dataesr/scientific-paragraphs-categorization huggingface dataset:
Folds representation The Morphologist pipeline of the BrainVISA software ( www. brainvisa. info ), a widely used resource for studying cortical  anatomy, allows first to represent the folds as a set of voxels corresponding to a skeleton of the cerebrospinal fluid filling the fold and then to label them using the SPAM model ( Fig. 5  ). This first step of fold segmentation is common to all the models presented in this article. It consists of three major steps: first, the segmentation of white and grey matter from MRI, then the extraction of the skeleton of cortical folds, followed by its division into elementary folds. The fragmentation into elementary folds satisfies topological and geometric constraints specific to the sulci's definition. It is first based on the topological characterization of a simple surface proposed by  Malandain et al.
Statistical probabilistic anatomy map (SPAM) models In this comparative study, the reference method corresponds to the one described in  ( Perrot et al. , 2011 ) , where they propose a coherent Bayesian framework to automatically identify sulci based on a probabilistic atlas (a mixture of SPAM models) estimating simultaneously normalization parameters. This method, currently available in the BrainVISA/Morphologist pipeline, has been widely used on very large databases for large-scale morphometric studies  ( Le Guen et al. , 2019 ). However, the model is still making obvious errors and we believe that this is due to the fact that the SPAM approach is based on a single template atlas, which prevents it from fully representing the high variability of folding patterns. Each sulcus can have several configurations, which may prove difficult to represent with a single average model.
9  ): d(P (S A ) → S B ) = 1 | P (S A ) | p A ∈ P(S A ) min p B ∈ S B [ d 2 E (p A , p B )] (2) Note that, in order to avoid border effects, the closest neighbor of p A is searched in the entire skeleton S B and not only among the skeleton voxels contained in the patch P ( S B ). Realigning and comparing all the patches in the database for each skeleton voxel to be labeled would be extremely expensive, making it impossible to label within a reasonable time. Additionally, it would increase the probability of spurious matching between remote areas in the brain while the images are already roughly aligned with each other. It is important to note that because we use binarized images, the risk of obtaining false positives is higher than usual. In  ( Romero et al. , 2017 ) , the Optimized Patch Match Label fusion (OPAL)  ( Ta et al.
, 2005 ). Thus, a score map is estimated for each label in the database. Concerning the "unknown" label, present in the manually labeled database, it is treated like a sulcus label. Patch-based MAS approach with High level representation of the data (HPMAS) Fig. 10. 3D representation of the HPMAS method. As for the Fig. 8  which represents the method in 2D, the approach is described in four steps: generating the virtual patches, registering them on the image to be labeled, propagating the labels of the selected virtual patches and finally merging the propagated labels to obtain the final labeling. As the standard way of extracting patches does not seem capable of exploiting the sulci geometry and the relations between them, which we believe to be the distinguishing features necessary for recognition, we have proposed a new virtual patch generation strategy based on a high level representation of the sulci  ( Borne et al.
, 2018 ). This framework is well adapted to leverage more information about the different folding configurations in the training dataset. Note that this method is the only one of the proposed new methods to have been specifically developed for the recognition of cortical sulci. It includes many arrangements specific to this application. Its complex design gives an idea of the scores that can be obtained by pushing as far as possible in this direction. To facilitate the understanding of this ad-hoc method, Fig. 10  represents the pipeline in 3D, which complements the 2D representation provided in Fig. 8. Patch generation. In order to take into account as much geometric information as possible, the idea was to define virtual patches containing whole sulci. These virtual patches correspond to a voxel cloud representing a pair of sulci, extracted from MNI space at the image resolution.
By defining patches as clouds of voxels and not as cubes, it allows to take into account the sulcus in its entirety without parasitizing the patch with all its surrounding sulci. Note that the shape of small sulci is not specific enough to prevent spurious hits. That is why we have chosen to aggregate two sulci to create discriminative local shapes. In the following, we define a type of virtual patches for each pair of sulci that are neighbors in the brain. In practice, a pair of sulci is selected in the circumstance that the two sulci are neighbors in at least one brain of the atlas dataset, according to the topology provided by the Brain-VISA/Morphologist pipeline that produces the folds. This pipeline endows the list of folds with a graph structure corresponding to either direct connections or to the fact that two folds are separated by a piece of gyrus.
