dataesr/scientific-paragraphs-categorization huggingface dataset:
Journeau et al. (2022) observed a similar signal separation and transition within the variables space obtained from the network's covariance matrix. In our case, the second-order scattering coefficients contain information about impulsive and continuous signal characteristics and, therefore, the separation of seismograms according to these characteristics is reasonable. Our findings show that the majority of the recording time is dominated by tremor signals. This agrees with the findings of  Makus et al. (2023)  where their reference correlation function for the same dataset is dominated by tremor activity.
Open Research The KISS dataset  (Shapiro et al. , 2015)  is available from the GEOFON data center of GFZ-Potsdam (https://geofon. gfz-potsdam. de). The data was processed with Python and visualized with Matplotlib  (Hunter, 2007) , available under the Matplotlib license at https://matplotlib. org/. The python packages ScatSeisnNet  (Steinmann, Seydoux, & Campillo, 2022) , ObsPy  (Beyreuther et al. , 2010)  and Scikit-learn  (Pedregosa et al. , 2011)  were heavily used for processing the data.
g. ,  [7, 8] ). However, the satisfiability problem of full DEL with public, semi-private and private communicative actions was shown to be NEXPTIME-complete  [9]. The situation is even worse in the context of epistemic planning: it is known that epistemic planning in public announcement logic (PAL) is decidable, while it becomes undecidable in full DEL, due to the fact that the epistemic model may grow as a consequence of a private announcement  [10]. In  [11, 12] , a variant of epistemic logic with a semantics exploiting belief bases is introduced. It distinguishes explicit belief from implicit belief. The former is modeled as a fact in an agent's belief base, while the latter is modeled as a fact that is deducible from the agent's explicit beliefs. The main advantages of the belief base semantics for epistemic logic compared to the standard possible world semantics based on multi-relational structures (so-called Kripke models) are (i) its compactness, and (ii) its closeness to the way artificial cognitively-inspired agents are traditionally modeled in AI and in the area of knowledge representation and reasoning (KR) by adopting a database perspective  [13].
Moreover, each time p t is added, △ m p t is also added. The reason is that in the case where the agent does not explicitly believe that p (¬ △ m p t ) and we want to add p t to the belief base of the agent, we have to remove ¬ △ m p t from the base. For example, in the initial state, the agent does not know the color of the card x (that is, for any color c, ¬ △ m col 0 x,c belongs to the base). As soon as he observes the color of a card (which is green, for example), then ¬ △ m col 1 x,g must be deleted from the base, which will be done automatically by our revision module as soon as △ m col 1 x,g is added to the database (in addition to col 1 x,g ). Note also that △ t+1 h c ′ ∈COLORS △ m col t+1 x,c ′ reads "agent h believes that m knows the color of card c ′ ", that is quite different from △ t+1 h △ m c ′ ∈COLORS col t+1 x,c ′ that reads "agent h believes that agent m believes that card x has at least one color (among COLORS)".
Volcanic ash provides information that can help understanding the evolution of volcanic activity during the early stages of a crisis, and possible transitions towards different eruptive styles. Ash consists of particles from a range of origins in the volcanic system and its analysis can be indicative of the processes driving activity. However, classifying ash particles into different types is not straightforward. Diagnostic observations for particle classification are not standardized and vary across samples. Here we explore the use of machine learning (ML) to improve the classification accuracy and reproducibility. We use a curated database of ash particles (VolcAshDB) to optimize and train two ML-based models: an Extreme Gradient Boosting (XGBoost) that uses the measured physical attributes of the particles, from which predictions are interpreted by the SHAP method, and a Vision Transformer (ViT) that classifies binocular, multi-focused, particle images.
