dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to thank Sebastian Paeckel, Salvatore Manmana, Michel Ferrero, Tarun Grover, Thomas FIG. 15. Time series of energy and magnetic structure factor measurements from 5 different random intial states. Data shown for a 32 × 4 cylinder with parameters T /t = 0. 300, U/t = 10. We used time evolution parameters Dmax = 3000 and ε = 10 -6 and S x collapses. After initial thermalization no autocorrelation effects are apparent. (a) energy measurements at half-filling, p = 0. We observe several initial plateaux in the energy before thermalization. These plateaux are metastable states that correspond to antiferromagnetic domains. (b) energy measurements at hole-doping p = 1/16. (c,d) measurements of the magnetic structure factor S(k) evaluated at ordering vector k = (π, π) at p = 0 and p = 1/16.
Geological model A 3D geological model was built based on a range of field investigations including topographic surveying, geological drilling, electrical resistivity tomography, manual augering and probing, and analysis of existing outcrops. This model is detailed in  Duranel (2015)  and is summarised here. Three different datasets were aggregated to produce a digital elevation model (DEM). Within the mire, surface elevation was measured using a differential geo-positioning system (DGPS) at approximately 5 m resolution and with a vertical accuracy of around 10 cm. On mineral soils in the southernmost part of the catchment, topographic data were extracted from the IGN BD Topo, a nationwide DEM with a resolution of 25 m and a root mean square error (RMSE) of 4. 8 m against DGPS ground-truthing points within the site.
The inversion was performed using the Res2Dinv software  (Loke, 2013; Loke and Barker, 1996)  and a L1 norm optimisation, leading to RMSEs of 5. 3 % for Profile A and 2. 2 % for Profile B. Both profiles are characterised by strong contrasts in resistivity values. The most striking feature is the presence on each side of the profiles of highly resistive material overlaying more conductive material. Highly resistive material is only recorded beneath mineral soils on hilltops and slopes and does not occur beneath the mire. The transition between these layers is very sharp, its altitude is largest beneath hilltops, and its depth greatest beneath steep slopes. Out of the four ERT profiles, the only location where conductive material was recorded above resistive material is on one hilltop (visible on the lefthand side of Profile A).
Hydrological model development The MIKE SHE/MIKE 11 modelling system  (Graham and Butts, 2005; Refsgaard et al. , 2010)  was used to model the mire and its catchment. MIKE SHE/MIKE 11 is a deterministic, fully distributed modelling system able to model actual evapotranspiration, unsaturated zone flow and storage, saturated zone flow and levels, overland flow and channel flow processes in an integrated manner. It has previously been successfully applied in a wide range of wetland environments (e. g. Al-Khudhairy et al. , 1999; Bourgault et al. , 2014; Clilverd et al. , 2016; Hammersmark et al. , 2010; House et al. , 2016a; Thompson, 2004; Thompson et al. , 2004 Thompson et al. , , 2017)). Originally fully physics-based, it has evolved to include a suite of modules that also allow for the use of simplified physical equations or even conceptual models based on distributed, semi-distributed or lumped approaches  (Refsgaard et al.
Water balance analysis A water balance was computed using the MIKE SHE water balance tool for two areas upstream of the main mire outlet corresponding to mineral and peat soils, respectively. These water balance domains cover 36. 9 and 125. 0 ha, respectively (Fig. 4 ). The water balance was not calculated over the entire model domain for two reasons: firstly to reduce potential errors caused by uncertainty in boundary conditions at the north-western end of the model domain as well as the relatively sparse hydrogeological and hydrological data downstream of Pont de Pierre; and secondly to constrain the focus of the analysis on the main designated mire area upstream of Pont de Pierre. The stream flowing through the mire was not included in the water balance of the mire (i. e. only fluxes between the stream and peat soils or the surface of the mire were considered, with flow in and out of the stream where it enters and exits the mire excluded).
