dataesr/scientific-paragraphs-categorization huggingface dataset:
5 latent heat flux output computed from the U 10m , q 2m , T 2m and SST given by eq. 4 and that LHF LR is the COARE3. 5 latent heat flux output obtained from the remapped U 10m , q 2m , T 2m and SST. These two LHFs show differences up to 10 W m -2 in coastal areas of the ECR. Weaker LHF values prevail by moving towards the OOR. The quantities in brackets over Figures  5A, B  represent the areaweighted means of the LHF differences. They are close to zero for both datasets. This means that, away from the locations where DSST is large (i. e. the ECR), the O (DSST 2 ) effects in the LHF controlling variables (U 10m , q 2m , SST and T 2m ) do not produce large LHF variations so that LHF evolves almost linearly with DSST. Accordingly, the spatial pattern of the LHF differences coincides with the size of the DSSTs shown in Figures  5C, D  for SeaFlux and ERA5 respectively.
Validation We validate our approach by means of the original highresolution LHF obtained from the original simulated WRF variables using the COARE3. 5 algorithm (Figure  8A ). This LHF dataset, which we denote as LHF WRF , is physically consistent with the fully compressible Euler equations and, thus, can be considered a reliable source of high-resolution information. To test the robustness of the downscaling approach, we look at the difference between LHF WRF and LHF LR,HR,therm. To estimate the downscaled LHFs we use the coupling coefficients computed from LHF LR that do not differ significantly from Seaflux (not shown). Results in Figure  8  show that LHF HR obtained from the downscaling best represents LHF WRF both in terms of spatial patterns (Figure  8D ) and in terms of spatial-temporal statistics (Figure  8B ).
Acknowledgments The authors acknowledge the contributions of  Carol Anne Clayson  and  Jeremiah Brown  from the  Woods Hole Oceanographic Institution  ( WHOI ) for providing the new, highresolution, SeaFlux dataset. In addition, the authors would like to thank  Dr. Dongxiao Zhang  from  NOAA  for the fruitful discussions on the different bulk flux algorithm as well as on his work highlighting the effect of surface ocean currents on air-sea fluxes which serves as inspiration for further research. The authors thank the two reviewers who helped to clarify results presented. This work is a contribution to the  EUREC 4 A-OA  project, funded jointly through  JPI Oceans and JPI Climate. Part of this work is an.
Funding PF is funded by a PhD grant of the  Doctoral School of Environmental Sciences in Île-de-France  N°129. FD is funded by  Piano Operativo Nazionale "Ricerca e Innovazione", Italian Ministry of University and Research  ( 2021-RTDAPON-150 ). MB is funded by the  Joint Programming Initiative ClimateOceans  (project  EUREC 4 A-OA ). AM has received support from the  European Space Agency (ESA)  as part of the  GLAUCO Climate Change Initiative (CCI)  fellowship ( ESA ESRIN / Contract No. 4000133281/20/I/NB ).
Data availability statement The data sets analyzed for this study can be found in the following open-access repositories. • SeaFlux, https://seaflux. org/seaflux data/ATOMIC current/ • ERA5, https://cds. climate. copernicus. eu/cdsapp!/dataset/ reanalysis-era5-single-levels?tab=form • MUR-JPL, https://thredds. jpl. nasa. gov/thredds/ncss/grid/ OceanTemperature/MUR-JPL-L4-GLOB-v4. 1. nc/dataset. html • WRF outputs, due to their large memory size, are available from the authors upon request. • The additional data used in the Appendix are available from the Centre for Environmental Data Analysis (CEDA) archive: https://dap. ceda. ac. uk/neodc/esacci/sst/data/CDRv2/.
Figures Figures 5A, B represent the averaged 2008-2018 DJF difference between LHF HR and LHF LR for SeaFlux and ERA5 respectively. Recall that for SeaFlux and ERA5, LHF HR represents the COARE3. 5 latent heat flux output computed from the U 10m , q 2m , T 2m and SST given by eq. 4 and that LHF LR is the COARE3. 5 latent heat flux output obtained from the remapped U 10m , q 2m , T 2m and SST. These two LHFs show differences up to 10 W m -2 in coastal areas of the ECR. Weaker LHF values prevail by moving towards the OOR. The quantities in brackets over Figures5A, Brepresent the areaweighted means of the LHF differences. They are close to zero for both datasets. This means that, away from the locations where DSST is large (i. e. the ECR), the O (DSST 2 ) effects in the LHF controlling variables (U 10m , q 2m , SST and T 2m ) do not produce large LHF variations so that LHF evolves almost linearly with DSST.
