dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets Dataset 1: 32 participants were selected from an ongoing tumor study, including 23 healthy controls, and 9 patients with a diagnosis of glioblastoma. All participants underwent a multi-shell diffusion acquisition with b-values of 300 (15 directions), 800 (30 directions), and 2000 s/mm 2 (64 directions) and 9 unweighted volumes. The data were acquired using a Siemens 3T TIM Trio scanner with a 32-channel head coil and echo planar (EP) sequence with TR=5216 ms, TE=100 ms, at a spatial resolution of 2 x 2 x 2 mm. T1, T2, FLAIR, and T1 contrast-enhanced (T1CE) scans were also acquired for the 9 brain tumor patients. Dataset 2: This dataset comprised 143 brain tumor patients (89 glioblastoma/54 metastasis), putatively representing a range of levels of tumor cell infiltration and extent of edema.
The age range of this data was 19 to 87 (77 females). Single-shell diffusion data were acquired on Siemens 3T Verio scanner with TR=5000ms and TE=5000/86ms, 3 unweighted volumes, and 30 diffusion weighted volumes at a b-value of 1000 s/mm 2. The acquired spatial resolution was 1. 72 x 1. 72 x 3 mm. Additionally, T1, T2, FLAIR, and T1CE were acquired. Dataset 3: This dataset comprised 216 healthy controls from the Philadelphia Neurodevelopmental Cohort (PNC)  [22]. The age range of these selected subjects was 18 to 23 years. There were 118 females and 98 males. The data were acquired on a 3T Siemens TIM Trio scanner, using a 32-channel head coil and a twice-refocused spin-echo (TRSE) single-shot EPI sequence with TR=8100 ms and TE=82 ms, at a b-value of 1000 s/mm 2 for a total 64 weighted diffusion images and seven unweighted images.
Data preprocessing: All datasets were investigated visually for quality assurance (QA). The datasets were denoised  [23]  and corrected for eddy current-induced distortions  [24]. Voxels were resampled to 2mm isotropic resolution. The b0 images were extracted and skull-stripped using FSL's BET tool  [25]. Bias correction of the b0 images was performed with N4BiasFieldCorrection from the Advanced Normalization Tools (ANTs) package. A DTI model was fit within the brain mask using an unweighted linear least-squares method. FA and MD maps of healthy controls in Dataset 1 and Dataset 2 were non-linearly registered to the JHU-MNI-ss ("Eve") atlas  [26]  using the SyN registration algorithm from the ANTs package. Automatic tumor and peritumoral region segmentation was obtained in the patients from Dataset 1 and Dataset 2 using GLISTR  [27] , after co-registering the T1, T2, FLAIR and T1CE images using FSL's flirt tool.
Comparison of single-shell and multi-shell free water estimations Since ground truth values of the free water volume fraction in human data are not available, we used the values estimated through a multi-shell estimation  [28] , utilizing all the shells available in Dataset 1, as a reference standard. We further extracted the b=800 s/mm 2 shell to obtain single-shell data on which the two initialization approaches for free water estimation were applied. Two analyses were performed. First, the single-shell methods were compared to the reference standard by calculating voxel-wise correlation coefficient and mean square error, using the 23 healthy controls which were co-registered to the same atlas. The second analysis was performed in the peritumoral regions of 9 tumor patients to compare the estimated free water of the two single-shell approaches against the reference standard.
The effect of bias field correction and constraints on free water estimation As shown in Equation  3 , the initialization depends on representative b0 signals in both tissue and free water compartments as well as the b0 signal itself. We applied a bias field correction  [29]  on the b0 image, and compared results of free water estimation with and without the application of bias correction. Analysis was performed using co-registered data from Dataset 3. At every voxel, the percentage of controls with artifactual initial estimates of the free water corrected tensor, defined as MD < 0. 40x10 -3 mm 2 /s  [30] , was calculated. In addition, we investigated the effect of constraining the initialized free water values to the extremes of the range [  ,   ], rather than the value 1 2 (  +   ), as described in section 2.
