dataesr/scientific-paragraphs-categorization huggingface dataset:
II. SIMULATION METHOD We use micromagnetic finite-element simulations to study the high-frequency magnetic properties of buckyball-type nanostructures and how these magnetic excitations depend on the magnetic configuration. The simulations are carried out in a two-step process, each involving different simulation methods. First, we simulate the zero-field static equilibrium state. For this part of the simulation, we use our micromagnetic software tetmag,  23  which is based on the numerical integration of the Landau-Lifshitz-Gilbert (LLG) equation 24 dM dt = - 1 1 + α 2 [γ(M × H eff ) + α Ms [M × (M × H eff )]], (1) where α is the Gilbert damping constant, γ is the gyromagnetic ratio, Ms = |M| is the material's spontaneous magnetization, and H eff is the micromagnetic effective field. 25 Despite implementing several advanced features, such as graphics processing unit (GPU) acceleration and H2-type hierarchical matrix compression allowing us to treat large-scale problem with almost linear O(N) complexity, the main operation principle of our tetmag software corresponds to that of traditional micromagnetic solvers.
VI. FIELD DEPENDENCE OF THE MAGNETIC MODES Having established the frequencies and mode profiles of the principal zero-field resonances of the structure, both with and without defect configurations, we proceeded to investigate how the high-frequency spectrum is modified by an externally applied magnetic field. For this purpose, we performed exhaustive simulations. First, we simulated the equilibrium magnetic structure at different applied field strengths, quasistatically varied in step sizes ΔH = 25 mT from an initial field of 0 to ±250 mT for both the highremanence state with the defect structures and the demagnetized, defect-free state. At each field value, using our MF-μLRS software, we then determined the dynamic magnetization in a frequency range between 100 MHz and 25 GHz. The results are summarized in Fig.
ACKNOWLEDGMENTS The authors acknowledge the  Interdisciplinary Thematic Institute QMat  ( ANR-17-EURE-0024 ), as part of the  ITI 2021-2028 program  of the  University of Strasbourg , CNRS ans INSERM, supported by the  IdEx Unistra  ( ANR-10-IDEX-0002 ) and  SFRI STRAT ' US  ( ANR-20-SFRI-0012 ) through the  French Programme d'Investissement d'Avenir. The authors acknowledge the  High Performance Computing Center of the University of Strasbourg  for supporting this work by providing access to computing resources.
MAGMA identified 15 of 54 (27%) GTEx tissues, 95 of 729 (13%) Mouse Atlas cell types, 27 of 119 (22%) Tubula Muris FACS, and 19 out of 75 (25%) Tubula Muris Droplet cells as enriched in the expression of genes associated with CAD. A total of 35 out of 209 tissues/cell types reached an FDR<0. 05 in DEPICT. MAGMA gene property analyses of a wide range of single-cell RNA datasets from mice as well as a more restricted set of cell types in humans highlight the relevance of the endothelial, stromal/fibroblast, and smooth muscle cells in the pathogenesis of CAD (Fig. 5a-b ) with DEPICT reinforcing these findings and further delivering strong signals for hepatocytes and adipocytes (Fig  5d ). The most significant system/tissue for both algorithms involved arteries, with MAGMA producing a top signal specifically for the 'coronary artery', a tissue almost exclusively made up of endothelial, stromal/fibroblast, and smooth muscle cells (Fig.
Genetic Relatedness We used KING, version 2. 0, to identify 20,881 related participants at a 3rd degree or closer  58. Among these individuals, we preferentially retained 5,289 unrelated cases and 4,909 unrelated non-cases in analyses and excluded the remaining individuals (1,023 cases and 9,601 noncases). Analyses of heritability across racial/ethnic groups We used GREML-LDMS-I as implemented in Genome-wide Complex Trait Analysis (GCTA) 1. 93. 0beta to estimate the multicomponent narrow sense heritability of CAD in our three HARE-define MVP groups and in the Biobank Japan dataset  18. GREML-LDMS-I method was adopted because it has been shown to be one of most accurate methods of estimation of heritability when considering common factors that may bias such estimates  64. To minimize the confounding effects of admixture, we ran heritability analyses in minimally admixed subsets of individuals in each of the HARE groups identified through PCA of MVP data with the 1000G data and selection of White, Black, and Hispanic subjects clustering most closely with the 1000G European, African and Peruvian populations, respectively.