Electrothermal Actuation and Identification in Open-Loop Conditions Electrothermal actuation measurements were performed by cutting the bilayer devices in a "U shape" and fixing one end on the measuring bench, whereas the other end was left to bend either freely or with payloads. The actuation was achieved by Joule heating the device and the resulting change of the tip position was detected by a noncontact, optical position sensor (Figure  5a ). In situ temperature maps of the devices were obtained by an infrared camera and photographs of the device are taken using a video camera. A custom-made Labview program allowed us to follow the tip position, the current intensity, and the device electrical resistance as a function of the time. The devices were tested both in open-and closed-loop control (Figure  5b ).
Electrothermal Actuation and Tracking in Closed-Loop Strictly speaking, the resulting open-loop identification, either in the form of a first-order or a second-order system, has no linear form since, beyond the presence of a nonlinear viscous component, at least one identified parameter (i. e. , the gain) varies with the current I. Nevertheless, following our previous results,  [49]  the use of a simple linear PID (i. e. , with constant gains) was considered for closed-loop control. The PID is the one implemented in the LabVIEW software: the output current I(t) is generated according to the following equation: IðtÞ ¼ k c ½eðtÞ þ T d e 0 ðtÞ þ 1 T i ∫ t 0 eðtÞdt , where e(t) is the position error, k c is the PID gain and T d , T i are, respectively, derivative and integral parameters. In the case of a nonlinear first-order system controlled by a PID, and in the case of a nonlinear second-order one controlled by a PD, the stability of the closed-loop system can be easily derived by means of Lyapounov's movement stability theory.
This means that the SCO properties of the iron complex were not altered sizably by the TPU matrix. Actuator Test Bench: Position tracking of the actuator devices was performed using a Micro-Epsilon opto-NCDT 2300 laser triangulation system. Electrothermal heating of the samples was achieved by a Keithley 2420 source-meter unit. The LabVIEW software was used for controlling the sample movement both in open-loop and closed-loop conditions. For the open-loop control of the sample, a current bias in the range between 0. 3 and 1. 9 A was applied in a stepwise manner. The duration of the step (typically 100 s) was set to ensure reaching the steady state. Due to the risk of damage to the sample by exposure to high temperatures for such long periods, no open-loop identification was performed beyond 1.
Individual patient data We selected patients initially diagnosed with temporal lobe epilepsy (surgical procedure: resection, surgical outcome: seizure-free, Engel score I). The patients underwent comprehensive presurgical evaluation, including clinical history, neurological examination, neuropsychological testing, structural and diffusion MRI scanning, Stereotactic-EEG (SEEG) recordings along with video monitoring as previously described in  Bartolomei, Chauvel, and Wendling (2008) ,  Proix et al. (2017). For the selected patients, SEEG electrodes were implanted in critical regions based on the presurgical evaluation. Each electrode had 10-15 contacts (length: 2 mm, diameter: 0. 8 mm, contact separation: 1. 5 mm). SEEG signals were recorded with a 128-channel Deltamed system (sampling rate: 512 Hz, hardware band-pass filtering: between 0.
16 and 97 Hz). To determine electrode positions, an MRI was performed after electrode implantation (T1 weighted anatomical images, MPRAGE sequence, TR = 1900 ms, TE = 2. 19 ms, 1. 0 × 1. 0 × 1. 0 mm 3 , 208 slices) using a Siemens Magnetom Verio 3T MR-scanner. To reconstruct patient-specific connectome (DTI-MR sequence, angular gradient set of 64 directions, TR = 10. 7 s, TE = 95 ms, 2. 0×2. 0×2. 0 mm 3 , 70 slices, b-weighting of 1000 s/mm 2 ), diffusion MRI images were also obtained on the same scanner. The SEEG data is re-referenced using a bipolar montage, which is obtained using the difference of two neighboring contacts on one electrode. Bipolar SEEG signal is extracted from 5 s before seizure onset up to 5 s after seizure offset. The onset and offset times of the epileptic seizures are set by clinical experts.
Network anatomy The structural connectome was built with TVB-specific reconstruction pipeline using generally available neuroimaging software (see Fig. 1A ). The current version of the pipeline evolved from a previously described version  (Proix et al. , 2017 (Proix et al. , , 2016;; Schirner, Rothmeier, Jirsa, McIntosh, & Ritter, 2015). First, the command recon-all from Freesurfer package  (Fischl, 2012)  in version v6. 0. 0 was used to reconstruct and parcellate the brain anatomy from T1-weighted images. Then, the T1-weighted images were coregistered with the diffusion weighted images by the linear registration tool flirt  (Jenkinson, Bannister, Brady, & Smith, 2002)  from FSL package in version 6. 0 using the correlation ratio cost function with 12 degrees of freedom. The MRtrix package in version 0.
