dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Performance on Increased Precursor Concentrations The simulations performed to create X10 and X100 data sets were carried out identically compared with the reference simulations starting at 10 ppt precursor concentrations, except that the initial precursor concentrations were increases by a factor of 10 (X10) and 100 (X100). The new data sets were then split into train, validation, and test data sets just as before, then transformed using the fitted scaling transformations on the original data sets. Then, the X10 and X100 test data sets were passed through MLP and GRU models that were trained on the original data set containing the smaller initial value of the precursor. Figure  9  shows the predictions of the GRU model on the reference test set of experiments (left column), and the expanded X10 and X100 test data sets (middle and right columns, respectively).
The Pearson coefficient, defined by Equation B3, which measures the correlation between two data sets, and MAE for each prediction task are listed in the sub-panels. The figure shows that the GRU trained on the smaller initial precursor concentrations made predictions on the X10 and X100 data sets that correlated strongly with the true values for precursor, gas, and aerosol, as is seen by high values of the Pearson coefficients for the different prediction tasks, but the MAE for each task increased by orders of magnitude with larger starting precursor concentrations. The figure also clearly indicates that the GRU model under-predicted the true values for gas and aerosol by approximately 1 and 2 orders of magnitude for the X10 and X100 data sets, respectively. For the precursor prediction task, the predicted decay times were significantly shorter compared to that observed in the GECKO-A experiments.
Data Availability Statement We would like to acknowledge high-performance computing support from Cheyenne and Casper (Computational and Information Systems Laboratory, CISL, 2020) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. The emulators described here and simulation code used to train and test the models are archived at https://github. com/NCAR/gecko-ml. All GECKO-A data sets created for this study are available at https://doi. org/10. 5281/zenodo. 5790042.
In the second step, masonry units are merged, and its topology is optimized to provide a macro representation of the media. Prone in-plane and/or out-of-plane failure mechanisms are a-priori assigned, and the location of the yielding surfaces is optimized by an upper bound limit analysis theorem coupled with a heuristic solver. At this stage, the third step is conducted, in which an ad-hoc script represents the sub-structure activated by the failure mechanism through a micro-scale representation. The outer domain, i. e. the rest of the structure that is not involved in the mechanism, keeps a macro and continuous representation. Finally, the concurrent FE multiscale model can be used to perform the structural assessment of the structure through linear/non-linear quasi-static/dynamic type of analysis and within a FE environment.
Parametric modelling of the structure The geometric modeling of the structure is the first step of the framework (node one in Figure  1 ). To this aim, it requires the knowledge of the masonry arrangement since a micro-modeling approach is assumed. Although a full representation of masonry units and joints can be cumbersome and time-consuming, the framework integrates a digital tool that was recently proposed by  Savalle, et al. (2021). This tool allows the pre-processing of the geometry via an automatic generation of the masonry arrangement, and it was implemented in the environment offered by Rhinoceros (+ Grasshopper) through C# programming language. It includes an initial discretization of the structure into elementary parts, i. e. walls arrangement, location of corners, location of T-connection, among other substructures, which can be assembled without any restriction aiming to form complex three-dimensional structures.
According to the distinct element method, a local damping factor is considered. The equations of motion are damped to reach a force equilibrium state as quickly as possible under the applied initial and boundary conditions. Damping is velocity-proportional (magnitude of the damping force is proportional to the velocity of the blocks) and it was assumed equal to 0. 8 in the present study. The adopted FE software is Abaqus (2014) contains the latter mentioned contact interface model. The constitutive law is automatically assigned to all the interfaces of the micro-domain m  through the General Contact algorithm (Abaqus, 2014).
Despite the ever-growing amount of ocean's data, the interior of the ocean remains under sampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000m and the Mixed Layer Depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in-situ profiles in the Gulf Stream region.
The training is done on raw in situ profiles alone (not interpolated fields) and the prediction are generalised for daily gridded fields at ¼ • horizontal resolution. Our framework also delivers a quantification of the uncertainty through a confidence interval of the model's prediction as well as the relative importance of each input variables. The proposed reconstruction method and resulting product are named OSnet for Ocean Stratification network. The paper is organised as follows. Section 2 introduces the datasets used as inputs and outputs of OSnet, as well as the products used as benchmark to evaluate the performance of our reconstruction. Section 3 presents the method composed of the neural network and the MLD adjustment. Section 4 evaluates the accuracy of OSnet predictions, and presents property maps and sections.
Temperature and salinity in situ profiles We use the in situ temperature and salinity (T-S) vertical profiles from CMEMS quality controlled Coriolis Ocean Dataset for Reanalysis (CORA) database  (Cabanes et al. , 2013; Szekely et al. , 2019)   (Wong et al. , 2020). After 2012, the amount of T-S profiles stabilises at ∼400 profiles per months.