multi-allelic SNPs, and iv. SNPs with minor allele count (MAC) <6. METAL 68 (classical standard error approach) was then used to apply a genomic control to each input dataset and meta-analyze GWAS results across genotype releases within each HARE group. For Whites, we also ran METAL with genomic control turned off to create a dataset suitable for LD score regression  21. X-chr association testing in MVP for both phenotypes was conducted stratified by sex in addition to HARE group. We implemented a standard logistic regression model assuming presence of X-chr inactivation (males coded as 0/2, females as 0/1/2). In the UK Biobank, Xchr analyses were restricted to unrelated subjects of White/European descent (34,541 CAD cases and 261,984 controls).
Meta-analysis with external datasets We used METAL to conduct 2 fixed-effect meta-analyses for the clinical CAD phenotype  12. The first involved the MVP Whites with the CARDIoGRAMplusC4D 1000G study 2 and the UK Biobank CAD study 3 and the second further incorporated the MVP Blacks, MVP Hispanics, and Biobank Japan  23. Genomic control was applied to each input dataset by METAL. This second trans-ethnic meta-analysis was also performed using MR-MEGA  13. METAL and MR-MEGA were also used to conduct a trans-ethnic meta-analysis of the CART derived burden of CAD. For the X-chr, we used GWAMA  69  to perform a meta-analysis of males and female strata within each HARE group followed by METAL to conduct a metaanalysis of the X-chr data in MVP Whites with the UK Biobank and the X-chr study by CARDIoGRAMplusC4D  70.
Definition of a locus including parameters for lead and candidate genetic variants We used FUMA  30  to define genomic risk loci including independent, lead, and candidate variants. First, independent genetic variants were identified as variants with a P below a specific threshold and not in substantial linkage disequilibrium (LD) with each other (r 2 < 0. 6). Second, variants in LD (r 2 ≥ 0. 6) with an independent variant and with p < 0. 05 were retained as candidate variants to form an LD block. Third, LD blocks within 500kb of each other were merged into one locus. Lastly, a second clumping of the independent variants was performed to identify the subset of lead SNPs with LD r 2 < 0. 1 within each locus. For our meta-analyses of Whites alone and our trans-racial/ethnic meta-analyses, we used a UK Biobank release 2b EUR reference panel of genotype data imputed to the UK10K/1000G SNPs created by FUMA including ~17 million SNPs.
Two-stage joint analysis of most promising findings in non-Europeans We sought replication of all promising genomic risk loci in our MVP Black and MVP Hispanic GWAS for clinical CAD in multiple external datasets. Replication was attempted not only for all lead SNP(s) with P<1´10 -5 but also for all other independent and candidate genetic variant members of these loci. In the same external datasets, we also sought replication for all SNP with local FDR < 0. 05 from our XPEB analyses as described below.
all subjects; ii. after excluding CAD cases; and iii. after further excluding subjects with other manifestations of atherosclerosis including peripheral arterial disease and ischemic stroke. We generated a network plot with the Yifan Yu proportional multi-level layout and Atlas 2 layout algorithms implemented in Gephi Software using the subset of significant individual novel SNP PheWAS associations. The node size was defined using the weighted in-degree network statistic with the directionality from SNP to phenotype. The edge size was defined by the number of connections between two nodes (SNPs and phenotypes) and only include associations between SNP and phenotype represent by the z-score statistic of the SNP-phenotype association. The size of the label of the node was proportional to the weighted degree statistic.
05) in our homozygote Africans Blacks among all SNPs reaching GWS (P<5´10 -8 ) in our meta-analysis of Whites and used these SNPs to construct haplotypes and perform a haplotype trend regression of this region using the R package haplo. stats. Downstream analyses to prioritize genes, pathways, cells, and tissues/systems relevant to CAD We conducted downstream analyses to prioritize genes, pathways, and tissues involved in the pathogenesis of CAD based on the results of our meta-analyses. We applied four analytic algorithms to the summary statistics including Multi-marker Analysis of GenoMic Annotation (MAGMA) v1. 09 for gene, gene-set, and gene-property analysis, as implemented in FUMA  30- 32  , a model-based enrichment method for GWAS summary data using biological pathways to define gene-sets, Regression with Summary Statistics exploiting Enrichments (RSS-E)  33  , Data-driven Expression Prioritized Integration for Complex Traits (DEPICT)  28  , and MetaXcan  29.
