dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample size calculation and statistical analyses Given the absence of literature data on the primary outcome measure, the target sample size (15 patients and 45 relevant TAIs) in this pilot study was based on the investigating centers' expected recruitment capacity and the length of the recruitment period, rather than on a statistical calculation. Each investigating center was asked to recruit 5 patients. The full analysis set (FAS) comprised all patients in the safety population with at least one evaluation of the primary outcome criterion. Descriptive statistics were performed using SAS software (version 9. 4, SAS Institute Inc. , Cary, NC, USA). Quantitative variables were defined as the mean ± standard deviation or the median (range). Categorical variables were defined as the number (%).
A : The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We compare it to previous state-ofthe-art scalar pattern recognition algorithms and show significantly faster processing and in some cases increased physics performance over all current alternatives. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance.
For this reason, during the LHC second long shutdown (LS2), the LHCb detector will undergo its first upgrade, as shown in Figure  1. The maximal physics reach of collider experiments like LHCb is directly proportional to the number of proton collisions which the detector can record per second. The primary goal of this upgrade is to allow LHCb to increase the number of collisions per second by a factor five. In addition LHCb is switching to a full detector readout capable of operating at 30 MHz. The full 4 TB/s data rate will be sent for real-time processing in an off-the-shelf data center, where a two-stage software trigger2 will process the data in real time and select interesting signals for further offline physics analysis. The first High Level Trigger stage (HLT1) performs a fast reconstruction and selects approximately the 1 MHz of most interesting LHC collisions for further analysis.
The second stage (HLT2), operating on the output of HLT1, performs a full reconstruction and real-time analysis of the data. Removing the previous hardware trigger, which was reducing the data volume by a factor 20, implies that the overall data volume processed will increase by a factor of around 100. On the other hand if this data can be processed, the additional information available for early decision making will increase  [2]  processing efficiency for key physics benchmarks by a factor between 2 and 10. In order to fully exploit this additional information, it is necessary to improve LHCb's reconstruction algorithms so that they are able to process the LHCb upgrade data volume within the available computing resources. Concretely, the data rate seen by the upgraded LHCb HLT is around 100 times larger than for the current detector, with a flat budget for computing resources.
The changes mainly concerned the tuning of tolerances and search windows. It was noted that large search windows were needed in the track extension step to allow the recovery of tracks not pointing to the beam-line, for which the R-z projection is not exactly a straight line, and low momentum tracks with large multiple scattering. In 2007, further tuning and analysis of the algorithm were performed  [10]. In 2011, a new implementation of the algorithm was introduced  [11] , motivated by LHCb's choice to run at twice the design's instantaneous luminosity. The consequently higher number of proton-proton collisions per bunch crossing and detector occupancy, required the reconstruction to be reoptimized in order to fit into the constraints of LHCb's real-time data processing. In addition, during the 2010 run it was found that the VELO could not come as close to the LHC beamline as expected.
Base Turbo Non-AVX / Light AVX2  Developing new algorithms that efficiently use such a kind of SIMD architecture requires heavy algorithmic modifications and can only be done efficiently if the specific instructions are available. Due to this complex process, it is hard for a compiler to provide vectorization support for irregular algorithms. Domain Specific Languages (DSLs) such as Halide  [38]  or SPMD  [39]  do not contain all patterns necessary for our problem and would also introduce significant additional complexity in the context of a physics codebase almost entirely written in C++. A less invasive option is to use SIMD libraries, like VC  [40] , UME::SIMD  [41]  or VecCore  [42] , that wrap the compiler intrinsics to provide a higher abstraction level to the developer. While these libraries work well for implementing most algorithms, they do not currently fully implement the latest SIMD ISA extensions.
Reconstruction efficiency While minimum bias events are representative of the data seen by the production system in real-time, the majority of them do not contain the physical signals whose efficiency we want to optimize. We therefore simulate a typical signal of interest for LHCb to measure the algorithm efficiencies. We choose B s → φφ, which is the same signal used in all LHCb historical publications on tracking. We present the efficiency of different track categories as a function of multiple physical parameters of interest. The efficiency as a function of the distance of closest approach to beamline  (docaz) is studied to ensure the efficient reconstruction of tracks produced in the decays of longlived particles. Because of the geometry of the VELO, it is also particularly interesting to plot the efficiency as a function of track pseudorapidity (η).
Acknowledgments The authors would like to thank the Physics Data Processing group from Nikhef and in particular  Tristan Suerink , for lending them the  AMD EPYC "Rome" 7702 system and Gvozden Nešković  from the  Frankfurt Institute for Advanced Studies  for the opportunity to have an early look at the  AMD EPYC "Rome " architecture. They would also like to thank the LHCb computing and simulation teams for their support and for producing the simulated LHCb samples used in the paper. VVG, RQ, and AH are partially supported by  ERC-CoG-724777  " RECEPT ".