Model results demonstrate that the Dauges mire is a groundwater-dependent system closely connected to the granitic aquifer. This has important implications for the management and conservation of this and other similar ecosystems. However, whilst the Dauges mire is representative of acidic valley mires commonly found in many Variscan uplands, further work is required to upscale the conclusions of this study and to investigate how groundwater upwelling rates influence the ecology and biogeochemistry of such systems. Financial support. This research has been supported by a UK Natural Environmental Research Council doctoral studentship to Arnaud Duranel (grant no. NE/H525203/1) and a grant to UMR 5600 CNRS EVS from the European Fund for Regional Development (FEDER Loire) and the Agence de l'Eau Loire-Bretagne, obtained as part of the Plan Loire Grandeur Nature managed by the Établisse-ment Public Loire (http://www.
Acknowledgements. The  Conservatoire des Espaces Naturels de Nouvelle-Aquitaine  and the  Réserve Naturelle Nationale de la Tourbière des Dauges  dedicated staff time to the collection of data used to develop the hydrological model. Météo-France  provided part of the meteorological data. The  Institut National de l'Information Géographique et Forestière, Areva (now Orano) , the  Conservatoire des Espaces Naturels de Nouvelle-Aquitaine  and the  Réserve Naturelle Nationale de la Tourbière des Dauges  provided geological, topographic and cartographic data.
Data availability. With the exception of secondary data derived under licence from third-party datasets, data used in this study are available upon reasonable request from the first author (arnaud. duranel@univ-st-etienne. fr). The meteorological data can be obtained from Météo-France. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-25-291-2021-supplement.
IV. IMPLEMENTATION WITH HDBSCAN* The HDBSCAN algorithm is a clustering algorithm for exploratory data analysis that extends the DBSCAN algorithm  [30]. It can operate correctly up to 100-dimensional data. The notion of outlier is particularly interesting when considering IOHMM learning. In this paper, we consider two types of outliers: (i) ← { y ∈ ( y (k) ) K k=1 |∃i ∈ [0, K -1], f ( y) = i} 2 C ← {c (i) |i ∈ [0, K -1]}// set of clusters with their associated observations 3 Q ← {x ∈ N | 0 ≤ x ≤ |C| -1}// 1) Intrinsic outliers depend on the degree of conservatism desired in model learning, governed by the min_samples parameter. The larger the value of min_samples, the more conservative the clustering, i. e. , clusters are restricted to more dense regions with, as a result, a higher number of outliers 5 (Fig.
A. APPLICATION TO A SYNTHETIC DATASET The first experimentation is carried out on the synthetic dataset generated from the model depicted in Fig. 7. It contains two inputs and two outputs (denoted by <name>->[mean,stdev]). Given this model, the generated dataset is applied to the Algorithm 1 implemented with the incremental extension of HDBSCAN* (i. e. , the FISHDBC algorithm). To this end, the dataset is split into several parts incrementally fed into the algorithm with min_samples parameter set to 80. Learning results are depicted in Fig. 8. The experiments carried out here reinforce the notion of distinguishability discussed earlier. A certain number of observations are needed in order to densify regions (i. e. , reinforce the knowledge) and obtain the expected model. This is illustrated by the results of the incremental learning of the topology and the parameters λ for nb_observations ranging from 250 to 1500, depicted in Fig.
B. APPLICATION TO A REAL-WORLD USE-CASE The second experimentation is carried out on a real-world dataset gathered in a smart-home equipped with 54 Internet of Things (IoT) devices offering up to 228 sensors/actuators (Fig. 10 ). Several IoT protocols are used to communicate with all the devices (WiFi, ZWave, etc. ). Computational resources (Arduino Uno/Nano, Raspberry Pi) are made available at the edge of the IoT infrastructure where software components are deployed through Docker containers. These software components implement the control logic for some actuators (e. g. , roller shutters, light bulbs) or extract features from sensor/probe observations (e. g. , sound features from the microphone signal, operating status of the TV, etc. ). Observations are recorded into a database (InfluxDB) on a regular basis.
