dataesr/scientific-paragraphs-categorization huggingface dataset:
A recent review of literature has shown that the game, used as interventional tools, can influence behavior by incentivizing, reinforcing, educating, providing feedback loops, prompting, persuading, or providing meaning, fun, and community. However, not all game elements will appeal to all consumers equally, and different elements might work for different people and in different contexts  [13]. In our work, we focus on the analysis of behavioral tendencies in a digital simulation game to estimate its effectiveness and to understand users' behavior and limitations of game simulation. The analyzed dataset has been collected from the game-based simulation for students from Nursing Schools, which is called Clinical Organizer Nurse Education (CLONE). The study proposes a data analysis model with the implementation of data mining tools.
We call these learners -outliers, and the process of finding them -outlier detection. The importance of outlier detection is caused by the fact that outliers in a dataset can bring significant information for application domains. Outliers exist in almost every dataset and might be summoned for a variety of reasons. They can appear because of human error, instrument error, natural deviations in populations, fraudulent behavior, changes in the behavior of systems, or faults in systems  [47]. Many data mining algorithms try to minimize the influence of outliers or eliminate them altogether. However, this could lead to the loss of important hidden information since one persons noise could be another persons signal  [48]. Therefore, outliers can be relevant, especially for cases that result to make a decision based on data analytics, such as an education.
Most of them target technical skills. The game-based simulation called CLONE is also dedicated to nurses while it intends to develop non-technical skills. CLONE is a software which provides various real-life-like professional cases. Nurse students are supposed to develop skills such as work organization, decision making, and situation awareness. This digital environment includes game mechanisms and interactive features such as task scheduling or shifting and decision-making. The designing process of CLONE contained three steps: the domain analysis, the human activity modelling, and the scenario, which are described in  [60]. Fig. 1  presents the graphical user interface.
Sequences Analysis The data extracted from the game represent sequences of actions, which students have made during the game session. For a symbolic sequence, the simplest way is to treat each element as a feature. However, the sequential nature of sequences cannot be captured by this transformation. To keep the order of the elements in a sequence we have exploited the n-gram models. N-gram approach is commonly used in computational linguistics (e. g. natural language processing) and computational biology (e. g. DNA sequencing or protein sequencing). An n-gram is a contiguous subsequence of n elements from considered sequence. For example, for a sequence of elements {1, 2, 3, 4} we can form three 2-grams {12, 23, 34} and two 3-grams {123, 234}. Given a set of n-grams, a sequence can be performed as a vector of the presence and the absence of the n-grams or as a vector of the frequencies.
The weight of the edge is the distance between vectors. Hence, according to a predefined threshold for edge weight (distance between points), instances are detected as outliers if at least one point in their k-neighborhood is further than the threshold. kNN algorithm works well for detecting global outliers -objects which are outlying for the entirety of the dataset in which it is found. The results of the algorithm are very simple to understand and equally easy to interpret. The algorithm requires predefined parameters: k -number of nearest neighbors, d -the threshold. In the current work, we have considered k = 3. As a thumb rule, in the outlier detection field, the partition of the outliers usually does not exceed 10%; however, it depends on the application domain and other circumstances related to the certain context.
Data Collection In this work we have analyzed log data that include information about actions of students during game sessions and their timestamps. It is worth mentioning that we have not focused on time series and have not considered the time for each action. However, we have concentrated on continuous sequences of actions and their row in the game session. In other words, we have not compared data objects according to time, when they happened, but have analyzed their order. The actions or sets of actions have been denoted as activity types and after have been coded. The code for each action had the following representation: Type Info Time, where Type corresponds to activity type, Info refers to the Patient ID or extended information about activity, and Time corresponds to the virtual time at schedule panel.
