dataesr/scientific-paragraphs-categorization huggingface dataset:
A priori requirements As with all computational software, meaningful results in EMBER will only be achieved with appropriate dataset. Investigations should be limited to entirely glassy embayments, sampled from rapidly quenched deposits (e. g. , <2 cm sized tephra), exhibiting a geometry that is close to that of a cylinder with constant radius. More complex geometries would void the core assumption of unidirectional diffusion and necessitate 3D diffusion modeling. [e. g. , deGraffenried and Shea. , 2020], which is not currently supported by EMBER. The longer the embayment, the more likely it is to display a concentration plateau in the diffusion profile indicative of C i and the starting pressure of ascent (P Start ). In absence of such condition, we recommend the user to determine P Start , C i and T from melt inclusions and geo-thermobarometry studies.
Finally, volatile concentration measurements on surrounding glass are required to assess P end , the pressure of quenching. While we used VolatileCalc and SolEx as the main degassing path generators in the examples below, it should be noted that EMBER can also read degassing paths from any other software as long as the input files comply with the required format (see the tutorial in the additional instructions). Hence, the choice of degassing path software is ultimately up to the user's preference.
Otherwise, we estimated the possible range of grid search from the reported uncertainties for decompression rates and initial concentrations. Degassing paths were calculated respecting P, T, and volatile contents used in the original studies (typically, we use SolEx or VolatileCalc when authors specified it). Similarly, the reanalysed results were calculated using the same set of values for M 0 as the original literature data to which they are compared. In detail this was M 0 = 0. 1, 1. 6, 1. 6, 0. 8 wt. % respectively for IkiE1, ReticE1, ReticE2 and KeaE1  [Ferguson et al. , 2016] , M 0 = 0. 4, 0, 0, 3. 2 wt. % respectively for AF2, AD5, AE38 and PG11  [Moussallam et al. , 2021]  and M 0 = 0 wt. % for all embayments from Ambae  [Moussallam et al. , 2019]. For all other studies of which the exsolved gas content was not specified we used M 0 = 0 wt.
For AO02 the shape of the histogram is pointy, unimodal and skewing right with a long tail. With higher dP/dt values (i. e. , sharper diffusion profiles), modelled diffusion profiles become more and more similar, making it harder for the software to find the best solution. This results in a skewed solution histogram with a long tail and a more noticeable difference between mean and median value. For such cases, stability is reached for m = 101. We recommend 101 iterations (EMBER default iteration value) as it was enough to reach a stable solution for our most uncertain case.
Another strategy is to use the volatile content of melt inclusions as starting conditions for the diffusion model instead of the measured plateau values in the embayment interior. This was the strategy adopted by  Myers et al. , [2021]  for the Minoan eruption of Santorini, were a fixed initial concentration of 5. 2 wt. % H 2 O (except for one embayment at 5. 6 wt. % H 2 O) was used for all model calculations. Such strategy was also used by  Humphreys et al. , [2008]  for Mt St Helens calculations. In this study, we recalculated all embayment profiles using the same strategy throughout, but we do not pass judgement on the validity of one strategy over another. Our aim is to present a dataset which is as comparable as possible. The data presented in Fig. 7  and Table  2  are hence all calculated leaving the exsolved volatile content (M 0 ) as unconstrained (i.
Conclusion  We present EMBER, a user-friendly GUI program that calculates decompression rates from H 2 O, CO 2 and S concentration profiles along embayments of basaltic to rhyolitic compositions.  We found that decompression rate calculations are particularly sensitive to variations of M 0 especially for the H 2 O diffusion profile. Variations of P end are also accompanied by a variation of the best fit exsolved gas content, but not necessarily by a variation of associated decompression rate.  We recalculated decompression rates from previous studies twice: first, to validate and test how well EMBER reproduced existing results using the parameters from the original studies, and secondly, to homogenize determined decompression rates applying the same protocol to the existing raw data from previous studies, in order to improve inter-study comparison.
 In the first case, recalculated decompression rates are in the same order of magnitude as original calculations but notable differences do occur such as for the 1980 Mt St Helens eruption which recalculated decompression rate are at 0. 15-0. 41 MPa/s, half of the previously reported values  [Humphreys et al. , 2008].  In the second case, recalculated dataset shows no significant correlation between magma decompression rate and eruption magnitude when considering the entire dataset and shows a weak correlation when considering the subset of decompression rates of basaltic magma (Pearson coefficient of 0. 24 with a p-value of 0. 35 and R²=0. 47. ) The correlation is significant when considering only the maximum decompression rates of each basaltic eruption (Pearson coefficient of 0. 93 with a pvalue of 0.