Gene and cell/tissue/system specificity/prioritization analyses incorporating gene-expression data into their algorithms were restricted to Whites given a majority of the gene-expression data incorporated into these analyses are derived from Whites. We harmonized gene level results by MAGMA, RSS-E, DEPICT, and MetaXcan, and compared to the DEPICT analyses performed on the CARDIoGRAMplusC4D and UK Biobank meta-a alone  8. MAGMA gene-set analyses were run on 10,678 gene sets (curated gene sets: 4,761, GO terms: 5,917) from MSigDB v6. 2 while gene-property analyses were conducted on GTEx V8 and multiple single cell RNA-seq databases incorporated into the FUMA bioinformatic pipeline including the Mouse Cell Atlas, the Tabula Muris dataset (FACS and droplet) and several datasets of human brain, pancreas, and blood.
For RSS-E, gene-sets were derived from nine databases (BioCarta, BioCyc, HumanCyc, KEGG, miRTarBase, PANTHER, PID, Reactome, WikiPathways) that are archived by four repositories: Pathway Commons v7, NCBI Biosystems, PANTHER (v3. 3), and BioCarta. We downloaded preprocessed pathway and gene data from http://doi. org/10. 5281/zenodo. 1473807 on October 29, 2018 and used a list of 3,803 pathways that contains between 2 to 400 autosomal protein-coding genes per pathway in the present study. Fig. 1 : Design of multi-ethnic genome wide association study (GWAS) of coronary artery disease (CAD) and estimates of heritability of CAD using GREML-LDMS-I for 4 racial/ethnic groups a, Phenotyping using the EHR in the MVP identified ~120,000 genotyped cases with CAD and ~285,000 genotyped controls. GWAS was first performed stratified by racial/ethnic group.
The thickness of the edges is correlated with the strength of the SNP-phenotype association (z-score). The size of the labels is dictated by the number of connections to phenotypes or genes and the strength of association. Network plot was created using Yifan Yu proportional and Atlas 2 layout algorithms as implemented in Gephi software. Figures Figure 1 Design of multi-ethnic genome wide association study (GWAS) of coronary artery disease (CAD) and estimates of heritability of CAD using GREML-LDMS-I for 4 racial/ethnic groups. a, Phenotyping using the EHR in the MVP identi ed ~120,000 genotyped cases with CAD and ~285,000 genotyped controls. GWAS was rst performed strati ed by racial/ethnic group. GWAS for Whites was then meta-analyzed with 2 existing GWAS for initial discovery among Whites.
b, Manhattan plot (right) of trans-ethnic meta-analysis of GWAS for burden of coronary atherosclerosis as estimated by the number of coronary obstructions >50% on coronary angiogram (example left, "angiogram one" by j l t is licensed under CC BY-NC-SA 2. 0) where 15 loci reach GWS. All but LPL, COL4A1, and TGFB1 reach GWS in Whites and LPL was the only locus to reach GWS in AFR (details not shown). No locus reached GWS in Hispanics alone. wide PRSs were derived in external datasets using either a standard pruning and thresholding strategy, 'Prun & Thresh', modeling linkage disequilibrium, 'LDPred', or through the meta-analysis of the weights of 3 separate scores, 'metaGRS'. a, PRS were tested in MVP Whites, Blacks, and Hispanics, separately. In addition to all cases combined, subgroups of incident only cases (after enrollment), severe cases with evidence of either a myocardial infarction and/or a revascularization procedure, and early onset vs older onset cases (divided by median age of onset) were tested.