(9) For the purpose of fitting VEP to brain epileptic patterns, we trained NFs by maximizing the log-likelihood of the observed data under our model (Eqs (1) and (  2 )), equivalently, minimizing the Kullback-Leibler divergence or discrepancy between the true posterior distribution p(θ | y) and the variational approximation of Eq. (  9 ) denoted by a family of densities q φ , through learning the variational parameters φ. In practice, this is performed by adjusting the weights ψ of a neural network F so that q F (y,ψ ) (θ) ≃ p φ (θ | y). Assuming that our dataset has N samples {(θ i , y i )} N i=1 with parameters drawn from the prior p(θ) and observations generated from the forward model p(y | θ), the posterior p(θ | y) is obtained by minimizing the loss function L(ψ ) = - N ∑ i=1 log q F (y i ,ψ) (θ i ) (10) over network parameters ψ.
(  2021 )), or simulation-based inference (SBI;  Cranmer et al. (2020) ). In Bayesian terminology, SBI enables us to approximate the posterior distribution of parameters of interest, conditioned on observed data, with the aid of only forward simulations, avoiding the computation of potentially intractable likelihood and its gradient. Given a prior over parameters, a stochastic simulator, and the observations, SBI returns the posterior distribution that best explains the data. The sequential methods based on neural density estimators can be divided into three main categories: (i) sequential neural posterior estimation (SNPE;  Greenberg et al. (2019)   2020 )). The PyTorch-based SBI package  (Tejero-Cantero et al. , 2020)  implements state-ofthe-art algorithms for neural network-based density estimation based on sequential sampling methods (SNPE/SNLE/SNRE).
The SBI package works with any simulator as long as it can be wrapped in a Python callable, and offers a flexible choice of network architectures  (Gonçalves et al. , 2020; Tejero-Cantero et al. , 2020). The simulation step can be easily run on multiple CPU/GPU cores to benefit from effective computational parallelization, and the likelihood function is encapsulated as a feed-forward ANN, allowing for parallel evaluation by design, referred to as likelihood approximation networks (LANs; Fengler, Govindarajan, Chen, and Frank (  2021 )). To perform SBI using SNPE, three inputs are needed to be specified  (Gonçalves et al. , 2020) : (i) a prior distribution describing the possible range of parameters from which the samples can be drawn easily, (ii) a mechanistic model as a simulator that takes parameters as input and generates simulated data as output, (iii) a set of observed data (or low-dimensional data features) as the target of fitting.
Taking prior distribution p(θ) over the parameters of interest θ, a limited number of simulations (N) are carried out to generate a dataset {(θ i , y i )} N i=1 , where θ i ∼ p(θ) and y i is simulated data given model parameters θ i. In other words, the simulated data set is a set of N independent and identically distributed samples from the generative model p(θ, y) = p(θ)p(y | θ). Then, we estimate the posterior q φ (θ | y) by training the NFs on the generated data set {(θ i , y i )} N i=1. Once the distribution is learned, for new observed data y obs , we can readily approximate the true posterior p(θ | y obs ). Active learning can be used in this approach to adaptively reduce the number of simulations over multiple simulation rounds by adding their loss terms together  (Lueckmann et al.
This is a dynamical system from which we can generate samples, but we cannot exactly evaluate the likelihood function. In particular, normalizing flows (NFs) embedded in the SBI approach, such as sequential neural posterior estimation (SNPE;  Gonçalves et al. (2020 ), Lueckmann et al. (2017) ), enable us to directly estimate all joint posterior distributions and bypass the need for MCMC, while also potentially capturing degeneracy or multi-modalities. To perform this step, three inputs are needed: (i) a prior distribution describing the possible range of parameters from which samples can be easily drawn, (ii) a simulator in computer code such as the VEP that takes parameters as input and generates data from drawn parameters as output, (iii) a set of low-dimensional data features for training a neural density estimator.
Collecting a simulated dataset by repeatedly drawing samples from the specified prior distribution over parameters, a deep neural density estimator such as masked autoregressive flow (MAF;  Papamakarios et al. (2017) ) is trained to learn an invertible transformation between data features and parameters of an approximated posterior distribution. Due to this amortization strategy (i. e. , a single pass through the ANNs), the SBI-VEP is able to rapidly approximate the full posterior of parameters for new observations, hence, affording clinicians the ability to evaluate different hypotheses with a negligible computational cost for the inference (in the order of a few seconds).
