dataesr/scientific-paragraphs-categorization huggingface dataset:
Alg. 1, thread management is handled by OpenMP. The number of threads can be smaller than the number of active spheres. The multithreading stage transliterates the one of Alg. 5. Statement 2ι of Alg. 5 is implemented through a constraint graph (see Section 3. 1). Statements 10ι through 13ι are expressed as follows in MultiThreadECMC. where the memory_order qualifier may take on different values (see Section 3. 2). Important differences with Alg. 5 are discussed in Remarks 11 and 12. Output is as for Alg. 1. Code availability. Alg. 6 is implemented in MultiThreadECMC. cc. It is executed in several validation and benchmarking scripts (see Section 3. 2).
and validation suites. The Scripts directory provides the following bash scripts to compile and run groups of programs and to reproduce all our results: Script Summary of usage Setup. sh Prepare C t=0 , G, L ref SequentialC4. sh Test suite for Alg. 5 with N = 4 SequentialC5. sh Test suite for Alg. 5 with N = 5 (see Remark 12) Ordering. sh Test suite for Alg. 6 with N = 5 (see Remark 12) ValidateG. sh Validate constraint graph PValidateECMC. sh Validate Alg. 4 against L ref CValidateECMC. sh Validate Alg. 6 against L ref BenchmarkECMC. sh Benchmark MultiThreadECMC. cc, generate Fig.
Alg. 1, thread management is handled by OpenMP. The number of threads can be smaller than the number of active spheres. The multithreading stage transliterates the one of Alg. 5. Statement 2ι of Alg. 5 is implemented through a constraint graph (see Section 3. 1). Statements 10ι through 13ι are expressed as follows in MultiThreadECMC. where the memory_order qualifier may take on different values (see Section 3. 2). Important differences with Alg. 5 are discussed in Remarks 11 and 12. Output is as for Alg. 1. Code availability. Alg. 6 is implemented in MultiThreadECMC. cc. It is executed in several validation and benchmarking scripts (see Section 3. 2). cc: 10ι → j->tag. compare_exchange_strong(. static,. 11ι → if (j->tag. load(memory_order) == iota) 12ι → if (tau < distance) 13ι → if (x == j->x),.
and validation suites. The Scripts directory provides the following bash scripts to compile and run groups of programs and to reproduce all our results: Script Summary of usage Setup. sh Prepare C t=0 , G, L ref SequentialC4. sh Test suite for Alg. 5 with N = 4 SequentialC5. sh Test suite for Alg. 5 with N = 5 (see Remark 12) Ordering. sh Test suite for Alg. 6 with N = 5 (see Remark 12) ValidateG. sh Validate constraint graph PValidateECMC. sh Validate Alg. 4 against L ref CValidateECMC. sh Validate Alg. 6 against L ref BenchmarkECMC. sh Benchmark MultiThreadECMC. cc, generate Fig. 3] GenerateG3. py Generate G (3) (Section 3. 1) PruneG. py Prune G (Section 3. 1) GraphValidateCellECMC. cc Validate G against cell-based ECMC GlobalTimeECMC. py Alg. 2 (Section 2. 1) SingleThreadLocalTimeECMC. py Alg.
g. ,  Shiota et al. , 2014) , IPS-ENLIL (e. g. ,  Odstrcil, 2003; Jackson et al. , 2015) , or EUHFORIA (e. g. ,  Pomoell and Poedts, 2018). The solar wind plasma parameters measured onboard one of the spacecraft can also be propagated with the help of a simple 1D MHD model such as Heliopropa (http://heliopropa. irap. omp. eu) to another planet, comet, or spacecraft in order to provide a virtual solar wind monitor and predict the properties of the solar wind in another heliospheric location. 3) Solar Energetic Particles (SEPs). The study of their generation, acceleration and transport processes and their flux-time profiles at different locations in the solar wind (e. g. ,  Miroshnichenko, 2018). Moreover, combining in situ SEP data with type-II radio burst data, and global MHD simulations may provide information regarding the structure of the background interplanetary magnetic fields and the propagation of ICMEs.
AMDA Tool AMDA is an online database and analysis tool (http://amda. cdpp. eu/) developed for 15 years by the CDPP (http://www. cdpp. eu/). It gathers a large variety of plasma data from space missions and ground instruments together with simulations and models. It also offers a dedicated work space to all registered users where plot layouts, data mining conditions, and event lists can be stored and reused. Hundreds of user accounts have been provided over the years from space physics students to senior researchers. Several papers describe the basics of AMDA and its various applications in heliophysics and planetary sciences  (Génot et al. , 2021, and references herein). Registration is done by sending an mail to amda@irap. omp. eu, and a public access (with no workspace) is also available.
, 2020). The quadrature geometry and the combination of the in situ and remote sensing measurement would allow to observe the global structure of the eruptive events as they evolve in the interplanetary space and contribute to the understanding of their initiation at the Sun. In a recent study,  Davies et al. (2021)  could investigate in situ the propagation of an ICME through the interplanetary medium as Solar Orbiter, BepiColombo, and Wind spacecraft were radially aligned, and could observe its global shape and expansion using the remote observations from STEREO-A as it was in a quadrature geometry with respect to the Sun-Earth line. A detailed description of AMDA's data mining conditions can be found in the Bepi-CoObs WG AMDA manual (see data availability statement). It is worth noting that the travel time of the plasma from one spacecraft to another was not taken into account when selecting the different events and this should be taken into account when considering the radial alignment case studies.
WINDOWS OF OPPORTUNITY AND FUTURE PLANNINGS Using the AMDA tool and applying the method described in section 3, the investigation of the joint observations with BepiColombo by the CoObs WG, has led to 921 windows of opportunity between September 1st, 2020 and November 30th, 2025. We note that this large number of events is mainly due to the "Parker" geometry condition, that was applied assuming different ranges of the solar wind speed. The full list of events is publicly available on AMDA, and will be shared on BepiColombo's Cruise Science study group ESA's website (see data availability statement). The timetable includes an identifier (ID) for each case study, the starting and ending time of the observation window, the geometry, the name of the spacecraft or objects, whether the event coincides with any particular flyby of BepiColombo, Solar Orbiter or PSP, or to an EP period.
It is supported by a database (The COVID-19 HLA and Immunogenetics Consortium, 2020b) and its role is the centralization of relevant HLA and clinical data for COVID-19 study. It contains HLA data of 2,892 individuals from nine projects. These data are freely available and new data can be easily uploaded upon account creation. In addition, the website allows HLA allele frequencies visualization, and use of HLA data management and analysis tools. An HLA Imputation Portal (HIP) is set up to allow geneticists to infer individuals HLA alleles using SNP genotyping data, relying on multi-ethnic models from Zheng et al. (Zheng et al. , 2014). This tool may help leverage SNP data to gain power in HLA association studies. The CHIC also produced a broad review on immunogenetic parameters (e. g. , HLA, KIR, complement, cytokines and chemokines receptors) and their role in COVID-19  (Aguiar et al.
Researchers now routinely identify novel HLA alleles  (Nilsson et al. , 2018; Ralazamahaleo et al. , 2019; Loginova et al. , 2020; Ananeva et al. , 2021a; Ananeva et al. , 2021b; Cheranev et al. , 2021; Loginova et al. , 2021)  using NGS and confirm them using SBT with PCR-SBT error often responsible for non-concordance between the two. NGS-based sequencing of multiple exons and introns has led to increases in the growth of the IPD-IMGT-HLA Database collection  (Robinson et al. , 2015; Robinson et al. , 2019). Unfortunately, the total number of new alleles may be underestimated as it is not uncommon for new alleles to be NGS-typed without Sanger validation. So-called third-generation NGS generates unambiguous, phased HLA genotypes, using instruments like the PacBio SMRT  (Mayor et al. , 2015)  or Oxford Nanopore Technology  MinION (De Santis et al.
HLA Data Formats The modern and legacy nomenclature systems are still in use, which often makes data comparison and meta-analysis difficult. In addition, HLA alleles are stored in multiple formats which impact their use with bioinformatic tools. TSV or CSVs have been used to store HLA genotypes, usually organizing individuals in rows and HLA genes in columns (with two columns for each gene). Such files are often generated manually, but are used by multiple population genetic and disease-association applications  (Lancaster et al. , 2007; Excoffier and Lischer, 2010; Pappas et al. , 2016). More strictly-defined bioinformatic-oriented formats include HLA PED (or HPED)  (Choi et al. , 2021) , an HLA-focused extension of the PED format  (Purcell et al. , 2007) ; Variant Call Format (VCF), as used by BEAGLE  (Browning et al.
, 2018) , in which HLA allele names are recoded as multiple binary identifiers, and Histoimmunogenetic Markup Language (HML), an XML format developed specifically for exchanging HLA and Killer-cell Immunoglobulin-like Receptor (KIR) genotype data  (Milius et al. , 2015). The IPD-IMGT/HLA Database releases new and updated reference sequences and allele names every 3 months. Individuals datasets may have been generated under any release version, which is why tools like the Allele Name Translation Tool (ANTT) have been developed to standardize datasets to a common release version. (Mack and Hollenbach, 2010). Development of a standardized means of storing and sharing data is still underway. In 2015, the MIRING reporting guideline  (Mack et al. , 2015)  introduced standardized data elements and a controlled vocabulary for HLA genotype data and meta-data, which were implemented in HML  (Milius et al.
, 2015). An HML message includes information on the IPD-IMGT/HLA Database version, the entity and how they generated the data, as well as references to external sources (e. g. , reference sequences and aligned read). HML is used to transmit HLA genotyping data to the National Marrow Donor Program (and other similar registries and donor centers), but has yet to be adopted for genetic-analysis applications. Most of the existent HLA analysis applications require fewer data elements than are included in an HML message. Given the number of different applications of HLA data, new informatics tools can influence the interpretation of this information. Multiple ancillary tools have been developed for HLA research. Whether they allow researchers to run rapid association analyses, extract new information from data, or link HLA genotypes to novel fields of translational research, all contribute to the advances in the HLA research (Table  1 ).
, 2015). Moreover, general pipelines for analyzing NGS data from WGS do not work for HLA genes; because they present high sequence similarity, it is very common that a short read (a sequence generated in NGS procedures) from one gene aligns to another gene (cross-mapping), leading to genotyping errors (e. g. , HLA-A and HLA-H, or HLA-C and HLA-B)  (Castelli et al. , 2018). The intense polymorphism observed in HLA genes may bias read alignment when using a single genome reference, especially when one individual presents too many modifications compared to the reference genome. This issue overestimates reference allele frequencies and causes genotyping errors  (Brandt et al. , 2015). Therefore, it is mandatory to use methods tailored for HLA genes to get reliable genotypes and haplotypes at the SNP level from NGS data.
HLA Allele Imputation HLA genotyping data can also be generated using HLA imputation tools, which generate genotypes for individuals on the basis of LD between GWAS-derived SNP data for the MHC region and specific HLA alleles. These methods ultimately rely on reference datasets of HLA and SNP genotypes for the same individuals, and have become increasingly accurate in their predictions as new algorithms are developed. Following the opportunity brought by SNP to SNP imputation, SNP to HLA imputation algorithms offered a quick and easy way to obtain HLA genotypes from widely available GWAS SNP genotyping data  (McCarthy et al. , 2016). SNP to HLA imputation relies on reference panels of individuals with known SNPs and HLA genotypes, to generate links between SNPs, haplotypes, and HLA alleles using machine learning algorithms (Figure  2 ).
The first published algorithms, SNP2HLA  (Jia et al. , 2013)  and HLA*IMP  (Dilthey et al. , 2011) , were based on different implementations of hidden Markov models; SNP2HLA used BEAGLE  (Browning and Browning, 2009) , a haplotyping and SNP genotype imputation tool. In 2014, Zheng et al. proposed HIBAG, an attribute bagging method tailored for HLA data  (Zheng et al. , 2014) , which showed better performance than pre-existing tools, and at the time was the only method to provide population-specific reference panels for hundreds of individuals while enabling construction of personalized reference panels building. Initial independent reviews suggested that SNP2HLA performed better on 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository  (Karnes et al.
