dataesr/scientific-paragraphs-categorization huggingface dataset:
DGCNN Dynamic Graph CNN (DGCNN) architecture  [63]  was designed to integrate a convolution process of 3D points directly into the network, rather than a separate grouping process as done in PointNet++. The local neighbourhood of a point is represented with a graph structure. A process called EdgeConv is applied to extract edge features to encode the spatial relationship between a point and its K neighbours. Unlike the CNN structures used in regular grids, fixed graphs are not used. The graphs are updated since the K nearest neighborhoods of the points changes in each layer. Only in the first layer, geometrical proximity between nearest points are considered. In the following layers, edge convolution is performed between nearest neighbours that are close in feature space. That might be an advantage in terms of diffusing the information with respect to the proximity in the feature space; however, a multi-scale hierarchical local spatial grouping is not present in DGCNN.
We call these update networks S+II-trained networks. • S-trained networks updated with three real rosebush models (S+III): In this experiment, the S-networks were re-trained using the blocks extracted from three real rosebush model. We call these update networks S+III-trained networks. The hyper-parameters used to train the networks are given in Table  2. First we examine the results of the S-trained networks applied on the 8 synthetic validation models (Table  3 ). PointNet++, DGCNN, ShellNet and PointCNN were able to produce performance success over 90% for all measures. For the synthetic models, the local geometric variations between the points in the same class are limited to the generation rules of the L-studio software. Hence, the networks were easily able to learn underlying structures separating one class from the other.
We can observe that for DGCNN parts of main stems were classified as leaves and the flower class is not retrieved at all (27. 94% and 7. 12% recall rate for the flower and stem classes in Table  6 ). We conjecture that DGCNN is only encoding the geometric structure at the very local level; the spatial receptive field was limited to the K-neighbours of each point in 3D. The data imbalance in the training data in favor of leaves limited the capacity of DGCNN to learn features from stem and flower regions (Fig. 12d ). The effect of data imbalance was alleviated with incorporating synthetic data in training data as seen in Fig. 13 , where results obtained with S+III-trained networks are given. DGCNN was able to capture branch and flower structures (Fig. 13e ). Despite the incorporation of synthetic data, DGCNN's performance lacks behind PointNet++, PointCNN, and ShellNet.
Touching leaves resulting in thick structures are also a cause of error (Fig. 11c ). Another source of error with ShellNet is the interference of points from close parts, such as the misclassification of leaf points as stems in Fig. 11d. The segmentation results obtained with RIConv (Table  9 ) falls behind all the architectures except PointNet. The local regions were extracted in the same way as in ShellNet, however, use of rotation invariant features resulted in significant loss of geometric information about the constellation of the points, which is especially important in distinguishing plant parts. As a summary, we give the segmentation performances (in M IoU ) of all architectures with III-trained and S+III-trained networks in Table  10. The incorporation of synthetic data was significantly beneficial for all architectures except for PointNet.
PointNet was unable to learn local geometric structures since all points were processed independent of each other. RIConv performed poorly compared to other architectures due to the information loss with rotation invariant features. DGCNN used a single spatial receptive field at the very local level and opted for feature proximity in a non-local way; therefore missing the multi-scale spatial variability in plant parts. The best results were obtained with PointNet++ with or without the use of synthetic data for training. The hierarchically organized local regions for feature extraction with PointNet++ are defined in terms of metric radius, which are flexible and can be adjusted outside the network structure. The next best two methods are PointCNN and ShellNet, both of which hierarchically regroup points and aggregate features within the network.
Conclusion We applied six recent 3D point-based deep learning architectures, PointNet, PointNet++, DGCNN, PointCNN, ShellNet, and RIConv, to segment 3D models of real rosebush plants into their structural parts. We used the annotated 3D models in ROSE-X data set for training and testing the networks. We also conducted experiments where the networks were pre-trained with synthetic rosebush models generated by L-studio software, and then updated by real rosebush data. The results indicate that pre-training with synthetic data boosts the performace of all networks, except PointNet. The best segmentation results were obtained by PointNet++ with a mean IoU rate of 86. 19%. We attribute this success to the ease of determining the size of the hierarchical local regions to extract multi-scale features with PointNet++.
This delay in reproduction together with limiting breeding sites creates a high-competition scenario for reproduction in males, providing a good framework to investigate phenotypical differences between floaters and breeders. In this species, fights between females are not uncommon (pers. obs. ), suggesting that reproductive status in females may also be determined by phenotypical differences. In this work, we used data collected from long-term monitoring (2012-2020) of a wild colony of spotless starlings. We gathered longitudinal information of female and male spotless starlings belonging to 8 and 7 different cohorts respectively and followed them until the age at which males or females typically start breeding. By marking individuals with passive integrated transponder tags (hereafter, PITtags) we took advantage of their frequent intruding behaviour, deploying automatized PIT-tag readers in nest-boxes during reproduction to detect birds that would have gone unnoticed otherwise.