Here we carried out 10 4 model simulations from the reduced 2D-VEP model (see Eq. (  1 )) to prepare the training data set, while only the statistical moments (m = 4) of time-series, peak properties, and the seizure onsets were used as the data features. The parameters are drawn from a uniform prior in the ranges: η i ∈ U (-5, -1) and K ∈ U (0, 2), and SNPE was run using a single round. Finally, we used a MAF (with 5 autoregressive layers, each with two hidden layers of 50 units) to learn the invertible transformation between the feature datasets generated by samples drawn from the prior distribution, and the parameters of the approximated posterior distribution. As shown in Fig. 2B , the predicted timeseries are in very good agreement with the observations in terms of seizure initiation, propagation and termination (see Table  S2 ).
S2 ). Using SNPE, the only heavy computational task is running simulations to generate training data. However, this approach is efficient as it allows for running model simulations in parallel processors (each VEP run to simulate 100 s of wholebrain epileptic patterns takes only 0. 003 s using a JIT compiler). For SBI, the model simulations can be easily run independently to create the training dataset (the more simulations, the more informative posterior), whereas HMC is amenable to embarrassingly parallel execution of only independent chains as each HMC chain itself is a simulation of Hamiltonian dynamics. Using MAF, the posterior estimation took around 1000 s and generating 200 samples from posterior took less than 1 s, whereas generating 200 samples using HMC (with an efficient non-centered form of parameterization implemented in Stan  (Carpenter et al.
Moreover, with a feasible number of simulations, the uncertainty of the estimated posterior using SBI is higher than that of the MCMC, and a wider prior range demands a larger number of simulations. However, using high performance computing, the model simulations can be easily run independently to create a large training dataset for SBI (the more simulations, the more informative the posterior), whereas MCMC is amenable to embarrassingly parallel execution of only independent chains  (Hashemi et al. , 2021). On the other hand, in the presence of metastability in the state-space representation, MCMC methods require either more computational cost or intricately designed sampling strategies to accurately detect state transitions in the latent space  (Gabrié et al. , 2022; Jha et al. , 2022). Whereas SBI allows efficient Bayesian estimation in a black-box manner without access to the knowledge on the statespace representation of a system, or in a gray-box setup, which uses additional information about or from the simulator  (Cranmer et al.
Acknowledgments This work was funded by the  French National Research Agency  (ANR; https://anr. fr)as part of the  second ''Investissements d'Avenir'' program ,  ANR-17-RHUS-0004 , EPINOV, to VJ and FB, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 785907  and  945539 ,  Human Brain  Project  SGA2  and  SGA3  (https://ec. europa. eu/programmes/horizon2020) to VJ, by  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant Agreement  and No. 826421 , VirtualBrainCloud (https://ec. europa. eu/programmes/horizon2020) to VJ,  PHRC-I 2013  EPISODIUM (grant number  2014-27 ), the  Fondation pour la Recherche Médicale, France  ( DIC20161236442 ), and by  SATT Sud-Est ,  827-SA-16-UAM  (https: //www.
Chromatin state annotation To obtain functional annotation of MM. 1S cell line, we used ChromHMM (v1. 11)  (Ernst & Kellis, 2010 , 2012). Five histone marks available from ENCODE consortium (ENCODE Project Consortium, 2012) (H3K4me1, H3K4me3, H3K27ac, H3K36me3, and H3K27me3) in three different cell lines (MM. 1S, U266, and GM12878) were analyzed using hidden Markov model to identify 10 different chromatin states. Default parameters of chromHMM were used. Bam files were binarized into 200-bp genomic windows and the presence or absence of each histone mark was evaluated. Then, we employed biological analysis to annotate those chromatin states giving them biological meanings. Treatment of ChIP-seq data ChIP-seq sequencing quality was assessed with fastqc (v0. 11. 8)  (Andrews, 2010). ChIP-seq read adaptors were firstly trimmed using trimmomatic (v0.
