dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the intergenerational transmission of poverty implies that present levels of income inequalities would determine future ones, as suggested in  Barro and Lee (2013) ,  Corak (2013), and Hanushek (2013). Hence, the feedback effects of income inequalities are also potential causes of endogeneity in this framework. Therefore, we mitigate these drawbacks with the following dynamic equation, where we set all the first stages variables as endogenous. Gini it ¼ α 0 þ η t þ β 0 Gini itÀ1 þ β 1 Corrupt k it þ β 2 X it þ e it (1) where α 0 is the intercept; η t is the time-specific fixed effect; β i are coefficients to be estimated; X it is a set a of control variables; e it is the stochastic error term; i and t stand for countries and years respectively. The dependent variable is the usual proxy of income inequality (income inequalities), the Gini index is drawn from the Standardized World Income Inequality Database  (SWIID 7.
1, Solt, 2016). This database uses a custom missing-data multiple-imputation algorithm to standardize observations gathered from 5 of the most reputed databases combined with many other data from national statistical offices worldwide. In this way, the SWIID ensures that available income equality data are comparable over time for the broadest set of countries  (Solt, 2016). Corrupt k it are the types of corruption considered in this study. We distinguish between legislature corrupt activities, judicial corruption decisions and executive corruption. Legislature corrupt activities capture the extent to which members of the legislature abuse their position for financial gain, including accepting bribes or facilitating the gain of government contracts for firms owned by the legislator (or his relatives).
Similarly, corrupt judicial decisions indicate the frequency with which agents make undocumented extra payments or bribes to speed up or delay the judicial process to obtain favorable decisions. Values of these variables range from the least democratic to the most democratic, meaning that this variable's rise implies less corrupted activities. The executive corruption index measures either how routinely members (or agents) of the executive allow favors in return for any inducements (financial, material, or personal) or how often they misappropriate public funds for personal use. As this index runs from less corrupt to more corrupt activities in the original dataset, we replace it with its opposite to facilitate harmonized interpretations. With this baseline specification, we consider a twofold perspective as income inequalities (within or across countries) may result from either a difference between poor and rich in terms of resources and incentives for accumulation or instead, in returns of human capital that could reflect barriers or market failures  (Young, 2013).
Ma et al. [12]  have used an STM to detect bubble defects in lithium-ion polymer cell sheets. A complete STM overview with references and recent works can be found in  [24]. In the context of tensor learning, the multilinear principal component analysis (MPCA)  [25] , an extension of the principal component analysis (PCA) for tensor patterns, is also worth mentioning. As well as the PCA, the MPCA reduces data dimensionality and the correlation among the variables. The MPCA is very suitable for classification problems with multidimensional datasets, generating low-dimensional matrix or tensor patterns that can be fed into classifiers  [26]. In the present work, supervised tensor learning is used for classifying volcanic-seismic events. In particular, a tensor-based learning framework is proposed to classify the five main events of a volcano from seismic signals recorded at the Ubinas volcano, in Peru.
The data tensors are constructed by exploiting the use of multichannel triaxial sensors. Indeed, contrary to the standard approach of the literature of using only a single channel sensor, the database of the present work was recorded with sensors that have three channels: vertical, east, and north. The use of triaxial sensors has shown to offer a better representation of the seismic signals in comparison to single-channel sensors  [7]. The use of multiple sensors, operating simultaneously in more than one seismic station, is also exploited to build the tensor patterns. The 3-D feature arrays are constructed as follows: stations × channels × features. The tensor representation of the data is preserved in the proposed approach, avoiding the earlier mentioned drawbacks of the vectorization of the tensor.
The next step is the reduction of dimensionality done by the MPCA, which projects the data into a new basis, extracting useful information and eliminating correlation. Finally, multidimensional data are used to feed the tested tensor-based classifiers: the STM using the PARAFAC decomposition  [18] ,  [19] , denoted here by support PARAFAC machine (SPM), and the STuM  [20]. The proposed learning framework is fully tensorial, as the three main steps of the classification system (feature extraction, dimensionality reduction, and classifier) use multidimensional methods  [2] ,  [6]. The database used in this work was collected from two stations of the Ubinas volcano, located 70 km northeast of the city of Arequipa, in Peru, during a period of great activity in 2009. The data catalog was constructed by experts of the Volcanological Observatory of the Geophysical Institute of Peru (IGP).
The main original contributions of this work are summarized as follows: 1) proposition of a tensor-based learning framework for classifying the volcanic events. As earlier mentioned, no previous work has used tensor learning for classifying of seismic events; 2) the use of two seismic stations, each one with multichannel sensors, to measure the events of the volcano, contrary to previous works that usually use only a single channel and station. The presented methodology is scalable to use data from multiple stations and multiple sensors; 3) the use of a complete database with two stations and three channel sensors during a period of great activity of the Ubinas Volcano. In addition, the results showed a good performance of the proposed multilinear classification system, outperforming its vectorial counterparts and reaching a success rate of 91.
