dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are grateful to the  CNRS  for the  ATIP grant  " SymbioCode " to SC, the  Genoscope for DNA sequencing through "Bibliothèque du Vivant" and "Speed ID " projects, and the  Moorea Biocode team  and the  Gordon and Betty Moore Foundation  for financial and logistic support, including access to Malaise traps. We also thank  Julien Varaldi  for handy R scripts;  Alexander Anichtchenko ,  Daniel Grand ,  Hannelore Hoch  and  Manfred Asche  for invaluable taxonomic expertise; and finally  Marjorie Saillan  and the "entomology club" of the  Paopao School in Moorea  for their unique contribution to the collecting effort. This article is dedicated to  Roland Allemand  for his early encouragement, and more generally for his major contributions to the field of entomology.
Experiments Even though this system is a work in progress, several experiments have been conducted with it or part of it. This section presents two experiments, one with only the symbiont part of the system, linked to a non-evolving music generator, and the other one with a preliminary version of the whole system. The setup of each experiment is exposed, and illustrated with a video of the working system. In all these experiments, sensors collected the acceleration in three directions, the rotation speed in three directions and the magnetic field in three direction. The measurement frequency was 50 Hz. The tempo was set to 60 beats per minute, and the data were collected over each 1 second interval and aggregated by computing the mean and the variance of each measure. The resulting 18 descriptors over the last second of movement were then fed to the symbiont.
Our current solution, as described in section 5, is only a prototype, but might give some hints about how to infer subjective aesthetic evaluation from performer use of the system. The host and the symbiont have been designed and implemented, as well as the part collecting the data from the sensors and the bridge to the audio player. The host and the symbiont were integrated in the proposed commensal architecture. We reported experiments (recorded on videos) showing preliminary investigations of the possibilities given by the prototype. Many questions remain open. In particular, how to obtained a more autonomous system that could be used by a single performer (the one wearing the sensors) and without the need of a second performer guiding the evolution of the host? We have not enough feedback about how to co-evolve live a host-symbiont pair, during performances.
Acknowledgements This research has been supported by  EU-FET  grant  EvoEvo  ( ICT-610427 ). The authors would like to thank  Sophie Tabakov  and  Laurent Soubise  of the  Anou Skan Dance Company  (www. anouskan. fr)who were early testers of the symbiont. Many thanks also to  Leo Lefebvre  and  Anthony Rossi  who contributed to the development of some modules used in the system. Other support:  Christophe Rigotti  is a member of  LabEx IMU  ( ANR-10-LABX-0088 ).
3. 1]; Dewanji et al. [10] , Komarova et al. [19] , and Ycart  [30] ), fully efficient plating  [26, 25, 1] , or, as mentioned above, exponential distribution of division times. Using a model for estimation, when the data have been generated by another one, necessarily induces a bias on estimates. For instance, if cell deaths are neglected, mutation probability will be underestimated. Several informatic tools have already been developed for fluctuation analysis  [32, 16, 15]. These tools are quite user-friendly. However, they do not take into account all the possible model assumptions mentionned above. The package flan described here, is dedicated to mutation models, and parameter estimation with the three methods P0, ML, and GF. It includes a set of functions for the distribution of mutant cell counts (dflan, pflan, qflan, rflan) and a graphic function (draw.
clone). They treat general models, with fluctuating final numbers, cell deaths, and other division time distributions than exponential and constant. The general estimation function is mutestim. It returns estimates for the parameters α, π and ρ, with the three estimation methods, constant or exponential division times, and cell deaths. As a wrapper, a hypothesis testing function (flan. test) is provided. In order to make the package user-friendly, the functions have been designed to resemble classical R functions, like t. test or rnorm. The paper is organized as follows. Section 2 is devoted to the probabilistic setting: the hypotheses of the different models are described, and the asymptotic results are explained. In section 3, the three estimation methods are exposed, and the biases described above are discussed.
Comparison of the three estimators The Figures  1  and 2  (drawn using ggplot2) show "maps of usage" of the estimation methods under LD and H models. The methods are compared in terms of the relative MSE of ( α, ρ) defined as: 1 - α α 2 + 1 - ρ ρ 2. ( 8 ) The RGB code is used: red for GF, green for P0, blue for ML. Twenty values of α between 0. 5 and 10, and as many values of ρ from 0. 2 to 5, were chosen. Thus 400 couples were considered. For each of them, the following procedure was applied: 1. draw 10 4 samples of size 100 of the LD(α, ρ, 0, 1); 2. for each sample, compute ML, GF and P0 estimates of (α, ρ) under LD model; 3. from the 10 4 estimates, compute the relative MSEs of each method; 4. assign a RGB color according to the MSEs. For each method: • if the MSE is less than 0. 05, assign 1 to the corresponding RGB component; • if the MSE is greater than 1, assign 0 to the corresponding RGB component; • else, assign 1 minus the MSE to the corresponding RGB component.