In the following use-case, we consider a light bulb and two roller shutters whose control logic is implemented by a software component deployed on a Raspberry PI. Besides these actuators, we also consider indoor and outdoor luminosity sensors. The IoT infrastructure is depicted in Fig. 11  where, for the sake of visibility, only the devices used for the experiment are described. Based on this setup, the objective is to learn a model of the behaviour of the control logic of the light bulb and the roller shutters. This controller is primarily designed to maintain an acceptable level of indoor luminosity while taking into account the outdoor luminosity to optimize and reduce power consumption. The dataset thus used for the experiment is depicted in Fig. 12. The contextual inputs u of the IOHMM to be learned are defined by the roller shutter state (RollerShutter: 0 UP, 100 DOWN) and the light bulb status (WhiteLight: 0 OFF, 100 ON).
The outputs y are defined by the indoor luminosity level (Luminance) and the difference between the outdoor and the indoor luminosity levels (Diff). The dataset depicted in Fig. 12  is applied to the Algorithm 1 implemented with HDBSCAN* clustering algorithm with min_samples parameter set to 157. The IOHMM model learned from the dataset is depicted in Fig. 13  where distributions are fitted to Gaussian distributions, characterized by the mean and the standard deviation of each feature (i. e. ,<feature>->[<mean>,<stdev>]). The IOHMM model learned exhibits some unexpected behaviours. For instance, the state#2 (blue) corresponds to the case where the roller shutter is DOWN while the white light is turned ON. However, the outdoor luminosity being high (as denoted by the outdoor/indoor luminosity difference), it should be leveraged to enlighten the room.
Back to the dataset depicted in Fig. 12 , the HDB-SCAN* clustering results are assessed on each indicator for  different min_samples values. The results are provided in Fig. 14  where the figure at the bottom shows the number of clusters (i. e. , number of states in the model) obtained for each value of the min_samples parameter. Davies-Bouldin and Silhouette indexes obtain best score with min_samples = 157 and an IOHMM model with 6 states (corresponding to the IOHMM model depicted in Fig. 13 ). The Calinski-Harabasz index gets the best score for min_samples = 89 and an IOHMM model with 10 states. The IOHMM learning algorithm proposed in this paper is generic; one can use any continuous multivariate space clustering algorithm (incremental or not). For instance, results obtained with the GMM clustering algorithm (where the number of clusters to be identified is fixed to 6) are depicted in Fig.
COntrol, Data Acquisition and Communication (CODAC) real-time software codes are key elements for the operation of a fusion device both for the machine protection and for the optimization of the experiments. In 2013, following the WEST (W -for tungsten-Environment Steady-state Tokamak) upgrade of Tore-Supra, the whole legacy acquisition system has been re-factored. The WEST CODAC framework which inherited the older implementations used over 20 years on TORE Supra has been reorganized into primary software components corresponding to basic technical features, such as Database Access, Inter-process Communications, Real-time Device Management, Timing System Network, Finite-State Machine, and Shared Memory Network. In addition, a new modern middleware component named Westbox has been developed, to simplify developments for native diagnostic upgrades and cross-platform data acquisition hardware and software from guest collaborators.
Introduction The aim of the WEST (W -for Tungsten-Environment in Steady state Tokamak) project is to minimize technology and operational risks of a full tungsten actively cooled Divertor on ITER. It was started in 2013 and consists in transforming the Tore Supra tokamak into an X point Divertor device, while taking advantage of its long discharge capability. The WEST software architecture has been defined  [1]  based on the 20 years development of TORE Supra and re-factored as small components named "IRFM Framework". This framework includes features such as database access to read and write files, the timing system network management to synchronize controllers during experiments and the shared memory network based on Dolphins hardware to exchange plasma control data in real time between controllers.
Technical overview On WEST, the acquisition software infrastructure is distributed as a CMake based framework including some small low level C legacy libraries, a core C++ library, and a client implementation customized for each data acquisitions system. CMake is a cross-platform free and opensource software tool for managing the build process of software using a compilerindependent method. It supports directory hierarchies and applications that depend on multiple libraries. CMake can handle in-place and out-of-place builds, allows several builds from the same source tree, and cross-compilation. CMake can locate files, dependencies, and properties related to software version management. These information are stored in a cache to speed-up configuration stage. Finally, CMake can deploy files to targets.
