dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  Igor Mazin ,  Matthew Watson , and  Fabio Orlandi  for fruitful discussions and helpful feedback. This work was supported by  NSF  through the  Princeton Center for Complex Materials , a  Materials Research Science and Engineering Center   DMR-1420541 , by  Princeton University  through the  Princeton Catalysis Initiative , and by the  Gordon and Betty Moore Foundation  through Grant  GBMF9064  to L. M. S. F. J. acknowledges funding from the  Spanish MCI/AEI  through grant No. PGC2018-101988-B-C21. M. G. V. and F. J. acknowledge funding from the  Basque Government  through grant PIBA  2019-81. M. G. V. thanks support from  DFG   INCIEN2019-000356  from  Gipuzkoako Foru Aldundia. N. B. M. S. was supported by  Microsoft. The work of S. S. was supported by the  Swiss National Science Foundation  under Grant No.
The WRF and CHIMERE Models The regional models WRF 3. 7. 1  [22]  and CHIMERE 2020r1  [23]  are used. This version of CHIMERE is designed to run in offline or online modes, corresponding to the simulation of the direct and indirect effects of aerosols on radiation and cloud properties. How these effects are taken into account is described in  [24]  (for the direct effects) and in  [25]  (for the indirect effects). The model configuration is exactly the same as in  [26] : it includes emissions from anthropogenic, biogenic, sea salt, biomass burning, and mineral dust sources. The chemical evolution of gaseous species is calculated using the MELCHIOR2 scheme  [27]. The photolysis rates and Aerosol Optical Depth (AOD) are calculated using the FastJX radiation module (version 7. 0b)  [28]. The aerosols are modeled using the scheme developed by  [29].
In practice, except for very coarse resolutions, this factor evolves around 0. 97 and has in practice a low impact on the results, compared to the uncertainties of all others parameters. These uncertainties are generally compensated by adjustment factors to bring the model closer to the observations, as described in  [15]. This kind of CTH scheme was extensively tested (e. g. , in  [35] ). For example, the authors of  [13]  used an additional scaling factor of α=0. 05 to fit the number of flash after comparison to global yearly satellite data. For each model cell, the relative percentage of sea x sea is estimated using the land-sea mask from the land use database. Then, the flash frequency is estimated as F = c pr × (x sea × F m + (1 -x sea ) × F c ) (3) with F the final number of flash, divided by the surface of the cell to have number per km 2 , then in (# mn -1 km 2 ).
They are also averaged over the two months of July and August 2013. The TRMM Lightning Imaging Sensor (LIS) dataset was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth tropical and subtropical regions. These data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency  [42] [43] [44]. Model outputs and satellite data are not directly comparable as the model displays all calculated flashes while the satellite displays only events observed by the satellite when passing over a specific region. Having no information to extract the model data corresponding to the satellite leg, the comparison may be done only to see that modeled flashes are well distributed.
Statistical Scores All previous results are summarized as statistical scores in Table  2. These scores are defined in Appendix A. For AOD, we used data from 32 AERONET stations located in Africa and Western Europe. For pollutants surface concentrations, we used data from 35 stations located in Western Europe. The complete list of the stations used is presented in  [23]. R s is the spatial correlation, R t is the temporal correlation, RMSE is the Root Mean Square Error, and the bias is the difference (LiNOx-NoLiNOx). Details on these calculations are presented in Appendix A. Results are presented for ozone, NO 2 and PM 10 surface concentrations, and AOD. Results show good spatial correlation for the four variables. For the temporal correlations, based on daily mean values, the correlation is low for NO 2 (0.
Vibration analysis is an important component of industrial equipment health monitoring. Aircraft engines in particular are complex rotating machines where vibrations, mainly caused by unbalance, misalignment, or damaged bearings, put engine parts under dynamic structural stress. Thus, monitoring the vibratory behavior of engines is essential to detect anomalies and trends, avoid faults and improve availability. Intrinsic properties of parts can be described by the evolution of vibration as a function of rotation speed, called a vibration signature. This work presents a methodology for large-scale vibration monitoring of operating civil aircraft engines, based on unsupervised learning algorithms and a flight recorder database. Firstly, we present a pipeline for massive extraction of vibration signatures from raw flight data, consisting in time-domain medium-frequency sensor measurements.
