dataesr/scientific-paragraphs-categorization huggingface dataset:
The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource-intensive and time-consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph-based methods (such as graph edit distances, graph kernels, and graph neural networks) that are reviewed to enlighten their deep links with theoretical chemistry. To this aim, we establish the ORedOx159 database, a comprehensive, homogeneous (with reference values stemming from density functional theory calculations), and reliable resource containing 318 one-electron reduction and oxidation reactions and featuring 159 large organic compounds.
Subsequently, we provide an instructive overview of the good practice in machine learning and of commonly utilized machine learning models. We then assess their predictive performances on the ORedOx159 dataset through extensive analyses. Our simulations using descriptors that are computed in an almost instantaneous way result in a notable improvement in prediction accuracy, with mean absolute error (MAE) values equal to 5. 6 kcal mol -1 for reduction and 7. 2 kcal mol -1 for oxidation potentials, which paves a way toward efficient in silico design of new electrochemical systems.
Noteworthy, we will start from the very beginning by setting up from scratch a completely new database of organic molecules, with DFT reference values computed by ourselves. This departs from the common practice based on preexisting databases. Indeed, one of the usual drawbacks of them is that they might gather reference values from various origins, sometimes without clear source, and they may thus suffer from a lack of homogeneity that is not without incidence on the "trustability" of the results. Conversely, we will generate here our reference values with a unique and perfectly defined, controlled, and -also an important point -reproducible computational protocol. This database will be described more in details in the next section. Then, the computational details and the ML technics used in this study will be presented in a pedagogical way, so that it can build a bridge between the two communities (namely theoretical chemistry and data science).
II. THE OREDOX159 DATABASE The ORedOx159 database is a collection of 318 oneelectron reduction and oxidation reactions involving 159 large-size organic compounds routinely used as redox indicators, or involved in the development of molecular electrochemical storage and electrochemical sensors  [20]. For instance, it counts viologen derivatives which are well-known from decades to reversibly change color between violet and deep blue through reduction and oxidation  [21] , or 2,2'-bipyridiniums which are used as 'electron reservoir' for electrochemical storage  [22] , or phenothiazine compounds which has been recently considered as efficient redox mediators in electrochemical sensors  [23]. For each organic compound A, the database collects a one-electron reduction reaction A + e -→ A -, ∆ r G 0 Red (1) where ∆ r G 0 Red denotes the (standard) Gibbs free energy variation of the reduction process at room temperature.
e. , 96,500 C mol -1 ). It is thus equivalent to build a predictive model on Gibbs reaction free energies or on redox potentials. Regarding its chemical diversity, the database is composed by a large variety of compounds substituted by electro donor or acceptor groups such as aliphatic, etheroxy, keto, ester and halogen substituents, and derived from four different organic families that are clustered into the phenothiazine (1), quinone (2), 2,2'bipyridinium (3) and viologen (4) subsets. Figure  1  provides a representation of the main core derivative of each of them, and Figure  2  depicts the distribution of their respective Gibbs free energy variations in reduction and oxidation computed in gas phase at PBE0/def2-SVP level. More precisely, the phenothiazine subset counts 28 derivatives. They are 15 carbon-and 6 aza-substitued phenothiazines as well as 4 benzothiazines and 3 sulfoxided phenothiazines.
III. COMPUTATIONAL DETAILS With the collected database, we now perform the necessary computations and prediction of the redox potentials. FIG. 3  exhibits the workflow of the procedure, which breaks down into five steps. Three steps during the training procedure include: (I) Computing the redox potentials for each compound in training set using DFT. The corresponding computational method is detailed at the following part of this section. (II) Constructing the descriptor representation for each compound in the training set, which is detailed in Section IV A. (III) Training a ML model with the given descriptors and redox potentials. The list of models used in this paper is described in Section IV B. After that, a ready-to-use model is established. When a new chemical compound with unknown redox potentials is given, we (IV) construct the descriptors for this compound the same way as in step (II), and then (V) use these descriptors as the input for the trained model, and take the output of the model as the predicted redox potential.
