dataesr/scientific-paragraphs-categorization huggingface dataset:
Basic Idea The aim is a classification of lateral cephalometric images into 6 classes. Our dataset is too small and ROI (C2, C3, and C4) are located in the small portion of images. To address these issues, we exploit domain knowledge. The proposed framework is shown in Figure  1. We used the multi-scale attention mechanism to highlight the important features, surpass the irrelevant part of the image, and efficiently model long-range feature dependencies. The attention mechanism improves both the performance and interpretability of visual tasks, including image classification. In this work, we used spatial and channel soft attention. Our proposed network consists of three branches. The first branch extracts global features, and creates attention maps and related masks, the second branch uses the mask to extract discriminate local features for classification, and the third branch fuses local and global features.
Conclusions In this paper, we showed that the combination of the domain knowledge, mimicking of radiologists' behavior in CVM stage classification, and deep neural networks can improve the network accuracy. In particular, we utilized a novel multi-scale soft attention module to combine semantic information and long-range dependencies at different levels and create a mask for useful regions. In the second branch of network for ROI selection, we do not resize the image, and thus avoid detail information loss in ROI. To validate our approach, we conducted experiments on our dataset and compared the results with some state-of-the-art methods. Experiment results showed that the proposed model outperformed all previous approaches.
The overestimation implies that the local slope is not necessarily a good feature to locally adjust the melt rate. Normalising the melt by dividing by the 90th percentile (Fig. 8c ) provides an additional perspective on the pattern, more independent from biases induced by the tuning. We see that taking a constant Antarctic slope and the modified version of the plume parameterisation result in patterns closest to the observational estimates (Fig. 8a ), while the normalised melt is more   (1994, 2000, 2007, 2009, 2010, 2012). The Bedmachine Antarctica version 2 dataset  (Morlighem, 2020; Morlighem et al. , 2020)  is used for the geometric information. We averaged the parameterised basal melt patterns over the 6 years. homogeneously distributed in the other parameterisations. This result, based on one single ice shelf, implies that the modified plume might adapt better to increasing resolution.
Acknowledgements. We thank  David E. Shean  for sharing his observational estimates of the basal melt rates with us. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is supported by Grenoble research communities. The NEMO simulations were performed using HPC resources from  GENCI-CINES  ( MISOCS  project,.
https://doi. org/10. 5194/tc-2022-32 Preprint. Discussion started: 22 March 2022 c Author(s) 2022. CC BY 4. 0 License. Code and data availability. The NEMO simulation output is documented on https://github. com/pmathiot/NEMOCFG and the data will be uploaded on zenodo. All code used to handle the simulation output and produce the figures, as well as the package containing the parameterisations will be available on https://github. com/ClimateClara and uploaded on zenodo as well upon acceptance of this manuscript.
Moreover, we perform sparse inference procedures, which allow us to reconstruct a graphical model of neurons that interact together. Here we investigate two procedures designed for Hawkes processes to get the connectivity graph. The first one is called ADM4 and is implemented in the Python Library tick. The algorithm is developed in  [37]. ADM4 is a parametric method based on the exponential assumption for the Hawkes process. It relies on a likelihood with a LASSO penalty criterion and is adapted to high-dimensional networks. The second method is a nonparametric estimator proposed in  [21]. This estimator is derived from the optimization of a penalized least-squared criterion and does not make any assumption on the interaction functions. However, this estimator was developed for a small network and to the best of our knowledge its performance has not been demonstrated in a high-dimensional context.
In Section 3 we investigate the whole network with ADM4 and then we extract a sub-network of 17 neurons that are assumed to impact the central neuron (according to the outcome of the estimation of the adjacency matrix). We then re-estimate the parameters of both models from this subnetwork with both methods this time and compare the results. Furthermore, we apply the test procedure introduced in  [32]  on the subnetwork in order to validate the choice of the Hawkes process to model the interaction between neurons. This is an asymptotic test based on the Kolmogorov statistics, which is detailed in Section 3. 3. A code named UnitEvents implemented for the R software has been developed to detect connectivities between neurons from simultaneously recorded spike trains. The implemented procedure is based on a Gaussian approximation of the counts to determine coincidences between spikes.