9%, outperforming all the other tested approaches. The rest of this article is structured as follows. In Section II, the database is presented. In Section III, the used tensor decompositions are recalled. The proposed multilinear classification system is shown in Section IV, while Section V presents the obtained results. Finally, Section VI concludes this article. Notation and multilinear algebra concepts: The notation used in this article is presented here. Scalars are denoted by lowercase letters (a, b,. ), vectors as lowercase boldface letters (a,b,. ), matrices as uppercase boldface letters (A,B,. ), and tensors as calligraphic letters (A, B,. ). The element (i,j) of the matrix A is denoted by A i,j or a i,j , and the element (i 1 ,. , i N ) of the N th-order tensor A is denoted by A i 1 ,.
C. Multilinear Principal Component Analysis After the feature extraction step, the MPCA is applied for dimensionality and data correlation reduction  [25]. The MPCA is an extension of the PCA for tensor patterns that performs multilinear orthogonal projections that capture most of the original tensorial input variation  [25]. However, contrary to the standard PCA that generates fully uncorrelated data, the MPCA is not able to create perfectly uncorrelated variables. The MPCA method has a wide range of applications, such as gait, face, fingerprint, age, and fault recognition  [25] ,  [26] ,  [43]. As the available volcano-seismic database is a set of P = 856 third-order tensors X p ∈ R I 1 ×I 2 ×I 3 , for 1 ≤ p ≤ P , where I 1 = 2, I 2 = 3, and I 3 = 56, the MPCA algorithm can be summarized as follows.
IV. DATABASE DESCRIPTION The Ubinas volcano is an active andesitic stratovolcano, located 60 km east of the city of Arequipa, with an average occurrence of six to seven eruptions per century and persistent fumarolic and phreatic activity. The recent episodes are characterized by volcanic eruptions and by the extrusion of a lava dome. The Centro Vulcanológico Nacional (CENVUL) is a service of the IGP in charge to keeping in alert the volcano monitoring in Peru. Recently, three eruptive events were identified by the IGP, into the periods 2006-2009, 2014-2017, and 2019, with persistent seismicity and observed fumarole, such as the occurrences between 2006 and 2009  [33] ,  [34]. To monitor the volcanic activity, the IGP built up a seismic telemetry network composed of six stations around the volcano  [7].
In  [38] , it was shown that an overall acceleration of the number of LP leads to eminent explosion. 4) Volcano-tectonic: They are associated with stress changes in rocks, induced by magma movement, similar to earthquake events  [1] ,  [36]. The VT events are caused by high pressure inside the volcano, resulting in signals with high-frequency components. 5) Hybrid: They have characteristics of both LP and VT events, where the signal's spectral response corresponds to both high-frequency and low-frequency values  [1] ,  [36]. The database used in this was taken from the IGP catalog, during a period of great activity of the Ubinas volcano, in 2009. The signals considered in this work were registered from two seismic stations deployed on the north (3. 7 km from the crater) and west (2. 7 km from the crater) of the volcano, as shown in Fig.
5 , where UBN represents the north station and UBW the west station. Each station is equipped with three-component (north, east, and vertical) broadband seismometers, with a 100-Hz sampling rate and high-resolution digitizers Guralp 6TD. As earlier mentioned, it is considered that the signals recorded by different stations, in the same time interval, belong to the same class. A summary of the main information about the seismic database considered in this article is shown in Table  II , with the number of class samples being detailed in parenthesis in the third row. A total of 856 tensor samples are used, each one being organized as 2 × 3 × 6000 third-order tensor, with the first dimension corresponding to the number of sensors, the second dimension the number of channels per sensor, and the third dimension the number of time samples per signal.
V. RESULTS In this section, the results obtained with the database of volcano-seismic events described in Section IV are presented, with the K-fold cross-validation method being used. Some preliminary simulations, whose results are omitted, were carried out in order to find the best values for the constant C, the number of folds K, and the ranks of the SPM and STuM, with C = 100, K = 10, Q = 3, Q 1 = 2, Q 2 = 3, and Q 3 = 5 providing the best results. When not stated otherwise, these values were used in the rest of the results. For comparison purposes, the LR-SPM was also tested, as well as the standard PCA and SVM techniques using the vectorization of the tensor samples, which yielded pattern vectors of dimension 336. The proposed approach is also compared with a CNN inspired by the work  [8]  and with the method presented in  [7] , where the EMD is performed before attribute extraction, but using the same attributes of the present work.