O=C1OC[C@@H]2[C@@H](C[C@@H](OC)O2)O1 (A) Atom + Bond Types This does not mean that only interactions between bonded atoms exist. Actually, a full molecular graph will be in principle a complete graph (i. e. all pairs of nodes are linked -which is not a synonym of bondedtogether), and one can for in principle evaluate the interaction energies between all nodes (for instance, using Pendás' Interacting Quantum Atoms (IQA) scheme  [31]. However, such graphs rapidly become huge since they involve n(n -1) 2 edges (with n the number of atoms), so that their use would introduce a computing complexity that can be prohibitive for large databases of extended molecules. Conversely, a linear molecule (i. e. without rings or cages) exhibits (as a direct consequence of the Poincaré-Hopf relationship within the framework of Bader's Quantum Theory of Atoms-In-Molecules (QTAIM)  [32] ) only n -1 bonds, so that linear scaling may be possible with such graph assumption.
Atom + Bond Features Then, once the molecular graphs established, we consider in total 3 types of descriptors for graph-based models in this research. As we intend this paper to have some didactic content, we have decided here to describe them into more detail with no prerequisite on chemoinformatics. They are also represented in FIG. 4. (A) Atom + Bond Types: This descriptor considers element types (for instance carbon, hydrogen. ) as node features and chemical bond types (either single, double, triple, aromatic, see more details in type B) as edge features. Since these features are discrete, they are encoded in a one-hot manner. These descriptors, while affording a simplistic description of a molecule, are widely used in various databases for the evaluation of machine learning models, such as TUDataset  [33].
This matrix serves as the inner product in kernel machines and aids in finding the K nearest neighbors in K-Nearest Neighbors algorithms. The catch is that the computational time associated with this matrix scales linearly with the size of the training set, rendering it prohibitively timeconsuming for large datasets. As a result, the scalability of these models becomes constrained. In contrast, GNN models usually feature a linear or nearly linear time complexity during inference. This efficiency is intricately linked to the model's design, particularly the scale of weight parameters. In practice, GNN computations predominantly involve basic matrix operations like multiplication and addition. Consequently, GNN models excel in efficiently handling graphs of varying sizes, making them exceptionally well-suited for realworld scenarios.
Given the vectorfocused nature of these models, a graph-level pooling step is required beforehand to derive fixed size vectors from varying size graphs. For this purpose, we adopted the strategy outlined in  [71] , wherein statistics are computed for each feature over all nodes within each graph (stats). Indeed, the number of nodes varies along the molecular dataset, so that the vector gathering the node features would have, in general a different size for one chemical system to another one, precluding the use of standard ML methods. To avoid this, for a given feature, we compute the minimal, maximal, mean and standard deviation values on all nodes, resulting in four descriptors that can be evaluated for all molecules. Additionally, we introduced a novel pooling approach, where we quantified the frequency of each feature.
In the case of vectorbased models, we focus on the results obtained through count pooling as it consistently outperforms statistical pooling. This count pooling thus represents an interesting improvement over the methods previously used by us to deal with the non-constant number of nodes along the whole dataset. An exception is observed for the GPR model with the One-Hot descriptor, where statistical pooling yields better results, and thus we present both pooling methods. When comparing different descriptors, a clear trend emerges with the One-Hot descriptor prevailing in most models: 16 out of 19 for reduction Gibbs free energies and 15 out of 19 for oxidation Gibbs free energies. In the context of graph-based models, this dominance continues as 8 out of 11 models for both targets. The following is The Atom Bond Types descriptor.
Importantly, the performance gap among descriptors is significant on a model-wise basis, which often surpasses the differences in performance between models. This phenomenon is particularly pronounced for vector-based models, likely due to their limitation in handling only node features. The One-Hot descriptor, encoding implicit structural information, proves highly beneficial in this context. A similar pattern is observed for most GNN models, emphasizing the crucial role of feature engineering in their performance. An exception is for reduction values Red, where the One-Hot descriptor excels. This suggests the intriguing potential of transformer-like models in capturing core information for graphs with limited features, even on smaller datasets. Conversely, the Distance descriptor is evaluated only for models supporting continuous edge features.