Additional datasets We validate our results against other observational and synthetic datasets. The Sea Surface Salinity (SSS) CCI dataset is distributed at 0. 25 • x 0. 25 • horizontal grid resolution from 2010 to 2019  (Boutin et al. , 2021). We do not use SSS as an input variable for several reasons. SSS satellite observations only cover the period 2010-2019 and its quality is questionable in high latitudes and cold water  (Boutin et al. , 2021). As our prediction only depends on the input variables, it is risky to rely on data containing systematic errors. Moreover we tested an architecture with the SSS as input and the results were not improved significantly, only the surface salinity was slightly better. The relative importance algorithm also showed that SSS was not used significantly to make the prediction.
Method The method is composed of two steps (Fig. 2 ). Firstly, a neural network predicts T-S profiles and the MLD from satellite data. Secondly, an adjustment of the MLD combines the predicted T, S and MLD to correct the vertical shape of the profiles towards a physically consistent solution. The procedure is coded in Python with the help of several useful modules. The NN algorithm is coded with Tensorflow  (Abadi et al. , 2016)  and the Keras application programming interface  (Chollet and Others, 2015). It is explained with Shap  (Kaur et al. , 2020). Xarray  (Hoyer and Hamman, 2017) , Dask  (Rocklin, 2015)  and Numba  (Lam et al. , 2015)  are used for fast computation and the management of large datasets. The color palettes for maps are imported from Cmocean  (Thyng et al. , 2016)  and Colorcet  (Kovesi, 2015).
The inputs are linearly interpolated at each in situ profile's To further improve the prediction performance and assess the associated uncertainty, we exploit a bootstrapping scheme  (Breiman, 1996). More precisely, we bootstrap the training procedure 15 times using a different initialization and training dataset each time. Indeed because of the instability of the prediction method, the bootstrap can give substantial gains in accuracy. Overall, given 15 trained models, we compute the mean T, S and K profiles for each input data and the associated standard deviations (Fig. 3 , grey). The latter deliver an estimation of a confidence interval. This bootstrap method reduces the estimation bias. Finally, the T-S prediction is generalised on a daily ¼ • horizontal grid. The spatial resolution of the input data (Table  1 ) is unified to ¼ • in longitude and latitude by a nearest neighbour interpolation method.
Prediction of the mixed layer depth We define the mixed-layer depth H with a density deviation from the surface as proposed by  de Boyer Montegut et al. (2004). It is the depth at which the potential density referenced to the surface, σ 0 , exceeds by a threshold of 0. 03 kg m -3 the density of the water at 10m, σ 0 (z = -H) = σ 0 (z = -10m) + 0. 03 kg m -3. This definition is chosen for its simplicity of application, but tends to overestimate deep winter MLDs compared to the more sophisticated hybrid algorithm of  Holte et al. (2017). These regions of deep winter MLD are rarely observed in our dataset (1145 profiles with MLD deeper than 300m, i. e. 1. 7% of the dataset), comforting our choice of using the simple density threshold. For the NN, the mixed-layer is represented in the form of a unitless profile K of size Z = 51, that is filled with zeros between the surface and the mixed layer depth H, and with ones from H to -1000 m : K(z) =    0 if z > H 1 if z ≤ H (1) This form is preferred because it allows the NN to give an estimation of the gradients around the MLD instead of a single depth value (Fig.
After comparing different methods we thus choose to apply a MLD adjustment on the predicted profiles, in the same spirit as convective adjustment schemes used in numerical hydrostatic models  (Madec, 2015). We want to weight the vertical gradients of T and Ŝ by K, in order to reduce the gradients of T and Ŝ in the mixed layer, increase the gradients of T and Ŝ just below the MLD ( K = λ), while keeping the deeper gradients unchanged. So we modify the K into a new mask K * as follows : K * z =        Kz if Kz < λ 2 -Kz if λ > Kz > 1 Kz if Kz = 1 (2) Then we recompute iteratively the T-S profiles with the gradients adjusted with K * , starting by the bottom value (z = 1000m) where K * = 1 because the deepest MLD is never reaching 1000m (the maximum observed in the CORA dataset is H = 628m for our region).
The change in the output is representative of the importance of the input to predict the output. Shapley regression values  (Shapley et al. , 1953)  are representing the impact of an input on the output by removing it from the input set and retraining this model with the subset of inputs. This being computationally expensive, it is possible to obtain an approximation of the effect of removing a variable from the model by integrating over samples from the training dataset using the Shapley sampling values  (Štrumbelj and Kononenko, 2014). It produces an "importance" value for each particular prediction. 2. Metrics of accuracy for predictions of Armor3D, GLorys12, a Neural Network (NN) and and OSnet, compared to the in-situ CORA profiles. The σ0 inversions larger than 0. 01 kg m -3 are counted.
