dataesr/scientific-paragraphs-categorization huggingface dataset:
This results in a new functional volume (with 4 dimensions), with functional time-series on the white matter voxels, that can then be statistically analyzed in the same way as a classical fMRI volume. Here, we used the Functionnectome program to project the signal of two task-fMRI datasets onto the white matter (finger-tapping dataset, n = 46; working-memory dataset, n = 45). The functionnectome volumes of each participant were then statistically analyzed by generalized linear modeling (GLM) to reveal the corresponding activation maps. Both first-and second-level analyses were done using FEAT (FMRI Expert Analysis Tool, v6. 00), from the FMRIB Software Library (FSL), yielding a group-level activation z-map per task. In the present study, we created one set of white matter priors per set of tractograms, for a total of eight sets of priors, namely: two whole-brain sets, extended and non-extended; four association extended sets (all fibers, and separately short, medium, and long association extended fibers); one commissural extended set; and one projection extended set.
Enhancement of the activity detection in the white matter We first explored the effect of extending the WM streamlines in the GM on the structural-functional analysis. We therefore analyzed the involvement of the white matter in the right-hand finger-tapping task by contrasting the activation maps from the Functionnectome analysis using the priors generated with and without streamline extension. The resulting maps (Fig. 4 A-B ) showed that using the extension method significantly improved the statistics of the activated regions (i. e. , with z>3), resulting in an average increase of 12% (p<0. 0001) of the z-values across the map. The analysis was repeated with the working-memory dataset (Fig  5 A-B ), which also demonstrated a significant increase of 4% (p<0. 0001) in z-values across the map when using the extended tractograms.
While the increase in the significance of the activations was somewhat modest, it should be noted that the tractography used in the present study was very "aggressive", attempting to maximize the estimation of the brain connectivity by generating a vast number of streamlines. It is entirely possible that the streamline-extension procedure would have a much stronger effect on less dense tractograms. This method has thus the potential to become a standard step applied to tractograms when used in this framework. It should be noted, however, that for the same region the connectivity changes according to the layer of the cortex studied  (Pandya et al. 2015). While such estimation is beyond the resolution available nowadays, future endeavors based on higher resolution diffusion weighted imaging datasets should adapt streamline elongation to the anatomical principles of the cortical architecture.
This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 818521 ). Data were provided by the  Human Connectome  Project, WU-Minn Con-sortium (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the  16 NIH Institutes and Centers  that support the  NIH Blueprint for Neuroscience Research  and by the  McDonnell Center for Systems Neuroscience at Washington University. Victor Nozais  received financial support for this project from the  "Consulat Général de France à Québec " and the " Conseil Franco-Québécois de Coopération Universitaire " as part of the  Samuel de Champlain Program for the Development of Strategic Partnerships in Education and Research.
Other authors have chosen to describe the polarization curve taking into account the behavior of the electronic components  [22] ,  [24] ,  [30]. In  [31] , the electrochemical behavior of a PEMFC has been taken into consideration for the development of an ECM and a Matlab-Simulink ® model has been established. The authors in  [32]  used the equivalent double-layer capacitor to avoid step-transient voltage variations. This capacitor has been optimized based on the transient phase convergence and its lifetime. In  [33] , a state space model of a PEMFC DC/DC converter system has been presented. This model describes the relation between different electrical variables and can be applied to any FC operating point. The authors in  [34]  presented a simplified and novel dynamic ECM for PEMFC suitable for power system analysis and design.
Thus, with this model, it is possible to perform dynamic simulations of power electronic systems directly connected to the PEMFC stack without the need to parameterize with special EIS measurements. In  [43] , an electrically controllable ECM for PEMFC is developed and implemented in MATLAB ®. In addition, the authors presented a method to parameterize the models developed in MATLAB-Simulink ® through experimental results. The authors in  [44]  applied an ECM to fit the EIS measurements of laser coating samples measured at open circuit potential. The diagram of the circuit for this ECM takes into consideration the resistance of the solution, the charge transfer resistance at the electrolyte interface, and the Warburg resistance. In  [45] , a new diagnostic technique has been developed to determine the hydration of the PEMFC using an ECM.
