dataesr/scientific-paragraphs-categorization huggingface dataset:
Hyper-parameter tuning The XGBoost model has been tuned using the Optuna python library  (Akiba, Sano, Yanase, Ohta, & Koyama, 2019). This package is an optimization framework that searches for the best hyper-parameters of the space defined by the user. It uses some distributed computation and early stopping to improve the speed of the solution. It is implemented with various optimization algorithms. In our case, we use the sampling algorithm Tree Parzen Estimator Sampler (TPES)  (Bergstra, Bardenet, Bengio, & Kégl, 2011). It is a sequential modelbased optimization. As a bayesian optimization algorithm, it computes a probability model of the optimization function and selects the best hyper-parameter according to this probability model and the real cost result. For each step, the tuning is done using the mean of the F1-score of a 4 cross-validation method.
RESULTS AND DISCUSSION The results obtained for the three tasks are detailed in this section. Table  2  shows the F1-score for each task of the challenge. The training and testing set results are close, showing good generalization capability. It seems that our models avoid overfitting issues that are difficult to handle with this dataset. Indeed, the imbalance issue and the fact that some variables such as ComponentID have a lot of importance can lead to large bias. ComponentID could be thought of as a domainspecific key factor, if, for example, depending on the type of component, the reparation is possible / allowed or not. For instance, if there is an issue with a microchip, this is far more difficult and costly to solve than an issue with a simple resistance. The following most important features are the metafeatures being in the order of importance more critical the number of defective pins per figure, then component and final panel.
SHAP values The interpretation of the machine learning model described as a black block model is a really important topic in the industry. Indeed, for the acceptance of a model, it is mandatory to explain the model decision to the process experts. The interpretation allows validating the model by comparing the model and experts' explanation of a phenomenon. It also allows recommending some repair actions to the experts. SHAP (SHapley Additive exPlanation) interpretation  (Lundberg & Lee, 2017)  is based on the game theory  ( Štrumbelj & Kononenko, 2014). The idea is to compute an interpretation value called SHAP value and denoted ϕ j for all variables and each sample of the dataset. The output of the model is described by the sum of the SHAP values ϕ j. ϕ j = S⊆J\{j} |S|!(M -|S| -1)!(f (S ∪ {j}) -f (S)) M ! (1) where M is the number of variables, J is the ensemble of variables, f the model output and j the variable index.
Hence, even if an algorithm is proven correct, its translation into source code may introduce errors; • The second step is to use a compilation chain to generate executable code from the source code. Existing works such as the formally proven CompCert  [3]  compiler guarantee the equivalence between a C source code and the machine code generated from it, thereby ensuring that no errors are introduced in this step. Errors in the first step can be prevented by reasoning directly on the source code. This is the approach followed in the two major projects involving formal proofs in operating systems. The seL4 formally proven microkernel  [4]  contains a roundrobin scheduler that refines an arbitrary scheduling policy. Recent seL4 publications on execution flow transfer and scheduling, notably  [5] , have focused on temporal isolation and mixed-criticality systems.
Digger, a tool developed by the Pip kernel team  [15] , translates this specially crafted shallow-embedded C subset  into compilable C code. In this process, the monad (the interface with the verified code) is ironed out and implicitly integrated into our C program. During this process, the bind function from the monad disappears and corresponds to the notion of sequence in C. Similarly, calls to ret corresponds to the return of C. Moreover, calls to functions from the interface remain unchanged, but the translated code calls the actual function's implementation instead of their abstract model. The last feature of Digger is that it can translate specially designed recursive functions. The function must have its upper recursive bound as its first argument, and must decrement it on each call, as can be seen in Figure  2.
