dataesr/scientific-paragraphs-categorization huggingface dataset:
D. RESULTS This section provides a comprehensive overview on the results that we obtained from our experiments. It is divided into two major subsections, the Qualitative Results and the Quantitative Results. The Qualitative results deal with the functioning of our method and aim at illustrating the way classes are organized. This qualitative evaluation is also compared to other existing approaches, and it shows how well it is able to cluster different segments of handwritten Urdu characters with varying amounts of similarity, across common prototypes in the embedding space. In a complementary way, the Quantitative results give us an empirical overview into the accuracy of our method over the two datasets that we tested it on. As this study tends to prove that a few-shot learning approach is able to learn the Urdu handwritten features, we trained the Prototypical Network in 1-shot and 5-shot scenarios, taking the nearest and the 5 nearest points in the embedding space.
2) Quantitative Results Following this qualitative analysis, we propose formally assessing the relevance of our proposed approach in this section. To this end, we tested it on two major datasets (Urdu Handwritten Text (UHaT) dataset  [5]  and the one from  [18] ) and we propose hereafter a comparative study against other baselines. This comparison was firstly conducted on other few-shot learning techniques, including Matching Networks  [45] , Neural Statistician  [11]  and Model Agnostic Meta-Learning (MAML)  [12]. We also propose to study the genericity of the embedding models by training on one dataset and testing on the other one. Finally, we also propose a comparison with classical (i. e. non-few-shot learning) techniques which were published recently. (a) t-SNE of the embedding space from  [19]  (b) t-SNE of our embedding space FIGURE  7 : A two-dimensional projection comparison of the embedding space unsing t-SNE during training.
a: Comparison with few-shot learning approaches The classification accuracy of our models was computed by averaging the results obtained over 200 randomly generated episodes from the testing set for both test cases (5-way-5shot and 5-way-one-shot). We used the traditional measure of computing classification accuracy by comparing the values of the predicted class given by the network to the actual class in the dataset. We can mathematically depict the classification accuracy as: Ψ(y, ŷ) = n i=1 argmax( ŷi , 1) ∼ y i n (9) where Ψ(y, ŷ) is the classification score for the true value y and its predicted output ŷ. n is the total number of data-points in a randomly generated episode. We compute Ψ for 200 episodes and average the overall score to get the final accuracy for the model. Table  1  shows a summarized view of the results on training our network on Urdu characters, while table 2 deals with Urdu Numerals.
In a global overview, we can observe that our proposed method is competitive and outperforms stateof-the-art on the two datasets. In addition to computing the classwise accuracy for characters and numerals, we also compared our model against other techniques in terms of another quantifiable measure, inference time. We found that our few-shot learning approach can operate at a competitive inference time compared to other similar techniques. Table  2  shows the classification accuracy for our method Network/Dataset UHaT  [5]  Dataset used in  [18]  Method Applied 5-Way-5-Shot 5-Way-1-Shot 5-Way-5-Shot 5-Way-1-Shot Matching Networks  [45]  91. 92 ±0. 5% 87. 81 ±0. 4% 89. 22 ±0. 8% 80. 89 ±0. 04% Neural Statistician  [11]  93. We also propose a deep analysis of our performances on how our proposed network can accurately predict a given individual character or numeral.
c: Genericity evaluation of our approach The next part of our evaluation tends to highlight that our approach can embed some generic knowledge from one dataset to propagate it to a second one. To do so, we decided to put our model to the test by training it on a dataset and providing the query set from a completely unrelated set of images. By doing so, we were able to observe whether our model was robust to changes in the structure of the images, and was not learning shortcuts such as visual artifacts unique to a particular dataset, in order to optimize performance. With that in mind, we trained our model on the numerals from the UHaT dataset, and we used the query set of the numerals from the dataset used in  [19]. Our system reached a global accuracy of 90. 81% which is approximately 1. 9 points lower than the accuracy obtained on our previous result, for 5-way-5-shot learning.
