dataesr/scientific-paragraphs-categorization huggingface dataset:
Results on a full human dataset. To further validate the scalability of CONSENT, we present results on a full ONT human dataset. This dataset is composed of 113 Gbp, displays an error rate of 17%, and contains ultralong reads reaching lengths up to 1. 5 Mbp. Data were sequenced by 2 ] and are publicly available with accession number PRJEB23027. Further details are given in Supplementary Table  S1. In this experiment, we not only evaluate how CONSENT behaves on such a large dataset, but also study the impact of the correction / assembly order on the quality of the results. We thus correct the raw data with CON-SENT, and then assemble the corrected long reads, but also assemble the raw long reads first, and then polish the assembly with CONSENT. Alignment statistics of the raw and corrected long reads are presented in Table  5 , while statistics of the different assemblies are presented in Table  6.
We only report CONSENT results, since other tools could not scale to this dataset. Indeed, Daccord crashed upon start due to memory limitations, while FLAS and MECAT reported errors during correction, owing to the presence of ultra-long reads, as reported in previous experiments. Canu, on the other hand, did not crash, but required an unreasonable amount of time to Table  4. Statistics of the assemblies, before and after polishing with RACON and CONSENT. The missing contig for the CONSENT and RACON polishings on the D. melanogaster dataset is 428 bp long, and could not be polished, due to the window size of the two methods being larger (500). Best results for each metric is highlighted in bold. 1  For the assembly of the original reads on the H. sapiens (chr 1) dataset, QUAST-LG did not provide a metric for the NGA75.
run. We ran all the experiments on a cluster node equipped with 28 2. 39 GHz cores and 128 GB of RAM. Tools were thus run with 28 threads. Alignment statistics of Table  5  show that CONSENT managed to process the whole dataset in 10 days, and required less than 100 GB of RAM. More precisely, the more computationally expensive step, in terms of memory consumption, was actually the overlaps computation, and not the error correction itself. The corrected reads displayed a higher N50 than the raw reads, the longest read reaching 929 kbp. Moreover, almost 99% of the reads could be realigned to the reference genome. The average identity of the alignments reached more than 93. 5%, which is slightly higher, but consistent with the results on chr 1, presented in Table  2. Moreover, CONSENT managed to correct a large number of reads, and thus barely reduced the genome coverage of the original dataset.
Dataset Assemblies statistics of Table  6  are particularly interesting. Indeed, they show that, in addition to being extremely more computationally expensive, correcting the reads before assembling them produces less satisfying results than assembling the raw reads first, and then polishing the assembly. Indeed, the correction + assembly pipeline required more than 17 days and 1 TB of RAM, while the assembly + polishing pipeline ran in 7. 5 days, and consumed less than 400 GB of RAM. In addition, the polished assembly displayed better metrics than the assembly generated from corrected reads, reaching higher NGA50, NGA75, and genome coverage, and lower error rate per 100 kbp. These results underline the fact that, for large datasets and complex genomes, assembling the raw data first, and then polishing the assembly is much more efficient than correcting the reads and then performing assembly.
Discussion Experimental results on the human datasets are particularly promising. Indeed, they show that CONSENT is the only method able to efficiently scale to the ultra-long reads they contain. More precisely, on the human chr 1 dataset, CONSENT is almost four times faster than Canu, the only other method able to scale to the correction of ultra-long reads. Moreover, it also produces more accurate results, and thus allows to yield a more contiguous assembly. As such reads are expected to become more widely available in the future, being able to deal with them will soon become a necessity. In addition, results on the complete human dataset show that CONSENT manages to efficiently process such large datasets in 10 days, using less than 100 GB of RAM. Moreover, this memory consumption could easily be reduced by adapting the parameters of Minimap2, and reducing the size of the jobs queue used during the actual correction step.
