dataesr/scientific-paragraphs-categorization huggingface dataset:
Multi-resolution analysis (MRA) A discrete wavelet analysis was used to identify the contributions to the overall isotopic variability from signals of different periodicities (i. e. corresponding to decadal to multimillennial signal variability). We produced a multi-resolution analysis (MRA) using R software with the waveslim wavelet package  (Whitcher, 2020)  containing the MRA function with a Daubechies orthonormal wavelet filter. MRA is a mathematical analysis tool which decomposes a signal at different resolution levels. An important feature of MRA is its ability to capture temporally localized changes at its nearest neighbour. A low (high) resolution level corresponds to a coarse (detailed/high-frequency) component of the original signal. Each MRA level can thus be used to interpret the temporal variability within a frequency range.
Coherency of different analytical measurements Different analytical instruments and techniques have been used to determine δ 18 O and δD in the EDC1 and EDC2 ice cores at different laboratories (Table  1 ). To determine the coherency of the different datasets, two different comparisons were performed; (1) a comparison of the isotopic values from the same samples measured by different analytical techniques; and (2) a comparison of the 55 cm sample resolution data with the 11 cm sample resolution data using a five-point average. We averaged the 11 cm resolution in a five-point window to compare it with the 55 cm resolution measurements in exactly the same window.
9 % confidence level (t = 3. 5, N = 1000) with respect to the experimental uncertainties. Finally, the distribution of the differences between the 2010 and the 2019 δD measurements is not Gaussian and is not centred around zero (Fig. 2c ). Still, this distribution is narrow and is encompassed within a Gaussian distribution where 2σ = 1. 4 ‰ is associated with the classical analytical uncertainty of the δD measurements. Note that the analytical uncertainty associated with this CRDS measurement series was evaluated from the analysis of the difference between the same samples (1000 samples, which represent 10 % of the whole series) measured twice, 1 to 3 months apart. We thus conclude that the δD difference between the uranium reduction vs. CRDS datasets is smaller than the uncertainty associated with CRDS measurements, and thus that we can com-  bine the different datasets if we consider a 2σ uncertainty of 1.
557, N = 1000). Second, we compared the low-resolution (55 cm) and the high-resolution (11 cm) δ 18 O series after calculating the average δ 18 O of five 11 cm samples that overlap with the same sample depth as the 55 cm samples (Figs. S2 to S4 ). The difference between the two time series is 0. 008 ± 0. 001 ‰ (Fig. S4 ). A comparison between low-and high-resolution series for δD has already been performed by  Pol et al. (2011 Pol et al. ( , 2014)). In the 2011 paper, the coherency between the 55 and 11 cm samples was studied by calculating the average signal over five 11 cm data. They observed that the signal from the 55 cm samples is similar to the average signal but has a lower statistical accuracy (1σ = 0. 5 ‰) than the average signal (1σ = 0. 23 ‰). The two comparisons performed above suggest there is no statistically significant difference in δ 18 O and δD between the datasets compiled here (Fig.
Conclusion Here, we compiled and presented a EDC ice core water isotopic record (δ 18 O and δD) using new and previously published 11 cm data spanning the last 800 kyr. This compilation and the comparison performed between different series of measurements showed that the differences between water isotopic data measured by different laboratories and techniques over the last 20 years for the same samples are not statistically significant and are within analytical uncertainty. As a result, all the available EDC water isotope data were combined to produce a continuous high-resolution dataset at mostly 11 cm sample resolution. MRA decomposition of the water isotopic record at temporal resolutions varying between 10 and 2560 years shows that the variability during glacial periods at multi-decadal to multi-centennial timescales is higher than the variability during the Holocene and that the variability is enhanced over the early temperature optimum during MIS 5 and 9.
Data availability. The data for this study can be found at https://doi. org/10. 5281/zenodo. 7086012 (Grisart et al. , 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-2289-2022-supplement. Author contributions. VG, BV, BM, FP, BS, JJ, AL and AG performed the measurements. PN and MV helped on the statistic approach. AG led the data analyses and the writing of the manuscript with the active contribution of all co-authors. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "Oldest Ice: finding and interpreting climate proxies in ice older than 700 000 years (TC/CP/ESSD inter-journal SI)".
