dataesr/scientific-paragraphs-categorization huggingface dataset:
Any combination of map tasks and reduction tasks can be recorded and figured as map (min, max) and reduce (max, min) and combined in vertices. While the corresponding time series of any combination can be represented in triplets, in which way the methodology of bipartite hypergraph operable partitions is developed, which represents a new way of information processing of only non-contradictory solutions of any kind of triplet  [25]. A bipartite graph is shown based on the example in Figures  1 2 3 4 and the related code can be found under the GitHub link (https://github. com/OtmaneAzeroualDZHW/Creating_bipartite_graph, accessed on 4 February 2021). In this example, an equality diagram is generated where each edge weight is equal to the sum of the marks of both neighboring nodes. To determine the equality graph, the algorithm must find the maximum adjacent edge of each node and assign the node its weight as a marker.
The equality graph is determined using the node markings (black), as shown in Figure  2. tices. While the corresponding time series of any combination can be represented in triplets, in which way the methodology of bipartite hypergraph operable partitions is developed, which represents a new way of information processing of only non-contradictory solutions of any kind of triplet  [25]. A bipartite graph is shown based on the example in Figures  1 2 3 4 and the related code can be found under the GitHub link (https://github. com/OtmaneAzeroualDZHW/Creating_bipartite_graph, accessed on 4 February 2021). In this example, an equality diagram is generated where each edge weight is equal to the sum of the marks of both neighboring nodes. To determine the equality graph, the algorithm must find the maximum adjacent edge of each node and assign the node its weight as a marker.
Data Structure of Information on COVID-19: Current Issues Current issues indicate emerging trends towards fuzzy data flows and data structures on COVID-19, which are a common and well-known consequence of the data deluge in a shared new domain. The result is an overview of the current characteristics of the COVID-19 data structure from the related perspective of users and item issues. The first positive outcome is the end of the "valley of death" conditions of networks connecting academics and industry. Presently, publications and databases circulate on a global scale. However, this generates other problems such as overflow and heterogeneity of the information collected, which we will discuss in more detail. It is well known that the processing of data by computer technologies makes decisive contributions to the resolution of massive health crises, as has been the case in previous pandemic situations  [28].
Integration Integration is a challenge from every point of view; knowledge thinking develops in this way  [41]. What is more directly reachable here is the functional advantage of scalability from the user point of view. Graph users can be specialists as well as non-specialists and it is also in that range of application that scalable function of expression of user preferences finds its benefits with Hadoop. Domain-specific search engines give a good example when the zooming function of preferences has to be mobilized for the expression of nonspecialists, from a scientifically structured publication database  [42] , and explore Scientific Knowledge Graphs (SKGs) with my Domain-specific Insight Graphs (myDig) systems. In that direction, there is a matter of rhythm in knowledge acquisition that Hadoop can easily handle.
Results After explaining the Hadoop technology and its possible potential use in different areas, we now want to present our developed solution in this section, which allows the processing of large amounts of data in clusters. MapReduce is often incorrectly compared directly to relational database systems and presented as an inefficient way to manage data. The criticism overlooks the fact that MapReduce is not a database system, but a programming model with which data can also be evaluated, but which is not limited to this. In general terms, Hadoop and MapReduce can be used to solve tasks whose input data can be easily partitioned into pieces so that they can then be processed as independent sub-problems, which correspond to the division of data into individual blocks in HDFS. Examples of this are searching through text files or converting their format.
The simplicity of setting up and expanding our solution enables the users to start small in order to test their ideas for processing the big data. As soon as the idea is mature, scaling can be done very easily. ever, it can also be coded in other programming languages such as Python, R, C ++, etc. These program codes will process different data that are stored in various files and database systems. The aim and focus of this paper is to introduce a simple MapReduce programming framework that is extremely easy to set up and use. The simplicity of setting up and expanding our solution enables the users to start small in order to test their ideas for processing the big data. As soon as the idea is mature, scaling can be done very easily. As shown in Figure  5 , the calculation receives as input a list of pairs consisting of the document name and the document content.
The flow of the code can best be illustrated using a concrete example. Three documents are entered as name-content pairs (Algorithm 2): Algorithm 2 Documents as a name-content pair 1 ("doc_I", "Publication has one author") 2 ("doc_II", "Publication has no author") 3 ("doc_III", "Publication is free") The map function is called once for each of the input documents. For each of the words in the document, it produces an output pair consisting of the word and a "1". The following intermediate results result from the call (Algorithm 3). Algorithm 3 Map Function 1 map("doc_I","Publication has one author")= {("Publication", "1"), ("has", "1"), ("one", "1"), ("author", "1")} 2 map("doc_II", "Publication has no author")= {("Publication", "1"), ("has", "1"), ("no", "1"), ("author", "1")} 3 map("doc_III", "Publication is free")= {("Publication", "1"), ("is", "1"), ("free", "1")} The execution environment summarizes the results of all map calls.