This is an alternative to loops, which unfortunately cannot be expressed naturally in functional languages. However, the code inherits the termination guarantee from Coq, and although recursive calls are not as optimal as loops in the general case, compilers are now able to recognize them and automatically convert them to standard assembler loop structures. Let us use the function insert_new_entries_aux to illustrate what the tool actually does when translating code. This function covers all of Digger's previously announced features. The Coq code and its translated counterpart are shown in Figure  2. Fixpoint denotes a recursive function in Coq. The first argument of the function, timeout, is an upper bound on the number of items in the new_jobs list. timeout is translated into rec_bound. The match on timeout is translated to a if and timeout1 is translated to rec_bound -1.
This is the most complicated transformation Digger will ever perform. One can see that the translated code has exactly the same structure as the original code; sequences, conditional statements, and function calls remain intact. Digger is actually a word-to-word translator that does not attempt to interpret the code it processes. It operates only at the syntax level. Even though this translation is not proved, we believe that Digger is a sufficiently trustworthy computational base to include and rely on, and we argue that the proofs written for the original Gallina code also apply to the C code generated by Digger. However, we are aware that one can argue against such a claim. We discuss this further in Section VI.
D. Abstract Types All types used by the verified code are abstract types (black box types). These types have their own interface. There are abstract types for Boolean values, integers, readonly job structures, and read-write job structures. Most of the primitives are simple and straightforward functions for specific data types. For example, there are 3 primitives for the boolean type CBool : not, and, and or. Here one can see the model of type CBool together with the boolean primitive or: Definition CBool := bool. Definition or (b1 b2 : CBool) : RT CBool := ret (orb b1 b2). The actual implementation is defined as follows: typedef int coq_CBool; static inline coq_CBool CBool_or(coq_CBool b1, coq_CBool b2) { return b1 || b2; }; Mutable structures such as type Entry are given primitives for creating the structure and for accessing and modifying their fields.
C. Reasoning on Gallina code and compiled code trust We believe that a toolchain that unquestionably proves every property down to the compiled code is the gold standard when working with formal methods. We strive to achieve this goal, and in this section we elaborate on the topic of trust in the compilation chain. As mentioned in Section III-C, Digger, the automatic translation tool we use to translate the Gallina shallow embedded C to C, currently does not provide formal semantic-preservation guarantees. The lack of a such a guarantee may shed doubt on whether the formal properties proved on the Gallina code really hold for the automatically generated C code (even if we strongly believe that they do). The next step for us is to formally prove that the semantics of the Gallina code is preserved when compiled to C.
There is already related work in the community on this topic. OEuf  [19]  allows a large subset of idiomatic Gallina code to be compiled to C, with the guarantee that valid calls to OEuf-compiled functions will behave in a manner equivalent to the user's original Gallina implementation. -  [19] , Section 2. 1 The Gallina program is compiled down to CompCert's Cminor, so that it can be further compiled with CompCert. Similarly, the CertiCoq project  [20]  aims at verifying the compilation of any Gallina programs in Clight, which can then be further compiled by any C compiler, including CompCert. An important aspect of these two tools is that they add a garbage collector to the compiled C code. A garbage collector is a complex piece of software that allows the developer to take advantage of features that are available in Gallina but not in the native C language or on trusted hardware.
However, similarly to our own work, it is assumed that their compilation tool (front-end) from C to Coq as well as the semantics of Caesium are correct. This type of approach has also been taken by the Verified Software Toolchain (VST) project. One of their most recent contributions is VST-Floyd  [22]. VST-Floyd is an ecosystem of lemmas and tactics designed to facilitate the use of Verifiable C, a higher-order separation Hoare logic. Verifiable C is provably sound with respect to the operational semantics of CLight, which is formalized in Coq. This makes it possible to reason on almost any CLight program and provides methods and tools for end-to-end program verification. Taking cues from all of our peers, we are currently working on a verified Gallina to CompCert C compiler. CompCert C expressions are almost identical to those of C.