However, we can observe that a large part of the results remain relevant, in both 5-way-5-shot as well as 5-way-1-shot, and unchanged even with varied datasets. Moreover, in order to propose a fully comparable evaluation, table 6 presents a comparative study of results obtained on each number. We can observe that the changes in the accuracy have a degree of variance (denoted by Delta). Delta ranges from 1. 1 to 2. 9 for 5-way-5-shot learning, and from 0. 7 to 2. 0 for 5-way-1-shot. We can then conclude that the proposed embedding space is generic enough to be learned on one dataset and tested on another one, even with a very limiter number of samples in the training set.
Network/Dataset UHaT  [5]  Dataset used in  [18]  Method Applied 5-Way-5-Shot 5-Way-1-Shot 5-Way-5-Shot 5-Way-1-Shot Matching Networks  [45]  87. 77 ±0. 8%  replicating the results obtained with very large set of training data used in conventional approaches  [9] ,  [10] ,  [19] ,  [34]. Table  7  shows the comparison between our method and other conventional handwritten character recognition methods for the Urdu language (from the second dataset  [19] ). While our network is not able to outperform these, it comes fairly close to emulating the results with a comparable accuracy for characters. For numerals, classical deep learning approaches using large training sets remains the best.
We found that our proposed approach is more efficient as well as more simpler than other meta-learning approaches, such as Matching Networks and Model Agnostic Meta-Learning (MAML), and is able to produce state-of-the art results on two independently collated datasets consisting of individual examples of Urdu characters and numerals. In our experiment, we trained a Prototypical Network pipeline to classify handwritten Urdu characters and numerals with a minimal number of examples. We also employed an episodic training process, which was proved to be more effective for training  [41] , and proposed a pipeline for its training, consisting of a preprocessing step, an encoding network, the Prototypical Network acting as the classifier, and a post-processing step to account for diacritics. Our method was able to give a good enough accuracy compared to OCR solutions for other languages  [26]  and was able to manage it with a minimal amount of examples.
We used four spectral windows of 1. 875 GHz with 2 MHz channels. For MRC 0114-211 and MRC 0156-211, one of the spectral windows was centered around the redshifted CO line, while the remaining three covered line-free continuum emission. For MRC 2048-272, which had a wider overall CO(1-0) signal (Paper I), two of the four spectral windows were stitched together to cover the CO line, while the other two observed only continuum emission. A standard strategy for bandpass, phase, and flux calibration was adopted as part of the ALMA calibration plan. The ALMA data were reduced with the Common Astronomy Software Applications (CASA; CASA Team et al. 2022). Calibration was performed with the scriptForPI. py calibration scripts that were included with the archival data set, using CASA v. 4. 5. 1 with pipeline v.
Notes. a Units for I CO(J,J-1) are in Jy bm -1 × km s -1. Uncertainties include the uncertainty in the Gaussian fitting, plus an assumed 5% uncertainty in absolute flux calibration of the ALMA data. b The value of r J,J-1/1-0 is the line ratio of the CO luminosities ¢ -L CO J,J 1 ( ) between the ALMA (J = 3/2) and ATCA (J = 1) data from Paper I. This value reflects the excitation conditions of the molecular gas. c For MRC 0114-211, no CO(1-0) is detected in region A in the velocity range of the CO(2-1) detection. We assume a 3σ upper limit across the FWHM of the CO(2-1) detection. d For MRC 0156-252, the CO(3-2) in region A and CO(1-0) in region B1 occur at the same redshift. Because the spatial shift between the CO peaks in these regions is less than half the FWHM of the synthesized ATCA beam, we obtained the line ratio by comparing the peak CO(3-2) emission in region A with the peak CO(1-0) emission in region B1, despite the fact that they are not exactly cospatial in Figure  2  radio sources tend to align with the major axis of the mass distribution on large scales.
Conclusions We presented the detection of CO(2-1) or CO(3-2) emission in the environments of three high-z radio galaxies, MRC 0114-211 (z = 1. 41), MRC 0156-252 (z = 2. 02), and MRC 2048-272 (z = 2. 05). Our ALMA data support previous results from a CO(1-0) survey performed with ATCA, which detected CO(1-0) emission from cold molecular gas in the halo environments of these sources. We derive the following conclusions: 1. The CO is found along the radio axis but beyond the main radio lobe. This confirms that the "alignment effect", which is often observed for different constituents of highz radio galaxies, also applies to cold molecular gas on large scales in these sources. 2. For MRC 0114-211 and MRC 0156-252, part of the observed molecular gas is likely ISM from the host or a companion galaxy, respectively.
