dataesr/scientific-paragraphs-categorization huggingface dataset:
Theories and Data Languages The automata learning algorithm SL * learns automata with data registers and data input and output. The data ranges over specific theories that have the following shape. We assume that all elements of any D in this paper are denotable. The current implementation of SL * comes with two theories, namely the IntegerEquality theory N, {=} and the DoubleInequality theory R, {=, <, >}. An alphabet E is a set of actions which can be split into an input alphabet E in and an output alphabet E out , with E = E in ∪ E out and E in ∩ E out = ∅. A parameterized symbol α(p) is an action α ∈ E with a formal parameter p. For some fixed theory D, R , a data word is a concatenation of data symbols α(d) with α ∈ E and d ∈ D, i. e. , α 1 (d 1 ) • α 2 (d 2 ) • • • α n (d n ) with α 1 , α 2 ,.
For example consider some theory N, {<} and a data word u = α(1) • α(2). We have that u is not 1-extendable, because for u = α(2)•α(4) we have u ≈ R u but for α(d ) = α(3) there is no α(d) such that α(1) • α(2) • α(d) ≈ R α(2) • α(4) • α(3). A theory is said to be strongly extendable iff all data words are ∞-extendable and a theory is said to be weakly extendable iff for all data words u and for all k ∈ N, there is a data word u with u ≈ R u that is k-extendable. Note. SL * requires a theory to be either weakly extendable or strongly extendable. The two theories currently implemented in RALib, namely N, {=} and R, {=, <, >} are both strongly extendable. A data language L is a set of data words, such that for all two words w and w that are R-indistinguishable, we have that w ∈ L ⇐⇒ w ∈ L. A word w is said to be accepted by L iff w ∈ L, and rejected otherwise.
Experimental setup As the main purpose of this paper is to investigate the suitability of SL * in an industrial environment, the algorithm has been applied to several case studies l0 l1 l2 l3 ?enter[int] | true r1 := v int !ok | true ?enter[int] | v int > r1 r1 := v int ?enter[int] | v int == r1 ?enter[int] | v int < r1 r1 := v int !nok | true Fig. 4. Result of SL * after learning the SUL of Figure  2  extracted from the coding environment of ASML. In this section we elaborate on the case studies themselves and on applying the algorithm to these case studies. Case studies. In some departments within ASML, a modelling environment called ASD:Suite  [6]  (see also  [15]  in this proceedings for a compact explanation of ASD) is used to model the behavior of software from which the source code is generated.
Using ASD:Suite, a major component can be decomposed into many smaller components. In particular there is a very large component within the code base of ASML that is decomposed into 200-300 smaller components. For the purpose of this paper, several of these smaller components are considered as case studies. It is important to note that components modeled in the ASD:Suite use guards to make control flow decisions based on state variables, i. e. , variables used to describe the state of a system, as the use of such state variables provides a more compact model of the behavior of the component. Any component using state variables however, can also be modeled as a component without state variables, resulting in a more extensive behavioral model with more explicit states. Visually, this means that the second model has a layered structure, where different layers of states represent the different values of an otherwise present state variable.
Component c 3 communicates only with c 2 , which receives calls from c 1 concerning the list and forwards them to c 3 whilst also communicating with two other components, indicated in gray in is the simplest component of these three, as it only functions as a communicator between other components and c 2. Each component is learned separately, observing the running times and studying the results, as well as a combination of c 1 and c 2 , and a combination of all three components, to observe the learning results and running times when dealing with increased complexity. To illustrate the strength of SL * when it comes to learning software behavior with (abstract) data parameters, the same set of test cases is learned with concrete data parameters. For this purpose, the input alphabet of each test case is extended such that for each parameterized input i, five concrete but arbitrary inputs are created instead, with concrete values v 1 , v 2 ,.
