dataesr/scientific-paragraphs-categorization huggingface dataset:
Seasonal calendar A direct comparison of the ice-core reconstructed net accumulation/precipitation records with precipitation amounts measured at meteorological stations contains seasonal and annual dating uncertainty. The boundaries of warm and cold seasons at meteorological stations can vary significantly depending on the geographical location and altitude. Seasonal variations of ammonium for which the main source is vegetation in the active phenological phase corresponding to the warm half-year (April-September) were used for identification winter and summer layers in WP ice core. Ammonium concentration in the ice core is maximum during the period of active convection and the accumulation sum over this period corresponds to precipitation of the warm half of the year. As a seasonality predictor, we used the convective available potential energy (CAPE) calculated from the ERA-Enterim reanalysis data for 1979-2010 which has a pronounced seasonal variation and reflects convective movements in the atmosphere due to buoyancy forces.
Spatial distribution of precipitation in the North Caucasus The interpretation of the reconstruction of accumulation relies on a comparison with meteorological information gathered during the period of instrumental observations, utilizing weather station data, global precipitation data sets and reanalyses. This comparison enables us to assess the accuracy of annual and seasonal accumulation amounts, as well as to determine the area where this reconstruction holds significance for different timescales. Additionally, we can establish the statistical and physical relationship between resulting accumulation and precipitation amounts, while mitigating the potential impact of confounding factors such as wind drift, avalanche supply, evaporation, and sublimation rates. The spatial correlation function for the observed annual precipitation amount was calculated relative to the Terskol meteorological station closest to the drilling site.
Comparison with gridded data Spatial comparison with the gridded GPCC v2020 0. 25 precipitation dataset revealed a statistically significant correlation for both seasons between ice core accumulation and precipitation estimates (Fig. 8 ). Summer accumulation moderately correlates with precipitation observations over Ukraine, South Russia, and the Black Sea. Winter accumulation shows an even stronger correlation with gridded datasets over the entire Black Sea region and North Caucasus. We also found a strong correlation  The correlation between seasonal accumulation and gridded precipitation data GPCC v2020 0. 25 remains below 0. 4 for the majority of the territory. However, during the cold period in a substantial area of the North Caucasus steppe zone, the 390 correlation significantly exceeds 0.
We observed a robust correlation between 10-year averaged summer and winter accumulation data and precipitation data from the EKF400 Version 2 (Fig. 10 ). The region exhibiting a stronger correlation is situated to the south of the Eastern European plain for both summer and winter seasons. This correlation remains consistently strong throughout the instrumental observational period since 1850 CE. However, prior to 1850 CE, the datasets do not align. The summer paleo precipitation records for the region display an unexpected and unsupported strong negative trend, with a decrease from 170 mm a -1 to 120 mm a -1. Other records do not corroborate this trend. Similarly, winter records also exhibit discrepancies before 1850, possibly attributed to a decrease in the number of observations included in the EKF400 v.
2 dataset. The WP accumulation record effectively captures the decadal and long-term variability for a larger region during both summer and winter seasons. The most recent little ice age (LIA) glaciation maximum was observed in the Caucasus in the middle of the 19th century and is well reflected in the dendrochronological data from this region. The general glacier retreat in the North Caucasus began in the late 1840s, which was interrupted 4-5 times in  1860 -1880 , as well as in the 1910s, 1920s, and 1970 -80s (Solomina et al. , 2016)). According to our results, the period of glacier retreat was preceded by a long period of negative precipitation trend in summer, which was accompanied by an increase in summer temperature, reconstructed from the tree ring widths  (Dolgova, 2016). The period of short readvances at the end of 19 th century coincide with several intervals of increased summer and winter accumulation.
Comparisons of the distribution functions and spectral density of annual precipitation between meteorological stations in the Caucasus and icecore records provided clear agreement, validating the reconstructed data as representative of precipitation. The comparison between the obtained ice core accumulation record from WP and gridded precipitation datasets, as well as paleo reanalysis data, revealed that the accumulation record represents the precipitation regime on decadal and long-term timescales in a larger region encompassing the Northern Caucasus, Black Sea, and South-Eastern Europe. The analysis showed that precipitation variability in the WP was primarily influenced by the summer season, with a significant decrease in the contribution of summer precipitation over a span of 260 years, corresponding to the transition from the Little Ice Age to modern climatic conditions.