It takes over the division of the input quantity into small partial calculations, the summarization of the partial results, and the assembly of the final result. With MapReduce, the typical problems that classic relational databases have with processing large unstructured data volumes can be eliminated. Another decisive advantage of MapReduce compared to other distributed methods of data processing is the low load on the network, since not the data but the programs are sent over the network. Another advantage compared to other methods of data processing in the context of big data is that the data does not have to be partitioned beforehand according to predefined criteria. The functions of MapReduce are relatively easy to program, and the effort to change for other suitable use cases is relatively low.
Either way, recent observations in "nature" regarding a severe "replication crisis" should be taken into account  [50]. Faced with these threats, the positive solutions brought by Hadoop MapReduce are of course not a panacea, but with the support of the referenced advantages described by this paper, they represent decisive tools to promote massive resilient responses that have the advantage, for the first time in history, to be shareable on the scale of the whole of mankind, as are currently the results for COVID-19. Further works for big data solutions should focus on the best of flexible and innovative technological development in computer science, relying in particular on advances in the natural language processing (NLP), and semiotic analysis. It is not a question here of finding solutions by means of automatic processing but of promoting the best combinations of information for the best consensual choices of experts.
Conclusions It is time now to find information retrieval techniques that could fit with such a challenge and we are aware that they are not available yet, but that conditions to let them appear are strongly evidenced by the Hadoop toolkit as well as with a series of innovative applications of knowledge reasoning with graphs. There are many reasons to believe that solutions are close to us. Which ones? First, the questions are global and the answers are local, which is always the best way to bestow expertise and common sense as well as operational links between any type of "goodwill" approaches with highly referenced software like Hadoop and local talents of highly skilled developers. Second, we face a common threat, and this is a unique and strong opportunity to unify our information-sharing attitudes.
We must consider the unprecedented break in commercial barriers in the publishing industry to imagine similar solutions emerging in the software industry. This could be the case in the perspective of a "Hadoop Fusion" approach on which we plan to offer several principles of common codification in a subsequent paper. Finally, there is a third reason which is, in our opinion, the most stimulating: "integration" has been described just a bit earlier as a strategic condition for data processing in the context of COVID-19, and we suggest that this last condition be developed in various contexts in which knowledge learning is at work to create optimization between data production of "flotillas" and data production of "squadrons". As a research question, this last challenge is a stimulating idea in the flourishing industry of user-generated content.
This includes post-pandemic predictive modeling on an unprecedented scale, that is, on a mankind scale. It will include the best of big data capabilities and models in the directions we have discussed above, while it has to secure new capacities in assessing "the extent to which epidemic modeling has aligned with the rise of big data, and related expectations that the volume and variety of the data could make up for problems in sourcing, sampling and calibrating the data". This will of course rely on supported Hadoop-like modeling. In summary, Hadoop MapReduce is a powerful technology that deals with handling large amounts of data. The technology offers a systematic solution for data storage, data processing, system administration, and integration of further modules. Hadoop can serve as a flexible processing and analysis tool for all types of data and can be used as an inexpensive framework in both small and large businesses.
Typically, such changes are observed over the course of one or two years of schooling  12, 13  , making it difficult to disentangle the contributions of learning from normative brain development. To address this, we used a short-term cognitive training program  14  to investigate how learning dynamically alters the mix of strategies used, whether it improves the efficiency with which memorybased strategies are applied  11, 15, 16  , and how these changes are related to learning-related reorganization of the functional brain networks in children. Our cognitive training program, adapted from MathWise  17, 18  , combined conceptual instruction with speeded retrieval of math facts and involved 22 lessons of increasing difficulty delivered over 8 weeks (see "Methods" for details). The training program centered on improving number knowledge and relations within and between numerical operations that facilitate the use of sophisticated counting procedures and memory-retrieval-based processes.
Brain regions identified in Module 1 were among those most consistently activated during numerical problem solving across multiple studies, as determined by meta-analysis using Neurosynth  77  (see "Methods"). After training, individual functional subregions of the MTL were no longer distinctly segregated and instead formed a more integrated three-module structure with parietal, frontal, and ventral temporal-occipital cortical regions involved in numerical problem solving. Next, to determine whether training significantly altered modular network organization across individuals, we assessed the mutual information, a nonlinear measure of distance between modular structure before training and modular structure after training, in each participant. Distance between two modular structures was computed by subtracting mutual information between the two module affiliation vectors (ranging from 0 to 1) from 1.