ACKNOWLEDGMENTS We would like to sincerely thank  Samuel Hym  for reviewing a significant part of the paper. We also address our heartfelt thank you to  Florence Quèbre , who provided feedback on how to improve the writing style of the document. This work was partly financed by the  TinyPART  project in the context of the French-German cooperation on cybersecurity (MESRI-BMBF conventions n°ANR- 20-CYAL-0005  and  16KIS1395K ).
Recently, our research group conducted a pilot study to assess which type of ADL visual stimulus was the most effective in inducing motor excitability during action observation. EEG signals were recorded in 20 patients with stroke in chronic stage during the observation of task-oriented upper limb tasks (including self-care and feeding actions) and nonfinalized actions. The comparison of EEG rhythms between the 2 groups of motor actions (ie, task-oriented and non-task-oriented) revealed that an increased suppression of mu and beta bands occurred during the observation of the former category  [37]. The procedures described in this protocol, including recruitment, assessments, and visual stimuli for motor action, have been tested in a single-group uncontrolled feasibility study. Preliminary analysis from a qualitative observation of a few collected data by using the proposed protocol showed desynchronization effects of all cerebral rhythms (especially of the beta rhythm) after the AOT treatment (ie, pretest vs posttest assessment).
AOT Protocol for EG@Home After appropriate training of patients and caregivers, the use of tablets will allow home-based treatment. In particular, a tablet will be enabled with a web-based program that will be used to train the patients (and receive feedback on their progress). Participants enrolled in EG@Home will be asked to carefully observe the videos showing different daily actions on the tablet. The web-based program will present each motor act for 2 minutes. At the end of each motor act presentation, the web-based program will ask the participants to execute the observed motor sequence for 1 minute (by audio or video message) with the affected upper limb. Participants enrolled in EG@Home will perform rehabilitation treatment at their homes. A 7-inch tablet with Android software 11. 0 will be used for the experimentation.
Before handing the tablet to the patient, the researcher will schedule the AOT program via the web, defining the days when the patients should perform the treatment independently at home. Patients can check the days on which treatment will be performed by consulting the web-based application, specifically the section where the scheduling of sessions is provided, on the tablet. The research teams will be remotely monitoring the progress of the EG@Home sessions using a dedicated web platform. The web-based application was developed by a specialist following the researchers' specification.
Kinematics Kinematic parameters of the pointing motor task will be acquired at 100 Hz by using a 7-camera motion capture system (Vicon, Oxford Metrics Ltd). Spherical reflective markers for motion tracking will be placed on specific body landmarks. In addition, an inertial measurement unit will be placed over the metacarpophalangeal joint of the middle finger. The data acquired during the assessment trial with respect to the hand trajectory (ie, metacarpophalangeal joint of the middle finger) will be evaluated using the following metrics, related to the entire turn [52]: 1. Number of peaks of the speed profile: If a point-to-point reaching movement has a low number of peaks, it means that few acceleration and deceleration periods are present  [53]. 2. The smoothness described by Teulings's index is the rate of change of the acceleration in a movement  [54] ; a lower value of Teulings's index indicates a smoother movement.
RenderX of training effects (data from T2-T3) will be separately investigated using mixed effect models. Clinical-neurophysiological correlations will also be analyzed: the correlation between the ERD and factors such as the clinical scale score will be studied. To evaluate the differences in trends related to the clinical evolution of the patients, we will define a new variable to group the participants according to the presence or absence of relevant clinical improvements. The relevant clinical improvement will be defined by a clinical specialist for this purpose.
Data Collection and Management The study data will be collected and managed using REDCap electronic data capture tools hosted at the IRCCS San Raffaele Roma Hospital and Casa di Cura del Policlinico Hospital  [62, 63]. REDCap is a secure web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources.
Only those participants who provided informed written consent before starting will be included. During the study, written data will be stored in a closed cabinet; all data, after anonymization, will be entered into a REDCap electronic data capture repository. The input data will be double-checked by another research assistant. Personal data will be discarded after 5 years. The results of this study will be disseminated through peer-reviewed journals and national and international academic conferences only by the professionals directly involved in the clinical trial.