This has led to the creation of reference panels with limited HLA diversity. While accuracy values are often reported as the ultimate answer to a model viability, these values can be misleading. For a rare HLA allele in a validation dataset, a 90% accuracy value can be achieved if that allele should be imputed 20 times out of 2,000 alleles (i. e. , 1,000 individuals) but is never predicted. Therefore, other metrics (e. g. , sensitivity, specificity, or F1 score  (Cook et al. , 2021) ), must not be overlooked. Admixed populations are formed by individuals from different genetic backgrounds in variable proportions, and HLA imputation can be sub-optimal if the reference panel is only drawn from one of the ancestral populations. Conversely, a reference panel from an admixed population with a different overall genome proportion from the individuals being imputed may also provide inaccurate results.
To effect worldwide improvement in HLA imputation efforts, we led the creation of an international consortium, the SNP-HLA Reference Consortium (SHLARC), whose aim is to gather data to represent the extreme diversity of HLA alleles, fostering accurate imputation  (Vince et al. , 2020b). We further advocate for improvements to current HLA imputation tools and for the development of a platform promoting easy access to HLA imputation for immunogeneticists. Though HLA imputation is not yet suited for clinical settings, generalization of HLA association studies offers a new way to investigate immune pathologies  (Meyer and Nunes, 2017). New versions HLA*IMP  (Motyer et al. , 2016)  and SNP2HLA have been released (e. g. , MHC*IMP  (Squire et al. , 2020) , CookHLA  (Cook et al. , 2021) , and Deep-HLA  (Naito et al.
HLA Allele Frequencies The diversity of HLA alleles across geographically separated populations is thought to be the result of balancing selection due to local pathogens  (Meyer and Thomson, 2001). The allelefrequencies. net database has the most extensive collection of HLA allele frequencies in diverse populations  (Middleton et al. , FIGURE 2  | HLA imputation from GWAS data. Reference panels are created from individuals with known SNP and HLA data. Depending on the method, an algorithm will deduce the probability of a specific HLA allele in the population given a SNP haplotype. These new found links are stored for that reference panel and applied to new SNP data to infer HLA genotypes. HLA-A is given as an example with a truncated list of alleles; other MHC genes are imputed using the same method.
2003 ). In addition, HLA typing conducted by bone marrow registries may constitute a local estimation of HLA allele distribution in a population  (Sacchi et al. , 2019; Schmidt et al. , 2020). It is possible to statistically analyze the correlation (e. g. , via linear regression or Pearson coefficient) between a quantitative value, such as the number of COVID-19 cases, and the HLA allele frequencies obtained from a different sample in every studied population (e. g. , in a database or registry). However, while these correlations are faster and easier to obtain than new HLA genotypes, they may result in spurious correlations because: 1) most of the HLA alleles (and observed haplotypes) have a low frequency. For example, according to allelefrequencies. net, in the 416,581 individuals from the African-American NMDP population in the United States, two-thirds of the 321 HLA-B alleles at two-field resolution have frequencies below 0.
In silico Peptide Binding HLA molecules present endogenous and exogenous peptides, however, affinities for these peptides vary greatly depending on the peptide conformation and the peptide cleft topology and chemistry. Whether an HLA allele presents several or few peptides derived from one specific pathogen is one mechanism potentially explaining the strong immune response or tolerance towards it. Researchers can use prediction tools, such as NetMHCpan  (Nielsen and Andreatta, 2016; Jurtz et al. , 2017) , trained on binding affinity and elution assays, to evaluate the number of potentially bound peptides for any HLA class I allele. The "pan" methods, contrary to the "allele-specific" methods, use similarities in sequence data to predict the peptide binding capacity of HLA alleles for which no information is available.
HLA Allele Association Studies Association studies of HLA alleles offer a more relevant biological explanation, based on peptide presentation. HLA allele data can come from different sources, including various epochs of HLA typing and HLA imputation from SNPs (see above). These data can be analyzed as is, or low resolution HLA data can be "upgraded" using the HLA-Upgrade tool from the Easy-HLA website, which statistically impute the most probable two-field genotype based on a haplotype database  (Geffard et al. , 2020). Once HLA data from multiple sources have been standardized for allele content and resolution, a frequency cut-off value is usually applied to test only those alleles with sufficient occurrences in the dataset to guarantee statistical power in the analysis. HLA alleles being highly polymorphic, they often display lower frequencies, and a larger sample size is usually required to obtain significant results compared to SNP analyses.