-correct: whether or not the learned result is correct. This correctness is evaluated by visual inspection. Notations 'N. A'. and '??' denote cases that do not have a final model to inspect or cases that are too big to evaluate by visual inspection, respectively. For each test case, two values are listed to indicate the result for the test case with abstract and with concrete data parameters. The absence of two values indicates a similar result for both cases. In the test cases that resulted in an out of memory, the DoubleInequalityTheory has also been tried, resulting in the same outcome. For two out of five test cases the experiments with data parameters led to out of memory. In both cases one parameterized input (the same input in both cases) was removed from the input alphabet in order to be able to obtain a partial result.
Conclusion Learning well known software with data parameters using SL * that fit the available theories is quite impressive. Applying SL * in an industrial environment can be of use, but there are quite some limitations to consider. First of all, two direct shortcomings were found that prevented SL * from learning the correct results. Industrial systems do not have a strict alternation of input and output. Furthermore, there are too many software flaws in the available implementation of SL *. Both had to be dealt with in order to allow SL * to correctly learn the results of the industrial case studies. Especially, the latter is not only very time consuming, but it also obfuscates conclusions about the quality of SL *. While applying SL * to said industrial cases, another weakness became apparent, namely the limited availability of data theories, forcing the use of integers and doubles and limiting the operators usable in guards to equality, < and >.
Acknowledging that there is no available method to identify all causal genes following GWAS discoveries, we prioritized genes for pathway analyses while allowing several different hypotheses for mechanisms of actions (9). In brief, we prioritized genes that: (i) were cis-eQTLs in any of the eQTL data sets outlined above, (ii) had at least one exonic variant at r 2 >=0. 1 with any of the 200 effects, (iii) had high score of regulatory potential using a cell specific network approach, (iv) had a similar coexpression pattern as identified using DEPICT. (33) Sensitivity analyses were performed including different combinations of the above categories, and including genes with intronic variants at r 2 >=0. 5 with any of the 200 effects (9). Overall, we prioritized 551 candidate MS genes (Table  S18 ; Table  S19  for sensitivity analyses) to test for statistical enrichment of known pathways.
(4) Given the over-representation of immune pathways in these databases, ambiguity remains as to where some variants may have their effect: neurons and particularly astrocytes repurpose the component genes of many "immune" signaling pathways, such as the ciliary neurotrophic factor (CNTF), nerve growth factor (NGF), and neuregulin signaling pathways that are highly significant in our analysis (Table  S20 ). These results -along with the results relating to microglia -emphasize the need for further dissection of these pathways in specific cell types to resolve where a variant is exerting its effect; it is possible that multiple, different cell types could be involved in disease since they all experience the effect of the variant. Pathway and gene-set enrichment analyses can only identify statistically significant connections of genes in already reported, and in some cases validated, mechanisms of action.
Materials and Methods Detailed materials and methods are listed in the Supplementary Material (9). In brief, we analyzed genetic data from 15 GWAS of MS. For the autosomal non-MHC genome, we applied a partitioning approach to create regions of ±1Mbps around the most statistically significant SNP. Then we performed stepwise conditional analyses within each region to identify statistically independent effects (n=4,842). We replicated these effects in two large-scale replication cohorts: i) nine (9) data sets genotyped with the MS Replication Chip, and ii) eleven  (11)  data sets genotyped with the ImmunoChip. Chromosomes X and Y were analyzed jointly across all the data sets, i. e. the discovery and replication. The extended MHC region was also analyzed jointly across all data sets. We further imputed HLA class I and II alleles and corresponding amino acids.
Statistically independent effects in the autosomal non-MHC genome were group into 4 categories post-replication: i) genome-wide effects (GW), ii) suggestive effects (S), iii) non-replicated (NR), and iv) no replication data (ND). Narrow sense heritability was estimated for various combinations of these effects, and the extended MHC region, to quantify the amount of the heritability our findings could explain. Next, we leveraged enrichment methods and tissue/cell reference data sets to characterize the potential involvement of the identified MS effects in the immune and central nervous system, at the tissue and cellular level. We developed an ensemble approach to prioritize genes putative associated with the identify effects, leveraging cell-specific eQTL studies, network approaches, and genomic annotations.
