dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Discussions In this study, we developed a selective logging module in FATES and parameterized the model to simulate different logging practices (conventional and reduced impact) with various intensities. This newly developed selective logging module is capable of mimicking the ecological, biophysical, and biogeochemical processes at a landscape level following a logging event in a lumped way by (1) specifying the timing and areal extent of a logging event; (2) calculating the fractions of trees that are damaged by direct felling, collateral damage, and infrastructure damage, and adding these size-specific plant mortality types to FATES ; (3) splitting the logged patch into disturbed and intact new patches; (4) applying the calculated survivorship to cohorts in the disturbed patch; and (5) transporting harvested logs off-site and adding the remaining necromass from damaged trees into coarse woody debris and litter pools.
Future work Currently, the selective logging module can only simulate single logging events. For regionalscale applications, it will be crucial to represent forest degradation as a result of logging, fire, and fragmentation and their combinations that could repeat over a period. Therefore, we will enable structural changes in FATES to track disturbance histories associated with fire, logging, and transitions among land use types. Nevertheless, this study lays the foundation to simulate land use change and forest degradation in FATES, leading the way to direct representation of forest management practices and regeneration in Earth System Models. We also acknowledge that as a model development study, we applied the model to a site using a single set of parameter values and therefore we ignored the uncertainty associated with model parameters.
Acknowledgements This research was supported by The  Next-Generation Ecosystem Experiments -Tropics  project through the  Terrestrial Ecosystem Science (TES) program  within  US Department of Energy's Office of Biological and Environmental Research (BER). RF acknowledges the  National Science Foundation  via their support of the  National Center for Atmospheric Research. M. L. was supported by the  São Paulo State Research Foundation  ( FAPESP , grant  2015/07227-6 ).
Code and data availability FATES-CLM has two separate repositories for FATES and CLM at: https://github. com/NGEET/fates/releases/tag/sci. 1. 6. 0_api. 3. 0. 0 https://github. com/NGEET/fates-clm/releases. Site information and data at km67 and km83 can be found at http://sites. fluxdata. org/BR-Sa1 and http://sites. fluxdata. org/BR-Sa13. A README guide to run the model and formatted datasets used to drive model in this study will be made available from the open-source repository XXXXX upon acceptance of the manuscript.
Autonomous driving systems tightly rely on the quality of the data from sensors for tasks such as localization and navigation. In this work, we present an integrity monitoring framework that can assess the quality of multimodal data from exteroceptive sensors. The proposed multisource coherence-based integrity assessment framework is capable of handling highway as well as complex semi-urban and urban scenarios. To achieve such generalization and scalability, we employ a semantic-grid data representation, which can efficiently represent the surroundings of the vehicle. The proposed method is used to evaluate the integrity of sources in several scenarios, and the integrity markers generated are used for identifying and quantifying unreliable data. A particular focus is given to real-world complex scenarios obtained from publicly available datasets where integrity localization requirements are of high importance.
LiDAR A subset of LiDAR data containing points that lie inside a 3D region of interest (ROI) is selected. Points on the road and on the edges of the road are classified using 3D gradients. The ROI is divided into smaller patches in the XY plane, and the points belonging to each patch are examined for their Z values. This helps to differentiate between road segments, curbs, dividers, vegetation, etc. using the technique presented in  [21]. Points with high reflectivity are also selected as they correspond to the bright surfaces such as lane markings and railings. These are further classified into reliable lane marking detections by combining their position with road regions. As a result of these detection steps, every point in the ROI is classified with lane markings, roads, other surfaces, or unclassified.
The min-max normalization operator x min-max is defined as xnorm = xxmin xmaxxmin. Hence, the total representation of data from sources will have two components: the labels and their importance, which is denoted by the weights. Other source-specific weighting functions using homography of image transformations and LiDAR data acquisition models can also be used for this purpose. However, data sources like maps use uniform weights for all the cells in their FGs due to the fact that they are not measured but just extracted.
The block diagram for the localization optimization in the ego frame of the vehicle with decision criteria is given in Algorithm 1. In the first step, new position and orientation measurements from GPS and IMU are compared with the current best localization estimate. If the new measurements (x m : [x m , y m , θ m ]) are not within the non-holonomic constraints of the current state (X state : [x state , y state , θ state ]) of the vehicle, they are detected as an outlier  [26]. Conversely, consistent position and orientation measurements are used to render a map from the database and the coherence between FGs of the map and other sources is computed. If sufficient coherence is observed (greater matching than the empirically-derived threshold for f m (s i FG, s j FG) considering different sensors and scenarios), localization optimization is not performed and the data representations from each source are used for integrity assessment.
