dataesr/scientific-paragraphs-categorization huggingface dataset:
a strong correlation. The mean autofluorescence inferred by the Hill fitting CH exhibits negative values, which shows that the Hill fitting is not optimal for the pWUS::GFP-ER fluorescence profiles. (D-E) Study of how meristem size relates to the pWUS::GFP-ER expression domain at the inflorescence meristem, using the features from the generalised exponential fit. Both pWUS::GFP-ER domain size and total intensity correlate very well with the inflorescence meristem size at different growth conditions. Meristem size has been manually computed as in [12]. Domain area is computed assuming it spans a circular region of radius LE. The legend found within panel A applies to all panels. At the bottom of panels A-B and D-E, the Pearson linear correlation coefficient r and its p-value are specified. Dataset extracted from [12, 16].
An example from the Beta maṣāḥǝft Project 14 shows how a dierent sort of collection can be served in the same way: in the case of a manuscript transcription having the URI https://betamasaheft. eu/BLorient718, the collection resource attached betamasaheft. eu/BLorient718) will provide metadata and segment identiers for them. In the case of manuscripts the smallest reference will be to lines, but in case additional referenceable structures are encoded, these will also become available as references. 16 The Epigraphic Database Heidelberg's implementation shows how the same collection model holds true if this is an inscription on stone, for example: https://edh-www. adw. uni-heidelberg. de/edh/ inschrift/HD000001 (but note that this implementation does not currently support navigation). To link these three resource models and make them available, DTS provides specications for three endpoints (or routes) that are respectively called Collections, Documents, and Navigation (see gure 2).
• The MyCapytain implementation from the capitains. org project, which is a Python implementation in the form of a library (Clérice et al. 2019). MyCapytain, via Nautilus, 31 and the TEI Publisher both provide a server API, with the latter having the same limitations as the client (i. e. , only Collection and Document endpoints are supported). Another implementation for small corpora is oered by the DTS-Demo-Server in Python with an SQL database. 32 It supports one level only in navigation (Dartois, Vieillon, and Clérice 2019).
21 See https://github. com/distributed-text-services/specications/issues/164 and the minutes of the meeting https://github. com/distributed-text-services/meeting-notes/blob/master/ notes/2019-10-11. md. 22 See https://betamasaheft. eu/DSintro. html. Example from https://betamasaheft. eu/api/dts/ navigation?id=https://betamasaheft. eu/LIT5068MalkeaGabraManfas, accessed October 29, 2019. :cts:latinLit:phi0893. phi005. perseus-lat2, accessed October 29, 2019. 25 The next and prev link relations are registered with IANA (https://www. iana. org/assignments/ link-relations/link-relations. xhtml), and dened in https://html. spec. whatwg. org/multipage/ links. html#sequential-link-types. 26 Accessed October 29, 2019, https://dev. chartes. psl. eu/api/nautilus/dts. 27 Accessed October 29, 2019, https://texts.
Beta maṣāḥǝft collects written artefacts from the highlands of Ethiopia and Eritrea mainly in Gǝʿǝz (Classical Ethiopic). 29In the collection are present both transcriptions of manuscripts and editions of textual units. The scarce availability of transcriptions as well as available editions means that the actual text contents are few in comparison with the textual units and written artefacts identied and described. ⚬ Alpheios. net provides a small collection of Latin and Greek texts which have been aligned with linguistic annotation for learning ancient languages. 27 • Medium Sized Corpora (> 1000 texts) ⚬ Perseids serves all textual resources available from Perseus within the Ancient Greek and Latin corpora as well as some resources in Hebrew and Farsi. 28 ⚬ ⚬ Epigraphische Datenbank Heidelberg holds around 80,000 short texts from Latin epigraphic databases.
The present study In the current study, we seek to answer three main research questions using wayfinding data collected with the Sea Hero Quest -a mobile game application played by over 4 million users globally. While we previously reported individual and cultural differences of self-estimates, quantified and explained the gap between the self-reported navigation abilities and wayfinding performance across 46 countries and 11 cultural clusters  (Walkowiak et al. , 2023) , we now explore cross-cultural differences and correlates of wayfinding overconfidence such as age, gender, and education. Secondly, we identify and describe characteristics of participants who are prone to the Dunning-Kruger effect with particular focus on those who performed poorly while displaying excessive overconfidence across multiple wayfinding tests.