The scheduled panel contains six time gaps with one-hour duration and one time gap with thirty minutes duration, where the start time is 7:00, and the end time is 13:30, thereby the code for gap 7:00-8:00 is 1, and code for gap 13:00-13:30 is 7. Depending on the activity, the code may consist of one, two, and three parts. For example, the code of such activity as Communication is C, the code of planning of the breakfast Pm bkf, the code of planning an activity pertaining to personal care for a Patient 4 at 8:00-9:00 time gap is P 4 2. The full list of codes and its descriptions are represented in Table  1. Thus, the collected data contained in-game actions for 353 game sessions which have been made by 222 students. The students from 11 Nursing Schools played the same scenario during the 2018-2020 period.
, 2018). Here, Py-GC-MS samples were handled using an auto sampler (EGA/PY-3030D, Frontier Lab, Fukushima, Japan), all samples were analyzed with a GC2010-QP2010-Plus GC/MS (Shimadzu, Noisiel, France). All the interfaces from the pyrolyser to the ion source were set at 300 • C. After pyrolysis at 700 • C for 1 min, the pyrolysis products were injected with a split of 10. Separation of the products was achieved on a RXi-5 ms R column (60 m, 0. 25 mm, 25 µm thickness) (Restek, Lisses, France). The helium flow, oven program and mass parameters were kept constant. Finally, F-Search software 4. 3 was used to identify the polymer from the total ion chromatogram (TIC) program. Identification was considered successful when similarity percentages reached 80% or higher, or when marker compounds were clearly highlighted.
µ-Raman analyses were performed on a XploRA PLUS V1. 2 (HORIBA Scientific, Palaiseau, France). Samples were analyzed with a laser at a wavelength of 785 nm. Time of acquisition was set at 2 s with 10 accumulation. Spectra were acquired from 200 to 3200 cm -1 with a 100x objective. Slit aperture and hole were respectively set at 200 and 500 µm. The polymer identification was carried out using the KnowItAll R spectroscopy software. Identification was considered as good where hit quality index was above 80. FT-IR analyses were performed on a Spotlight TM 400 FT-IR device equipped with a MCT. The micro attenuated total reflection automatic module equipped with a germanium crystal was used to perform measurements. All the spectra recorded were obtained using the same parameters, i. e : wavelength range from 4,000 to 600 cm -1 , with a resolution of 4 cm -1 and 25 accumulations.
Measures were performed in the transmittance mode. Spectra were then compared to Flopp and Flopp-e databases  (De Frond et al. , 2021)  and confirmed using the OpenSpecy database  (Cowger et al. , 2021). Identifications were validated when identification were higher than 0. 7. To generate MP samples from the collected macroplastics, the items were washed, ground and sieved. To avoid contamination of the cell culture, organic matter present on the surface of the plastics was removed using 0. 22 µm filtered 30% (w/w) hydrogen peroxide solution (H 2 O 2 ) (ref: 216763, Sigma-Aldrich, Saint-Quentin Fallavier, France). A ratio from 25 to 45 mL/g of 30% H 2 O 2 was used to pre-treat the samples under a fume hood for 24 h at room temperature. Then samples were rinsed four times with 0. 22 µm filtered analytical grade water  (ref.
PROAST Analysis for Dose-Responses Dose-response analyses on the cytokine secretion data were performed using the European Food Safety Authority (EFSA) BMD webtool (EFSA Web Application for PROAST (version 69. 0) available from: https://r4eu. efsa. europa. eu [accessed November 13, 2020]), which is based on the R-package PROAST version 69. 0 (RIVM, Bilthoven, Netherlands). PROAST is a software package that fits dose-response curves for a given data set. Based on the dose-response curve fitting, the package calculates a confidence interval around a dose (benchmark dose -BMD) at which a predefined response (benchmark response -BMR) would occur. The lower (BMDL) and higher (BMDU) bounds of this confidence interval (90% double sided) obtained after model averaging are conventionally described as BMDL and BMDU respectively.