At the expense of an increased runtime, CONSENT could thus process a full human dataset on a simple laptop. Using 8 threads, setting Minimap2 parameter -I 900M, and reducing the size of the jobs queue to 50,000, we indeed estimate CONSENT would run in a month, consume at most 16 GB of RAM, and require 5 TB of disk space to process a full human dataset. The 5 TB disk space requirement comes from the overlaps file, which can easily be stored on a external hard drive. Further experiments should therefore focus on the correction of larger and more complex organisms. However, the runtime of CONSENT's correction step tends to be higher than that of other state-of-the-art methods. We discuss how to further reduce these computational costs below. Our experiments show that the runtime of the correction step tends to rise according to the complexity of the genome.
sapiens dataset. Raw corresponds to the assembly generated from raw reads. Corrected corresponds to the assembly generated from corrected reads. Polished corresponds to the assembly generated from raw reads, and polished with CONSENT. Runtime and memory consumption are reported for the whole correction + assembly or assembly + polishing pipelines. QUAST-LG did not provide a metric for the NGA75 of the assembly generated from corrected reads. Best results for each metric is highlighted in bold. the best possible chaining according to the coverage or the repetitive elements of a given window, the method could be more robust and more efficient by computing smaller MSA. Finally, it is essential to note that CONSENT uses Minimap2 as its default overlapper, but does not depend on this tool. As a result, CONSENT will benefit from the progress of future overlapping strategies, and will therefore allow to propose better correction quality as the overlapping methods evolve.
Finally, the polished consensus is locally realigned to the read, in order to correct it. Our experiments show that CONSENT compares well to, or even outperforms, other state-of-the-art selfcorrection methods in terms of quality of the results. In particular, CONSENT is the only method able to efficiently scale to the correction of ONT ultra-long reads, and is able to process a full human dataset containing reads reaching lengths up to 1. 5 Mbp in 10 days. Although very recent, such reads are expected to further develop, and thus become more widely available in the near future. Being able to deal with them will thus soon become a necessity. CONSENT could therefore be the first self-correction method able to be applied to such ultra-long reads on a greater scale. CONSENT's assembly polishing feature also offers promising results.
In particular, our experiment on a full human dataset shows that assembling the raw reads and then polishing the assembly allows to greatly reduce the computational costs, but also provides better results than correcting and then assembling the reads. This conclusion raises the question of the interest of long-read error correction in assembly projects. Moreover, as the processes of long read correction and assembly polishing are not much different from one another, one can also wonder why more error correction tools do not offer such a feature. It indeed seems to be affordable at the expense of minimal additional work, while providing satisfying results. We believe that CONSENT could open the doors to more error correction tools offering such a feature in the future. Finally, it would also be interesting to evaluate already published correction tools on their ability to polish assemblies, at the expense of minimal modifications to their workflows.
Dataset Corrector Number of bases (Mbp) Error rate (%) Recall (%) Precision (%) Overlapping Correction Total Runtime Memory (MB) Runtime Memory (MB) Runtime Memory (MB) Original 12. 2862 _ _ N/A N/A N/A N/A N/A N/A Canu 0. 4156 99. 7647 99. 5887 _ _ _ _ 19 min 4613 E. coli 30x Daccord FLAS 0. 0248 0. 2720 99,9965 99. 9291 99,9757 99. 7385 1 min _ 6813 _ 13 min _ 639 _ 14 min 12 min 6813 1639 MECAT 0. 2569 99. 9302 99. 7533 25 s 1600 1 min 14 s 1083 1 min 39 s 1600 CONSENT 0. 3111 99. 9328 99. 6934 18 s 2345 7 min 16 s 532 7 min 34 s 2345 Original 12. 2788 _ _ N/A N/A N/A N/A N/A N/A Canu 0. 7404 99. 4781 99. 2658 _ _ _ _ 24 min 3674 E. coli 60x Daccord FLAS 0. 0214 0. 1547 99. 9971 99. 9546 99. 9790 99. 8526 3 min _ 18,450 _ 51 min _ 1191 _ 54 min 38 min 18,450 2428 MECAT 0. 1714 99. 9547 99.