Due to the high frequency of the measurements (51 kHz), the vibration data are represented as spectrograms. Traditionally, experts perform a visual analysis of the spectrograms to detect anomalous patterns. The goal is to automate this process using algorithms and numerical methods. In  (Lacaille, 2013) , spectrogram patches are queried against a database of reference patterns, using dimensionality reduction through non-negative matrix factorization (NMF). (Abdel-Sayed et al. , 2015)  propose an automatic anomaly detection procedure also based on NMF. This line of work differs vastly from our contribution, firstly because we are interested in a fleet of operating engines, and not a test bench. The nature of our data is also different, as we have medium-frequency time-domain signals, already aggregated by the flight recorder, but measured during entire flights.
Unsupervised learning for engine data analysis As more and more data are collected on modern aircraft, datadriven approaches and machine learning have become useful tools for condition monitoring. Supervised learning allows to build predictive models when target values or labels are available. Unsupervised learning, on the other hand, can be used for data exploration, anomaly detection, monitoring, etc. We have seen previously that dimensionality reduction allows to compress and extract information from high-dimensional data  (Abdel-Sayed et al. , 2015). Another major tool is clustering, also known as unsupervised classification. Clustering is a family of unsupervised learning techniques that try to discover groups of similar elements in a dataset, providing information on the structure of the underlying data distribution.
DATA DESCRIPTION This section describes the acquisition, ingestion and storage process of the sensor data used in this work. Continuous Engine Operational Data (CEOD) designates the data recorded by modern civil aircraft. It consists in a set of parameters and sensors that are recorded during entire flights, from engine start to landing. Due to the large number of parameters and their possibly high frequency (up to 66 Hz), the volume of such data is very important. Finally, the studied vibration signatures are presented.
GENERIC BIG DATA PROCESSING PIPELINE For the large-scale computation of vibration signatures on a fleet of civil aircraft engines, we use the generic big data processing pipeline introduced in  (Forest et al. , 2018). This pipeline has been designed to analyze operational flight data on a Hadoop cluster and is based on the Apache Spark distributed computing engine (Apache Spark, 2014). It allows to deploy custom functions containing the engineers' business logic without knowledge of distributed programming, and is composed of several modules presented in the next paragraph.
Description The first step in the pipeline is basic preprocessing and selection queries against the flight database. The second step is generic feature extraction, using predefined or user-provided functions to compute flight features. This is where domain knowledge is incorporated. Signature computation happens at this step. Finally, learning algorithms are trained to obtain models, and results are visualized on a visualization application to extract insights. Each step is configured using a flexible configuration file with JSON syntax. All the processing is based on the Spark framework.
Vibration signature computation Signatures are computed by a custom function taking the parameters of a single flight as input, and outputs the resulting signature. The code is written in Python with standard numerical and data analysis libraries. This function has five parameters: the names of both input signals (x-and y-axis), the period and range of the x-axis (e. g. the regime range), and the type of operation used to aggregate points in each bin of the x-axis (e. g. average, max, quantiles, etc. ). Parameters are set in the configuration file, allowing to extract various signatures using the same generic code. The feature extraction module applies this function on flights in parallel across the cluster, as illustrated in Figure  7. In this work, we use a period of 5% regime bins in the [25%, 100%] range, thus each signature can be viewed as a 15dimensional vector, or a one-dimensional curve of length 15.
The resulting map for signature 4 (HP-ACC2 vs N2) is displayed Figure  8  and will be investigated further in the next paragraph. We use a distributed, data-parallel implementation of batch SOM using the map-reduce paradigm and the Apache Spark framework  (Apache Spark, 2014). This allows to leverage the production cluster to train SOM models on very large datasets of several million flights. The code is open-source and available online at https://github. com/FlorentF9/sparkml -som.
INTRODUCTION In recent years, the massive deployment of digitisation technologies and the increasing availability of digital data describing the past have made of those "big data of the past" a major challenge for research in information science and digital humanities. They represent an enormous potential for knowledge and understanding of our social, cultural and geographical heritage and its evolution over time. These data may be very heterogeneous, e. g. statistical data, ancient texts, iconography, 3D objects, etc. They are usually distributed in silos in diverse GLAM (Galleries, Libraries, Archives, Museums) institutions and poorly interconnected. Their richness, facing their variety, complexity and lack of global structure, makes them particularly interesting and challenging in the digital humanities domain, both for ICT researchers who have the ambition of proposing tools to better organize and develop them, and for SSH (Social Sciences and Humanities) researchers who can benefit from explanatory models of historical evidence that may enrich narrative and even lead to new, plausible ones.
