dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we study the last approach and leverage the coupling structure of the simulation code to replace the chemical reaction solver with a neural network. The advantages of this approach are twofold. First, the definition of neural networks allows us to easily vectorize their calls on array-like data structures such as meshes. It is a solid computational advantage compared to the original simulation code, which has to call the chemical reaction solver in each mesh cell. Second, the chemical reaction solver is not costly when executed as a standalone application. Consequently, it is possible to build a large training database and substantially improve the neural network's accuracy without additional cost during inference. Besides, the neural network only approximates the chemical reactions of the problem, thereby being applicable, once trained, to any simulation which involves the same chemical reactions.
Obviously, for efficiency, the surrogate model has to be well-chosen. The classical reflex at this stage would be to build and use some abacuses, as offline calls to Mutation++ can be made. This is classical for tabulated equations of state for example. Those abacuses can then be loaded into memory and interpolated during the simulation  [43, 44]. However, this method becomes intractable when the input/output dimensions increases (here we have d in = n e + 2 1 or d out = n s + 5 1, see section 3. 3) because the number of points N needed to obtain a fine interpolation increases exponentially fast with it, together with a complexity for the search in the database which strongly depends on N too. For this reason, in the following section, we study the possibility of building different surrogate models of Mutation++ from gathered data.
• u(X, θ) = P k=0 θ k X k , i. e. a polynomial approximation and a linear application θ → u(X, θ) with respect to variable θ. • together with N = P + 1 = d θ. This ML model ensures u(X i ) = u(X i , θ) ∀i ∈ {1,. , N }. Besides, spectral convergence can be achieved  [47] , hence very good accuracies, but the convergence behavior strongly depends on the choice of the experimental design (X i , w i ) i∈{1,. ,N } discretising (X, dP X ) (see the divergence of the approximation of Runge's function with uniform points  [52, 51] ). Finally, if N is huge (i. e. in a big data context), P ∼ N is huge and the run-time of the ML model strongly depends on the size of the database. It may become prohibitive for the desired accuracy. Polynomial regression and generalised Polynomial Chaos. [53, 54, 55, 10, 11, 56, 12, 57, 14, 58, 13, 59 ] are very popular, especially in problems of Uncertainty Quantification (UQ).
Since the seminal work of  [10] , it is extensively used in (non-intrusive) uncertainty propagation  [11, 56, 12, 57, 14, 60, 61, 59 ]. These ML models are based on the choices: • L(x, y) = (x -y) 2 in equation (7), • u(X, θ) = P k=0 θ k φ k (X) , is linear with respect to θ and (φ k (X)) k∈{0,. ,P } are orthonormal polynomials with respect to the scalar product defined by dP X. • The number of parameters d θ = P + 1 is not constrained by N. Spectral convergence with respect to P is ensured  [62]. The orthonormality of the basis helps with round-off errors and conditioning  [63]  while having a model for which N and P are not correlated anymore (i. e. we can take N P , big datasets, with a model having a run-time depending on P N ). Gaussian Process regression or Kriging. Popularized for ML by  [64]  and intensively used in Uncertainty Quantification (UQ)  [65, 15, 66, 67, 68, 69, 70, 71, 72, 73] , this technique has established to a leading position in surrogate modeling.
As a consequence, these models are not well suited to highdimensional problems. Neural networks complexity scales linearly with d in , and is famous for its recent breakthrough on high dimensional test cases like image or text processing. In addition, the complexity of one prediction is independent of N , so we can leverage very large databases. • The implementation of the inference of neural networks boils down to a succession of matrix-vector products, which can be easily vectorized. As a result, they can process array-like data structures very efficiently, which makes them perfectly suited to computations on meshes. Classical surrogate models do not offer such implementation properties. In the next section, we introduce a profiling experiment that gives an idea about the potential computational gains of neural network-based hybridization.