Acknowledgments We thank the patients who have generously donated their genetic material to enable this study. We thank the  Harvard Aging Brain Study  ( HABS -P01AG036694 ). We thank the  Biorepository Facility  and the  Center for Genome Technology laboratory personnel  (specifically  Sandra West ,  Simone Clarke ,  Daniela Martinez , and  Patrice Whitehead)  within the  John P. Hussman Institute for Human Genomics at the University of Miami  for centralized DNA handling and genotyping for this project. The IMSGC wishes to acknowledge William and  Lois Edgerly ,  John  and  Elaine Carlos ,  Martha Crowninshield ,  William  and  Cindy Fowler , whose enduring commitments were critical in creation of the Consortium. We thank the volunteers from the  Oxford Biobank  (www. oxfordbiobank. org.
uk) and the  Oxford NIHR Bioresource  for their participation. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the  Department of Health. German Ministry for Education and Research, German Competence Network MS (BMBF KKNMS ). the  Medical Research Council  grant  G0000934  and the  Wellcome Trust  grant  068545/Z/02 ) and the  UK National Blood Service controls  (funded by the  Wellcome Trust ). The study was supported by the  Cambridge NIHR Biomedical Research Centre, UK Medical Research Council  ( G1100125 ) and the  UK MS society  ( 861/07 ). NIH/NINDS :  R01 NS049477 ,  NIH/NIAID :  R01 AI059829 ,  NIH/NIEHS :  R01 ES0495103. Research Council of Norway  grant  196776  and  240102. NINDS/NIH   R01NS088155. Oslo MS association and the  Norwegian MS Registry and Biobank  and the  Norwegian Bone Marrow Registry.
Research Council KU Leuven ,  Research Foundation Flanders. AFM, AFM-Généthon ,  CIC ,  ARSEP ,  ANR-10-INBS-01  and  ANR-10-IAIHU-06. Research Council KU Leuven ,  Research Foundation Flanders. Inserm ATIP-Avenir Fellowship and Connect-Talents Award. German Ministry for Education and Research, German Competence Network MS (BMBF KKNMS). Dutch MS Research Foundation. TwinsUK is funded by the  Wellcome Trust, Medical Research Council, European Union , the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust  in partnership with King's  College London. The recall process was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre Programme.
Italian Foundation of Multiple Sclerosis  ( FISM grants ,  Special Project  " Immunochip "  2011/R/1 ,  2015/R/10 )). NMSS  ( RG 4680A1/1 ). MultipleMS  EU project. Lundbeck Foundation and Benzon Foundation  for support (THP). This research was supported by grants from the  Danish Multiple Sclerosis Society , the  Danish Council for Strategic Research  [grant number  2142-08-0039 ],  Novartis, Biogen Biogen (Denmark) A/S , and the  Sofus Carl Emil Friis og Hustru Olga Doris Friis Foundation and the Foundation for Research in Neurology. The  Observatoire Français de la Sclérose en Plaques (OFSEP)  is supported by a grant provided by the  French State  and handled by the  "Agence Nationale de la Recherche ," within the framework of the  "Investments for the Future" program , under the reference  ANR-10-COHO-002 , by the  Eugène Devic EDMUS Foundation  against multiple sclerosis and by the  ARSEP Foundation.
36 ). This approach allows doublets and aggregates to be easily identified and excluded from the analysis. As with all experiments, controls should be included. Chicken and trout erythrocytes have been proposed as internal standards for analysis of DNA content by cytometry in order to control and maintain consistency in the staining and measurement approaches. However, it should be noted that the ploidy of DNA in fish can also vary, and so it is important to be aware of ploidy when using cells as a standard  [317]. It is also crucial to exclude nonviable cells from any analysis, as the presence of these can introduce heterogeneity into the datasets that are generated. Although DNA analysis, by its nature, requires that cells are fixed and therefore nonviable, it is possible to stain cells using nonfixable dyes (protein-binding dyes) prior to their fixation for DNA staining.
