dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis: Although it has more trainable parameters, Resnet18 based models perform equally with simple U-Net or even worse for difficult classes (mineral materials and bare soil). It seems that our dataset is too small to be able to benefit from the complexity of Resnet18. Loss performances are quite equivalent for this experiment. We note that the use of Binary Cross Entropy degrades measured performances and combo loss (BCE + Jaccard loss) improves IOU for many classes but to a relatively limited extent.
CONCLUSIONS This article gives hints to build a training dataset for classes asphalt, bare soil, buildings, grassland, mineral materials (permeable artificialized areas), forest and water from scratch when full manual annotation is not an option. DeeplavV3 architecture shows very good results for semantic segmentation of aerial images. Dataset quality is the most important criteria to obtain good results on prediction. Prediction concatenation is a real challenge when a seamless heat map is expected. This experiment has been conducted within a project whose objective is modernizing production lines for land cover products on French territory. Land cover maps produced on Gironde department are then derived to a generalized product and will be used by public authorities to monitor the land consumption.
or terrestrial images (Kaiming He et al. , 2015; Liang-Chieh Chen et al. , 2018)  and others have transposed those solutions to earth observation with high resolution images taken from satellite (Nagesh Kumar Uba, 2019), airplane (CeZhang et al. , 2019)  or drone(Zhang et al,  2019). Multiple competitions have been recently carried out to improve semantic segmentation results on earth observation images with a provided dataset (Spacenet challenges, ISPRS semantic labelling contest). Lots of work on roads or building extraction from satellite images (ZhengxinZhang et al. , 2017)   have been published. We propose to reuse top-class semantic segmentation architectures (U-Net with ResNet encoders, DeeplabV3+) on very high resolution aerial images.
loss building forest grass asphalt mineral bare soil water CE table presents IOU results (in percentage) computed on the test dataset which has 1935 samples. 77. 76 84. 14 74. 56 77. 68 40. 81 72. 82 96. 12 BCE 77. 9 84. 74 75. 23 77. 42 41. 20 74. 91 95. 85 WCE 72. 99 80. 39 69. 08 62. 48 20. 50 57. 86 93. 04 Combo 77. 60 84. 75 74. 80 77. 56 39. 44 72. 12 95. 69 CE 77. 27 84. 75 73. 67 71. 62 21. 67 36. 96 93. 24 BCE 76. 60 84. 06 72. 31 71. 07 21. 64 9. 990 93. 09 WCE 76. 55 83. 40 72. 37 69. 85 27. 26 32. 73 94. 31 Combo 77. 27 85. 27 73. 61 71. 12 21. 38 36. 67 94. 38 CE 82. 66 85. 83 77. 78 79. 36 46. 59 82. 57 96. 43 BCE 82. 32 86. 95 78. 42 77. 98 41. 85 86. 77 97. 46 WCE 81. 78 83. 97 77. 26 76. 63 41. 57 89. 54 96. 75 Combo 82. 49 87. 47 78. 87 77. 70 45. 23 77. 35 97. 59 model.
The present study aimed to work out a peptide-based multi-epitope vaccine against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We predicted different B-cell and T-cell epitopes by using the Immune Epitopes Database (IEDB). Homology modeling of the construct was done using SWISS-MODEL and then docked with different toll-like-receptors (TLR4, TLR7, and TLR8) using PatchDock, HADDOCK, and FireDock, respectively. From the overlapped epitopes, we designed five vaccine constructs C1-C5. Based on antigenicity, allergenicity, solubility, different physiochemical properties, and molecular docking scores, we selected the vaccine construct 1 (C1) for further processing. Docking of C1 with TLR4, TLR7, and TLR8 showed striking interactions with global binding energy of -43. 48, -65.
There are four other coronaviruses, HCoV-229E and HCoV-NL63 (alpha coronaviruses) and HCoV-HKU1 and HCoV-OC43 (beta coronaviruses), responsible for mild respiratory tract infections that cause complications or fatalities in elderly immuno-compromised individuals and young children  [6]. Currently, there are some antiviral treatments like Chloroquine, Remdesivir, Ribavirin, Lopinavir, traditional Chinese medicine etc. , under trial to combat SARS-CoV-2. In silico drug screening against viral proteins using natural products, FDA approved drugs, and other antivirals have also been attempted  [7]. A whooping 657 clinical trials for various drugs against COVID-19 have been registered till 20 April 2020 (https://clinicaltrials. gov/ct2/results?cond=COVID  -19). Although some studies report the use of these drugs in some COVID-19 patients, their concordant status against the disease is yet inconclusive and no satisfactory treatment for COVID-19 exists till to date.