01 and R²=0. 86). Additionally, there is no significant correlation between decompression rate and plume height when considering the entire dataset. However, once again, a statistically significant trend appears when considering only the maximum decompression rate of the basaltic eruptions (with a Person coefficient of 0. 84, a p-value of 0. 007 and R²=0. 88).  Our results suggest for the first time, a significant positive correlation, between embayment-calculated maximum decompression rate and eruption explosivity parameters such as magnitude and plume height, for basaltic eruptions.
Rose-Koga  Hardware used: EMBER was run on a computer with 4 core (4. 5GHz) and 16GB of RAM  Link to the software, tutorial and test using data from  [Ferguson et al. , 2016]  DOI: 10. 6084/m9. figshare. 13663811  Software required: MATLAB Runtime Environment 2019b and up https://www. mathworks. com/products/compiler/matlab-runtime. html The outputs are a decompression rate and an initial (dissolved) volatile concentration as well as their associated confidence interval. This process of calculation is repeated for each value of exsolved volatile content (M 0 ). Estimation of the M 0 value and its related uncertainty is done in a similar manner but by considering the range of M 0 variation as the 4 th dimension of the starting matrix. ReaE1 studied previously by  Ferguson et al. , [2016]   Lloyd et al.
Acknowledgement We would like to thank  M. Humphreys  for providing the raw data of the Mt St Helens embayments. GG was supported by a PhD fellowship from the  French Government "Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation". ERK  acknowledges partial funding from  Laboratory of Excellence  initiative  n°ANR-10-LABX-0006 , the  Région Auvergne  and the  European Regional Development Funds. Y. M acknowledges funding from  INSU  and the  Région Auvergne Rhone Alpes. This is Laboratory of Excellence ClerVolc contribution number  463. We thank  Madison Myers  for sharing her code during the review process of our manuscript. We would like to thank  Madison Myers  and an Anonymous reviewer for their comments on the original manuscript and  Marie Edmonds  for editorial handling.
For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption.
Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.
g. , museums, restaurants, and hospitals)  [17]. To show the suitability of our approach, we develop a first version of this framework focused on emotion detection in text and tested it with a case study of tour-guide robots for museums. The robots receive the text directly from people or by converting speech to text. Then, we used NLP transformers to make sentence embeddings, transforming the text to a vector representation. Thus, the classification problem was solved in this new vector space, resulting in emotion labels for each text. The first version of the framework relies on (i) a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library; (ii) a neural network to label the emotions in texts based on NLP transformers; and (iii) EMONTO, an ontology to represent emotions integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in museum visitors for further analysis.
Nevertheless, these limitations have been overcome by an attention-based transformers architecture  [29] , which is inspired by the encoder-decoder architecture used for sequence to sequence (Seq2seq) tasks. In high-level terms, it consists of two stacks (one of encoders and one of decoders), where the encoder and decoder use some form of self-attention (https://jalammar. github. io/illustrated-transformer/ (accessed on 18 December 2020)) and a feed forward neural network. Each encoder in the stack receives its input from the previous layer, except the first one that receives a series of tokens. Then, the final encoder in the stack sends its output to every decoder. Then, each decoder uses this and the input from the previous layer in the stack to return its output. Afterward, the output for the final decoder is passed to a linear + softmax function to finally return the output of the network.
Currently, there are many available implementations of these transformers, such as the Hugging Face Transformers library  [36] , which provides a wide variety of implemented pretrained transformers that work with a classic NLP workflow; the Simple Transformers library (https://github. com/ThilinaRajapakse/simpletransformers (accessed on 18 December 2020)), which is a simplification of the previous one, suitable for non-expert researchers; and the Sentence Transformers library  [37]. Our classification model is based on a transformer architecture, supported on the Simple Transformers library.
Multimedia Processing In this step, the robot converts the multimedia raw data into proper representations for emotion analysis. For example, feature vectors from images and videos, normalized spectograms from audio, and text from speech. For the first version of our framework, in this step, the robots can receive text directly or by converting audio to a text format. We developed a speech-to-text algorithm using the available Google API  [44]  and the SpeechRecognition Python library, which is specialized in speech processing, audio reading, and noise reduction, based on a Hidden Markov Model for speech recognition and an RNN to tackle the sequence problems, where the timing is variable. For the case of images and videos, features extracted can obey traditional machine learning models as well as more complex models such as multimodal-based ones that are becoming popular for emotion recognition in images and videos.
