dataesr/scientific-paragraphs-categorization huggingface dataset:
2011). A more extensive analysis of SNe II-P and the physics relevant to nebularphase modeling has been presented in a series of papers by  Jerkstrand et al. (2014 Jerkstrand et al. ( , 2012 Jerkstrand et al. ( , 2015b)). This work was also made possible by the acquisition of nebular-phase spectra for nearby SNe, although even today the published sample remains limited to a Article number, page 1 of 23 arXiv:2007. 02243v1 [astro-ph. SR] 5 Jul 2020 handful of objects (see, for example, the dataset presented by  Silverman et al. 2017 ). An important conclusion from these works is that the [O i] λλ 6300, 6364 doublet flux, or its ratio with that of [Ca ii] λλ 7291, 7323, can be used to constrain the progenitor mass. Applied to the existing dataset, nebular-phase modeling indicates initial progenitor masses below about 17 M , with a noticeable absence of 20 -25 M progenitors (e.
We then prescribe the mass ratio for the dominant species of each shell, following the results from our MESA calculations. The H-rich shell is made of 68 % H and 28 % He (all meant by mass). The He-rich shell is made of 99 % He and 0. 5 % N (some models were inadvertently made with 2 % N). The O-rich shell is made of 90 % O and 10 % Mg. The Si-rich shell is made of 90% Si and 10 % Ca. We prescribe a 56 Ni profile (function of the adopted mixing; see below). At nebular epochs, 56 Ni has decayed into a mix of 56 Fe, 56 Co, and 56 Ni that have the same distribution in velocity space and a cumulative mass equal to the initial 56 Ni mass ( 58 Ni and 59 Co are also included as part of the solar metallicity mixture). In all shells we include the metals at their solar metallicity value whenever their atomic mass is greater than that of the A&A proofs: manuscript no.
It is well known that both resolution and network size alter the results of simulations of massive star evolution  (Farmer et al. 2016 ) and explosion  (Paxton et al. 2015) , but not to an extent that would alter the results of the present study. For example, the s-process can lower the Ca and Fe abundance within the O-rich shell. The s-process is ignored by our small network and we find instead that the Ca and Fe mass fractions in the O-rich shell are essentially constant in our MESA simulations. In the SN model s15 of  Woosley & Heger (2007) , based on a 15 M star initially, the s-process influences the Ca and Fe A&A proofs: manuscript no. ms abundances so that they show a depression relative to solar by a factor of 5 -10 in the O/C shell, and by a factor of 2 -3 in the O/Ne/Mg shell. Ultimately, such features should be properly handled, but we ignore them for the time being.
5  At the critical density, this rate is equal to N u A ul. Thus N e (crit) = 1. 16 × 10 7 g u A ul √ T 4 Υ lu cm -3. ( 7 ) 5 The values listed in the table are those used in the present calculations. Experimental lifetime measurements, and theoretical calculations, suggest that a better estimate for A for the Ca ii transitions is 1. 1 (see  Meléndez et al. 2007 ). The value listed in the NIST database is 1. 3 (Kramida & NIST ASD Team 2019). It comes from a calculation by  Osterbrock (1951)  and has an indicated error of greater than 50%. The O i values are slightly lower than those in the NIST database Kramida & NIST ASD Team (2019) (5. 6 × 10 -3 and 1. 8 × 10 -3 ; E < 7%) however  Baluja et al. (1988)  suggest even higher values (6. 7 × 10 -3 and 2. 3 × 10 -3 ). While the adoption of different cross-sections will (slightly) alter line strengths, they will not affect any of the conclusions made in this paper.
Acknowledgements. We thank  Bill Paxton  and  Frank Timmes  for their help with MESA, specifically in setting up the proper controls that prevent shell merging during Si burning. Hillier thanks  NASA  for partial support through the astrophysical theory grant  80NSSC20K0524. We thank the referee,  Anders Jerkstrand , for their thoughtful comments on an earlier version of this paper. This work was granted access to the HPC resources of CINES under the allocation  2018 -A0050410554  and  2019 -A0070410554  made by  GENCI, France. This research has made use of  NASA's Astrophysics Data System Bibliographic Services.
