dataesr/scientific-paragraphs-categorization huggingface dataset:
The vinde term was not identified on the surface of the colour space in the leave-one-out mode by RST. A McNemar test  (Dietterich, 1998)  showed that the two proportions were significantly different χ 2 (1, N = 160) = 32, p < 0. 01 with a Yates' correction. Given that we found a 6th dispensable modal term in Roberson's data, we also tested the k-means algorithm with k = 6 but its performance deteriorated further with a classification accuracy of 54%. Considering the new Himba data that also cover the interior of the colour space, the k-means with k = 10 (equal to the number of the modal terms) classified 40% of the 600 samples correctly while the RST produced again superior performance with a classification accuracy of 93% including the major modal terms plus a smaller vinde category in the interior of the colour space.
Discussion Theories of augmentation have been essentially conjectural as there is little actual augmentation data but we now provide, along with  Kuriki et al. (2017)  for Japanese and  Mylonas and Mac-Donald (2016)  for English, information concerning the language of a remote group where we have seen just such a development. In 2005, the Himba were reported to use a 5-colour-term grue language. Today, they have seven colour categories. If a regular pattern of colour term evolution exists as suggested by  Kay et al. , (1991) , Himba has evolved from a Stage V to a Stage VI language with 7 colour terms using their classification scheme. One of these new categories (the brown term vinde) could have been present in 2005 since it is only with our present methodology that we can investigate the desaturated interior of colour space.
It still could be that the colours were slightly different and the new boundary between their green and blue terms would be in a different place if naming had been measured from paper swatches. However, the boundary between GREEN and BLUE in our new data is where it would be either if the category had been imported from another culture or if it were perceptually driven. So, there is no reason to believe that the new computerised procedures by themselves would have produced any different outcomes than that obtained with paper swatches. We therefore turn to consideration of what are the driving forces for augmentation of colour terms. One hypothesis for the Himba grue term having split in the last 15 years is that there might be optimal ways of dividing colour space and so predict how a 5-colour-term grue language might form 6 categories  (Regier et al.
, 2007 ; see also  Kay and Regier, 2007). It was argued that these proposed optimal partitions could be based on the uneven shape of perceptual colour space where several large "bumps" of saturation presumably produce areas with greater consensus among speakers across languages  (Jameson and D'Andrade, 1997; Jameson, 2005). Indeed, the speed with which the augmentation has taken place might argue that the change "was waiting to happen". However, the new bluish (burou) and greenish (grine) categories were not latent in the GRUE category of the old data  (Roberson et al. , 2005)  as has been suggested  (Kay and McDaniel, 1978; Lindsey et al. , 2015; Regier and Kay, 2004). The colour chips with the highest confidence of the GRUE category in our earlier data have become boundary colours between the new grine and burou categories in our current data.
Alike, the location of the chips with the highest confidence for grine and for burou in our new data were boundary colours in our earlier data. So, there is no evidence that latent categories were responsible for augmenting the colour lexicon in the adult Himba nor indeed that they drive colour naming in the development of colour naming in Himba children  (Roberson et al. , 2004). Further insight into augmentation can be obtained from our machine-learning data but it is important to issue a caveat. All the machine-learning models are statistical techniques and by themselves do not predict the underlying mechanisms of change. Even the k-means models do not necessarily entail that there is a physiological underpinning of the perceptual structure that they discover  (Davidoff, 2015; Jameson and D'Andrade, 1997).
However, our evaluation of supervised (RST) and unsupervised (kmeans) machine-learning approaches suggests that perceptual similarity alone cannot explain colour categorisation and that linguistic similarity is the driving force for facilitating effective colour communication with the Himba at different time periods, for any number of categories, on the surface and across the colour gamut and in different colour spaces. Even for a language with only five terms, the unsupervised (perceptual structure) is suboptimal compared to the consistent performance of supervised learning. In contrast to supervised cultural learning, the communication accuracy of unsupervised perceptual learning falls short mainly because minimising the variance in the data tends to produce equal size clusters that fail to fit human colour categories of various sizes and also because exploiting statistical regularities found in data is sensitive to scaling and produces diverging results in different coordinate systems  (Steels and Belpaeme, 2005).
