dataesr/scientific-paragraphs-categorization huggingface dataset:
To avoid triggering on subsequent samples also exceeding the threshold but belonging to the same glitch, we introduce a window length in which no further glitch can be triggered. This parameter can be thought of as glitch minimum length. We note that this parameter is smaller than the typical glitch length for VBB and SP, allowing our detection algorithm to detect polyglitches. A glitch simultaneously occurring on multiple components is detected on each affected component but the respective start times may slightly differ. However, after modeling of the full glitch waveform (section 4) we can retrospectively establish that such glitches occur at the same time to within milliseconds. This holds true for all multicomponent glitches observed to date on either VBB or SP, also for data with the highest available sampling frequency of 100 Hz.
Hierarchical Glitch Detection (UCLA) This MATLAB-based method took into account that glitch amplitudes follow a power law distribution with many more very small glitches than larger ones (see Figure  SI2 -3 in Text S2 in the supporting information). Therefore, the strategy was to remove the largest glitches first and repeat the process on the smaller ones. 10. 1029/2020EA001317 Earth and Space Science SCHOLZ ET AL. In this method, the raw UVW VEL channel data are inspected for glitches and their spikes. The instrument response to a step in acceleration was termed Green's function. The 20 sps data were decimated to 2 sps and each channel was tested for correlation with the response function as follows. An inverse filter was designed that turned glitches into narrow Gaussians with rise times equal to the glitch so that each glitch represented one peak without the overshoot.
10. 1029/2020EA001317 Earth and Space Science SCHOLZ ET AL. exponential decay, which adds an extra parameter, significantly improves the fit, but runs the risk of overfitting data. To minimize this effect, the approach is only applied to data that show >0. 9 correlation coefficient with the glitch corresponding to our acceleration step model. Glitch (sometimes broadened) and spike templates were fit to the glitches and spikes, respectively, using NLSQ. Because of the delta-like   Lognonné et al. , 2019)  between June-August and June-October 2019. The data are shown before and after temperature decorrelation (TD), the latter of which is needed to hunt for Phobos' tidal signal in the SEIS data  (Pou et al. , 2019; Van Hoolst et al. , 2003). The deglitched data (DG, ISAE method) after TD show reduced spectral peaks caused by glitches.
10. 1029/2020EA001317 Earth and Space Science SCHOLZ ET AL. shape of the spike over one or two sample intervals, the starting model must find the location to within a fraction of a sample interval (e. g. , 0. 05 s). Glitches are easier to fit than spikes, being low frequency, and requirements on the starting model are less stringent. Spikes are much smaller in 2 sps data relative to glitch sizes. Thus 2 sps data were used to generate a glitch catalog (section 2). The starting parameters from the 2 sps fits were then used to fit glitches in the 20 sps data and residuals were calculated. The residuals were examined for the presence of a spike in the data before the glitch peak, by requiring its amplitude to be greater than 5 standard deviations of the residuals after the peak. If true, an iterative forward model was run by shifting the phase of the spike template about the corresponding peak in the residuals (in steps of sample interval/10), and finding the amplitude and phase of maximum cross correlation.
Glitch Mitigation Given the abundance of glitches and their influence on the data analysis, the question arises how glitches could be mitigated for future installations. For thermally related glitches, the most obvious action is to decrease the thermal amplitudes the seismic sensors are exposed to. While for the SEIS instrument great care was taken to achieve just that (Wind-Thermal Shield, Remote Warm Enclosure Box, Evacuated Container, heaters, Thermal-Compensation Device; see  Lognonné et al. , 2019) , the daily temperature cycle still exceeds those of fine terrestrial stations by 4 orders of magnitude, that is, around 15 K compared to a few mK. Given the harsh environments typically found on extraterrestrial, planetary bodies, it may not be easy to achieve  Earth and Space Science SCHOLZ ET AL.