Dataset Statistical analyses reported in this manuscript were based on samples obtained from the full behavioural and demographic dataset of 4. 1 million participants who between April 2016 and April 2019 downloaded and played a mobile game application called Sea Hero Quest. The game, which comprises forty-five wayfinding, fifteen path integration, five radial maze and further fifteen strictly game engagement levels, was designed to measure typical behaviours of healthy individuals on navigation and spatial tasks across the population. During the wayfinding levels, which were of our main interest in this study, the participants had to navigate a virtual boat from the origin to the destination by following the sequence of numbered buoys which functioned as checkpoints along the route. Before each wayfinding level, a map with the layout and locations of both origin and destination points as well as positions of buoys was displayed to enable the players to plan and memorise the route.
Ethics and informed consent Informed consent was obtained from all participants who played the Sea Hero Quest mobile application game. The starting screen of the game displayed debriefing information including goals of the study, what data were collected and how they were used for research and analysis. Participants could also access the full explanation of the study and the Sea Hero Quest game during the gameplay through the 'journal' icon. They could also withdraw from playing any part of the game or sharing their data at any point during the study. This study was conducted as part of a larger research project which has been approved by the University College London Ethics Research Committee under the project number: CPB/2013/015. All experiments and methods including data collection, processing and analysis were carried out in accordance with relevant guidelines and regulations.
Data cleaning procedures and sampling The full Sea Hero Quest dataset included 4. 1 million users worldwide and 50 million gameplays at fortyfive wayfinding levels. Our final sample was restricted to only those players who provided self-estimates of their navigation skills, answered socio-demographic questions such as age, gender, education level, average daily commute time, typical amount of sleep and the type of home environment they grew up in, represented countries which were included in the cultural clusters identified in the Definition of cultural clusters subsection of Methods, and with at least 500 valid users at Level 12 (for baseline measurement of overconfidence) or at Level 38 (for estimation of dropout rate as a result of the Dunning-Kruger effect). These selection criteria were implemented to ensure a large enough sample for each country at the last task to allow statistically meaningful between-countries comparisons.
Finally, based on the wayfinding performance metric calculated for baseline levels (i. e. , defined in the Measuring wayfinding performance subsection of Methods), we have excluded players who performed two standard deviations below and above the sample mean on these tasks. Following these data cleaning and processing activities, the final sample of participants who completed all baseline levels (up to and including level 12) consisted of 376,836 individuals representing 46 countries with at least 500 valid players: 172,368 females (45. 74% of the overall sample; MeanAgeFemales = 37. 86 ± 14. 71) and 204,468 males (54. 26%; MeanAgeMales = 37. 47 ± 13. 48). Descriptive statistics for this sample with a breakdown by gender, country and cultural cluster are presented in Tables  S1  and S2  of the Supporting Information.
The models listed above were designed to consider several socio-demographic, environmental and behavioural covariates we collected from the participants. More specifically, we used age and gender of participants as the main demographic independent variables, but our models also included their education attainment (with four levels: 'no formal', 'high school', 'college', and 'university'), type of home environment they grew up in (i. e. , whether 'rural', 'mixed', or 'city'), their average daily commute time (with three levels: 'up to 30 mins', '30 mins to 1 hour', and '1 hour+'), and average amount of sleep in hours. We used these variables as there has been a growing evidence of their effects on wayfinding performance reported recently with the use of the same data e. g. Coutrot et al. (2022a)  showed that those who grew up in low street network environments displayed better navigation performance at SHQ game levels with regular layouts, whereas those who grew up in rural areas or high-entropy cities excelled at levels with irregular layouts.