VI. CONCLUSION In this article, a tensorial framework was proposed for classifying volcano-seismic signals into five different classes using tensor learning techniques such as the MPCA and STM. The proposed method integrates feature extraction, dimensionality reduction, and classification into a framework that can be fed with multidimensional data. The database used in this work consists of 3-D data samples recorded during a period of great activity of the Ubinas volcano, Peru, in 2009. The tensor structure of the patterns, organized as stations × channels × features, is built by exploring the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations. The results showed the very significant gain in performance provided by the tensorial classifiers, as well as by the MPCA, when compared with their vector-based counterparts.
We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disappears. We also find that the impact of sentiment on returns is concentrated on the period around the Bitcoin bubble. However, the magnitude of the effect is rather small making it impossible for a trader to make economic profits by trading on the information published on social media.
2018 , Demir et al. 2018 , Aalborg et al. 2019 , Dastgir et al. 2019 , Poyser 2019). In this paper, we explore a novel source of data -social media messages sent on the platform StockTwits -to answer a key question regarding the Bitcoin market: what is the role played by investor sentiment in the price dynamics process. This issue has important implications for academics working on the efficient market hypothesis, but also for investors trying to beat the market and for regulators wanting to better understand the drivers of this volatile market to protect unsophisticated traders. We provide empirical evidence on this question by analyzing if investor sentiment on social media contains robust information for Bitcoin returns predictability at various time-frequency (1 minute to 24 hours). When it was created in 2009, and until the end of 2014, the Bitcoin market was very illiquid and little known to investors.
Similar results have been found when considering proxies of investor attention by using data from social media and Google Search  (Urquhart 2018 , Shen et al. 2019 ): past information do not forecast returns in the recent period. To explore the relationship between investor sentiment and Bitcoin returns, and following the methodology of  Renault (2017) , we extract all messages about the Bitcoin sent by individual users and traders on the largest social network for finance: StockTwits. From a database of 988,622 messages sent between 2017 and 2019, we construct investor sentiment indicators -at various frequencies from 1 minute to 24 hours -by computing the average sentiment of messages posted on StockTwits during each time interval. We also construct a measure of investor attention by considering the number of messages sent on StockTwits during a given time interval.
Bitcoin price data We download complete historical Bitcoin trade data on www. bitcoincharts. com 1. We consider BTC-USD exchange rate from the US-based cryptocurrency exchange Kraken, as in  Dyhrberg et al. (2018) , one of the leading cryptocurrency exchange platform in the world. From trade data, we construct BTC-USD exchange rate time series at various frequencies: 1min, 2min, 5min, 15min, 30 min, 1 hour, 2 hours, 6 hours, 12 hours and 24 hours. 2. As in  Baur et al. (2019) , we do not find any evidence of time-of-day or day-of-week returns anomalies when considering data up to 2019. 3.
StockTwits messages StockTwits is a microblogging platform where users can share ideas and opinions about the stock market. Since its creation in 2008, more than 180 million have been sent on the platform by a total of more than 100,000 users. For researchers in finance and computing science, StockTwits is a very valuable source of data as users can use a toggle button to selfclassify their messages as bullish (positive) or bearish (negative) before posting a message on the platform. Data from StockTwits have been used, among others, by  Oliveira et al. (2017) ,  Mahmoudi et al. (2018)  and Cookson & Niessner (2019). 4   We use the StockTwits API to download all data related to the Bitcoin since the creation of the website in 2008. However, before July 2017 and the introduction of a specific symbology for cryptocurrencies on the StockTwits website 5  , the number of messages related to  Replicating  Caporale & Plastun (2018) , we find a day of the week effect on Monday for the period 2013-2017.
4 Users on StockTwits might also use the platform to release false or misleading information to affect cryptocurrencies prices (see  Li et al. (2019)  for an example of the use of the encrypted messaging app "Telegram" for pump-and-dump schemes. ) Bitcoin and other cryptocurrencies was very low. As we are considering intraday intervals, and in order to have a sufficient number of messages per time interval, we focus our analysis on the period from August 1st, 2017 to December 1st, 2019. We end up with a database of 988,622 messages. Figure  1  shows an example of three messages: two messages classified as negative (Bearish tag) and one message classified as positive (Bullish tag). The correlation between the daily number of messages sent on StockTwits and Bitcoin prices is high (0. 61). Figure  2  illustrates the relationship between the number of messages and Bitcoin prices during the 2017-2019 period at a daily frequency.
