dataesr/scientific-paragraphs-categorization huggingface dataset:
Database Implementation The conceptual multidimensional snowflake schema described in Section 3 can be directly translated into the database schema presented in Figure  2. This database schema is used in both Spark framework and Hive data warehouse. Fig. 2 : TextBenDS logical snowflake schema However, for MongoDB, we need to adjust it to a JSON representation. Thus, when translating the multidimensional snowflake schema into the MongoDB collection, each dimension becomes a nested document inside the same record. Figure  3  presents a document representation.
Experiments In this section, we first present TextBenDS's performance metrics and execution protocol. Second, we present the hardware architecture and the software configuration used for our benchmarking experiment. Third, we discuss the dataset, as well as the query complexity and selectivity. Forth, we present a comparison by data management distributed system and weighting scheme. Finally, we compare the runtime of these data management distributed system implementations w. r. t. the scale factor (S F) and weighting schemes.
Performance Metrics and Execution Protocol For TextBenDS, we use as metric only the query response time. We note the response time for each query as t(Q i ) and t(Q i ) ∀i ∈  [1, 4]. All queries Q 1 to Q 4 and Q 1 to Q 4 are executed 10 times for both top-k keywords and top-k documents, which is sufficient according to the central limit theorem. Average response times and standard deviations are computed for t(Q i ) and t(Q i ). All executions are warm runs, i. e. , either caching mechanisms must be deactivated, or a cold run of Q 1 to Q 4 and Q 1 to Q 4 must be executed once (but not taken into account in the benchmark's results) to fill in the cache. Queries must be written in the native scripting language of the target database system and executed directly inside said system using the command line interpreter.
The Spark Clients are the worker nodes. For the MongoDB tests, we used the same cluster infrastructure with the following specifications: 1 node with the MongoDB Configuration Server and MongoDB Shard Server (mongos) and 5 nodes with MongoDB shards. The MongoDB Configuration Server stores the metadata for a sharded cluster. This metadata reflects state and organization for all data and components within the sharded cluster. The MongoDB Shard Server is a routing service for the MongoDB shard configurations. It processes queries from the application layer, and determines the location of this data in the sharded cluster, in order to complete these operations. The number of Spark executors was fixed to 16 with one vnode and 3GB memory each for the Spark experiments. Moreover, we use the Spark SQL and Dataframes libraries for the Spark experiments together with the Scala programming language.
The Hive Server Heap Size and the Hive Metastore Heap Size are both set to 2GB each, while the Hive Client Heap Size is set to 1G. Each reducer can process 1GB of data at a time. The dataset is stored on HDFS for both Hive and Spark experiments under the ORC format. The code of all Hive queries and Scala code for the Spark experiments, together with benchmarking results, are available on Github  1 The query parameterization is provided in Table  1.
Dataset The experiments are done on a 2 500 000 tweets corpus. The initial corpus is split into 5 different datasets equally balanced between the number of tweets for gender, location, and date. These datasets contain 500 000, 1 000 000, 1 500 000, 2 000 000, and 2 500 000 tweets, respectively. They allow scaling experiments and are associated to a scale factor (S F) parameter, where S F ∈ {0. 5, 1, 1. 5, 2, 2. 5}.
Database Implementation Comparison The following set of experiments analysis the time performance of the different database implementations w. r. t. S F and weighting schemes for the top-k keywords and documents queries. Hive has the overall worst runtime for all queries. In comparison, Spark has a constant execution time regardless of S F. Whereas, with the increases of S F, the runtime gap decreases between Spark and Hive by a factor between 2x and 1. 8x for all queries. This runtime gap can be explained by the fact that Hive uses Tez as query execution engine while Spark uses direct in-memory data processing. Tez is built on Hadoop MapReduce and relies extensively on HDFS, thus with the number of I/O operations the execution time increases. Spark's runtime performance is directly influenced by the chosen planning policy of the YARN resource manager, and implicitly by how the resources are allocated for each task.
