dataesr/scientific-paragraphs-categorization huggingface dataset:
Design of experiments A common strategy to design an emulator consists in considering the simulator as a "black-box" and build the emulator based on a synthetic dataset of input and corresponding output. The first step of this strategy is to define a design of experiments (DOE) to generate the datasets that will be used to build the emulator and evaluate its approximation error. Given input dimension and model complexity in the present study, we expect a large number of simulations (∼ 10 5 at the very least) will be required for an emulator to have good accuracy. The DOE relies on a Latin Hypersquare Sample (LHS) in [0, 1] 46 , which is a popular space-filling design. For all elements of the LHS, we apply an affine transformation from [0, 1] 46 to the hyperrectangle whose boundaries are defined by the ranges in are interesting alternatives, however their computational requirements (regarding time and/or memory space) can become prohibitory when there are both a high dimension (d = 46) and a large sample size (≥ 10 5 ).
If one considers the fields of elevations and fuel parameters h, σ f , and S v restricted to this square, given their 80-m spatial resolution, this amounts to four input fields of size 256 × 256 for emulation, raising the need for a method that is adapted to handle such high-dimensional data as well as the remaining scalar inputs. Neural network models appear suitable for emulation of fire spread simu-lations, not only because they usually perform well when trained on a large dataset, but also because they can handle several types of data. In particular, CNNs proved to be quite successful in the classification of 2D inputs such as images (e. g. Krizhevsky et al. , 2012) , but also for regression (e. g. Xie, Xing, Kong, Su, & Yang, 2015), which is our target. Here, the simulations are also significantly influenced by the other (scalar) inputs, notably wind speed and FMC, so a network with a hybrid architecture to process both types of inputs (2D and scalar) seems well suited to our problem.
Accuracy metrics and training strategy Among a dataset of size n, u i denotes the i-th set of simulation inputs, y(u i ) the resulting output, and ∼ y(u i ) the corresponding value returned by the emulator. Several metrics can be used to evaluate the accuracy of ∼ y, the emulator of function y. In this study, we use the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the standardized mean square error (SMSE, cf. Rasmussen & Williams, 2006) , which are respectively defined as follows: MAE = 1 n n i=1 | ∼ y(u i ) -y(u i )|, (3. 1) MAPE = 1 n n i=1 ∼ y(u i ) -y(u i ) y(u i ) , (3. 2) SMSE = n i=1 ∼ y(u i ) -y(u i ) 2 n i=1 y(u i ) -ȳ 2 , ( 3. 3) where ȳ = 1 n n i=1 y(u i ) is the sample mean of the emulated function. The SMSE can be seen as a mean squared error normalized by the sample variance of y, and would be equal to 1 if the emulator was a constant function equal to the sample mean ȳ.
The lower these scores, the more accurate the emulator. The emulator can also be evaluated in terms of mean error, similarly to the MAE but without the absolute value, that will be referred to as "bias" in the following. The accuracy metrics need to be computed for the test dataset as the error is expected to be much lower for the training dataset, which is used to determine the parameter values of the network. In order to quantify overfitting, the accuracy metrics may also be computed for the training dataset. The procedure used to train the network's parameters relies on a MAE loss function with an Adadelta optimizer  (Zeiler, 2012) , without any regularization due to layer parameters. To enrich the train dataset, a form of data augmentation is carried out: over one epoch, each member of the training dataset is used exactly once, but possibly after a geometric transformation (rotations, axial symmetries, or a combination of both).
The geometric transformation is applied to the 2D field inputs as well as (W x , W y ), the wind speed vector, and (δ x , δ y ), the relative coordinates of the ignition point. There is a 0. 5 probability of having no transformation, whereas the other transformations (seven different nonidentity applications) each have a 1/14 probability of being applied. We know that in such a configuration, the simulated burned surface would be the same, so this allows us to enrich the dataset (virtually, by a factor of eight) without running additional ForeFire simulations, and might limit overfitting  (Shorten & Khoshgoftaar, 2019).