This observation can be illustrated by the growing popularity of national and international initiatives, such as E-RIHS 1  (European Research Infrastructure for Heritage Science) or the LSRI (Large Scale Research Initiative) Time Machine 2  , that gather hundreds of institutions in Europe. Many of those contents can be associated, directly or indirectly, with a spatial information, from a simple street name to a position and orientation in 3D space. This information provides a common framework for their analysis and exploration, and may facilitate their exploration through time. Today, however, solutions allowing the joint exploitation of these spatialized data remain rather basic, most of the time it is a question of visualizing them on a 2D map with a GIS, layer by layer. In this context, the main contribution of the work presented here relates to the proposal of a web platform able to centralize different modalities of data and integrating several spatio-temporal visualization tools in a 3D environment, with the objective of enlightening the reading of sociologists and historians, and even to bring new points of view, in the joint interpretation of the data manipulated.
The third table gives information on dwelling units and their occupancy and equipment. It shows how many dwellings had running water or central heating in each block. The number of dwellings in acceptable or critical overcrowding situations is counted. The last table concerns the built environment. This makes it possible to identify the nature of the buildings (dwelling houses, hotels, etc. ), the date of construction or the number of floors. 2. 1. 2 Oblique imagery as visual data. The data considered are aerial imagery about the French territory at several time periods, made available by the French mapping agency (IGN) and the Departmental Archives of Hauts de Seine area (AD92). Since the 1920s, IGN has performed regular surveys covering the territory with vertical imagery, as well as other acquisitions, through oblique imagery, targeted on particular areas such as those that have undergone significant changes over time  (destructions, reconstructions, etc.
Different levels of localization information Classically, the census data were spatialized in a GIS. For each municipality, a plan of the "îlots" is provided in the volumes. They were digitized, then georeferenced (Lambert 93 projection) and finally vectorized using the QGIS software. Positioning the image of the collections in a 3D environment supposes the knowledge of an information of localization. Here, such contents are associated with different levels of geolocalization, with which we have to deal with: • Textual metadata providing an information of geolocalization through toponyms: department, city, locality, etc. It is often the case with collections from preservation institutions, such as the ones of AD92 used here. Geocoding algorithms, together with reference datasets, are usually exploited to determine a 2D position related to the centroid of the area described; • Historical pose estimations (i.
Furthermore, the subsequent visualization of the image requires of the user that he does not move the camera around, otherwise losing the initial (correct) position of the image that follows the camera as it moves. Finally, the project ArchiMediaL 12  works toward automatic linking of image content for comparative investigation of contemporary and historic built forms. To this end, an annotation platform 13  of historical images is proposed, partly based on Mapillary's visualizer, in order to enrich the data content with semantic information. Hence images are registered as street-view or interior for instance. Furthermore, if changes have occurred, they are semantically registered ("building destruction", "new building built", etc. ). Such images can be used in conjunction with other historical data to help researchers in various fields.
Web-based interactive image geolocalization Because some of the contents managed here rely on historical photographs potentially not precisely localized in the 3D scene, one important functionality of the system is based on their geolocalization. We have adopted the semi-automatic approach previously proposed in  (Harrach et al. , 2019). Localization is achieved with 2D-3D registration by adopting an interactive approach where the user identifies at least 4 corresponding points on the image to localize and the 3D reference model. This 3D model is at least a Digital Elevation Model in order to create the 16 https://github. com/itowns/itowns whole virtual globe. However additional 3D information may be added, such as Digital Surface Model or 3D point clouds for instance. Here, we exploit a 3D representation of the buildings in order to improve the accuracy of our geolocalization.
Publishing massive georeferenced image datasets. To support massive image datasets, image poses and urls are streamed with space (bounding box) and time (interval) filters, using a standard webservice. Only images of interest are thereby downloaded, based on the user's location and time selection, ensuring smooth navigation and low bandwith. In practice, images are published using a Web Feature Service (WFS) of points, where the point geometry is the 3D camera location and the other camera parameters and image content url are provided as feature attributes. This way, image content is not duplicated but fetched from the conservation institution, preventing copy of its images from going around unchecked. The conservation institution only needs to suitably setup its cross origin (CORS) policy in order to authorize specific accesses.
Navigation via the image collection. To browse easily through the images of the collection, a carousel has been added at the bottom of the web browser in order to display a gallery of images: it displays images that are present on the area visualized ; see an example in Figure  6. While many images may be present in the same displayed area, the carousel can only show a limited subset, making available the rest by scrolling horizontally. These images may be accurately geolocalized or just geocoded. By clicking on one image, if its precise pose is known, then the camera will center itself on the position of the image's camera in the scene. Otherwise, the image will be ready for registration in order to compute its precise pose and update the database. If the study is done on a specific area, as in the uses cases presented in section 5, it may be interesting to find images of the collection that show a specific area: a tool was then implemented in order to be able to find all precisely geolocalized images that can see a given point.