We find that the XGBoost has an overall classification accuracy of 0. 77 (macro F1-score), and specific features of color (hue mean) and texture (correlation) are the most discriminant between particle types. Classification using the particle images and the ViT is more accurate (macro F1-score of 0. 93), with performances across eruptive styles from 0. 85 in dome explosion, to 0. 95 for phreatic and subplinian events. Notwithstanding the success of the classification algorithms, the used training dataset is limited in number of particles, ranges of eruptive styles, and volcanoes. Thus, the algorithms should be tested further with additional samples, and it is likely that classification for a given volcano is more accurate than between volcanoes.
g. ,  Pardo et al. , 2014). Whilst a standardized analytical procedure of juvenile particles has been proposed  (Ross et al. , 2022) , the step of particle classification relies on observer's experience, making it subject to varying interpretations, and hindering comparison of datasets produced by different labs. An approach commonly employed to address such classification challenges in various domains is through the utilization of Machine Learning (ML). ML-based models can classify complex images in a wide range of situations  (He et al. , 2015). ML-based models are capable of learning patterns to classify objects, and use them for classification of future datasets, such as mushrooms  (Lee et al. , 2022)  or leaf diseases  (Sujatha et al. , 2021) , and have already been used for classification of ash particle shapes  (Shoji et al.
, 2018). In this study, we trained two models using the VolcAshDB curated dataset  (Benet et al. preprint)  with the objectives of: (i) identification of the most important features for discrimination of particle types, and (ii) obtaining a particle classifier as accurate as possible. The results of our study should be a step forward towards a universal and unbiased classification of ash particles as more data becomes available and better algorithms are developed.
VolcAshDB dataset We used the data from the open-access database VolcAshDB, which comprises images and measurements (here referred as features) of more than 6,300 volcanic ash particles (https://volcash. wovodat. org/). These were obtained with the binocular microscope and processed to obtain multi-focused, high-resolution images  (Benet al. , preprint). The images have been classified with a dichotomous key (Figure  1 ), using some key particle features as reported in  Benet et al. , (preprint). The database contains ash particles from 12 samples from 8 volcanoes and 11 eruptions from a range of magma compositions and eruptive styles (Table  1 ). These include  (1)  phreatic eruptions of Soufrière de Guadeloupe (Lesser Antilles) in 1976 and 1977  (Feuillard et al. , 1983) , the early activity of April 1991 of Mt.
The shape features in the database have been used in previous studies  (Cioni et al. , 2014; Dellino & La Volpe, 1996; Dürig et al. , 2018; Leibrandt & Le Pennec, 2015 ; E. J. Liu et al. , 2015) , and include those sensitive to particle-scale cavities, (e. g. , solidity), perimeter-based irregularities (e. g. , convexity), and form (e. g. , elongation  ; Liu et al. , 2015). The textural features in VolcAshDB were obtained from calculations of the distribution of pixel intensities in grayscale across several particle regions based on the socalled Gray Level Cooccurrence Matrix (GLCM,  Haralick et al. , 1973). From the GLCMs we obtained features that indicate a more uniform texture (e. g. , Homogeneity), and those that indicate a more complex or heterogeneous texture (e. g. , Dissimilarity; Hall-Beyer, 2017).
Data processing The dataset consists of 33 features measured from each particle (variables; Table  S1 ) and the particle types (target variable; Figure  2 ). The dataset is made of 6,300 particles and was divided into a training set (80% of the total particles) to optimize and fit the models, and a test set (20%), not used during the model's learning process. The original feature distributions are heterogenous and were standardized using the Scikit-learn's function StandardScaler, as it is commonly done to ease convergence of ML models  (Géron, 2017). The standard scaler redistributes the values of each feature with the mean at 0, and the first standard deviation at 1 and -1. The features from the test set were also standardized according to the scaler that was fit into the training set to avoid data leakage.
Any outliers, defined as values higher and smaller than two standard deviations  (Verdhan, 2020) , were kept after visually confirming that the source images had no errors. Highly correlated variables were kept for estimating their importance for classification in the step of feature permutation (more details are reported in 'Explaining the model's predictions' in Section 2. 3. 4). Highly correlated variables may cause multi-collinearity issues in regression models, but these haven't been reported in tree-based models  (Kotsiantis, 2013). The VolcAshDB dataset contains more altered material than juvenile and lithic particle types, and free crystals are relatively scarce (Table  1 ). Such uneven distribution of particle types may cause an imbalanced dataset problem. We addressed this issue by oversampling the less abundant particle types, using the SMOTE package, which uses a K-Nearest Neighbor algorithm (KNN) to generate synthetic data  (Brownlee, 2020).
