performing natural language inference such as fact-checking  [9, 22, 40] , semantic parsing  [10, 41, 42] , retrieving relevant tables  [24, 29, 38] , understanding table metadata  [11, 14, 34] , data integration  [8, 26] , data to text generation  [37]  and data imputation  [11, 21]. Since these applications involve both structured data and natural language, they are built on new data representations and architectures that go beyond the traditional DB approaches.
1). We describe and contrast the most recent works according to five dimensions: datasets, data pre-processing, extensions to the transformer architecture, output characteristics, and usage (Sections 2. 2 and 2. 3). Finally, we discuss limitations of existing works and open research problems tailored for a DB audience (Section 2. 4). In the second part, we conduct a hands-on session where the audience will be guided in the execution of Python notebooks to explore both vanilla language models and those specialized for tabular data (Section 3). We start with investigating the general data pipeline from input data formatting to processed data (Sections 3. 1 and 3. 2). We proceed with the actual pretraining procedure (Section 3. 3), which is followed by an exercise on fine-tuning and evaluating pretrained models for the downstream task of data imputation (Section 3.
Characterization of the Methods We then detail the dimensions to describe and categorize the different proposals. We focus our tutorial on the extensions to the original transformer architecture for developing representations of relational tables. While several solutions have contributed to the transformer original architecture to better represent tabular data, the alternative innovations to model and consume the encoded data are scattered over the process. We aim at bringing clarity in this space by providing an overview with a set of dimensions that let us highlight the main ideas and trends spanning the different proposals. We use five dimensions summarized below. More details on the proposed dimensions can be found in our survey paper  [3]. (1) Training Datasets: comparative summary of characteristics of datasets used for learning the table data representations along with some representative samples.
Four datasets are typically exclusively used for pretraining, e. g. , WikiTables  [6] , WDC Web Table Corpus  [25]. The majority of the datasets include extra manual annotations to enable their usage for fine-tuning or evaluation. Examples of such datasets include TabFact  [9] , WikiSQL  [44] , FEVEROUS  [1]  and SPIDER  [43]. (2) Input Processing: textual and tabular pre-processing steps of the training data prior to feeding it to the neural network. • Data Retrieval and Filtering: to meet the limits of transformer based architectures or to reduce noisy representations. Take-away: the audience can grasp the characteristics of the different existing solutions and classify upcoming ones along the same dimensions for easier comparison.
Latest Works in the Field After detailing the dimensions in Section 2. 2, we analyze a sample of the latest research efforts in the field based on those dimensions. We briefly discuss how 20 surveyed works  [9, 11, 14-16, 18, 19, 21, 24, 27, 29, 34-36, 38-42]  address the five dimensions following the framework in Fig. 1. Most works opt for pretraining ((1) in Fig. 1 ) followed by finetuning and consuming the representations to tackle downstream tasks ((2) in Fig. 1 ). A few exceptions either fine-tune existing LMs or use them as part of their features set  [14, 24, 34]. For developing tabular representations, most of the works aim at supporting significantly large datasets, up to millions of tuples, by combining multiple datasets for more accurate generalized representations. The steps in the Input Processing part (first module for both (1) and (2) in Fig.
e. , cell, row, column or table representations. For instance, Herzig et al. generate cell representations for the QA task, Wang et al. use table representations to facilitate table retrieval (TR) task, and Liu et al. utilize token embeddings for semantic parsing. These representations are then either fine-tuned using labeled downstream tasks datasets  [29]  or utilized as features of training data points  [14]. Take-away: the audience can match a target application to the most effective solution. They also have a good understanding of the main technical challenges from a data perspective.
OUTLINE OF THE HANDS-ON SESSION In the second part, we focus on providing hands-on experience with transformer models. Attendees will better understand transformer models for tabular data on a conceptual level through practical exercises, while also becoming familiar with their implementation details and impact in downstream applications. We will demonstrate the general pipelines from input data and data processing to training of various models and fine-tuning for downstream tasks. The structure and content of the hands-on session align with the structure of the first part of the tutorial (Section 2). The hands-on session will be facilitated by notebooks in Google Colaboratory  4. Throughout the session, we will investigate the synergies and differences between a vanilla language model (BERT  [12] ), and two specialized tabular models (TURL  [11] , TAPEX  [27]  and TaBERT  [41] ).
Off-the-shelf Model Inputs and Outputs We start with a few example input tables and will use the pretrained models off-the-shelf to obtain numeric vector representations of them. First, we load a given table from a CSV file and format the table such that the respective pretrained model can process it. For BERT, we programmatically linearize the raw table header and values into sequences compatible with BERT, to illustrate basic design choices behind linearization. We proceed with loading the model and using it to encode the formatted table, such that we obtain a vector representation of the table. After each step, we will inspect the intermediate object that is obtained. The code snippet in Figure  2a  illustrates this exercise on a high level. Finally, we compare the input formats and output encodings across the three different models (BERT, TAPEX and TaBERT).