A multitude of demographic, health, and genetic factors are associated with the risk of developing severe COVID-19 following infection by the SARS-CoV-2. There is a need to perform studies across human societies and to investigate the full spectrum of genetic variation of the virus. Using data from 869 COVID-19 patients in Bahrain between March 2020 and March 2021, we analyzed paired viral sequencing and non-genetic host data to understand host and viral determinants of severe COVID-19. We estimated the effects of demographic variables specific to the Bahrain population and found that the impact of health factors are largely consistent with other populations. To extend beyond the common variants of concern in the Spike protein analyzed by previous studies, we used a viral burden approach and detected a protective effect of low-frequency missense viral mutations in the RNA-dependent RNA polymerase (Pol) gene on disease severity.
Here, we prospectively analyzed data from 869 COVID-19 patients receiving inpatient or outpatient care in Bahrain between March 2020 and March 2021. We used paired viral sequencing and non-genetic host data to understand host and viral determinants of severe COVID-19 in Bahrain. Using multivariable regression and a viral burden approach, we detected a protective effect of rare missense viral mutations in the RNA-dependent RNA polymerase (Pol) gene, the target of the drug remdesivir, on disease severity. We find that rare mutations in Pol lead to a small but statistically significant increase in predictive ability for severe COVID-19, and that Pol is one of the most strongly constrained genes in the viral genome. Our results contribute to and support study of COVID-19 in diverse populations and highlight the benefits of studying rare viral mutations.
Discussion The development of severe COVID-19 is determined by patient demographics and medical risk factors in a complex interplay with viral and host genetics. In this study we sought to disentangle this interplay in the Bahraini population by using a large dataset enriched for severe COVID-19 with available viral sequences and metadata on relevant non-genetic host risk factors. We reproduced the expected impact of age and comorbidities on COVID-19 severity. However, we did not find a significant effect of gender on COVID-19 severity, whereas other studies have found that the fatality rates are higher for men than in women  (Beaney et al. 2022 ), risk differences by gender have been shown to vary considerably with geography and time, suggesting that this interaction is mediated by social rather than biological factors  (Danielsen et al.
Study population and data collection We prospectively collected, through the chart abstraction from the Bahrain Ministry of Health (BMOH) medical records, demographic and health information for 1,151 patients in Bahrain with PCR-confirmed SARS-CoV-2 infection between March 4th, 2020 and March 8th 2021. These SARS-CoV-2 infected patients were chosen for sequencing based on (1) sampling to evenly represent asymptomatic or mildly symptomatic SARS-CoV2 patients across the different Bahraini geographic districts, or (2) the development of severe COVID as defined by an O2 saturation <90% or PaO2 <60%, and a physician determined need for ICU hospitalization, and the presence of respiratory disease including ARDS, or viral pneumonia or the need for mechanical ventilation or death due to COVID-19. Host level characteristics including comorbidities were abstracted from the patient electronic health records and physician questionnaires.
SARS-CoV-2 whole genome sequence analysis Consensus SARS-CoV-2 genomes were generated from read data using a reference-based assembly pipeline (https://github. com/broadinstitute/viral-pipelines,  Lemieux et al. 2020 ) with NC_045512. 2 as the reference  (Wu et al. , 2020). We then filtered sequences based on quality summaries implemented in Nextclade using default parameters for SARS-CoV-2  (Aksamentov et al. , 2021 , https://clades. nextstrain. org). Sequences were given a "bad" missingness quality designation if they had over 3,000 sites missing. After removing sequences with a "bad" missingness score, no sequences with "bad" scores on any other quality metric remained. Of the 1,151 patients with isolates submitted to viral sequencing, 988 remained after this quality filtering. Of the 988 isolates, an additional 118 were excluded because they lacked clinical severity data.