Implementation Since most functions involve loops that are more expensive in R than in C, flan has been implemented with Rcpp modules. This paradigm provides an easy way to expose C++ functions and classes to R. There are four main classes in the C++ implementation: • FLAN_Sim: random generation for M M and M M F N distributions. One of its members is a variable of the following type; • FLAN_SimClone: random generation for clone size distribution according to the lifetimes distribution; • FLAN_MutationModel: computation of the descriptive functions (probabilities, PGF,. ) for LD and H distributions. One of its members is a variable of the following type; • FLAN_Clone: computation of the descriptive functions for clone size distribution according to the lifetimes distribution. The Rcpp interface enables also to import into the C++ code any R function.
In particular, it is interesting to import the R functions which are already implemented in C. Thus no external C/C++ library is required. The installation remains basic, and the size of the installed package is reduced. For example, the computations of LD distributions involve numeric integrations. The C libraries integration and alglib compute integrals with an accuracy close to machine precision. We could use those libraries but the R function integrate is actually implemented in C. The R function can then be directly called into the C++ code. However, such imports increase the computational time and memory consumption. This is thus unsatisfactory. Another solution uses the package RcppGSL  [11, chap. 11] , which creates an interface between Rcpp and the C library gsl. Several integration methods are thus available.
Since it requires only that gsl is correctly installed, this solution is a good compromise between easy setup and computational cost. Computing the probabilities for the H distribution with δ > 0 involves squaring high degree polynomials. Such polynomials are easily treated by the package polynom. However its implementation raises memory issues, because of the degree of the polynomials involved. A more efficient way is to use the Fast Fourier Transform. It is provided by the C library fftw3, which can raise some installation issues. The R function fft could be directly called into the C++ code. For the same reasons as for the integrate function, it is more adequate to use the package RcppArmadillo  (Eddelbuettel [11, chap. 10] ; Eddelbuettel and Sanderson  [12] ). This package links Rcpp to the C++ library armadillo, which is dedicated to linear algebra.
In particular, it includes a performing Fast Fourier Transform. Finally, likelihood optimizations in the ML and P0 methods are done with a bounded BFGS optimizer. The package lbfgsb3 provides the eponymous function which is implemented in Fortran. It is much faster than the basic R function optim. werhoff$samples$W11$mc ##  [1]   0 0 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 4 4 4 4 5 5  This sample does not contain any jackpot, then the theoretical fitness in a mutation model, should be very large. If the GF method is used, it will output a warning message and set the fitness at ρ = 1, as customarily done in the litterature  [14] : W <-werhoff$samples$W11$mc # Compute GF estimates of mutations number and fitness mutestim(mc = W, method = "GF") ## Warning in mutestim(mc = W, method = "GF"): Impossible to estimate 'fitness' with 'GF'-method: 'fitness' is set to default value 1 and only mutations number is estimated.
[7, Tab. 4 ]. The author studied mutations of Escherichia coli from sensitivity to nalidixic acid resistance. 23 samples of resistant bacteria counts are provided. As in the original paper, the 23 samples are concatenated as one. The three estimation methods are compared in terms of computational time on the resulting sample of size 1104. The package microbenchmark is used, evaluating 10 4 times each method on the sample. The methods are compared when only α is estimated (ρ = 1), and when the couple (α,ρ) is estimated. In both cases the estimates are computed under the model LD with δ = 0 and ζ = 1.
Classification of multiple days to identify consensual congestion patterns. Now our objective is to find commonalities in the 35 daily congestion patterns, and, ideally, summarize these with a fewer number of "consensual" patterns. To this end, we first have to define a common link network for all the 35 days, see supplementary S4. This is necessary because some links may have insufficient observations on particular days to be assigned with a significant value. The procedure has 3 main steps as outlined in Fig. 3(a). In step 1 we obtain 3D speed maps related to each daily pattern, by running the k-means algorithm with 9 targeted clusters over all the 35 days of the dataset. After this, each observation, i. e. a couple composed by a link and a time period, is assigned a cluster ID i. Each day k can then be synthesized into a single ordered vector of all observations π k , whose values are the cluster ID.