For example,  (Sharma et al. , 2020)  in India showed a decrease in PM 2. 5 and PM 10 concentrations up to respectiveley 43 and 31% and an increase of 17% ozone concentrations from 16 March to 14 April 2020 confirming the analysis based on observed datasets. For this study, the WRF-CHIMERE modelling system was used on a spatial domain covering Western Europe. An emission scenario was set up using Apple public data (https://www. apple. com/covid19/ mobility). Two simulations were carried out for the month of March 2020, (i) a reference simulation with 'business as usual' emissions and (ii) the scenario reflecting the 'reality' of emission changes due to lockdown measures. The difference between both simulations makes it possible to quantify the impact of the consequences of lockdown measures.
The anthropogenic emissions scenario The anthropogenic emissions for the MED60 domain are from the CAMS database (available at https://eccad3. sedoo. fr/#CAMS-GLOB-ANT, Granier et al. (  2019 )) for the available year 2019. For the EUR20 domain emissions are from the EMEP (European Monitoring and Evaluation Programme,  (Mareckova et al. , 2019) ) database released in 2019 and available for year 2017. This dataset correponds to the most stateof-the-art and up-to-date dataset for anthropogenic emissions in Europe. For the reference simulation, REF, this dataset was used with the EMISURF pre-processing program, developed by the same group as the CHIMERE model. This program is able to read many differents format of raw anthropogenic emissions to prepare files with data projected on the CHIMERE grid, with one fields per week day for a given month and for the chemical species used for the simulation.
Many details about this preparation program are presented in  Menut et al. (2012)  and  Mailler et al. (2017). Estimating emissions during the lockdown period, the CVD simulation, is the most challenging part of this study. Only anthropogenic emissions for EUR20 are changed to account for lockdown measures. The Apple company has provided a dataset as an estimate of "driving", "transit" and "cycling" daily activities from 13 January 2020 for tens of countries in the world. This dataset is a compilation of anonymized and aggregated data retrieve from mobile Apple devices when users search map information. Most of European countries are covered by this dataset. We use the "driving" dataset to estimate the reduction or increase of traffic activity base on a reference state. Because emission data in CHIMERE already account for a weekly factor to distinguish emissions from Monday to Sunday we have normalized for each available country the Apple dataset by dividing by the activity dataset of a "normal" day of week before the lockdown measures.
g. less than 2% of NO 2 emissions during the Landing-Take-Off aircraft operations of total NO 2 emissions in France). Conversely, as people are asked to stay at home, an increase of the use of heating systems during this period has been observed. Some first estimates in France showed that an increase of 20% of emissions could be observed. Then, for each country a factor -f 7 /4 is applied for the residential sector (SNAP 2), so that the more traffic emissions are reduced the more residential emission increase. This formula caps the residential increase to +25%. This method provides daily multiplication factors for each country and activity sectors. Application of these factors to the 'business as usual' emissions that are used for the REF emission dataset allows to produce emissions for the CVD simulation that include the effect of lockdown measures.
Introduction Studying complex living systems by the evaluation of the various gene programs defining organ/tissue architecture, is part of the current challenge on Systems Biology. In fact, while till recently accessing at the gene programs in tissues was performed by global (bulk) gene expression analyses, recent advances on single cell transcriptomics managed to move from an "average view" towards a single-cell gene program readouts  1. This being said, cell dissociation by enzymatic methods -necessary for single-cell assays -tend to modify the transcriptional patterns  2  , it destroys for at least a fraction of the cells composing the tissue, and it does not conserve tissue architecture. 3 analogy to pixels commonly described as units composing raster images in digital imaging. Hence, MULTILAYER process ST maps as a digital image on which gexel patterns revealed from agglomerative clustering allows to highlight biologically relevant tissue substructures.
Similar to contextual classification strategies used for image analysis from pixels information  8  , MULTILAYER detects gene expression patterns by the use of an agglomerative strategy over contiguous Gexels. This module generates a cleaner view of over-expressed genes within the tissue. Furthermore, it allows to identify multiple patterns for the same overexpressed gene within the same tissue, which per see is lost in all other strategies relying on aggregating independent Gexels by applying for instance the t-distributed stochastic neighbor embedding (tSNE) method (Supplementary Figure  3 ). Having detected patterns for all over-expressed genes, MULTILAYER compares their spatial localization to infer their degree of co-expression behavior (Tanimoto and Dice similarity coefficient implemented; see methods on line).