Finally, each type is made up of the instances of the pair of sulci in the atlas dataset, most of the time as many shapes as atlases (some atlases miss a few small sulci) ( Fig. 10. 1). Note that only the unknown sulcus label is not selected to form virtual patches, as it does not constitute a coherent structure like the other labels. Thus, unlike the previous PMAS method, the unknown label is not treated like other sulcus labels. Distance calculation. For the distance calculation step, the set of folds of the brain to segment and the virtual patches of the library are represented by point clouds. In order to find an optimal alignment of each virtual patch into the skeleton point cloud of the brain to segment, the well-known iterative closest points algorithm  ( Besl and McKay, 1992  ) is used, with the robust implementation of  Holz et al.
In order to augment the training dataset, a rotation in a random direction with a random angle (following a Gaussian distribution N (0 , π 16 2 ) ) is applied to the images at each epoch. At the output of the neural network, a score per label present in the database is obtained per voxel. Concerning the "unknown" label, it is treated like a sulcus label. Training design. Initialization of the weights of the neural networks was done as in  ( LeCun et al. , 2012 ). Stochastic gradient de-scent was used for training, with learning rate and momentum determined by 3-folds inner cross validation. The learning rate was halved when the loss function had not improved for two consecutive epochs. After four consecutive epochs without improvement, training was stopped. The selected trained neural network corresponds to the epoch obtaining the lower error rate E SI , described in  ( Perrot et al.
, 2011 )  and in the following section. The loss function used is the cross-entropy loss. In most cases, for unbalanced problems, the loss function must be weighted to avoid favoring the labels most involved in backpropagation, due to their higher presence in the database. Although the average size of each sulcus is extremely unbalanced, we have chosen not to weigh this loss function because large sulci are also the most interesting from a neuroanatomical point of view and need to be better recognized than small ones. Patch-based model with a 3D CNN (PCNN) PCNN method adapts the approach proposed in  ( Ciresan et al. , 2012 ) , addressing a segmentation problem as a classification of each voxel based on its environment contained in a patch. Here, only voxels belonging to the skeleton are selected to participate in the classification.
05 and in red otherwise. Regularization by elementary folds significantly improves results. Automatic fold re-division also significantly improves results. All regularized models are significantly better than the SPAM model. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) cross-validation and not by leave-one-out cross-validation. Moreover, the addition of the four new sulci labels and our refined labeling of the training dataset may also have impacted the results.
Error rate comparison During the 10-folds cross validation, each fold contained approximately 6 hemispheres labeled to test the model's performance. Error rates are calculated by hemisphere and then averaged over the entire database to obtain the mean error rates per model. When not specified, the average error rate includes the right and left hemispheres. In order to compare the models in pairs, a Wilcoxon signed-rank test was performed between the er-ror rate lists for each hemisphere. If the p -value was less than 0. 05, the error rates were considered significantly different.
Most importantly, the automatic re-division of these elementary folds also significantly improved the four methods. Thus, the use of top-down refinement of bottom-up regularization is particularly relevant in this paper. Third, by comparing the new models in pairs, the models seem to demonstrate equivalent performance. Concerning the PCNN and UNET models, this paper consequently demonstrated the incredible efficiency of neural networks, even for the recognition of structures as variable as cortical folds. However, it is surprising that the UNET model was not better than the PCNN model due to its deeper architecture. The fact that these four models do not stand out radically on this dataset suggests that these models may have reached the limit of what can be interpreted from this database, probably due to its insufficient size to represent the high variability of cortical folds.