Funding/support This research is based on data from the  Million Veteran Program, Office of Research and Development, Veterans Health Administration , and was supported by  Veterans Administration  awards  I01-01BX03362 ,  I01-BX004821 ,  I01-BX003340  and  VA HSR RES 13-457. The content of this manuscript does not represent the views of the Department of Veterans Affairs or the  United States Government. The eMERGE Network was initiated and funded by  NHGRI  through the following grants: Phase III:  U01HG8657  ( Kaiser Permanente Washington/University of Washington );  U01HG8685 (Brigham and Women's Hospital) ;  U01HG8672  ( Vanderbilt University Medical Center );  U01HG8666  ( Cincinnati Children's Hospital Medical Center );  U01HG6379  ( Mayo Clinic );  U01HG8679 (Geisinger Clinic) ;  U01HG8680  ( Columbia University Health Sciences );  U01HG8684  ( Children's Hospital of Philadelphia );  U01HG8673  ( Northwestern University ); U01HG8701 ( Vanderbilt University Medical Center serving as the Coordinating Center );  U01HG8676  ( Partners Healthcare/Broad Institute ); and  U01HG8664  ( Baylor College of Medicine ) Phase II:  U01HG006828  ( Cincinnati Children's Hospital Medical Center/Boston Children's Hospital );  U01HG006830  ( Children's Hospital of Philadelphia );  U01HG006389  ( Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University ); U01HG006382 (Geisinger Clinic);  U01HG006375  ( Group Health Cooperative/University of Washington );  U01HG006379  ( Mayo Clinic );  U01HG006380  ( Icahn School of Medicine at Mount Sinai );  U01HG006388  ( Northwestern University ); U01HG006378 ( Vanderbilt University Medical Center ); and  U01HG006385  ( Vanderbilt University Medical Center  serving as the Coordinating Center).
If the project includes data from the eMERGE imputed merged Phase I and Phase II dataset, please also add U01HG004438 (CIDR) and  U01HG004424  (the  Broad Institute ) serving as Genotyping Centers. And/or The PGRNSeq dataset (eMERGE PGx), please also add U01HG004438 (CIDR) serving as a Sequencing Center. Phase I:  U01-HG-004610  ( Group Health Cooperative/University of Washington );  U01-HG-004608  ( Marshfield Clinic Research Foundation and Vanderbilt University Medical Center );  U01-HG-04599  ( Mayo Clinic );  U01HG004609  ( Northwestern University );  U01-HG-04603  ( Vanderbilt University Medical Center , also serving as the Administrative Coordinating Center);  U01HG004438 (CIDR)  and  U01HG004424  (the  Broad Institute ) serving as Genotyping Centers. The  Population Architecture Using Genomics and Epidemiology (PAGE) program  is funded by the  National Human Genome Research Institute (NHGRI)  with co-funding from the  National Institute on Minority Health and Health Disparities (NIMHD) , supported by  U01HG007416 (CALiCo) ,  U01HG007417  (ISMMS),  U01HG007397 (MEC) ,  U01HG007376  (WHI), and  U01HG007419  (Coordinating Center).
The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the  NIH. The PAGE consortium thanks the staff and participants of all PAGE studies for their important contributions. The complete list of PAGE members can be found at http://www. pagestudy. org. The  MultiEthnic Study (MEC)  was supported by  U01 CA164973. The Women's  Health Initiative (WHI) program  is funded by the  National Heart, Lung, and Blood Institute, National Institutes of Health, U. S. Department of Health and Human Services  through contracts  HHSN268201100046C ,  HHSN268201100001C ,  HHSN268201100002C ,  HHSN268201100003C ,  HHSN268201100004C , and  HHSN271201100004C. Scientific Computing Infrastructure at Fred Hutch funded by  ORIP  grant  S10OD028685.
Additional support was provided through  R01AG023629  from the  National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at  CHS-NHLBI. org. The provision of genotyping data was supported in part by the  National Center for Advancing Translational Sciences, CTSI  grant  UL1TR001881 , and the  National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC)  grant  DK063491  to the  Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health. BioBank Japan  (BBJ) was supported by the  Tailor-Made Medical Treatment Program of the Ministry of Education, Culture, Sports, Science, and Technology and Japan Agency for Medical Research (AMED)  under grant numbers  JP17km0305002  and  JP17km0305001.
Healthy Aging  in Neighborhoods of Diversity across the Life Span (HANDLS): was funded by the  Interlaboratory Proposal Funding of the Intramural Research Program of the National Institute on Aging (NIA) , the  National Institutes of Health (NIH), Baltimore, Maryland. Funding number: [AG000989]. X. Z. was supported by the  Stein Fellowship. J. A. L. and K. M. L. are supported by the  U. S. Department of Veterans Affairs  ( IK2-CX001780 ). Y. L. is supported by  NIH   R56HL150186. S. Koyama  and  K. Ito  were supported by  AMED  under Grant Numbers JP20km0405209 and JP20ek0109487. K. E. N. is supported by  NIH   R01HL142302. R. D. is supported by  NIH   R35GM124836 & R01HL139865. F. C. is supported by  NCI   T32CA229110. B. F. V. was supported by the  NIH   DK101478  and a  Linda Pechenik Montague Investigator Award.