Data science tools and statistical software Accessing the full dataset and sampling procedures were implemented with custom-made code written in Python programming language (version 3. 9. 12). All analyses reported in this manuscript, including data wrangling operations, transformations, statistical tests, survival analysis models and graphical visualisations, were performed in the R language (version 4. 2. 2) and RStudio Desktop (version 2022. 12. 10). We used the tidyverse family of R packages  (Wickham et al. , 2019)  for data wrangling, transformations, data aggregations and cross-tabulations. Custom-made implementations of native R language were used for inferential hypothesis tests (some of them were also obtained with the rstatix package, Kassambara, 2023) and multivariate logistic regression models.
The multicollinearity of covariates was tested with the car package  (Fox & Weisberg, 2019) , whereas the survival curves and Cox models were estimated with the survival package  (Therneau, 2023). Graphical visualisations e. g. survival curves and percentage-at-risk tables were designed with the help of the ggplot2  (Wickham, 2016)  and survminer  (Kassambara, Kosinski, & Biecek, 2021)  packages. All analyses were performed on a Mac Book Pro (2022) with 16 GB of RAM and Apple M2 processor.
Does the baseline overconfidence or the Dunning-Kruger effect lead to task disengagement and higher dropout rate? Firstly, we investigated whether the overconfidence estimated at baseline levels was related to the higher dropout rate at wayfinding test levels. Using a sample of 295,474 participants who played all baseline levels and completed at least one wayfinding follow-up level, we have applied the Mantel-Haenszel test with the Kaplan-Meier survival estimate to compute the difference in survival between overconfident and not overconfident users. Table  S9  in the Supporting Information presents the level-by-level game dropout ratios for not overconfident and overconfident participants across wayfinding follow-up levels. The test calculations and survival curves were estimated with the survival package in R language  (Kassambara, 2023).
Acknowledgments This manuscript was written as part of the Ph. D. research conducted by S. W. whose work was funded by the  Alan Turing Institute in London. S. W. would like to thank the  Alan Turing Institute  for academic support and financial contribution during his doctoral research. Furthermore, the authors wish to thank  Deutsche Telekom  and  Alzheimer's Research UK  ( ARUK-DT2016-1 ) for funding the research and analysis based on the Sea Hero Quest mobile game application. Finally, the authors are grateful to  Glitchers Limited  for the SHQ game production, and to  Saatchi & Saatchi London  for the project management and creative input.
Data availability Access to the full Sea Hero Quest dataset (including all wayfinding trajectories generated by the players) is available upon registration through a dedicated server at https://shqdata. z6. web. core. windows. net/. Please contact the Lead Contacts directly (H. J. S. and E. M. ) to obtain the sample of the full dataset used in this study. Lead Contacts: Hugo J. Spiers (h. spiers@ucl. ac. uk) and Ed Manley (e. j. manley@leeds. ac. uk). Additionally, researchers interested in using the Sea Hero Quest mobile game application can invite participants to play the game and generate wayfinding research data for non-commercial purposes via the SHQ game portal at https://seaheroquest. alzheimersresearchuk. org/.
When using our extension to Creusot, every for loop benefits from this structural invariant: we change the way these loops are desugared into the more primitive loop construct, by adding ghost variables init_iter and produced and the new invariant init_iter. produces(produced, iter). More precisely, a simple for loop for x in iter {<body >} is desugared into: let init_iter = ghost! { iter }; let mut produced = ghost! { Seq::EMPTY }; #[invariant(structural, init_iter. produces(produced, iter))] loop { match iter. next() { None => break, Some(x) => { produced = ghost! { produced. concat(Seq::singleton(x)) }; <body > }, } } Interestingly, the ghost variable produced can be referred to in a user invariant to relate the state of the loop with the iteration state. In the piece of code in Figure  2 , we use a variable count to count the number of elements generated by an iterator, and use such an invariant to verify its intended meaning.
Evaluation In this section we measure the performance of both the proofs of iterators and their clients, using the Creusot  [4]  tool for verification of Rust programs. It allows for verification of Rust programs, and requires some annotations to verify the functional correctness of Rust programs. Verification is performed by translating annotated Rust code into a pure, first-order functional program. Then, Creusot uses Why3  [15]  to generate verification conditions, which are discharged using automated solvers such as CVC5, Z3 or Alt-Ergo. The results in Figure  3 , were gathered using a Macbook Pro with an M1 Pro CPU and 32 GB of RAM, running macOS 12. 2. Why3 was limited to using four provers simultaneously among Z3 4. 11. 2, CVC5 1. 0. 2, and Alt-Ergo 2. 4. 1. Why3 supports proof transformations: manual tactics which can be used in combination with automated solvers.