2 While the upcoming requirements and challenges are presented in  [8] , the work in  [9]  focuses on the technical solutions needed to address them and to establish the network slicing paradigm as targeted by 5G NORMA. The key elements of the architecture are Software Defined Mobile Network Controller (SDM-C) and Software Defined Mobile Network Coordinator (SDM-X). These controllers are responsible for managing the network functions within a slice as well as the multiplexing gain over shared components. Even though the authors present an architecture with NFV orchestration that ensures an efficient utilization of resources and provides support for slice mobility 2 https://5gnorma. 5g-ppp. eu/. Accessed 31. 07. 2021. while keeping a satisfactory QoS/QoE level, there is no implementation or performance evaluation of such an architecture.
D. Traffic Control Mechanism: Hierarchical Token Bucket In order to enable co-existence of numerous heterogeneous applications, 5G networks require efficient rate control mechanisms. According to the 3GPP standards for the Next Generation Radio Access Networks (NG-RAN) in 5G  [12] , each flow should have its assigned guaranteed bitrate (GBR) and maximum bitrate (MBR). There is no simple ready-to-use OMNeT++ module that emulates network slicing and at the same time supports such two-level bitrate guarantees. Therefore, we implemented a new OMNeT++ compound module called HTBQueue that adopts the classful queuing approach -Hierarchical Token Bucket (HTB). We decided to emulate slicing with HTB for several reasons. Firstly, with the HTB tree-based structure we introduce the hierarchy needed for the realization of slices, as shown in  [23].
In general, only the leaves have queues and priorities assigned to them. We tested and validated that our implementation of HTB in OMNeT++ behaves as expected. In our evaluations we compared the HTBQueue performance with the expected outcomes of the same scenarios in Linux. We also made sure that HTB and TCP are interacting in the correct way in terms of analysing the throughput values, RTT, and inter-departure times as well as congestion window and queue sizes.
1 versus 63. 0 dB(A), p = 0. 02). Additionally, we included children participating in the longitudinal INMA (INfancia y Medio Ambiente, Environment and Childhood) Sabadell birth-cohort study. In INMA, 778 pregnant women were recruited in the first trimester of pregnancy between 2004 and 2006, and were followed from then onwards  (Guxens et al. , 2012). In this study, we included the INMA children who participated in the follow-up visit at age 10-12 and attended Sabadell primary schools in the 4th, 5th and 6th years (n = 481) during the same period of the ECHOCAT study. Similar protocols for data collection, outcome assessment and exposure assessment were used in ECHOCAT and INMA. In both studies, all parents or tutors/guardians signed a consent form and the studies were approved by the Clinical Research Ethical Committee (ECHOCAT N • = 2016/6635/I, INMA N • = 2016/6708/I) of the IMIM-Parc de Salut MAR, Barcelona, Spain.
Weight-related behaviours Weight-related behaviours including diet, physical activity, sedentary behaviour, and sleeping duration were collected from parents through questionnaires. Psychological well-being was collected directly from the children. Data about dietary intake and dietary behaviours were obtained through a specific questionnaire and a modified version of a validated food-frequency questionnaire (FFQ)  (Vioque et al. , 2019). The food frequency questionnaire referred to the child intake in the previous year, and it was completed by the parents, and the responses were transformed to times/week of intake. We selected two items as indicators of unhealthy diet: fast food consumption in restaurants and consumption of sugar-sweetened beverages. The fast food consumption in fast food restaurants was obtained from this question "How often does your child eat fast food (eg.
Exposure assessment: the urban environment A wide range of urban environment exposures were estimated using geographic information system (GIS) platforms, using protocols developed for the HELIX project  (Nieuwenhuijsen et al. , 2019; Robinson et al. , 2018; Vrijheid et al. , 2020). We estimated the urban environment in the following exposure groups: ambient air pollution, green spaces, built environment, food environment, road traffic, and road traffic noise. Data sources and time points are specified in Table  1. The exposures were estimated at the geocoded residential address of the children and were averaged over the year before the clinical examination. The assessment of ambient air pollution included nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), particulate matter (PM) with an aerodynamic diameter of less than 10 μm (PM 10 ) and of less than 2.