Overall study design. Training sessions. Children took part in an 8-week cognitive training program adapted from MathWise  17, 18. The training program combined conceptual instruction with speeded retrieval of math facts  14. Similar to MathWise, the training involved a total of 15-20 h of training, but it was condensed to 8/9 weeks with longer lessons in order to equate overall time on training  14. The training consisted of 22 lessons of increasing difficulty  14. Lessons 1 through 4 reviewed adding and subtracting 0, 1, and 2, as well as low ties (from 1+1 to 6+6 and corresponding subtraction facts, e. g. , 12-6)  14. These lessons also taught the commutative property of addition (i. e. , changing the order of the operands does not change the sum), as well as the additive identity property of zero (i.
Statistics and reproducibility. The current study used the following analytical approaches: (i) neurocognitive process model of latent memory processes to examine training-induced changes in the use and efficiency of memory-based problem-solving strategies and (ii) quantitative functional brain network analysis to investigate training-induced changes in modular organization of functional brain circuits. Two-tailed paired t-tests and Wilcoxon signed rank paired tests were performed for comparisons between pre-and post-training for behavioral and brain measures respectively, and Spearman correlation was used for analysis on brain-behavior relation, unless otherwise specified. Effect sizes, Cohen's d, and Spearman's rho, were estimated in Matlab. All statistical analysis is based on the sample of 35 children.
Brain imaging Functional MRI data acquisition. fMRI data were acquired using whole-brain imaging with a T2*-sensitive gradient echo spiral in/out pulse sequence at a Signa LX (GE Medical Systems) 3T scanner with the following parameters: echo time (TE) = 30 ms, repetition time (TR) = 2 s, flip angle = 80°, field-of-view = 200 mm, 29 axial-oblique slices parallel to the anterior commissure-posterior commissure line, dimensions 3. 125 × 3. 125 × 4 mm with 0. 5-mm skip. To reduce blurring and signal loss from field inhomogeneity, an automated high order shimming method based on a spiral acquisition was used prior to the acquisition of functional MRI scans. Cushions were placed around participants' heads to minimize head movement. Structural MRI data acquisition. High-resolution T1-weighted images were acquired in each child at both scan sessions (that is, pre-and post-training) to facilitate anatomical co-registration of fMRI maps.
This condition served as the control task for fMRI data analyses to better isolate brain activity related to numerical problem solving, controlling for low-level perceptual processing of visual stimuli and motor responses required to complete verification tasks. The task design also included a total of six rest periods-10 s each, which occurred at jittered intervals during each run to achieve an optimal event-related fMRI design. The rest periods were not explicitly modeled. Accuracy and median reaction times of correctly solved problems were computed separately for each participant for each of Addition and Control conditions (Supplementary Table  5 ). We used performance on Addition condition to assess numerical problem-solving ability. Functional MRI preprocessing. Data were analyzed using SPM8 (http://www.
3a ). Community detection was used to determine the optimal global modular structure within the functional connectivity matrix by grouping nodes into nonoverlapping communities or modules that maximize intramodular connectivity and minimize intermodular connectivity. The Louvain algorithm implemented in the Brain Connectivity Toolbox (http://www. brain-connectivitytoolbox. net) was used to detect community structure in the functional connectivity matrix. This algorithm optimizes a quality function Q*, defined as the difference between the observed intramodular connectivity and the intramodular connectivity expected by chance, while penalizing assignment of nodes with negative correlations to the same community. The Louvain algorithm automatically determines the number of underlying communities, and the resulting community structure is characterized by high positive and low negative connectivity within each community.
Brain Connectivity Toolbox was used to compute the mutual information between two modular organizations. Modular organization at the regional level was characterized by computing diversity coefficients of each of the 30 nodes belonging to the numerical problem-solving brain network. Diversity coefficient is a measure of how uniformly a brain region interacts with regions in other modules. Specifically, a high value for the diversity coefficient would indicate that interactions are more evenly distributed across modules. Diversity coefficient, is a more relevant measure than participation coefficient. Crucially, diversity coefficients are not influenced by the number of modules and thus consistent across different partitions of the same network  105. In contrast, participation coefficients are influenced by the number of modules and thus variable across different partitions of the same network.