Acknowledgments We thank  Donald Terndrup  for useful discussions during the preparation of this paper. We also thank the referee for valuable feedback. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2015. 1. 00897. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI ( Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO , and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the  National Science Foundation  operated under cooperative agreement by  Associated Universities, Inc. The Australia Telescope Compact Array is part of the  Australia Telescope National Facility  (https://ror.
PATIENTS AND METHODS Data collection and validation We invited participating centers to report their data for 2021 using the activity survey as shown in Table  1. Patients receiving their first transplant in the survey year are reported by disease, donor type and stem cell source. Additional information on the numbers of subsequent transplants performed due to relapse, rejection, or those that are part of a planned sequential protocol are reported in summative form. Information on the number of patients receiving un-manipulated donor lymphocyte infusions (DLIs), non-myeloablative or reduced intensity HCT, and the number of pediatric HCT were also collected. In addition, in Table  2 , centers reported information on different types of cellular therapies qualifying as advanced therapy medicinal products (ATMP).
Transplant and cellular therapy rates Transplant rates, defined as the total number of HCT per 10 million inhabitants were computed for each country, without adjusting for patients receiving their HCT in a foreign country. Cellular therapy rates were defined as the numbers of patients receiving a cellular therapy treatment per 10 million population. Population numbers for the European countries in 2021 were obtained from Eurostats: (https://ec. europa. eu/ eurostat) and the World Bank database for the non-European countries: (https://databank. worldbank. org).
We showed partial recovery after the decrease of transplant activity in 2020. The impressive continuous increase in CAR-T cell activity reflects the wide adoption of a newly approved modality supported by academic activities. We show that this expensive new technology is developing almost exclusively in high-income countries. Despite EBMT recommendations for indications for transplant aiming to standardize practice  [4] , there appears to be no 'ideal' transplant activity rate across countries, even with similar economies. Ongoing studies using the EBMT benchmarking model with registry and survey data aim to assess the impact during the Covid-19 pandemic and, more generally, the impact of international variation in activity and clinical practice across countries with similar and variable economies on survival outcomes.
ACKNOWLEDGEMENTS Special thanks go to the cooperation of all participating centers and their staff (listed in the Supplementary Online Appendix). The staff at the EBMT Co-ordination offices; Barcelona, Paris and London. The National Registries; Austria (ASCTR),  Belgium, Czech Republic, France (SFGM-TC) ,  Germany (DRST) Italy (GITMO) , The  Netherlands, Spain (GETH) ,  Switzerland (SBST) ,  Turkey  and the  United Kingdom (BSBMT). The authors also thank  D. John  for database support.
Documentation Pictures of the herring larvae in different stages were taken with a single-lens reflex camera (Canon EOS 80D, lenses: Canon MP-E 65 mm, Sigma EX 105 mm) as well as stereomicroscope (Leica M165C) equipped with a dedicated Leica DFC425 camera using the software Leica Application Suite (Leica Microsystems, version: 4. 9. 0). The pictures were edited using Adobe Photoshop 2022. The final figures for each stage were compiled using Abode Illustrator 2022. Graphs were created using R Studio (4. 0. 5, 31 March 2021, "Shake and Throw"). SL (tip of snout-body-parallel distance -posterior margin of the hypurals or if no hypurals are present to the end of the notochord, respectively) or total length (TL, tip of snout-end of caudal fin) and body height (measured vertically at the insertion of the posterior-most dorsal fin ray, if present, in smaller larvae the measurement was taken at the point of the largest body height of the trunk) of each larva were measured under the microscope to the nearest 0.
This would contradict the stage duration hypothesis and once more illustrates that pure length data are not well suited as proxy for development rates. To evaluate long-term data series, for example, herring larvae surveys, which mostly only record length data, population, or stock specific size-per-stage ranges could be calculated as an approach to analyze the existing length data sets. Therefore, larvae from different years and, if possible, different temperature regimes should be analyzed regarding their stagelength relationship. Then probabilities can be calculated, which show how likely it is, that larvae of a specific size belong to a specific stage for a certain stock or population. With the calculated stock or population-specific probabilities, it would also be possible to analyze data within time series where staging of larvae is not possible anymore.