Therefore, the fold variability is such that manual labeling of a brain raises many questions and it may be possible that the models have reached the human-level performances. Unfortunately, since manual labeling is based on consensus among several experts, it is impossible for us to assess human-level performance on this database. Finally, with regards to the computation time required to label a hemisphere, the SPAM model takes about 5 min, while the UNET model takes about 20 s, PCNN takes slightly more than a minute, PMAS and HPMAS take several hours. Although the PMAS model could be much faster by optimizing the codes as in  ( Giraud et al. , 2016 ) , the UNET model is currently by far the fastest. Thus, since the UNET model has the lowest error rates and is the fastest, we propose to study in more detail the differences between this model and the SPAM model in the following section.
Experiment on an external database demonstrating the clinical advantage Here, the SPAM model and the UNET model were trained on the entire manually labeled database. The hyperparameters of the UNET model were estimated over the entire database, using the same procedures as during inner cross-validation, i. e. by performing a 3-folds cross-validation to select the hyperparameter values that minimize error rates. The database used by  Sun et al. (2012)  to study the effect of handedness on the shape of the central sulcus was labeled manually and automatically by these two models. This database contains 23 consistent age and sex matched natural dextrals (mean age 34, range 22-59 years; 17 males, 6 females) and 18 similar natural sinistrals (mean age 36, range 25-56 years; 12 males, 6 females).
The database used in  Sun et al. (2012)  also contains a group of 34 forced dextrals that is not studied here. We propose to investigate the asymmetry index I of the central sulcus length along the brain hull between the left l S. C. _ le f t and right hemispheres l S. C. _ right : I = l S. C. _ le f t -l S. C. _ right l S. C. _ le f t + l S. C. _ right ( 10 ) Note that in the nomenclature proposed in this paper, two sulci labels belong to the central sulcus: "S. C. " and "S. C. _sylvian. ". Therefore, the lengths of these two "sub-sulci" are added together to obtain l S. C. With manual labeling, there is a significant difference between left-handed and right-handed people ( Fig. 17  ). Therefore, lefthanded people have on average a longer central sulcus in the right hemisphere than in the left, and vice versa for right-handed people.
Unknown label In this paper, except for the HPMAS model, the "unknown" label in the manually labeled database is treated like the other sulci labels. However, although the "unknown" label represents about 0. 5% of the skeleton voxels of manual labeling, this proportion is null if we consider the labels of the HPMAS model. Moreover, the PMAS and PCNN models label around 0. 02% of voxels as "unknown" and the SPAM and UNET models 0. 04%. These figures show that treating the "unknown" label as other labels is insufficient. Models should also assign the "unknown" label to structures where it is not sufficiently confident. However, since all the new methods were compared to the SPAM model, which treated the unknown label as sulci labels, we chose not to address this point in this paper.
Finally, in order to take advantage of the large unlabeled databases currently available, a semi-supervised strategy would be particularly attractive to better represent the variability of the cortical folds. In the near future, considering that the labeling model seems sufficiently reliable to us, we would like to reconsider the number of sulci used in the nomenclature on the basis of the sulci most often confused by the model. Indeed, the error rates of some small sulci are still too high to be used in morphological studies. By allowing the user to choose the level of granularity of the nomenclature, he will be able to use sufficiently stable labeling of the structures of interest to him.
Results Volumetric Mesoscopic White Matter Features. The mean AD indices in the corpus callosum (CC) were fitted on diffusion MRI volumes of the monkey brain and exhibited the expected trend in diameter from the splenium to the genu, but with overestimated diameters (19) (Fig. 1A ). In the splenium, along the interhemispheric connection between visual cortices V1/V2, the mean AD index was 1. 3 μm. The connection length was delineated by tractography to 49. 7 mm (SD = 1. 9 mm) as described in Materials and Methods, resulting in a conduction delay of 4. 8 ms. To further investigate the microstructure underlying the diffusion MRI estimates with XNH, we extracted and processed cylindrical tissue volumes of 1 mm in diameter. Tissue was taken from the CC and a crossing fiber region, located in the anterior centrum semiovale at a point where the diffusion MRI data indicated the existence of crossing axon bundles (SI Appendix, Fig.
