dataesr/scientific-paragraphs-categorization huggingface dataset:
Tuning the model using acoustic data from the LES We discussed earlier that the errors in the LES database cause the correlation between the forcing and the acoustic field to be partially contaminated, yielding the response generated by the first RESPOD forcing mode, χ (1) , to globally underpredict the optimal SPOD mode of the acoustic field. As the forcing model in (5. 2) is constructed based on the supersonic part of this forcing mode, the effect of the errors in the database is inherited in the model, ξ. To minimise this effect, we tune the model using the acoustic data obtained directly from the LES. The tuning process is summarised as follows: we first correct the mode amplitude using the acoustic field from the M04Mc00 case to better capture the overall sound level and its change with respect to frequency.
1, the hydrodynamic fluctuations reach the acoustic field as reported by  Nekkanti & Schmidt (2021) , who used the same database for the cases M09Mc00 and M07Mc00; and at St = 1. 5, the acoustic level is already 20 dB less than the peak in all the cases. For the range St = [0. 4, 1], the model accurately predicts the acoustic field for all the cases, despite some underestimation for the cases M04Mc00, M07Mc15 and M09Mc30 at θ = 25 •. However, at slightly lower propagation angles, the model starts to yield better predictions for these three cases as well, as can be seen in figures 16, 19 and 20. At St = 1. 5, the model yields accurate predictions in all the cases except for the cases M07Mc00 and M08Mc00 at θ = 15 •. At frequencies below St = 0. 4, the model starts to overpredict the acoustic level which becomes evident at St = 0.
1 in all the cases. We expect the model to be valid only above a certain frequency. The forcing amplitude is scaled by 1/St 3/2 in (5. 11), which tends to infinity as St → 0. The overprediction remains within 3 dB at St = 0. 2 for all the cases except M07Mc00, M07Mc15 and M09Mc30, where it reaches up to 5 dB. Another reason that may explain the poor performance at low frequencies is that the identity of the leading resolvent mode switches from 965 A18-30 Finally, we test our model against experimental data available in the literature. We choose the cold jet experiments conducted in NASA SHJAR  (Khavaran & Bridges 2009)  denoted with set point 7, for which an LES database was generated by  Markesteijn & Karabasov (2017)  and the mean flow and acoustic field data were provided by  Gryazev, Markesteijn & Karabasov (2023).
5, for which the Nyquist limit to avoid aliasing is given as 6. 25  (Nyquist 1928; Shannon 1948). A detailed analysis of aliasing the LES database was given by  Karban et al. (2022b) , where it was shown that significant aliasing was observed in the forcing data even though it is negligible in the response. As resolvent analysis is performed in the frequency domain, aliasing appears as an error source for both forcing identification based on RESPOD and the resolvent-based prediction of the response. These errors accumulate in the LES database. It is not possible to accurately quantify contributions from each error source, but one may use the difference between the state obtained directly from the LES and its prediction obtained using the resolvent tool as a global measure of the total error included in the database.
In figure  23 , we show a comparison of the PSD of the pressure in the acoustic field, i. e. at r = 5D, directly extracted from the LES data, and its resolvent-based prediction for the case M04Mc00 at St = 0. 6. The acoustic field is predicted with reasonable accuracy, except the most upstream part, x < 1D, where the resolvent-based prediction suffers from a boundary condition effect. These error levels are similar to those observed by  Towne et al. (2021)  when comparing the PSD extracted from an LES database and obtained from a forced resolvent model for a supersonic jet. These results show that, although being contaminated by errors to a certain extent, the current database can be used to investigate noise generation mechanism in jets at this Mach number. At higher Mach numbers, aliasing is seen to affect the spectral estimates at such a level that the resolvent-based analysis using forcing data becomes unreliable.
Funding. This work has received funding from the  Clean Sky 2 Joint Undertaking  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement no. 785303. U. K. received funding from  TUBITAK 2236 Co-funded Brain Circulation Scheme 2  (project no. 121C061 ). A. T. was supported in part by  ONR  grant  N00014-22-1-2561. Declaration of interests. The authors report no conflict of interest.
5. The metabolite resonances were identified and quantified using the LC Model software. In this particular example, after 1. 5 hour of CED of RSM-932A solution, a 15% increase of the amplitude of lactate resonance at 1. 3 ppm was observable, while a 7 % decrease of the tCho resonance at 3. 2 ppm and a 30% decrease of tNAA at 2 ppm were noticeable. The spectra acquired during the CED of aCSF solution in the two sham animals showed no evolution of the amplitude of any resonance. In total the evolution of eighteen metabolites were assessed using LC Model software following the infusion of ChoK𝛼 inhibitor. Three of them, tCho, tNAA and lactate/lipids showed a significant change in their concentration relative to that of tCr. The evolution of the concentration of these three metabolites for the six C6-bearing animals with CED of ChoK𝛼 inhibitor is summarized in Fig.