Experiments and Discussions Experiments are conducted with scenarios available in the KITTI benchmark suite  [27]  to establish proof of concept. Real-Time Kinematic (RTK) GPS fixes in these datasets are added with noise generated using the GPS-noise simulation model proposed by  [28]  to simulate poor GPS localization fixes. Outliers that are higher than the 2σ variance of the GPS-noise simulation model are used to replace RTK GPS fixes at random sections of the trajectory. Finally, 5% of the RTK GPS fixes are randomly removed from the trajectory to emulate GPS outages as they may occur in generic GPS receivers. Since different data sources have different spatial ranges, a 3D region of interest (ROI) in the vehicle's ego frame is established. its limits in XY plane are chosen as 25 m in front of the vehicle (positive X axis), 15 meters behind (negative X axis), and 15 m at each side (Y axis).
Since vision cannot provide data in the back of the vehicle as well as to the front bumper of the vehicle, the ROI of vision is limited from 3. 5 m to 25 m along the positive X axis. Even though the vision data used in this work does not cover the back view of the vehicle, the other two major sources-LiDAR and map-can provide information in the back of vehicle, hence justifying the choice of the limit in negative X axis. The discussion on the results has three parts. Firstly, comparing the performance of the proposed method to the method in  [18]. This includes a comparison of integrity markers in the datasets presented in  [18]  and showcasing the improvements provided by the new method in handling fault and feasibility predictors (FPs) produced by the previous method. FPs are the markers generated when the fitting of the common model to the data sources is not possible or feasible.
These markers suggest the limitations of the method in  [18] , which mainly arise when the method is applied on non-highway scenarios. The set of five FP markers defined is • FP m : Not enough nodes in the map for model fitting; • FP v : Not enough lane markings for model fitting; • FP g : GPS measurement is not available or an outlier; • FP s : Vehicle not moving or moving very slowly; • FP t : Vehicle performing a hard turn. The second part of this discussion considers more datasets in semi-urban and urban scenarios to evaluate the integrity estimation of sources in complex situations such as junctions, road splits, and merges, etc. In the final part, we compute classical integrity markers from our framework and compare them with values presented in  [3].
Integrity Marker Comparison In this section, we compare the results presented in  [18]  with the results obtained from the new method. The key difference between these two methods is the parameter they use for the integrity computation. The former uses the error observed in model fitting to evaluate integrity, whereas the latter uses coherence between data representations to achieve the same. Hence, the contribution of error by each sensor and the contribution of coherence by each sensor are used for this analysis of the results of these methods, respectively. The same errors are introduced in the GPS for each algorithm, and the results obtained from the dataset 2011_09_26_drive_0029 are shown in Figure  4. The primary advantage of the proposed method is the ability to evaluate the integrity in conditions where FPs are produced due to the limitations of the model-based integrity analysis employed in  [18].
In Figure  6 , the results of integrity assessment in a highway scenario are presented, where the old method reliably performed. The FP m instances observed in this dataset are due to the lack of map nodes to reliably fit the polynomial model in straight line road sections. In the new method, the model fitting is replaced with FG data representation, which eliminates such errors in modeling. The comparison of integrity markers in specific cases presented in  [18]  with the integrity markers provided by the new method is given in Table  1. A general tendency of improved integrity values is observed across all datasets and scenarios. For example, in the second row of Table  1 , the integrity weight of vision computed using the old method was lower due to the improper detection of curved lane markings as straight lane markings.
Complex Situations This section is dedicated to analyzing the behavior of the integrity assessment system in some of the selected complex scenarios present in the KITTI dataset. In Figure  7a , an example of a semi-urban road junction is shown. Due to the lack of information from the map, the rendering process failed to reconstruct the continuity of lanes at the intersections. On the other hand, vision and LiDAR data detected all of the branch roads at the junction and managed to perceive the width of each of these road sections accurately. This results in a lower integrity value for the map at this junction (Frame numbers: 310-320) compared to other sources, shown in Figure  4c. One of the main reasons behind the proposed data representation is the fact that it is an improvement over other existing geometrical models for intersections, which fail to accommodate partially correct data.
Conclusions This work presents a framework for integrity monitoring of sources used in the localization of autonomous vehicles. The limitations of common geometrical models in representing multimodal data sources are identified in this work. To overcome these issues, a semantic feature grid model is proposed that can geometrically represent different features using labels. A function for coherence evaluation between feature grids is formalized to iteratively optimize the localization as well as to assess the integrity of data sources. The framework is tested using different scenarios from datasets, and the results show the versatility of the proposed model, which is able to provide reliable and consistent integrity estimation in highway as well as semi-urban and urban environments. This method is proven robust against inconsistencies in feature detections such as partial detections, occlusions, and poor map rendering.