The resulting glitch waveforms of these adaptations are still close to those corresponding to a zero-rise time acceleration step, allowing however to fit for glitches varying from our simple step model. It is these adaptations where the removal codes diverge from each other, but the basic concept and general success is equal (if the parameters are chosen accordingly). Analyzing differences in the removals can be worthwhile for glitches deviating from our step-like acceleration model in more important time periods such as during marsquakes. Overall, the removal of glitches following the approaches presented in this study allows to improve on the quality of seismic data and hence helps to accomplish InSight's high scientific goals. 10. 1029/2020EA001317 Earth and Space Science SCHOLZ ET AL. As no glitch removal algorithm can warrant a perfect clean-up of all glitches and their spikes, we prefer to not provide a deglitched time series of all available data.
Instead, we have assembled our algorithms for glitch detection and removal into one Python/ObsPy toolbox. Some convenient functions for data retrieval and handling are also implemented. The package further holds MATLAB scripts to perform glitch detection and removal tasks as presented. Its link is at this site (https://pss-gitlab. math. univ-paris-diderot. fr/data-processing-wg/seisglitch). Documentation is available. Together with this code we also provide deglitched data for a selection of seismic events.
Experimental setup The AEVO, EVO and EVI registration methods were evaluated within the context of two clinically relevant applications of IGRT: CT/conebeam CT(CBCT)-guided radiotherapy for lung cancer and MR-guided radiotherapy of prostate cancer. The two anatomical sites were also chosen due to the considerably different deformation patterns showcased by the respective structures present within the field-of-view, i. e. compressible areas (lungs, bladder and rectum) situated in the immediate vicinity of near-incompressible structures (such as the pathological tissue itself). In the following we will describe the medical image data used for this purpose, together with the employed evaluation methodology for the algorithms.
Selection of the medical image datasets For CT/CBCT-guided radiotherapy for lung cancer, we have selected a 4D-CT image sequence from five patients, with each sequence sampling the 3D anatomical changes over the course of their respiratory cycle (from 0 to 90 %, with an increment of 10%). The data was downloaded from "The Cancer Imaging Archive", originally collected in the scope of the following studies  (Roman et al. 2012 , Clark et al. 2013 , Balik et al. 2013 , Hugo et al. 2016 , Hugo et al. 2017). For each dynamic image volume of the selected 4D-CT sequences, the curators of the database have also provided contours for the lungs, the tumor and several other structures. Out of each 4D-CT sequence we have only selected image pairs which showcase the largest anatomical differences between them.
In effect, the fullinhalation image (the 0% phase) was established as reference image for the registration process, whereas the 40 -70 % images were considered to be due for registration. The resulting dataset was then used to evaluate the performance and behavior of EVO, EVI and AEVO for CT to CT registration. Note that for the AEVO method, the lungs, the heart and the area outside the body were considered to be compressible, while the rest of the structures present in the FOV were deemed near-incompressible. In order to evaluate the performance of the algorithms for CT to CBCT registration (potentially beneficial for applications described in detail in  (Zachiu et al. 2017b )), a set of CBCT images were synthesized from the 40 -70% CT images mentioned above. This was achieved by a three-fold down-sampling of their sinogram and subsequent reconstruction via the TIGRE toolbox  (Biguri et al.
2016). This resulted in a set of synthetic CBCT images with a quality similar to that provided by a regular CBCT imaging device (see  (Zachiu et al. 2017b)  for further discussion on this topic). Thus, registering the synthetic 40 -70% CBCT images to the original 0% CT via the three algorithms, allowed their evaluation for CT-CBCT registration. In terms of acquisition parameters, the original CT images were acquired on a Varian Trilogy scanner, in-plane image size 512 × 512 and a variable number of slices, voxel size 0. 97 × 0. 97 × 3. 0mm 3. The evaluation of the algorithms for MR-guided radiotherapy for prostate patients was performed on image data acquired as part of a cohort study on the Unity MR-Linac installed at the UMC Utrecht, Utrecht, The Netherlands. In-line with the potential requirements of adaptive IGRT on the MR-Linac, we have evaluated the algorithms for: 1) Mono-modal MR image registration; 2) Multi-modal/cross-contrast MR image registration and 3) CT to MR image registration.