Epitope Prediction For chimeric vaccine development, the surface topology of the glycoprotein was selected to identify immunogenic determinants for the vaccine construct. Identification of immunogenic T-Cells (MHC-I and II) was carried out by using the Immune Epitopes Database (IEDB) (https://www. iedb. org/) [13]. It employs a different prediction method for MHC epitope binding analysis. MHC class I and II molecules were predicted through the Stabilized Matrix Method (SMM) scoring and neutral-network based tool (net MHC-1. 1) [14] [15] [16]. Epitopes were prioritized from the predicted ensemble, based on a threshold value of IC50 ≤ 200 nM. B-Cell immunogenic determinants were projected using BCPRED (http://ailab. cs. iastate. edu/bcpreds/), with the default parameters. BCpred predicts linear B-Cell epitopes  [17] , which are important for stimulating a humoral immune response, which activates B lymphocytes for antibody production  [18].
Epitope Prediction For chimeric vaccine development, the surface topology of the glycoprotein was selected to identify immunogenic determinants for the vaccine construct. Identification of immunogenic T-Cells (MHC-I and II) was carried out by using the Immune Epitopes Database (IEDB) (https://www. iedb. org/) [13]. It employs a different prediction method for MHC epitope binding analysis. MHC class I and II molecules were predicted through the Stabilized Matrix Method (SMM) scoring and neutral-network based tool (net MHC-1. 1) [14] [15] [16]. Epitopes were prioritized from the predicted ensemble, based on a threshold value of IC 50 ≤ 200 nM. B-Cell immunogenic determinants were projected using BCPRED (http://ailab. cs. iastate. edu/bcpreds/), with the default parameters. BCpred predicts linear B-Cell epitopes  [17] , which are important for stimulating a humoral immune response, which activates B lymphocytes for antibody production  [18].
Molecular Docking and MD Simulation Molecular docking is used to predict the possible binding orientation of the vaccine constructs  [34, 35]. TLR7/8 complex (TLR8 PDB ID:3w3g) was downloaded from Protein Databank (PDB) (https://www. rcsb. org). Online servers PatchDock (https://bioinfo3d. cs. tau. ac. il/PatchDock/), HADDOCK webserver (https://doi. org/10. 1007/978-1-4939-0366-5_12), and FireDock (http://bioinfo3d. cs. tau. ac. il/FireDock/) were used for molecular docking and docking refinement, respectively. FireDock refines and re-scoring the docked complexes and ranks the complexes based on binding score and global binding energy  [36]. An online server CABS-flex (http://212. 87. 3. 12/CABSflex2/job/7e5d9345a1a8235/) was used for molecular dynamics simulation, to check the stability of the complex.
Codon Optimization and In Silico Cloning A codon optimization approach was used to enhance recombinant protein expression. Codon optimization is important because degeneracy of genetic code permits most of the amino acids to be encoded by multiple codons. Java codon adaptation index (JCAT) (http://www. jcat. de/) and EMBOSS Backtranseq (www. ebi. ac. uk/) were used in the codon system of E. coli to obtain the codon adaptation index (CAI) values and GC contents  [39]. The optimized sequence of the final vaccine construct was inserted in a vector for expression, by using the Snapgene tool available at (https://www. snapgene. com/free-trial/). A compatible plasmid vector pET-28a (+) was used to integrate the optimized sequence and clone the constructed chimeric vaccine. pET-28a (+) is for expression of N-terminally 6 × His-tagged proteins and usually, N-terminal tags are advantageous over C-terminal tags, leading to enhanced purification, protein recovery, and stronger response.
Results The T cell, B cell, and IFN-inducing epitopes of the antigenic surface glycoprotein were predicted with the help of Immune Epitope Database (IEDB) and IFNepitope servers. The predicted T cell epitopes were able to bind with MHC-1 and MHC-2 molecules to activate the adaptive immune response in the host. We searched the overlapped epitopes as shown in Table  1  (the epitopes bind with both MHC molecules, B cells, and IFN-gamma) to take advantage and produce humoral, cell-mediated immune responses and induce interferons which were our ultimate objective. Five different constructs were built by the integration of two antigenic peptides and several adjuvants and linkers. There were a lot of possible arrangements of segments but since the analysis of such a huge number of constructs was impossible, we designed these five constructs as shown in Table  2 , as samples for our study.