Radiofrequency simulations and characterization of microcoils The distribution and the amplitude of the B1 RF magnetic field generated by the microcoil were simulated using the modeling software COMSOL Multiphysics 5. 5 (COMSOL AB, Stockholm, Sweden) based on finite element method (FEM). The unloaded quality factor (the resonance frequency over the full width at half-maximum of the reflection coefficient) of microcoils were measured using a vector network analyzer (VNWA 3EC, SDR-Kits, Melksham, United Kingdom). The quality factor of the loaded coils positioned within the magnet were measured using the network analyzer of the MRI spectrometer. The microcoils were centered and positioned vertically within the volume coil. The normal to the elliptical surface of the microcoil was placed perpendicular to the B0 static magnetic field.
MR spectrum processing. The SNR values of the metabolites in solution were obtained using the TopSpin software (Bruker, Ettlingen, Germany). MR spectra obtained in control animals and C6-bearing animals for comparison of amplitude of metabolites were processed using a java-based version of the MR user interface package jMRUI. A first order phasing and a Lorentzian apodization of 5 Hz were performed and the residual water components were removed using the HLSVD (Hankel-Lanczos Singular Value Decomposition) algorithm. The Quest (QUantum ESTimation) module was used for the identification of the resonance of the metabolites. Spectra obtained in animals were fitted and quantified with the QUEST (quantitation based on Quantum ESTimation) procedure from jMRUI  33. A basis set of 12 metabolite spectra was simulated at 7 T for the in vivo experimental protocol with the NMR Scope-B.
The basis set included choline and phosphocholine (tCho), lactate (Lac), creatine and phosphocreatine (tCr), N-acetylaspartate and Nacetylaspartylglutamate (tNAA), glutamate (Glu), glutamine (Gln), myo-inositol (mI) and taurine (Tau). Only peak quantifications with a Cramer-Rao lower bound greater than 20% of the SNR were considered sufficiently reliable  34. LCModel (Linear Combination of Model spectra) software  35  was used to estimate metabolite peak-areas during CED of ChoK𝛼 solution. To compute the absolute quantification of each metabolite, the experimental spectrum was fitted between 0. 5 and 4. 1 ppm with a group of basis sets for the spectrum of a specific metabolite for the PRESS proton MRS sequence for a TE equal to 15 ms. No baseline correction, zero-filling, no using unsuppressed water signal were applied to the in vivo data before the analysis in LCModel.
Competing Interests The authors declare no competing interests. Bottom spectrum was acquired 1. 5 hour after starting the CED of RSM-9321A solution. Spectra were obtained using the microcoil as a receiver and a PRESS sequence with a total acquisition time of 8 min and 32 s. LC Model software was used for the identification and quantification of the metabolites. Grey and red curves correspond to the acquired non processed spectra and to the LCModel fitted spectra respectively. ChoKα inhibitor. The ratios for these three metabolites demonstrated statistically significant changes following the CED with * corresponding to p<0. 05. N=5 for tNAA/tCr concentration ratio due to the absence of detectable tNAA resonance in one of the animals Table  1. FOG for the microcoil relative to a surface coil for rodent brain for five resonance lines of metabolites present in the brain.
Acknowledgments: This study was achieved within the context of the  Laboratory of Excellence   TRAIL   ANR-10-LABX-57  with funding of the research program  Insight. Dr. Imad Benkhaled  acknowledges a funding from  ANR program   SEMB. The authors thank  François Lux  and  Olivier Tillement  ( Institut Lumière Matière, Lyon, France ) and  Laurent David  ( Ingénierie Des Matériaux Polymères, Lyon, France ) for providing the Gd-based contrast agent.
The EUOS/SLAS challenge has its goal to develop reliable algorithms to predict solubility of small molecules experimentally measured aqueous solubility of 100k compounds. In total, hundred teams took part in the challenge to predict low, medium and highly soluble compounds as measured by nephelometry assay. This article describes the winning Top I rank model which was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM) and which is publicly available at https://ochem. eu/article/ 27. We describe in detail the assumptions and steps used to select methods, descriptors and strategy which contributed to the winning solution.
Challenge evaluation The original 101,017 small molecules were made available to participants in a training set of 70,710 compounds with activity data and a test set of 30,307 compounds without activity data. A public performance score based on a subset of the test set was published immediately after submission. The competition was evaluated using the performance score based on the remaining test set data, which was made public only after the end of the challenge. The two evaluations involved the creation of two sets, the leaderboard set and private set, which combined contained a total of 30,307 test set compounds. Randomly stratified sampling was used to ensure that the same fractions of low, medium, and high soluble compounds were present in the training, leaderboard, and private sets. The challenge participants were limited to one submission per day and per team, and each participant was required to join only one team during the challenge, which ran from September 19th to December 31st.