Results and discussion The computational time of a simulation (with ForeFire) of wildland fire  than what would be observed in reality; and this amount would probably be higher were it not for non-burnable zones that significantly contribute to limit fire spread in some cases. This is mostly due to the fact that the simulations rely on simplifying assumptions where wind speed and FMC are constant in time and the DOE allows these inputs to vary in very large intervals. Therefore, it is not surprising to obtain a very large burned surface in a simulation where the wind speed is extremely high, the FMC extremely low, and no unburnable zone is reached during a whole hour of spread. Although somewhat unrealistic, the extremely high values of simulated burned surfaces were not removed from the dataset.
This might make the emulation more difficult but the ability to discriminate between a wide range of situations, even extreme ones, is relevant in wildland fire spread. more training epochs would result in a significant decrease of the error metrics for the test dataset. Consequently, the model with the best SMSE over the test set, which was obtained at the end of the 94-th epoch, was selected to define the emulator. The emulator with the best MAE was not selected because its MAE was only slightly lower (80. 7 ha instead of 81. 2 ha), while the other scores were all better for the model with the best SMSE. The error metrics of the emulator are reported in Table  3  and Table 4  it is much lower compared to that of the simplistic model (461. 9 ha). The SMSE of 6. 0% means that 94. 0% of the variance in the test dataset output is explained by the emulator, which is very good given the range of variation in simulation inputs.
The relative error is also satisfactory with a MAPE of 32. 8% on the test dataset, especially when compared to that of the simplistic model (2266. 0%). As for computational time on a 32-CPU machine, the outputs for the test dataset are obtained in about half a second with the emulator against 56 s with the whole DNN, which corresponds to a speedup by a factor of about 100. Also, the corresponding ForeFire simulations would have been obtained in about two hours with parallel computations on the 32-CPU machine, meaning that the emulator allows a speed-up by about 15,000 times. For a dataset where the simulated burned surface tends to be higher, the average computational time with ForeFire could be higher, which is not the case for the emulator for which computational time does not depend on the output fire size, meaning that the resulting speed-up factor would be higher.
For more insight regarding the approximation, the emulator output for each member of the test dataset is plotted against the actual values of simulated burned area in Figure  7. The vast majority of the emulated values are close to their simulated counterparts and 9,332 out of 10,000 are at most either twice higher or half lower. In 157 cases, the emulator returns the minimum value of 0. 45 ha, while the actual simulated value may go up to  10 ha; this corresponds to the apparent "black vertical bar" at the lower left of the graph in Figure  7a. There are 29 simulations for which the emulated burned area is at least five times lower (11 of them being equal to 0. 45 ha) and 43 simulations for which the emulated value is at least five times higher. In the latter cases, most of the simulated burned surfaces are small (≤10 ha in 32 simulations out of 43), which usually contributes to a higher relative error; but not all of them.
The 10,000 simulated burned surface areas of the ensemble are compared to their emulated counterparts in Figure  8. Similarly to the test dataset, most emulated values fall into the range of half to twice the simulated value, leading to a MAPE of 22. 7%. A MAE of 18. 7 ha is obtained and individual errors result in a distribution of the emulator output that is less dispersed than that of the simulated output, as shown in Figure  8b , with a bias of -9. 6 ha and a standard deviation of 77. 7 ha against 86. 1 ha. The overall agreement between simulation and emulation is good for this simulated fire case, and the simulations were computed in 20 minutes while the emulator predictions only took a bit more than a second. The speed-up factor is about 1000 this time, which is lower than the several thousands obtained for the test dataset; this is explained by the lower simulation time for this fire case (20 min instead of about two hours for the test dataset).
The proposed approach consisted in training a DNN used for regression. The network has a hybrid architecture to deal with 2D fields of environmental parameters and with scalar inputs. On the one hand, the 2D fields are restricted to a square of 20 km side centered around the ignition point to filter out information that is, for the most part, not used during the simulation, and go through convolutional blocks due to their similarity to images. On the other hand, the remaining scalar inputs go through dense blocks, are concatenated with last layer of the convolutional part, followed by more dense blocks. Training was carried out with a large dataset of size 5 × 10 6 obtained from a LHS sample, which could be augmented during training, and a complementary test sample of size 10 4 was obtained from a low-discrepancy sequence.