The code (in R and Python) for the whole procedure is available on the repository https: //github. com/charlottedion/HawkesForNeuro. Some details are given in Sections 4 and 5. The paper is organized as follows. Section 2 is dedicated to the description of the data. We present both inference procedures for the study of the spike trains in Section 3. The inference results on the data, together with the result of the goodness-of-fit test, are given in Section 4. In Section 5 the jump-diffusion model is presented and implemented on the data. We present the estimation of the coefficients obtained on the data. With a simulation of new trajectories obtained from the inferred process, we obtain a validation of the model through the depth motion of Tukey. Section 6 gathers the discussions on modeling and inference on this data.
Data description The data consist of two kinds of signals from the lumbar spinal of a red-eared turtle: a continuous signal from the central neuron, the membrane potential of which has been recorded, and a discrete signal representing the spike trains of the neurons around. They are presented, for example, in  [30]. The two types of data were acquired with different recording systems and were synced using a trigger signal. They are measured simultaneously, and an external stimulus is applied to the turtle after 10s during 10s. The first type of data is called intracellular. It is an intracellular recording of the membrane potential of one neuron in mV during 40s. The sample time step is 0. 048ms for the path. An invasive recording method allows getting this type of information that describes the observed neuron's electric dynamic.
We use the R-package STAR. The result is presented in Figure  2. On the left, we have represented the spike trains for the first trial. On the right, the PSTH is presented. We choose to investigate the time interval  [11, 24] s for the following inference. Limits of classical tools Different methods are based on histograms of pairwise cross-correlation functions of neurons  [24]. From these procedures, some imprecise results may result in confounding neurons present in the network and not used in these histograms. The following methods of inference avoid this issue.
Study of the spike trains This section aims to use the extracellular data to reconstruct a connectivity network containing neurons that interact with the central neuron. We first estimate the adjacency matrix A on the whole network of M = 248 neurons from the n = 9 trials using ADM4. We extract from this estimation procedure a sub-network of M = 17 neurons that impacts the central neuron. Then we perform the goodness-of-fit test with the estimated coefficients obtained from the two inference procedures: ADM4 and NPL.
We provide the outcomes of this goodness-of-fit test in Table  4. Although some acceptance rates are pretty low, most of them are greater than 50% for both ADM4 and NPL procedures. As a comparison, we generated a dataset according to an exponential Hawkes process with an adjacency matrix that corresponds to the one estimated with ADM4: the results are denoted as "synthetic data" in Table  4. We observe that, as expected, the acceptance rates are high on average, but even on this synthetic dataset, some acceptance rates are lower than 50%, which comforts us in validating the Hawkes assumption on our neuronal dataset. process assumption for the real data using ADM4 and NPL, and for the "synthetic data" simulated from ADM4 5 Diffusion process to study the membrane potential In this section, we consider the intracellular signal, which is a membrane potential recording.
2. From  [1] , we know how to estimate σ 2 and a thanks to the wealth of the present dataset. Then, from  [12] , we can deduce an estimator of the drift. For the sake of simplicity, the estimators are not given here. We recall the definitions of the different estimators in the Appendix, Section 7. We refer the reader to the original papers for more details concerning the definition of the estimators and the theoretical results. The code with the implementation of the three estimators b, σ 2 and a is available on the GitHub page. From this model, with the estimations of the parameters b, σ 2 and a, we are able to recover the trajectories of the membrane potential of the central neuron between spiking phases. In the following, we describe the procedure more precisely.
Conclusion and discussion In this paper, we propose a complete study of real neuronal data. We integrate both intracellular and extracellular signals jointly in a unified framework to represent the dynamic of the membrane potential of a neuron considering the spikes from other neurons around. We first extracted a subnetwork of neurons that interact with the neuron of interest. We then studied the relevance of considering a Hawkes process to model the interactions between neurons based on a validation test. Finally, Model (5. 1) was introduced to take into account both types of data. Theoretical results to guarantee the performance of the proposed estimators were proposed in  [13] ,  [12]  and  [1]. In this paper, we work with this model on neuronal data to investigate whether it could improve the fit to the real data compared to a standard diffusion process.