The Armor3D and Glorys12 statistics are computed on the subsampled products at CORA's profile locations. The mean and the standard deviation is provided. ( * )All the MLDs are computed with the density criterion of 0. 03, except for Armor3D for which a different criterion is used to bypass their density inversion issues. direction in which the input influences the output, relative to the averaged output. The SHAP algorithm being computationally expensive it was not possible to run it over the full dataset. After some tests, we found that 300 random samples were representative enough to obtain stable results for the average feature of importance across the entire dataset.
Accuracy of the predictions compared to observations Table  2  presents different metric to evaluate OSnet, Armor3D and Glorys12 relatively to CORA. Each dataset is predicted or subsampled at the location of CORA's profiles in longitude, latitude, depth and time. The two first lines indicate the number and size of vertical density inversions, indicating that the MLD adjustment of OSnet suppresses almost all density inversions, from 16. 41% to 0. 29% of profiles. Meanwhile, Armor3D has about 50% of profiles with density inversions and Glorys12 almost none (0. 01%). Regarding the amplitude of the density inversions, the MLD adjustment suppresses well large inversions and decreases by one order of magnitude the mean amplitude of inversions, from 10 -3 to 10 -4 kg m -3. The RMSE of the MLD (table 2) indicates that the MLD adjustment improves the MLD RMSE of OSNet from 50 m to 40 m, which is of the same order of magnitude than Glorys12 (38.
Temperature and salinity maps Let us examine a daily map of temperature and salinity at 1/4°resolution (Fig. 6 ). We chose a date in the pre-ARGO era to show the great generalisation of OSnet product. The maps reveal coherent horizontal structures. At the surface, the warm Gulf Stream is detaching from Cape Hatteras and meandering further East, transforming into the North Atlantic Current (Fig. 6a, b ). The surface confidence intervals are maximum for the cold and fresh waters near the edge of the continental slope and inside the cold and fresh core eddies and meanders (Fig. 6c, d ). On average the confidence intervals are highlighting the cold waters north of the Gulf Stream (Fig. 7a, b ) which is consistent with the error of prediction maps presented in Buongiorno Nardelli (2020). This could be due to the lack of profiles containing these cold waters in our dataset.
197 • C/decade, close to the SST trend of 0. 190 • C/decade (Fig. 12a ). For comparison the surface averaged temperature of Armor3D is warmer in the pre Argo years, probably due to their background global average that is mostly composed of Argo floats. This is a validation that OSnet generalises well and is not biased towards the recent years of in situ observations. Regarding the surface salinity long term trends, no significant trend is captured over the 1993-2019 period (Fig. 12b ). There is no clear agreement between the different datasets, except in the last period 2010 to 2019, where all averages increase like the SSS signal. Armor3d mean surface salinity drops significantly during the last two years 2018 and 2019, out of the SSS error margin. Note that OSnet does not include the areas over the continental shelf and does not predict deeper than 1000 m.
A large part of the climatic signal takes place in the coastal regions  (Ezer et al. , 2013; Davis et al. , 2017)  and an improvement of OSnet would be to deal with profiles of different length in order to include these regions. The mean seasonal variation of surface temperature, salinity and MLD is presented in figure  13. We compute it by averaging the signal by day of the year (week of the year for Armor3D). The surface temperature seasonal signal is well reproduced for each dataset, as expected considering that SST is included in the input of OSnet and used to produce both Armor3D and Glorys12 (Fig. 13a ). A close observation of the curves in figure  13a  shows that the seasonal cycle of OSnet surface temperature is too cold between May and September (Fig. 13c ). This is due to the MLD adjustment because the direct prediction of surface temperature with the NN gives a very precise seasonal cycle of SST (red line in fig.
First, Glorys12 displays a deep patch of MLD around 41 • N, north of the Gulf Stream, that is not observed by the CTDs, nor predicted by OSnet (Fig. 15d ). This deep MLD could be due to the nearest neighbour selection of profile that is not exact in the case of Glorys12, or it could be an artefact of their model. Indeed deep patches of MLD are also visible on daily MLD maps of Glorys12 but are absent of OSnet daily MLD maps (Fig. 8 ). Second, the salinity reconstruction of Glorys12 differs more from the observed data north of the Gulf Stream, compared to OSnet (Fig. 15g ). Salinity is more difficult to reconstruct than temperature, especially in this highly variable region. OSnet performs well along this section, in comparison to the reanalysis Glorys12.