Assessing the capabilities of neural networks in terms of accelerations To test the potential computational gains of hybridization, we construct a simple benchmarking code that compares the run-times of neural networks and Mutation++ for a given number of input points. In our case, this number, N D , is the size of the simulation mesh. This code (C++) only corresponds to the extraction of the for loop of algorithm 1 for Mutation++ and of the call surrogate model of algorithm 2. The neural networks are implemented within this code via the Tensorflow C API and a wrapper, CppFlow 5. In the next studies, the neural networks have n layers = 5 hidden layers, and the number of neurons in each layer n units is constant per layer and chosen as a parameter of the code. In other words, we only study the influence of hyperparameter n units, the number of neurons per layer, even if many others exist (dropout rate, the different architectures, and their parameters, the different optimizers and their parameters, etc.
The neural networks are trained using Tensorflow in python on a training dataset of 170000 points, and the hyperparameters are selected using a test set of 20000 points. The training and test sets are constructed by sampling ρ and , respectively, uniformly and log-uniformly within the intervals [0. 1, 3. 8]×[2. 07503×10 7 , 3×10 8 ], defined based on the execution of the simulation code without chemical reactions. Figure  6  displays the histogram of the L 2 errors obtained after a random search  [88] , i. e. a uniform Monte Carlo sampling of N M C = 3 × 10 3 samples on the hyperparameter space described. It shows several important properties: • first, there is a non-zero probability of having poor performance in terms of accuracies: the L 2 errors go up to ≈ 10 6. For this reason, it is important performing an optimization of the hyperparameters and not only rely on one test.
Deep Learning-based hybridization with guarantees In this section, we revisit the motivating example of section 1 with a hybrid reentry simulation code. The sketch of the code is recalled below in the algorithm 3. 1 # general initialization (mesh, quantities on mesh etc. ) 2 initialise guess vector of unknowns on mesh(U 0 ,x 0 ) 3 while convergence criterion not satisfied do 4 U n+ 1 2 =solve Euler equations(U n ,x n ) 5 x n+1 , U n+1 =call neural network(U n+ 1 2 ,x n ) 6 U n ← U n+1 7 x n ← x n+1 8 end Algorithm 3: Core of the code with a call to a neural network surrogate model of Mutation++. In function call neural network, we plug the neural network approximating Mutation++ described at the end of section 3 using the Tensorflow C API and a wrapper, CppFlow 6. Note that Algorithm 3 is slightly different from Algorithm 1 because, in the former, the neural network is called in a batch fashion to take advantage of the vectorial optimizations, while in the latter, Mutation++ has to be called sequentially.
• NN (for neural network) denotes the results obtained with the hybrid reentry code with the simulation of chemical reactions using the neural network approximating Mutation++ obtained with the methodology described in section 3. Figure  9  echoes figure  2  and presents the results obtained with the three above reentry codes. As in section 1, we can observe, by comparing figure  9  (a) and (c) that the perfect gas closure (PG) is a coarse model for our reentry problem as its results in terms of pressure field considerably differ from the one obtained with chemical reactions (MPP), i. e. which takes into account finer physics. On the other hand, the pressure field obtained with the hybrid reentry code (NN) is not visually distinguishable from the results obtained with Mutation++. Now,  low is for low-resolution mesh (30 × 100), high is for high-resolution mesh (90 × 100) and is 3 times more refined in the direction of the shock.
Discussion and Perspectives In this work, we studied the acceleration of a simulation code involving the coupling between hypersonic fluid dynamic and chemical equilibrium. The simulation code is computationally expensive because of the chemical equilibrium simulator (Mutation++) that has to be called at each cell of the simulation mesh. This motivated using neural networks as surrogate models approximating chemical reactions because (1) their execution can be easily vectorized, so they can be called efficiently on a whole mesh, and (2) they can be trained on an extensive database constructed out of Mutation++. By taking care of constructing neural networks not only accurate but also cost-effective thanks to the methodology of  [89] , we achieve an acceleration factor of 18. 7 for the hybrid simulation code (NN).
Towards a general approximation of Mutation++ In this section, the methodology for approximating Mutation++ consists of constructing a training database and fitting a neural network. This is a strong advantage of this method since the neural network can be used in any simulation code involving the same chemical equilibrium setting (as we saw in section 5. 3). However, the neural network is trained for a fixed output dimension corresponding to the number of species. It cannot be used for test cases that involve different species because it requires constructing a new training database for each different chemical setting. It would be interesting to investigate the use of transfer learning to make the approach easily applicable to other chemical reactions. One could pre-train a neural network once for a high number of different species and with a large database constructed out of Mutation++.