Porting the compilation rules from older operating system dependent Makefiles to CMake was a big step in modernizing and simplifying the inter-dependencies between all WEST CODAC software components and the rest of the WEST infrastructures. It also allowed introducing a continuous integration chain  [4]  in order to check source code quality before deployment over the acquisition units in production. The libwestbox is based on Qt: open-source toolkit for creating complex cross-platform applications  [11]. Applications designed with Qt can run on various platforms such as Linux and Windows, with little or no change in the underlying code-base while still being a native application with native capabilities and speed.
Libwestbox: a complete toolbox The Libwestbox is the new C++ component from the IRFM Framework  [3] [4] : It includes all objects required to perform data acquisitions at IRFM. The architecture of the Libwestbox (fig. 1 ) is composed of the following main modules: -Acquisitions Manager: used to control the data acquisition hardware with multithread and multi-core support. The data can be streamed in real time through the IRFM network if necessary to the dedicated part of the "ARCAD" database, the legacy measurements data storage used at IRFM. -Chrono Manager: used to catch events to synchronize data acquisitions from the real time network. This object uses the libtschrono component which is an interface to the hardware connected to the IRFM real time network. -Database Interface: used to read / write parameters and files from the database.
This object uses the libtsdb component from the framework which is an interface to the legacy IRFM "ARCAD" database. -Events Listener: used to communicate with the top level acquisitions manager "Supervision" over the local network. This object uses the libtsmom component from the framework which builds with the Message Oriented Middle-ware (MOM) used at IRFM (as RTWorks™ -a legacy and proprietary communication software component). -Modular Finite State Machine: used to intercept and respond to the top level Supervision events during experiments. This is the core component of a data acquisition unit installed at IRFM. -Log Manager: used to archive and rotate the data acquisition unit events into files. This component allows broadcasting to a web interface, or to a remote service located on the network.
-Data Streamer: used to encapsulate and format real time data to network frames to stream measurements on the database. -Websocket Interface: used to simplify communication between the Westbox State Machine and delegate measurement controllers (aka National Instruments ™ devices). -MOM definitions: declare all common values and structures used by the Message Oriented Middle-ware, aka the topics, the data container, and the communication grammar. -Widgets Collection: a set of common graphical software components used to construct users friendly GUI related to the real time acquisitions, aka curves, statistics, graphs, etc. -WestBox Client: the last framework component is the Westbox Controller "Client" application which uses all of the previous software components on the target acquisition unit.
Managing the Data Acquisitions The controller's purpose is to store the acquired data to the database for post processing and analysis by physicists. Two options are implemented: -Streaming: used to record long acquisitions for slow measurements, such as temperature, pressure, etc. The data is stored and streamed in frames which are received and decoded on the fly on a delegate server in charge of database storage. Data is then quickly available to physicists at the end of shot. -File storage: used to manage fast acquisitions which the necessary bandwidth and the amount of data can be huge. To save network bandwidth for Plasma control, file uploading is delayed until after experiment. Post processing can be done by the controller for reduction data. A dedicated WEST format is preferred to store files in legacy IRFM "ARCAD" database, but in the future new formats will be proposed such as MDSPlus  [9]  and IMAS  [8].
A Versatile Controller One goal of the Westbox framework is to be able to host new guest devices not developed originally for the WEST project. A typical example is to integrate an extra Labview™ controller, without rewriting too much of the guest implementation. For this purpose, the Westbox controller can be configured in a "Gateway Mode". In this mode, the finite state machine transitions are forwarded to the Delegate Controller using for instance Websockets technology (fig. 8 ). Websockets are a standardized protocol that allow opening two-way interactive asynchronous communication sessions between a user application and a server. With this protocol, you can send messages to a server and receive event-driven responses without having to poll the server for a reply. Websockets are also used to open a communication channel between the delegate controller and the WEST database server: the delegate controller will be able to load settings and to store data files directly.