Acknowledgements: This work was carried out within the framework of the  Russian Science Foundation  project  17-17-01270. The study was 710 completed in the laboratory created within  Megagrant  project (agreement no. 075-15-2021-599 ,  08. 06. 2021 ). We thank all the people who provided support at all stages of the work, participated in the field work, carried out the sampling and analytical processing of the ice core. We are grateful to  V. Matskovsky  for his help in interpreting dendrochronological data for the Caucasus.
We also characterize the criticality of the errors that modify the GEMM output when used as the core of the DNNs operations. • We present a complete characterization of the fault model at the software level on GEMM and DNNs. On this fault propagation evaluation, we can identify which types of simulated faults affect the DNN output similarly to the beam experiments. • We craft an improved version of NVBitFi that includes multiple thread fault models and compare them with the standard single-bit flip. The rest of this paper is organised as follows. Section II presents the background on radiation-induced errors on DNNs. Section III introduces the proposed analysis and shows the experimental methodology for the beam experiments and the fault simulation at the software level. The GEMM and DNNs error rate is presented in Section IV.
3) Silent Data Corruption (SDC): undetected output corruption, that is, the application finishes, but the application output is not correct and no flag or indication of an error is set. The SDCs on DNNs have a particular characteristic compared to other typical GPU applications. The SDCs can be separated into two classes: (1) Tolerable SDCs are the errors that change the output of the last layer of the DNN, but they do not modify the classification or object detection. (2) Critical SDCs are the errors that change the classification or detection partially or entirely. The Critical SDCs are particularly dangerous for safety-critical applications, as a decision based on an incorrect inference can lead to a catastrophic event. For instance, Figure  1  shows the result based on a real scenario extracted from Caltech Pedestrian detection dataset  [18].
B. GPU devices We consider Kepler (Tesla K40) and Volta (Tesla V100) NVIDIA GPUs. The tested NVIDIA K40 (Kepler) is built with the Kepler ISA and fabricated in a 28nm TSMC standard CMOS technology  [47]. Tesla V100 (Volta) are designed with the Volta microarchitecture and built with TSMC FinFET 12nm  [4]. Volta GPUs feature hardware acceleration for three IEEE 754-2008 floating-point point precisions: double (FP64), single (FP32), and half (FP16). We also evaluate the reliability of Volta GPUs' tensor cores, i. e. , a specific hardware that performs 4 × 4 Matrix Multiplication from GEMM kernels in one cycle with FP16 precision. Tensor cores can also perform matrix multiplication for FP32 precision, however, the data will be cast to FP16 in the low-level operation. Both GPU architectures have available Single Error Correction Double Error Detection (SECDED) Error Correcting Code (ECC) to protect the register file, shared memory, and caches.
Then, we check if the error created a false positive (i. e. , add non-existing objects) or if the error made the DNN misdetect objects. For the beam experiments and the fault injection, we evaluate the reliability of the DNNs with two datasets: Caltech Pedestrian detection  [18]  and VOC2012  [52]. Note that, evaluating with fault injection all the frames tested on neutron experiments would be unfeasible as the fault sites grow exponentially. Thus, to reduce the fault space for the SASS-level fault injections, we select the frames from the Caltech dataset that generated critical SDCs on the beam experiments.
E. SASS-level fault injection To perform the SASS-level fault injection, we have updated the available NVBitFI framework  [19]. NVBitFI allows instrumenting the GPU kernels at the assembly level (i. e. , at the microinstruction level). Other fault injectors, such as GPUQin, CAROL-FI, Kayotee, GPGPU-SIM, SASSIFI  [38] ,  [45] ,  [55] ,  [56]  neither inject at the SASS level nor offer support for Volta and newer architectures, nor inject in CUDA libraries. NVBitFI can inject transient errors in the GPU's ISA visible states, modifying the SASS instructions output of a code being executed on a real GPU. By default, NVBitFI allows for the users to select the fault model to inject single and double bit-flip, single zero value, and a single random value. We modify the injection procedure to inject the multiple thread faults, Warp random value and Warp zero value.