General modeling framework Open Chemical database and Modelling environment (OCHEM)  11  and its recent open source release -openOCHEM (https://github. com/openochem) were used to develop and disseminate models. OCHEM contains numerous descriptors and machine learning methods, which include both commercial and publicly available tools. openOCHEM contains only open source code (GPL, LGPL, AGPL) or binary codes, which were released under permissive licenses and can be used without restrictions for academic, educational, recreational or evaluation purposes. All models were validated using 5-fold cross-validation (5CV). The statistical coefficients calculated using 5CV protocol were also used to evaluate model performances and select models for the challenge as described below.
Molecular descriptors In order to ensure the reproducibility and sharing of the developed model, the selection of descriptor packages and machine learning approaches was limited to those available in open-source openOCHEM. To eliminate redundancy, unsupervised filtering was employed by removing descriptors with less than two different values per training dataset and those with strong correlation (Pearson correlation > 0. 95). The number of descriptors for each package after filtering is presented in Supplementary Table  S1. Based on previous studies indicating that the use of 3D descriptors may not significantly improve solubility prediction, we focused on 2D descriptors. 3  Following preliminary analysis, we selected nine descriptor packages to build our models. These packages are briefly described below.
Additionally, we also used a number of representation learning methods implemented within KGCNN,  33  which were adapted to be used in the OCHEM (Table  2 ). These methods do not need descriptors but learn internal features based on representation of molecules as SMILES or graphs. The differences in methods appear due to various attributes of nodes and vertices of graphs (i. e. , atoms and bonds for molecules), different training algorithms, model pretraining and/or use of 3D information, which was generated internally using RDKit 24 from SMILES. 12  We also used the Transformer CNN,  34  which is based on analysis of internal latent representation learned during SMILES canonisation of 1. 7M molecules from the ChEMBL database.
A value of zero shows an alignment, which would occur at random considering the categorical frequencies. (1) For the calculation of the quadratic kappa metric the confusion matrix O i,j and the expected matrix E will be needed. The latter is calculated as the outer product of the two histograms, one coming from the actual labels and the other from the predictions. After normalizing the two matrices, the quadratic kappa value can be calculated as follows using a 3 × 3 weights matrix w with a quadratic distance function. (2) The Kaggle challenge has provided code and examples of usage of this coefficient. 47  Two strategies were mentioned to optimize performance of models for QWK: 1) Consider the modeling as regression model and optimize the root mean squared error (RMSE) 2) Use QWK directly as error (loss) function Since the QWK loss function was not available in OCHEM we considered the classification problem as a regression model with numerical values 0, 1 and 2 for the respective classes and selected models with minimal RMSE, which is a traditional loss function for regression tasks.
Descriptor-based model analysis Individual machine learning models were analyzed using an initial descriptor set of AlogPS and OEstate. Internal five-fold cross-validation results and external test QWK scores from models available in openOCHEM are shown in table  2. Post hoc analysis showed that descriptor-based models included some of the best performing models in both leaderboard and private QWK scores. An exception was ASNN, which contributed the highest score for the Leaderboard dataset but had a lower score for the private set. Overall the RMSE correlated with the QWK scores. Based on Table  2  data we calculated R=-0. 38 and R=-0. 46 for leaderboard and private scores, respectively. Thus, indeed, RMSE could be a proxy to identify models with high QWK score in the absence of a direct loss function for the latter one.
We also analyzed whether linear machine learning methods could be used instead of DNN and CatBoost. For this study we developed a consensus model based on 18 descriptor-based models using Partial Least Squares (PLS) and Multiple Linear Regression Analysis (MLR). Despite the 5CV RMSE of this model (#11, Table  3 ) was similar to that of the winning model, QWK scores for test sets were lower. After the end of the challenge Dr. Bernhard Rohde described results of his submission and indicated that there was a variability of the proportions of insoluble compounds per plate. 50  He determined the information about plate and position of each sample on the plate from the data labels based on assumptions of the enumeration of compounds within each assay. His submission calculated higher scores QWK=0.
This result highlights an importance of consensus modeling as well as shows the high potential of the OCHEM platform to contribute best accuracy models. The 1st EUOS/SLAS solubility Challenge was a great experience for all authors, in particular to the first author, who learnt structure-activity relationship study approaches when working on this challenge and preparing her BSc thesis. One remark would be that the organizers could have described the challenge rules better to avoid any confusions as well as to eliminate any information (e. g. , data labels, or data ordering) which is not relevant for the challenge. The absence of such information would avoid misinterpretation of the challenge rules by some participants, who contribute high scoring, but ineligible solutions. Another example was "Can we use other dataset?" discussion.