39)  (Bolger et al, 2014)  and then reads were mapped using bowtie2 (v2. 1. 0)  (Langmead et al, 2009)  to the Human genome UCSC hg19 (GRCh37)  (Kent et al, 2002). Only one mismatch was allowed. After alignment step, unmapped reads, low-quality mapped reads (mapQ < 30), and reads mapped to ENCODE blacklist regions  (Amemiya et al, 2019)  were removed with samtools (v1. 3. 1)  (Li et al, 2009)  for analysis. We also removed reads that were like to be optical and/or PCR duplicates using picard MarkDuplicates (v2. 23. 5) from GATK  (McKenna et al, 2010). ChIP-seq-enriched regions defined as peaks were called using macs2 (v2. 1. 1)  (Zhang et al, 2008)  versus input (sequencing without immunoprecipitation). We only retained peaks higher than specified P-value threshold (P-val < 1 × 10 -7 ).
Treatment of ATAC-seq data All ATAC-seq data were processed based on Kundaje laboratoryproposed pipelines  (Koh et al, 2016; Liu et al, 2019)  available on github. Quality of sequencing assessment, read adaptor trimming, read mapping to human genome hg19, and filtration were performed the same way as ChIP-seq reads. Before peakcalling steps, and because of the Tn5 insertion, mapped reads were shifted with, respectively, 5 and 4 bp for strand + and strandwith samtools. Finally, enriched regions defined as ATAC-seq peaks were called using macs2 only significant peaks were retained (FDR < 0. 05).
HiChIP data treatment and differential analysis We employed HiC-pro  (Servant et al, 2015)  to process HiChIP data from raw data to normalized contact maps. All reads were mapped to hg19 genome using bowtie2 (global parameters: --verysensitive -L 30 --score-min L, -0. 6, -0. 2 --end-to-end --reorder; local parameters: --very-sensitive -L 20 --score-min L, -0. 6, -0. 2 --end-to-end --reorder). Contact maps were generated at different resolutions  (1, 2, 15, 20, and 40 kb)  and normalized by the iterative correction and eigenvector decomposition method. HiC-pro output directory was then used as input to hichipper  (Lareau & Aryee, 2018)  with MboI restriction site position for loop calling. Differential analysis of chromatin loops was performed with function exactTest of package edgeR  (Robinson et al, 2010) , with default parameters except for dispersion, which was set to "trended.
Global treatment of single-cell data Preprocessing steps for single-cell data were done using CellRanger Software suite, respectively, cellranger (v5. 0. 0)  (Zheng et al, 2017)  and cellranger-arc (v1. 0. 1) for scRNA-seq and scMultiome-seq  (Satpathy et al, 2019). For both types of data, the hg38 genome assembly provided by 10xGenomics was used for alignment. Further analyses were performed on R (v3. 6). For scRNAseq, Count matrices were loaded into R using the Seurat package (v3. 9. 9)  (Satija et al, 2015). For each cell, we calculated the percentage of mitochondrial reads (percent. mt) and the percentage of nuclear-retained lncRNA (percent. nc). We also used the CellCycleScoring function from Seurat to assign a cell cycle state to each cell (Phase); the assignment of the cell cycle state is based on the S.
score and G2M. score calculated by this function. Cells were then filtered on the following criteria: 5 < percent. mt < 25, percent. nc < 10, a minimum of 2,000 reads, and 1,500 different genes expressed. Normalization was performed using the Seurat NormalizeData function with standard parameters. The function FindVariableFeatures was then used to select the 3,000 most variable features, those features have been scaled with Seurat ScaleData function, because cell cycle was a major part of the variability, we added S. score and G2M. score to the vars. to. regress argument of the function. We reduced dimension using RunPCA from Seurat; only the first 30 dimensions were used for downstream analyses. We also calculated a 2D embedding of our cells with RunUMAP; neighbor search and clustering were performed using FindNeighbors and FindClusters functions, with default parameters.
Acknowledgements We thank the  Genomics and Bioinformatics core facility of Nantes (Geno-BiRD, Biogenouest, IFB)  for its technical support. We thank the  Fondation Française Pour la Recherche contre  le My élome et les gammapathies monoclonales (FFRMG), the  Programme d'investissements d'Avenir   I-SITE NexT  ( ANR-16-IDEX-0007 ) the  Pays de la Loire , the  SIRIC ILIAD  ( INCa-DGOS-Inserm-12558 ), and  Celgene  for supporting this study.
Data Availability ChIP-seq, HiChIP-seq, ATAC-seq, RNA-seq, scRNAseq, and scMultiome have been deposited at the European Genome-phenome Archive (EGA, https://www. ebi. ac. uk/ega), which is hosted by the EBI and the CRG, under dataset accession EGAD00001011136, EGAD00001011138, EGAD00001011135, EGAD00001011137, EGAD00001011139, and EGAD00001011140, respectively.