Discussion In this paper, we questioned the regularity of day-to-day mobility patterns at the macroscopic level. The global analysis of Amsterdam link speed data over 35 days shows a high degree of regularity when comparing the daily congestion patterns. In our case, four consensual 3D speed maps related to four groups of days are sufficient to describe the daily traffic dynamics at the city scale. This is remarkable given the fact that these consensual 3D speed maps are very parsimonious: for our case study, they consists of 9 clusters (collections of link and time ID) only, each characterized by a single mean speed value. A key contribution here was to use consensus learning methods to turn the cluster shapes of different days belonging to the same group into a single common pattern. Note that if more days are available for the learning, it is possible to keep the same level of quality for the consensual shape by increasing the number of groups.
Methods Initial dataset. In this study, link speed data are reconstructed from trip travel time observations. In Amsterdam, 127 cameras are recording license plates at the critical points of the major street networks (excluding freeways). This defines 314 single origin-destination (OD) pairs. For each OD pair the shortest path in distance is determined using the OpenStreetMaps GIS database  46. The final network consists in all the links included in all the shortest paths, i. e. 7512 links in total. We apply an algorithm that merges together successive links in the same direction between two intersections. Internal links for intersections are also merged into a single node that only reproduces the available turning movements. At the end, the network has 208 links and 214 nodes  20. The final step is to calculate the link speed information for 10 min time intervals from the individual travel times between OD pairs.
We have a complete database of 35 days where we select the time period between 7am and 3 pm (morning peak hour and lunch time). The mean number of individual travel time records per day is 171000. Each individual travel time information provides both the departure and the arrival times. All travel time data that exceeds a given threshold added to the current moving average for a given OD are considered as outliers and then disregarded (7% in total). The remaining information are then matched to links assuming a constant travel speed. We used a 10 min time window for link speed data, meaning that all observations coming from vehicles that drive through a link during the same 10 min period are averaged into a single link speed value. A complete description of the data preparation can be found in  20.
Note that the data processing in this paper is not restricted to the data we used for the Amsterdam network but can be applied to any network with link speed information. Ncut algorithm. Ncut is a clustering algorithm based on a similarity matrix S(i, j) that defines the level of similarity between two elements i and j of the dataset  15. In this paper, we use two different metrics to define the similarity: the Snake similarity  17  when determining the original clustering for each day and the NMI, eq. 2, when gathering days with similar patterns. More details about the Snake similarity are provided in supplementary S1. The different steps of the Ncut algorithm are: 3. Calculate the eigenvalues of L and increasingly order the eigenvectors with respect to the eigenvalues 4. To obtain a partition in 2 m clusters, select the 2nd to the + - m (2 1) th eigenvectors in the ordered list.
We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones.
Data sets The data sets  [16, 47]  are from healthy volunteers from the database at the National Society for Epilepsy's MRI Unit. They had no neurological, medical, or psychiatric condition and their MRIs had been read as normal by two experienced neuroradiologists. Ethical approval had been obtained from the Joint Medical Ethics Committee of the Institute of Neurology and the National Hospital for Neurology and Neurosurgery, University College London, Queen Square, and all subjects had given written informed consent. Data sets consisted of 30 MRIs from healthy volunteers (15 women), scanned using a 1. 5 Tesla GE Signa Echospeed scanner (GE Healthcare, Little Chalfont, Buckinghamshire, UK). The inversion recovery prepared fast spoiled gradient recall T1-weighted sequence yielded images with voxel sizes 0.
Delineation software We used RView software  [51]  to delineate regions, construct 3D models, and calculate cortical volumes for each region. The software provides three orthogonal viewing planes, transverse, sagittal, and coronal, in addition to a 3D-rendered surface view that can be tilted at arbitrary angles, permitting optimal viewing of the parietal convexity, which is hard to visualize on traditional orthogonal slices. The surface view provided by the current RView software occasionally indicated that changes were needed where the previously described boundaries had not been sufficiently accurate. Even though the most suitable plane to define the SMG and AG is thought to be the coronal plane  [52] , each of the orientations was referred to in this work, and in difficult cases a consensus decision was reached through discussion between the main investigator (HMW) and the senior investigator with a >20-year track record of developing protocols and performing delineations (AH).
Morphological and volumetric evaluation Several morphological characteristics were noted (see Tables  1 2 3 ). After the final round of checking, we extracted volumes for all parietal regions combining grey matter (GM) and white matter (WM). MRIs were then segmented into three tissue classes using FSL FAST software 5. 0. 1  [53]. The resulting binary grey matter mask includes voxels where grey matter is the most likely out of the three tissue classes. This mask was then multiplied with the atlas images. Volumes for GM only were again extracted. To account for effects of different head sizes between participants, the same intracranial volumes as in our previous studies  [54, 55]  were used, determined via Exbrain 2. 8. 4  [56]. MRIs were spatially normalized to the Montreal Neurological Institute MNI152 reference space (MNI space) using SPM8 and the Unified Segmentation iterative spatial normalisation technique  [57].