MRI scanning. The diffusion MRI dataset was collected on an experimental 4. 7tesla Agilent MRI scanner with a maximum gradient strength of 600 mT/m. An optimized three-shell ActiveAx MRI protocol based on a maximal gradient strength of 300 mT/m for ex vivo tissue as in Dyrby et al. (19)  was used. The noncollinear diffusion weighting encoding directions were obtained from the Camino Toolbox (RRID: SCR_001638)  (59). Further details of the protocol are included in SI Appendix. Prior to AD fitting and tractography, the diffusion MRI datasets were denoised  (60)  and processed to remove Gibbs ringing artifacts  (61)  in the MRTrix3 software toolbox (RRID: SCR_ 006971). AD estimation. AD estimation was performed using the ActiveAx framework, based on the four-compartment minimal model of white matter diffusion (MMWMD) as described in ref.
We used a constrained spherical deconvolution method  (63)  implemented in MRTrix3 to obtain voxelwise multifiber reconstructions of the whole brain. Probabilistic streamline tractography was then implemented with the SD-STREAM function in MRtrix3 using standard parameters to extract 2,000 streamlines  (64). Further details are included in SI Appendix. Tractography-based estimation of conduction velocity. To estimate the end-to-end conduction delay of the interhemispheric connection between the V1/V2 border regions and passing through the splenium, the tract was assumed to have a g-ratio, g, of 0. 7 as in other studies  (10, 31). The CV was calculated according to the following (9): CV = 5. 5/g•d, where d is the inner AD. The inner AD used was the AD index obtained by fitting ActiveAx to the diffusion MRI dataset of the monkey brain, as previously described.
Acquiring one full scan took ∼4 h. Segmentation of cell clusters, blood vessels, and vacuoles. We implemented an inhouse intensity-and morphology-based approach in MATLAB to segment the cell clusters, blood vessels, and vacuoles from the four consecutively acquired XNH volumes (Fig. 2A ). This used classic low-level image analysis operations such as intensity thresholds, morphological operations, and connected components analysis. Further information can be found in SI Appendix. Segmentation of axon diameters. The XNH volumes were downsampled by a factor of 5 by extracting every fifth slice, and performing a slicewise cubic interpolation and Gaussian smoothing (kernel width of 5 pixels) in MATLAB (version 2019b) to achieve volumes of dimension 410 × 410 × 410 voxels with isotropic voxel size of 375 nm.
A rough segmentation of the axons from these volumes was then performed using the adaptive paintbrush in ITK-Snap (RRID: SCR_002010). This allowed for the extraction of an approximate centerline by a simple slicewise estimate of the axon centroid. With the centerline as input, we employed a MATLAB-based, in-house segmentation method to extract the axons and update their centerlines from the highresolution volumes of voxel size 75 nm. This is described in SI Appendix. Quantification of Axonal Dispersion of Segmented Axons. For each of the 54 axons, the point-to-point vectors within their centerlines were averaged to obtain a main axon direction. The average of all 54 main axon directions was defined as the main bundle direction. The OD of each axon was defined as the inclination angle between the main bundle direction and the main axon direction.
We introduced the concept of microdispersion at different length scales, L, to quantify the deviation of axons from their linear main directions. To calculate the microdispersion, we first aligned the axons with the z axis. Then, the cumulative distance along the centerline was calculated, and the centerline was divided into segments of length, L, corresponding to the sampling length. A principal-component analysis was performed on the set of points within each segment to determine the segment direction in MATLAB. Thereafter, the inclination angle between the segment direction and the main axon direction was calculated. The average inclination angle of all segments (weighted according to the number of points in each segment) was defined to be the mean inclination angle of the axon at a length scale L.