Although training resulted in some overfitting, this did not seem to have any adverse impact on the emulator prediction in the test dataset. The last layer of the convolutional part of the DNN for all fuel cells (∼ 1. 2 × 10 6 ) of the map of Corsica for which ignition is possible in the simulation is precomputed. This allows to reduce computational time since the resulting positional information can be used together with the scalar inputs to run computations with only the remaining part of the DNN, which was chosen as emulator of burned surface area. The emulator showed satisfactory performance. In the test dataset, it explains 94. 0% of the variance of the output, it has a MAPE of 32. 8%. Also, compared to the ForeFire simulations for fire danger mapping, the emulator computations are carried out thousands of times faster on a 32-CPU machine.
Python scripts are used to process the data, generate the training and test datasets, build and evaluate the DNN. Keras library, which is a high-level neural networks API that is running on top of TensorFlow, is used for building the DNN. Training and accuracy evaluation of the DNN up to the retrieval of the actual emulator are carried out on a GPU accelerated compute node. The computational time of the actual emulator is evaluated on a machine with 32 CPU. The size of the datasets are n train = 5 × 10 6 and n test = 10 4. Training is carried out for 100 epochs with batches of size 400, and the hyperparameters of the Adadelta optimizer are a decay rate of 0. 95, a conditioning constant of 10 -7 , and a learning rate of 0. 3, which is an extra factor in the right-hand term of Equation (.
spread took an average of approximately 25 s. This time highly depends on the input of the simulation and can range from about 0. 1 second to more than an hour. Overall, the larger the simulated burned surface, the more computations are carried out during the simulation. Given the simulation settings presented in Section 2, the obtained burned surface areas range from 0. 45 ha to 24 804. 4 ha among the training dataset. Some statistics of this output in the training dataset are presented in.
position" scalars linked to the 2D fields surrounding the ignition point stemming from the convolutions and the remaining 46 scalar inputs. Approximate SHAP values are computed for each member of the test dataset by means of expected gradients; this procedure relies on the assumption that the model to explain is linear and that the features are independent. While these assumptions are not verified with the emulator, this method allows for computation of approximate SHAP values in a reasonable amount of time. Although these values should be taken with care when analyzed individually they can still provide some insight on the overall input influence over a whole dataset. For each member of the test dataset, the expected gradient is estimated based on a subset of size 50,000 sampled randomly from the training dataset.
SHAP values associated to the perturbation of ∆H are also consistent with our expectations. Regarding the rankings of the inputs when looking at the absolute SHAP values averaged over the test dataset in Figure 9a, the three most influential inputs are the FMC and the coordinates of wind speed. Position is ranked fourth, perturbations on fuel parameters that affect all fuel types (∆H and ρ d ) are ranked fifth and sixth, and the remaining ranks are attributed to the other perturbations of fuel parameters, as well as δ x and δ y (ranked last). Interestingly, when the positional inputs are not summed, their individual influence.
" position" scalars linked to the 2D fields surrounding the ignition point stemming from the convolutions and the remaining 46 scalar inputs. Approximate SHAP values are computed for each member of the test dataset by means of expected gradients; this procedure relies on the assumption that the model to explain is linear and that the features are independent. While these assumptions are not verified with the emulator, this method allows for computation of approximate SHAP values in a reasonable amount of time. Although these values should be taken with care when analyzed individually they can still provide some insight on the overall input influence over a whole dataset. For each member of the test dataset, the expected gradient is estimated based on a subset of size 50,000 sampled randomly from the training dataset.