Potential improvement of the method Seeing the very promising results of our study, an obvious future work would be to apply OSnet on other more challenging regions, with less data or more complicated vertical structures and different dynamics. The 3D geostrophic velocities of OSnet product could be estimated using the thermal wind equation combined with surface altimeter geostrophic currents  (Mulet et al. , 2012). The climate change signal in the ocean's interior is difficult to observe due to a lack of in situ observations  (Silvy et al. , 2020). The global Ocean Heat Content (OHC) is generally well-constrained among ocean reanalyses, but regional differences may warrant further investigation  (Palmer et al. , 2017). The boundary currents are choke points of the OHC estimation accuracy, because the relatively sparse data or low resolutions of the objective analysis products are inadequate to resolve the mesoscale structures  (Liang et al.
In that case, we could predict properties over shallow bathymetry and also deeper than our rather arbitrary 1000 m limit. OSnet produces coherent horizontal and temporal patterns even though each profile is predicted independently. Yet we wonder how the horizontal and temporal surface gradients as inputs could improve predictions, especially in frontal regions. To test this, we would need to work with three dimensional (lon/lat/time) patches of input data for each profile's location and to build a different NN architecture (e. g. Ouala et al. , 2018; Jouini et al. , 2013; Tandeo et al. , 2013; Lguensat et al. , 2018)  that takes patches of data as input and profiles as output. Convolutional neural architectures accounting for irregularly-sampled space-time observations might also be appealing  (Fablet et al.
, 2021). The expected result would be sharper fronts, as we observe that the OSnet fronts are smoother than observations for the example of figure 15. We wonder if the NN could learn from the temporal surface gradient to anticipate vertical changes of stratification. Prediction intervals (PI) also called "coverage probability" could be computed in supplement of confidence intervals  (Khosravi et al. , 2011). While the confidence intervals (Fig 7 ) gives the range of variation of a set of NN with different initializations and training datasets, the PI gives the probability for the observation associated with the prediction to be in a range of values. The PI can be obtained by adding output variables to the proposed architecture. Here, the PI could be represented as temperature and salinity profiles around the prediction, representing the 95% interval.
Simulation Data The cosmological simulation HR5  (Lee et al. 2021 ) covers a volume of the universe in an 1. 15 cGpc 3 scale volume while achieving a resolution down to ∼1 pkpc. We adopt the cosmological parameters of Ω m = 0. 3, Ω Λ = 0. 7, Ω b = 0. 047, σ 8 = 0. 816, and h = 0. 684 that are compatible with the Planck data  (Planck Collaboration et al. 2016 ). The linear power spectrum is computed using the CAMB package  (Lewis et al. 2000)  and the initial conditions are generated at z = 200 using the MUSIC package  (Hahn & Abel 2011)  with second-order Lagrangian perturbation theory (2LPT;  Scoccimarro 1998; L'Huillier et al. 2014). We use a modified version of the adaptive mesh refinement code RAMSES  (Teyssier 2002)  for HR5. The Poisson equations and Euler equations are solved based on the Particle-Mesh method  (Guillet & Teyssier 2011 ) and the Harten-Lax-van Leer contact wave Riemann solver  (Toro et al.
2013). In HR5, we define a lump of stellar particles in an SBO as a galaxy. In principle, the MBP and the galaxy of an SBO should be closely bound to each other, but, in practice, they are sometimes decoupled due to complicated internal interactions, branching into different structures in the next step (see Case 3 in Figure  1 ). In merger trees, this case can be seen, as if galaxies suddenly appear or disappear with no legitimate descendants or progenitors. To minimize such cases, we additionally use the tree-building code ySAMtm  (Jung et al. 2014; Lee et al. 2014 ) that forms merger tree branches based on individual particle IDs. ySAMtm has been originally developed to construct halo merger trees using dark-matter particles that do not newly form or disappear throughout the duration of the cosmological simulations.
We thank  Katarina Kraljic  for helpful comments. C. P. and J. K. are supported by  KIAS Individual Grants  ( PG016903 ,  KG039603 ) at  Korea Institute for Advanced Study. J. L. is supported by the  National Research Foundation of Korea  ( NRF-2021R1C1C2011626 ). J. S. acknowledges support from the  National Research Foundation of Korea  grant ( 2021R1 C1C1003785 ) funded by the  Ministry of Science, ICT & Future Planning. B. K. G. and C. G. F. acknowledge the support of  STFC  through the  University of Hull  Consolidated Grant  ST/ R000840/1 , access to VIPER, the  University of Hull High Performance Computing Facility , and the European Unions  Horizon 2020 research and innovation program  ( ChETEC-INFRA- Project No. 101008324 ). This work benefited from the outstanding support provided by the  KISTI National Supercomputing Center  and its  Nurion Supercomputer  through the  Grand Challenge Program  ( KSC-2018-CHA-0003 ).
Despite the advances in mobile robotics, the introduction of autonomous robots in humanpopulated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot's presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people's natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people's presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks.
This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatiotemporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.