Emotion Detection According to the multimedia data considered, a wide range of emotion detection techniques can be implemented, mostly based on machine learning models. For the first version of our framework, in this step, we approached the emotion detection task as a multi-label classification problem, modeled with a Transformer architecture and meta-learning. We considered eleven emotions: anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust. The classification task consisted of labeling a text as "neutral or no emotion" or as one or more of the previously mentioned emotions, i. e. , a binary vector indicating if each emotion was detected  (1)  or not (0). Based on the available pretrained transformers, we designed a stacked ensemble architecture, as shown in Figure  3.
Each weak learner classifier consists of a transformer, used to create a sentence embedding and a Multi-Layer Perceptron (MLP), with two hidden layers of sizes 500 and 300 (see Figure  4 ). To avoid overfitting of our MLP, we used an early stop in the training, using 10% of the training data to validate in each iteration if the training is still effective. For implementation of the transformers, we used the Sentence Transformers library (https://github. com/UKPLab/sentence-transformers (accessed on 18 December 2020))  [37] , since they are specifically pretrained transformers to perform semantic textual similarity tasks, leading to sentence embeddings. This is a mapping of sentences to a vector space such that sentences with similar semantic meanings are close in this new space. From the Sentence Transformers library, we used the roberta-large-nli-stsbmean-tokens, distilbert-base-nli-mean-tokens, and bert-large-nli-stsb-mean-tokens transformers to perform the sentence embeddings.
g. , artworks, candidates, and plates). An Event can produce several Emotions. The entities emo:Object and emo:Person are general classes that can connect other ontologies, such as museum and artwork ontologies  [17]  as Object or userprofile ontologies  [49]  as Person. The idea is to make EMONTO extensible and flexible to be easily adopted in scenarios where data related to the recognized emotions need to be stored for further analysis. The ontology provides the modality (emo:Modality) of the information used to recognize the emotion (e. g. , emo:Gesture, emo:Face, emo:Posture), and the type of annotator (emo:AutomaticAnnotator and emo:HumanAnnotaton). Moreover, a datatype property emo:hasIntensity is associated to the category to express the level of confidence (a float value between 0.
Querying Our Emotion Ontology The semantic repository enables the possibility of implementing simple and advanced queries in order to retrieve specific values or to infer new knowledge, in both cases for data analysis. For example, a simple query can retrieve information about the emotions produced by a specific artwork. The pseudocode presented in Algorithm 2 retrieves the emotions produced by the artwork The_Starry_Night: in line 1, libraries related to the RDF manipulation are imported; then, a graph should be initialized to read the RDF triples (lines 2 and 3); the namespaces used in the ontology have to be added (line 4); and finally, following the SPARQL syntax, a query is performed (lines 5-15). Algorithm 2: Obtaining the artwork The_Starry_Night. 1 import RDF libraries 2 g = Graph() 3 g.
read("database_emotions. ttl", format="ttl") 4 g. add_namespace("emo", "http://www. emonto. org/ (accessed on 18 December 2020)") 5 qres = g. query("SELECT ?emotion_l Other information such as the artworks that produce the emotion "surprise" can be also obtained (see Algorithm 3). In these examples, the Object entity is an artwork, but an object could also be plates in restaurants, movies, or any entity that produce emotions. The extensibility of EMONTO allows researchers to integrate any specific domain ontology.
Experimental Evaluation of the NLP Transformer Model In this section, we show the performance of our transformer-based method compared with the NVIDIA-AI model  [15] , which is also based on a transformer architecture. We detail the procedures for obtaining adequate data for training and testing, some important implementation details, and the comparative results. We used the dataset for SemEval-2018 Task 1  [28] , specifically the dataset E-c. This dataset contains tweets in English, each with its own ID and contents, that are labeled with a vector of eleven binary numbers, corresponding to the eleven considered emotions (anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust). If the value at position k is 1, the tweet contains the emotion k; otherwise, the value is 0.
Text Target Has anyone else had a bad experience with Poun. [1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0] #America finding #gratitude amidst the sadness. [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] Let us not burden our remembrances with a heav. [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0] @AdsByFlaherty And you're cheerfully defending. [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] The irony and hilarity of making Taylor Lautne. [0, 0, 0, 0, 1, 0, 0, 0, 1, 0 Training of the model was performed with the 2018-E-c-En-train file that contains 6838 tweets labeled as indicated previously, and all the result metrics were calculated using 2018-E-c-En-test-gold that contains 3259 tweets. Training was performed using all 6838 samples of the 2018-E-c-En-train dataset, since validation was done on the 2018-E-c-En-test-gold dataset; thus, there was no need to split the data used for training.