We have larger scale examples where iterators are used in the context of a larger verified development: hillel is a port of a prior Creusot solution to Hillel Wayne's verification challenges  [16] ; knights_tour is the same for the Knight's Tour problem. In both of these cases, updating the code to use for-loops and iterators actually reduced the number of lines of specification. Because our lines of specification include the assertions which test functional properties, we believe the resulting overhead is reasonable, especially in our client examples. Additionally, our specifications for iterators seem to have low impact on verification times. We compared hillel and knights_tour with alternative versions that only differ by using traditional while loops instead of iterators, verification times are 0.
Related and Future Work RustHorn  [7]  and RustHornBelt  [8]  show how the non-aliasing guarantees of Rust can be used for reducing the verification of Rust programs into the proof of first-order logic formulas. These works serve as theoretical foundations for Creusot  [4] , which we use to evaluate our specification scheme for iterators. Prusti  [1]  is a semi-automatic verifier for Rust built on the Viper  [10]  separation logic verification platform. Prusti models mutable borrowing and ownership using separation logic permissions, unlike our choice of using a prophetic mutable value semantics. This leads to differences in the specification languages: whereas Creusot uses the ^operator to reason about borrows, Prusti uses a notion called pledges. Pledges are assertions which must be true at the end of a specific lifetime.
Transitions t 1 x 1 and t 2 x 1 represent the update of node x 1. Of course, in any marking t 1 x 1 and t 2 x 1 cannot be both enabled. Then, the fully asynchronous update scheme in N corresponds to the classical firing scheme in P where only one of the enabled transitions for a given marking will be fired  [12]. Note that given a Boolean network in the standard SBML-Qual format  [27] , i. e. , the package of SBML v3  [28]  for such models, one can easily obtain its Petri net encoding in the Petri Net Markup Language (PNML) 2 standard using the bioLQM 3  library. This piece of software extracted from GINsim  [29]  and part of the CoLoMoTo 4   [30]  software suite allows for easy conversion between standard formats. It also accepts many other common formats for Boolean networks, notably the.
Evaluation To evaluate the performance of the newly proposed methods (implemented as a Python package named Trappist and available on the Python package index  9  ) and the state-of-the-art methods (bioLQM 10  , PyBoolNet ), we compared them on both PyBoolNet's own model repository and many real-world models from various sources in the literature. To our knowledge, these models are a highly representative sample of Boolean models currently available. It is worth noting that mpbn  [9]  only handles locally-monotonic models, whereas the other methods can handle general models. To obtain a more comprehensive comparison, we also used random models generated by a third-party software BoolNet R package  [31]. As explained in Section 5, in our benchmarks, we only searched for the first 1000 minimal trap spaces for each model.
Upon closer inspection, for the Boolean CSP characterizing conflict-free siphons, CP seems to be something that is a "less-efficient-SAT", handling mostly Boolean constraints and making little use of the global constraints only added for the iterative 11 https://github. com/hklarner/pyboolnet/tree/master/pyboolnet/repository part. For ILP, it may be even worse, since the problem is purely Boolean (no real or integer numbers whatsoever). This is confirmed by the observation that for some quite large models (e. g. , the grieco mapk, zhang tlgl, and zhang tlgl v2 models), Trappist-ILP is much slower than Trappist-CP. Note that the inferiority of ILP compared to ASP with respect to the trap space enumeration has been reported in  [7]. Hereafter, we shall compare the best variant of Trappist (i.
BBM repository The research group behind the BBM repository  [48]  has recently undertaken considerable effort for building a collection of real-world Boolean models from various sources used in systems biology. It aims to be a comprehensive collection suitable for benchmarking and testing new tools and methods. BBM consists of 211 models (24 out of them are non-locally-monotonic), peaking at 321 nodes, 1100 regulations among the nodes, and 133 source nodes, respectively. It is released and maintained at https://github. com/sybila/ biodivine-boolean-models. We here tested all the compared methods on this model repository. first see that Trappist-ASP and Trappist-MaxSAT are still the two best methods as they can handle every model within 1s and always can handle more models than all the remaining methods on every time limit.