Gexel agglomerative compression module Most of the current available ST maps are issued from glass slides on which barcoded-polyT DNA probes are printed. The manufacturing constraints of these DNA arrays provide a resolution of ~100 μm (equivalent to ~10-40 cells per gexel) with a number of spots ranging between ~1000 to ~5000 (when considering the recent commercial upgrade of the original ST protocol), covering a surface of ~6x6 mm 3. An alternative strategy, based on the use of uniquely DNA-barcoded beads deposited onto a glass coverslip enhanced the ST resolution to 10 μm. This methodology known as Slide-Seq, allowed to generate high-resolution ST maps within a circular surface of 3 mm of diameter and ~70 000 uniquely DNA-barcoded beads  20. Aiming to use MULTILAYER to analyze high-resolution Slide-Seq maps, but concerned by the technical constraints related to (i) the low number of read counts per gene retrieved within Gexels (Supplementary Figure  6 ) and (ii) the high number of Gexels within the ST map impacting the computational performance (including the display functionalities); we have implemented a complementary script allowing to reduce the ST map complexity.
This ad-hoc module, called "MULTILAYER compressor", generates super-Gexels by agglomerating contiguous gexels defined by a user-provided compression factor. This approach, previously described for image segmentation strategies  21  , allowed to enhance the number of counts per gene within super-Gexels to even comparable levels as those retrieved on regular ST maps (Supplementary Figure  6 ). Furthermore, it reduced the computation performance, such that MULTILAYER could decorticate the functionally relevant digital tissue complexity. This last aspect has been highlighted by the analysis of public Slide-Seq data, including mouse hippocampus and sagittal cortex maps  20, 22. In both cases, raw Slide-Seq maps composed by ~ 70 000 high-resolution gexels, were compressed by factors of 60x, 100x and 175x respectively, leading to grid sizes compatible with the performance of MULTILAYER (Supplementary Figure  6 7 8 9 and Figure  3 ).
Furthermore, it reduced the number of detected spatial gexel communities (13 and 8 spatial communities for 100x and 175x respectively), but retained the global digital tissue substructures (Figure  3C, E  & G ). Finally, the functional relevance of the various spatial gexel communities were confirmed by Gene Ontology enrichment analysis (ARCHS4 tissue database  23  ) assessed for the described three compression factor conditions. (Figure  3D, F  and H ). A Similar analysis performed on the cortex map (reduction factor of 60x), allowed to identify spatially distinct over-expression signatures for factors like the gene Transthyretin (TTR) and the Calcium/Calmodulin Dependent Protein Kinase II Inhibitor 1 (CAMK2N1) (Supplementary Figure  8 ), Their gene expression association to the mouse cortex has been confirmed by gene ontology term analysis performed over the spatially co-expressed genes at different digital compression factor reduction levels (100x and 175x in addition to 60x) (Supplementary Figure  8E-I ).
The extension of the co-expression pattern detection over all genes within the tissue map allowed to infer > 40 super-gexel communities on the cortex map issued from a 60x compression factor (Supplementary Figure  9A ). A gene ontology analysis performed by MULTILAYER (ARCHS4 tissue database) revealed the enrichment for terms like cerebral cortex, superior frontal gyrus, dentate granule cell, motor neuron, neuronal epithelium, dorsal striatum, spinal cord (Supplementary Figure  9D ). The use of a compression factor of 100x reduced the digital tissue stratification to 15 communities (Supplementary Figure  9B ) and only to 7 communities when a 175x compression factor has been applied (Supplementary Figure  9C ). In both cases, the major spatial tissue stratification remained visible, further supported by their associated Gene ontology terms (Supplementary Figure  9E-F ).