Conclusion In this article, we have presented a new tracking algorithm for the VELO detector of the LHCb experiment specialized to take advantage of SIMD general purpose multicore processors. We compared it to previous State-of-the-Art pattern recognition algorithms and showed a significant speedup and in some cases increase in efficiency over all previous alternatives. This allows the SIMD algorithm to be used for all stages of LHCb's real-time data processing. We also evaluated the algorithm on two high-end systems from Intel and AMD and showed the impact of the SIMD extensions on the performance.
In their contribution, Schlüter and Vetter present a powerful new web-based tool for the exploration of raw Google Books Ngrams data, christened (an:a)-lyzer. Purpose-built with the Shiny R package, the interface allows for a flexible and speedy management of a truly enormous dataset encompassing over 468 billion words across five centuries, allowing users to categorise lexemes based on custom phonological and etymological factors. The application is currently tailored to a key problem in the historical phonology of English: the restoration of consonantal corelates of word-initial <h>, as diagnosed by the allomorphy of a preceding indefinite article (a/an). The paper guides readers through the possibilities of the app and the numerous visualisation and statistical summaries that it makes available.
The possibilities of visually tracking categories, subcategories and individual lexical items across such a vast dataset allows users to find patterns in the data with fine-grained internal structure conducive to lexical diffusion and exemplar-based studies, amongst others. The presentation of the resource as both a standalone, downloadable tool and as a web-based application, furthermore, provides maximal access and flexibility, hopefully boosting research on additional aspects of the available dataset. Most importantly, however, the tool serves as a proof of concept for applications that can manipulate and visualise extremely large 'messy' data sets for hypothesis generation and testing. Our issue concludes with a paper that applies visual mapping of sound-spelling relationships to a concrete problem in the historical development of Scots.
Acknowledgements As editors we would like to thank all the authors included in the current issue for their continued support and enthusiasm towards bringing together these materials in the midst of the various challenges and delays imposed by the COVID 19 pandemic. Our thanks also go out to the thirteen external reviewers that greatly improved the quality of the individual contributions. We are also grateful to the main editor of the  JDMDH, Laurent Romary , for agreeing to host this unique set of papers, and facilitating and encouraging us through the inevitable technical challenges of editing an online, open-access journal.
In addition, the database includes the abundances of 72 fish species collected from 1527 sites in France, sampled between 2005 and 2015; and fish taxonomic and biological information. Twenty random forest models provided at least good performances when evaluating impairment probabilities of fish communities by those pressures. The best performing models indicated that fish communities were impacted, on average, by 7. 34 ± 0. 03 abiotic pressure categories (mean ± SE), and that hydromorphological alterations (5. 27 ± 0. 02) were more often detected than chemical ones (2. 06 ± 0. 02). These models showed that alterations in longitudinal continuity, and contaminations by Polycyclic Aromatic Hydrocarbons were respectively the most frequent hydromorphological and chemical pressure categories in French rivers.
Fish and environmental data This approach is based on three large datasets gathering information on i) the spatial and temporal distribution of French stream fish communities ("Fish record dataset"), ii) the taxonomic, biological, and ecological characteristics of fish species ("Fish characteristic dataset"), and iii) the levels of chemical, hydromorphological, and biological pressures impairing sites at each sampling event ("Pressure dataset"). The Fish record dataset contains electrofishing samples performed from 2005 to 2015 in 1527 sites belonging to the French Water Framework Directive's surveillance monitoring, covering the mainland France (c. a. 550 000 km²). The standardized electrofishing protocol followed the recommendations of the European Committee for Standardisation's standard  (CEN, 2003; Marzin et al.
, 2014). This protocol was conducted during low-flow periods (from May to October) with a sampling design that depended on river width and depth. Fishes were identified to the species level, counted and then released back into the river. On average, the sites were sampled 5. 5 ± 0. 1 times (Mean ± SE; min = 1; max = 11) over the 2005-2015 period. The Fish record dataset was thus composed of the abundances of 72 fish species distributed in 8529 sampling events (site x year; Table  A ). The Fish characteristic dataset gathered information about 56 fish characteristics (260 categories) compiled from different sources of information (Table  B ). Hereafter, we considered 'fish characteristics' as any descriptive information related to the physiology, morphology and behaviour (i. e. functional traits sensu stricto; N = 53), life history strategy (lifespan; N = 1), and taxonomy (family and order levels; N = 2) of fish.