This can be tested by adding the staining Ab to Indo-1 AM loaded cells and detecting the resulting Ca 2+ levels. Since kinetics may vary, the period of acquisition for these tests should be for at least 10-15 min. If the Ca 2+ baseline shifts in response to the staining Ab, that Ab should not be used. To test whether one of the staining Abs interferes with binding of the Ab used for stimulation, the measurement should be compared in the presence and absence of the respective cell surface Ab. If datasets from different days have to be compared, it is recommended that you keep the times between loading, staining, and data acquisition constant for all of the samples. The UV laser should be turned on at least 15 min beforehand to allow it to stabilize prior to use, since it is highly sensitive and more prone to fluctuation than other lasers.
It should be noted that different software uses the terms division index and proliferation index with differing definitions, so they should always be clearly defined when used, the division index used here was calculated by FlowJo software. When both percentage divided and division index are used to measure proliferation in the same population, it may be seen that while the results are broadly similar, division index is able to measure appreciable suppression at low Treg ratios that are less clearly different when using percentage divided (Fig. 78 ). In the assay calculating the percentage suppression of antigendriven T cells (Fig. 76 ), the resulting T cell proliferation can be detectable by using the MFI of CFSE-stained T cells better than by using percentage of divided T cells or the division index.
Complementary manual and automated analysis methods: FCM data analysis presents a complex problem because of recent rapid increases in the number of parameters measured, and because of some peculiarities of flow data. Current datasets include 20 or more parameters even by conventional fluorescence cytometry, and other methods yield 35 or more channels. Traditional bivariate gating (manually drawing boundaries on sequential two-parameter plots) can still be performed on high-dimensional data sets, but this becomes progressively more time consuming and less thorough as the parameter number increases. The current generation of flow cytometers is capable of simultaneously measuring 50 characteristics per single cell. These can be combined in 3 50 possible ways using traditional bivariate gating, resulting in a massive data space to be explored  [1798].
There has been rapid development of unsupervised clustering algorithms, which are ideally suited to biomarker discovery and exploration of high-dimension datasets  [599, 1795, 1796, [1799] [1800] [1801] [1802] [1803] [1804] , and these strategies are described in more detail in Chapter VI, Section 1. 2. However, the directed identification of specific cell populations of interest is still critically important in flow analysis for providing "reality checks" for the results returned by different algorithmic strategies, and for the generation of reportable data for clinical trials and investigations. This is the approach used by investigators who prefer to continue manual gating for consistency with previous results, now complemented by the availability of supervised cell population identification methods.
Curation of datasets: In some datasets, curating names and metadata may be necessary, especially when following the MIFlowCyt Standard (See Chapter VIII Section 3 Analysis presentation and publication (MIFlowCyt)). The manual entry error rate can be greatly reduced by using an automated Laboratory Information Management System (e. g. , FlowLIMS, http://sourceforge. net/projects/flowlims) and automated sample data entry. As manual keyboard input is a major source of error, an LIMS system can achieve a lower error rate by minimizing operator input through automated data input (e. g. , by scanning 2D barcodes) or pre-assigned label choices on pull-down menus. Although compensation is conveniently performed by automated "wizards" in popular FCM analysis programs, this does not always provide the best values, and should be checked by, e.
Post-processing of subpopulation data: Comparison of experimental groups and identification of significantly altered subpopulations: Regardless of the primary analysis method, the output of most FCM analyses consists of the sizes (cell numbers) and MdFIs of many cell subpopulations. Differences between samples (e. g. , in different groups of a clinical study) can be performed by standard statistical analysis, using methods appropriate for each particular study. It is very important to address the problem of multiple outcomes, and this is even more critical in high-dimensional datasets because the potential number of subpopulations is very large, and so there is a large potential multiple outcome error. By automated analysis, hundreds or even thousands of subpopulations can be identified  [1801, 1805] , and manual analysis also addresses similar complexity even if each subpopulation is not explicitly identified.