Tractography Tractography was performed on Dataset 2 (143 brain tumor patients) using Diffusion Toolkit  [31] , on tensor images calculated with and without FERNET free water elimination. Tensors calculated without free water elimination are referred to as the "standard" tensor fit. The second order Runge-Kutta algorithm was used for tracking, with an angle threshold of 45 ο , a step size of 1 mm, and an FA threshold of 0. 2. Five bundles of interest (corticospinal tract, inferior longitudinal, inferior fronto-occipital, uncinate and arcuate fasciculi) in each hemisphere were extracted from each tractogram, using the RecoBundles algorithm  [32] , with a pruning parameter of 7 mm. Finally, the "edema coverage", defined as the percentage of voxels in the peritumoral edema region containing one or more streamlines belonging to any of the ten bundles of interest, was computed for every patient with and without free water elimination.
Tractography: Fig. 6  shows the effect on tractography as a result of free water elimination in a large dataset of 143 brain tumor patients (Dataset 2). Fig. 6A  is an example case, showing a comparison of the arcuate fasciculus between standard tensor-based tracking and FERNET-based tracking. In extending this tractography to all the patients, we found that the tractography based on FERNET covered more of the peritumoral region than tractography using the standard tensor fit. This is depicted in Fig. 6  B by a histogram of percent difference in coverage of the edematous region by tracts. In a majority of patients, the percent difference in edema coverage was positive, indicating that tractography on free-water-corrected tensor maps traveled through a larger portion of the peritumoral region.
However, correlations are higher with FERNET (r=0. 71) than with b0 initialization (r=0. 45). Effect of bias field correction and heuristics on corrected diffusion indices and free water volume fraction. Evaluation of FERNET and b0 initialization on a large dataset (Dataset 3) of healthy controls showed that A) data without bias field correction produced more physiologically implausible voxels (defined as corrected MD < 0. 4x10 -3 mm 2 /s) using FERNET than data with bias field correction (B), especially in GM regions and in the cerebellum, which are the areas impacted most by bias. The colorbar represents the percentage of controls with physiologically implausible fits. C) Constraining initial free water estimation to the boundaries of feasible free water values instead of the mean value (see Equation  4 ) yields a free map in the peritumoral region that is more plausible and more aligned with the FLAIR contrast.
DISCUSSION We have introduced a new initialization strategy, FERNET, for the free water elimination problem in singleshell data, aimed at improving the characterization of peritumoral edema using clinically feasible diffusion MRI. The efficacy and wide applicability of our method in estimating free water fraction has been demonstrated comprehensively both on simulated data representing vasogenic and cytotoxic edema, as well as datasets of healthy controls and tumor patients of different tumor types. We have shown that our strategy improves the accuracy of free water elimination for peritumoral regions. Most free water elimination methods to date have been designed for healthy tissue. Developing a method for a pathology, like tumors, is very challenging due to the absence of ground truth. We aimed to address this issue by generating simulated data representing vasogenic and cytotoxic edema using different ground truth diffusivities, anisotropies, and free water volume fractions, and tested our proposed interpolated initialization against the existing b0 initialization method.
A comprehensive investigation of different tractography algorithms, deterministic and probabilistic, is beyond the scope of this manuscript and will be investigated in the future. Our free water map delivers valuable information about the free water content of brain tissue in the presence of pathology. This may be used to distinguish different types of brain tumors and their genetic underpinnings based on the nature of the peritumoral tissue, paving the way for better radiomic markers of cancer. The applicability of FERNET in clinically feasible acquisitions is expected to facilitate investigations of large tumor datasets like ABTC (http://www. abtconsortium. org), that have single shell acquisitions. The tractography tested here was based on single fiber estimation, and approaches with more complicated fiber models may assist in simultaneously resolving partial volume and complex fiber architecture in the context of peritumoral regions.