Acknowledgments The authors would like to thank  Dorothee Moll ,  Anne Georgi  and  Paul Kotterba  from the  Thünen Institute of Baltic Sea  fisheries for their important input toward this study and the constant support with sampling and processing of the herring larvae. The authors would also like to thank  Norbert Rohlf  and  Matthias Kloppmann  for providing larval samples from the  North Sea  and therefore enabling an important extension to this study. Furthermore, the authors would like to thank  Lee A. Fuiman  for providing important literature. Open Access funding enabled and organized by Projekt DEAL. Submitted 27 September 2022 Revised 10 April 2023 Accepted 21 April 2023 Associate editor:  Paul F. Kemp.
| Detecting potential recovery areas We identified potential areas for forest recovery (hereafter "potential recovery areas") as areas that had undergone an initial tree cover transition between 2000 and 2005, before monitoring "realized forest recovery" for the subsequent period, 2005-2020 (Figure  3a ). We identified these using the "Tree Cover Multi-Year Global" (Table  1 ) dataset from Global Forest Cover Change (hereafter "GFCC") as pixels that had <10% tree cover in 2000 (i. e. , <10% tree cover in an area is commonly considered to be unforested;  Putz & Redford, 2010) , which had increased by 2005 to >20% tree cover (i. e. , early succession) and thus showed a substantial initial forest transition, such as that following land abandonment. We chose 20% as a threshold to ensure that the change was substantial and not within the variability and uncertainty of the dataset.
| Assessing realized recovery trajectories We used surface reflectance layers from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI as the highest-resolution datasets continuously available for our study period and selected seven bands (Table  1 ). We masked clouds and shadows using the quality assessment band and clipped the time series to the potential recovery areas. We computed yearly dry season mosaics on a pixelby-pixel basis using Tropical rainfall measuring mission (TRMM) data (NASA, 2021) by creating a mosaic for the driest month across the We harmonized the time series of Landsat 7 ETM+ and Landsat 8 OLI surface reflectance using a harmonization function with linear regression coefficients  (Roy et al. , 2016). We then computed the vegetation indices (Enhanced Vegetation Index -EVI, Normalized Difference Vegetation Index -NDVI, Difference vegetation index -DVI, Relative vegetation index -RVI, Modified Soil Adjusted Vegetation Index 2 -MSAVI2 and Normalized Difference Water Index -NDWI;  Xue & Su, 2017).
| Monitoring of forest recovery trajectories in potential recovery areas To calculate realized forest recovery trajectories, we used three methods: time series of NDWI recovery, forest cover change detection and our newly developed classification of realized recovery trajectories using Z-scores. We computed time series of annual NDWI using loess-smoothing functions, because forest recovery tends to show nonlinear temporal patterns  (Decuyper et al. , 2022). We computed aggregated mean time series for the six tropical Andean countries and for five elevation belts of 500 m width between 1500 and 3500 m a. s. l. We extracted tree cover values from the GFCC dataset for the Across all potential recovery areas in the tropical Andes, Colombia represented 42. 2% (115 km 2 ) of all potential recovery areas.
| Validation of our method Our own adapted version of a Z-score to classify recovery trends in each 5-year interval allowed us to look at short-term temporal dynamics within the 15-year time series, accounting for residual variability of NDWI between time steps, to establish 5-year forest recovery trends by detecting anomalous deviations in NDWI. Our pre-processing of data layers that involved driest month pixel selection and exclusion of steep slopes helps to minimize cloud effects and geometric distortions, which are two common challenges of mountain remote sensing  (Weiss & Walsh, 2009). This gives us confidence that the yearly median mosaic values of NDWI are as accurate as possible and can feed into a reliable Z-score computation. The findings from our realized recovery classification align with the Global Forest Cover Change dataset, which shows analogous instances of disrupted recovery through frequent shifts between low and high tree cover classes between 5-year intervals.