A. Deep Neural Networks FIT rate Figures  4a  and 4b  show the normalized SDC and DUE rates, respectively. We show results for YOLOv1, Faster R-CNN, and ResNet on Kepler and YOLOv3 on Volta GPUs. We separate the Tolerable and Critical SDCs. The reported results are normalized to not reveal business-sensitive information. Even if the data is normalized, it allows a direct comparison among configurations. The reported values are relative to YOLOv1 ECC ON SDC FIT for Kepler and YOLOv3 ECC ON SDC FIT for Volta. Experimental data is presented with a 95% confidence interval. Figure  4  shows that DUEs are more probable than SDCs for all tested configurations. DNN kernels have a high level of reuse that requires several device-host (CPU-GPU) synchronizations. A transient fault during these synchronizations could potentially result in a GPU DUE.
Equivalently, on Volta GPU, the DUE rate grows as the data representation increases. Comparing The SDC rate is separated between Tolerable errors (i. e. , the inference is not modified) and Critical SDCs (i. e. , the inference is modified). FP16 and FP32 versions of YOLOv3, the memory size grows 2×. As the FP32 version of YOLOv3 performs more memory transfers than the FP16 version, the DUE is expected to be higher for FP32. Additionally, with the ECC ON on Volta, and when caches and register usage increases to store the FP32 precision, the double bit flips in the memories are more frequent. Figure  4a  shows that the SDC rates are related to the DNN model complexity and accuracy. Compared to YOLO, the complex structures used on Faster R-CNN and Resnet increase their SDC rate by more than 10×.
From the obtained results, the corruption of internal resources, necessary to perform specific Warp-level synchronizations of the tensor core, is particularly prone to generate a DUE. When the tensor cores are used to improve the performance of the DNNs, they may further increase the DNNs' DUE rate. Figure  5a  shows that GEMM with tensor cores has a lower SDC FIT rate for all SDC types when executed in FP16 precision for Volta GPU. This attests that the tensor core circuit is slightly more reliable than the combination of ADD, MUL, and the loop control variables needed to implement matrix multiplication in software. When GEMM is executed in FP32 precision, the tensor core SDC FIT rate increases significantly, being, on average for the SDC types, 20% higher than the software GEMM FIT rate.
We are able to quickly simulate the sources of critical errors in software without having to employ fault injections using RTL or microarchitectural simulations. Lower level fault injections are in fact more accurate, but very slow. A simple GEMM evaluation for one configuration as presented in Figure  6a  would take 4. 8 × 10 5 hours to simulate according to recent works  [34]. This would  make the reliability evaluation of DNNs unfeasible. We acknowledge that our approach presents some limitations as the fault injection is based on SASS-level instrumentation, that is, software level. Consequently, our fault injection is limited to the user-accessible resources, making it impossible to simulate faults that affect resources like memory buffers. However, despite the proposed fault models' limitations, we can still simulate the syndrome of a fault in a shared resource of GPU faults at the SASS level.
ACKNOWLEDGMENT This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie (MSCA)  grant agreement No  899546 , from The  Coordenacão de Aperfeicoamento de Pessoal de Nível Superior -Brazil (CAPES)  - Finance Code 001 , from  BIENVEN ÜE Bienvenue Fellowship MSCA  Co-funded  program , and  ANR   RE-TRUSTING  ( ANR-21-CE24-0015-02 ). Neutron beam time was provided by  ChipIR  ( DOI:10. 5286/ISIS. E. RB2200004-1, 10. 5286/ISIS. E. RB2000137-1, 10. 5286/ISIS. E. 101136531 ) thanks to  Chris Frost ,  Carlo Cazzaniga , and  Maria Kastriotou  and by LANSCE thanks to  Steve Wender  and  Gus Sinnis.