A similar desaturated LIGHT BROWN category (color de coyuche) borrowed from Spanish and denoting the colour of organic cotton was reported by  MacLaury (2007)  in Zapotec (see  Jameson, 2018  for a digitised archive of MacLaury's dataset). The augmentation that we have shown for the Himba seems unlikely to be simply the result of schooling as suggested by  Grandison et al. (2014)  but very much as if the Himba, especially the younger ones  (Griber et al. , 2021) , have imported a green term, probably from Herero who recently started using the term ngirine  (Nguaiko, 2010)  instead of the earlier tarazu  (Kolbe, 1883) ; a process we refer to as cultural transfer or simply loanwords. The centroid of the newly acquired Himba word for GREEN was indeed located at much the same place in colour space as it is in English, but it is also in the same place in the neighbouring language of Herero where its words for green and blue come from European languages  (Roberson et al.
Acknowledgements The authors thank  Debi Roberson  for making available the earlier Himba colour naming data,  Andrew Stockman  for the use of facilities at the  Colour Vision Research Laboratory of University College London  and  Jerone Andrews  for his support towards the computational model. This work was supported by the  British Academy/Leverhulme  Grant  SG171176. DM was partly supported by the  University College London (UCL) Computer Science-Engineering and Physical Sciences Research Council, UK ,  Doctoral Training Grant :  EP/M506448/1-1573073  and by the  FY22 TIER 1 Seed Grant  from  Northeastern University, USA.
Achievement With 20 years of development, Choco-solver is now a stable, flexible, extensible, powerful, and user-friendly library. There is a community of users and contributors who actively participate in improving the library. In addition, Choco-solver relies on software quality standards (unit and performance tests, continuous integration, code review, etc. ) and frequent updates are made. Finally, the choice of Java as programming language makes the integration of the library simple into both academic and industrial projects.
Community tools integration Several useful extra features are also available such as parsers to XCSP3 format and MiniZinc format. Choco-solver is also embedded in PyCSP3, a Python library for modeling and solving combinatorial constrained problems. In addition to offering alternatives to modelling in Java, it also allows participation in the two major constraint solver competitions : MiniZinc Challenge and XCSP3 Competition. Finally, although it is originally designed to solve discrete mathematical problems, Chocosolver also supports natively real variables and constraints, and relies on Ibex-lib to solve the continuous part of the problems  (Fages et al. , 2013). Equally, a Boolean satisfaction solver (based on MiniSat) is integrated to offer better performance on logical constraints. These aspects consolidate the place of Choco-solver as an important tool in the CP community.
Acknowledgements We acknowledge contributions from (in alphabetical order)  Hadrien Cambazard ,  Arthur Godet ,  Fabien Hermenier ,  Narendra Jussien ,  Dimitri Justeau-Allaire ,  Tanguy Lapègue ,  Alexandre Lebrun ,  Jimmy Liang ,  Xavier Lorca ,  Arnaud Malapert ,  Guillaume Rochart ,  João Pedro Schmitt  and  Mohamed Wahbi.
Results and Discussion In vivo measurement of intraneuronal transport. To investigate the axonal transport in the CNS of Zf larvae imaged by fast nonlinear microscopy, we synthesized single crystal nanoKTP (Supporting Figure  S1A ) in solution in deionised water as we reported previously. 26 Using differential centrifugation we narrowed their size distribution, measured by nanoparticle tracking analysis, leading to an average size of 123. 8±2. 4 nm, with a standard deviation of 42. 7 nm (Supporting Data S1 and Figure  S1B ). This solution was injected in the left optic tectum of 3 days post-fertilization (dpf) larvae at a concentration of ≈ 1 mg/mL (Fig. 1A ). We conducted the nonlinear microscopy fast-scanning recording 24 hours later, at 4 dpf, when tectal neurons are mature and respond to visual stimulations.
Inset: zoom on optic tectum displaying periventricular neurons (PVN) projecting parallel and coplanar axons in the neuropil. (B) Immobilization at 4 dpf of 8 anesthetized injected larvae in channels molded in agarose, with dorsal orientation towards the microscope objective for SHG microscopy. (C) Left: schematics of a single monocrystalline nanoKTP embedded in a vesicle connected to a microtubule (displayed as a section, MT) by a dynein (blue) and a kinesin (green). The black arrow onto the nanoKTP indicates the direction of the second order nonlinear polarization (mostly dipolar), in response to the pulsed-laser excitation at 1040 nm wavelength. Right: in green, the SHG radiating pattern of one nanoKTP. (D) Analysis pipeline with the Python program MINT we developed,  36  consisting, from left to right, in two parts of analysis.
