dataesr/scientific-paragraphs-categorization huggingface dataset:
Extra Virgin Olive oil δ 18 O This dataset comes from a study on the geographic traceability of Italian Extra Virgin Olive Oil through isoscape analysis  [62]   65] and Oxygen  [66] [67] [68]  isotopic composition of precipitation and source water  [69]. The δ 18 O isoscapes, in particular, are related to the elevation and to the distance from the sea and correlate with the year's precipitation  [66, 67] ; the original results in  [62]  have been obtained by standard spatial statistical procedures. The Timescape interpolation of the same data gives results comparable with  [62]  for the first year only (figure  15 , left), while the subsequent years (figure  15 , centre and right) do not match well with the source events. Note in particular how the extreme values are averaged out in 2010 and 2011: this example shows clearly that the Timescape algorithm is not a universal tool, in this case, each year's data are self-contained and independent of each other, so mixing three years of observations is not correct.
Precipitation δ 2 H This example is based on the renowned Global Network of Isotopes in Precipitation (GNIP) dataset  [71, 72]  from the International Atomic Energy Agency  [73]. The collection of isotopic abundance data started in the early 1960s and the stations network has grown dramatically over time. For historical and technical reasons, the spatial coverage is uneven (figure  16 ), with the earlier, denser coverage in central Europe. The interpolation algorithm chosen is a smooth version of the Inverse Distance Weighting method, with a weight function W u, x = 1 ∆(u, x) 2 + m 2 (22) where m 2 is a positive inertial term, that prevents the overweighting of the closest source events. In our case m 2 = 2. Moreover, a maximum number of ten neighbors has been imposed. Nonetheless, the computation is CPU-intensive since the geodesic metric be ordered by distance, then trimmed retaining only the closest events to x; this is based on the heuristic observation that only the closest elements contribute significantly to the weight functions.
Both Python and Java Timescape implementations allow this kind of trimming. This example shows how the interpolation results are affected by the addition of new sampling sites. The dates covered are form year 1975 to 1984 (included). This is of course a very crude model, based only on the GNIP δ 2 H observations, further improvements could benefit of a regionalised regression based on climatic ancillary data  [67, 74]. An acknowledged source of Hydrogen isoscapes is the web-based application IsoMAP  [75] , that also provides insightful information about precipitation hydrogen and oxygen isoscapes in general  [68].
Performance Analysis The interpolation throughput has been estimated in terms of evaluated target events per second, typical values range from a few tens to more than one thousand, depending on three factors: the number of source events |S|, the spatial interpolator (see the method column in table 2, Kriging is slower than IDW), the distance function (geodesic is much slower than Euclidean) and the causal cone form factor (periodic is slower than straight). The cone shape and width also influences the number of null target events. The lowest throughput is associated with the temperature model (section 3. 2). In fact, this model associates a large source set, a periodic form factor and a Kriging spatial interpolator, each factor adding its computational load. A fair estimate of the python Timescape throughput can be set roughly about a few hundreds events per second with a standard hardware configuration.
12 We can explore further topological diversions modifying the structure of distance; section 4. 1 introduces a few suggestions. Altering the definition of the causal cones has a deep impact on causality, so the results can be unexpected even if the mathematical procedure itself is sound. A possible scenario could be inferring the past from the future (properly, retrodiction), exchanging the role of past end future cones C - x and C + x. Accuracy assessment is the most delicate phase of Timescape modelling. On the one hand, statistical interpolators provide an accuracy figure σ(x) along with the estimated field Φ(x) at running time, but it is also possible to apply jackknifing (16) on the finished target for every interpolator. The Python Timescape module  [26]  offers a set of functions (see figure  9 ) for an initial guess of model's accuracy.