The classification matrix (Figure  S2 ) is typically used in ML to show the proportions of TP, TN, FP and FN for each class. Based on these proportions, we can calculate four well-known metrics to evaluate the models' performance (e. g. ,  Verdhan, 2020) : Accuracy = !"#!$ !"#!$#%"#%$ (1) Precision = !" !"#%" (2) Recall = !" !"#%$ (3) F1-score = & * !" & * !"#%"#%$ (4) Classification scores in this study are reported based on the F1-score, as it combines the precision, dependent on the FP, and recall, dependent on the FN, into a single metric  (Verdhan, 2020) , and is recommended for imbalanced datasets when FN and FP are equally important  (Brownlee, 2020). We use the unweighted average of the F1-scores (the so-called macro from macro-averaging) of the four particle types to evaluate the overall model performance, as opposed to the weighted averaging, where the average is multiplied to a coefficient based on the number of particles per class  (Verdhan, 2020).
Explaining the model's predictions Explainable AI (xAI) is a set of methods that provide explanations on the variables that drive the model's predictions  (Gianfagna & Di Cecco, 2021; Mishra, 2022; Molnar, 2021). We used the method called "permutation feature importance" to assess the contribution of the 33 features to the model's prediction across all instances (i. e. , the feature values from all particles), and the SHapley Additive exPlanations (SHAP;  Lundberg and Lee, 2017)  method to estimate the contribution of the features for each particle and, by aggregation, their global importance  (Molnar, 2021). In the permutation feature importance, the values of each feature from the dataset are shuffled to measure the increase in prediction error. We used Scikit-learn's function permutation on the test set from which we obtained a ranking of the features' contribution between two end-members: "important" features, which cause an increase in prediction error when shuffled, and "unimportant" features, where the error remains unchanged or decreases  (Molnar, 2021).
Effect of the training and test data split on the XGBoost scores As noted above, we first split the dataset into a training (80% of all particle features in VolcAshDB) and a test set (20%) and used the latter to evaluate the XGBoost's performance. However, as splitting process is random it may affect the precision and accuracy of the measured F1-scores. To estimate this error, we re-trained and evaluated the XGBoost at 1,000 different values of random state, i. e. , the hyperparameter that controls randomness. We obtained an average accuracy (macro F1-score of 0. 76; Table  S4 ) that is like the accuracy from the test set (macro F1-score of 0. 75). The free-crystal type shows the widest variability (standard deviation of 0. 04) and is the most inaccurate (F1-score of 0. 57; Figure  3 ) amongst the particle types.
Development of a particle classifier using VolcAshDB image dataset We used four steps to develop an optimized classifier for the image dataset (Figure  4 ): data augmentation, fine-tuning, selection, and evaluation. We compared the performance between three state-of-the-art models that have top accuracies in the reference dataset ImageNet (Jia  Deng et al. , 2009) : ResNet  (He et al. , 2016) , which is the prevalent model of the so-called convolutional neural networks (CNN), Vision transformer (ViT;  Dosovitskiy et al. , 2020) , which superseded ResNet in image classification, and ConvNeXT (Z. Liu et al. , 2022) , which is an optimized convolutional neural network that has surpassed performances of vision transformers. The models are available in the Hugging Face library (https://huggingface.
co/), which also provides application programming interfaces (API) for their deployment. The model that yielded highest classification score was the ViT. We augmented the training dataset with an array of variations from the original images (see below), and the ViT reached a macro F1-score of 0. 93, outperforming the XGBoost classifier. The images of the ash particle in VolcAshDB were obtained from processed multi-focused binocular images, but this is not the standard practice, and thus we also tested the ViT's ability to classify standard single-focus binocular images used in most studies of ash particles. (3) Fine-tuning with the augmented dataset and final evaluation using the test set. The ViT classifier can be then applied for prediction of particle types. See more details in main text and subsequent figures.