We have previously reported on 123 scans of children and adolescents, the majority of whom had disability  [20, 21]. Reviewing these data, 63% of scans demonstrated patients' data from the Hospital dataset was not approved by the Royal Brisbane and Women's Hospital (RBWH) Human Research Ethics Committee. Therefore, the images from the Hospital dataset cannot be shared. For the HCP dataset, the FOD and T1 images (both real and synthetic) are available at https://data. csiro. au/ collection/csiro:53349v2. All numerical results reported in this paper are detailed per subjects in Annexes S1-S5. motion artefacts in the diffusion scan but only 3. 3% had 15 or more motion-affected volumes out of the 60 acquired. By contrast, 25% of structural scans displayed artefacts that precluded both structural and atlas-driven diffusion analyses  [16].
In-vivo experiments We applied the proposed method to several diffusion datasets in order to assess the quality of the resulting images in terms of qualitative appearance, quantitative tissue contrast, and fitness for use as an integral component of a diffusion tractography pipeline. 2. 2. 1 Input data. Three datasets, named herein as the 'Human Connectome Project Multishell' (HCP-M), 'Human Connectome Project Single Shell' (HCP-S) and 'Hospital' datasets, were used in the current study. Scans in these datasets were mostly a convenient sample which had been preprocessed as part of a recent tractography study  [9]. The HCP-M dataset included 10 healthy participants from the Human Connectome Project Young Adults 1200 Release  [34]. Participants had been scanned twice on a Siemens Skyra Connectom scanner (Siemens Healthcare, Erlangen, Germany) with a 32-channel head coil.
We used the preprocessed  [35]  T1w MPRAGE (1 mm isotropic; α = 8˚; TE = 2. 14 ms; TI = 1000 ms; TR = 2400 ms; acquisition time 7m 40s), T2w SPACE (1mm isotropic; TE = 565 ms; TR = 3200 ms; acquisition time 8m 24s) data, and preprocessed multishell diffusion data derived from a high quality acquisition (18 @ b = 0s/mm 2 ; 90 directions @ b = 1000s/mm 2 ; 90 directions @ b = 2000s/mm 2 ; 90 directions @ b = 3000s/mm 2 ; 1. 25mm isotropic). For testretest experiments (See Section 3. 2. 4), we utilised structural scans from two time points. For other experiments, only the first time point was used. Ethical consent was granted for use of HCP data. The HCP-S dataset included the same participants as the HCP-M dataset, excepting that raw data were used (see below) for the diffusion data, which were downsampled and volumes were removed such that only a single shell remained (2 @ b = 0s/mm 2 ; 60 directions @ b = 3000s/mm 2 ; no directional repeats; 2mm isotropic).
This dataset was designed to represent a modest single-shell HARDI acquisition often seen in the literature. The Hospital dataset consisted of 12 neurosurgical patients scanned once on a Siemens Prisma scanner with a 64-channel head coil at the Herston Imaging Research Facility in Brisbane, Australia. Images were acquired as part of a clinical trial involving temporal lobe resection for epilepsy (n = 3 before surgery, n = 6 after surgery), glioma resection (n = 2 before surgery), or removal of arteriovenous malformations (n = 1 before surgery). This study included acquisition of a T1w MPRAGE scan (1mm isotropic, α = 9˚; TE = 2. 9 ms; TI = 900 ms; TR = 1900 ms; acquisition time 4m 18s) and a modern multishell diffusion acquisition (12 @ b = 0s/mm 2 ; 20 directions @ b = 2000s/mm 2 ; 30 directions @ b = 1000s/mm 2 ; 60 directions @ b = 3000s/mm 2 ; 2mm isotropic; acquisition time 11m 30s) that is in use in a variety of studies  [36] [37] [38].
Diffusion MRI processing. Diffusion data from the HCP-S and Hospital datasets were processed automatically using the CONSULT neurosurgical planning pipeline  [9]. This Table  1. Magnetic properties of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) used for the T1w and T2w signal simulations. These magnetic properties were measured in previous studies conducted at 3T  [31] [32] [33]. pipeline prepared raw diffusion data for tractography using standard libraries and algorithms without requiring associated structural images. Steps included denoising via MRtrix 3. 0  [39] , removal of motion-corrupted volumes, brain mask calculation using MRtrix's dwi2mask, and eddy-current distortion correction using a combination of FSL's topup (http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/TOPUP) and eddy_cuda 8.
