dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing of the functional data In order to minimize the influence of eye movements on BOLD signal fluctuations, only runs with fixation rates above 85% were considered for further analyses. For our two monkeys (M01 and M02), we collected 20 runs for each condition of the face localizer (i. e. , 20 runs with human faces and 20 with monkey faces). We also collected 14 control runs with phase-scrambled versions of faces (7 runs with phase-scrambled versions of human faces and 7 other runs with phase-scrambled versions of monkey faces) and non-faces objects. Fewer runs were recorded in this case since, as shown below in the "Results " section, it was largely enough to demonstrate that our face-selective responses were not driven by low-level visual cues. The volumes of each run were first rigidly realigned with each other, and a mean functional image of the runs was computed for co-registration with the functional template.
We then compared the Z-scores obtained in these face-selective clusters during the even runs to those obtained during the phase-scrambled controls. Responses to face stimuli were considered as significantly higher for differences of Z-scores greater than 3. To avoid double-dipping in the statistical comparison between responses to human versus monkey faces (see Fig. 7  ), we estimated the Z-scores of responses to monkey faces within face patches defined from runs with human faces and Z-scores of responses to human faces within face patches defined from runs with monkey faces (see Supplementary Table  1 ). Data used to define the face patches and estimate the Z-scores are therefore independent.
Test-retest reliability To assess the reliability of the paradigm, we performed the analyses on two subsets, defined from the even and odd runs of our data. We quantified the overlap between the face-selective nodes identified in the two datasets using the Dice coefficient (as in  Gao et al. , 2018 ) , a measure of the extent of overlap between the two thresholded activation maps obtained: 𝑂 ij = 2 × 𝑉 ij ∕ ( 𝑉 𝑖 + 𝑉 𝑗 ) where O ij is the consistency score between even run i and odd run j, V ij is the number of face-selective nodes in both datasets, V i is the number of face-selective nodes in even run i , and V j is the number of face-selective nodes in odd run j. We calculated three scores of consistency within each animal's anatomically defined STS: on the unthresholded data, on the thresholded activation maps at Z = 3, and on the activation maps presented in our figures (see the "Results " section, Z = 7 for M01 and Z = 4 for M02).
Responses to human and monkey faces Finally, we investigate the responses of the face-selective clusters to human and monkey faces, tested across interleaved runs. Fig. 7 -A  presents the corresponding activations on inflated reconstructions of the left and right cortical hemispheres of M01 and M02. Z-scores in response to human and monkey faces are respectively shown on the upper and lower rows (colored contours of these activation maps are provided to facilitate the comparison). We can observe that the two networks overlap well and are generally associated with similar activations. Although the maps shown here were computed from half the data, they are in excellent agreement with those reported during the analysis of the entire dataset (see Section 3. 1. ) with face-selective clusters along the STS on both animals and in the prefrontal cortex in M01 and M02 to a lesser degree.
It could also be used in marmoset, which is a rising NHP model in cognitive neurosciences and which can now be used for awake behaving fMRI recordings  ( Hung et al. , 2015 ; Cléry et al. , 2020 ; Schaeffer et al. , 2020 ). The experimental paradigm, stimuli, and analysis scripts can be found here: https://osf. io/6r8wz/?view_only = 3d264c959e4b4d30a114ce2b76f3f079.
Fundings This research was supported by a grant from the "Agence Nationale de la Recherche " (ANR-16-CE37-0002-01, ANR JCJC 3D3M) awarded to Benoit R. Cottereau, (ANR-18-CE37-0022, ANR CIM2) awarded to Jean-Baptiste Durand and a PhD fellowship from the Université de Lorraine to Marie-Alphée Laurent.
Instead, RaGAN's discriminator distinguishes that "real data isn't like average fake data" or "fake data isn't like average real data". According to  (Wang et al. ) , this helps to learn sharper edges and more detailed textures. The perceptual loss helps the network to capture perceptually relevant differences like textures or the clays in the rock images. These are determined from features maps obtained from VGG19 before the activation. We do not use the weights of the pre-trained VGG19 network, instead we re-train it with rock micro-CT images in order to have more relevant features and we have observed an improvement of the results after that. Finally, the content loss is the L1 pixel-by-pixel between the generated and the high-resolution images. The loss for the generator is therefore: 𝐿 𝐺 = 𝐿 𝑝𝑒𝑟𝑐𝑒𝑝 + 𝜆𝐿 𝐺 𝑅𝑎 + 𝜂𝐿 1 where 𝐿 𝑝𝑒𝑟𝑐𝑒𝑝 is the perceptual loss, 𝐿 1 is the 1-norm distance between recovered image G(xi) and the ground-truth y, and λ ( equal to 5 x 10 -3 ), η (equal to 1 x 10 -2 ) are the coefficients to balance different loss terms.
4. 1-Factor 4 enhancement As mentioned previously, the training is performed using two scans of the same small volume at 1. 45 microns image and 5. 8 microns image resolution respectively. This is followed by applying the trained model on a second full low-resolution dataset of Bentheimer from another acquisition. An up-sampling in the Z direction was done on the low-resolution image that was next passed to the trained neural network. The generator was applied slice by slice on 2D images with 796 x 820 pixels to generate a 3D image of 3184 x 3280 x 12928 with voxel size of 1. 45 microns. After that, we consider a test subset from super resolution image, and we compare it to a high-resolution zoom of the same volume.
Unfortunately, the extraction code needs large amount of memory to extract large images (more than 60 GB of RAM for 1500 3 voxels image). Furthermore, although OpenFOAM simulations are parallelized, they are computationally expensive and require a large amount of resources and/or simulation time for large images. Therefore, to overcome these limitations, a stitching process has been developed on networks extracted from overlapped sub-volume of a given image. Fig 22 illustrates the stitching process and shows that the stitched network is formed by first part conserved from PNM1, a second part conserved from PNM2 and a third part obtained on a transition zone where rules inspired from the ones used by GNextract are defined to choose the elements to conserve --some elements being obtained from PNM1 whilst others from PNM2.