For the next set of experiments, focused on computing the top-k documents, the performance gap between TF-IDF and Okapi BM25 is decreasing for all of the deployed distributed systems. The smallest difference is found in running with Spark and MongoDB, where the runtime gap reaches a factor of 1. 1x, and even 1x. As an analysis of the overall performance of the three distributed systems, it resulted that the best outcomes are obtained in some cases by MongoDB with MapReduce, and in other cases by Spark. Although we evaluate TextBenDS on social media data, our benchmark is generic and can be used on any textual data enhanced with metadata extracted through preprocessing and Natural Language Processing techniques, e. g. , part-of speech tagging, lemmatization, hashtag extraction, etc. As future work, we plan to improve the scalability of our solution by designing sampling strategies and aggregation queries.
The sampling methods will include constraints on tags and named entities while boosting the performance by lowering the query selectivity and complexity. Furthermore, we plan to expand TextBenDS's dataset significantly to achieve a big data-scale volume. Moreover, we considered in this paper TF-IDF and Okapi BM25 for machine learning tasks. The next version of our benchmark should include other weighting schemes, such as KL-divergence  [33] , to further improve the relevance of our solution.
Combining Clusters Clustering is classically used to improve the performance, by reducing the structural redundancy of docking solutions  [33] [34] [35]. In this section, we explore how to combine rescoring functions with the native scoring function of ZDOCK, in conjunction with structural clustering. We used the BSAS clustering algorithm, which takes into account the different scores, since clusters are initiated according to the scores. We have tested a combination of clusters. On the one hand, we computed clusters from the poses ranked by their initial ZDOCK scores. On the other hand, we computed clusters from poses reordered after consensus rescoring. We then combine the representative poses of the first N1 ZDOCK clusters, with the representative poses of the first N2 poses after rescoring, with N1+N2=10.
Simulations For our model simulations we employed the four climate models LOVECLIM, CCSM3, HadCM3 and FAMOUS. LOVECLIM is a coupled Earth-system climate model. In this study, we applied a version of LOVECLIM that dynamically represents the following components of the climate system: atmosphere, ocean and sea ice, and vegetation. LOVECLIM reproduces reasonably well the main characteristics of the observed surface temperature distribution and right zonal structure of precipitation  (Goosse et al. , 2010). CCSM3 is a coupled climate model of the oceaneatmosphereeseaiceeland system; with CAM3 as the atmospheric model, the Parallel Ocean Program (POP) for the ocean, the Community Sea Ice Model (CSIM) for sea ice, and CLM-DGVM for vegetation  (Collins et al. , 2006). Temperatures and atmospheric circulations in CCSM3 are generally consistent with modern day observations  (He, 2011; Jin et al.
Hydroclimate variables Total precipitation of the CCSM3, HadCM3 and FAMOUS simulations was analysed in the present study to reflect the wet or dry climate conditions, while the soil moisture data were used for the LOVECLIM simulation as a proxy of wetness. The main reasons for choosing the soil moisture rather than precipitation is that moisture variable is a better indicator for climate wetness in LOVECLIM, because the LOVECLIM model belongs to Earth system Models of Intermediate Complexity (EMIC) and includes a simplified representation for precipitation (with several processes related to precipitation being parameterized), while soil moisture shows water balance and thus wetness of climate  (Goosse et al. , 2010). Similar spatial patterns between soil moisture and precipitation was detected in LOVECLIM and also in CCSM3 (results not shown).
It is worth mentioning that, similar to the LOVECLIM simulation, soil moisture data of CCSM3 can alternatively be used in this analysis, but the data are only available for the annual mean. Since we wanted to analyse summer patterns besides the annual mean, we employed CCSM3's monthly precipitation data in this study. Additionally, soil moisture is closely relevant to biotic proxies (e. g. Anderson and Smith, 1994) , permitting realistic comparisons with some proxy-based data. The precipitation and soil moisture data of all models were normalized into percentage anomalies by reference to the presentday (PI, or 0 ka) climate, in order to avoid the potential uncertainties caused by different units and different ways of presentation of the wetness variable. The normalization procedures included calculating the anomalies of precipitation or moisture from the PI level, and then dividing these anomalies by the value of the present-day, before transferring them into percentage by multiplying by 100.