The success of statistical ML models in diverse applied settings stems from their ability to identify relationships across multiple noisy inputs. However, such models typically require access to large, curated/annotated datasets to make high-quality predictions  (Hastie, Tibshirani, & Friedman, 2009) , and often constitute "black boxes" with (sometimes) millions of uninterpretable parameters. Consequently, it becomes near impossible to determine why models make the decisions they do  (Gunning, 2017). Cognitive scientists, on the other hand, develop and implement theory-driven models capable of explaining and interpreting human empirical data  (McClelland, 2009). Such models usually have a fixed mathematical structure representing specific theoretical assumptions and ideally capture input data variations through a limited number of free parameters that map onto psychological measurements and cognitive processes.
The current study We are interested in potential ways to combine the strengths and mitigate the weaknesses of ML and cognitive modeling. The current work is a first exploration of this endeavor. Our central research question was whether insights from a cognitive model could be leveraged to enhance the predictive accuracy of an ML model. To start, we identified a promising largescale, naturalistic dataset to assess our integrated approach against. Second, we developed specific implementations of an ML model and a cognitive model of learning and retention for integration. The dataset we selected was from a naturalistic task in the domain of language learning. This dataset was from the 2018 Second Language Acquisition Modeling (SLAM) challenge organized by Duolingo  (Settles, Brust, Gustafson, Hagiwara, & Madnani, 2018).
Duolingo published learning data spanning the first month of new users on their platform (see the Dataset section below for details) and challenged the international research community to submit models that could predict user accuracy on withheld data. For the ML model, we chose a gradient-boosted decision tree (GBDT;  Friedman, 2001). GBDTs are available in multiple out-of-the-box implementations that are readily deployed and perform well on a wide range of prediction tasks  (Bentéjac, Csörgő, & Martínez-Muñoz, 2020). They are generally used to model tabular data and several top teams in the 2018 SLAM challenge also utilized decision tree ensembles (see table 2 in  Settles et al. , 2018). For the cognitive model, we chose the predictive performance equation (PPE). The PPE was developed as a knowledge tracing model that could account for the nonlinear, multiplicative effects of learning and forgetting over time, with a particular focus on predicting future performance  (Jastrzembski, Gluck, & Gunzelmann, 2006).
Specifically, the information contained in the training history that is most relevant to predicting retentionmodel time and stability-before the ML model is trained. This restricts the feature space of the dataset, which might make it easier for the ML model to search for the best prediction function. This "smoothing" should be beneficial if the cognitive model's mechanism captures relevant statistical regularities in memory over time. We expect that both of these potential benefits would emerge depending on how much data is available to train the model. With sufficient data, the GBDT will learn the optimal mapping with or without the cognitive insights from PPE. With limited amounts of training data, however, transforming the raw timing data using PPE's assumptions about underlying human memory processes is expected to yield benefits to the GBDT.
Consequently, we present explorations of various restricted data scenarios and demonstrate that, indeed, a state-of-the-art ML model's predictive validity is enhanced when it leverages the insights of a cognitive model. In the following sections, we will detail the methods of our approach, focusing on the dataset used and the specific implementations of our modeling approach. We will then present the results of our explorations, highlighting the conditions under which the PPE-enhanced model might be most advantageous. And finally, we will conclude by discussing our findings and presenting implications for future work.
Dataset For the current explorations, we used the dataset Duolingo released for the 2018 SLAM challenge  (Settles et al. , 2018). These data contain learning histories from three languages over a 30-day period. 1 Specifically, they "sampled from Duolingo users who registered for a course and reached at least the tenth row of skill icons within the month of November 2015. By limiting the data to new users who reach this level of the course, we hope to better capture beginners' broader language-learning process […]"  (Settles et al. , 2018, p. 56). The data from each of the three language tracks are entirely separate. We focused on the English track for our current analyses, as this track represented the largest subset of data. The user's task was to accurately respond to a range of language learning exercises that included various forms of translation and listening (see fig.