Introduction A medical decision, regardless of its nature, about the prognosis or diagnosis of a patient's response to prescription treatments or therapeutic plans, is a difficult task. Personalized medicine designs a medicine centered on an attitude adapted to patient profiles. These profiles can include information about behavioral preferences, environmental factors, key biomarkers, treatment history, demographics, genetic composition, and other useful information. This information, such as body temperature and blood pressure, is observed and organized by medical events. The electronic storage form of these profiles is referred to as Electronic Health Records (EHR). EHR offers a data source with a very detailed level of data. Data can be structural, nonstructural or semistructural with different qualities, such as nominal, numerical, Boolean, date-time, image, text, video, sound, and Extensible Markup Language (XML).
In addition, numerical and nominal medical events can be presented as time series or nominal sequences. Therefore, there is a considerable need for models to explore these datasets and facilitate decision-making. To facilitate medical decision-making, one can consider computer-aided procedures. The latter usually rely on classification techniques that operate on training data, the quality of both are important to achieve excellent results. The qualities and types of the considered data and the ways of representing and preprocessing data can be factors of decisive influence. As a sample, we can quote the example of structural data processing and the completion of missing values, as well as coping with both loss of information and data during the transformation process  [16]. However, choosing the best classification techniques regarding a specific medical problem is also difficult if one wants to conceive an efficient computer-aided medical decision-making system.
Different exploration results for the same data set may be returned due to each technique's computation strategies. The computation time of medical decisions is another crucial element of these systems, especially for urgent cases. The processed data volumes, data dimension reduction techniques, and complexity of the classifiers utilized are the main factors affecting the processing time taken to perform an automated medical decision. This paper proposes a model for automated medical decisionmaking based on personalized medicine datasets. The model is intended to classify new patient data as predictive and preventive measures for a given disease and estimate the appropriateness of treatment for a given patient to avoid adverse effects. To achieve this process, our model combines three main steps.
The dataset sometimes contains thousands of patients, which makes classification treatments computationally demanding. The third step reduces the previous numerical matrix dimensionality, using dimensionality reduction to simplify the calculations and reduce the processing time. The reduction is performed to obtain three dimensions. This choice is motivated by the visualization and interpretation needs of the classification results. The last step classifies the resulting 3D data and orients medical decisions by defining patient categories to be examined. We have identified the best combination of dimensionality reduction techniques with the best performing classifier. We implemented data mining techniques that produce an efficient automated medical decision-making model using these three steps.
Data dimensionality reduction Personalized medicine data exploration involves working with large and multidimensional data. Many works use dimensionality reduction and visualization to understand, analyze, and treat such datasets. One of the famous dimensionality reduction techniques is principal component analysis (PCA)  [1]. The PCA technique is based on transforming variables in a linear space and maximizing the variance to extract new characteristics in a reduced space that is referred to as principal components. In a study of Parkinson's disease, Shukla et al. [2]  employed the PCA technique for data dimensionality reduction in the dysphonia features' classification process. In another study on the disease "systemic lupus erythematosus"  [3] , researchers applied PCA followed by visualization and interpretation of the results.
The new distribution more closely represents the data that have a high probability of similarity. Thus, the most dissimilar data are represented farther from each other  [8]. After the data representation and similarity calculation, Zhang et al. [9]  utilized the TNSE technique to reduce dimensionality and applied a clustering technique to define Parkinson's disease subtypes. Workman et al. [10]  investigated the TSNE reduction technique for patients' temporal clinical data in a classification and analysis process that is intended to evaluate the effectiveness of deep learning on temporal data. This short overview shows that many studies have considered data dimensionality reduction techniques in EHR mining and classification. The main finding of these studies reveals an interest in the direct application of a single data reduction technology without comparison with others.
Classification techniques Sometimes exploration works use different classification techniques to compare the results and evaluate performance. For example, the work of Mohammad et al. [11]  applied seven classification algorithms for heart disease prediction: k-nearest neighbor (k-NN), decision tree, naïve Bayes (NB), logistic regression, support vector machine (SVM), neural network and vote (hybrid technique with NB and logistic regression). Other approaches  [12, 13]  predict coronary artery and heart diseases by the following classification techniques: multilayer ANN, SVM, NB, and decision trees (C4. 5). In ˙Ilkim et al. [14]  and Vital et al. [15] , six classification techniques are considered. The first technique applies the NB 1 Data utilized in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.
loni. usc. edu). The investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be obtained at:http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknow ledgement_List. pdf. classifier, decision trees (Classification and Regression Tree (CART), C4. 5, C5. 0, C5. 0 boosted), and random forest (RF) algorithms to analyze the effect of rheumatic fever on heart disease in childhood  [14]. The second technique applies the alternating decision tree (AD tree), decision trees (C4. 5), NB, BayesNet, K-Star, and RF to predict cancer disease and analyze the performance of the dataset  [15]. The classifier selection behaviors applied for these latter approaches  [11] [12] [13] [14] [15]  vary among unjustified, popular and recommended approaches.