Error from each sensor used in dataset 2011_09_26_drive_0029 using the previous method. Red: GPS error; Green: Map error; Blue: Vision error; Green dotted lines: FP m ; Blue dotted lines: FP v ; Light brown: FP s ; Light blue: FP t stop turn poor vision (b) Coherence observed for each sensor used in dataset 2011_09_26_drive_0029 using new method. Red: LiDAR; Green: Map; Blue: Vision Integrity Markers for dataset 2011_09_26_drive_0029 using the new method. Red: LiDAR; Green: Map; Blue: Vision.
Participants The datasets used in the present study were selected retrospectively from healthy control subjects of several imaging studies, across multiple centers and different populations across India. They included imaging data from the ongoing Indian multi-site developmental cohort study, the Consortium on Vulnerability to Externalising Disorders and Addictions (cVEDA)  [Sharma et al. , 2020 , Zhang et al. , 2020]  and from stored datasets contributed by researchers at the National Institute of Mental Health and Neurosciences (NIMHANS, Bengaluru, India). All of these studies were approved by the ethics review boards at the corresponding participating sites and informed consent was obtained from each participant (or from their parent, in the case of subjects below 16 years, along with participant's written assent) with a specific request to collect, store and share anonymized data for research.
Inclusion criteria included not having a personal history of prior brain injury, neurological disorder or psychiatric diagnosis. The sample was comprised of 466 subjects from a large number of states across India and acquired at multiple sites. Based on age and demographic distributions, subject datasets were divided into 5 groups: C1, late childhood (6-11 years); C2, adolescence (12-18 years); C3, young adulthood (19-25 years); C4, adulthood (26-40 years); C5, late adulthood (41-60 years). The sample size and demographic information of each cohort is summarized in Table  1.
Data Preprocessing and Initial Quality Assurance This processing primarily used programs in the AFNI (v19. 0. 20)  [Cox, 1996]  and FreeSurfer (v6. 0)  [Fischl, 2012]  neuroimaging toolboxes, as well as the "dask" scheduling tool in Python developed by the Dask Development Team  [2016]. Unless otherwise noted, programs named here are contained within the AFNI distribution. The following processing steps are shown schematically in Figure  1 , in the first column. Datasets were first processed using AFNI's "fat_proc_convert_dcm_anat". Using this, DICOMs were converted to NIFTI files using dcm2niix_afni (the AFNI-distributed version of dcm2niix  [Li et al. , 2016] ). For uniformity and initialization, with this tool, they were also given the same orientation (RAI), and the physical coordinate origin was placed at the volume's center of mass (to simplify later alignments).
Next, "fat_proc_axialize_anat" was applied to reduce the variance in the spatial orientation of brains for later alignment and for practical considerations of further processing steps, as described here. Each volume was affinely registered to a reference anatomical template (MNI ICBM 152 T1w) that had previously been AC-PC aligned; alignment included an additional weight mask to emphasize subcortical structure alignment (e. g. , AC-PC structures), and only the solid-body parameters of the alignment were applied, so that no changes in shape were incurred. Because datasets had been acquired with varied spatial resolution and FOV (see Table  2 ), the datasets were resampled (using a high-order sinc function, to minimize smoothing) to the grid of the reference base of 1mm isotropic voxels. All datasets were visually and systematically checked for quality of both data and registration using the QC image montages that were automatically generated by the previous program.
For these alignments, AFNI's "lpa" cost function (absolute value of local Pearson correlation)  [Saad et al. , 2009]  was used for high quality alignment of features between volumes of similar contrast. The cost function computes the absolute value of the Pearson correlation between the volume and the current template in patches of the volume at a time. As a practical consideration, we note that lower level alignments such as these have a general property of producing a smoothed brain, which has the additional effect of increasing the apparent size of the base dataset (i. e. , the edge is blurred outward). Therefore, in these initial levels we added a step to control the overall volume of the template. We calculated the mean intracranial volume (ICV) of all the subjects in the cohort V coh , and then calculated the volume of the initial mean-affine brain mask V a↵.
In order to quantify the inter-subject brain morphological variability for participants in each ageband, we calculated a region-wise mean deformation value (mDV) from the deformation warp fields generated during non-linear registration to the age-specific IBT. For this, the absolute warp value was summed across all three axes (L1-norm) and averaged across all the voxels within each ROI in the age-specific MPM atlas. A larger mDV indicates greater inter-subject brain morphological variability. To examine the utility of the IBTs on a real, representative dataset, a separate sample of Indian population data was included for validation and testing purposes. For each cohort, the validation group ("V1", matched with cohort C1; "V2", matched with cohort C2; etc. ) comprised 20 subjects within the corresponding age range.