| Avenues for further research Further research could elucidate drivers and solutions for forest recovery. Owing to the mosaicked nature of Andean ecosystem dynamics, high-resolution land-use classifications need to be used to establish environmental conditions related to land-use history and landscape connectivity. In the future, the recently launched Dynamic World classification at a 10 m resolution could be used for this purpose. At present, however, this dataset dates only 7 years back and does not allow the construction of a sufficiently long landuse history to capture legacy effects. In the future, our method could be re-applied and refined using these higher-resolution datasets to establish more fine-scale potential recovery areas and track future forest recovery during the UN Decade of Restoration.
time steps 2005, 2010 and 2015 for all pixels in the potential recovery classes (the dataset terminates in 2015). We grouped the tree cover into five tree cover classes(0-10, 10-25, 25-50, 50-75 and   75-100%)  and assessed changes in tree cover between time steps using Sankey diagrams. Lastly, we computed realized forest recovery trajectories through a look-up table classification of Z-scores for 5-year time intervals since the beginning of forest recovery (Figure3a,c). Z-scores are useful for this purpose because they provide a standardized and relative measure of change accounting for variability and noise of a given pixel and can help to elucidate relative trends over large areas, as opposed to arbitrary thresholds or cut-offs. Inspired by previous before-after comparisons of recovery using vegetation indices (Bright et al.
Forest recovery in potential recovery areas of 2000-2005 across the Andes. (a) Tree cover change from 2000 to 2015 (the dataset terminates in 2015). (b) Normalized difference water index (NDWI) mean time series in different elevation zones. (c) Realized recovery trajectories in different elevation zones. F I G U R E 6 Forest recovery trajectories in the Intag valley (Ecuador). (a) Normalized difference water index (NDWI) time series in potential recovery classes. (b) Tree cover change in potential recovery areas. (c) Map of recovery trajectories across the landscape and zoom into the restored water shed reserve El Paraíso. (d) Area in each recovery trajectory class. | 11 CHRISTMANN et al.
F Forest recovery in potential recovery areas of 2000-2005 across the Andes. (a) Tree cover change from 2000 to 2015 (the dataset terminates in 2015). (b) Normalized difference water index (NDWI) mean time series in different elevation zones. (c) Realized recovery trajectories in different elevation zones. F I G U R E 6 Forest recovery trajectories in the Intag valley (Ecuador). (a) Normalized difference water index (NDWI) time series in potential recovery classes. (b) Tree cover change in potential recovery areas. (c) Map of recovery trajectories across the landscape and zoom into the restored water shed reserve El Paraíso. (d) Area in each recovery trajectory class. | 11 CHRISTMANN et al.
The experimental data in the literature concerning the Paramagnetic Meissner Effect (PME) or also called Wohlleben effect are reviewed with the emphasis on the PME exhibited by metallic, s-wave superconductors. The PME was observed in field-cool cooling (FC-C) and fieldcool warming (FC-W) m(T)-measurements on Al, Nb, Pb, Ta, in compounds such as, e. g. , NbSe 2 , In-Sn, ZrB 12 , and others, and also in MgB 2 , the metallic superconductor with the highest transition temperature. Furthermore, samples with different shapes such as crystals, polycrystals, thin films, biand multilayers, nanocomposites, nanowires, mesoscopic objects, and porous materials exhibited the PME. The characteristic features of the PME, found mainly in Nb disks, such as the characteristic temperatures T 1 and T p and the apparative details of the various magnetic measurement techniques applied to observe the PME, are discussed.
This manuscript is organized as follows. In Section 2, we present the various experimental observations found in the literature. Section 2. 1 gives an introduction to the PME found first in polycrystalline Bi-2212 HTSc samples and compares these reults to the observations of the PME on bulk Nb disks. A third type of PME was observed in YBCO thin films, patterned YBCO structures, and YBCO nanowires, which has only some features in common with the other two cases. In Section 2. 2, the observations of paramagnetic signals upon field-cooling of various metallic superconductors are reviewed. Then, in Section 2. 3, we discuss the various methods applied to observe PME, which is very important to understand the data published in the literature. Section 3 focuses on details of the specific experiments performed in Detroit, Tokyo, and Nancy on bulk Nb disks to elucidate the nature of the PME.