Proxy records We collected published proxy-based records on the wet or dry conditions (e. g. precipitation and moisture) of Holocene climate from the East and North Asian domains (20e70 N, 60e125 E). These records are mainly lake sediment data, such as d 18 O and pollen, sediment deposition rate, CaCO 3 and magnetic susceptibility. Key information on these records is summarized in Table  SI1. It worth pointing out that we did not include cave d 18 O data, because we think that comparison of cave d 18 O data with modelling results deserves a separate study given the complexity of the interpretation of such data and the ongoing debate (e. g. Goldsmith et al. , 2017; Liu et al. , 2017). These records fall into four groups: individual quantitative reconstructions of total annual precipitation based on pollen data, composite pollen-based aridity index for Eurasian interior, composite moisture reconstructions from central Asia, and composite moisture index from the Chinese monsoon regions.
The composite reconstructions are employed as a whole to illustrate the hydroclimate conditions in a broad region, and we did not further manipulate them, for example by excluding few data from sites that are actually located outside the defined boundaries of our domain (further west), given the composite procedure that has been done by the original studies. Those records data are digitalized by the xyscan tool (http://rhig. physics. yale. edu/~ullrich/software/xyscan/) with a minimum of 3 points per thousand years for records with low-frequent variability (smooth time-series curve), and with a maximum of 13 points per thousand years for high-frequency variability (saw-tooth time-series curve). Thus, the accuracy in the digitalization is acceptable for representing the general trends, although uncertainties in exact values are unavoidable.
From 8 ka to 5e4 ka, the quantitative precipitation reconstruction and moisture data suggests a wetter climate than at 0 ka. This feature is weak in the simulations and absent in pollen-based aridity index reconstruction. From ~3 ka onward, both data and simulated wetness data indicate minor changes toward 0 ka. Overall, simulations and proxy data agree well in terms of the sign of climate wetness at onset of the Holocene and of general Holocene trend of wetness. For East Asia, proxy-based reconstructions of precipitation and moisture agree with simulated results, which show the decreasing Holocene wetness trend (Figs. 2  and 3B ). During the early Holocene, proxy datasets show a positive wetness range from 20% to 40% anomalies from 0 ka, although the trends in those two sets data are slightly different.
Acknowledgements Yurui Zhang has received funding from the  French National Research Agency  (under  ANR-17-EURE-0015  and  ANR-10-LABX-19 ). Heikki Sepp€ a was funded by the  Academy of Finland  (project  316702 , GRASS). Jianyong Li  was funded by the  Chinese Academy of Sciences  ( Y652001589 ,  Y651031589  and  XAB2016B01 ) and the  National Natural Science Foundation of China (NSFC 41801090 ).
Patient Sample Data from pre-term infants were collected as part of the Resilience Study, which has been carried out in the Neonatal Intensive Care Unit (NICU) of the University Hospitals Leuven, Belgium. Parents of pre-term infants born before 34 weeks gestational age (GA) and/or with a birth weight <1,500 g were approached for informed consent within the first 3 days after birth. A total of 136 patients was included in the cohort from July 2016 to July 2018. Exclusion criteria were as follows: parents' age < 18 years, limited knowledge of Dutch or English, medical (somatic or psychiatric) condition in the parent(s) that impeded participation, and the presence of a major congenital malformation or central nervous system pathology (grade 3 or grade 4 intraventricular hemorrhage or periventricular leukomalacia) at the time of consent.
The research protocol has been examined and approved by the Ethical Committee of University Hospitals Leuven, Belgium. The Resilience Study was performed in accordance with the Guidelines for Good Clinical Practice (ICH/GCP) and the latest   14 [28. 86-32. 43] 28. 86 [26. 86-30. 71] 30. 29 [27. 29-31. 71] Birth weight (g) 1475  1140  1225  PMA (weeks) 32. 14  [30-33. 43] 34. 14 [33. 86-34. 29] 38. 43 [37. 29-39. 57 ] LPS 1 [0-3] 0 [0-2] 0 [0-2] Data is reported as q 50 [q 25 -q 75 ], where q 50 is the median and q 25 -q 75 is the IQR. version of the Declaration of Helsinki. It has been registered at Clinical Trials. gov (NCT02623400).