A notable difference in the present study is that the first elements selected by the algorithm are used to complete the training sample only if they are valid (they are ignored otherwise); then, when the target size n train is reached, the next valid elements are used to form a test sample of size n test. This procedure aims at selecting the points of the test sample so that they are located far from each other but also far from the points of the training sample, where the approximation error is expected to be higher. Finally, based on the inputs of the training and test sample, the corresponding fire spread simulations are carried out as described in Section 2 and the resulting outputs complete the training and test datasets. 3. 2NeuralnetworkarchitectureSeveral techniques can be considered for emulation.
FLER (Fluorescence-Based Enhanced Reality) FLER has been extensively described previously  [8, 9, 21]. In a nutshell, the AR-Perfusion™ software version 1 (IRCAD, Strasbourg, France) computes the speed required for the fluorescence signal to reach its maximum, pixel-by-pixel, and the metric used for the current study was the slope of fluorescence speed (FLER slope: ∆intensity/time). Thus, with the same method previously explained elsewhere  [9] , the dynamic perfusion cartogram of the pancreas was created by averaging fluorescence signals for a 40 s video at a speed of 25 frames per second, and by assigning a color code based on the time required to reach the maximum intensity of each pixel. However, since the fluorescence signal intensity decreases with distance according to the inverse quadratic law, a calibration reference card providing a constant fluorescence signal (Green Balance™, Diagnostic Green GmbH, Aschheim, Germany) was placed in the surgical field beside the pancreas.
Confocal Laser Endomicroscopy (CLE) A quantitative histological evaluation was performed using confocal laser microscopy (CLE) (Cellvizio™, Mauna Kea Technology, Paris, France)  [22]. CLE provides a realtime virtual image of histology which could be important additional information on the biochemical changes of the pancreatic tissue undergoing ischemia. Immediately after a systemic injection of fluorescein (Fluocyne TM , Serb, Paris, France), the pancreatic surface was manually scanned at each ROI and recorded for approximately 45 s. The videos were analyzed using the IC Viewer software™ (version 3. 8. 5) (Mauna Kea Technologies, France). As previously described in bowel ischemia models  [22, 23] , the software automatically recognizes the vessels as elongated shapes containing contrast and computes the functional capillary density area (FCD-A) index.
Prediction of Local Capillary Lactates from the FLER-Slope and HSI-StO2 The relationship between LCL and the FLER slope and HSI-StO2 respectively followed an exponential trend, which was modeled by exponential regression analysis. This allowed us to generate a prediction algorithm of LCL values from the corresponding HSI-StO2 and FLER slope. In order to validate prediction models, a mean square minimization of the objective function exp (-ax + b) was performed on normalized LCLs using the SciPy, Python library  [32]. The prediction functions were obtained from the whole datasets because no significant difference in terms of error prediction could be observed when performing a leave-1 (pig)-out cross-validation. The accuracy of both models, indicated as the absolute difference between the sampled LCLs and the predicted LCL values of both prediction models, was computed on the whole data by the corresponding exponential equation.
Statistical Analysis Data are shown as mean ± standard deviation unless indicated otherwise. Statistics were performed using the GraphPad Prism 8 software and Python Scikit-learn library  [32]. ANOVA followed by Tukey's multiple comparison test was used to compare the results among the three zones. Spearman's rank correlation test was used to investigate variables presenting a non-linear relationship. Exponential regression was also used to investigate predicted LCL values based on HSI-StO2 and the FLER slope. The Wilcoxon test was performed for paired comparison of lactate prediction algorithms, since a non-Gaussian data distribution was observed. A p-value < 0. 05 was considered statistically significant.
Circadian rhythm analysis. RBCs were incubated at constant 37 °C for time course sampling, with a separate aliquot being removed from the cycler for analysis at each time point for each donor. The final transition to 37 °C was taken as t = 0 (or Zeitgeber time 0, ZT0). Measurements were taken every 3 h from ZT0 to ZT48. We also tested a single participant using blood taken via needlestick to the finger, approximately every 2 h for 24 h; a drop of blood was resuspended in 1 ml KHB and measured the ζ-potential immediately, with 3 measurements taken as before. Time course data were analysed using Prism 8 (Graphpad Software, La Jolla, CA). Curve fitting was used to fit a damped cosine in order to determine the circadian parameters baseline, period, amplitude and phase as described by Hirota et al.