All videos were recorded and time-stamped with Noldus Media Recorder (Version 2. 5, 2013). At the beginning of each day of observation, four cameras, filming children from different angles, were installed in the area where the discussions would take place before the children arrived in the classroom. The recording was initiated before the beginning of each whole-class discussion by a researcher, whose presence was as unobtrusive as possible. Before starting the first observation, children were told that they will be video-recorded to help researchers better understand how children learn to speak during preschool. The footage from the four cameras was synchronized and aggregated using Noldus Observer XT software (Version 14. 2, 2018). A coding scheme was established before data collection (see Figure  S1  in the online supplemental materials) and applied to code oral participation using Noldus Observer XT  (Zimmerman et al.
42 times as often after being called on again for follow-up as low-SES children did, IRR = 1. 42, p =. 007, 95% CI = [1. 10, 1. 83] (adjusting for proficiency: IRR = 1. 41, 1 As a robustness check, we also computed multilevel models with observations (Level 1) nested within students (Level 2) and sessions (Level 2), which were cross-classified. The third level (i. e. , classrooms) was treated as incidental and modeled with fixed effects (i. e. , a dummy variable was included in the model;  McNeish & Wentzel, 2017). These models were calculated with the R package lme4  (Bates et al. , 2015)  and yielded estimates similar to those obtained with our main estimation strategy. p =. 032, 95% CI = [1. 03, 1. 92]), consistent with H1b and H5 (see Figure  2B ).
Meta-analyses are useful even when they summarize small numbers of effect sizes, in part because they shift the focus from significance testing to estimating effect sizes with greater precision-the foundation of a cumulative science (e. g. ,  Cumming, 2014; Goh et al. , 2016). We conducted separate random-effects meta-analyses for each outcome and scenario using the metaprop  (Nyaga et al. , 2014) , metan  (Harris et al. , 2008) , and meta commands in Stata 16. 1  (StataCorp, 2019). The data and Stata code   52 (55. 3% ) "she wants to explain more things"; "s/he has a lot to say"; "s/he works hard" Extrinsic factors 5 (5. 3%) "the teacher likes her/him"; "the other children make trouble" 5 (5. 3%) "the question was longer"; "it was her birthday" Incoherent or irrelevant 16 (17. 0%) "because there is a lot of children"; "because we are in school" 14 (14.
This research was previously presented at the EASP (European Association This research was supported by the Grant  ANR-19-CE28-0007-PRESCHOOL  from the  Agence Nationale de la Recherche (France) , awarded to  Sébastien Goudeau  and by the  Grant Contrat de Plan Etat Région  (14e  CPER-INSECT-CONTEXTE ,  Université de Poitiers, France) , awarded to  Jean-Claude Croizet. The authors are grateful to the research assistants who coded the videos, particularly to  Emilie Guichard  and  Antoine Méry. Ming-Te Wang  and members of the  Cognitive Development Lab at New York University  provided helpful comments on previous drafts of the manuscript. The coding scheme, data, and R scripts used in the served in a supporting role for conceptualization. Frédérique Autin  contributed equally to data curation and served in a supporting role for funding acquisition and resources.
INTRODUCTION The genome sequencing landscape is no longer recognizable in terms of the technology used to sequence model organisms 25 years ago. Sequencing the genome of the first member of the Firmicutes [now Bacillota  (Oren & Garrity, 2021) ], Bacillus subtilis strain 168, involved more than 30 laboratories worldwide and 151 authors with different cloning and sequencing strategies  (Kunst et al. , 1997). This multi-national effort resulted in a patchwork of sequences derived from individual clones of the same strain propagated in the participating laboratories. The corresponding heterogeneity of the sequence quality required subsequent refinement into a final sequence derived from a single clone  (Barbe et al. , 2009). Our knowledge of B. subtilis in its various environments is improving rapidly and sequences from more than 100 isolates of the species are now routinely available in the International Nucleotide Sequence Database Collaboration (INSDC: DDBJ/ENA-EBI/GenBank  [Arita et al.
in the Institut Pasteur collection (https://www. paste ur. fr/fr/sante -publi que/bioba nques -colle ction s/colle ction -insti tut-paste ur-cip) was described in the first sequence and annotation update deposited at the INSDC (DDBJ/ENA-EBI/GenBank) that ironed out the differences stemming from the variety of clones that had been maintained in individual laboratories around the world  (Barbe et al. , 2009). It is expected that these isolates of the reference strain 168 did not evolve while staying in the repositories. However, isolates used regularly in the laboratory keep evolving  (Richts et al. , 2020; Shiwa et al. , 2013) , and consequently clonal genome sequences should be checked regularly. This is important since the sequencing of individual laboratory isolates often identifies SNP variants that in some cases can reveal additional knowledge  (Zhao et al.