0. Subsequently, intensity inhomogeneities were corrected using N4-ITK  [40]. A final brainmask was then recalculated using MRtrix's dwi2mask in conjunction with simple morphological operations. Diffusion data from the HCP-M dataset were downloaded from the HCP server in their minimally preprocessed form. This preprocessing included correction for b0 intensity inhomogeneities, EPI distortion, eddy currents, head motion, gradient non-linearities, as well as reorientation and resampling to 1. 25 mm isotropic. Brainmasks for these data were calculated in the same way as for HCP-S and Hospital datasets.
WM Tissue response functions were calculated using an unsupervised method  [41]  and FOD maps were calculated for white matter, grey matter, and cerebrospinal fluid using either multishell multitissue contrained spherical deconvolution (for multishell images)  [22]  or Single-Shell 3-Tissue constrained spherical deconvolution  [19]  (https://3Tissue. github. io; for HCP-S images). Fractional anisotropy (FA) maps were also calculated from the preprocessed data using MRtrix. Synthetic T1w and T2w scans were generated from the resulting FOD maps, for all the three datasets, using the method described in Section 3. 1.
Comparative performance in a diffusion tractography pipeline We tested whether the synthetic images were of sufficiently high quality to be used in a standard Freesurfer-based diffusion tractography pipeline, described below. Similar to other tractography-generating processes, this pipeline required structural images for parcellation and diffusion images for tractography. For each subject, we ran this pipeline using (A) in-vivo structural scans acquired during the same scan session as the diffusion data (all datasets); (B) in-vivo structural scans acquired during a different scan session as the diffusion data ('in-vivo repeat'; HCP-M and HCP-S only); or (C) the synthetically generated structural scans (all datasets). The diffusion FOD image did not differ between each subject's acquisitions.
Both the T1w and T2w images belonging to the HCP-M and HCP-S datasets were provided to Freesurfer. However, only the T1w images were utilised for the Hospital dataset because in-vivo T2w scans were not available. When structural images were acquired in-vivo, the resulting parcellation was spatially normalized to the diffusion space by affinely registering the T1w scan to either the FA image (HCP-M and HCP-S) or mean 'b0' image (Hospital dataset) using ANTS 2. 1  [42]. The mean 'b0' image was used for the Hospital dataset as it was already known to produce registrations of better quality for this particular dataset. When structural images were synthetic, no spatial normalization was required because image synthesis naturally creates images in native diffusion space. The left superior thalamocortical tract was delineated using probabilistic tractography, using labels extracted from the Freesurfer parcellations.
Similarly the synthetic T2w scans displayed comparable tissue contrasts to those of the in-vivo T2w scans, including the typical hyper-intensity of the CSF signal. Synthetic images derived from single and multi-shell diffusion scans produced similar tissue contrast, despite relying on meaningfully different diffusion FOD algorithms and resolutions. Notably, images were also realistic for patients with tumours and post-surgical cavities. The quantitative brain tissue contrast measurements obtained for both T1w and T2w synthetic images were close to their in-vivo counterparts for the HCP-M, HCP-S and Hospital datasets (Tables  2  and 3 ). The brain tissue contrasts were not significantly different between in-vivo and synthetic images (Tables  2  and 3 ), except for the T1w WM/GM contrast in the Hospital dataset (Bonferroni-corrected Wilcoxon signed rank test, p = 0.
02) and the WM/CSF and GM/CSF contrasts in the HCP-M dataset (Bonferroni-corrected Wilcoxon signed rank tests, both p = 0. 04). The contrast difference between in-vivo and synthetic images remained very small for these cases (Tables  2  and 3 ). A very strong zero-normalized cross-correlation was measured between the co-registered T1w in-vivo and synthetic images for the Hospital (mean: 0. 87; range: 0. 82-0. 90), HCP-M (mean: 0. 86; range: 0. 84-0. 87) and HCP-S (mean: 0. 86; range: 0. 83-0. 88) datasets. The zeronormalized cross-correlation measured between the co-registered T2w in-vivo and synthetic images was strong for both HCP-M (mean: 0. 75; range: 0. 67-0. 80) and HCP-S (mean: 0. 68; range: 0. 64-0. 73) datasets. In-vivo and synthetic T1w and T2w images obtained from the HCP-M (far left), HCP-S (middle left) and Hospital (middle and far right) datasets.