Outlook and conclusion A versatile Westbox Controller was design during 4 years and has been successfully used with different guest devices as part of the distributed data acquisition infrastructure of WEST since 2015. It's now routinely used to achieve measurements (fig. 12 ). Ongoing developing work includes new guest delegate controllers based on Python, Matlab, and EPICS  [10]. MQtt MOM backend will be also integrated within the finite states machine. Also, the database will be expanded to support the MDSPlus file format and the ITER compatible IMAS shared storage.
Labview controller for the crystal position adjustment. A rotating table is controlled at preparation stage to place the right crystal in front of the detector. When this operation is completed, the crystal position is archived to the database. The first spectra measured with DXICS were obtained in December 2018, during WEST C3b campaign. All crystals were properly aligned and a low but sufficient signal was obtained (fig. 11). DXICS uses two remote controllers (fig. 10): -Dectris Pilatus controller for data acquisition which provides a network server that can be driven during the transition stages, by the Westbox Controller through standard sockets with a proprietary protocol. The Westbox controller passes a large amount of settings at preparation stage to the Pilatus. Finally, the Pilatus generates more than one hundred large TIFF files which are uploaded after the Plasma discharges to the database.
In our previous work  [27] , we proposed a combination of a multilingual end-to-end entity linking method with several techniques to minimize the impact of issues frequently found in historical data. Our EL approach made use of entity embeddings, built from Wikipedia in multiple languages, along with a neural attention mechanism that analyzes context words and candidate entity embeddings to disambiguate mentions in historical documents. To adjust to historical documents, we developed several modules to handle multilingualism and errors stemming from the output of OCR systems. In this paper, we present MELHISSA, a Multilingual Entity Linking architecture for HIstorical preSS Articles, which extends our previous work on EL  [27]. Specifically, we present an EL analysis on two recent historical datasets: CLEF HIPE 2020  [13]  and NewsEye  [28]  that are composed of documents in English, Finnish, French, German, and Swedish.
This deep analysis enabled us to improve our approach and achieve better results for both datasets and all languages. This paper is organized as follows. We present an overview of EL approaches and a survey on historical data for the EL task in Sect. 2, before describing our multilingual approach in Sect. 3. Next, the CLEF HIPE 2020 and NewsEye datasets are described in Sect. 4. Then, the experimental setup is introduced in Sect. 5, while the results are presented in Sect. 6. We discuss the results in Sect. 7. Finally, we provide conclusions and final comments in Sect. 8. (a) 1857 German newspaper  [59]. (b) 1890 American newspaper  [60]. (c) 1936 French newspaper  [61]. (d) Illegible words, such as Berlin  [61]. (e) Old spelling, Jeudy instead of Jeudi (Thrusday)  [62].
For instance, Onoe and Durrett  [30]  proposed a disambiguation system to overcome the risk of EL methods of overfitting to the domain (the genre of text or the particular distribution of entities), and, in consequence, to generalize effectively. The model does not rely on labelled entity linking data with a specific entity distribution. The authors derive a large inventory of types from Wikipedia categories and use hyperlinked men-tions in Wikipedia to distantly label data and train an entity typing model. With this domain-independent setting, their approach achieves strong results on the CoNLL dataset  [34]. While most disambiguation systems employ entity representations embeddings bootstrapped from word embeddings to assess topic-level context compatibility, they also tend to neglect the context of the mention.
g. places that changed name over time), they proposed a method based on honorifics, generic geographic labels, and linguistic environments to recognize entities, while they made use of gazetteers, biographical information, and general linguistic knowledge to disambiguate these entities. Other works  [23, 24]  focused on author names in French literary criticism texts and scientific essays from the 19th and early 20th centuries. They proposed a graph-based method that leverages knowledge from different linked data sources to generate the list of candidates for each author mention. It then crawls data from other linked datasets using equivalence links and fuses graphs of homologous individuals into a non-redundant graph in order to select the best candidate. Dedicated end-to-end EL systems for historical documents have also been developed  [22, 25, 26].