The T1w and resting state functional MRI (rs-fMRI) data acquisition information and demographics of these additional groups are provided in supplementary text. For each IBT, in comparison to the MNI ICBM-152 template, the following validation tests were conducted using the T1w and resting functional data. We first used the deformation field to characterize the difference between the two templates (IBT vs MNI). For each subject in the validation cohort, we calculated the absolute amount of displacement needed to move a voxel location from native space to the target in the new age-specific IBT and the standard MNI ICBM-152 templates, for non-linear registration. A median absolute distance along each axis (LR = left-right; PA = posterior-anterior; IS = inferior-superior) was calculated from the dimensional deformation field in each voxel.
The median absolute distances when warping to MNI and cohort-specific IBT along each axis were compared using a paired sample Wilcoxon's signed-ranks test. Finally, the practical benefits of using the IBT as reference volume for FMRI alignment were investigated by processing resting state FMRI data from age-specific validation cohorts using the same pipeline twice: once with the IBT, and once with the standard MNI template. AFNI's afni_proc. py command was used to generate the full fMRI processing pipeline and the exact command is provided in the supplementary text. We used AFNI's 3dReHo  [Taylor and Saad, 2013]  to calculate a common resting state FMRI parameter, ReHo (region homogeneity, which is Kendall's Coefficient of Concordance, W, in statistics  [Kendall and Smith, 1939, Zang et al.
While the differences tend to be smallest along the LR axis (particularly for C4), warps to MNI are nevertheless significantly larger for 4/5 of the cohorts along this axis, as well. average warp distances along each of the main volumetric axes are shown separately. In all cases, alignment to an IBT dataset required much less overall displacement on average. Warps to MNI were highly significantly greater (p < 0. 05, corrected for N = 3 ⇥ 5 multiple comparisons) along the PA and IS axes in all cases. Along the LR axes, differences were smaller but still significant at the same level for 4/5 cohorts (again, warps to MNI being larger); the C4 cohort showed no significant difference along the LR axis, but overall differences for this group were still large, due to the warps along the other axes.
Overall, registration to the IBTs from the Indian population validation groups required much less deformation of the input datasets and resulted in more accurate stereotactic standardization and anatomical localization. The relative differences in warping along the major axes of the brain were shown here using validation groups from the local population. The differences in warping magnitudes varied both by axis and by the age of subjects. Thus, the structural differences in templates are not trivial, i. e. , just scaling, but instead reflect shape variations that are likely to significantly affect the overall goodness-of-fit and anatomical alignment across a group study. Such aspects were highlighted in the differences of outcomes in fMRI processing when using IBT vs MNI templates: the IBT-based output tended to have higher ReHo values among ROI pairs.
The latter fact in particular suggests that the IBTs provided better function-to-anatomical alignment across groups, so that voxel with functionally similar time series tended to be grouped together more preferentially. One might expect this to be a relatively small effect, because alignment to the MNI templates still appears generally reasonable; one would expect the overlap pattern differences to be occurring fractionally within ROIs and predominantly at boundaries. Indeed, the FC differences were relatively small, but with a noticeable trend toward higher values in the IBT-based datasets. It is important to emphasize that these structural differences are only with regards to morphology; they do not relate to functional or behavioral outcomes, nor to intelligence, etc. The purpose and goal of population-specific templates is for the practical consideration of maximizing the matching of structures across a group during an alignment step of processing, as well as to better match functional regions to structures.
Indeed, the Indian population (currently over 1. 3 billion people) is spread across a wide range of geographies with diversity in linguistic-ethnic compositions as well as extensive genetic admixtures  [Basu et al. , 2016]. In this study, the final mean template for each cohort contained variability. However, this was relatively low compared to the mean dataset values, and the final mean template contained a large amount of clearly defined structure. Moreover, the fractional overlap of ROIs when generating the maximum probability map atlases showed a high degree of agreement across the group through most of the brain. The variability present in the template generation is also observable in the atlases. The intersubject variability (as measured by the mean deformation values for various regions during non-linear registration to age and population-specific template) also correlated positively with the expansion of MPM volumes, in all age groups (see Supplementary Figure  S7 ).
There are several methodological strengths and limitations related to the current study that should be noted. We used combined state-of-the-art linear and non-linear averaging techniques using AFNI's completely automated pipeline "make_template_dask. py", which uses the Dask python parallelization to efficiently make a template from a large group of subjects. We addressed several specific challenges involved in the template creation, such as intensity normalization from different scanners, scaling, resizing of the overall brain size to be representative of the cohort at each iteration, and anisotropic smoothing with preservation of edges. While the overall sample size of the study was relatively large, the late childhood and the late adulthood templates had relative modest sample sizes. Therefore, it will be of benefit for the constructed templates to continue to be updated with larger sample sizes as we collect more MRI datasets.