Key parameters for quantitative analysis of axonal transport with nanoKTP. Imaging data sets were further analyzed to extract quantitative parameters required to assess axonal transport defects. We first evaluated the precision of localization and applied a denoising strategy before processing data through the automated pipeline of transport analysis schematized on Fig. 1D. Trajectories of nanoKTP-labeled vesicles are inferred from consecutive positions of the nanoparticles (Materials and Methods) obtained as the position of the center of the best Gaussian fit to each SHG spot. Focusing on directed motions, we first filter-out "trajectories" corresponding to confined Brownian motions (possibly from particles in intercellular space) that are characterized by a bounded mean-square displacement.
The following step of the automated analysis consists in parsing the regularized trajectories in phases of "slow" (further called Stop) and "fast" (Go) motions (Fig. 1D ). To this aim we apply the same heuristic method as in our previous work. 22 Briefly, we calculate at each trajectory position i a confinement ratio R i on a sliding window of 6 consecutive points width, defined as the net distance between the first and last point divided by the sum of the length of the two segments. R i = 1 corresponds to straight line, and therefore a Go phase, while R i = 0 is associated to a zero net motion, hence a Stop phase. To attribute a phase state to all other intermediate values of R i , we searched for a confinement ratio threshold R th emerging from the datasets. To this aim we examined the distribution of R i for all trajectories of all conditions (including heterozygote larvae) in kif5aa larvae (Supporting Data S5 and Figure  S5 ) and noticed that it has a minimum at 0.
To this aim, we selected dynapyrazole-A (DYNA), a new conformational isostere of the first-in-class dynein inhibitor ciliobrevin, that has a higher selectivity in blocking only dynein's microtubule-stimulated activity (Figure  4A ) and not the ATPase one. 33 n in vitro assays DYNA strongly inhibits dynein's microtubule-stimulated activity at micromolar concentrations. In absence of data available in vivo, we first immersed Zf larvae in different concentration (10 and 15 µM) of dynapyrazole for 25 to 45 min at 28 • C in a bath. The larvae were then washed and observed to monitor the appearance of potential toxicity effects right after or 1 h post exposure. Thirty minutes incubation at 15 µM lead to severe malformations (body curvature) detected 1 h post exposure, which evolved into larvae death (data not shown).
Note that we are not able to extract the azimuthal angle defining the orientation of the SHG dipole in the sample plane, as we are not resolving the polarization of the collected light. Then in order to quantify the fluctuations of vesicle orientation, we first applied a Savitzky-Golay filter to SHG intensity time traces in order to mitigate the high sensitivity of the intensity-to-polar angle inversion process to a large intensity fluctuation close to the extrema. We determined θ(t) from the filtered intensity time trace and calculated its standard deviation σ θ in each phase of motion within a trajectory, from which we inferred an average value σ Go/Stop θ j per trajectory j. We focused our analysis on kif5aa data set. Figure  6A  displays the distribution of these values for all the trajectories of WT and MUT conditions.
In order to check the consistency of our observations in different conditions, we also computed the vesicle orientation fluctuations for the dynapyrazole dataset (Supporting Figure  S12 ). We observed similar distributions and median values of σ θ than in kif5aa data, with larger σ Stop θ than σ Go θ for both control and DYNA. Here, the inhibition of functional dyneins by dynapyrazole leads to an increase of 14% of σ θ in the Go phase, and no changes in the Stop ones (Figure  S12B ).
Immunostained samples were acquired by confocal and 2-photon microscopy using the same Leica SP8 and a coverslip corrected HCX IRAPO L 25X/0. 95NA water immersion objective (Leica Microsystems). Alexa Fluor 488 and Alexa Fluor 594 were excited with lasers emitting at 488 nm and 552 nm wavelengths respectively. Fluorescence was detected with PMT detectors. SHG signal from nanoKTP in fixed larvae was acquired as in live imaging experiments. Automatic extraction of nanocrystal-labeled vesicle trajectories and transport parameters Videos were first exported from the Leica LIF proprietary format as stacks of 8-bits TIFF files with a custom Fiji macro relying on the import function of the Bio-Formats plugin. 62 The subsequent data analysis process is automated, using a set of Python scripts that we developed and made freely accessible online.