The above processes for extracting volumes were repeated in MNI space (which effectively standardises head size,  [58] ) to yield structure volumes in MNI space, and both for GM+WM and GM-only atlases (using the FSL FAST GM mask). The main analyses were performed either in native space or using atlases spatially normalised with SPM8. A newer software version-SPM12-has been shown to yield different intracranial volumes  [59]  which were closer to manual reference volumes than those obtained with SPM8 for 1. 5T scanners (cf. caveat in  [60]  for varying field strengths). We therefore repeated the volumetric analyses for SPM12 (via the Segment procedure) with standard settings.
Multi-atlas label propagation In earlier work we applied the previous version of the Hammers_mith atlas (with 83 regions) as a source of labels for multi-atlas label propagation using MAPER software, showing that automatic segmentation is feasible and accurate with this approach  [61]. We carried out the same leave-one-out experiment (treating each subject image as a target, using the other 29 atlases as label sources) with the new Hammers_mith atlas version (95 regions, i. e. incorporating the new regions described here, and new insular subdivisions described in  [55] ). Using the manual labels as reference, we assessed the quality of the generated labels by determining overlap and volume aberration. We report here the results for the new parietal lobe regions.
Morphometric features We explored the volumetry data for relationships between the anatomical features recorded for the 60 hemispheres from 30 adult brains. The descriptive data was tested for correlations. Expected correlations like the relationship between ICV and brain size translated into correlations between ICV and PL region size and served as a plausibility test. The results show clear correlations (negative and positive) between the number and distribution of gyri in relation to the major intraregional sulci, many of which have intuitive interpretations. There was a clear negative correlation between the number of additional gyri superior to the horseshoe shapes of AG and SMG and the vertical extent of the major intraregional sulci. This may be purely a mechanical effect of limited distance between the dorsal end of the sulcus (SF or AS) and the IPS.
Automatic segmentation of the parietal lobe via multi-atlas label propagation The present work investigated morphological characteristics in the parietal lobes of 60 hemispheres. Thanks to existing methods exploiting the knowledge incorporated in manual delineations for automatically anatomically segmenting large numbers of brain images  [48] [49] [50] , it is possible to use the present atlases for the study of new control and disease cohorts. Future studies could hence also explore the correspondence between the current anatomical delineations and fMRI distributions of activity. The leave-one-out experiment we carried out using MAPER software yielded the expected results for postCG, SupPL, and SMG, indicating that these regions can be automatically propagated to newly acquired images with a high level of accuracy.
Acknowledgements J. W. was funded by  PROMETRO-INMETRO 550124/2012-8 ; J. C. , U. L. and M. F. were funded by  Brazilian CNPq ,  CAPES  and  FAPERJ. D. A. B. is supported by  U. S. National Science Foundation (NSF)  grant  EAR-1423939. J. W. , J. C. , K. D. , O. E. , U. L. , M. F. were funded by  International associated laboratory "AEMB" between the CNRS of France  and  UFRJ of Brazil. CTL was funded by the  French National Research Agency  ( ANR-16-TERC-0025-01 ).
In that context, mathematical optimization algorithms are very efficient, and can lead to find the best design, and avoiding trial and error tryout procedures  (Ning et al. 2009). Indeed, the combination of finite element analysis and optimization procedure  (Sanjay et al. 2011) , enables to precisely predict stent deployment and detect product defects as stress concentrations and fractures in a design stage, thereby reducing design and prototyping costs to a considerable extent. However, numerical simulations and optimization algorithms have to be sufficiently robust in order to avoid the high computational time and guarantee severe manufacturing precisions. In this work, in order to improve the mechanical properties of stents, the Sequential Quadratic Programming (SQP) algorithm is coupled with the FE simulation using Abaqus software and python script.
Numerical Modelling The stent is characterized by ring elements (Figure  1 ) made of stainless steel material AISI316L, only a fraction of the ring will be modelled and optimized considering the strut parametrized by two design variables (A, B, Figure  1 ). The FEA is conducted using Abaqus finite element code using linear hexahedrons elements (C3D8I). Two loading conditions are considered: radial expansion and bending combined with torsion of the stent. The radial strength of the stent is explored by expanding the stent in the radial direction using a cylinder. The expansion is simulated by constraining only the displacement of the strut in the axial direction and a radial displacement Ur = 2Rstent is imposed to the cylinder, Rstent being the initial radius of the stent.
We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset.