dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the chemistry is solved, providing the densities of about 200 species at each position. About 3 000 reactions are considered, both in the gas phase and on the grains. The chemical reaction network was built combining different sources including data from the KIDA database 5   (Wakelam et al. 2012 ) and the UMIST database 6 (McElroy et al. 2013) as well as data from articles. For key photoreactions, we used cross sections from  Heays et al. (2017)  and also taken from Ewine van Dishoeck's photodissociation and photoionization database 7. The successive resolution of these three coupled aspects (radiative transfer, thermal balance, chemistry) is iterated until a global stationary state is reached. A full run is computationally intensive and typically lasts a few hours. The code provides density profiles of the chemical species and the temperature profiles of both the grains and the gas.
It also outputs the line intensities emerging from the cloud that can be compared to observations. As of version 7 (yet to be released), a total of 5 409 line intensities are predicted from species such as H 2 , HD, C + , C, CO, 13 CO, C 18 O, 13 C 18 O, SO, HCO + , OH + , HCN, HNC, CH + , CN or CS. We choose to work on the Meudon PDR code because we consider it a representative element of the most complex ISM models. Multiple complex ISM codes compute numerous observables from a few physical parameters  (Ferland et al. 2017; Sutherland et al. 2018). The complex physical and chemical processes taken into account in such codes make the relations between the line intensities and the input parameters highly nonlinear and thus challenging to emulate. Often, ISM numerical models focusing on a subset of physical processes included in the Meudon PDR code, such as radiative transfer and excitation codes, yield simpler relations between observables and input parameters and might thus be simpler to emulate.
Dataset generation In this work, we restrict ourselves to constant pressure models as they appear to better reconstruct observations for typical   6 ) The distribution of grain radius a is a power law  (Mathis et al. 1977). PDRs  (Marconi et al. 1998; Lemaire et al. 1999; Allers et al. 2005; Goicoechea et al. 2016; Joblin et al. 2018; Wu et al. 2018). We approximated the code with respect to the D = 4 input parameters that are most relevant for inference  (Wu et al. 2018; Palud et al. in prep). The three main ones are the thermal pressure, P th , the scaling factor, G UV , of the interstellar standard radiation field and the size of the slab of gas measured in total visual extinction, A tot V. As in  Wu et al. (2018) , we consider a wide variety of environments with P th ∈ [10 5 , 10 9 ] K cm -3 , G UV ∈ [1, 10 5 ] and A tot V ∈  [1, 40]  mag.
The Meudon PDR code computes line intensities for multiple angles α between the cloud surface and the line of sight. In the Meudon PDR code, this angle α can cover a [0, 60] deg interval. A face-on geometry corresponds to α = 0 deg and α = 60 deg is the closest to an edgeon geometry. To enable analyses of PDRs with known edge-on geometry such as the Orion Bar  (Joblin et al. 2018) , this angle is added to the considered physical parameters. Table  1  details the values of the main input parameters and of other parameters, fixed at standard values from the literature. We generated two datasets of Meudon PDR code evaluations to assess the approximation quality of the Meudon PDR code: a training set and a test set  8. The training set is used to fit all surrogate models. It contains N train = 19 208 points, structured as a 14 × 14 × 14 × 7 uniform grid on (log 10 P th , log 10 G UV , log 10 A tot V , α).
This uniform grid structure is chosen to simplify the outlier identification procedure (see Sect. 4. 1) and to include spline interpolation in the comparison. We note that all the other considered methods, and ANNs in particular, do not require such a structure for the training dataset and using other dataset structures generated, for instance, with Latin hypercube sampling  (McKay et al. 1979) , stratified Monte Carlo  (Haber 1966) , or low discrepancy sequences used in Quasi-Monte Carlo methods  (Asmussen & Glynn 2007, chapter 9 ) might improve accuracy. The Meudon PDR code predicts line intensities that are strictly positive and span multiple decades. To avoid giving more weight in the regression to lines with high intensities and disregarding the faintest ones, in the following, y ∈ R L denotes the log-intensities.
Similarly, P th , G UV and A tot V are considered in log scale. Even in log scale, the parameters of interest cover intervals with quite different sizes. For instance, log 10 G UV ∈ [0, 5], while log 10 A tot V ∈ [0,  1. 602]. In other words, A tot V covers an interval more than three times smaller than G UV. Both interpolation methods and ANN based regression typically suffer from this difference. The D parameters are thus standardized to have a zero mean and a unit standard deviation. This simple transformation generally improves accuracy for both interpolation methods and ANNs (Shalev-Shwartz & Ben-David 2014, chapter 25). The test dataset was used to assess the accuracy of surrogate models on data not used in the training step. It contains N test = 3 192 points. These points were generated with 456 independent random draws from a uniform distribution on the (log 10 P th , log 10 G UV , log 10 A tot V ) cube and with a uniform grid of 7 values on α.
To ensure consistent preprocessing between the two sets, both the input values x and output values y of the test set undergo the same transformations as for the training set. In particular, the standardization applied to its input values x relies on the means and standard deviations obtained on the training set, and its output values y are considered in decimal log scale. Numerical codes may yield numerical instabilities. In its domain of validity, the Meudon PDR code produces few of them. However, the considered complex nonlinear physics can also lead to physical bistabilities or multistabilities. For example, the H 2 heating process can produce bistable solutions  (Burton et al. 1990; Röllig & Ossenkopf-Okada 2022). In such a case, profiles computed by the code, for example, of a species density or of the gas temperature, can oscillate between the possible solutions at each position in the modelled cloud.
The line integrated intensities computed from these profiles can contain errors of up to a factor of 100 and thus are highly unreliable. The code being deterministic, an input vector x consistently leads to a unique output vector y. However, in the regions of the parameter space with such multistabilities, variations of intensities can be very chaotic and challenging for a surrogate model to reproduce. Such chaotic values thus lead to the deterioration of the accuracy of any surrogate model, interpolation, or ANN, thus they should not be used. Unfortunately, as of today there exists no simple or complete procedure to check the physical validity of a precomputed model of the Meudon PDR code. With a first scan of the datasets, we remove a few lines that are particularly affected. The total number of considered lines is therefore reduced from 5 409 to L = 5 375.
This simple filter leaves other outliers in the training and test datasets. Although we observe that these outliers are rare (i. e. , less than 1% expected), we do not have any specific a priori knowledge on their location nor on their exact proportion. To manually check the validity of each value is unrealistic given the sizes of the two datasets. The most informative hypothesis we can make on outliers is that if one line in a precomputed model is identified as an outlier, then it is likely for this precomputed model to contain other outliers, especially in the lines of the same species or of isotopologues. This hypothesis is exploited in the more thorough outlier detection method using an ANN, which is presented and described in Sect. 4. 1. Overall, the Meudon PDR code version to emulate is a function f : x ∈ R D → y ∈ R L , with D = 4 and L = 5 375.
Comparison metrics Interpolation methods and ANNs are compared on evaluation speed, memory requirements, and approximation accuracy. We describe here the metrics used for the comparison, regardless of how the surrogate models are defined or trained. The evaluation speed is measured on the full set of L lines for 1 000 random points. The measurements are performed on a personal laptop equipped with a 11th Gen Intel(R) Core(TM) i7-1185G7, with eight logical cores running at 3. 00 GHz. The ANNs and interpolation methods are run on CPU to obtain a meaningful comparison. Running ANNs on a GPU could further reduce their evaluation times. The implementations of interpolation methods are from the SciPy Python package, popular in ISM studies  (Wu et al. 2018 ). Nearest-neighbor, linear, and RBF interpolation implementations allow for the evaluation of a vector function at once.
The 99th percentile and maximum provide upper bounds on the error. The maximum is very sensitive to outliers while the 99th percentile is more robust. To illustrate this sensitivity of upper bounds, consider a fictional dataset of error factors including 0. 5% of outliers at much higher values. The maximum is affected by the outliers, which induces a pessimistic bias for the corresponding error upper bound estimation. The 99th percentile is not significantly affected by the outliers, and provides a more relevant estimator of the actual upper bound of the error factor for this fictional dataset. This example shows that the choice of percentile is a trade-off based on the expected proportion of outliers. Lower percentiles (e. g. , 90 or 95) underestimate the upper bound on the error factor and percentiles higher than 99.
Designing and training adapted ANN The choice of architecture and training approach of ANNs are now discussed. In the following, ANNs are trained with the MSE loss function. In addition, we assume the Meudon PDR code to be differentiable. To derive an ANN satisfying this constraint, we set the activation function g to the exponential linear unit (ELU,  Nwankpa et al. 2021). Unless explicitly mentioned, our ANNs have H = 3 hidden layers of equal size. This choice may not be optimal. A hyperparameter optimization step could improve the network performance, but would require a validation dataset and the training of many networks. As the results of Sect. 5 will show, this step is not necessary to obtain satisfactory results. The specificities inherent in ISM models such as the Meudon PDR code, namely, the presence of outliers and the unusual dimensions of the problem, very few inputs to predict many outputs.
In the following, a masked version of the MSE relying on the binary mask M is used when this outlier removal step is taken into account. Existing implementations of interpolation methods, on the other hand, lack flexibility to handle such a mask during the fit. As some points of the grid are removed for some lines, the spline interpolation cannot be applied on the masked training set. Nearest-neighbor, piece-wise linear and RBF interpolation methods can be applied but would require line by line fits and predictions, as outliers don't occur for the same training points x for all lines. Such a line-by-line manipulation would be extremely slow with a Python implementation. To present a somewhat meaningful comparison between ANNs and interpolation methods on the masked dataset, the masked values are imputed.
Exploiting correlations between line intensities Line intensities computed by the Meudon PDR code come from the radiative de-excitation of energy levels. While non-local effects are accounted for in the radiative transfer, the excitation of many lines is affected to a large extent by local variables such as the gas temperature or density. As a result, high correlations between some lines are expected. Figure  4  shows the L × L matrix of absolute Pearson correlation coefficients, with lines grouped by molecule. We indeed find some strong correlations. In particular, lines from the same species are often highly correlated, especially for water isotopologues and molecular hydrogen. However, some species produce lines that are not correlated. For instance, high energy lines of SO have a very small correlation with low energy lines, as the corresponding submatrix has a diagonal shape.
In an ANN such that D ≪ L, most parameters belong to the last hidden layer, as illustrated in Fig. 2. The size of this layer is thus critical to obtain a good accuracy. Too large a layer might lead to overfitting, while too small a layer could not capture the nonlinearities of the dataset. In regression, this last hidden layer applies an affine transformation. We therefore set its size to the estimated dimension L. To predict the L intermediate values of the last hidden layer, which are then used to predict all L line intensities, the first two hidden layers are set with the same size. For the networks trained on the four clusters of lines obtained in Sect. 4. 2. 1, the size of the last hidden layer is also set to the minimum number of principal components that ensures a decompression with mean error factor below 0.
Performance analysis The proposed ANNs outperform all interpolation methods on all aspects by a large margin: they are between 100 and 1 000 times faster than reasonably accurate interpolation methods and between 14 and 38 times lighter in terms of memory. Interpolation methods handle the prediction of L lines as L independent oper- to predict all lines, which limits redundant computations and effectively compresses the dataset. They can thus be fully described with between 2. 7 and 7. 8 million parameters, that is to say, between 43 MB and 118 MB. Finally, the proposed ANNs are roughly twice as accurate as the best interpolation methods on average and between two and three times as accurate with respect to the 99th percentile. Overall, the proposed ANNs are the only surrogate models that yield a mean error factor lower than 10% and thus that are suited to a corison with actual observations.
Acknowledgements. This work was partly supported by the  CNRS  through  80Prime  project  OrionStat , a  MITI interdisciplinary program , by the  ANR  project " Chaire IA Sherlock "  ANR-20-CHIA-0031-01  held by  P. Chainais , and by the national support within the  programme d'investissements d'avenir   ANR-16-IDEX-0004 ULNE  and  Région HDF. It also received support from the  French Agence Nationale de la Recherche  through the  DAOISM  grant  ANR-21-CE31-0010 , and from the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP , co-funded by  CEA  and  CNES. JRG and  MGSM  thank the  Spanish MCINN  for funding support under grant  PID2019-106110G-100. Part of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Young NMR tube under inert atmosphere and light-protected using the corresponding [Au I (PPh 3 )] + [X] À catalyst (0. 0066 mmol), 2 (0. 33 mmol), and dried CD 2 Cl 2 (0. 6 mL). [b] Yield determined by 1 H NMR spectroscopy using 1,3,5-trimethoxybenzene (0. 33 mmol) as an internal standard. [c] Experiment performed using [Au(PPh 3 )]-1 in situ prepared from [{Au(PPh 3 )} 2 (μ-Cl)]-1 and Ag-1 (1 equiv. ) and after removal of AgCl. [d] Experiment performed using [Au(PPh 3 )]-1 prepared in situ from (PPh 3 )AuCl and Ag-1 (1 equiv. ) and after removal of AgCl. [e] Data extracted from Ref. [19]a. [f] Data extracted from Ref. [19]b. a [M] Cat × T [°C] t (h) 3 [%] [b] TOF [h À 1 ] 1 ( PPh [g] Blank experiment using Ag-1 as a catalyst upon otherwise identical conditions. To fully discriminate any significant contribution of 1 and unreacted (Ph 3 P)AuCl, two blank experiments were designed for the cycloisomerization of 2 using Ag-1 or (Ph 3 P)AuCl as catalysts (entries 7 and 1, resp.
Acknowledgements Financial support from the  CNRS , the  Université Paul Sabatier , and the  Agence Nationale de la Recherche  ( ANR-JCJC-20-CE07-0023 ; acronym Ni4R f ) is acknowledged. L. D. and H. T. are thankful to MESR and  ANR Ni4R f  for Ph. D funding, respectively. P. Lavedan  and  M. Vedrenne  are acknowledged for 1 H DOSY NMR analyses of [Au(PPh 3 )]-1, [{Au(PPh 3 )} 2 (μ-Cl)]-1 and (PPh 3 )AuCl. We are thankful to  Dr. N. Mézailles  for insightful discussions and continuous support.
Acknowledgements Financial support from the  Centre National de la Recherche Scientifique , the  Université de Toulouse  and the  Agence Nationale de la Recherche  ( ANR-19-CE07-0037 ) is gratefully acknowledged. This study is a part of the  FRINATEK  project  315004  funded by the  Research Council of Norway  (stipend to M. H. ). The NMR and XRD services of  ICT (Pierre Lavedan, Sonia Mallet-Ladeira ) are acknowledged for assistance with the variable-temperature NMR experiments and single-crystal analyses, respectively. The " Direction du Numérique " of the  Université de Pau et des Pays de l'Adour and Mésocentre de Calcul Intensif Aquitain (MCIA)  are acknowledged for the support of computational facilities. This work was also granted access to the  HPC resources of IDRIS  under the allocation 2022-[AD010800045R1] made by  GENCI.
Bottom-Up (BU) proteomics is based on a proteolytic digestion of proteins from a complex mixture prior to analysis by mass spectrometry (MS). Peptides resulting from the digestion are separated by liquid chromatography or capillary electromigration  [1, 2]  coupled to MS for further sequencing and identification via a protein database search. Furthermore, intensities of the identified peptides can be used as a surrogate of the protein abundance in solution in order to compare protein expression of large biological systems  [3, 4]. This technology is now well established and has benefitted from dramatic improvement in MS instrumentation (resolution, mass accuracy, sensitivity and acquisition frequency). However, the beforehand proteolytic digestion results in a massive loss of information and precludes the analysis of a large diversity of proteoforms arising from combinatorial post-translational modifications (PTMs), splicing mechanisms, polymorphisms, protease degradation  [5].
MS spectra were acquired at a resolution of 70 K, with a mass range of m/z 500-2500 and an AGC target value of 1e6 for a maximum ion accumulation time of 500 ms. MS/MS data were acquired at similar resolution, in data-dependent top2 acquisition mode with an AGC target value of 5e5 for a maximum ion accumulation time of 250 ms. Parent ions were selected for fragmentation by Higher-energy C-trap Dissociation (HCD) with a dynamic exclusion time of 30 s. Histone sample was analyzed in positive ion mode on an Orbitrap fusion instrument from ThermoFisher Scientific (Waltham, MA, USA). MS spectra were acquired within a mass range of m/z 500-2500, with an AGC target value of 4e5 for a maximum injection time of 250 ms at a resolution of 60 K and 4 microscans accumulation. MS/MS data were acquired at similar resolution, with a first m/z of 100, an AGC target value of 1.