uniprot. org/uniprot/P0DTD1). Tblastn was used to obtain coordinates within the viral genome  (Gertz et al. 2006 ). To eliminate any ambiguity in the functional consequences of mutations, we ignored codons where more than one mutation was detected in at least a single individual. We also masked known problematic sites within the SARS-CoV-2 genome (De Maio et al. 2020, https://github. com/W-L/ProblematicSites_SARS-CoV2/). For the purposes of genetic association and phylogenetic analyses, we ignored all insertions, deletions, and premature stop codon mutations. A four nucleotide deletion which leads to a premature stop codon removing the last 20 amino acids of ORF3a was detected in 30 samples. Multiple mutations resulting in premature stop codons in ORF8 were detected, indicating that this gene is not essential for the virus  (Jungreis et al.
Viral phylogenetic and population structure analysis We assigned samples to phylogenetic clades using Nextstrain clade definitions as implemented in the Nextclade software  (Aksamentov et al. 2021 , https://clades. nextstrain. org). While all samples we analyzed were from Bahrain, this tool compared our sequences to a sample representative of global viral genetic diversity. We also summarized the overall genetic structure in our sample by conducting a principal component analysis (PCA) using the prcomp function in R (R Core Team, 2022). We did not variance-scale genotypes, so principal components (PCs) should be interpreted as reflecting patterns of common genetic variation.
We also analyzed a set of comorbidities that included individual predictors for hypertension, diabetes, and smoking based on previous literature implicating these characteristics with increased disease severity. We pooled additional comorbidities that were individually rare into an 'other comorbidity' variable comprising immunocompromising disease, renal disease, chronic lung disease, cardiovascular disease, sickle cell disease or trait, and cancer. Throughout the text we include vaccination status when referring to comorbidities. Sampling date, age, and gender were missing for a small number of patients with good quality sequencing data. In order to retain these individuals in the data set, sampling date was imputed as the median date within the Nextstrain-assigned cluster, age was imputed as the median overall age, and gender was imputed by coding as 0/1 and setting missing individuals to the mean value.
We tested the top 50 most frequent mutations in the sample to assess whether their presence or absence was associated with COVID-19 severity. Statistical significance was evaluated using a Bonferroni correction and family-wise error rate of 0. 05. We examined the relationship between vaccination and clades in our patient panel by grouping patients by vaccination status and clade for the months of January, February, and March 2021 (when vaccination became common). We balanced the data for each month by randomly dropping patients so that equal numbers of vaccinated and non-vaccinated individuals remained within each month. After pooling the data across the three months, we conducted a Fisher's Exact Test between vaccination status and a binary variable indicating Alpha versus non-Alpha clade status.
We first tested the RNA-dependant RNA polymerase (Pol) and S glycoprotein (Spike) genes, as both are large genes with well-understood biological functions and assessed statistical significance at the nominal 0. 05 level. We then calculated and tested burden scores for the remaining 12 genes. This second set contained many small genes for which we expect to have much lower power, and statistical significance was assessed using a Bonferonni-corrected threshold of 0. 0024 (family-wise error rate: 0. 05). To assess whether regression results were due to the composition of our sample rather than gene-specific effects, we also simulated burden scores by randomly sampling missense mutations with the same observed frequencies for each gene. Regression analyses were then performed on simulated scores, and we calculated the percentile of each gene's effect size in the real data within this simulated distribution of frequency-matched, hypothetical genes.
Acknowledgements We would like to thank  Luca Freschi  for his assistance in the early stage of this project, as well as members of the  Sunyaev lab  for their constructive feedback. This study was funded by an award from the  Bahrain Ministry of Health  to AH (Fund # 102075 ). AH was supported by the  US National Heart, Lung, and Blood Institute  ( NHLBI K8HL150284 ), and the  American Heart Association. EK and SH were supported by the  US National Institutes of Health  grant  R35GM127131  to SH.