The simulator output the mean-squared displacement, AE(Δx) 2 ae, of the particles in the directions parallel and perpendicular to the main bundle direction. The reported ADCs were calculated from AE(Δx) 2 ae by the Einstein relationship (68): AE(Δx) 2 ae = 2 • ADC • D t. The AD estimations were based on the analytical expression for diffusion perpendicular to cylinders (69): λ 2 = R 2 =2, where λ 2 is the mean-squared displacement perpendicular to the cylinder and R is its radius. Data Availability. The four XNH image volumes of the monkey brain splenium and the segmentation of cells, vacuoles and blood vessels are publicly available on the download center of the Danish Research Centre for Magnetic Resonance (https://www. drcmr. dk/axon-morphology-dataset). The diffusion MRI datasets of the same brain as the XNH image volumes are also available on the same link and include the raw diffusion MRI data covering the mid sagittal region of the corpus callosum, used for axon diameter fitting, and the whole-brain diffusion MRI data set used for tractography.
ACKNOWLEDGMENTS. M. A. and H. M. K. were supported by  Capital Region Research Foundation  Grant  A5657  (principal investigator: T. B. D. ). M. B. was supported by  Swedish Research Council  Grants  E0605401  and  E0605402. We acknowledge  ESRF  for providing beamtime for the experiment LS2702 at ID16A. We thank  Henrik Lundell  for valuable discussions and  Susanne Sørensen  for her assistance with the tissue preparation.
COMPUTATIONAL RESULTS In this section, we present the results of series of computational experiments, conducted to test both designed approaches (optimal and sub-optimal). They were tested on a PC with Intel R Core TM i3-2330M CPU @ 2. 20 GHz and 2. 00 GB RAM. The proposed MILP was coded in C++ and implemented using GUROBI optimization solver (GUROBI, 2014). In the following, we will first describe how test data are gen- erated. Secondly, we analyze performance of our newly proposed Pro-GA. In this analysis phase experiments results are described : the calibration process, a comparison between Pro-GA and a standard GA (without restart scheme) as well as results of large size problems compared to a proposed lower bound. Next, a comparison between the MILP and Pro-GA is depicted for small problem instances.
A mobile module is installed on the vehicle and consists of a satellite receiver, storage and transmission modules in coordinating data. Data on specific indicators for travel and standing time are actively collected employing a specially equipped vehicle for driving through the examined sections of the street and road network. A passive system is also applied and comprises specific indicators calculated regarding navigation data collected from various vehicles involved in road traffic. Such data are obtained from different sources like the servers of a transportation company. The passive approach to data collection allows increasing the accuracy of the system since more data are examined than gathered from different vehicles controlled by different drivers. The methodology of the passive experiment, along with the use of the Herman-Prigogine model, allows examining traffic flow parameters and constructing a decision-making support system for controlling transportation, selecting traffic routes and assessing possible changes in the transport system.
Using the two-fluid Herman-Prigogine model, a software system has been implemented and allowed making a quantitative assessment of traffic parameters using navigation data as well as visualising them using the Google Maps geographic information system (Figure  1 ). The software system is designed to collect and analyse navigation data on the further application for assessing traffic flow parameters based on the two-fluid Herman-Prigogine model  (Blinkin, & Tkachenko, 2009; Nelson & Sopasakis, 1998; Prigogine & Herman, 1979). The data are collected using a large number of navigation modules installed on the vehicles or smartphones of users. Data analysis is performed centrally on a high-performance computer, as it requires a considerable amount of calculations. The software package proposed for the solution of the aboveintroduced tasks is implemented on the client-server architecture basis (Ill.
1, Ill. 2). Thus, the data received from clients are transferred to the server end for further storage and processing and analysing the obtained information (Figure  2 ). The client end is represented by two types of equipment -a built-in navigation module integrated into the vehicle or a mobile application based on the Android OS. The built-in equipment automatically sends data on the vehicle location to the transportation company performing preliminary data collection with the subsequent provision of the system under consideration. The second case is a client application installed on the mobile device of the user. To send tracks, users must have a Global Positioning System (GPS) receiver activated on a smartphone or a vehicle and enable data transfer. When driving over a certain period, users receive their current location.