In Table  2 , we present the number of positive values per emotion among the samples of the dataset; there is some class imbalance as the surprise and trust labels are poorly represented. However, since this is a multi-label classification, this is not a trivial issue to handle and we ended up finding better results without upsampling the data. For metrics, as per the official competition rules, we used the Jaccard index to measure accuracy as this is a multi-label classification task, based on Equation (  1 ), where T is the set of tweets in the dataset and where, for any given tweet t, G t is the set or the expected labels for t and P t is the set of predicted labels for t. Accuracy = 1 |T| ∑ t∈T |G t ∩ P t | |G t ∪ P t | (1) Algorithm 4 shows the general process of training: the implementations needed for both the transformers and the MLPs are imported (line 1 and line 2, respectively); they can be either user-made libraries or publicly available ones; then, the training samples as well as their expected labels are loaded (line 4 and line 5); the transformer models that will be used for each of the weak learners are also loaded (line 7); afterward, the training of the weak learners is done (lines 8-15); once the training cycle is performed, all predictions made by the weak learners are combined (line 17) to be used to train the meta-learner MLP; and this final classifier is created (line 19) and trained (line 20).
This algorithm is available in Python (https://colab. research. google. com/drive/16Ja5MUFb_ A0RmaBVKvcjYxGSmvmSTtMN?usp=sharing (accessed on 18 December 2020)). Table  3  shows the obtained results using the sklearn built-in functions. We include the results of F1 score, precision, and recall from our model, both the micro average (i. e. , calculating the number of positives and negatives globally among all classes) and the macro average (i. e. , calculating the metric for each label and then finding their unweighted mean (https://scikit-learn. org/stable/modules/generated/sklearn. metrics. f1_score. html) (accessed on 18 December 2020)). The best results are in precision, which means that the model is good at avoiding false positives. The F1 scores show an overall ability for detecting true positives, up to 76%.
Accuracy on the other hand seems to indicate that our model, on average, strays around 47% from the expected emotions per instance. Compared with the NVIDIA-AI model, the results presented in Table  5  show that our model is behind the other by at most 7% across all evaluated metrics. This leaves us at a good starting point as. Unlike the NVIDIA-AI model which was fine tuned for the tweet dataset, fine-tuning our Transformers for this task and possibly changing our MLP classifiers for more refined models could lead to better results. We explore these possibilities further in the Discussion section.
Improvements for the Classification Model The classification method can be improved in several aspects. One of them is in the transformers. The ones we used were fine-tuned for semantic textual similarity of tweets, in which a lot of emojis and abbreviations are present. Conversations in museums are quite different; thus, a more appropriate dataset, with more appropriate texts, can be used to fine-tune the transformers. As explained in Section 5, an imbalance in the dataset generates some problems when it came to training the model, as some emotions could not be correctly detected. While in single-label classification problems, there have been many techniques developed to mitigate this, in the multi-label case, there are not many solutions available to solve this. For example, one common technique in the single-label case is to randomly upsample the data.
Funding: This research was supported by the  FONDO NACIONAL DE DESARROLLO CIENTÍFICO, TECNOLÓGICO Y DE INNOVACIÓN TECNOLÓGICA-FONDECYT  as an executing entity of  CONCYTEC  under grant agreement No. 01-2019-FONDECYT-BM-INC. INV  in the project  RUTAS: Robots for Urban Tourism Centers , Autonomous and Semantic-based. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Metabolic profiling of PDAC We performed metabolic profiling on 77 pancreatic cancer samples grown as PDTX. Unsupervised clustering analysis of these metabolic profiles revealed distinct clusters on the dendrogram, indicating key heterogeneity among the PDAC samples analyzed. The metabolic heterogeneity across samples was related to the intensity variations in metabolites that belong to different classes as shown in Figure  1A. Considering the entire metabolome dataset of all samples, a total of 502 metabolites were detected and included in further analysis. The vast majority of these metabolites were from the lipid class. Glycerophospholipids were the most represented class with 45% metabolites followed by glycerolipids (17. 1%), fatty acids (10%), sphingolipids (8. 2%), amino acids (5. 8%), nucleotides (2.
8%) and sterols (2. 2%), as summarized in Figure  1B. The other metabolites, representing 9% of all metabolites, included carbohydrates, such as monosaccharides and disaccharides, as well as alkylamines. All metabolite data corresponding to each patient is shown Dataset S1 and the annotation of metabolic feature in Dataset S2. Thus, the metabolomic profiles of human PDAC tumors grown as PDTXs display heterogeneity that is comprised largely of the lipid class of metabolites.