For instance, with the One-Hot descriptor, the performance of KNN closely rivals and surpasses the best graph kernels, while GED outperforms 3 out of 5 GNNs for reduction and 2 out of 5 for oxidation. Comparing GKRR and graph kernels, both employing Kernel Ridge Regression for predictions, GKRR achieves similar performance to three graph kernels, including WLSubtree, and surpasses one of them. It is important to highlight that with the One-Hot descriptor, numerous graph kernels, GED, and even vector-based models outperform GNNs with the Atom Bond Types descriptor, underscoring the critical role of descriptor engineering. On the whole, with the chosen low-level and computational-friendly descriptors, the best ML model achieves a prediction MAE of 5. 8 kcal mol -1 for reduction and 7. 2 kcal mol -1 for oxidation on the ORedOx159 database.
VI. CONCLUSION AND FUTURE WORK In this study, we proposed a comprehensive, homogeneous, and reliable database ORedOx159 (based on density functional theory calculations), which collects one-electron reduction and oxidation reactions involving large-size organic compounds of experimental interest. We then applied a series of machine learning models on this dataset to predict the reduction and oxidation Gibbs free energies using several descriptors computed from the structures of the compounds obtained at the cheap molecular mechanics level. The analyses of the results suggests that with the proper choice of descriptors and machine learning models, a relatively high prediction accuracy can be achieved, where the prediction time is considerably reduced. Looking ahead, future research work will include several dimensions.
Firstly, from the chemical point of view, the next important step will be to (i) include to our model solvation effects, and (ii) extend the training database to other chemical families of high interest in electrochemistry. Then, in comparison to tabulated reference potentials, our research output will become a robust built-in package to fast and accurately predict redox potentials of organic compounds in solutions, as it is for instance the case for NMR spectrum simulations. Secondly, state-of-the-art machine learning models may serve as better prediction tools, especially the ones tailored for our problem. For example, Graph Neural Networks and transformer-like models, particularly focusing on leveraging 3D coordinates and leveraging stateof-the-art models may unlock new capabilities and insights.
ACKNOWLEDGMENTS E. B. and L. Z. thank  Dr. P. P. Lainé  ( ITODYS lab, CNRS ) for fruitful discussions regarding the construction of the ORedOx159 database. E. B. gratefully acknowledges  ANR  ( Agence Nationale de la Recherche)  for the financial support of this work through the  MoMoPlasm  project ( ANR-21-CE29-0003 ). He thanks also  ANR  and  CGI (Commissariat à l'Investissement d'Avenir)  for their financial support of this research through  Labex SEAM (Science and Engineering for Advanced Materials and devices )  ANR-10-LABX-096 ,  ANR-18-IDEX-0001 , and acknowledges  TGGC  ( Très Grand Centre de Calcul du CEA)  for computational resources allocation (Grant No. AD010810359R1 ). This work has also been partially supported by  University of Rouen Normandy ,  INSA Rouen Normandy , the  "Centre National de la Recherche Scientifique" (CNRS) , the  European Regional Development Fund (ERDF) ,  Labex SynOrg  ( ANR-11-LABX-0029 ),  Carnot Institut I2C , the graduate school for research  XL-Chem  ( ANR-18-EURE-0020 XL CHEM ), and the  "Région Normandie".
Stereo photogrammetry and/or photoclinometry have long been applied to the production of 3D imaging datasets for planetary surfaces. However, insufficient image resolution or reductions in the resultant 3D resolution, the lack of adequate stereo coverage and where available, lengthy processing time, various matching artefacts or noise effects, and unsatisfactory quality and complexity of processing, have long been significant barriers for large-area planetary 3D mapping. In this work, we demonstrate how deep learning based solutions can address the above issues using a single image and a coarse 3D reference as inputs.