Acknowledgements. This work is a contribution to the European Project  for Ice Coring in Antarctica (EPICA ), a joint  European Science Foundation  and  European Commission scientific program  funded by the  European Union  and by national contributions from  Belgium, Denmark, France, Germany, Italy, Netherlands, Norway, Sweden, Switzerland  and the  United Kingdom. The main logistic support was provided by  Institut Polaire Français Paul-Emile Victor and Programma Nazionale di Ricerche in Antartide. Antoine Grisart acknowledges the support from the  European Research Council. We would like to thank  Nancy Bertler ,  Andrew Moy  and an anonymous referee for their careful evaluation and thoughtful comments. Financial support. This research has been supported by the  European Research Council ,  H2020   European Research Council (ICORDA  (grant no.
Prostate cancer diagnosis is performed under ultrasound-guided puncture for pathological cell extraction. However, determining accurate prostate location remains a challenge from two aspects: (1) prostate boundary in ultrasound images is always ambiguous; (2) the delineation of radiologists always occupies multiple pixels, leading to many disturbing points around the actual contour. We proposed a boundary structure-preserving U-Net (BSP U-Net) in this paper to achieve precise prostate contour. BSP U-Net incorporates prostate shape prior to traditional U-Net. The prior shape is built by the key point selection module, which is an active shape model-based method. Then, the module plugs into the traditional U-Net structure network to achieve prostate segmentation. The experiments were conducted on two datasets: PH2 + ISBI 2016 challenge and our private prostate ultrasound dataset.
(30). In summary, boundary information is helpful to improve segmentation accuracy by combining it with a neural network. However, boundary representation remains a challenge in two aspects: (1) the contour of the existing dataset is implicit because the ground truth always provides semantic segmentation (Figure  1A ), and (2) the actual contour is surrounded by many disturbing points (Figure  1E ). In our work, we proposed a model that combines boundary information with deep learning for prostate ultrasound image segmentation. The contributions of our work are as follows:  (1)  we built an end-to-end neural network for prostate ultrasound segmentation; (2) an ASM-based method was utilized for boundary information extraction; (3) the key point selection models were easy to plug into any network.
Materials Two medical image segmentation datasets, namely, PH2 + ISBI 2016 Skin Lesion Challenge  (31, 32)  and our private dataset, were applied in the experiments. The PH2 + ISBI 2016 dataset, which is a publicly available dataset for evaluating skin lesion segmentation, includes 900 multi-size skin lesion images and 200 dermoscopic images. The private dataset is from the Second People's Hospital of Changzhou Affiliated with Nanjing Medical University, which was approved by the Ethics Committee of the Second People's Hospital of Changzhou. All subjects agreed to participate in the study and signed the informed consent. All data, including 100 prostate images, are desensitization data. The ground truth was based on the average delineation from three radiologists.
BSP U-Net Implementation Details All the experiments were implemented in TensorFlow 1. 4. 0 with an NVIDIA GeForce RTX 2080Ti GPU. We used a robust model training schema by 10-fold cross-validation and randomly shuffled all images. We trained the model by Adam optimization algorithm with an initial learning rate of 1e-4, a maximum epoch of 1,200, and a batch size of 8. The automatically saved optimal model was used to evaluate the testing set. We evaluated the proposed method by the test set. We trained and tested other classic deep-learning based models, including U-Net  (27) , FCN (3), and Lee's network  (30) , using the same dataset in the paper to evaluate the performance of our network objectively.
We conducted experiments on two datasets. PH2 data: Compared with SCDRR, the segmentation accuracy of our method improved by 11. 56% for DSC and 16. 55% for Jaccard coefficient (JC) as Table  2  shown. Compared with JCLMM, the segmentation accuracy of our method improved by 15. 80% for DSC. Compared with MSCA, the segmentation accuracy of our method improved by 17. 62% for DSC and 22. 47% for JC. Compared with SSLS, the segmentation accuracy of our method improved by 22. 40% for DSC and 29. 36% for JC. Compared with FCN, the segmentation accuracy of our Comparison of boundary detection. The first column is the original image, the second column is the boundary detection based on Lee's method, and the third column is the boundary detection based on the proposed method. method improved by 7. 30% for DSC and 7.