A vaccine able to bind to different HLA allelic proportion of the human population is most important for its proper function inside the host. The docking of all vaccine constructs was performed with TLR7, TLR8, and TLR 4/MD2 complexes (PDB ID 2Z65). After analyzing all vaccine constructs, we finalized the vaccine construct (C-1) based on different physiochemical properties and docking scores. The C-1 TLR7 complex has good global binding energy score (-65. 88 Kcal/mol) obtained from PatchDock as shown in Table  5. Therefore, we considered construct 1, as a promising vaccine against the 2019 novel Coronavirus (2019-nCoV). Further molecular docking of the vaccine was performed These tertiary structures are considered as critical regarding their interactions with other proteins and molecules within the cell.
Tertiary structures of the constructed vaccines were generated by using the SWISS-MODEL server. PatchDock, HADDOCK web server, and FireDock were used for molecular docking and refinement, respectively, and then validated by RAMPAGE. The final 3D modeled structure of the vaccine construct is shown in Figure  3B  and the Ramachandran validation plot is displayed in Figure  3C. A vaccine able to bind to different HLA allelic proportion of the human population is most important for its proper function inside the host. The docking of all vaccine constructs was performed with TLR7, TLR8, and TLR 4/MD2 complexes (PDB ID 2Z65). After analyzing all vaccine constructs, we finalized the vaccine construct (C-1) based on different physiochemical properties and docking scores. The C-1 TLR7 complex has good global binding energy score (-65.
The deterministic G12 model Following  Gissinger (2012) , we consider the ordinary differential equations dQ dt = µQ -V D, dD dt = -νD + V Q, dV dt = Γ -V + QD, (1) where µ = 0. 119, ν = 0. 1, and Γ = 0. 9. Here, D is the dipole and the variable Q represents the quadrupole or, more generally, the non-dipole field; V is a velocity variable that couples D and Q. A change in the sign of D corresponds to a dipole reversal. We refer to this model as the G12 model. A typical simulation with G12 is shown in Figure  1. Here, model time t is scaled to represent the G12 millennium time scale (1 dimensionless time unit = 4 kyr), see  Morzfeld et al. (2017). The simulation is done by discretizing the differential equation by a fourth-order Runge-Kutta scheme (Matlab's ode45). et al. (2009)  derived a model for dipole reversals by considering the interaction of two modes.
(2017). The radius ratio of the inner-core boundary to the core-mantle boundary is set to its present-day value. The 3D model has no-slip boundary conditions on the inner-core and core-mantle boundaries, and it assumes that the inner core is conducting. The four non-dimensional control parameters, as defined e. g. in  Schaeffer et al. (2017) , are as follows. The Ekman number is 10 -4 , the Prandtl number is 1, the magnetic Prandtl number is 3 and the Rayleigh number is 15000. These choices result in an average hydrodynamic Reynolds number of 216 (recall that the hydrodynamic Reynolds number is defined as the product of the root-mean-squared velocity by the shell thickness divided by the kinematic viscosity). The open-source, freely available xshells code is used to numerically solve the equations.
This code combines the finite https://nschaeff. bitbucket. io/xshells/ difference method in the radial direction with a spherical harmonic representation of field variables in the horizontal direction, using the dedicated SHTns library  (Schaeffer 2013). To ensure numerical convergence, a hyperdiffusivity is applied beyond spherical harmonic degree 55. The resolution of the 3D model is defined by the triplet (Nr, max, mmax), giving the number of points used in the radial direction together with the maximum degree and order used in the horizontal approximation of field variables with spherical harmonics. Since five scalar fields are discretized, the total number of degrees of freedom of a simulation is O (5Nr maxmmax). Namely, the triplet defining the resolution is  (144, 79, 63) , which results in about 3.