Inter-tissue comparison was performed by constructing a graph, where spatial communities per tissue are associated to their relevant gene co-expression patterns. Such inter-tissue graph has been partitioned (Louvain methodology 9 ) on 9 "classes", highlighting the relationship between tissue substructures retrieved among all 12 biopsies (Figure  4C ). Indespite of the histological classification, MULTILAYER partitioning revealed that all tissues present molecular signatures related to prostate cancer progression on at least one substructure (Supplementary Figure  10, Figure 4C&D ). For instance, tissue biopsies histologically classified as "normal glands" (P1. 1, P2. 1, P3. 2, P4. 1, P4. 2 and P4. 3), presented gene co-expression patterns associated to factors like the membrane cell-junction protein Claudin-4 (CLDN4; known to be over-expressed on primary and metastatic prostate cancer  25  ), the growth/differentiation factor-15 (GDF-15; its over-expression has been associated with prostate cancer progression  26  ), The gene ACPP coding for the prostatic acid phosphatase, associated to prostatic hyperplasia, but also observed in prostate carcinoma (as revealed on the human protein Atlas database), the Kallikrein Related Peptidase 2 coding for a trypsin-like serine protease (KLK2; primarily expressed in prostate and its overexpression is considered as a prognostic marker for prostate cancer risk  27  ), or the activating transcription factor 3 (ATF3), shown to be upregulated on oncogenic stress, and described as a tumor suppressor response notably by its inhibitory effect on androgen receptor signaling  28  (Supplementary Figure  10 ).
Gene ontology analysis MULTILAYER counts with a Gene Ontology enrichment analysis implemented within the "gene co-expression patterns detection" and "Gexel communities" panels. For it, a collection of GO terms has been collected from the Enrichr libraries suite. MULTILAYER infers the GO terms enrichment confidence by comparing the list of genes issued from the gene coexpression patterns detection or within a spatial community, with those retrieved within the GO database (one-sided Fisher Exact test). As outcome, MULTILAYER provides a confidence barplot per enriched GO terms, as well as a heatmap matrix displaying the list of genes associated to the enriched GO terms.
MULTILAYER Compressor ad-hoc module Multilayer Compressor is an ad-hoc module for generating super-Gexel maps by aggregating the raw read counts of contigous gexels prior processing. This strategy allows to convert a larger matrix, like those retrieved in the case of high-resolution Slide-Seq data  20  , to a compressed format, counting with less number of gexels within the grid but with an enhanced number of read-counts per gexel. According to the user-defined compression factor parameters (number of gexels on X & Y coordinates), Multilayer Compressor transforms an input data (3 columns format composed by gexel coordinate, Gene ID and read counts per gene), into a data-frame compatible with MULTILAYER (matrix format composed by gexel coordinates on columns and Gene ID on rows). We recommend to use the MULTILAYER Compressor when raw ST maps are bigger than 120x120 gexel grids.
Data availability All processed spatial transcriptomics data within this article were obtained from public repositories and converted into a uniform format compatible with MULTILAYER requirements. Human heart development data generated by Asp. M. et al  10  ; and prostate cancer data generated by Berglund et al  24  ; were obtained from the Spatial Research portal. Hyppocampus and Cortex high-resolution Slide-seq maps  20  were obtained from the SpatialDB database  22. Pancreatic ductal adenocarcinoma data generated by Moncada et al  33  ; were obtained from GEO database (GSE111672). Compatible MULTILAYER versions of these data are accessible via https://github. com/SysFate/MULTILAYER.
g. when walking at comfortable speed, ZUPT detectors work well. But when the human dynamic increases, e. g. running, the performance of existing detectors tends to decrease  [5] ,  [6] , resulting in false or missed detected ZUPT. More recently, a threshold-less approach  [10]  and methods based on artificial intelligence (AI) have been proposed to meet the challenge of adapting the ZUPT detectors to the diversity of human dynamics  [11] ,  [12]. They alleviate the need for threshold tuning. The most advanced approaches rely on deep leaning techniques that are sometimes combined with parallel motion classification methods. They provide more robust ZUPT detection performances  [13]  but rise questions about the need for large training databases and even heavy computation costs, preventing real time applications.
As a series of research work concerning the IA-based ZVD,  [23]  trained an SVM classifier using foot mounted IMU to classify six motion types: walking, jogging, running, sprinting, crouch-walking and ladder-climbing. The mean test classification accuracy is 90% on a dataset with five different subjects. Then, according to the predicted motion type, the traditional SHOE detector is associated at the next step by using the pre-fixed optimal thresholds for each motion. The entire dataset consisted of 30000 training and 30000 test samples in six motion classes collected from five subjects. The complete algorithm is only tested for walking, running and a combined walking/running test. Then,  [12]  trained a long short term memory (LSTM) neural network to directly detect the stance phase of a gait cycle without prior motion classification and the threshold tuning.