For each of these instances, we have selected image pairs from five patients, with contours for the prostate, bladder, rectum, femurs and the pelvis being available for each individual image. The contours were validated for accuracy by two experienced radiation oncologists. For registration using the proposed AEVO method, the prostate and the bony structures (i. e. the pelvis and the femurs) were considered to be near-incompressible, while the remainder of the areas (including the region outside the body) were allowed to smoothly deform. The acquisition parameters for the reference and the moving images in the three datasets are reported in had a different FOV and/or voxel size, the moving image was initially re-sampled on the grid of the reference. This was subsequently followed by resizing both images to 256 × 256 × 256, for computational purposes.
The known deformations were generated via a finite element modeling (FEM) of the organ displacements in each of the two images. In summary, the available organ contours were initially imported into the FEM software PreView v2. 1. 4 (Maas et al. 2012) , which allowed establishing the physical properties of the tissues and the motion actuators. The latter implied a lung inflation for the CT data and an increase of the bladder volume and rectum for the MRI. This was followed by the effective finite element simulation of the resulting deformations via the FEBio v2. 9. 1 software  (Maas et al. 2012). Finally, the simulated deformations were inspected using PostView v2. 4. 4  (Maas et al. 2012 ) in order to confirm their similarity to the typical deformation patterns occurring in these anatomical regions.
Algorithm implementation and calibration All algorithms were implemented using the Compute Unified Device Architecture (CUDA) and executed on a Nvidia TITAN V graphics card. The regularization parameters α, β, γ and δ (see eq. 1, 3 and 5) were configured by an exhaustive search, maximizing the post-registration contour alignment of the clinical target volume (CTV). The calibration procedure was only performed on one of the image pairs in each of the datasets (CT-CT, CT-CBCT, MR Monomodal, MR Multimodal and CT-MR), with the parameter values being maintained for the remainder of the image pairs in each dataset. Such an optimization procedure is justified by the fact that in clinical practice, registration results are validated by a visual inspection of the post-registration organ boundary alignment.
Results 3. 1. Performance of the investigated algorithms for tracking organ boundaries 3. 1. 1. CT/CBCT-based tracking in lung cancer patients Figure  1  illustrates the statistical distribution of the Dice Similarity Coefficient (DSC) following the registration of the CT-CT and CT-CBCT datasets (see section 2. 2. 1). The pre-and post-registration DSC values were pooled together from the five patients, for each of the anatomical structures-of-interest, and displayed under the shape of a boxplot. The box limits of each boxplot are represented by the 25th and 75th percentiles, while the whiskers are approximately the 1st and the 99th percentiles, respectively. While all three algorithms lead to improvements of the post-registration DSC, there is a noticeable tendency of the EVI algorithm to under-perform for the lungs, compared to both EVO and AEVO.
2. 2. 1). The values in the table represent the DSC averaged over the five image pairs from each of the MR Monomodal, MR Multimodal and CT-MR datasets. The DSC values are reported individually for the bladder, prostate and rectum. Similar to the CT/CBCT tracking scenario, all three algorithms have led to a notable improvement of the DSC in all cases, compared to pre-registration. On the other hand, while for the prostate itself all three algorithms perform similarly, for the bladder and rectum, the EVI algorithm has a tendency to misregister the images in these areas, with differences of the average DSC of up to 7% compared to EVO and AEVO. The latter two algorithms, however, showcase no notable differences in terms of the DSC.
Anatomical plausibility of the estimated deformations Figure  2  illustrates the voxel-wise Jacobian determinant of the deformations estimated by the evaluated algorithms on one of the image pairs from each of the five datasets. It can be observed that in all cases, the EVO algorithm displays moderate to high deviations of the Jacobian determinant from one, regardless whether the underlying anatomy is near-incompressible or not. On the other hand, as the EVI algorithm penalizes deviations of the Jacobian determinant from one, such deviations are considerably reduced over the entire field-of-view. This also includes anatomical structures which are expected to change volume such as the lung and the rectum. The AEVO method, on the other hand, maintains a Jacobian determinant close to one for structures which were considered to be near-incompressible in the scope of this study (enclosed by the red dashed lines), while for the remainder of the structures moderate to high deviations can be observed.
Another interesting observation is that for the illustrated CT-CBCT case, the AEVO algorithm showcases a more uniform spatial distribution of the Jacobian determinant within the lung, compared to the EVO method. To support the observations stemming from figure  2 , figure  3  shows the statistical distribution of the Jacobian determinant provided within incompressible structures by the three registration algorithms. The illustrated boxplots were generated by pooling together the Jacobian determinant of the deformations within incompressible structures from all registered image pairs in each of the five datasets. It can be observed that, to different extents, the EVO algorithm leads in all cases to considerably larger variations of the Jacobian determinant compared to EVI and AEVO. On the other hand, within incompressible structures, AEVO demonstrates similar ranges of the Jacobian determinant to EVI.
In order to determine the contour propagation performance of the three algorithms, we have evaluated the pre-and post-registration dice similarity coefficient (DSC) for several structures of interest in each of the five datasets. As shown in figure  1  and table  3, all algorithms have led to a notable improvement of the DSC compared to the preregistration case. However, for anatomies which can undergo volumetric changes (such as the lungs, bladder and rectum), the EVI algorithm leads to systematically larger errors in terms of contour alignment, compared to EVO and AEVO. Such an outcome is, nevertheless, in line with the design of EVI, which globally penalizes deviations of the Jacobian determinant from one and implicitly any volumetric changes. Concerning AEVO, while there is a tendency to provide slightly lower values for the DSC compared to EVO, this situation only occurs within isolated cases, with limited differences of 1-2 %.
For this first iteration we analysed the 12 DDS initiatives we were already aware of. The second iteration took place between 2012 and 2014. We gathered data about all the DDS initiatives from multiple publicly available sources: prospectuses, annual reports, academic and professional articles, company web sites, press releases, initiative descriptions, white papers, web search engines. We analyse a total of 58 new DDS initiatives. We analysed both start-ups as well as established large corporations, spanning several countries. The third iteration took place at the end of the 2014. We downloaded all the articles available on the news aggregator Factiva1 starting from the first of January 2011 to the end of November 2014, containing as keyword "real-time data. " Overall, we downloaded and read 1,839 articles and among these articles, we identified 101 DDS initiatives.
We used the term "real-time data" instead of "digital data stream" to extent the search to all communications eventually referring to uses and exploitation of real-time data, thus configuring DDS initiatives. Every iteration was characterized by three steps. In the first step, we identified the initiatives to consider for defining the new dimensions to include in the taxonomy. In the second step, we defined the dimensions to add in the taxonomy. In the third step, we verified whether our ending conditions were achieved. Besides the results of the taxonomy development process, and with illustrative purpose, we further detail the application of the taxonomy to five distinct DDS initiatives. Overall, we analyzed 177 initiatives, the majority of which being business solutions marketed by professional vendors.
Scada Application Software of Chemtrols Industries Ltd company Chemtrols Industries Ltd is a manufacturing company of industrial equipment and offers a Supervisory Control and Data Acquisition (SCADA) software application. This kind of applications refers to the combination of telemetry and data acquisition. The company packages its SCADA software application in their CT Meerkat product. The CT Meerkat is a software application for real-time monitoring and control of a whole set of different, and eventually interconnected, equipment parts. The company targets, with its CT Meerkat product, station and substation automation. CT Meerkat affords real-time sensing thorough a terminal unit that collects data from the field. It affords also mass visibility since a master station collects and displays the data in real-time, using various colors and animations, via a communication system from remote terminal units.
Acsis ProducTrak of Acsis company Acsis is a supply chain operator and sells Acsis ProducTrak. It is a software suite for data collection and task management that adds functionality to major ERP systems, including SAP and Oracle. Acsis ProducTrak brings real-time connectivity to the different passage points in the supply chain, from incoming materials, through manufacturing, warehousing and distribution. Acsis ProducTrak provides the infrastructure to manage data collection points (e. g. , label scanners, PLCs, label printers, and a host of other real-time devices) and deliver the data to the supply chain actors on the shop floor to operate effectively and efficiently. At the same time the Acsis ProducTrak aggregates the data for the management and reports the eventual inaccuracies in the supply chain process.