In children, single-sided deafness (SSD) affects the development of linguistic and social skills and can impede educational progress. These difficulties may relate to cortical changes that occur following SSD, such as reduced inter-hemispheric functional asymmetry and maladaptive brain plasticity. To investigate these neuronal changes and their evolution in children, a non-invasive technique is required that is little affected by motion artifacts. Here, we present a research protocol that uses functional nearinfrared spectroscopy (fNIRS) to evaluate the reorganization of cortical auditory asymmetry in children with SSD; it also examines how the cortical changes relate to auditory and language skills. The protocol is designed for children whose SSD has not been treated, because hearing restoration can alter both brain reorganization and behavioral performance.
Protocol Design The protocol is for an exploratory, single-center, cross-sectional study, which aims to compare two groups of children: those with SSD and age-and sex-matched normallyhearing controls. There are 30 children in each group, aged 5-16 years. The children first undergo a baseline evaluation to confirm their inclusion; they then complete two sessions, each lasting maximum 2 h, separated by a gap of up to 3 months. For the first session (V1), the children undergo auditory and language testing. For the second session (V2), auditory cortical imaging is carried out using fNIRS. The fNIRS is non-invasive and uses a sensor-bearing cap. The raw intensity values are converted to optical density units and then, using the modified Beer-Lambert Law, to the relative concentrations of oxy-and deoxyhemoglobin.
Auditory Cortical Imaging Imaging is performed using fNIRS. This non-invasive functional imaging technique relies on a sensor-bearing cap, which can measure cortical activity during auditory stimulation. We will use the channel distances of 30 mm for all the channels and short channels located at 8 mm from the sources. These short-distance detectors pick up extracerebral signals close to the source and route these signals to an available detector channel through miniaturized fiber optics. Bundles of eight short-distance probes couple to a single detector channel. For this study, optodes are placed with a focus on the auditory cortex and the superior temporal area, as determined using fOLD software (Figure  1 ). We expect to observe activity mainly in the channels over the Te3 area, the secondary auditory cortex located at the lateral surface of the gyrus, and more posterior temporal areas involved in integrative auditory processing  [56, 92].
For the recording session, the participant is seated in a sound-proof room where there is no external source of light. An fNIRS cap is chosen according to the subject′s head circumference, and then centered on the head using the left and right preauricular points and the nasion (frontal) and inion (occipital). The optodes and detectors are calibrated using NIRstar software, which visualizes the cap using a model. During calibration, the optode images on the screen are color coded (red/orange/green) according to the signal to noise ratio. The cortical signals are recorded using NIRstar and begin 1 min before the first sound presentation to obtain the baseline levels of cortical activity. During the recordings, the sounds are presented through noise-cancelling headphones at a level of approximately 60 dB, as measured using a sound level meter.
Data Collection and Management For each participant, information is collected using a case report form (CRF) that is completed by the investigators. The CRF includes the child's identity, clinical observations, and medical follow-up data of relevance to the study. This document is kept in the ENT department, Toulouse Hospital. The study data is kept on a computer, with data entry carried out by the investigators and their colleagues. The patients′ CRFs are reviewed on-site prior to data entry, which is then carried out according to procedures determined by the study methodologist, statistician, and principal investigator. The imaging data is saved on an external storage device (CD, DVD) in compliance with the study′s anonymity requirements. This device is attached to the CRF for archiving.
Statistical Analysis As this is an exploratory study, descriptive statistics are run for the groups of children to summarize the primary outcome measures: the changes in oxyhemoglobin, deoxyhemoglobin, and their ratio. The associations between these fNIRS measures and scores on the auditory, speech, and quality of life assessments are also analyzed for each group. The Matlab-based Homer2 program is used to preprocess the fNIRS signals to obtain curves that can be further analyzed. The preprocessing involves motion correction, bandpass filtering, and principal component analysis (PCA), which detects the greatest amount of spatial covariance across all spatially distributed channels and then removes those components from all channels  [95]. The scalp-coupling index, which is used as a measure of the prominence of the photoplethysmographic cardiac signal  [96] , will be used to detect bad channels.