For green spaces, we included green space availability and accessibility indicators. We followed the Positive health Effects of the Natural Outdoor Environment in Typical Populations in Different Regions in Europe (PHENOTYPE) study  (Nieuwenhuijsen et al. , 2014)  to estimate the surrounding greenness (trees, shrubs, and parks) with the Normalized Difference Vegetation Index (NDVI). We obtained satellite data derived from the Landsat 4-5 Thematic Mapper (TM) with 30 m × 30 m resolution. We selected images of 2017 according to the following criteria: i) cloud cover less than 10%, ii) Standard Terrain Correction (Level 1 T) and iii) greenest period of the year (May-August). We estimated surrounding greenness within 100-m, 300-m, and 500-m buffers around each address. Additionally, we calculated the distance to the nearest major green spaces and the area of this space, considered as open areas more than 5000 m 2.
We created an unhealthy food environment index based on the NAVTEQ database  (Copernicus, 2020). This indicator equals the number of unhealthy facilities present divided by the area of the 300-m buffer (number of facilities/km 2 ). Among the 100 different subcategories of facilities in the NAVTEQ database we selected the subcategories related to unhealthy food (petrol/gasoline Station, bar or pub, coffee shop, restaurant, convenience store, shopping malls). Higher values indicate more availability of different unhealthy facilities. Road traffic indicators were calculated from traffic road network maps following the ESCAPE protocol  (Beelen et al. , 2013; Eeftens et al. , 2012). We estimated the following indicators: traffic load on all roads, traffic load on the major road within 100 m, traffic density on nearest road, and inverse distance to nearest major road.
Multiple imputations Before imputation of missing values, we transformed all skewed exposures, weight-related behaviours and covariate variables to achieve normality; variables were categorized if normality was not achieved. We applied chained equations to impute all exposures, weight-related behaviours and covariate missing values  (White et al. , 2011). Physical activity was imputed for the whole INMA study sample. We imputed 20 datasets and we restricted the number of predictors in the imputation models to fewer than 25 variables while ensuring that all outcomes were considered as predictors  (van Buuren, 2018). After imputation, we standardized all continuous exposure variables by the interquartile range (IQR). The following exposures were categorized below and above the median because they did not achieve normality for imputation: straight line distance to a green space, area of the nearest green space, population density, bus stops (100 m, 300 m and 500 m buffer).
Then, we performed generalised additive models (GAMs) with the R package 'mgcv' to assess departures from linearity in the relationship between selected urban exposure and each outcome (we selected the urban exposures included for the multiple exposure models below). Because almost all GAMs showed evidence of linearity as indicated by effective degrees of freedom close to 1 (Figure  S2 /S3), we modelled all exposure variables as continuous variables in subsequent analyses, assuming linear associations. Next, to assess the associations between the urban exposures and childhood obesity outcomes and weight-related behaviours, we applied linear regression models for the continuous outcome variables (zBMI, waist circumference and body fat percentages z-scores, total and vigorous physical activity, screen time and sleep duration) and logistic regression models for the dichotomous outcome variables (overweight/ obesity status, fast food consumption, sugar-sweetened beverage consumption, and psychological well-being).
Hierarchical clustering on principal components (HCPC) We applied a HCPC analyses to classify children into clusters with similar urban exposures patterns. We performed the HCPC analyses on a reduced exposure dataset including only the continuous exposure variables (N = 20). Previously, a study in Sabadell found that children from higher area-level SES were associated with higher levels of air pollution, noise, traffic, and lower levels of green spaces  (Robinson et al. , 2018). Thus, SES may play an important role in the association between the urban exposure patterns and childhood obesity outcomes and weightrelated behaviours. We therefore standardized all exposure variables by the mean within each quintile of the deprivation index before running the HCPC; Thus, the standardized exposures explore gradients in the particular exposure among individuals with a similar deprivation index.