, 2019). This is especially important when discussing the role of specific genes and phenotypes  (Gallegos-Monterrosa et al. , 2016). Two related collections, with knock out mutants carrying kanamycin or erythromycin resistant inserts, BKK and BKE, respectively  (Koo et al. , 2017)  are available from the Bacillus Genetic Stock Center (BGSC https://bgsc. org). The American Type Culture Collection (ATCC: https://www. atcc. org) and the Japanese Bioresource Project (https://shigen. nig. ac. jp/ bsub/) also keep relevant collections of mutants. As time goes on, new information on genes and sequences of various strains of the B. subtilis species continues to accumulate  (Steinke et al. , 2021). The corresponding information is collected in a variety of repositories and databases. Currently, the most popular database, SubtiWiki, follows a simple 'wiki' data structure, striving to obtain up-to-date basic information on B.
subtilis genes from a variety of sources  (Pedreira et al. , 2022). A counterpart has long been maintained within the MicroScope platform collection  (Vallenet et al. , 2020). There, annotation is explicitly designed to help users to enter the reference data update deposited at the INSDC whose unique role is to act as the internationally accepted upstream reference data source that can be used by other repositories for value-added approaches (specialized databases). Importantly, the annotation in the MicroScope platform is substantiated systematically using comparative genomics. The annotation data management system in MicroScope provides the user not only with up-to-date annotation but also with access to a variety of tools (see next paragraph) designed to help users verify and extend the annotation  (Médigue et al.
, 2019). The annotation reported here was performed using MicroScope and the presented data refers to that found on 31 December 2022 (https://mage. genos cope. cns. fr/micro scope/ mage/viewer. php?O_id=7). The metabolic profile reported at the site can also be used to update BsubCyc (https://bsubc yc. org), based on the latest annotations from MicroScope with Gene-Reaction links using data from the Enzyme Commission, EC  (Martínez Cuesta et al. , 2015) , MetaCyc  (Caspi et al. , 2020)  or Rhea  (Bansal et al. , 2022) : https://mage. genos cope. cns. fr/micro scope/ searc h/export. php?forma t=csv&S_ id=843. With reference accession number AccNum AL009126. 4, this marks the fourth update of the B. subtilis 168 reference sequence since the debut of the genome sequence in November 1997  (Kunst et al.
Nevertheless, the combination of a variety of software and information sources helps users obtain accurate information. The MicroScope platform F I G U R E 1 Twenty-five years of knowledge of the genome sequence of Bacillus subtilis. With these unknowns the B. subtilis model can be an essential partner in our quest to understand the background physical properties of biological agents responsible for the apparent 'animation' of chemical biology, a feature essential to proceed with synthetic biology constructs. combines a database reporting annotations on individual genomic objects with a graphical web interface, called MaGe, which provides a set of tools for the user to validate the quality of annotations. For example, in order to benefit from comparative genomics, the display of a genomic sequence with counterparts can be aligned with the large number of genomic sequences present in MicroScope, allowing the visualization of synteny  (Vallenet et al.
, 2013). In parallel, for each gene, the PhyloProfile functionality allows the user to follow a list of genes co-evolving with the gene of interest  (Engelen et al. , 2012). The platform also provides direct access to generic databases of proteins: UniProt/SwissProt (https://www. unipr ot. org) and Seed/ FigFam (http://thefig. info/index. html), as well as protein structures (InterProScan: https://www. ebi. ac. uk/ interpro), evolutionary protein genealogy (eggNOG: http://eggno g5. embl. de/#/app/home) and metabolic pathways (available under different flavours in the platform). It is also designed to help users to explore the metabolism of the organism via the establishment of metabolic profiles and pathway curation based on three major metabolism databases: BRENDA (https://www. brend a-enzym es.