Data Acquisition During the NICU stay, pain levels were daily recorded with a multidimensional scale for premature infants known as the Leuven Pain Scale (LPS). This scale varies in the range [0,14] and is obtained as the sum derived by seven categories (such as crying, grimace or heart-rate)  (Allegaert et al. , 2003 (Allegaert et al. , , 2013)). LPS scores were routinely daily recorded by bed-side nurses, every hour for the intensive care patients and every 3 h for the intermediate care. Based on the association between stress and pain, perinatal stress has been defined as the presence of non-zero LPS in the patient record the day before the recording, i. e. , LPS > 0, which means experience of any pain the day before the recording. According to the clinical protocol, EEG, ECG, and SpO 2 measurements were recorded for at least 3 h in three monitoring groups: the first measurement took place around 5 days after birth (5days), while the second and the third recording were respectively planned around 34 weeks post-menstrual age (PMA) (34w) and in the week before discharge home.
, 2007). The most interesting attributes of the singularity spectrum are the location of the maximum and its width which are usually defined as c 1 , c 2  (Jaffard et al. , 2007). According to  Jaffard et al. (2007) , c 1 is usually considered the main Hurst exponent (H exp ) of the multifractal signal, while c 2 is a variational index to represent the amount of fractals inside the signal. Wendt et al. (2007)  reported further details of the methodology and of the WLBFM toolbox implemented in MATLAB to estimate the fractal parameters. The parameters c 1 , c 2 were estimated for each EEG channel and the associated δ oscillations.
Bradycardia-Based Classification A customized software tool was developed with MATLAB libraries to detect whether each bradycardic event belonged to a patient with or without stress burden. In summary, the following groups of features were derived for each hypoxic event: • Temporal and periodicity features: 14 features in total for HRV, • Spectral features for both HRV and EEG: 8 features for HRV and 16 features for EEG • Non-linear features: 4 features for HRV, 4 features for SpO 2 and 32 features for EEG • Brain-heart connectivity topological indices: 168 features in total for HRV, EEG and SpO 2. A complete overview is reported in Tables  2, 3. Given the fact that features were derived for three epochs (before, during and after each bradycardia), the total number of extracted features was 748.
The tuning in training-set followed a 10-fold cross-validation and the following set of performance indices were derived each monitoring group: the area under the curve (AUC) and Cohen's kappa between machine learning labels and the clinical labels. It is important to remind the only one set of indices was obtained for each classifier since they were obtained by pooling all test sets of the different patients together. Given that the number of features should be below one tenth of the training dataset, the subspace of features has been restricted to 1/10 (one-tenth) of the data  (Misra et al. , 2017). However, before tuning of the model, features were further reduced before the subspace ensemble algorithm was applied. The considered attributes had intra-feature correlation below 90% and the highest F-scores.
Based on the fact that the most dangerous de-oxygenation happens with bradycardias  (Pichler et al. , 2003) , the pursued strategy of this investigation focused on events that looked both to desaturations and bradycardias, but might be possible to reconsider the entire analysis to have only bradycardias. However, the long-term studies on stress aim to quantify the impact on the development of early-life experiences in the NICU and the specific effect of hypoxia was proven detrimental for the development outcome of preterm patients  (Janvier et al. , 2004). The current study might also be complemented by a longitudinal analysis, using repeated measurement ANOVA or a balanced linear mixed-effect model. However, the current study presents an event-based dataset, where the number of bradycardias vary for each patient and recording time.
FUNDING This research was supported by  Bijzonder Onderzoeksfonds KU Leuven (BOF) :  C24/15/036  The effect of perinatal stress on the later outcome in preterm babies, EU:  H2020 MSCA-ITN-2018 :  INtegrating Functional Assessment measures for Neonatal Safeguard (INFANS ), funded by the  European Commission  under Grant Agreement # 813483. This research received funding from the  Flemish Government  under the  AI Research program. Funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because the clinical metadata of patient (such as hospital ID, age, recording time-stamps, pain scales) are subject to the European data-privacy policy. Requests to access the datasets should be directed to mlavanga@esat. kuleuven. be. The authors will try to provide an anonymized version of dataset in compliance with the privacy policy of the University Hospitals of Leuven, which is the owner of the data.