The preprocessing pipeline is: raw measurement → transformation to optical density → bad channel rejection→ transformation to concentration → PCA → motion correction → band-pass filter. At the first stage, the fMRI-like general linear model analysis will be applied  [97]  to test the differences between the conditions. Functional responses are calculated as block average waveforms for each condition. Various parameters will then be extracted from the waveforms, using Matlab scripts, for further statistical analysis. The two main parameters are the peak value and the area under the curve (Figure  4 ). We will use Bonferroni correction for the family-wise error rate (FWER) and the Benjamini-Hochberg correction for the false-discovery rate (FDR). In a second step, the relative activity levels in the two hemispheres are calculated.
The asymmetry index (AI) is calculated for each monaural condition using the formula: AI = (Contralateral hemisphere activity-Ipsilateral hemisphere activity)/(Contralateral hemisphere activity + Ipsilateral hemisphere activity). AI values are positive when there is greater contralateral activity, and negative when there is greater ipsilateral activity. The aural preference index (API) is calculated for each hemisphere using the formula: API = (Contralateral ear condition-Ipsilateral ear condition)/(Contralateral ear condition + Ipsilateral ear condition). API values are positive when the contralateral ear has a greater influence (Figure  5 ). The within-and between-group analyses are carried out using STATA and R software. Between-group comparisons are run using generalized mixed-effect models, after the data have been checked, outliers corrected, and the data 'frozen'.
Confidentiality In accordance with the regulations, those with direct access to the data take all necessary precautions to ensure the confidentiality of information relating to the study participants, in particular their identity and results. During the research or at its conclusion, the data collected is made anonymous. For the children with SSD, only the first letter of their name and surname are recorded, along with a coded number indicating the order of their inclusion, and the letter S (for SSD). For the matched control children, the same code is assigned plus the letters NH. For each child, the investigators ensure that the child's legal guardians provide written consent for the team to access the child's data that is necessary for the research. No information enabling the identification of participants is communicated to third parties other than those legally authorized to hold this information (and who are bound by professional secrecy).
Demethylation of enhancer regions is enriched in hypomethylated tumors We then analyzed the distribution of specific regions in the genome which were stringently more susceptible to DNA methylation changes in the hypomethylator phenotype (average β ≤ 0. 3 and β difference ≥ 0. 2, false discovery rate [FDR] < 0. 05). In this manner, we identified 237 CpG probes from the TCGA-KIRC cohort that lost DNA methylation specifically in the hypomethylator phenotype. Notably, we found predominant enrichment for demethylated enhancers as compared to the Illumina 450K-array background (23% vs 2%, P < 0. 0001; Figure  2d ). To validate such regionspecific enrichment, we performed unsupervised analysis using another independent ccRCC cohort (GSE61441) assessed by Illumina 450K-array using the same strategy applied for TCGA-KIRC.
Therefore, we evaluated the distribution of sarcomatoid and rhabdoid differentiation in the TCGA-KIRC cohort for which all the pathology reports were reviewed and analyzed according to the literature  18. Strikingly, among 29 ccRCC cases with sarcomatoid differentiation, 18 cases belonged to  + , indicating that  + was significantly enriched in sccRCC comparing to  -(26. 9% vs 4. 7%, P < 0. 0001). In addition, all four identified rccRCC cases belonged to  + instead of  -(9% vs 0%, P = 0. 002). We then investigated the reproducibility of the  phenotypes in the GSE61441 cohort in an unbiased manner. To this end, we identified 1,177 enhancer probes with high methylation in matched normal tissues. Notably, a total of 1,084 enhancer probes (enhancer signature) were shared between TCGA-KIRC (92.