Field methodology For this work, we gathered longitudinal data involving 8 and 7 cohorts of female and male birds respectively (cohorts fledged from 2012 to 2019, with breeding data collected up to 2020), encompassing a total of 10,091 nestlings. Hatching date of nestlings was monitored by visiting the nest-boxes every day after the completion of the incubation period (11-12 days). Nestlings from each cohort were marked with a unique number-coded metal ring and a PIT-tag (ID-100 Trovan Unique Implantable tags, Trovan Ltd, UK) and measured when they were 14 days old. We measured body weight with a digital scale (Ohaus, Model CS200, Pine Brook, New Jersey) to the nearest 0. 1 g and tarsus length with a digital calliper (Mitutoyo Absolute, Japan) to the nearest 0. 01 mm. For the analysis, we selected birds marked and measured as nestlings that were captured as adults at the age in which males or females typically start breeding, that is, 1 year after birth in the case of the females, and 2 years later in the case of males  (Veiga et al.
2012, D. Gil, unpublished data). We did not find a change in the strength nor in the direction of results using this stricter criterion, and thus we adhered to the original criterion in the results of the study. Analyses made with the alternative stricter criterion can be found in the online resources 7, 8 and 9. Our dataset for adult traits analyses consisted of a total of 280 males and 241 females (online resource 10). In the case of the analysis using nestling predictors, we had data from 276 males and 267 females (online resource 10). Differences in sample size between adult and nestling analyses are due to missing data in some adult measurements.
Statistical analyses All analyses were performed in the R language v. 3. 6. 1 (R Core Team 2020). We ran separate analyses for females and males due to differences in the age of first reproduction. We used the lme4 package  (Bates et al. 2015)  to build generalized linear mixed models (GLMMs) with a binomial distribution and a logit link function. We built four binomial GLMMs: two for males and two for females (one for adult traits and one for nestling traits). The dichotomous response variable 'reproductive status' had two possible outcomes: floater versus breeder. All analyses were conducted using 'floater' as the reference level. To obtain a robust index of body size, we took the first component of a principal component analysis (PCA) on three morphological traits: beak, wing, and tarsus length.
In the case of males, body condition was calculated as the residuals of a linear model in which body mass depended on the time of capture and the body size index (PC1). In the case of females, body condition was calculated as the residuals of the relationship between body mass and body size index and date of measurement. We tested the possibility that effects varied between years by building models that included the interaction of year with each trait (data not shown), but we found no relevant interaction and chose to consider year as a random effect in the models. In the case of models using early life predictors, 'reproductive status' was again used as the binary response variable. Body condition, hatching date and brood size were included as continuous predictors. In the analyses, nestling body condition was calculated as the residuals of a linear model of body mass dependent on tarsus length, corrected by the observer identity.
Path analysis allows calculating (1) direct effects (path coefficients of arrows directly connecting two variables), (  2 ) indirect effects (calculated as the multiplication of path coefficients of two variables that share an intermediary) and (3) total effects (computed as the sum of direct and indirect effects). We built two-path analyses (one for each sex) using the package piecewiseSEM  (Lefcheck 2016). Conditional independence claims were evaluated by directed separation tests to determine the goodness of fit of each model  (Pearl 2009). The result of these tests is summed in the Fisher's C statistic  (Shipley 2000). Temporal tiers were used to orient the path, as events that happen earlier in life can only be parental nodes of the variables that occur later in life. Moreover, within a tier, we excluded the presence of colliders by calculating the independencies and dependencies of all triplets of variables  (Scheines et al.
Path analysis Both male and female path analysis satisfied the conditional claims (Male path: C 6 = 3. 01, P = 0. 80, N = 261, Fig. 3a ; Female path: C 14 = 14. 24, P = 0. 43, N = 231, Fig. 3b ), showing that the structure of the paths was supported by our dataset. In the case of male models, only nestling body condition had a direct causal effect on reproductive status (Fig. 3a ). Path analysis showed that hatching date was indirectly and negatively linked to reproductive status via nestling body condition (indirect effect = -0. 307 × 0. 161 = 0. 05). Neither brood size, ornamentation nor adult body condition was linked to reproductive status, neither directly nor indirectly. Independently of their effect on reproductive status, we found that nestling body condition was positively associated with adult body condition, and this, in turn, was positively linked with a secondary sexual character, feather length.