This is not the kind of space one expects to find in ecological modelling: D T (x, y) = 0 means that x an y coincide in space and time, unlike D M (x, y) = 0 that includes all the light-like events' pairs. 14 The topological correspondence is: Minkowski space-like interval → Timescape ∆ = ∞, time-like → ∆ < ∞, light-like → ∆ < ∞ too. There is nothing special to the events laying on the boudary of the causal cones. section 3. 3. On the other hand, when the input dataset can be clearly partitioned as a bunch of time series, the algorithm can be applied, but it is unnecessarily complicated and time-consuming; in this case, the result is comparable with ordinary interpolated one-dimensional time series.
The Timescape algorithm has been already implemented as a Python module and as two Java stand-alone GUI-based applications. The software is distributed under the GNU-GPL open license. The software is meant to be used with georeferenced input data and can be included in a GIS workflow, eventually exporting geotiff files. Our modelling tool can cope with seasonality by playing with the shape of the causal cone, without subsetting the input set season-by-season. Moreover, the algorithm can be applied with a variety of interpolation methods. Ideally, any spatial interpolator would do, the choice being driven by the input data and the user's taste.
Pour ce qui est de la traduction automatique en elle-même, il convient de faire deux remarques. Premièrement, certains articles journalistiques ont été abrégés car la version de DeepL qui a été utilisée pour constituer le corpus n'accepte pas les textes au-delà de 5 000 caractères. Pour cette adaptation, le découpage initial en paragraphes et les enchaînements logiques ont été scrupuleusement respectés. Deuxièmement, partant du principe qu'il existe pour chaque texte « une pluralité de traductions parfaitement recevables »  (Keromnes, 2016 : 102) , DeepL propose une multitude de choix de traduction. Pour cette étude, c'est la première version en langue cible proposée par le système de TAN qui a été choisie. Autrement dit, la sortie brute n'a subi aucun traitement. Une fois les textes traduits, le logiciel MkAlign (Fleury & Zimina, 2007) a permis la création du bi-texte en alignant les segments de la version en espagnol (appelé volet source) avec ceux de la version en français (volet cible).
Première approche par le prisme textométrique L'analyse textométrique prend appui sur les données textuelles d'un corpus numérique et vise à réaliser une description statistique basée sur le vocabulaire. Elle permet l'observation quantitative des cooccurrences, des segments répétés ou des concordances des unités choisies (cf. Fleury & Zimina, 2007). L'analyse du vocabulaire du corpus parallèle faisant l'objet de cette étude a été réalisée avec le logiciel MkAlign. Ce système constitue des listes de mots (appelées « dictionnaires ») pour chacun des volets en ordonnant les lexèmes (« formes ») dans l'ordre croissant ou décroissant en fonction de la fréquence d'apparition. À partir de ces informations, un échantillon des termes les plus fréquents du volet source a été sélectionné. Les unités suivantes ont donc été retenues : España, pandemia, economía, crisis, COVID-19, año, empresa.
(Zimina, 2005). C'est précisément là que réside l'intérêt de la représentation cartographique. Une fois la carte créée, MkAlign permet de réaliser des requêtes par mots clés dans l'un des volets. Le système entoure alors les carrés où se trouvent les occurrences du lexème en question. Il est également possible de choisir un mot précis pour chacun des volets ; le terme ayant été saisi en premier dans le système est considéré comme le terme d'induction et constitue le repère qui amorce le processus de résonance. À l'aide d'un code couleur spécifique, le logiciel distingue alors l'absence ou la présence simultanée des unités recherchées dans chacun des volets. Par exemple, l'image 1 montre le résultat obtenu de la requête du couple de mots empresa (dans le volet source) et entreprise (dans le volet cible).
Les occurrences qui sortaient du champ sémantique visé ont été écartées de manière à améliorer l'analyse ainsi que la représentation des résultats sur les tableaux présentés ci-après. En résumé, les informations obtenues à chacune des trois étapes interagissent. Suivant cette méthodologie en trois phases à l'aide du logiciel de textométrie MkAlign, l'exploration a consisté à observer d'un point de vue quantitatif la mise en correspondance des unités entre les deux volets pour ensuite réaliser une évaluation qualitative du bi-texte. Autrement dit, l'analyse part des correspondances pour arriver aux équivalences traductionnelles proposées par DeepL.
Dans l'exemple 1, le système de TAN arrive à déterminer que España de Noche ne correspond pas à un nom commun, mais la base de données de DeepL ne comporte vraisemblablement pas assez d'informations concernant ce nom. Le processus de traduction débouche sur une proposition hybride, mêlant l'espagnol et l'anglais : le toponyme est exprimé en anglais et le reste du nom propre est emprunté à l'espagnol. 2  Outre le mélange de codes linguistiques, la qualité de la traduction peut être jugée moindre car les marques typographiques indiquant un emprunt sont absentes. L'exemple 2 illustre un cas particulier car le nom propre est en anglais et non pas en espagnol dans le volet source (Spain Startup-South Summit). Le système opère une traduction littérale vers le français de chacun des mots. 3  D'un point de vue pragmatique, le résultat n'est pas pertinent car un faux-sens est créé : le South Summit correspond au nom d'une plate-forme d'innovation et non pas à un sommet proprement dit.
Des traductions problématiques L'acronyme COVID-19 peut également être considéré comme une unité à correspondance quasi-univoque selon les données du tableau de fréquences présenté plus haut (cf. Tableau 2). En outre, étant donné que la fréquence est moindre dans le volet cible, l'hypothèse d'une perte d'informations durant le processus de traduction semblerait justifiée. La représentation cartographique du corpus permet de localiser les deux contextes de non correspondance du bi-texte. Le premier a déjà été présenté dans la sous-section précédente. La traduction de covid par crisis se rapporte à un procédé qui, dans l'ensemble, permet de transférer de manière fidèle le message original. Le deuxième cas de non correspondance concerne une erreur grave de traduction (exemple 8). ¿la vacuna evita la covid grave? le vaccin prévient-il la covariectomie sévère ? La base de données de DeepL ne semblerait pas encore suffisamment volumineuse pour que le système puisse reconnaître les occurrences de COVID-19 dans tous les contextes.
Les données disponibles sont soumises à évolution et ne sont pas équivalentes pour toutes les combinaisons de langues. Les systèmes peuvent en effet « apprendre » ou être nourris par les nouvelles informations reçues. Par conséquent, il convient de souligner que les conclusions de cette recherche ne sont pas transposables telles quelles à d'autres combinaisons linguistiques. Cet article s'est proposé d'évaluer un corpus parallèle espagnol-français issu de la traduction automatique. L'attention s'est portée plus précisément sur le transfert d'informations au cours du processus de traduction. Il a été question d'interroger la fidélité de la traduction en termes de conservation des informations dénotatives. Pour ce faire, certaines fonctionnalités du logiciel MkAlign ont permis de définir une méthodologie tripartite.
Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of Patlak K i can be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images of K i. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of Patlak K i.
Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise.
Due to the poor statistics and high noise associated with TAC measurements at the voxel level, in particular for DWB acquisitions, parametric image estimates can be heavily corrupted by noise and potentially biased. The use of direct dynamic reconstruction has been proposed to improve on this task by making use of dynamic models directly in the reconstruction. These techniques allow for more accurate modelling of the noise from the raw PET data in the generation process of parametric images and can improve parametric image noise and reduce bias  (Reader and Verhaeghe 2014). For DWB acquisitions specifically, it has been shown using simulated and real data that direct dynamic reconstruction provides reduced noise, bias and improved parametric image contrast when compared to postreconstruction parametric imaging  (Karakatsanis et al 2016).
So far, dynamic reconstruction has been used in DWB applications using the Patlak model and a generalised Patlak model for K i parametric imaging, using only frame data after sufficient time from injection to satisfy the steady state conditions of the Patlak model. In this work, we evaluate the performance of dynamic reconstruction algorithms for DWB fluorodeoxyglucose ([ 18 F]FDG) PET imaging for various dynamic reconstruction methods and different DWB acquisition protocols. The novelty in this work is the application of a spectral analysis based dynamic reconstruction on DWB data for Patlak K i parametric imaging, which enables the use of all acquired DWB data from injection in the reconstruction. The evaluation is based on simulations of a unique bed position for a SB and various multi-bed dynamic acquisition protocols and results are illustrated in a real dynamic FDG PET study.
• The first DWB protocol (DWB-1) considers a step and shoot (S&S) acquisition using measured system delays from an experimental DWB protocol on the Signa PET/MR, resulting in a delay of 6 s between adjacent bed positions and 36 s between whole-body sweeps, for a total of 8 whole-body sweeps in the duration of the study. As the system has no dedicated DWB protocol, a large fraction of the system delays between whole-body sweeps is caused by data transfers and internal processes. • The second DWB protocol (DWB-2) differs from DWB-1 by use of system delays that mimic a CBM acquisition of the same length, with no delays between adjacent bed positions and 12 s delay between wholebody sweeps to account for bed speed and acquisition overscan (used to obtain reasonable axial sensitivity at the edges of the acquisition  (Panin et al 2014) ).
These sampling uniformity differences are eliminated at the central bed position and maximised at the edge positions (first and last bed positions) of the assumed 5-bed DWB protocol. The choice of the second bed position was made as a compromise between the central and edge bed positions, to include non-uniformity effects in the analysis of the DWB-3 protocol results. The PET data simulations were conducted solely for this bed position using the framing of the DWB protocols described above. The exact framing information is available in the supplementary material.
Digital phantom and analytical simulation The Zubal brain phantom  (Zubal et al 1994)  was chosen for the simulations of PET [ 18 F]FDG data, even though its anatomy doesn't correspond to the anatomy that would be found in the axial location of the simulated bed position. The choice of the phantom was made to incorporate higher complexity structures than those offered from common lung/chest phantoms. Furthermore, the placement of the brain phantom in the centre of the FOV resulted in no area of the phantom falling in the overlapping regions of the DWB acquisition protocols. Therefore, the simulation of adjacent bed positions was not necessary for this study. The behaviour of structures falling in the overlapping regions of DWB studies as well as the treatment of the overlap data during and after reconstruction is yet another subject for investigation.
Real data A SB dynamic examination centred over the lungs region was used to test the performance of the evaluated algorithms and to compare results against the simulation findings. Approval for the retrospective use of the real patient data was obtained for this study. The data had been collected with approval from a local ethics committee. The original dataset was acquired on a Signa PET/MR, starting at the injection of 177 MBq of FDG tracer to the patient, for a duration of 1 hour. The imaged patient had been diagnosed with a non small cell lung cancer at the left lung. The raw list-mode dataset was retrospectively reprocessed (replayed) to create two new datasets. One dataset using the framing of the simulated SB study and one dataset using the framing of the simulated DWB-1 study (DWB) including temporal gaps.
The TOF information available with the original dataset from the Signa PET/MR was not used in the reconstruction process. An IDIF from the ascending aorta was measured on activity image data from 3D reconstruction and used for 4D reconstruction and postreconstruction analysis. The two datasets were reconstructed using the same 3D and 4D dynamic reconstruction algorithms that were used in the simulation study using identical parameters. No respiratory motion correction or gating was applied to the data. Similar to the simulation study, post-reconstruction Patlak analysis at the voxel level was performed with OLS to generate parametric images of K i. VOIs were drawn over the tumour, the tumourʼs background (left lung) and the liver, as shown in figure  2 , to compare between reconstructions and against the findings of the simulation study.
Using these the contrast to noise ratio (CNR) was estimated according to ( ) CNR SD , 9 tumour bkg bkg liver q q q = where SD VOI is the spatial standard deviation of a VOI. Similarly, CNR was calculated using the simulation study data to enable direct comparison with the real data. In this case, the eroded thalamus VOI was used as the target region and the white matter as the background for both contrast and noise estimation.
Comparison between SB and DWB protocol data The VOI and voxel based metrics comparing 3D and 4D Patlak reconstructions for the SB and DWB-1 protocols are shown in figure 3. For both voxel based and VOI based metrics the 3D reconstruction followed by postreconstruction Patlak fitting using DWB data resulted in higher CoV values, compared to 3D reconstruction of SB data at matched bias. For the first few iterations the 3D reconstructions of both datasets resulted in similar bias values, while further iterations resulted in a wider range of bias values for the DWB data compared to SB data within 40 iterations. The use of 4D Patlak reconstruction on DWB data produced results with lower CoV on both evaluated metrics and VOIs, compared to the 3D reconstruction of the same data at matched bias, and a shorter range of bias values within 40 iterations.
For the VOI metrics, CoV values of the 4D Patlak reconstruction on both evaluated regions approach those of 3D reconstruction of SB data. Furthermore, the 4D Patlak reconstruction of DWB data resulted in eroded thalamus bias values that evolved towards a steady value of positive bias, at approximately iteration 12, after which further iterations resulted in small step changes towards lower bias. For the voxel metrics, similar behaviour is seen on early iterations of 4D Patlak reconstruction on DWB data for the CoV, with values approaching those of 3D reconstruction of SB data. But at further iterations the CoV for the 4D Patlak reconstruction in both VOIs surpasses values from 3D reconstruction on DWB data. On the eroded thalamus this was the case beyond iteration 24, while for the cortex from iteration 22 and beyond.
Comparison between 4D dynamic reconstructions The VOI and voxel based metrics are shown in figure  4  for comparison of 4D Patlak and 4D Spectral reconstructions of DWB and SB data. On both metrics and for both regions the use of Spectral reconstruction with 6 basis functions provided the lowest CoV values at matched bias compared to other 4D reconstructions, on DWB and SB data respectively. However 4D Spectral reconstruction with 6 basis also provided the highest respective bias values in the eroded thalamus. On the cortex the difference on bias metrics was relatively small between all 4D reconstructions of respective DWB and SB data. The 4D Spectral reconstruction with 9 and 17 basis functions resulted in similar bias and CoV values at both regions on DWB data. Their use resulted in lower CoV compared to 4D Patlak reconstruction and 3D reconstruction of SB data, but higher compared to 4D Spectral using 6 basis.
Nonetheless, at the eroded thalamus use of 9 and 17 basis provided improved bias values at matched CoV when compared to the use of 6 basis, closer to bias values from 4D Patlak reconstruction. On SB data, a greater separation in rms Bias is seen between 4D Spectral reconstructions with 9 and 17 basis functions. When the 4D Spectral reconstructions of  DWB data were provided with the same frame data as the 4D Patlak reconstructions (4 frames with t > t ss ), instead of all data (8 frames for DWB-1), it resulted in a noticeable increase of the CoV values with very close noise versus bias trade-offs between 6 and 9 basis. Their trade-off curves got closer to the one of the 4D Patlak reconstruction, with lower bias values on both metrics but higher rms CoV compared to the 4D Patlak reconstruction.
Comparison between DWB protocols A comparison of 4D Patlak and 4D Spectral reconstructions between the three simulated DWB protocols is made in figure  6. For VOI based metrics on both regions and 4D reconstructions, data from all three DWB protocols resulted in bias values within the expected range of the underlying systematic bias in Patlak analysis. These biases are caused by the sampling differences of the DWB protocols, which result in the values shown in table 4 for noiseless TAC data. Differences in CoV at matched bias values are more profound in VOI metrics of the cortex region, where data from protocol DWB-2 resulted in the lowest CoV values and DWB-1 and DWB-3 data resulted in closer CoV values. This level of reduction in CoV was not seen on the eroded thalamus, neither on VOI or voxel based metrics.