Image augmentation and processing The binocular images of ash particles in VolcAshDB are multi-focused, and contain four color channels: red, green, blue and alpha. The alpha channel is a binary mask that takes a value of 1 or 0 to separate between the particle pixels and those of the background (more details in the segmentation step in  (Benet et al. , preprint). We split the dataset into a train (80% of the total images in VolcAshDB) and test set. Then, we augmented the number of images in the training set based on six standard methods  (Ayyadevara & Reddy, 2020) : rotation (at 45°), translation (at 25 pixels), up-down and left-right flipping, and adding random noise and Gaussian blur at sigma values of 0. 155 and 0. 55. Increasing the amount of images allowed us to balance the distribution across particle types (~2900/class), and is generally recommended to increase model's robustness  (Brownlee, 2020).
Results We used the VolcAshDB ash particle features and images to train the XGBoost and ViT models and to evaluate their ability to classify them into altered material, free-crystal, lithic or juvenile types (Table  2 ). We found that overall, the ViT classifies very accurately, with a macro F1-score of 0. 93, whereas the XGBoost is less performant with a macro F1-score of 0. 77 (Table  2 ) but allows for explaining the model's predictions by interpretable AI methods. We describe below the model performance through the two datasets by particle type and some particle subgroups, such as those divided by the volcano, or one class versus another.
32) at Nevados de Chillán. Classification of lithics is rather accurate for samples of dome explosions (F1-score of 0. 77) but inaccurate (F1-score of 0. 28) for those of phreatic events. Such fluctuations indicate limited robustness by the classifier and care should be taken for its application to other datasets on a case-by-case basis. The likelihood that a particle belongs to a given type according to the model is reflected in the distribution of the confidence scores, and varies across particle types. Within the True Positives (TP), almost 90% of the juvenile TP have confidence scores > 0. 9, whereas ~40% of the free-crystal TP have confidence scores between 0. 4-0. 9 (Figure  6A ). This means that the XGBoost is almost certain when predicting juvenile particles, but more unstable for free crystals.
Particles with combined traits are common in samples from Nevados de Chillán Volcanic Complex (Figure  15C ), which originated from a relatively long-lived dome-forming eruption cycle. An additional challenge is that the ViT confidently classifies as lithics some particles that are labelled as juvenile and, since petrographic classification was not always straightforward  (Benet et al. , preprint) , it is difficult to decide whether these are False Negatives, or instead, petrographic classification errors (Figure  15D ), especially when ML-based image classifiers have surpassed human performances in other fields  (He et al. , 2015). (   al. , 2014). The high score suggests that the ViT can discriminate between them to some extent (Figure  15E ), but a more robust labelling by a team of experts and a larger dataset containing SEM images is necessary to obtain more robust conclusions.
This finding agrees with previous studies that use a dull luster (which corresponds to low values of value_mode) to identify lithic particles  (Miwa et al. , 2013). On the other hand, juvenile particles have high Shapley values for the saturation_mode. This feature is related to high color intensities as observed under the binocular, but it was not recognized before as a diagnostic observation of the particle type. These two examples belong to particle types that are well classified and for which the Shapley values are reliable. Shapley values obtained from particles that yielded lower accuracies, such as the free crystals, are not reliable, and thus overall performances should be improved. This could be achieved by enhancing the quality and quantity of VolcAshDB dataset by (i) adding particles to balance the dataset, (ii) refining the particle contour in the multi-focused images, so that shape features can measure micro-scaled cavities  (Benet et al.
Deploying the ViT for automatic particle classification A main goal of our research is to obtain a classifier of ash particles that is as accurate as possible, and which can be applied to objectively classify new datasets in a reproducible manner. The ViT model (macro F1-score of 0. 93) currently performs very accurately for some samples (e. g. , Soufrière de Guadeloupe; macro F1-score of 0. 95) but is less accurate for others (e. g. , Merapi; macro F1-score of 0. 80). This variation is also found within subgroups of particles. For instance, elongated, highly-vesicular, glossy particles from basaltic lava fountaining (Cumbre Vieja, 2021) or pumice fragments (Kelud, 2014) are very accurately classified, but high crystallinity, blocky, dark particles from dome explosions (Nevados de Chillán, 2016-2018) are less accurately classified.