Acknowledgements The MS improved considerably as a result of reviewers' and editor's comments. IR was recipient of a  FPU (Formación de Profesorado Universitario ) grant from  Spanish Ministry of Education. JM was supported by a postdoctoral grant from the  Juan de la Cierva Subprogram  ( FJCI 2017-34109 ), with the financial sponsorship of the MICINN. We are very grateful to all the people that have contributed to collating the data presented in this study, including field technicians:  Oscar Magaña ,  Hector Bintanel  and  Lara Moreno ; PhD students:  Concepción P. Salaberría ,  Stefania d'Arpa  and  Eduardo Gómez ; MSc students:  Blanca Jimeno ,  Louise Froud ,  Elodie Faux ,  Irene García Celada ,  Thomas Crouchet  and  Pablo Salmón ; and volunteers:  Lucía Manzanares ,  Robyn Womack ,  Cristiano Gala ,  Evan Burnett ,  Hannah van Noordt ,  Benoit Vallas ,  Joao Heriberto de Oliveira ,  Angelica Camacho ,  Jorge García Campa ,  Lara de la Cita ,  Aida Rodrigues  and  Simon Degrise.
1. Stochastic population dynamics simulations are essential for many ecological and epidemiological studies to generate time series and genealogies that capture the relatedness between individuals. Many software packages allow one to simulate phylogenetic trees but these tend to suffer from one or two major limitations. First, the underlying population dynamics model is often simplistic (e. g. constant population size or exponential growth). Second, the software packages are not appropriate to simulate a large number of trees. 2. We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespie's exact or approximate stochastic simulation algorithm, or a newly proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards-in-time approach (i.
e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function. 3. TiPS is flexible, easy-to-use and available on the CRAN at https://cran. r-proje ct. org/packa ge=TiPS. Plus, benchmarking analyses show that TiPS is faster than existing packages. 4. This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies.
In the R software environment, it is implemented in packages such as GillespieSSA  (Pineda-Krch, 2008) , adaptivetau  (Johnson, 2014)  or epimdr  (Bjørnstad, 2018). The computational speed of these software packages is facilitated by the fact that they do not keep track of the history of the process, that is, the trajectory. Conversely, in this same environment, geiger  (Pennell et al. , 2014) , phytools  (Revell, 2012) , ape  (Paradis & Schliep, 2019) , and TreeSim can simulate phylogenies. However, the underlying model is often very simple, for example birth-death model. Also in R, some packages such as nosoi  (Lequime et al. , 2020)  allow the user to implement detailed agent-based models but their computational time is slow and the outputs are difficult to compare to classical compartmental models based on differential equations.
However, some software packages can simulate both trajectories and genealogies. In R, rcolgem, which was updated to phydynR  (Volz, 2012) , combines an Euler-Maruyama integration and the structured coalescent to allow the user to rapidly simulate phylogenies from any compartmental model. This will be our main reference in the following in terms of accuracy and computational speed. Another exception is the software package MASTER  (Vaughan & Drummond, 2013)  in the BEAST2 platform  (Bouckaert et al. , 2014). Although MASTER is a useful tool to simulate both time series and genealogies, the specification of the model of interest in the XML language is not as intuitive as the packages of the R environment. Furthermore, although MASTER is one of the fastest options to simulate a few phylogenies (because it does not need to compile the code), its execution time quickly becomes limiting when simulating thousands (or millions) of phylogenies.
We introduce TiPS, a flexible and easy-to-use R package to rapidly simulate population trajectories and phylogenies using a backwards-in-time, that is coalescent, process with either predefined sampling dates or a stochastic sampling scheme. We also introduce a new approximate version of the Gillespie algorithm to increase the calculation speed. A brief benchmarking analysis shows that TiPS is faster than adaptivetau to simulate trajectories, especially for large populations (Figure  3a ). It is also at least one order of magnitude faster than phydynR to simulate phylogenies (Figure  3b ).
| Package overview TiPS generates two types of stochastic simulation output: population dynamics trajectories and phylogenies. These are obtained using a continuous-time individual-based model defined in R as a system of reactions. The model is first transcribed in C++ and then compiled, before being linked back to a simulation function in R thanks to the Rcpp package  (Eddelbuettel & Francois, 2011). The general structure of the pipeline is illustrated in Figure  1. The TiPS package is available on the CRAN at https://cran. r-proje ct. org/web/packa ges/TiPS/index. html and is maintained on GitLab at https://gitlab. in2p3. fr/ete/tips/.