[13] synthesizes the previous two research work and expands the motion types to a stair-climbing case. It also provides a quantitative comparison between the previous two approaches and several classic detectors. Training and test data are collected from five subjects and each subject traveled approximately 110 m for each motion type. Every subject repeated each motion type three times, which results in a total of 45 motion trials covering approximately 6. 6 km. The test results show that both the proposed two approaches outperform the existing threshold-dependent ZVD. But the computational cost was not reported here and the deep learning methods are generally computationally expensive.
III. DESCRIPTION OF THE TRAINING DATASET A. Inertial Signals collected in a large motion laboratory 1) Experimental equipment: The hardware used to collect data for building the training dataset has been designed by GEOLOC laboratory  [24]. It is the PERSY unit (PEdestrian Reference System) that is used as a positioning reference system for pedestrian navigation research. Mounted on the foot, it outputs location estimates relative to a starting position with a 0. 3 % positioning mean error of the traveled distance  [14]. These performances depend on the good recognition of quasi-static phases of the acceleration and the magnetic field records that are used to mitigate the impact of inertial sensor errors in the positioning algorithm. Outdoors and in light indoor spaces, GNSS phase measurements are added to the strapdown Extended Kalman Filter to improve the positioning accuracy.
PERSY comprises a tri-axis accelerometer, a tri-axis gyroscope, a tri-axis magnetometer and a single-frequency, dual-constellation GNSS receiver. All data are synchronized with GPS time and recorded at a 160 Hz sampling frequency. The training database was collected with PERSY mounted on the foot in a large motion laboratory facility. The cluster of markers, needed by the motion laboratory tracking system, was also fixed on PERSY (Fig. 1a ). It is used by the motion capture system to estimate the position and the orientation of the cluster's center at 250 Hz with a millimeter level accuracy. These estimates are used as ground truth in this research. As shown in Fig. 1b , the motion capture system is installed in a large gymnasium (30 × 20 m) with two force plates (120 × 60 cm) in its centre allowing realistic human motion analysis.
2) Description of the scenarios: 7 test subjects of variable size, weight and age participated in the data collection. Among them were 4 women and 3 men who performed running, walking and sidestepping motions during the experiment as shown in Fig. 1c  and Fig. 1d. The motions were performed 3) Two classes to describe the recorded human gaits: The walking, running and sidestepping motions were classified in two classes, which are the "single support" and "double float" classes. The single support class corresponds to human gait where at least one foot touches the ground during motion. It is inspired by the biomechanics definition  [25]  of walking but includes more motions such as standing, stairs climbing/descending and sidestepping at low and comfortable speeds. The class double float corresponds to movements with an aerial phase: a period when both feet are off the ground.
B. Pre-processing of the inertial data Data preprocessing is the first step in machine learning. It transforms and structures the data into a state more appropriate for the learning phase that eases the interpretation of the data characteristics. Our preprocessing process comprises the three following steps: • Synchronization of the data • Labeling of the data • Calibration of the data 1) data synchronization: Our two data sources are recorded at different sampling frequencies. The IMU data is recorded at 160 Hz while the motion capture data is recorded at 250 Hz. Data must first be time-synchronized before being segmented and used for identifying the ZUPT instants. Let's note that these instants vary according to the individuals and the motion dynamics. The more segmented data there is, the more accurate will be the estimated ZUPT instants at higher dynamics.
Although this link is linear, it is too weak to properly model our phenomenon. Other variables are needed to increase the percentage of the total variance for ZUPT phenomenon. b) Logistic regression: This supervised learning technique was adopted to identify more relevant statistics. It studies the link between a qualitative variable Y and a set of explanatory variables X j , j = 1,. , n. The variables whose p-value is lower than 1‰, i. e. very significant, are considered as influential for the binary ZUPT variable {0, 1}. The logistic regression analysis identified the following variables: • r ay,az : the correlation coefficient between a y and a z • r ax,az : the correlation coefficient between a x and a z 3) Data augmentation: Data augmentation was applied to enrich the database using rotations and additive noises.