28% for DSC, and 2. 08% for JC. The boundary preservation performance in the encoder layer was better than that in the decoder layer; the segmentation accuracy   improved by 2. 57% for PA, 1. 45% for MIOU, 1. 44% for DSC, and 2. 63% for JC. The reason is that the decoder layer also can obtain boundary information using boundary preservation in the encoder layer. The shape constraint is augmented into the information forward transmission. This work focused on tumor and gland segmentation for ultrasound images with smooth and non-convex targets. The generation of key points would drop some points when encountering the bottleneck. In the future, we will conduct further research on tissues containing a bottleneck and to make our method effective on convex and non-smooth objects. In addition, considering the relatively small dataset of ultrasound images, the 10-fold cross-validation was adopted to evaluate the proposed method.
Yan et al. proposed a discrete deformable model guided by partial ASM for transrectal ultrasound image segmentation (20). Bi et al. proposed a fast and accurate segmentation method using ASM with Rayleigh mixture model clustering for prostate ultrasound images (21). Remarkably, authors started to combine shape information and deep learning. Mishra et al. proposed an FCNN with attention to boundaries conducted on the MICCAI 2011 IVUS challenge dataset resulting in a Dice index value of 0. 91 (22). Chen et al. developed a new model based on deep learning, which takes into account boundary information (23). Murugesan et al. proposed a Psi-Net that joins shape and boundary into a multitask deep network that aids in ensuring the smoothness of segmentation prediction (24). Nguyen et al. proposed a consecutive deep encoder-decoder network combined with a boundary-emphasization data augmentation (25).
Acknowledgements This publication was made possible through support provided by the  IRD-DPF  and the  MAGNET Program  of CONCYTEC Nº007-2017. This work was supported by the  International Joint Laboratory "PALEOTRACES" (IRD, France ;  UPMC, France ;  UFF , Brazil; UA, Chile;  UPCH ,  Peru ) and  ANR-15-JCLI-0003-03 BELMONT FORUM PACMEDY. Grain-size analyses were performed on the ALYSES facility ( IRD-Sorbonne University, Bondy, France ), which is supported by grants from  Région Ile-de-France. This work is a contribution of the Collaborative Research Project  754  " Climate-Biogeochemistry interactions in the Tropical Ocean " (www. sfb754. de),which is supported by the  Deutsche Forschungsgemeinschaft (DFG). We would like to thank the crew and scientists aboard R/V Meteor cruises M77/2 in 2008.
15 O(α,γ) 19 Ne is regarded as one of the most important thermonuclear reactions in type I X-ray bursts. For studying the properties of the key resonance in this reaction using β decay, the existing Proton Detector component of the Gaseous Detector with Germanium Tagging (GADGET) assembly is being upgraded to operate as a time projection chamber (TPC) at FRIB. This upgrade includes the associated hardware as well as software and this paper mainly focusses on the software upgrade. The full detector set up is simulated using the ATTPCROOTv2 data analysis framework for 20 Mg and 241 Am.
These measurements will proceed via the 20 Mg(βpα) 15 O decay sequence using β decay of 20 Mg followed by a proton-emission to the 4034 keV 19 Ne state. We are currently upgrading GADGET's Proton Detector into a TPC with the goal of resolving and identifying the proton and the α-particle tracks. A high granularity MICROMEGAS board with 1024 (2. 2×2. 2 mm 2 ) pads and high-density GET electronics  [8]  has been installed to accommodate the large number of electronics channels. In order to test the functionality of TPC, a 241 Am source is placed inside the detector near the upstream end. The TPC has been simulated using the ATTPCROOTv2 data analysis framework based on the FairRoot package for 20 Mg and 241 Am decay events. LISE++ simulations were also performed in order to model the FRIB beam properties and purity.