Quantitative comparison and ranking We compute MCC skill scores to quantitatively compare the skill of threshold-based predictions for the various models. To avoid over-fitting we now compute skill scores on verification data, i. e. , data that are not used for computing the optimal warning threshold, as described in Section 3. 3. We generate training and verification data as follows. For the G12, P09 and DW models, the training data are the long simulations that were also used in Section 3. 2. The verification data are ten independent simulations, each of length 10 4. For the 3D model, we create training and verification data by "chopping up" the overall simulation as follows. We split the simulations into two parts of equal length and use one for training and the other for verification. We then repeat the procedure, but split the simulation into three equally long portions, using one for training and two for verification.
Robustness of skill to a short training period Motivated by the fact that the observational record is short (the PADM2M and Sint-2000 reconstructions that we investigate in Section 5 extend over 2 Myr and contain only six low-dipole events), we investigate the robustness of the optimal WT and corresponding skill with respect to the duration of the training data. For each model we compute an optimal WT for several training data sets of different durations, and, hence, containing a different number of events. Results for a prediction horizon PH = 1 are shown in Figure  10(a). For G12, we note that the optimal WT is nearly independent of the duration of the training data set. This means that, for this model, one can find a useful warning threshold from a rather short training period. For all other models, we observe a variation of the optimal warning threshold as we vary the duration of the training period.
Impact of data filtering Threshold-based predictions for the 3D and DW model have a low skill compared to P09 or G12. This could be due to the quick changes in polarity that we observe in the 3D and DW models, and that occur on short time scales (recall Figure  1 ). These are absent from the P09 or G12 models. Palomagnetic reconstructions such as PADM2M and Sint-2000, which are inherently smoothing the field they record through the slowly depositing sedimentary process, also fail to show such a behavior. One may thus wonder if the 3D or DW models could become more amenable to threshold-based predictions if the dipole is smoothed in an analogous way. To test this possibility, we first consider the 3D model, and rely on the secular variation time scale τ = 415 years, which we already used to scale time for this simulation.
2017). We note that PADM2M and Sint-2000 are "data" of the same process, namely Earth's dipole intensity over the past 2 Myr. Nonetheless, there are differences between PADM2M and Sint-2000, which are due to variations in the processing and interpretation of raw data, and also the raw data that goes into the two reconstructions. This means that differences between PADM2M and Sint-2000 indicate the level of uncertainty that is caused by difficulties with observing Earth's dipole over millions of years (see also  Morzfeld & Buffett (2019) ). Moreover, the fact that the observational record is short (2 Myr sampled in 1 kyr increments), implies that it is difficult to determine if any differences are (statistically) significant. It is important to keep this "minimum level of uncertainty" in mind when evaluating threshold-based predictions for the paleomagnetic reconstructions (note that we essentially treat the paleomagnetic reconstructions as "data," but we are aware that these reconstructions are themselves "models").
We keep in mind that standard deviations for the paleomagnetic reconstructions may be corrupted by insufficient statistics, since the data document only six events. The situation, however, is different when considering average decay times. The average decay times of the paleomagnetic reconstructions are much larger than those of the 3D (shortest), DW and P09 models, but comparable to that of the G12 model (longest). The standard deviations are much larger than that of the G12 model, and substantially larger than those of the P09, 3D and DW models (P09, DW and 3D models are comparable). This could be due to insufficient statistics or to data uncertainties, as suggested by the disagreement between the different values obtained with the PADM2M and Sint-2000 data sets. From the perspective of average decay times, it thus appears that the data are consistent with the G12 model.
Figures  13(c ) shows the curve traced out by the MCC as when varying the warning threshold for PADM2M and Sint-2000 (MCC is computed over the entire 2 Myr time window). Again, we note that the curves corresponding to the paleomagnetic reconstructions are qualitatively similar to the corresponding curve of the P09 model (see Figure  11 ). We also compute optimal MCC scores for PADM2M and Sint-2000 via training and verification. We use the first 0. 95 Myr, containing four events, for training (finding an optimal warning threshold) and use the remaining 1. 05 Myr, containing two events, for verification. Table  1  lists these MCCs for PADM2M and Sint-2000, together with the MCCs of the four models, when computed with training data containing a comparable number of events (four events during training for the paleomagnetic data and five events during training for the models, see Section 4.