We applied first a principal component analyses (PCA) to reduce the dimension of the data and selected the number of components that explained at least 80% of the variance in the data. We then applied an ascending hierarchical classification (AHC) to identify clusters of exposure based on the components obtained from the PCA. We applied AHC after the PCA, rather than directly to the original dataset, because the PCA removes noise in the data and results in more stable clustering analysis  (Husson et al. , 2017). We selected the numbers of clusters by applying the Ward's criterion based on the decomposition of the total inertia (i. e. , total variance) in between-and within-group variance. The Ward's method consists in aggregating two clusters such that the growth of the within-inertia, characterizing the homogeneity of a cluster, is minimum  (Husson et al.
(d) As children spend a large amount of time at schools, we evaluated the associations between the urban exposures estimated at the schools and zBMI. The exposures at schools were only available for the ECHOCAT children. (e) Gender differences may exist in the use of the urban environment and in weight-related behaviours; we evaluated the effect modification of sex by introducing an interaction term into the model and evaluating the p-value for interaction with the likelihood ratio test. For this sensitivity analysis, we applied single exposure associations selecting one urban indicator within each exposure group. (f) to evaluate the quality of the imputation, we compared the distribution of the covariates in the imputed and non-imputed dataset, and we repeated the single exposure analyses using zBMI as an outcome only in the complete case dataset.
Acknowledgement We acknowledge support from the  Spanih Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. We want to especially acknowledge all the parents and children participating in this study, and particularly to the schools  Amadeu Vives ,  Andreu Castells ,  Can Deu ,  Can Rull ,  Carmelites, Catalunya, Cifuentes, Claret, Enric Cassases, Escolàpies ,  Font Rosella ,  Jaume Viladoms ,  Joanot ,  La Inmaculada ,  La Pia ,  La Romànica ,  La Roureda ,  La Salut ,  Martí i Pol ,  Mas Boadella ,  Pau Casals ,  Ribatallada ,  Sallares i Pla ,  Sant Julià , Salesians, Sant Nicolau, Serra, Servator,  Teresa Claramunt ,  Torreguitart.
Charline Warembourg holds a  Sara Borrell fellowship  from the  Instituto de Salud Carlos III  [ CD18/00132 ]. Maribel Casas  holds a  Miguel Servet fellowship  ( CP16/00128 ) funded by  Instituto de Salud Carlos III  and cofounded by  European Social Fund  ' Investing in your future'. Talita Duarte-Salles  is funded by the  Department of Health of the Generalitat de Catalunya , awarded on the 2016 call under the  Strategic Plan for Research and Innovation in Health (PERIS)   2016-2020 , modality incorporation of scientists and technologists, with reference  SLT002/16/00308.
understanding of parliamentary discourse as well as its wider societal impact, in particular with studies that represent diverse parts of society (women, minorities, and marginalized groups) and cross-cultural studies (Hughes et al. 2015). The most distinguishing characteristic of records of parliamentary debates is that they are essentially transcriptions of spoken language produced in controlled and regulated circumstances and are rich in valuable (sociodemographic) metadata. They are also easily available under various Freedom of Information Acts set in place to enable informed participation by the public and to improve eective functioning of democratic systems, making the datasets even more valuable for researchers with heterogeneous backgrounds. For these reasons, and because parliamentary proceedings are often available online, many researchers have already compiled corpora of parliamentary proceedings.
In the process of the developing the recommendations, we also produced the second version of the siParl corpus (Pančur and Erjavec 2020; Pančur et al. 2020), a carefully encoded and linguistically annotated collection of parliamentary debates from the Assembly of the Republic of Slovenia from 1990 to 2018, with over 1 million speeches and 200 million words. This was the rst substantial corpus to be fully Parla-CLARIN encoded, where we had the good fortune that the rst version of siParl was the basis for an extended tutorial on how corpora can be used to investigate language use and communication practices in the context of political discourse (Fišer and Pahor de Maiti  2020). The work on this tutorial revealed various shortcomings of the rst version, which were addressed in the encoding and structure of siParl 2.