The latter is that the estimator r must remain close to the acquired trajectory r m (t). With our hypothesis on the noise, we can write that ||r m -r d ||  2  2 ≈ nσ 2 xy , where σxy is the average standard deviation of the Gaussian noise w G (t), n is the number of frames in the trajectory, and 2 stands for the l2-norm. This leads to the following convex minimization problem: To solve this problem, we use cvxpy (version 1. 1. 7)  65, 66 Python package that relies on the convex optimization SCS solver 67,68 (version 2. 1. 2). Part 2 consists in parsing the trajectory into Go and Stop phases, corresponding to fast and little to no movement, respectively, using the same confinement ratio (R) method used in our previous work. 22 We determined the threshold R th = 0. 64 from the minimum of its distribution when considering all trajectories (Supporting Figure  S5 ).
Acknowledgement We thank  B. Link  for sharing the dync1h1 mw20 mutant line. We are grateful to  Clément Laigle  ( Leica Microsystems, France ) for his explanation of the conversion of the hybrid detector intensity in photon counts. We thank  Eric Larquet  for HRTEM acquisition,  Carsten Janke  for suggesting to use dynapyrazole in replacement of ciliobrevin,  Sophie Donnet  and  Adeline Leclercq Samson  for their insight in the statistical analysis of the datasets,  Jonathan Piard  for the nanoKTP hydrodynamical measurement,  Aurélien Ricard  for help in data analysis, and  Isabelle Garcin  for assistance in carrying out first tests in 2D-primary mouse cortical neuron grown on a glass coverslip. Zebrafish experiments were performed at  INRAE Infectiology of Fishes and Rodents Facility (IERP-UE907, Jouy-en-Josas Research Center, France doi.
org/10. 15454/1. 5572427140471238E12 )which. belongs to the  National Distributed Research Infrastructure for the Control of Animal and Zoonotic Emerging Infectious Diseases through In Vivo Investigation (EMERG'IN DOI: doi. org/10. 15454/90CK-Y371 ). The authors thank  Magali Bouvet  and  Dimitri Rigaudeau  at  IERP , for zebrafish husbandry. This work has received financial support from the  CNRS  through the  MITI interdisciplinary programs  (to F. T. ), from  University Paris-Saclay Strategic Research Initiative "Brainscopes"  ( French Investments for the Future program  grant number  11-IDEX-0003 ) to F. T. , from  ERANET Euronanomed 3 program , through the  French National Research Agency (ANR , grant number  ANR-18-ENM3-0002  to F. T. ), from  IHU FOReSIGHT  ( French Investments for the Future program  grant number  ANR-18-IAHU-0001  to F.
D. B. ), from  ANR  ( ANR-20-CE13-0011-02 CodeAX  and  ANR-19-CE16-0017-01  iReelAx to F. D. B. ) and from  Fondation pour la Recherche médical  (grant # MND202003011485  to F. D. B. ). V. B. is funded by a postdoctoral  FWO Fellowship  ( 12Y9120N ).
INTRODUCTION Bone cross-sections show many characteristics that have been used since the 1820s and 1830s to infer various properties of the individual or taxon from which they were prepared (Ricqlès 2021): life history traits  (Amson et al. 2014) , locomotor patterns  (Houssaye et al. 2016) , habitat  (Jaquier and Scheyer 2017) , metabolism, or growth rate  (Faure-Brac and Cubo 2020; Chinsamy and Warburton 2021)  are some examples. However, bone microstructure is very complex, and few objective, quantitative methods (other than very simple ones, like the cortico-diaphyseal index or the global compactness) were available to describe bone sections in a way that enables statistical inferences or comparisons  (Castanet and Caetano 1995; Buffrénil and Francillon-Vieillot 2001). In 2003, a computer program named Bone Profiler was produced to model bone tissue distribution in sections  (Girondot and Laurin 2003).