Acknowledgements The research leading to these results is receiving funding from the  UKSA Aurora programme  ( 2018-2021 ) under grant no. ST/S001891/1. The research leading to these results also received partial funding from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013) under iMars grant agreement  n˚607379. SJC is grateful for the financial support of  CNES  in support of her HiRISE work.
In most public speaking datasets, judgements are given after watching the entire performance, or on thin slices randomly selected from the presentations, without focusing on the temporal location of these slices. This does not allow to investigate how people's judgements develop over time during presentations. This contrasts with primacy and recency theories, which suggest that some moments of the speech could be more salient than others and contribute disproportionately to the perception of the speaker's performance. To provide novel insights on this phenomenon, we present the 3MT French dataset. It contains a set of public speaking annotations collected on a crowd-sourcing platform through a novel annotation scheme and protocol. Global evaluation, persuasiveness, perceived self-confidence of the speaker and audience engagement were annotated on different time windows (i.
e. , the beginning, middle or end of the presentation, or the full video). This new resource will be useful to researchers working on public speaking assessment and training. It will allow to fine-tune the analysis of presentations under a novel perspective relying on socio-cognitive theories rarely studied before in this context, such as first impressions and primacy and recency theories. An exploratory correlation analysis on the annotations provided in the dataset suggests that the early moments of a presentation have a stronger impact on the judgements.
g. ,  Ramanarayanan, Leong, Chen, Feng, and Suendermann-Oeft (2015) ;  Wörtwein et al. (2015) ). Others focused on the possibility to assess the speaker's performance by looking at thin slices taken randomly from the overall performance  (Chollet & Scherer, 2017). In this paper, we present a new dataset, the 3MT French dataset, aiming at facilitating the analysis of public speaking judgements, addressing some challenges of existing corpora. In particular, 3MT French contains human annotations given during different moments of a presentation. This would allow for the analysis of a presentation quality under a novel perspective relying on socio-cognitive theories rarely studied before in this context, such as first impressions and primacy and recency theories.
Related Work Several works focused on multi-modal modelling of public speaking behaviour in different contexts, such as student presentations (e. g. ,A. -T. Nguyen, Chen, and Rauterberg (2012) ), job interviews (e. g. ,  Hemamou, Felhi, Vandenbussche, Martin, and Clavel (2019) ;  Naim, Tanveer, Gildea, and Hoque (2015) ), simulations of different topic presentations (e. g. ,  Batrinca, Stratou, Shapiro, Morency, and Scherer (2013) ;  Chen, Leong, Feng, Lee, and Somasundaran (2015) ;  Ramanarayanan et al. (2015) ;  Wörtwein et al. (2015) ), academic talks  Curtis, Jones, and Campbell (2015) , or political speech (e. g. , S. Scherer, Layher, Kane, Neumann, and Campbell (2012) ). In this section, we review important studies in this field, discussing the datasets used for their analyses, their criteria to assess public speaking quality, and whether they took into account the temporal location of behaviours.
Existing Corpora Ad-hoc experimental corpora. Researchers often analyse ad-hoc datasets collected for the purposes of their study. For example,  Wörtwein et al. (2015)  provide a multi-modal corpus collected in the context of their experimental study investigating the potential of interactive virtual audiences for public speaking training. Data from 45 speakers, each giving four presentations, was gathered to compare pre-and post-training performance in front of the Cicero virtual audience system  (Chollet, Sratou, Shapiro, Morency, & Scherer, 2014)  providing different feedback. This dataset has been analysed in further studies (e. g. ,  Chollet, Marsella, and Scherer (2021) ) and integrated with additional annotations on three 10-second thin slices randomly selected from each video  (Chollet & Scherer, 2017).