Measurement protocols. Data collection was performed using three methods in parallel, timed to ensure that measurements were as close in time to one another as possible (within 1 min). Passive electrical properties. Passive electrical properties were measured using dielectrophoresis (DEP)  47. The cell population was counted using a haemocytometer and adjusted to 1. 15 × 10 6 cells/ml (± 15%). 100 cell radii were measured using Image J software (National Institute of Health, Maryland, US) for use in the analysis. Cell suspensions were analysed using a 3DEP reader (Heathfield, UK)  22. Cells were analysed for 60 s at five points per decade (10 kHz-20 MHz). Five technical repeats were performed for each of the four donors taken, and all data were averaged before fitting to a single-shell model  48  using MATLAB (The Mathworks, USA) to determine C eff , G eff and σ cyto.
Introduction: The Joint United Nations Programme on HIV/AIDS (UNAIDS) projections of paediatric HIV prevalence and deaths rely on the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium for mortality estimates among children living with HIV (CHIV) receiving antiretroviral therapy (ART). Previous estimates, based on data through 2014, may no longer be accurate due to expanded paediatric HIV care and treatment eligibility, and the possibility of unreported deaths in CHIV considered lost to follow-up (LTFU). We therefore estimated all-cause mortality and its trends in CHIV (<15 years old) on ART using extended and new IeDEA data. Methods: We analysed (i) IeDEA observational data from CHIV in routine care globally, and (ii) novel data from an IeDEA tracing study that determined outcomes in a sample of CHIV after being LTFU in southern Africa.
on HIV/AIDS (UNAIDS) 90-90-90 goal  [3]. In many countries, these HIV statistics cannot be directly estimated, and we rely on mathematical models to derive them. The UNAIDS Spectrum model (Spectrum)  [4]  generates robust global HIV estimates from country-specific service statistics, survey and surveillance data, and epidemiological parameters derived from scientific studies. Previously, Spectrum incorporated estimates of mortality among CHIV on ART from the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium using data through 2014  [5]. However, paediatric HIV programmes have evolved over time  [6] [7] [8] [9]. Improved prevention of mother-to-child transmission, early infant diagnosis and PCR testing at birth, as well as earlier ART start and universal treatment, are likely to have impacted mortality, previously modelled in Spectrum as constant over time.
M E T H O D S 2. 1 Data sources The IeDEA collaboration collects and harmonizes HIV/AIDS data globally  [13] [14] [15] [16]. As of 2020, it had consolidated data on approximately two million people living with HIV from participating routine care treatment programmes, each comprised of multiple centres. In this analysis, we included all routine programmatic data (referred to as the "routine data," below) on CHIV younger than 15 years, who were receiving ART (a combination of at least three antiretroviral drugs) initiated for the first time between 2001 and 2017. Six IeDEA regions contributed data: central Africa; East Africa; southern Africa; West Africa; Asia-Pacific; and central America, South America and the Caribbean (hereafter 'Latin America'). We also utilized data from a recent IeDEA tracing study in six treatment programmes in five countries (Zambia, Malawi, Zimbabwe, Lesotho and Mozambique) in southern Africa.
From the traced sample of 3256 persons classified as LTFU by participating programmes  [11, 12] , we used the subsample of CHIV aged younger than 15 years, who had not visited the clinic for at least the previous 120 to 180 days, depending on local definitions of LTFU. The children's outcomes were determined using phone calls and home visits. The sample was identified and traced during 2017 to 2019, with last clinic visits occurring during 2013 to 2017. Within IeDEA  [14] , each participating programme has ethical approval from appropriate local institutions to collect and share patient data. Each region's data centre, which consolidates the data, has ethical approval to analyse the deidentified data. At the time of ART initiation, CHIV or their caregivers provided consent for being traced.