6º C and a mean annual precipitation of 28. 6 mm. These climatological data confirm that all samples were collected from hyper-arid polar desert environments. While analyzing rocks that were previously collected and returned from the Antarctic is efficient and allowed us to perform this research without the need for costly field work, the use of these samples creates two causes for concern. First, the clasts originate from throughout the Antarctic and correspond to a wide range of environments, from low-elevation coastal regions to high-elevation mountainous and polar environments. Second, the exposure histories of these clasts are largely unknown, which precludes our ability to determine the length of time over which surface alteration processes have been occurring. Regardless, if we assume that the entirety of the Antarctic hosts cold, dry, and stable environmental conditions, and that surface alteration proceeds along the same alteration trends regardless of clast location or exposure history, we are still able to determine whether oxidative weathering processes dominate these sedimentary clasts, whether oxidative weathering is overprinted by more mature alteration processes, or whether any alteration is seen at all.
Mineralogy Measurements XRD analyses were performed on 21 pairs of sample powders at the University of Michigan's Electron Microbeam Analysis Laboratory using a Rigaku Ultima IV diffractometer (3º -65º 2θ) using a Cu-Kα radiation source at 40 kV and 44 mA. The goniometer radius is 285 mm, and the divergence, scattering, and receiving slits are 0. 67º, 0. 67º, and 0. 6 mm, respectively. Data were acquired at a step of 0. 05º at a speed of 1º per minute. Rietveld refinement was performed using the MDI Jade software subsequent to these analyses to quantify variations in mineralogy between sample interiors and surfaces.
CIA values associated with these sedimentary units vary from < 35  (Hurowitz et al. , 2017)  to > 60  (Frydenvang et al. , 2018)  which, if assumed that all sedimentary rocks were basaltic to start  (Taylor and McLennan, 2009) , indicates little to moderate amounts of aqueous alteration has likely occurred under fluctuating climate and environmental conditions during transport, deposition, and lithification  (Hurowitz et al. , 2017). Based on these and additional data from the instruments onboard Curiosity  (Vaniman et al. , 2014; Johnson et al. , 2016; Treiman et al. , 2016; Hurowitz et al. , 2017; Rampe et al. , 2017; Wellington et al. , 2017; Frydenvang et al. , 2018) , it is clear that the oxidation state of sedimentary rock interiors in Gale crater varies widely (Fig. 9 ). Should we expect oxidative weathering to dominate the sedimentary rock surfaces under investigation by the Curiosity rover in Gale crater and, if so, might these weathering processes influence our ability to accurately characterize surface compositions? Unfortunately, determining the specific role of oxidative weathering in Gale crater is a difficult task, as Curiosity's payload does not include a Mössbauer spectrometer and is thus not capable of quantifying the Fe 3+ /FeTotal ratio of geologic materials.
Relationship to Global Oxidative Weathering Processes on Mars Oxidative weathering products were identified using VNIR and TIR spectral datasets as globally significant components of the martian surface  (Salvatore et al. , 2014). Considering that much of the martian surface is composed of sediments and sedimentary rocks (e. g. ,  Grotzinger et al. , 2011; Le Deit et al. , 2015; Edgett, 2016) , which we show are not likely to be dominated by oxidative weathering processes on their surfaces, why might oxidative weathering products be a spectrally significant component of the martian surface, as proposed by  Salvatore et al. (2014) ? To address this seemingly incongruent set of observations, one must consider processes at both the scale of individual rocks (as we have done in this current investigation) as well as those that dominate broader geologic landscapes.
Acknowledgements The authors would like to thank  Dave Murray ,  Joe Orchardo ,  M. Darby Dyar ,  Eli Sklute ,  Jerry Li , and  Chris Raupers  for assistance with laboratory analyses and data interpretations. We would also like to extend our gratitude to  Anne Grunow  at the  Ohio State University's Polar Rock Repository (PRR). This manuscript was improved by the thoughtful comments by two anonymous peer reviewers. This research used samples provided by the  PRR , which is sponsored by the  National Science Foundation Office of Polar Programs. Original sample.