IV. PARAMETER ESTIMATION In this section, the parameters of (  10 ) are estimated. To adequately estimate the parameters, the five different PEMFC current tests (I i , where i takes the value between 1 and 5) were analyzed. Each test I i was estimated using Matlab ® Curve Fitting Application. Among all the functions available in this application, the function with the best fit for each I i was the Fourier series of 4 terms, see  (11). I i (t) = a i,0 + 4 j=1 (a i,j • cos[w i • t] + b i,j • sin[w i • t]). (11) Table  4  shows the different values of the parameters for the Fourier series of each current test I i. Fig. 6  and 7 illustrate the behavior of the five current tests and the behavior of their corresponding Fourier series. Fig. 6  shows the behavior of the three tests of increasing current while Fig.
These voltages are calculated using  (20). η act,i,0 + η con,i,0 = V ini -R mem • I i (0) -V fc,i (0), (20) where η act,i,0 + η con,i,0 = η act,c,i,0 + η con,c,i,0 + η act,a,i,0 + η con,a,i,0. From this result, the parameters of (10) were calculated (i. e. the parameters V ini , R mem , C c , C a , τ c , and τ a ). Due to the monotonic behavior of the polarization curve, it was estimated V ini = max[V fc ] (i. e. the maximum experimental voltage obtained, V ini = V fc (0) for increasing current tests and V ini = V fc (t f ) for decreasing current tests, where t f is the final time of the test). Besides, to facilitate the construction of the electronic circuit, the capacitors must be equal, so it is assumed that C c = C a. To calculate the parameters R mem and C c , a script was developed using MATLAB ® software.
The script and the MATLAB command lsqcurvefit (based on the least-square regression algorithm) were applied to adjust the parameters of the model to the voltage data obtained experimentally for the different electrical current tests. The values obtained were R mem = 0. 50781 Ω, and C c = 1. 8857 F. According to the references  [55] -  [57] , τ c and τ a are dependent on voltage and current. However, as a result of the script implementation, the values obtained for τ c and τ a were estimated as functions that only depend on the PEMFC voltage as follows. τ c = 0. 61 • exp - V fc -37. 48 1. 689 2 , (22) τ a = 0. 832 • exp - V fc -35. 75 7. 172 2. ( 23 ) In the next section, parameters estimation is used to validate the proposed model through a comparison of the numerical simulations with experimental results.
ACKNOWLEDGMENT The authors are grateful for the support provided by  Consejo Nacional de Humanidades, Ciencia y Tecnología (CONAH-CYT) Mexico. Furthermore, the authors extend their gratitude to  Dr. Luis H. Diaz-Saldierna  from  IPICYT  and  M. S. Carlo A. Beltran  from the  School of Sciences-UASLP , for their invaluable contributions in the experimental aspect.
Patient cohorts Solve-RD collects phenomics and genomics data from patient cohorts  7  and releases them as data freezes. The present study employs data selected from the 2020 Data Freeze 1 containing data of 8,730 cases and their unaffected relatives, a case being de ned as an index case or a relative case affected by a RD, submitted by ERNs to the RD-Connect Genome-Phenome Analysis Platform (GPAP)  12. This initial dataset contains 1,101 "solved cases" (patients annotated as "solved" in the GPAP platform by the ERN data submitter, the "Solve-RD solved cohort") and 7,269 "unsolved cases" (patients annotated as "unsolved" by the ERN data submitter, therefore with an inconclusive whole exome/genome sequencing WES/WGS result, the "Solve-RD Cohort 1").
Ontologies used To ensure data standardization, Human Phenotype Ontology (HPO)  13  , Orphanet Rare Disease Ontology (ORDO)  14  and HOOM (HPO-ORDO Ontological Module)  15  were used. HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. For this study, we employed the HPO 2020-10-12 release (including 15,656 unique HPO terms). ORDO is a structured vocabulary for rare diseases derived from the Orphanet knowledge base including relationships between diseases, genes amongst other features, enabling for computational analysis of rare diseases. This study uses ORDO v3. 1, Dec 2020 including 9,338 active clinical entities. Clinical entities in ORDO are designated in this paper by their unique identi er in Orphanet, the ORPHAcode. HOOM is an ORDO module that quali es the association between a clinical entity and its HPO-based phenotypic abnormalities according to their frequency.