Fifty-five fish characteristics were coded using a full disjunctive system (i. e. presence/absence of various categories). One fish characteristic was structured using a fuzzy-coding technique ('food'; Table  B ). The number of categories defined per characteristic was rather low (between 2 and 10), except for the characteristics related to taxonomic classification (N = 20 families/categories; N = 13 orders/categories). The Fish record and Fish characteristic datasets were then used to calculate 1576 metrics for each of 8529 fish sampling events. These metrics covered: (i) the relative frequency of the log-transformed abundances of organisms using each of the 260 categories belonging to the 56 fish characteristics (260 metrics), (ii) taxonomy-based diversity indices calculated at the scale of each trait category (e.
g. the proportion of piscivorous taxa, the Shannon diversity of rheophiles; 1255 metrics), (iii) Rao functional diversity indices calculated at the scale of each characteristic  (Rao, 1982 ; 56 metrics), (iv) the level of diet specialization  (Mondy and Usseglio-Polatera, 2014 ; one metric) and (v) taxonomy-based richness or diversity indices (e. g. the species richness, the Shannon diversity index; four metrics; Table  B ). The Pressure dataset was obtained by defining 28 pressure categories that describe the impairment of water quality (WQ: chemistry; 13 categories; 188 individual parameters taken into account; Table  C ), the impairment of habitat (HD: hydromorphology; 14 categories; 29 individual parameters; Table  D ), and the contamination of local fish assemblages by invasive alien species (IAS: 1 category; 1 individual parameter; Table  A ), at each site for each sampling date.
Model performances To assess model performances, each RF model was built on a learning dataset and then tested against a test dataset  (Larras et al. , 2017; Mondy and Usseglio-Polatera, 2013). Learning and test datasets corresponded to independent subsamples of the input database, i. e. , the metric and pressure data measured for all the sampling events. To take into account the temporal correlations among sampling events, the sites were randomly allocated either to the learning or to the test dataset (75% and 25% of the sites, respectively). Model performance was then evaluated with the Area Under the Curve (AUC). The AUC values indicate "good" model performances if they are equal to or higher than 0. 70 (i. e. , maximizing both sensitivity and specificity;  Pearce and Ferrier, 2000). We used this AUC criterion to assess model performances using mainly the test (AUCtest) datasets, and to a lower extent the learning (AUClearn) one.
This mean scaled rank was considered as the final measure of metric importance. The ecological validation of our approach was then performed by investigating the relationship between the fish metric values and the impairment probabilities generated by each RF model. This relationship was illustrated for the most important metrics (based on their mean scaled ranks) and the most accurate abiotic and biotic models (AUC > 0. 7). The strength of those relationships was assessed using non-parametric Spearman correlations. The diagnosis-based approach was implemented in the ecodiag-package in R (R Core Team, 2018) and is currently hosted on GitHub (Table  E ).
Model performances The integration period over which the WQ pressures were assessed had a pressure-dependent effect on the performances of the corresponding RF models and varied from three to five years (Table  1 ; Table  F ). For example, the best model performances were obtained using an integration period of three years for "Nitrogen compounds" whereas they were obtained using an integration period of five years for "Phosphorous compounds". Due to an insufficient amount of data corresponding to significantly impaired conditions regarding "Acidification", this WQ pressure was discarded from further analyses. All the HD, WQ and IAS models had AUClearn values higher than 0. 75, except for "Mineral micropollutants" (AUClearn = 0. 67; Table  1 ). In contrast, some models exhibited "poor" (four models) or only "fair" (three models) performances with the test dataset.
Most of the models, however, displayed good performances with both the learning and test datasets (AUC > 0. 70; HD: 10/14; WQ: 9/12; IAS: 1/1), resulting in clearly distinct distributions of impairment probabilities across pressure levels (Fig. A ). For the 19 abiotic models, we found no significant differences in AUCtest between the ten HD and the nine WQ pressure categories (Wilcoxon rank sum test: W = 55. 5, p-value = 0. 41; similar results were obtained with AUClearn: W = 29. 5, p = 0. 22).
Reliability of the diagnosis-based approach Combining a diverse array of taxonomic and functional information on fish communities has enabled us to build efficient models predicting community impairment by individual anthropogenic pressure categories in a multi-pressure context. Compared to many other studies on fish assemblages, we have constructed an unparalleled fish characteristic database by gathering and harmonizing information from various published functional trait databases, enriched by additional information on life-history strategies (e. g. , longevity) and taxonomy (at family and order levels) of fish (Table  B ). We have taken into account a large set of pressure categories related to water quality (n = 13), habitat degradation (n = 14), and invasive alien species (n = 1), unlike most of previous fish studies investigating the effects of pressures related to WQ  (Azimi and Rocher, 2016) , HD  (Adamczyk et al.