The main idea was to reduce bone section complexity to a limited number of variables that can be further compared using statistical tests. The development of software associated with this method was necessary to ensure reproducibility and precision of results. The original publication has been cited 129 times according to Google Scholar (as of September 15, 2021), which demonstrates its interest in ecology and paleobiology. The software was developed first in FutureBasic for MacOS and then converted into REALbasic to produce code for Windows, MacOSX and Linux systems. REALbasic was discontinued in 2006 and transformed into Xojo (Xojo, Inc. ), but the original Bone Profiler code was no longer compatible with this compiler. This situation prevents adding new functionalities to the software and new enhancements of the original methodology.
More recently, we were contacted by several researchers noting that the last available version of the software had stopped working on recent computers. They expressed concerns, because the method described in  Girondot and Laurin (2003)  has become a standard method in paleobiology, and it is important to continue to make it available for paleobiological and comparative studies. We produced a new enhanced version of the method and the software to fulfill this need. Below, we describe the method and software (new version is named BoneProfileR) with emphasis on the enhancement as compared to the original method and software (named Bone Profiler). The method is applied to two taxa: the extant terrestrial mammal Erinaceus europaeus and the extinct amphibious temnospondyl Eryops megacephalus. Visually, they show microanatomical differences in cross-section that may be related to their respective lifestyles and possibly other factors.
SYSTEMS, METHODS AND ALGORITHMS The standard procedure to analyze bone sections is the same as for the original 2003 version: 1) digitize a histological section or a drawing thereof; 2) open a bone section image; 3) locate the center of the bone section; 4) estimate bone compactness in concentric surfaces; 5) model the bone compactness using a mathematical function; and 6) compare bone compactness from various bone sections. Bone Profiler was able to automatically handle all these steps, making the comparison easy and reproducible. The new version is developed as an R package available in CRAN (https://CRAN. Rproject. org/package=BoneProfileR) and uses the same procedure. However, it is more convenient to use the statistical functions available in other R packages for step 6. A web-based version with a GUI (Graphical User Interface) has also been produced to help researchers unfamiliar with the R command line interface to use the method within the R package (http://134.
Opening Images Most standard graphic formats (JPEG, TIFF with or without Lempel-Ziv-Welch compression [lzw], BMP, PICT, PNG, GIF) can be read by the program using the function BP_OpenImage(). The TIFF image import can be done using the ijtiff package  (Nolan and Padilla-Parra 2018)  to handle special properties of some TIFF images generated by  ImageJ (Rueden et al. 2017). After reading an image, the first step is to detect background (unmineralized) and foreground (mineralized) colors. This action can be done manually using BP_ChooseBackground() or BP_ChooseForeground() and preferentially using automatic detection with BP_DetectBackground() and BP_DetectForeground() to ensure good reproducibility. A pixel will be considered as being background or foreground color depending on the minimal Euclidian distance between the RGB color of the pixel and the RGB color of background or foreground colors.
Consequently, as in previous versions, if a drawing (rather than a photograph) is used, it should ideally be binarized before analysis, or at least, what represents mineralized tissues should be colored differently from other areas. If a photograph is used, any inclusions or bubbles that may prevent automated thresholding should be edited before analysis. These preliminary steps need to be performed using other software such as ImageJ  (Rueden et al. 2017). The same procedures and algorithms were used in the 2003 Bone Profiler software, and it ensures that the same results will be obtained with BoneProfileR, which is important in the context of research reproducibility. Bone center detection is an important step in the analysis. It can be done manually by using the function BP_ChooseCenter().
Statistical Comparison of Profiles When several models are fitted to the same datasets of observations, the comparison of the performance of the different models can be assayed using AIC (Akaike Information Criterion)  (Akaike 1974). AIC is a measure of the quality of fit, while simultaneously penalizing for the number of parameters in the model (Equation  5 ). It facilitates the selection from a set of models that represent the best compromise between fit quality and overparametrization. (5) With L j being the likelihood of the model and p j the number of parameters of the model j. When a set of k models are tested, the model with lowest AIC is the best non-overparametrized fit. It is important to note that the AIC value itself is not strictly informative in terms of absolute model fit. When a set of models is compared, it is possible to estimate the relative probability that each model is the best among those tested using the Akaike weight (Burnham and Anderson 2002) (Equation  6 ): (6) We use both AIC and Akaike weight to compare the fitted logistic and flexit models.