e. multiple soils types, climate zones, land-use types and soil management options;  Ehrhardt et al. , 2018; Smith et al. , 1997). If necessary, the models can be further developed or parameterized using data from the same long-term experiments, or from shorter term experiments, before being evaluated again against a data set not used in development or parameterization  (Smith & Smith, 2007). When the model(s) are deemed to be reliable, they could be applied (a) to derive IPCC Tier 2 emission or SOC stock change factors, which are specific to the region and conditions represented within the region (e. g. Begum et al. , 2018) ; or (b) spatially over the whole landscape (or the entire land area of a country) using spatial databases of soil characteristics, and land cover, management and climate data (Figure  3 , item 4), to directly simulate SOC change and GHG emissions, thereby delivering a Tier 3 methodology to report emissions  (Smith et al.
continuity and consistency under changing boundary conditions, such as biophysical site conditions, climate change, methodologies, socio-economic setting and policy context; (b) adoption of a scientifically and politically (e. g. for GRA, UNFCCC, UNNCCD) appropriate spatial and temporal resolution for the measurements; (c) ensuring continuous quality assurance at all stages of the measurement and monitoring process; (d) measurement/observation and documentation of all potential drivers of SOC and GHG change; and (e) soil monitoring network-collated, georeferenced samples archived and the associated (harmonized) data made accessible through distributed databases to enhance the value of the collated data for multiple uses. In addition to this, soil monitoring networks should be included in a broader cross-method validation programme to ultimately permit spatially and temporally validated comparisons both within and between countries.
An open-access database, where short-or longterm soil C measurements could be uploaded and shared (e. g. https :// datav erse. org/ or an online collaborative platform as used in the CIRCASA project: https ://www. circa sa-proje ct. eu/), would also be of great benefit for progressing a global MRV system. As indicated, the implementation of soil monitoring networks poses several scientific, technical and operational challenges. From an operational point of view, to implement an integrated monitoring system, it will be crucial to overcome initialization costs and unequal access to monitoring technologies. For developing countries, this will require international cooperation, capacity building and technology transfer (de Brogniez et al. , 2011), which could be facilitated within GRA, CCAFS and similar organizations, in synergy with relevant funding mechanisms, or via the recently established 'GSOCseq' programme of the UN FAO (FAO, 2019b).
While other components of a soil MRV framework could be added, the components outlined in Figure 3 could certainly fulfil all of the functions necessary for an MRV system. As seen in Sections 4-7, the existing capacity in terms of existing benchmark sites, soil monitoring programmes and access to models in different countries varies greatly. While some countries are already using Tier 2 and 3 monitoring of soil C change, others have barely begun to build capacity. Recently, the UN FAO has established a programme called 'GSOCseq' (FAO, 2019b) which aims to build this capacity internationally. Programmes such as this could pave the way for making this proposed MRV framework a reality. ACK N OWLED G EM ENTS P. S. , J. F. S. , C. C. , N. B. , M. K. , C. A. and J. O. acknowledge support from the CIRCASA project which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 774378.
Database of the Canadian Soil Information System perennial crops and increase in annual crops) (2014) Change in tillage practices Change in area of summer fallow account cropland management activities. Table 3 provides an over-view of the methods used in GRA countries for estimating carbon stock change and emissions associated with agricultural land use and Land use, tillage, type and amount of input Crop residue, farmyard manure and presence or absence of vegetative cover management activities on mineral soil. There are still high levels of uncertainty in the estimates; however, Denmark uncertainties are relatively low for Annex I countries due to their well- Perennial and organic management systems Average SOC calculated annually per soil type and region based on process-based model (C-TOOL) using developed statistical systems and capacity to use higher tier meth-Tier 3 ods.
In contrast, national inventories of many developing countries data on temperature and estimated C input from crop residues and manure using national databases generally have greater uncertainty and are not sufficiently rigorous Crop type and crop yield Cover crops Residue management Taghizadeh-Toosi and Olesen (2016) to enable monitoring of emissions. For Tier 2 inventory development, Manure application countries could use the expertise of other GRA members, for instance Grassland management France from those countries that have adopted a Tier 3 method (see Table 4) The IPCC Guidelines and OMINEA database to estimate soil organic C stock changes in agricultural land. Tier 1 Land use CITEPA (2019) With increased obligations for reporting on GHG emissions Tillage and Nationally Determined Contributions (NDCs) under the Type and amount of input Japan Paris agreement, it is important that all countries are able to es- Average carbon stock changes in each year by land-use timate their GHG emissions to maximize transparency, accuracy, Tier 2 Carbon input from crop residue Shirato and Taniyama subcategory (rice fields, upland fields, orchards and pastural land) calculated by the Roth C model by the mineral soil area of each prefecture obtained from completeness and consistency.