DataSift platform of DataSift company DataSift is a data management company and offers a platform for real-time social media data-filtering and aggregation. The DataSift platform sorts user conversations from sources like Twitter, making real-time sensing. The same platform is able to gather hundreds of millions of social media posts per day to finds granular and relevant insight, giving real-time mass visibility. The platform is able to filter social media data for such as demographic information, online influence, sentiment, location, gender, or language. Through this solution, organizations can access to real-time data published on social media that could be impactful to their business for brand monitoring and other applications. Organizations allow to easily and quickly detect and respond to major trending events, social behaviors, customer preferences, and ultimately, avert any impending crises.
Nous proposons, dans le cadre de cet article, de mettre à l'épreuve cette conclusion à partir d'un nouveau corpus, de presse, constitué des 290 articles (223 545 occurrences, 15 102 formes et 6 232 hapax) parus dans la presse écrite nationale quotidienne (Le Figaro, Libération, Le Monde, Le Parisien, L'Humanité et La Croix) dans les 72 heures qui ont suivi la prise en charge de l'incendie de la cathédrale Notre-Dame de Paris dans la soirée du 25 avril 2019 (du 25 au 28 avril 2019) et qui a ravagé l'aiguille et le toit (nef, choeur et transept) du bâtiment. Ces textes ont été rassemblés à partir d'une requête dans la base de données Europresse ii avec le mot clé Notre-Dame pour la période cible. Notre objectif est double (i) éprouver l'articulation, prévue par la SPA, entre la description sémantique et conceptuelle de l'objet patrimonialisé et les déploiements discursifs dont il a effectivement fait l'objet dans ce contexte (lors et immédiatement à la suite de l'incendie) ; (ii) identifier les éventuelles transgressions du protocole sémantique ; celles-ci étant à même de (iii) mettre au jour différents degrés de patrimonialisation de l'objet analysé et/ou des valeurs spécifiques associées à la patrimonialisation en fonction des groupes d'appartenance et surtout des stratégies identitaires iii  (Lipiansky, 1998 ; Poutignat et Streiff-Fénart, 1995)  Dans cette perspective, nous exposerons, dans un premier temps, notre cadre théorique et notre description sémantique et conceptuelle de cathédrale et de patrimoine culturel.
Des genres discursifs auxquels appartiennent les textes réunisun corpus médiatique vs une base de données patrimoniales -émerge une explicitation de valeurs affectives et hédoniques du patrimoine par l'expression d'émotions et de sentiments. Leur convocation en contexte participe à construire deux représentations du lien entre patrimoine culturel et communauté pour laquelle il est commun et au sein de laquelle celui-ci est transmis entre générations : celle d'une communauté au sens large, jusqu'à l'universel qui reconnait des valeurs communes (historiques, esthétiques) au bâtiment et celle d'une communauté qui revendique d'autres valeurs en partage, religieuses, qui elles sont menacées comme/ou indépendamment de celle du bâtiment. Ces appropriations spécifiques se manifestent dans les discours non pas par une modification de la représentation sémantique d'église mais par une reconstruction discursive des propriétés les plus saillantes dans cette représentation.
méthodologiE Les matériaux empiriques mobilisés sont les suivants : -Une table-ronde dans le cadre du colloque « Marques, publicité et création audiovisuelle » que nous avons organisé à la Sorbonne Nouvelle en février 2018, réunissant des professionnels occupant des postes à responsabilité dans le secteur de la presse autour de la question « Que changent les alliances des éditeurs dans la data face aux GAFAM ? » ; -Deux entretiens menés en janvier et en mars 2020 avec des éditeurs de presse numérique dans le cadre du programme ANR Pluralisme de l'Information en Ligne (PIL) (ANR-17-CE27-0010) (2018-2021), conduits avec Lorreine Petters ; -Une interview vidéo publique de Ludovic Blecher (head of innovation pour Google News Initiative) par Philippe Couve (Samsa. fr), 24 juillet 2019. « Google appuie les innovations médias en Afrique et au Moyen-Orient », 8mn29s.