6%) and GSE61441 (92. 1%) cohorts, indicating the stable hypermethylation of enhancers in normal kidney tissues (Figure  4a ; Supplementary Table  S4 ). Consensus PAM triumphantly reproduced a phenotype of  + (n = 14; Figure  4b ), which showed a significantly higher MeTIL score (P = 0. 001; Figure  4c ) than  -(n = 32). ChAMP analysis identified 584 hypomethylated DMPs between  + versus  -ccRCC (Supplementary Table  S5 ); those DMPs were tightly associated with inflammatory response (P = 0. 0008, FDR = 0. 019), interferon-γ response (P = 0. 0009, FDR = 0. 15), and EMT (P = 0. 047, FDR > 0. 25) Hallmark pathways (Supplementary Table  S6 ). To explore the role of the enhancer signature in distinguishing tumor malignancy and immune infiltration, we conducted supervised clustering on an independent cohort of ccRCC enriched for tumors of a high-grade (G4: n = 27 [ Of note, we found that  + in the PSL cohort was tightly associated with advanced tumor grade (G1+G2: 5.
Additionally, we found that samples with unmethylated cg23602092 were enriched in the  + phenotype (53. 7% vs 17. 1%, P < 0. 0001), and they showed significantly higher expression of TET1 than that of those methylated (P = 0. 011; Figure  6g ). Likewise, such a negative correlation was also observed in the CCLE dataset (ρ: -0. 42, P = 0. 089; Figure  6h ); in addition, two out of three cell lines with the  + phenotype in the CCLE dataset harbored unmethylated cg23602092 but none harbored in  - (66. 7% vs 0, P = 0. 045), and cg23602092 unmethylated cell lines presented with upregulated TET1 at the transcription level (P = 0. 053; Figure  6i ). Subsequently, we wondered whether such a  + phenotype appeared only in specific samples that expressed molecular characteristics suitable for ICI. To this end, we combined the nivolumab plus ipilimumab arm B and the TKI arm C in our BIONIKK cohort, and we conducted unsupervised analysis (Supplementary Figure  S7a ).
Therefore, unsupervised analysis was conducted on the everolimus therapy arm from the CheckMate cohort. Although three regulon phenotypes were identified with similar activity patterns (Supplementary Figure  S9a ), no statistical significance was observed in terms of OS among the three phenotypes (P = 0. 31; Supplementary Figure  S9b ). These data suggest the potential prognostic and predictive value of regulons derived from  +. Additionally, unsupervised clustering of the regulon activity profile in the CCLE dataset revealed three regulon phenotypes, and we found two out of three ccRCC cell lines in the activated regulon phenotype belonged to  + (Supplementary Figure  S10 ); statistical significance did not approach due to small sample size (P = 0. 127), though. Of note, these regulons might be cancer-specific considering the proportions of active status for seven regulons that varied in the cell lines (17-50%).
ICI To screen for potentially effective chemical compounds for  + with resistance to ICI treatment, we performed a Connectivity Map (CMap) analysis as a preliminary method to investigate the therapeutic potential of candidate agents. To this end, 150 upregulated and 150 downregulated genes with the most significant fold changes from differential expression analysis were submitted to CMap (Supplementary Figure  S11a ), which identified seven chemical compounds under pharmacologic CMap classes (score < -90; Supplementary Table  S23 ). Notably, five out of seven (71. 4%) inferred agents were HDAC inhibitors (Supplementary Figure  S11b ). In this context, we further estimated the half maximal inhibitory concentration (IC50) for three HDAC inhibitors (i. e. , vorinostat, entinostat, and PCI-34051) that were available in the Genomics of Drug Sensitivity in Cancer (GDSC) database for human cancer cell lines.
This pinpoints to a mechanism by which the epigenome is remodeled, leading to coordinated activation of a set of transcription factors which might shape tumor aggressiveness and the associated microenvironment. Limitations should be acknowledged, however, including the sample size of the BIONIKK cohort treated with combination ICI, although the data are related to the first biomarker driven clinical trial in ccRCC, and we have been able to show the existence of  in several datasets; in addition, matched RNA-Seq for samples assessed for DNA methylation was not available. Finally, we have not been able to assess the changes of histone modifications (i. e. , H3K27ac), due to paucity of material available, although this can be assessed in the future. Despite these limitations, our study provides several pieces of evidence for the key role of  in immune evasion.