Parameter Selection For statistical analyses, we selected 19 parameters, based on their suitability for further interpretation (e. g. , excluding FTIR spectra, and using the type of plastic instead), as well as having a sufficient number of data points for further analyses. Included parameters were classified as biological (IL-1β, IL-6, IL-8, TNF-α, WST-1), chemical (PE, PET, PS, PP, nylon, all as binary options), or physical (particles concentration, melting point, density (e. g. , floating or sinking), size fractions, median diameter, percentages particles in the 0-20, 20-50, 50-100, and 100-200 µm ranges). Analyses were done in R version 4. 0. 0 or later.
Other Statistical Analyses The particle size distribution was visualized using the polynomial fit function in MS Excel based on the number of particles within defined bins for particles sizes. Statistical significant differences between conditions was calculated using Nonparametric Kruskal-Wallis one-way ANOVA's in GraphPad Prism 8. 4. 1 (GraphPad Prism software Inc. San Diego, USA) and R version 3. 6. 0.
1 H-NMR analysis of PET, PS, and nylon samples confirmed their identification (Supplementary Figure  2 ). All three polystyrene size fractions gave similar 1 H-NMR-spectra, that were typical for atactic polystyrene. Where on the one hand all three-size fractions of each type of polymer were chemically similar, they exhibited differences in polymer chain length as determined by GPC-UV (Table  2 ). Unfortunately, due to low solubility of the PE and PP samples in solvents suitable for NMR, these samples could not be analyzed, even at elevated temperatures. We also performed DSC to determine thermal properties and insights into crystallinity to further characterize the polymer configuration of the MP particles. From the DSC heat flow graphs (data not shown), we could extract melting point temperatures and melting enthalpies for all except the PS samples, and glass-transitions for PET, PS and nylon samples (Table  3 ).
Using image analysis software (ImageJ version 1. 53c) we measured the Feret diameter and surface area of each individual particle larger than 1 µm on the images (Supplementary Figure  4 ). We observed a significant difference in mean particle size within each sample. For PET_Land, PE_Land, and PS_Land, the increase in mean particle size was in line with the intended size fractionation. We next calculated the particle size distribution within each sample. Particle sizes were based on surface area, as this is an important dose metric for exposure of cells  (Schmid and Stoeger, 2016). Particle size distribution was plotted relative to the total surface area. For this, particle areas in µm 2 of particles in one bin were added up and divided by the total surface in µm 2 of all particles. The resulting distributions show what percentage of the total particle-covered surface is covered by particles falling into each bin of the accompanying Feret diameters of those particles (Figure  3 ).
Dose-Response Analysis of the Cytokine Secretion Data To further characterize the effect of MP exposure, the effect of MP particle size and material on cytokine release was further assessed using BMD analysis in which a 20% increase in cytokine release was considered a relevant effect (see Section PROAST Analysis for Dose-Responses). Sinking particles induced the most robust responses as seen in the narrow ranges between the BMDL and BMDU (IL-1β in Figure  5 , TNF-α and IL-8 in Supplementary Figure  8 ). For all three cytokines, the response was higher for MPs in the smallest size range, whereas for the fraction with the largest particles an BMD could not be derived with certainty for cytokine levels. This directly indicates a size dependent effect on cytokine secretion of MPs. For the smaller fractions, we found a dose-dependent increase in cytokine secretion.
(De Frond et al. , 2021)018)r the selected items, before transformation to microplastics. Figures presented in italic between brackets corresponds to the identification scores obtained with the different techniques. bAlthoughinferior to 80%, the identification as PET has been validated, controlling specific peaks as presented inHermabessiere et al. (2018). cScoresobtainedwith Flopp-e database(De Frond et al. , 2021). dScores obtained with Flopp database(De Frond et al. , 2021).