The sequencing run of DID and TUM1 isolates indexed on an Ion 318v2 ™ Chip resulted in approximately 1. 66 GB of data with a mode reads of 369 bases. A total of   2 ). DID, TUM1 and CHR reads which were not mapped against the C. parvum genome in this step were then mapped iteratively against the C. hominis and finally the C. muris genomes. Only two C. hominis CDSs were commonly found in DID, TUM1 and CHR sequences that were not found in C. parvum IOWA reference genome. No C. muris CDS was found in the remaining DID, TUM1 and CHR reads. The two C. hominis CDSs were Chro. 60630 and Chro. 60599, both belonging to subtelomeric regions of chromosome 6. After a blastn analysis of these two C. hominis CDSs, we found that they corresponded to sequences found in C. parvum chromosome 6, complete sequence; segment 4/4 (GI BX538353).
A poor gene annotation of the C. parvum IOWA reference genome at this location could explain this result. At the end of the iterative mapping step, 918,741 reads were still unmapped for the TUM1 isolate, 340,965 reads for the DID isolate and 1,821,200 reads for the CHR isolate. The de novo assembly of the residual unmapped DID, TUM1 and CHR reads resulted in 10,476 contigs (of which 152 > 5,000 bp), 23,498 contigs (of which 226 > 5,000 bp) and 6,987 contigs (of which 3,339 > 500 bp) for DID, TUM1 and CHR, respectively. De novo contigs> 5,000 bp were then blasted against the nr database and results were parsed to only retrieve the organisms and size of the alignment. A semantic search for the terms "virus", "onco", "cancer", "virulence" has returned no results. Various bacterial sequences have been found again in TUM1 data.
Custom bio-informatic analysis of DID, TUM1 and CHR C. parvum isolates. The bio-informatic analysis of DID, TUM1 and CHR reads was performed using a custom pipeline (see Methods). A bacterial contamination (mainly by Lactobacillus species) was also detected for TUM1 thanks to Kaiju program (Suppl. File 2 in Suppl. File). After mapping of the non-contaminated reads to C. parvum IOWA reference genome, a total of 1,136,427 mapped reads for DID, 766,759 mapped reads for TUM1 and 3,573,343 mapped reads for CHR were obtained, with a genome coverage breadth of 91. 5%, 72. 4% and 99% for DID, TUM1 and CHR isolates, respectively. Variant calling found 270 common variants on CDSs between the three isolates, compared to MICRA that found 190 common variants on CDSs between these strains (Suppl. Table  S1 ).
e. a stop lost (Table  3 ). Finally, of the 125 common SNVs, 81 appear to have a moderate impact on protein function and are all missense variants (Suppl. Table  S1 ). To test our results, verification by Sanger sequencing was performed on regions of 8 randomly selected CDS encompassing SNVs of interest for DID and TUM1. For all regions, obtained sequences were consistent with the MICRA and custom pipeline analyses of Ion Torrent sequencing results, validating therefore the 16 identified SNVs (Suppl. Table  S2 ). Also, all of the 125 SNVs were already identified, at least once, in CryptoDB database (http://cryptodb. org/) for other C. parvum isolates like the anthroponotic C. parvum isolate TU114  7  and/or C. parvum UKP isolates 2 to 8  9. Interestingly, when trying to identify hotspots of variation, we found that a lot of SNVs detected in this study were in the subtelomeric regions of chromosomes 1, 3, 5 and 6 as shown in Suppl.
File 3 (in Suppl. File). Blast2GO version 4. 1. 9 was used to assign GO terms to the annotated proteins of interest. To note, on 3,805 total genes for C. parvum IOWA, only 2,223 were Blast2GO annotated, and on 90 genes of interest, only 58 were Blast2GO annotated. Briefly, a BLASTX-fast search to the nr database was carried out, the accession numbers were mapped to the Gene Ontology database and only those with an e-value lower than 1. 10 -6 were kept. The Blast2GO analysis showed that CDSs of interest are mostly involved in metabolic and cellular processes, while the molecular functions are clearly divided between binding (48%) and catalytic activity (45%) (Fig. 4 ). The Blast2GO analysis of cellular components revealed that a large number of SNVs are identified in membrane proteins (Fig.