SA plays an important role in the field of XAI as it can answer a part of these questions without being dependent on a certain machine learning model or even a sampling strategy. Especially when the number of model inputs is large, recognizing the factors on which to focus resources in data collection and data-driven modeling efforts becomes crucial. SA is used in many real-world applications, including but not limited to: understanding This publication is part of the project XAIPre (with project number 19455) of the research program Smart Industry 2020 which is (partly) financed by the Dutch Research Council (NWO). This paper is submitted to IEEE Access. how chemical models work  [1] , measuring the influence of input parameters on biological models  [2] , and understanding and analyzing factors affecting CO2 emissions in the construction industry  [3].
In this paper, a wide set of popular and different GSA methods or pseudo-GSA methods (i. e. , methods that can be used for GSA without this being their main application) are compared both qualitatively and quantitatively by testing their robustness to different sample sizes and their accuracy to a large number of randomly generated linear functions with different dimensions. Suggestions and a real-world example of using GSA methods on very high-dimensional data are given to help data scientists and machine learning experts use these GSA methods as efficiently as possible. An open source library (GSAreport) is proposed, that allows users to create GSA reports for any model or data set, the Figures used in Sections 2, 3, 4 and 5 are generated by the proposed software. The paper is organized as follows: In Section 1, an overview of related literature is given.
Afterwards, the GSA XAI methods compared in this work are presented and explained: Variance-based methods in Section 2, Derivativebased methods in Section 3, Density-based methods in Section 4, and Model-based methods in Section 5. Then, in Section 6, the first quantitative experiment is introduced to investigate the robustness of the different methods under different sample sizes and number of dimensions. The second experiment in Section 7 focuses on the accuracy of these methods under different conditions for (relatively simple) randomly generated linear functions. In Section 8, we combine the two previous quantitative results with a qualitative comparison of the different methods. In Section 9, we propose the open-source software package GSAreport, which allows to easily use the aforementioned XAI and GSA methods to gain a deep understanding of the feature sensitivities of any model or process.
OPEN SOURCE GSA REPORTING SOFTWARE In order to make Global Sensitivity Analysis methods and to combine the information that can be extracted with this large variety of methods and automatically select the right methods, we propose the GSAreport application. With GSAreport one can create a detailed and interactive HTML report for a specific dataset, machine learning model or realworld process. The application selects the best GSA methods to use based on the number of dimensions and samples per dimension. If the number of dimensions is over 64, Sobol and PAWN are omitted. If the number of samples per dimension is less than 50, Sobol, Delta, and PAWN are not applied. These rules follow the observations from Table  2. FAST and DGSM are not included in the application due to their relatively low performance.
The GSAreport application can be easily installed by either downloading the executable, using the provided Docker image, or setting up the Python dependencies. No programming skills are required to use the package. The software generally works with two different steps, the sampling step and the analysis and report generation step. In the sampling step, the software can generate different designs of experiments required for the application of all applicable global SA methods. These designs of experiments are stored as csv files and can be used as input for real-world or model evaluations. The evaluations should be stored in the same order and format. Alternatively, an existing data set with input and output samples can be provided. In this case, a surrogate model (Random Forest) is built to allow for methods that require a specific sampling scheme.
REAL-WORLD USE CASE: IDENTIFYING IM-PORTANT SNPS IN DNA DATA The Morris method is here applied to provide an example of GSA on a specific real-world scenario dealing with genomic 1. https://github. com/Basvanstein/GSAreport prediction, i. e. , prediction of phenotype (output) based on genomic data (input). The target of the application is to understand which parts of the genome, i. e. , Single Nucleotide Polymorphisms (SNPs), have a major role in the predicted outcome. Among the other GSA techniques, the Morris method was chosen for this particular application because it can handle the very large number of discrete variables characterizing this application study and includes multidimensional averaging, i. e. , is able to account for interaction effects when ranking factors. It also allows for working with groups of factors, which is advantageous when dealing with large data sets.