Fig. 4A ). These data therefore suggest that the shift in opsin gene expression is controlled by TH. We next investigated if TH could also induce a shift in visual preferences. We treated stage 3 larvae (i. e. , before metamorphosis) for 72 hours with either T3 or DMSO as a vehicle and measured the color preference of the larvae in a dual chamber, as described above. Interestingly, we observed that control larvae spent significantly more time in the blue compartment than T3 treated larvae (53% vs 1% respectively, p-value=5,2x10 -7 , Fig. 3E ), which can be correlated with the higher expression of opnsw2B observed in control larvae (Fig. 3D ). Surprisingly, T3 treated larvae did not spend much time in the orange compartment compared to DMSO treated larvae (11% vs 16% respectively). Instead, they remained most of the time in the central compartment (88% vs 31% for the DMSO controls, p-value=1,3x10 -5 ).
Software Transcriptomic analyses are performed by the Altrabio Company (Lyon, France) using the following software: FasQCversion 0. 11. 8 and fastq_screen version 0. 13. 0, Salmon version 0. 14. 1, R version 3. 6. 1 (2019-07-05), x86_64-apple-darwin15. 6. 0.
Pre-treatment (read quality, quantification, filtering, normalization) Quality of raw reads is assessed using the FastQC quality control tool1. Sample contamination is assessed using the fastq_screen quality control tool2. The Bowtie2 indexes for Amphiprion ocellaris are generated from Ensembl "Amphiprion_ocellaris. AmpOce1. 0. dna. toplevel. fa" file. Amphiprion ocellaris transcript sequences and annotations (Ensembl release 97) are downloaded from the Ensembl website3 (files Amphiprion_ocellaris. AmpOce1. 0. cdna. all. fa, Amphiprion_ocellaris. AmpOce1. 0. ncrna. fa and Amphiprion_ocellaris. AmpOce1. 0. 97. gtf). Some genes which had no official symbols in Ensembl are given the following symbols: ENSAOCG00000023317 =tg, ENSAOCG00000008531 = mct8, ENSAOCG00000005709 = dio3a, ENSAOCG00000017526 = edn3b, and ENSAOCG00000019494 = bnc2.
Transcript expression quantification is performed from the raw read data (FASTQ files) using Salmon4 (with parameters --gcBias, --seqBias, --validateMappings, --discardOrphansQuasi, and --consistentHits). They are then summarized as gene counts using function tximport from package tximport5 (with parameters type="salmon" and countsFromAbundance="lengthScaledTPM"). Genes that did not have more than 0. 502 count per million counts in at least three samples are filtered out. To generate the normalized signals, the effective library sizes are first computed using function estimateSizeFactors from package DESeq26. The raw count values are then transformed using a variance stabilizing transformation (function VarianceStabilizingTransformation from package DESeq2 with parameter blind=TRUE).
Hierarchical clustering Hierarchical clusterings of samples are performed using the Wards agglomerative method, passing the euclidean distances between samples to function hclust from package stats (with parameter method="ward. D2"). Cluster stability is estimated by multiscale bootstrap resampling using function pvclust from package pvclust7 (with parameter nboot=10000). Hierarchical clusterings of genes are performed using the complete agglomerative method, passing the Euclidean distances between centered (for expression data) or uncentered (for fold change data) and scaled gene signals with function hclust from package stats.
Sample processing Samples of each experiment were processed in multiplexed reaction including, in each case, six negative probes (to determine the background) and six positive control probes. Hybridization reactions lasted 16 hours at 67°C. Data were then imported into the nSolver analysis software (version2. 5) for quality checking and normalization of data according to NanoString analysis guidelines, using positive probes and 7 housekeeping genes (Tuba1, PolD2, g6pd, eif4a3, tbp, rpl7 and rpl32)  (Kulkarni, 2011).