2. To evaluate the web application in the context of photo-elicitation, three complementary profiles of people were identified to answer the interviews: • An inhabitant of Nanterre, specialist in its history and geography for decades; • Two inhabitants of the city, former inhabitants of one of the shantytowns; • The director of the Nanterre tourism office, who does not live in the city and is just discovering its history. To each profile, a protocol of interview was established: the sociologist has associated a series of photographs, as do dedicated interviews. Commonly, the images are selected according to a time period with the time selector (section 4. 3. 2) and displayed in the 3D environment together with additional data such as simple representations of buildings (similarly to those of Figure  5 ).
ACKNOWLEDGEMENTS The authors would like to thank  Ordnance Survey GB  (https://www. ordnancesurvey. co. uk) and 1Spatial (https://1spatial. com/) for sponsoring the publication of this paper. This work is supported by  I-SITE FUTURE  and  ANR (French National Research Agency ) respectively within the  Archival City Tremplin project , and the  ALEGORIA  project under Grant  ANR-17-CE38-0014-01. We also would like to thank  Martyna Poreba  and  Nathalie Abadie  for their help in making available the images of Nanterre and  Benjamin Lecat  for his work on the census data.
Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes.
One advantage of SEEG monitoring is to allow the recording of several seizures from the same patient, with the same measurement points (or nodes), yielding thus as many realizations of identically distributed dynamical networks of FC. Hence, several seizures from the same patient can be investigated that may share some connectivity features but with different dynamics. Methodological tools have to be proposed to extract relevant information from those large datasets. Ideally, such methods should summarize the dynamics of the seizures by providing several epileptogenic networks, with stable network structures, that characterize the different steps of the seizures and that are involved in the production and the propagation of the ictal events. Those network states may be common to the different seizures of the patient, but the timeline activation should follow a pattern that remains specific to each seizure  (22).
Description of the Brain-Wide Time-Varying Network Decomposition Method For each patient, the dataset is composed of several SEEG recordings for different seizures. Seizures can have different durations. The proposed strategy can be summarized in four steps: (a) We chop each recording into short segments; (b) for each segment, we estimate via FC measures the connectivity for each pair of electrode contacts; (c) we rearrange the FC measures into a list of matrices representing the time evolution of FC for each seizure of a patient; (d) the list of matrices representing the multi-seizures brain-wide time-varying network is decomposed into FC subgraphs characteristic of one patient but common to all his seizures, along with their activation profile specific to each seizure. The main steps of the method are illustrated in Figure  1.
Based on an Elbow criterion and on visual inspection, we compare the quality of the resulting temporal activation and subgraphs, identifying thus the best value for K. Finally, we normalize each subgraph such that their connectivity strengths are between 0 and 1. We only retain connections above some threshold (empirically set to 0. 2 in our experiments) to eliminate non-significant interactions. For more details on the practical use of the method, see the Supplementary Materials. We provide the URL 1 for a Github repository with Matlab implementation of the proposed BTND method. 1 https://github. com/FrusqueGaetan/BTND.
SEEG Recordings Intracerebral multi-contact electrodes (5-15 contacts, diameter 0. 8 mm, length 2 mm, and 1. 5 mm apart) were implanted according to Talairach's stereotactic method (3). Electrode location was verified with post-implantation MRI. Prolonged extra-operative recordings were performed to capture each patient's habitual seizures. SEEG data were acquired with a 256 channel video EEG monitoring system, Micromed video EEG acquisition system (SD LTM express, Micromed, Treviso, Italy), using the following parameters: sampling rate 256 Hz, high-pass filter 0. 15 Hz, lowpass filter 200 Hz, notch filter at 50 Hz. The median number of bipolar contacts recorded per patient was 101 (range 64-130). The main cerebral structures targeted by intracranial electrodes for each patient are listed in the Supplementary Table  1.
As a whole, for all patients, the medial temporal lobe (anterior hippocampus, posterior hippocampus, amygdala, entorhinal cortex), the temporal lateral neocortex and the insular cortex were always targeted. Depending on the electroclinical findings of each patient, frontal lobe, parietal lobe, and occipital cortex were targeted. For 9 patients, all available seizures were extracted, forming a dataset composed of a total of 27 seizures. For 6 patients, 3 seizures were analyzed. For 2 patients, 4 seizures were analyzed and for 1 patient, a single seizure was available for analysis. For each seizure, we extracted signals for at least 1 min before the onset of the seizure and the whole course of the seizure. The duration of the seizure was largely heterogeneous both at the inter-individual and intra-individual level.