The synthetic T1w images display contrasts qualitatively similar to that of the in-vivo scans for all datasets, even in presence of tumours (middle right) and post-surgical cavities (far right). The T2w contrast was similar between the synthetic and in-vivo scans for the HCP-M and HCP datasets, including the expected hyper-intensity of the cerebrospinal fluid signal. For the Hospital dataset, the in-vivo T2w images were not acquired but synthetic images provided similar contrast to that which could be expected from an in-vivo scan, including in the areas of pathology. https://doi. org/10. 1371/journal. pone. 0247343. g002.
Comparative performance in a diffusion tractography pipeline Freesurfer succeeded for all but one participant. This participant, from the Hospital dataset, presented with a glioma in the left temporal lobe, preventing adequate Freesurfer performance when provided with either in-vivo or synthetic input data (Fig Tractography was successfully performed for all the datasets for which Freesurfer parcellation did not fail. Tractography was qualitatively similar between in-vivo-and synthetic-structural pipelines (Fig  4 ). Median Dice coefficients for binarised tractography between the invivo-and synthetic-structural pipelines were 0. 93 (HCP-M), 0. 93 (HCP-S), and 0. 90 (Hospital) with respective worst-case performances of 0. 90, 0. 90, and 0. 88 (Fig  5 ). Structural scans were available from a second time point for all HCP datasets.
The tractography pipeline was repeated using the same diffusion data but with these alternative in-vivo structural scans. These in-vivo vs in-vivo-repeat comparisons demonstrated similar median Dice coefficients to, and poorer worst-case performance than, the aforementioned in-vivo vs synthetic comparisons (Fig  5 ). Numerical differences between the in-vivo-repeat and the synthetic Dice coefficients were not statistically significant for HCP-S or HCP-M datasets (Bonferroni-corrected Wilcoxon Signed-Rank Tests, both p>0. 1; see  Fig 5). The poorer worst case performance of the in-vivo-repeat analysis may be explained by variations in the quality of registration between structural and diffusion scans: a step not required by the synthetic pipeline which guarantees perfect alignment between modalities.
FA values sampled from tractography did not differ significantly between pipelines for the HCP-M or HCP-S datasets (Bonferroni-corrected Wilcoxon Signed-Rank tests versus the invivo-structural pipeline FA values: HCP-M Synthetic, p = 1; HCP-M in-vivo repeat, p = 1; HCP-S Synthetic, p = 0. 11; HCP-S in-vivo repeat 0. 37). Although a statistically significant difference was found between in-vivo and synthetic pipelines for the Hospital dataset (p = 0. 02), this median difference was only 0. 01, or 2. 2%, which is well below both inter-group and longitudinal differences typically reported in imaging studies  [4, 45, 46]. MD values sampled from tractography did not differ significantly between pipelines for the HCP-S dataset (Bonferroni-corrected Wilcoxon Signed-Rank tests versus the in-vivo- as no in-vivo T2w scan was acquired for this dataset.
The synthetic HCP-M dataset WM/CSF and GM/CSF contrasts are significantly higher than their in-vivo counterparts. Although the absolute WM/CSF and GM/CSF contrast difference remained very small. The other synthetic contrasts were very close to their in-vivo counterparts, despite the use of different magnetic resonance sequences to acquire and simulate the signals. https://doi. org/10. 1371/journal. pone. 0247343. t003 structural pipeline MD values: HCP-S Synthetic, p = 1; HCP-S in-vivo repeat, p = 1). In other datasets, all other comparisons with the first in-vivo pipeline showed differences that were significant, or bordered on significance (HCP-M in-vivo repeat, p = 0. 01; HCP-M synthetic, p = 0. 06; Hospital synthetic, p = 0. 08), but these median differences (0. 33%, 0. 24%, and 0. 78% respectively) were again well below inter-group and longitudinal differences of clinical or scientific significance [E.