Raw data were processed with the Thermo Xcalibur v3. 0. 63 software. Peaks were integrated using the Genesis peak algorithm, a Boxcar smoothing value of 3 points and enabling valley detection in peak detection settings. For each peak, retention time (t R ), area and height were determined on the Total Ion Current (TIC) trace and used to calculate surrogate estimators of chromatographic performance and described below as "output variables": The average area of integrated peaks was used to monitor sensitivity. The relative t R difference between two consecutive peaks Δt R = (t R2 -t R1 ) was used as a surrogate of selectivity because of difficulties to calculate retention factors accurately with the lack of clear unretained peak information using solely the TIC trace. Full width at half maximum (FWHM) and resolution (RES) were calculated according to the following formula  [26] : FWHM = 0, 939 × Area Height RES = (t R2 -t R1 ) (FWHM 1 + FWHM 2 ) FWHM, peak area and Δt R taken as an average from the six peaks given by the Intact Protein Standard Mix were finally used as the three output variables of interest to be optimized during the DOE study.
Data analysis 2. 7. 1. NISTmAb and histone samples Protein mass spectra were deconvoluted using PMI Intact software (Protein Metrics, Cupertino, CA, USA)  [27]. Theoretical masses were computed by PMI Intact with chemical element values deduced from MIDAs isotopic distribution exact center of mass  [28]. GlycanMass tools provided by the Expasy website  [29]  and Unimod  [30]  were used to calculate average masses for PTMs.
Ribosome preparation The S. cerevisiae proteome from Uniprot was used as database (UP000002311 release 2022_01, 6062 entries) for all of the identification searches. Raw data were processed by Prosight 4. 0 on the Proteome Discoverer 2. 5 interface from ThermoFisher Scientific (Waltham, MA, USA). The ProSightPD High/High cRAWler node was used with default parameters and ProsightPD 4. 0 Annotated Proteoform node was run with mass tolerances of 20 ppm and 2. 2 Da for fragment and precursor masses, respectively. Subsequence search was used with a mass tolerance of 20 ppm for both fragment and precursor masses, then another ProsightPD 4. 0 Annotated Proteoform node was used for a less stringent search with mass tolerance of 20 ppm and 100 Da for fragment and precursor masses, respectively. N-term methionine excision, N-term acetylation, acetylation (K), methylation/dimethylation (K, R), phosphorylation (S, T, Y) and oxidation (M) were set as variable modifications.
A FDR (False Discovery Rate) of 1 % was applied for the identification. Raw data were also converted into an mzML format using msCon-vertGUI  [31]  (ProteoWizard, version 3. 0. 19106-4a85cc56b). Then, mzML files were processed using OpenMS tools  [32] , TOPFD and TOPPIC (OpenMS-2. 6. 0-pre-HEAD-2020-10-23). TOPFD was used to deconvolute data with the following parameters: HCD fragmentation, maximum charge of 50, maximum mass of 100,000 Da, MS1 S/N ratio of 3. 0, MS2 S/N ratio of 1. 0, precursor windows size (m/z) of 3. 0 and a m/z error of 0. 02 Da. TOPPIC was used to analyze ms2. msalign files obtained with TOPFD. TOPPIC was set with the following parameters: Decoy Database, an Evalue of 0. 01 was applied at both PrSM and proteoform levels, a mass error tolerance of 20 ppm and a PrSM cluster error tolerance of 1.
In the next experiments, a DOE-like study (see material & methods) was conducted in order to quickly optimize a couple other variables and to thereby obtain balanced performance in terms of protein separation, peak sharpness and time/method throughput. Three variable parameters were studied as input factors i. e. , temperature, flow rate and the eluent gradient slope. These are parameters that are known to be critical for chromatographic reverse phase separation  [38]. In this experiment, DFA was kept at a constant mobile phase concentration of 0. 1 % to limit the number of tests in the DOE study. FWHM, Δt R and peak area were used as output factors (as surrogate indicators of separation efficiency, selectivity and sensitivity). Fig. 2  shows the analysis of variance obtained from a set of 17 injections after fitting the data with a first order model.