Simulation of complex selective forces together with demographic processes remains a difficult problem for coalescence-based simulators  [19] ; other coalescent-based simulators that are not specific to bacteria (e. g. , ms  [21] , msprime  [22] ) suffer from the same constraints, and additionally most cannot simulate bacterial recombination (similar to gene conversion). On the other hand forward simulators such as SFS CODE  [23]  enable complex models including varying demography and multiple types of selection; however, this software suffers from poor performance  [24]. Yet computational efficiency is crucial for supervised methods trained on large simulated datasets, such as ABC, machine learning, and deep learning. Using forward simulation instead of coalescent-based methods can therefore be problematic, given that forward simulation has traditionally been several orders of magnitude slower than the coalescent.
The nonWF framework, on the other hand, is more individual-based, emergent, and realistic. It allows a greater breadth of possible scenarios to be simulated, but we cannot derive expectations of the same quantities. Thus, we will provide results for the same scenario under both models to confirm that they behave similarly (according to the WF expectations). In the main text we will present the protocol for simulating bacterial populations with the nonWF framework, since it is a more powerful framework on which other users can build more complex scenarios. The underlying simulation, however, corresponds to a Wright-Fisher population, so we can compare to the theoretical expectations. The corresponding annotated WF script is available in a public repository (https://github. com/jeanrjc/Bacte rialSlimulations), along with the nonWF script detailed below.
At equilibrium, the probability that a given bacterium will divide is about 0. 5, so half of the bacteria produce two offspring and die, while the other half produce no offspring and die, so the population size is then (stochastically) constant. The neutral mutation rate was set to 10 -9 , and the recombination rate was 10 -7 with a mean recombination tract length of 500bp. The figures were generated using the random number seed 2049327378235, and snapshots were taken in SLiMgui. The code to reproduce this simulation can be found in the same repository: https://github. com/jeanrjc/BacterialSlimulations.
Impact of rescaling We compared 9 different rescaling factors (RFs): 1 (no rescaling), 2, 3, 4, 5, 10, 25, 50, and 100. For RFs above 2, we generated 100 replicates for each RF and each SLiM model (WF, nonWF); for RF 1, 30 replicates were used, and for RF 2, 50 replicates. We generated 300 replicates when running FastSimBac and ms for comparison. Rescaling was not applied to backward simulators, since they do not simulate the entire population. Without rescaling, the generation of a single replicate takes about a day (Figure  1 ). This is too long if one wants to run millions of simulations; however, it is possible to do a few such runs for other purposes, such as confirming that rescaling did not introduce a bias when implementing a new script. This might also be useful to test a method on a dataset produced without rescaling, since even minor artifacts introduced by rescaling could conceivably bias or confuse inference methods.
The final model, of bacteria on a Petri dish, certainly could not be run in any coalescent simulator. These simulations were made both within SLiM's Wright-Fisher framework and within the more individual-based nonWF framework, to showcase these two possibilities for the user. The recipes for our WF and nonWF models are freely available on our dedicated repository (https://github. com/jeanrjc/BacterialSlimulations), where we encourage everyone to propose their recipes for more complex scenarios. It might be worth mentioning why one would choose between SLiM's WF and nonWF model types, since this fundamental choice will guide much of the model development that follows. The WF model is simpler in many ways: it involves more simplifying assumptions and less individual-level behavior. For example, population size in the WF model is automatically maintained at a set level, whereas the nonWF model requires you to write script that regulates the population size via mechanisms such as density-dependence or -appropriately for pathogens, perhaps -host mortality.
Acknowledgements We thank  Peter Ralph ,  Eduardo Rocha , and  Philippe Glaser  for fruitful discussions. JC and FJ thank DIM One Health 2017 (number  RPH17094JJP ) and  Human Frontier Science Project  (number  RGY0075/2019 ) for funding. The authors of this preprint declare that they have no financial conflict of interest with the content of this article. Guillaume Achaz  is one of the PCI Evol Biol recommenders and is part of the managing board. Version 5 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https: //doi. org/10. 24072/pci. evolbiol. 100123).