Some of the authors of this manuscript interpreted the remark that ". any extra dataset competitors might use, if produced with the same protocol we described, can anytime be used",  51  as permission to use additional measurements coming from the same protocol. In this case the extension of experimental data with new measurements could provide significant advantages to some participants, who have an access to such data. This caused some frustration, but did not stop us from participating in the challenge. Additionally, the participation in a challenge requires significant resources and keeps participants active during its whole duration. However, often a participant can dedicate significant resources only for a short period of time, frequently near to a deadline. In this respect a possibility to submit more than one submission per day (but limiting the total number of them) could be important to participants, who were not able to join from the very beginning and/or can dedicate only some time to the challenge.
We have noticed that Leaderboard scores were always higher than the private scores. We observed this difference for all our submissions as well as for all 100+ individual teams submissions from the challenge website. While models submitted by other teams could have inflated QWK scores for the Leaderboard set due selection of models with higher scores for this set, that was not a case for our analysis: we compared results without any model selection. Both the Leaderboard and private set scores were highly correlated (R 2 = 0. 978) based on 100 submissions. The only exception was the submission of Dr. Rohde, which used plate-specific information. An inclusion of the plate-specific descriptors provided a model with a higher score for the private set also for our study. This result may indicate that there was some bias in selection of compounds for the Leaderboard and private sets, which could be due to the use of stratified data sampling for test sets or/and other reasons.
This was also echoed in the discussion about the last section of the test set. 52  This section included a significantly higher amount of low-soluble compounds and therefore highly skewed the prediction of anyone using id information for their prediction, both those using it based on informed assumption, such as Dr. Rohde, and those using it by chance, such as the leaderboard first place. This was also supported by our analysis of Dr. Rohde's intuition that plate position did not significantly improve our results, whereas plate number did significantly improve results. In summary, we have shown that consensus modeling by averaging predictions contributed by different machine learning models available in OCHEM was a winning strategy. 2) Update database using (request the file from the authors) tar -zxf solubility.
tar. gz docker exec -i ochem-mariadb sh -c 'exec mariadb fragment_demo' < fragment_demo. sql docker exec -i ochem-mariadb sh -c 'exec mariadb --max_allowed_packet=1G ochem_demo' < ochem_demo. sql 3) Restart docker docker-compose -f docker/ochem-demo. yml stop docker-compose -f docker/ochem-demo. yml up The local version will be available at the same address http://localhost:8080 as the initial docker. To see models: a) Select from menu /Database/Articles c) Click on the Article profile to see all published models 4) To apply models, select the published consensus model and apply it to a single molecule or few compounds. In order to use it with a large number of molecules you will need to attach additional servers (contact us for details). 5) To get a prediction for the challenge set (only possible if you have an access to molecular structures available from the Kaggle challenge web site).
Acknowledgement We acknowledge the  OPENSCREEN consortium  and thank them for organizing the 1st  EUOS/SLAS Joint Challenge : Compound Solubility. Many thanks to the group of  Prof. Dr. Eyke Hüllermeier  of the  Ludwig-Maximilians university in Munich  for enabling the challenge participation in the course of a bachelor thesis. This study was partially funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Actions , grant agreement " Advanced machine learning for Innovative Drug Discovery (AIDD) " No. 956832. M. S. was partially supported by the  Ministry of Education, Youth and Sports of the Czech Republic  (project number  LM2023052 ). We also would like to thank  Dr. Bernhard Rohde  for useful discussions.
Instructions to install and use standalone model 1 It provided higher QWK scores than individual models or consensus based on the same machine learning method. Amid individual models, Transformer CNN contributed the highest accuracy. CatBoots model based on EFG descriptors was not included in the final submission by mistake. DimeNetPP and GraphSAGE provided the same predicted values for the test set compounds and could be excluded without affecting consensus model results. ) Build and start docker as indicated at https://github. com/openochem/openochem/tree/main/docker Check that it is correctly installed and available at http://localhost:8080 http://localhost:7080/metaserver MOLD2 0. 381 0 0. 127 0. 090 0. 381 0. 013 0. 107 0. 085 QNPR 0. 382 0 0. 102 0. 084 0. 384 0 0. 115 0. 088 EFG 0. 38 0.
Assessing past distributions, variability and trends in the mountain snow cover and its first-order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modeling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in situ, gridded observational datasets and satellite observations) across the European Alps from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation).
We assess multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales, mainly for the winter period (from November through April). ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution. However, a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration, especially the melt-out date. CNRM-AROME and CNRM-ALADIN simulations and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. The analysis of the interannual variability and trends indicates that modeling snow cover dynamics remains complex across multiple scales and that none of the models evaluated here fully succeed to reproduce this compared to observa-.