The particularity of this corpus is that, given the purpose of the study, the difference between pre-and post-training performance is measured. That is, judgements about the actual performance of each presentation are not provided. In addition, the presentations are collected in an experimental setting in front of a virtual audience. Another dataset, analysed in several studies (e. g. ,  Chen et al. (2015) ) contains audio, visual and Kinect data from 17 speakers, each giving four 4-5-minute presentations. Human ratings about the presentation quality are provided. Similarly to  (Wörtwein et al. , 2015) , this corpus is collected in an experimental setting where presentations are simulated without the presence of a real audience. In addition, it contains a relatively low number of speakers.
Monologues. In the context of audio-video-based job interviews, a relatively large amount of corpora were created, which usually contain monologues of candidates answering to questions from mock structured interviews, along with experts' annotations of hirability and automatically extracted audiovisual features (e. g. ,  Chen et al. (2017) ;  Naim et al. (2015) ;  Rasipuram and Jayagopi (2016) ). The largest one is that from  Chen et al. (2017) , containing 1891 monologues from 260 online participants. In contrast to the above corpora featuring mock interviews, L. S. Nguyen, Frauendorfer, Mast, and Gatica-Perez (2014)  gathered data from 62 real job interviews, providing audio-visual features form both the interviewees and the interviewer. Another corpus of monologues but not related to job interviews is the Persuasive Opinion Multimedia (POM) dataset  (Park, Shim, Chatterjee, Sagae, & Morency, 2014).
Another dataset of naturalistic presentations was gathered by  Curtis et al. (2015). It contains recordings of 31 academic talks given at an international conference. The particularity of this corpus is that it contains both the videos of the speaker and the audience. Audience engagement and presentation quality have been manually annotated online on 30-second segments. Limitations. To summarise, several existing corpora were previously used to model public speaking behaviour. Some of them are not publicly available, for example for privacy reasons (e. g. ,  Hemamou et al. (2019) ) or because they were only released for specific challenges (e. g. ,  Ochoa, Worsley, Chiluiza, and Luz (2014) ). Those created ad-hoc for specific research purposes often provide a limited amount of speakers, and are collected in an experimental setting without a real human audience.
Motivation and Contributions Being motivated to face most of the limits discussed in the previous section, and to facilitate the investigation of how people's judgements develop during a speech, we present the 3MT French dataset of public speaking presentations. Compared to previous work, this dataset allows for a novel perspective for the analysis of a presentation quality, relying on first impressions and primacy and recency theories (see Section 2. 3). With this work, we aim at providing two types of contributions. On one hand, the 3MT dataset with its particular properties: • A relatively large amount (248) of naturalistic presentations given in front of a real audience; • The speakers are not necessarily experts in public speaking, which means that the quality of the presentations is highly heterogeneous; • The presentations all have the same duration (180s) and follow a similar structure; • Information about speakers who won audience and/or jury prize is included.
The 3MT French Dataset In our 3MT French dataset, we focus on the French edition of the competition, held in 2019, which was the last year where the presentations took place in presence in front of the audience, without any physical restriction (the 2020 and 2021 editions were held partially or totally virtually, due to the pandemic). We selected the presentations from the regional rounds, the first phase whose videos were published online (upon participants' agreement). Since they still are at the beginning of the competition, we can find a high variety of presentations, and not only high-quality ones as may be the case for the national final. The 3MT French dataset contains videos from 248 presentations, 135 of female and 113 of male speakers, annotated on several time windows (see Section 5.
Jury and Audience Prizes At each regional round, a 1st prize is awarded by a jury composed of experts in public speaking and scientific mediation, and another prize is awarded from the votes of the audience. According to the regional rules, a 2nd and a 3rd jury prize may be awarded. We can consider the jury prizes as an objective judgement of the presentation, as the jury members followed a list of specific criteria, while the audience prize is a more subjective judgement, as the audience was just asked to vote for their favourite presentation, without any instructions. The dataset is unbalanced with respect to the number of winners, with 58 out of 248 (23%) of presentations winning a prize (9 speakers won both a jury and the audience prize). On the other hand, there is no effect of gender on winning a prize: neither for the jury prize (χ 2 (1) with Yate's correction= 0, p = 1) nor for the audience prize (χ 2 (1) with Yate's correction= 0.