As in the analysis of microarray and deep sequencing data, it is important to consider the false discovery rate, using a strong multiple outcomes correction such as the  Benjamini-Hochberg strategy [1830]  or alternative strategies  [1831]. Applying corrections to data from automated analysis is relatively easy because the total number N of subpopulations is known  [1832] , but it is very difficult to identify N for manual bivariate gating, because a skilled operator exploring a dataset will consider many subpopulations before intuitively focusing on a smaller number of "populations of interest. " To avoid errors in evaluating significance due to multiple outcomes in manual gating, strategies include: performing the exploratory gating analysis on half of the data, and calculating the statistics on the other half; or performing a confirmatory study with one or a few predictions; or specifying the target subpopulation before starting to analyze the study.
Integration of cytometric data into multiomics analysis-While FCM enables detailed analysis of cellular systems, complete biological profiling in clinical settings can only be achieved using a coordinated set of omics assays targeting various levels of biology. Such assays include, transcriptomics  [1867] [1868] [1869] , proteomics  [1870] [1871] [1872] , metabolomics analysis of plasma  [1873] [1874] [1875] , serum  [1876] [1877] [1878]  and urine  [1879, 1880] , microbiome analysis of various sources [1881], imaging assays  [1882, 1883] , data from wearable devices  [1884] , and electronic health record data  [1885]. The large amount of data produced by each of these sources often requires specialized machine learning tools. Integration of such datasets in a "multiomics" setting requires a more complex machine learning pipeline that would remain robust in the face of inconsistent intrinsic properties of these high throughput assays and cohort specific variations.
Such efforts often require close collaborations between biorepositories, laboratories specializing in modern assays, and machine learning consortiums  [1795, 1813, 1886, 1887]. Several factors play a key role in integration of FCM and mass cytometry data with other high-throughput biological factors. First, much of the existing data integration pipelines are focused on measurements of the same entities at various biological levels (e. g. , genomics  [1867, 1888]  profiled with transcriptomics [1869] and epigenetics [1889] analysis of the same samples). FCM, being a cellular assay with unique characteristics, lacks the biological basis that is shared among other popular datasets. This makes horizontal data integration across a shared concept (e. g. , genes) challenging and has inspired the bioinformatics subfield of "multiomics" data fusion and integration  [1890] [1891] [1892] [1893].
In order to facilitate meaningful sample subgroup discovery and to uncover between-modality correlations, recently developed methodologies apply a variety of existing machine learning techniques, such as, matrix factorization and latent space modeling  [1894, 1895] , graph-based analysis  [1896, 1897] , consensus clustering [1898], and canonical correlation analysis  [1899]. The implementation of principled multiomics analysis techniques therefore help to reveal the joint biological system and crosstalk between all measured biological datasets. A second consideration for integration of FCM data with other omics modalities is the targeted nature of FCM. While FCM typically produces fewer measurements compared to genomics, transcriptomics, and proteomics datasets, the panel of markers measured is often carefully curated to target key cell types and signaling pathways.
Therefore, if FCM data are simply combined with other high-throughput and high-content data modalities, the predictions will be primarily driven by the larger datasets  [1900]. This is further complicated by the facts that large untargeted datasets often include highly correlated measurements (e. g. , a large number of gene expressions measured from the same biological pathway). Therefore, an FCM data set, despite its smaller size, may have a higher information context than traditional untargeted assay. An example of this is demonstrated in a recent study of normal pregnancy in which a mass cytometry data set, despite its relatively small number of cell types and signaling pathways measured, required a higher number of principal components to account for 90% variance than large microbiome and transcriptomics datasets with tens of thousands of measurements  [1901].