In this context, we wish to point out that we did not use other global models, e. g. , PISO-1500  (Channell et al. 2009 ), because it is biased towards the North Atlantic region due to the fact that only stacks with a high sedimentation rate are used (see, e. g. , Figure  5  of  Panovska et al. (2019) ). Indeed, PISO-1500 is less representative of the (global) axial dipole field than PADM2M and Sint-2000 (see also  Ziegler et al. (2011) ). Exploring the consequences of such differences is beyond the scope of our work. Finally, we want to bring a few details to the reader's attention. In particular, we want to emphasize that the average dipole intensity and the average event duration for threshold-based predictions for PADM2M or Sint-2000 are computed using the entire 2 Myr record. One could also envision to compute the average intensity based on training data only.
The code we use to implement the prediction is available on github (https://github. com/kjg136/Threshold). We archived the code used to generate all figures in (https://doi. org/10. 5281/zenodo. 4267116). A first major conclusion is that the skills of intensity threshold-based predictions vary surprisingly widely within the hierarchy of numerical models we investigated (G12, P09, DW and 3D models). The only model that leads to a high skill (implying that the intensity threshold-based predictions are reliable) is the G12 model. This result is in line with the results obtained by  Morzfeld et al. (2017) , who identified a high skill of intensity threshold-based predictions for this model, using a simpler strategy and a less robust analysis. All other models lead to lower skills, implying that the intensity threshold-based predictions are less reliable.
ACKNOWLEDGEMENTS KG acknowledges that this work was supported by  NASA Headquarters  under the  NASA Earth and Space Science Fellowship Program -Grant  " 80NSSC18K1351 ". This work was supported in part by the  French Agence Nationale de la Recherche  under grant  ANR-19-CE31-0019  (revEarth). All authors would like to thank  Nathanael Schaeffer  ( ISTerre, CNRS, Université Grenoble Alpes ) and  Thomas Gastine  ( Université de Paris, Institut de Physique du Globe de Paris)  for allowing us to use the dipole time series of the 3D model. We acknowledge  GENCI  for access to the  Irene resource (TGCC)  under grants  "Grand Challenge "  GCH0315  and  A0060407382. We thank  Maggie S. Arvery  ( UC Berkeley ) and an anonymous reviewer for helping us improve this paper. We thank  Cathy Constable  ( Scripps Institution of Oceanography, University of California, San Diego ) and  Bruce Buffett  ( UC Berkeley ) for meaningful discussions.
As mitigating car traffic in cities has become paramount to abate climate change effects, fostering public transport in cities appears ever-more appealing. A key ingredient in that purpose is easy access to mass rapid transit (MRT) systems. So far, we have however few empirical estimates of the coverage of MRT in urban areas, computed as the share of people living in MRT catchment areas, say for instance within walking distance. In this work, we clarify a universal definition of such a metrics -People Near Transit (PNT) -and present measures of this quantity for 85 urban areas in OECD countries -the largest dataset of such a quantity so far. By suggesting a standardized protocol, we make our dataset sound and expandable to other countries and cities in the world, which grounds our work into solid basis for multiple reuses in transport, environmental or economic studies.
Few works  27, 28, 18  , however, have considered investigating catchment areas of MRT stations, i. e. looking at the share of population living close to MRT stations, for instance within walking distance. Such conditions have however proved to be essential in explaining commuting behaviours and mobility patterns  18. The most detailed definition of such catchment metrics is the People Near Transit (PNT), and originates from a 2016 publication from the Institute for Transportation and Development Policy (IDTP)  27. It produces a rigorous dataset of the share of population living close to transit (less than 1 km) for 25 cities in the world (12 in OECD countries). However, definitions of urban areas and rapid transit systems in that dataset are multiple and need to be refined while the number of cities must be expanded.
Hence, in order to expand our global knowledge of urban mobility, we need a common, unified and universal definition of access to public transit as well as sound measures of such a quantity. In this paper, we clarify its definition and propose what is to our knowledge the largest dataset of PNT globally. Our analysis uses functional urban areas (FUA) in OECD countries, a consistent definition of cities across several countries  29. We restrict our measures to mass rapid transit, usually referring to high-capacity heavy rail public transport, to which we added light rails and trams. In our sense, mass rapid transit thus encompasses: • Tram, streetcar or light rail services. • Subway, Metro or any underground service. • Suburban rail services. Buses are not comprised in that definition. In contrast with  27  , we do not exclude any form of commuting trains based on station spacing or schedule criteria.