F I G U R E 1 General structure of the TiPS pipeline. The equations and outputs correspond to the SIR epidemiological model  (Keeling & Rohani, 2008). The functions of the R package are in blue. The simulator of trajectories, which is built as a function, is in orange. The variable traj, in red, is the output trajectory of class simutraj. It can be plotted using our plot method. TiPS used the simulated trajectory and 100 sampling dates that we generated (variable sampling_dates in green) to simulate a sampled phylogeny. The output simulated phylogeny is a phylo class object from the ape R package  (Paradis & Schliep, 2019) , which can be used for plotting. The output of a trajectory simulation is a named list containing the simulated trajectory and some details about the model and the simulation, such as the reactions of the model, the parameter values, the time range, the algorithm used to perform simulations, the time step in case the algorithm is the GTA or the MSA, and, for reproducibility purposes, the random seed used to initialise a pseudorandom number generator.
| Benchmarking To evaluate TiPS's performances, we designed a benchmarking analysis on both modules of the software package, that is, the trajectory and the phylogeny simulators, comparing with existing R packages (adaptivetau and phydynR). Table  S1  summarises the main features of the approaches used. We first evaluated the computational speed and the accuracy of the trajectories (i. e. populating dynamics). For five initial population sizes and three different R packages, we simulated 10,000 trajectories of the epidemiological Susceptible-Infected-Recovered (SIR) model and measured the execution time. We then compared the time to under an SIR model. We varied the sampling proportions to obtain target phylogenies of various sizes (10, 100, 500, 1000, and 1500 leaves). We simulated 1000 phylogenies under each sampling scheme using phy-dynR and our package.
Furthermore, assuming a more detailed epidemiological model with two host types, as described in  (Danesh et al. , 2021) , we simulated an epidemiological trajectory using the tau-leap algorithm and a complete phylogeny, such that each end of infection event corresponded to a leaf in the tree. The simulation generated a full transmission chain corresponding to a phylogeny with 154,507 leaves. From this complete phylogeny, we generated 10 subtrees by randomly sampling and keeping 1000 leaves for each subtree. We then used TiPS and phydynR to simulate 1000 phylogenies with each package under the same epidemiological model with the same parameter values. Note that the 1000 dates of each target subtree were imposed when simulating these phylogenies using a backwards-in-time approach. To compare the 2000 simulated phylogenies with the target one, we computed 60 summary statistics for each of them using the methods described in  (Saulnier et al.
| RE SULTS In Figure  3a , we show the median execution time for one trajectory simulation and the 50% interquartile envelope. TiPS and adaptivetau rely on Gillespie's direct method (GDA), whereas phydynR uses the Euler-Maruyama integration (EMI). As expected, the population size, and hence the number of events per unit of time, increases the execution time for GDA-based packages but not for the EMI-based package. However, TiPS remains faster than the other two software packages for large populations (10 7 individuals). In Figure  3b , we perform the same simulations using approximations of the Gillespie algorithm with fixed time steps. The computational speed of this GTA implemented in TiPS and adaptivetau is comparable and much faster than the GDA. Our new MSA algorithm outperforms existing methods and improves computational speed compared to our GTA, especially for small population sizes.
| DISCUSS ION We developed a flexible R package to rapidly simulate trajectories and phylogenies from compartmental models. Its structure allows the user to include several sources of heterogeneity between individuals or between populations, for example different life stages or metapopulation structures. The simulation of the phylogeny relies on the trajectory and involves a coalescent approach. Our benchmarking analyses show that TiPS is comparable to or outperforms existing R packages in terms of speed when generating numerous trajectories or phylogenies. The same is true for the accuracy of the simulation outputs. The first asset of this software package is its flexibility since it can readily be used for any compartmental model. Another asset is its computation speed as it can simulate trajectories in a matter of milliseconds on a regular desktop computer.
These properties have already been used for infection phylodynamics studies involving approximate Bayesian computing (ABC)  (Danesh et al. , 2021)  or to illustrate the effect of superspreading events  (Alizon, 2021). In the context of infectious diseases, when simulating phylogenetic trees with TiPS, we assume that the time of a transmission event is the same as the time of coalescence in the tree. However, this is a simplification since the time of coalescence in the phylogenetic tree should take place before transmission, during the infection of the 'donnor' host  (Ypma et al. , 2013). A variety of within-host evolutionary processes may further weaken this assumption. This limitation is common to virus phylodynamics studies and an active line of research  (Volz et al. , 2017). Beyond epidemiology, this software package can be used more broadly to simulate population dynamics and associated genealogies.
C. 3D images Scanning electron microscope (SEM) images were acquired at a tilt angle of 0 • , then tilted along a vertical axis at 5 • and/or 8 • using a FEI Versa 3D (ACMiN, AGH UST) field emission scanning electron microscope (FESEM) operating at 5 or 10 kV. The pairs of SEM images were merged to make a red-cyan anaglyph image and 3D projection of the shell surface using MountainsLab 9 software (Digital Surf). A number of these 3D projections were taken with different inclinations, which were afterwards processed using the 3DF Zephyr Lite 6. 5 trial version (3DFlow) software. Using 3DF Zephyr, 3D models of the surfaces were created. See the Supplemental Material  [66]  for more examples. The videos shown in the Supplemental Material were also recorded using this software. 3D reconstructions were constructed by using the following presents: for camera orientation, urban and deep; for dense point cloud creation and surface reconstruction, urban and high details; and for texturing, general and high details.