Australian Bureau of Statistics; GRA, Global Research Alliance of Agricultural Greenhouse Gases. Models used to estimate carbon dioxide emissions and removals from the cropland remaining cropland soils component (Tier 3 method) in GRA countries GRA country Tier Land management activities Reference Spain SOC values calculated by use and province, together with the reference values of the management factors provided by the IPCC Guidelines United Kingdom Review UK relevant literature on the effects of crop-land management practices on soil carbon stocks to model UK-specific emission factors (methodology developed in Defra project SP1113) United States Published literature to determine the impact of management practices on SOC storage. Activity data based on the historical land use/management patterns recorded in the 2012 NRI (USDA, 2018) Model Australia The Full Carbon Accounting Model (FullCAM) Estimates emissions from soil through a process Tier 1 Land use Crop rotations Amount of input Tillage Tier 1 Manure Residue inputs Crop type (perennial, cropland, set-aside) Tier 2 Tillage Tier 2 Tillage Cropping rotations Intensification Land-use change between cultivated and uncultivated conditions Reference involving all on-site carbon pools (living biomass, dead organic matter and soil) on a pixel by pixel (25 m × 25 m) level Richards (2001) Canada CENTURY Process model used for estimating CO 2 emissions and removals as influenced by management activities, based on the National Soil Database of the Canadian Soil Information System Parton, Schimel, Cole, and Ojima Rovira et al.
The Merge-Label-Gap Transition System The Merge-Label-Gap transition system (henceforth ML-GAP) combines the distinction between structural and labelling actions from  Cross and Huang (2016b)  and the SHIFT-REDUCE-GAP (SR-GAP) strategy with a split stack from  Coavoux and Crabbé (2017a). Like the SR-GAP transition system, ML-GAP is based on three data structures: a stack S, a double-ended queue (dequeue) D and a buffer B. We define a parsing configuration as a quadruple S, D, i, C , where S and D are sequences of index sets, i is the index of the last shifted token, and C is a set of instantiated discontinuous constituents. We adopt a representation of instantiated constituents as pairs (X, I), where X is a nonterminal label, and I is a set of token indexes. For example, the discontinuous VP in Figure  1  is the pair (VP, {1, 2, 3, 4, 6}), because it spans tokens 1 through 4 and token 6.
Data To evaluate our models, we used the Negra corpus  (Skut et al. , 1997) , the Tiger corpus  (Brants et al. , 2002)  and the discontinuous version of the Penn Treebank  (Evang and Kallmeyer, 2011; Marcus et al. , 1993). For the Tiger corpus, we use the SPMRL split  (Seddah et al. , 2013). We obtained the dependency labels and the morphological information for each token from the dependency treebank versions of the SPMRL release. We converted the Negra corpus to labelled dependency trees with the DEPSY tool 3 in order to annotate each token with a dependency label. We do not predict morphological attributes for the Negra corpus (only POS tags) since only a small section is annotated with a full morphological analysis. We use the standard split  (Dubey and Keller, 2003)  for this corpus, and no limit on sentence length.
For the Penn Treebank, we use the standard split (sections 2-21 for training, 22 for development and 23 for test). We retrieved the dependency labels from the dependency version of the PTB, obtained by the Stanford Parser (de  Marneffe et al. , 2006). We used the relevant module of discodop 4 (van Cranenburgh et al. , 2016) for evaluation. It provides an F1 measure on labelled constituents, as well as an F1 score computed only on discontinuous constituents (Disc. F1). Following standard practice, we used the evaluation parameters included in discodop release (proper. prm). These parameters ignore punctuation and root symbols.