The scattered points between 10-30 mm in Figure  1  (b) refers to the β-particles from the decay of 20 Mg to 20 Na. 241 Am α-decay events were also simulated as shown in Figure  1  (c) and Figure  1 (d) , respectively. Based on these simulations, a machine learning algorithm is being developed that will be integrated with the ATTPCROOTv2 analysis framework to identify candidates in the data. Transfer learning will EPJ Web of Conferences 260, 11034 (2022) NIC-XVI https://doi. org/10. 1051/epjconf/202226011034 be used to refine the machine-learned models after the experiment using real in-situ data on 20 Mg(βpα) 15 O events for single protons and 20 Mg(βα) 16 O daughter-decay events for single alphas. LISE++ simulations were performed using narrow slit settings in the pre separator stage to reduce the momentum acceptance to 0.
Details on this classification harmonization and mapping are reported in a dedicated publication  (Bassignana et al. , 2021). It is important to highlight the following features: 1. A total surface of ∼69 km 2 was mapped (i. e. , 17% of the grassland surfaces of the park), corresponding to the main pastoral districts (Fig. 1 ). These were digitized and stored as polygonal vectors. 2. Thirteen categories were established based on the physiognomy of the pasture, the topography (i. e. , altitude, aspect and slope), the productivity and the presence of dominant species (Table  1 ). In particular, productivity ranges and potential stocking rates were assigned to the thirteen categories based on a historical database of dry biomass production  (Bornard et al. , 2006; Jouglet, 1999) , thus establishing a direct linkage between pastoral categories and productivity (Table  1 ).
, 2019). Values of this index in autumn are particularly effective in the discrimination between grasslands and shrubs such as Ericaceae  (Bayle et al. , 2019). The Copernicus tree cover density layer for 2018 (20-m spatial resolution, Copernicus, 2018c) was used to define dense and sparse forest areas. The Copernicus Grassland layer for 2018 at 10 m spatial resolution (Copernicus, 2018b) was used as a comparative layer for the grassland absence/presence map. Finally, a maximum composite Leaf Area Index was computed from Sentinel-derived S2 multi-temporal Leaf Area Index images obtained with the biophysical processor available in the Sentinel-2 toolbox  (Xie et al. , 2019; Weiss and Baret, 2016). Although this layer cannot be considered as fully independent, being itself derived from Sentinel-2 data stream, it was used as a comparative layer for the grassland productivity map.
Dynamic time warping analysis Multitemporal NDVI and NARI stacks were used to perform a dynamic time warping analysis (DTW,  Giorgino, 2009). DTW is a suite of algorithms that allows computing distances between time series. We used an implementation of the algorithms specific for satellite data available in the R package dtwSat  (Maus et al. , 2019). We first identified areas with distinct trajectories of NDVI and NARI belonging to 1) dense forest (tree cover density 80%), 2) sparse forest (tree cover density ∼30%), 3) highly productive pastures, 4) medium productive pastures, 5) low productive pastures, 6) bare soil. For each of these land cover categories we extracted average NDVI and NARI seasonal trajectories representing the templates which each pixel was compared to (supplementary material, fig.
2, model 13L). The caret package was used to fit RF models. It allows for self-tuning of the mtry parameter (the number of variables randomly sampled as candidates at each split) via resampling methods. We chose the repeated cross-validation method with 10-fold CV and 10 repetitions. This means that the 10-fold crossvalidation is repeated 10 times, each time varying the mtry parameter. The parameters mtry of the final models chosen after self-tuning was 5 for all three models. The other three random forest parameters were deliberately kept constant for the three models, i. e. , ntree was set to 500, nodesize was set to 1, as recommended for classification problems, and maxnodes was not set (i. e. , trees can grow to the maximum possible number of terminal nodes). Table  3  summarizes the predictors used in the models.