Acknowledgements The authors greatly appreciate the contributions of children and their parents without which this work would not be possible. This research was supported by grants from the  National Institutes of Health  ( HD059205 ,  HD094623 ,  EB022907 ,  MH121069 ) and  National Science Foundation  ( DRL-2024856 ) to V. M. , Stanford  Innovator Award  and a grant from the  Stanford Maternal and Child Health Research Institute  through the  Transdisciplinary Initiatives Program  and the Taube Maternal and  Child Health Research Fund  to K. S. , and  Stanford Maternal & Child Health Research Institute Postdoctoral Support Award  to H. C. and P. M. K. S. is a Taube Family Endowed Transdisciplinary Investigator for Maternal Child Health.
Deep learning has revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence has emerged as an area of research that goes beyond pure prediction improvement by extracting knowledge from deep learning methodologies through the interpretation of their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called Deep-COMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers' decisions by applying layer-wise relevance propagation as one example from the pool of explanation techniques.
The resulting importance scores are eventually used to determine a subset of the most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw P-value thresholding and other baseline methods. Two novel disease associations (rs10889923 for hypertension, rs4769283 for type 1 diabetes) were identified.
3  tion of heritability. The genetic architectures and variances of most traits and diseases remain largely unexplained. This effect, often referred to as 'the missing heritability', is assumed to--at least partially--be caused by the way GWAS datasets are traditionally analyzed  (19, 20). The classic approach--which we refer to as raw P-value thresholding (RPVT)--consists of carrying out a statistical association test to assign a P-value to each SNP under investigation and subsequently assessing its statistical significance via comparison to a predefined threshold t *  (16). This standard approach to analyzing GWAS is therefore based on testing SNPs individually and in parallel, which intrinsically ignores any potential interactions  (20, 21)  between or correlation structures among the set of SNPs under investigation  (22, 23).
Studies fail to identify multi-locus effects by using the traditional RPVT approaches and a large amount of potentially available information is lost  (24). Only very few diseases rely on single genetic defects with large effects. Most complex diseases are caused by epistatic interactions of multiple genetic factors with small effect sizes, which are further influenced by correlation structures due to both population genetics and biological relations  (15). Brute force multivariate approaches to identify such dependencies are oftentimes computationally too expensive for large GWAS datasets and are limited by low statistical power due to excessive multiple testing. A few attempts have been made to identify genetic interactions, but most of them were not able to find strong, statistically significant associations  (21, (25) (26) (27).
To overcome these limitations of traditional approaches and following the rise of machine learning in data science and an increasing amount of available large-scale GWAS datasets, a number of methods have been proposed to introduce machine learning tools for the analysis of such studies. Linear approaches such as multivariate logistic regression and sparse penalized methods including Lasso have been applied to GWAS datasets. In general, penalized models achieve better performances than nonpenalized methods  (4, (28) (29) (30). Some of the top-performing models combine statistical testing and machine learning for the identification of SNP disease associations  (6, 7, 31). While most of these methods do not provide validation on real data comparing to the GWAS database, very few provide a full evaluation of identified genetic variants in terms of comparison to previously published GWAS.
Other proposed nonlinear models, such as random forests, gradient boosted trees and Bayesian models  (4, 28, 32, 33) , investigate interactions and correlations in the genetic architecture of traits but were mostly found to be outperformed by linear penalized methods  (4, 25, 28, 33). To harness even more sophisticated nonlinear machine learning methods for the analysis of GWAS, attention has recently been drawn to deep neural networks (DNN). This powerful tool for learning nonlinear relationships between an input and an output variable by transferring information through 'a' computing system made up of a number of simple, highly interconnected processing elements'  (34)  has seen an unprecedented rise in data science  (35)  and created enormous progress in numerous fields, e. g. image classification  (36, 37) , natural language processing  (38) , speech recognition  (39)  and quantum chemistry  (40).
DNN have been applied to the analysis of GWAS datasets  (41, 42) , but most of the corresponding publications focus on risk prediction  (28, (43) (44) (45)  and only very few methods have been proposed for the identification of SNP disease associations  (28, 46). Romagnoni et al. (28)  present a thorough comparison of conventional statistical approaches, traditional machine learning-based techniques and state-of-the-art deep learning-based methods in terms of both prediction rates and the identification of SNP associations on a Crohn's disease immunochip dataset. Classification performances of numerous methods (Lasso as a reference, penalized logistic regression, gradient boosted trees, DNNs) were compared and found to be similar for most methods (linear and nonlinear) implicating potentially 'limited epistatic effects in the genetic architecture'  (28).