Impairment probabilities across spatial and temporal scales Based on the large spatial-temporal extent of our datasets, we have been able to investigate spatial and temporal patterns in fish community responses to anthropogenic pressures, via RF model results. The spatial patterns of river degradation found in this study were consistent with those observed in recently published papers; e. g. the widespread impact of longitudinal continuity impairment (Fig. 2A ) on European  (EEA, 2018; Schinegger et al. , 2018)  and North American fish communities  (Cooper et al. , 2016). In addition,  Schinegger et al. (2012)  showed that, overall, both WQ and HD pressures impaired lowland rivers whereas HD pressures dominantly impaired mountain and headwater streams, resulting in lowland streams being prone to a higher risk of degradation than mountain streams.
Implications of the diagnosis-based approach The present work has proposed an approach to help scientists and environmental managers for identifying the individual impacts of various anthropogenic pressures potentially impairing stream fish communities, both taxonomically and functionally (step II). We have designed the R ecodiag-package to allow assessment of pressure-specific impairment risks and to investigate the responses of communities to contrasting pressure levels. This R package produces convenient visualizations of model outputs (radar charts), enhancing the simultaneous evaluation of multiple pressures and the comparison of impairment risks over time. This last point is particularly useful for stream managers to rank the effects of cooccurring pressures for prioritizing management measures and implementing the most appropriate ones, for restoring river integrity (step III).
Thus, larval dispersal is often a key parameter when marine metapopulations are examined to understand connectivity patterns and develop conservation management models (Puckett and Eggleston 2016). Metapopulation studies involve either demographic modelling approaches or the analysis of the migratory component linking source and sink populations  (Neubert et al. 2006 , Mullineaux et al. 2018 ). The latter is based on the concept of a larval pool, through which populations are connected. Yet, the main question to be answered is how do larval interactions within their dynamic environment contribute to species and ecosystem resilience. In order to answer this question, we need more data and information on the temporal trends of connectivity, recruitment and survival.
Stace Beaulieu and Dhugal Lindsay suggested some adaptations (fine mesh sizes and longer nets or double-net systems to separate nekton and plankton), but Craig Young argued that such adaptations may bring other operational issues. Anna Metaxas mentioned that data and results obtained by the SyPRID system need to published in order to understand its effectiveness in different environments and for different purposes. It was recognized that comparisons between sampling tools are difficult, and that results are better compared in terms of diversity patterns than densities. Strategies to overcome the limitations set by the time-consuming process of sorting and identifying larvae were also debated. Jim Birch asked how humanpower could be replaced or aided by an automated system in this process. The consensual perspective of the participants about this was that automated solutions, although desirable, are not achievable in the near future mainly because current equipment such as the Continuous Plankton Recorder (CPR) and other video plankton recorders do not have sufficient resolution, and reference databases to assist both genetic and morphological identification are lacking.
Reference databases and taxonomical expertise A major limitation to our knowledge of meroplankton is the identification of deep-sea larvae, most of which remain undescribed with no recognition of their adult counterpart. The use of genetic barcoding is promising as an accurate tool to link adult forms with their respective larval forms, but depends heavily on the availability of reference data which are still scarce for the deep sea (some data are available through disparate databases: e. g. , BOLD, WORMS, EOL, OBIS). Although relevant work has been carried out on some taxa from specific deep-ocean environments (e. g. and would allow automated (potentially in situ) recognition of larvae using supervised machine-learning methods. However, such tools require prior learning with annotated training sets and the development of computer-based classification tools for highthroughput identification.
In the future, supervised machine-learning methods for image analysis might be possible, but this obviously would require extensive validation and the existence of a certified image database. Rob Young indicated that a similar problem persists with genomic data, but the use of NGS-multi-loci indicators is beginning to improve the resolution of genetic analyses. Luciana Génio suggested focussing on adult metagenomics as a precursor to larval genetics-based identification as a way forward, which, however, is currently hampered by a lack of reference genomic data.
Several workshop participants raised the point that molecular identifications of deep-sea larvae are presently hindered by the limited amount of data on adult molecular sequences. Species-level identifications of larvae necessitate large reference databases of sequences from described species in order to match larvae to adults. Basic research to build up public databases would significantly enhance the utility of deep-sea larval observations. In the meantime, larvae can be identified morphologically. Many Classes and Orders of invertebrates have distinct larval forms (see  Young et al. 2001) , so larvae can be matched to these higher taxa. Question 2. 3. How can we develop an observing system for deep-ocean meroplankton? The participants agreed on the following important steps for developing a sustained observation system for deep-ocean meroplankton: • Identify priority scientific questions for meroplankton research and define a strategy with long-term goals but short-term actions that can be implemented relatively quickly.