All predictors were normalized prior to model fitting. Random forest performance was evaluated by means of the overall accuracy and the kappa (unweighted) coefficient  (Cohen, 1960). A p-value from McNemar's test  (McNemar, 1947)  was also computed and only significant (p<0. 05) models are discussed. When pertinent, omission and commission errors were also evaluated. For more than two classes, these errors were calculated comparing each factor level to the remaining levels (i. e. , a "one versus all" approach). For each of the three models, a multiple linear regression model was fitted with the same data input in order to provide a benchmark null model useful to evaluate RF model performance. Variable importance was calculated as mean decrease in accuracy. For the 3L model, partial dependency was evaluated with the pdp R package  (Greenwell, 2017).
Grassland mask (GM) A subset of CDN (90 k pixels, approximately 10% of the data, randomly selected) was used for the random forest model. Data was then split: 75% was used for model training and 25% for a first assessment of validation. Model output represents a 3 level outcome (grasslands, forests and ice/water/rocks). This outcome was then converted to a binary absence/presence vector and, after prediction over the spatial extent of the GPNP, converted to a gridded grassland mask at 20 m spatial resolution (GM). Model performance was assessed trough five steps, summarized in Table  4. First, accuracy was assessed on the 25% validation subset (labelled GM-V1). Second, a confusion matrix was computed on the extent of the CDN map by comparing the predicted map GM against CDN (labelled GM-V2).
This represents an extension of crossvalidation because it includes all pixels not used for training. Third, GM was compared against the Copernicus Grassland layer (labelled GM-V3). This layer is considered as a partially independent layer, being derived itself from S2-data but trough completely different processing steps compared to our approach (Copernicus, 2018b). Fourth, GM was compared against a spatial subset of the Copernicus Grassland layer (see Fig. 2 ), in a region of GPNP where calibration data (CDN) was not available (GM-V4). This represents a spatially independent validation. Fifth, GM accuracy was tested against the 69 km 2 mapped in PASTOR-ALP surveys (GM-V5). Because field mapping was deliberately not exhaustive of all the grassland surfaces of the park, only omission errors could be quantified in GM-V5.
Productivity (3L) and pasture category (13L) models Data collected in the framework of the PASTORALP project surveys was used as training data. Random forest models were trained on bestquality ground truth data, corresponding to fully grazeable areas (see Table  1  for the definition and acronyms of grazeable areas). The following validation steps are valid for both the 3L and the 13L models described above. Similar to the GM model, data was split (75% for model training and 25% for a first assessment of validation). This first type of validation is labelled 3L-V1 and 13L-V1 for the 3L and 13L models, respectively. Second, models were validated against the whole set of data from the surveys including partially grazeable surfaces (HG) and poorly grazeable surfaces (PG, see Table  2 ). This second validation exercise is labelled 3L-V2 and 13L-V2 for the 3L and 13L models, respectively.
The complete ground truth dataset was used to train two additional random forest models (training data constituted by FG +HG, and by FG+HG+PG), in an attempt to improve model prediction by increasing the quantity of ground truth data at the expense of its quality. These additional data sets were used to fit both 3L and 13L Models. To examine the capacity of our models to discriminate between the various pasture types, we selected four markedly different pastoral categories (A-II, A-I , S-II, and S-I ) and extracted NDVI trajectories for 500 randomly selected pixels for each category. These NDVI time series were spatially and temporally averaged resulting in an average-year trajectory for the four pasture categories. All the statistical analyses, processing and graphics were conducted with the R Software for Statistical Computing, version 3.
Detection of grasslands We predicted a total grassland area of 300 km 2 , corresponding to ∼ 42% of the GPNP surface. The distribution of grassland surfaces (grassland mask, GM) was modelled with an accuracy ranging between 0. 76 and 0. 88, depending on the product used as ground truth (i. e. , CDN or Copernicus Grassland). Pazúr et al. (  2021 ) classified grassland distribution using random forest classifiers across the whole Switzerland achieving accuracies ranging between 0. 85 and 0. 93, depending on the layers used for comparison. Similarly,  Guo et al. (2020)  reported accuracies of 0. 85-0. 96 in mountain grassland detection in China, using deep learning methods. These results, obtained in similar environments, and with comparable approaches, are in line with our findings. Some studies coupled multisource optical remote sensing with other data sources such as SAR, followed by data reduction techniques  (Badreldin et al.