Data management The overall Solve-RD data management is described elsewhere  7, 8. Brie y, after data submission to the RD-Connect GPAP it is shared within the Solve-RD consortium via the European Genome-Phenome Archive (EGA)  16. We retrieved data for our analysis through a cloud-based central database RD3 (rare disease data about data) using the MOLGENIS open-source data platform  17. RD-Connect GPAP ensures data standardisation, pseudonymisation and harmonisation according to GA4GH-approved standards (Global Alliance for Genomics and Health)  18  , in a computer-readable format (Phenopacket)  19  which enables exchange of phenotypic and family information. Data and models employed Figure  1  a. Study population: From the 2020 Freeze 1 (8,730 cases submitted by the four core Solve-RD partnering ERNs) we built our study population by ltering the cases as depicted in Fig.
1A:  (a) 644 cases with no causative gene were removed from the solved cases pool and added to the unsolved cases sub-cohort, as cases without a causative gene are considered unsolved in this analysis; (b) cases without phenotype annotations were removed from both populations (72 solved and 3,289 unsolved cases) corresponding to (1) records of patients' relatives for which there were no phenotypic data (therefore not considered as "cases" in this analysis) and (2) non-annotated cases that are not exploitable for a phenotypic comparison. Cases bearing at least 1 HPO annotation, either patients or affected patients' relatives, were considered as cases for the purpose of this study. The resulting nal population used therefore includes 385 solved cases (4,6% of the initial cohort) and 4,624 unsolved cases (55,2% of the initial cohort).
Reference data: Reference datasets were obtained after applying the following ltering to the ORDO v3. 1 dataset (Fig. 1B ). "Groups of disorders" and "inactive clinical entities" were removed. Of the resulting active entities (6,171 disorders and 993 subtypes of disorder) only entities associated with (a) at least one gene (3,789 entities) and (b) at least one HPO term (3,955 entities) were kept. Thus, the resulting 2,259 clinical entities which satisfy (a) and (b) serve as reference data (31. 5% of Orphanet active clinical entities).
Work-up design A 3-step work ow was designed using a solved case as a starting point (triggering case of a phenotypic similarity calculation cascade, represented in Fig. 2 ). Each step in the work ow generates a cluster around each point of interest in the cascade thus maximizing the one-to-all phenotypic similarity explorations through in-house Python 3. 8 scripts. Step A ("solved case analysis") aims to check that the solved case phenotype corresponds clinically to the corresponding known RD caused by mutations in the same gene (A1). In case of clinical inconsistency, the similarity algorithm is run in order to detect the rst 50 similar disorders; their causative genes are then re-analysed for the triggering case based on data present in RD-Connect GPAP to eventually detect further candidate variants (A2).
It is located in the part of the gene coding for the protein motor region and it was found in a case at rank 16 of similarity, with a phenotypic description of pure spastic paraplegia. Despite the fact that parent's DNA is not available and no functional analysis can be carried out, this nding is likely to solve the case. The SPAST missense variant identi ed (c. 134C > A;p. Pro45Gln, (Supplementary Table  A )) in an unsolved case within the SPAST cluster bears con icting evidence of interpretation in reference databases and it does not segregate in the other symptomatic siblings; therefore, this case remains unsolved. The variant found in the TBL1XR1 gene (c. 1184T > A (p. Tyr395Phe)) for the unsolved case in the TBL1XR1 case phenotypic cluster did not explain the typical Amyotrophic lateral sclerosis (ALS) phenotype of the patient based on the feedback from the clinical expert, although it is described as likely pathogenic, pointing out the need for variant reclassi cation.
Step C was intended to use the solved case-Orphanet disorder similarity to expand the phenotypic clusters and therefore the genes proposed to be re-examined. In step C1, the causative gene for both the triggering solved case and its related Orphanet disorder was examined. Five SPAST pathogenic variants were found in 5 cases clustering around Autosomal spastic paraplegia type 4 (ORPHA:100985) that were solved in the meantime, and were therefore considered as positive con rmations of our analysis. Five KIF5A candidate variants were found in four unsolved cases in a SPG10 (ORPHA:100991)-centred cluster (Fig. 3 ). Despite their classi cation as likely pathogenic in ClinVar, they were discarded because not in the motor region of KIF5A gene, suggesting a variant misclassi cation in reference databases.
This approach enables to rapidly lter genomic data from thousands of datasets by a speci c gene list generated for each case and step as an output of the similarity algorithms. Resulting is a list of candidate variants based on any pre-de ned ltering approach, in this case rare variants with a high or moderate impact at the protein level. The whole process is scalable and can be automated. Current limitations lie in data interpretation as the list of candidate variants can be fairly long (> 10 variants per case) in steps comparing up to 50 individuals / disease entities. The elevated number of patients assessed made it impossible for clinicians to evaluate all variants, therefore we had to restrict the submission of results to variants classi ed as likely pathogenic or pathogenic according to ACMG criteria, missing variants of uncertain signi cance that could be re-classi ed as pathogenic after expert evaluation.
ACKNOWLEDGEMENTS The authors would like to thank the data submitters of our triggered cases,  Vincenzo Nigro ,  Rebecca Schule ,  Laurence Faivre , and all the clinicians who attended to the 4 jamborees for their participation and data interpretation. This study was supported by the  European Reference Network (s) [ERNs:  ERN-ITHACA , ERN-RND,  ERN-Euro NMD ,  ERN-GENTURIS ] (https://ec. europa. eu/health/ern/networks_en). The authors are grateful to  Charlotte Rodwell  who contributed to the organisation of the work presented in this article as well as proof-reading.
Test execution overheads alone can limit the scalability of the technique. For instance, in our experiments, we needed around 48 hours to execute the mutants for a single component of the systems we examined. At the same time the manual effort required by testers is escalated with larger programs as the number of mutants grows proportionally to program size. To reduce application cost, it is imperative to limit the number of mutants to those that are actually useful, prior to any manual mutant analysis or test execution. Thus, we need to identify which mutants are killable in order to limit the manual effort involved in their identification, and also to identify the mutants that are subsuming (disjoint) 1 , in order to reduce unnecessary computations, and to provide accurate adequacy measurements  [46].
Machine Translation Machine Translation can be considered as a transformation function transformðXÞ ¼ Y , where the input X ¼ fx 1 ; x 2 ;. ; x n g is a set of entities that represents a component to be transformed, to produce the output Y ¼ fy 1 ; y 2 ;. ; y n g, which is a set of entities that represent a transformed (desired) component. In the training phase, the transformation function learns on the example pairs ðX; Y Þ available in the training dataset. In our context, X contains the source code with an annotation that indicates the location and type of the mutation operator applied, and Y contains the same information, plus a label that indicates whether the mutant is subsuming or not. The transformation function is trained to append the label to a given mutant by training the function on the example pairs (Code+MutationAnnotation, Code+MutationAnnota-tion+Label), where Code+MutationAnnotation represents the source code with an annotation in the statement to indicate the mutation operator type applied.
r. t. test suite T ). If test suite T detects some bug in program P , then P has to be fixed and the same mutation testing procedure can again be employed. It is worth mentioning that there are two major cost factors in mutation testing, these are the equivalent and subsumed mutants. This is because they introduce overheads both during test generation and test execution, leading to minor test effectiveness improvements. Therefore, to reduce mutation testing effort while preserving its effectiveness, it is essential to focus on subsuming mutants. Hence, we develop Cerebro, a machine learning technique that learns from mutants' surrounding context to predict which mutants are subsuming. Given the input program, P and the set M of mutants, Cerebro selects a subset M 0 of mutants that is probably subsuming (predicted as subsuming by Cerebro), to be used for mutation testing (to guide testers and evaluate test effectiveness).
Pairs Generation The mutation operation (ReturnValsMutator 5 ) shown in Fig. 4c  represents a mutant in which the sentence return null is replaced by throw new java. lang. RuntimeException() exception. Notice that this mutant is labeled as subsuming in our dataset, since there is only one test that can kill it, when the input option is null. Hence for training we consider S as the label to be learned by the translator to predict this mutant as subsuming. To do so, we train in pairs (MutantAnnotation, Muta-ntAnnotation+Label), where the first component is the annotated code shown in Fig. 4c , and the second component is the same code with the predicted label, i. e. , MST [ReturnValsMutator]MSP[S] in our case, to indicate that the mutant is subsuming. The resulting text is arranged in a single sentence to represent a sequence of space-separated entities (the representation supported by the machine translator).
RESEARCH QUESTIONS We start by checking the prediction ability of Cerebro and ask: RQ1 Prediction Performance: How effective is Cerebro in predicting subsuming mutants? We leverage two datasets, made of C and Java programs, for which extensive mutation analysis has been performed to identify subsuming mutants. We reimplemented 2 techniques that we use as baselines in our analysis. The first baseline is a Random mutant sampling, while the second is a supervised machine learning method based on manually designed features that were used by previous work  [11]  (e. g. , data flow, control flow, etc. ). These features are used to train a binary classifier in order to predict whether a mutant is subsuming or not. Further details about the baselines can be found in Section 6. 3. After analyzing the predictions, we turn our attention to the envisioned application scenario; measuring test effectiveness of the predicted mutants.
Benchmarks and Ground Truth In order to show that our approach is language agnostic, we make our evaluation on a set of C and Java programs. C-Benchmark: To perform our study that requires strong test suites, we used an independently built dataset from related work  [12]. It includes C programs from the GNU Coreutils, 6 that consist of file, text and shell utility programs widely used in Unix systems. The data-set is composed of 48 GNU Coreutils (v8. 22) programs aka subjects (mentioned in Table  1 ), each packaged with an accompanying system test suite, generated by developers. The size of these programs ranges from 1,000 to 14,000 lines of code (LOC), with a median size of 3,500 LOC. For each subject, the data-set includes a mutant-test killing matrix that records, for each mutant, a set of tests that kill it.
1, and label each mutant as either subsuming or nonsubsuming. To make the problem as balanced as possible (to assist in machine learning), we mark as subsuming all mutants in the top of the hierarchies, including mutually subsumed mutants. Needless to say, it is possible to have some noise in our labeling process in the sense that mutants labeled as subsuming may be non-subsuming. The data-set reduced this noise by augmenting the test suites with multiple large and diverse 6. https://www. gnu. org/software/coreutils/ test suites generated by different state-of-the-art tools. Please refer to the threat in Section 9 for a related discussion. Java-Benchmark: For Java we select a set of well-tested open source projects from GitHub. We select projects from the Apache Commons Proper 7 repository of reusable Java components, Joda-Time 8 -a date and time library, and Jsoup 9 -an HTML manipulation library.
Baselines We consider 2 baselines. The first one is the Random mutant sampling that samples uniformly from the entire set of mutants. The second baseline is a Decision Tree classification based on the features proposed by related work  [11] ,  [29]. Previous works showed a strong connection between mutant utility and surrounding code (utility captured through CFG, data flows, AST, etc. features). Thus, we use the mutant features to predict subsuming mutants in both C and Java. Features belong to 4 categories: Mutant Type related features, Control-Flow graph related features, Control and Data dependency related features, and AST related features. In total we used the 28 features, used by the related work  [11] , for the C programs, and implemented 16 of those features for Java 11. We excluded features such as AstChildHasIdentifier and Ast-ChildHasLiteral that we found unfeasible to implement in the employed tools, i.
We found 15 epochs to be a good default for our validation sets. Once model training is complete, we follow the procedure explained in Section 4. 4 to predict whether an unseen mutant annotation sequence is subsuming or not. The codebase of C and Java programs with mutant information, abstracted code, and mutant annotation sequences that the encoder-decoder model trains on and predict, with mapping to the original code, are publicly available at https://github. com/garghub/Cerebro. In addition to our dataset, we have made available our source code and trained models as well.