A ViT particle classifier for volcano monitoring From an operational viewpoint, volcano observatories and laboratories are often equipped with binocular microscopes that can acquire standard, single-focus binocular images, and that are used to classifying ash (componentry analysis). This could be done near-real time, and it usually takes from one to a few days  (Re et al. , 2021) , or it could also be done a posteriori to obtain a time series data of ash componentry that can be compared to other monitoring data to better understand how the volcanic system works  (Benet et al. , 2021; Suzuki et al. , 2013). Our dataset and analysis are based on multi-focused images and therefore, we performed a preliminary test of ViT's ability to classify single-focus images from a small dataset of ~1,200 images from Nevados de Chillán  (Benet et al.
, 2021). The dataset contains images of about 400 particles, with 3 images per particle at different focus depths. Since using the same split ratio (80:20) would yield very small training set, we used all particles for training, except 28 representative particles of the types of ash as described in  Benet et al. (2021)  as test. Fine-tuning the ViT took only 3 hours and we obtained decent accuracies (macro F1-score of 0. 84) on the test set (Figure  17 ). This suggests that volcano observatories could potentially use a ViT and obtain an objective score on a particle-by-particle basis relatively rapidly. otherwise, the False Negative rate (lighter), all percentages with the corresponding number of particles per predicted type. Note that given the limited data we used all particles for training except 28 for the test set.
Abstract Volcanic ash provides information that can help understanding the evolution of volcanic activity during the early stages of a crisis, and possible transitions towards different eruptive styles. Ash consists of particles from a range of origins in the volcanic system and its analysis can be indicative of the processes driving activity. However, classifying ash particles into different types is not straightforward. Diagnostic observations for particle classification are not standardized and vary across samples. Here we explore the use of machine learning (ML) to improve the classification accuracy and reproducibility. We use a curated database of ash particles (VolcAshDB) to optimize and train two ML-based models: an Extreme Gradient Boosting (XGBoost) that uses the measured physical attributes of the particles, from which predictions are interpreted by the SHAP method, and a Vision Transformer (ViT) that classifies binocular, multi-focused, particle images.
We find that the XGBoost has an overall classification accuracy of 0. 77 (macro F1-score), and specific features of color (hue_mean) and texture (correlation) are the most discriminant between particle types. Classification using the particle images and the ViT is more accurate (macro F1-score of 0. 93), with performances across eruptive styles from 0. 85 in dome explosion, to 0. 95 for phreatic and subplinian events. Notwithstanding the success of the classification algorithms, the used training dataset is limited in number of particles, ranges of eruptive styles, and volcanoes. Thus, the algorithms should be tested further with additional samples, and it is likely that classification for a given volcano is more accurate than between volcanoes.
VolcAshDB dataset We used the data from the open-access database VolcAshDB, which comprises images and measurements (here referred as features) of more than 6,300 volcanic ash particles (https://volcash. wovodat. org/). These were obtained with the binocular microscope and processed to obtain multi-focused, high-resolution images  (Benet al. , preprint). The images have been classified with a dichotomous key (Figure  1 ), using some key particle features as reported in  Benet et al. , (preprint). The database contains ash particles from 12 samples from 8 volcanoes and 11 eruptions from a range of magma compositions and eruptive styles (Table  1 ). These include (1) phreatic eruptions of Soufrière de Guadeloupe (Lesser Antilles) in 1976 and 1977  (Feuillard et al. , 1983) , the early activity of April 1991 of Mt.
, 1982). 111 Table  1. Main sample characteristics, and proportion of main particle types in VolcAshDB. 112 The associated error is calculated using the equation of margin of error  Benet et al. , (preprint)  113 at a confidence interval of 95% and expressed in absolute values. In addition to ash images, VolcAshDB also includes: (i) the value of 33 features of each ash particle related its shape, texture, and color, (ii) a label with the identification of the types of particle (free-crystal, altered material, juvenile, and lithic; Figure  1 ), and (iii) metadata for each particle, such as the sample grain-size fraction, the number of magnifications used for image acquisition, amongst others. The shape features in the database have been used in previous studies  (Cioni et al. , 2014; Dellino & La Volpe, 1996; Dürig et al.