The NaviEar Geomagnetic augmentation signals the direction of geomagnetic north. This approach has been introduced to Figure  1. Rationale behind our approach to perceptual feel. The learning process must start with the acquisition of skills to master the device as a tool. We assess participants' mastery of the NaviEar through data from the training tasks. A stronger learning success is automatization, in which the participants internalized completely the functioning of the device and directly react to the device without any cognitive effort. Automaticity implies a high level of learning and tool mastery. We test automatization through our test battery. Finally, a perceptual feel develops, that is, a distinctive subjective qualitative character of not mere using but of perceiving with the augmentation device.
Apparatus and materials The NaviEar consists of three parts (see Figure  2 ): a headmountable orientation sensor (YEI technology, 3-space Bluetooth sensor  (YOST Labs, 1998 -2015) ), boneconduction headphones (Aftershokz Sportz M3 AS450), and an Android phone with low latency in orientation to sound mapping (LG G3 16GB). The orientation sensor consisted of three types of sensors: a gyroscope, an accelerometer, and a compass. The raw output of these sensors was processed through a Kalman filter to provide the final orientation reading. The sensor's recording rate was 100 Hz. Nominal orientation accuracy was on average 1 deg and orientation resolution <0. 08 deg  (YOST Labs, 1998 -2015). Before each use, the sensor was zeroed in on north by comparison with a manual geomagnetic compass. We implemented the NaviEar orientation-to-sound coding as well as the training tasks in a custom-made Android App (for source code, see https://github.
Our hope was that the need for precision would naturally lead participants to integrate the NaviEar signal in their estimation of orientation. There was no guarantee that this would work; but we thought it would be good to have a diverse set of tasks to increase the likelihood that users internalize the NaviEar signal. This task was only used for training, and no data was recorded. Trials of these tasks were presented in random order within one session. The only exception was the Angle-Difference task. It involved more complicated instructions and required more concentration than the other tasks. So, we thought it good to present it as one block at the end of each session. Each session involved all 5 types of tasks, with an overall of 74 trials that took between 45 and 60 min to be completed.
Results Data is available at Zenodo  (Witzel, Lübbert, O'Regan, Hanneton, & Schumann, 2022). As shown in Table  1 , two participants completed less than 8 training sessions (f5 and f2), and two participants missed one of the test sessions (f1 and f6). Participant f2 skipped one training session for unknown reasons. Participant f6 completed unrotated instead of rotated-signal tasks in the second test session by accident (hence 9 training sessions for f6). The rotatedsignal and interference data of participant 1 is missing for unknown reasons. The two missing training sessions of f5 had to be removed due to an error in handling the app. For the same reason, participant f3 was exposed to and trained with the 90-deg-rotated signal after the first rotated-signal test. Conditions were recoded accordingly (i.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by  ERC Advanced Grant "FEEL" (H2020 European Research Council ), number  323674  to  J. Kevin O'Regan  and in part by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -number  395696283  to  Frank Schumann  and by  FrontCog  grant  ANR-17-EURE-0017.
The research work presented in this paper introduces a feature-less algorithm designed to perform historicalto-nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span).
, 2019). While providing accurate solutions, such techniques are laborious at small scale and unfeasible when applied on massive datasets acquired over wide areas and coming from different epochs. Recently reported automatic frameworks exploit feature-based approaches  (Feurer and Vinatier, 2018) ,  (Zhang et al. , 2021) , being suitable for scenes representing structured environments. Therefore, introducing fully automatic techniques capable to provide accurate georeferencing of multi-epoch aerial imagery acquired in natural feature-less environments remains an open issue. This paper addresses the automatic georeferencing problem for multi-views partially overlapped historical aerial images, acquired in unstructured natural and rural environments. Generally, the key issue standing behind the automation of the multiepoch georeferencing process is represented by the capability * Corresponding author to extract homologous "features" or "landmarks" in presence of consistent environment changing which characterizes multiepochs acquisition scenarios.