Inclusion and exclusion criteria, and data collection Inclusion criteria were patients aged ≥ 18 years, with normal renal anatomy and pelvic-calyceal system who underwent only RIRS for renal stones of any size and number and localized in the lower pole. Exclusion criteria were children/ adolescents, renal collecting system anomalies, ureteral stones, concomitant bilateral procedures, and stones located in the upper and medium pole, and in the pelvis. Patients with missing data were also excluded. The following data were gathered: patient clinical characteristics, stone specification (i. e. , Hounsfield Units (HU), greatest transverse diameter, and single vs multiple), type of instruments and lasers, lithotripsy technique, intraoperative data, postoperative complications, and SFR. Surgical time was estimated as the time from the start of the procedure to the placement of a bladder catheter.
Statistical analysis The Kolmogorov-Smirnov test was used to check data distribution for normality. Continuous data are reported as mean and standard deviation. Categorical data are presented as absolute numbers and percentages. A multivariable logistic regression analysis was performed to assess factors associated with RF. Data are presented as odds ratio (OR) and 95% confidence interval (CI). Statistical tests were conducted using the SPSS software package version 25. 0 (IBM Corp. , Armonk, NY).
Discussion RIRS for LPS is one of the most intriguing procedures for renal calculi due to patients' and stone characteristics that affect its success  [11]. New scoring systems have been proposed to select the most appropriate endoscopic treatment to improve the single-stage SFR for 1-2 cm LPS, and the best approach for kidney stones has not yet been determined  [12]. Even a three-dimensional software, named Kidney Stone Calculator, was developed to improve surgical planning, estimating the stone volume and lithotripsy duration (DL)  [13]. Although the software demonstrated reproducibility and accuracy, LPS showed a higher difference between the expected and actual DL in case of no relocation of the stone. As most single-center studies have different results on SFR, we endeavor to have a real-life global series on the practice patterns and outcomes in LPS.
Conclusion Despite its limitations, to the best of our knowledge, the FLEXOR study is the first large volume real-world global multi-center data that undeniably validate RIRS for LP stones as a safe and efficacious procedure and clearly shows that disposable scopes, pre-stenting, and TFL are key elements to a successful surgery. Patients with large or multiple LPS require a follow-up to determine if a second intervention is needed or, perhaps, PCNL can be considered the right choice in these patients.
Social networks are tied to population dynamics; interactions are driven by population density and demographic structure, while social relationships can be key determinants of survival and reproductive success. However, difficulties integrating models used in demography and network analysis have limited research at this interface. We introduce the R package genNetDem for simulating integrated network-demographic datasets. It can be used to create longitudinal social network and/or capture-recapture datasets with known properties. It incorporates the ability to generate populations and their social networks, generate grouping events using these networks, simulate social network effects on individual survival, and flexibly sample these longitudinal datasets of social associations. By generating co-capture data with known statistical relationships it provides functionality for methodological research.
, 2012; Pradel, 2005). These models also have potential as tools in animal social network analysis  (Clements et al. , 2022; Fisher et al. , 2017) , especially when not all associations are detected. However, it is challenging to provide universal guidance on the applicability of these approaches given the diversity of animal social systems and sampling designs used to study them. Here we introduce the R package genNetDem to simulate co-capture datasets. We define a co-capture dataset as one in which a capture-recapture data also provides information on social structure, such as when individuals are caught or observed in groups (see also  (Silk et al. , 2021) ). The package generates integrated longitudinal social network and capturerecapture datasets with known statistical relationships. This provides functionality for methodological research, power analyses and sampling design.
genNetDem overview genNetDem is a set of R (R Core Team, 2021) functions that generate longitudinal social network and/or capture-recapture datasets with known underlying properties. Functionality can be split into four broad groups: a) population features; b) survival features; c) social network features; and d) observation features. The package is modular meaning specific components can be used in isolation or user-generated code can be integrated to extend functionality to different ecological or social contexts. Here we provide an idea of potential workflows when using genNetDem including a detailed example (Fig. 1 ) and an overview of key functions (Table  1 ; with more detail provided in the Supplementary Materials). genNetDem is available on GitHub (https://github. com/NETDEM-project/genNetDem).
genNetDem workflow While genNetDem is designed to be modular so that individual components can be adjusted to perform a range of tasks, many of the functions fit well within specific workflows. We illustrate one such common workflow (Fig. 1 ), but various other applications are demonstrated in package vignettes. The workflow illustrated here generates a population with a known, underlying social structure and then simulates grouping events (or associations) using this underlying social structure alongside demographic change, sampling from the grouping events to simulate an observation process.
Simulate an observation process Finally, genNetDem also provides tools to simulate a capture and observation process based on the simulated grouping events (interactions) such that it is inherently influenced by the underlying social structure. These samples can be used to generate co-capture datasets to test the power to detect social network effects on demographic rates (as illustrated in the case studies) or other research questions of interest. In a typical workflow these stages can be linked together to generate longitudinal datasets. For example, in Figure  1 , we repeat steps 4, 5 and 6 to generate a co-capture dataset that provides a window into how social network structure and demographic rates are linked in our simulated population. Adapted versions of this workflow are used for the case studies below.
Survival features The covariates_survival() function allows survival probabilities to be calculated for each individual based on individual traits and the position of an individual within a population social network (this could be any network provided to the function; the underlying social networks, simulated interaction network or a separate user-specified network). Individual traits specified in the dataframe generated by indiv_info_gen() can be used as covariates. There is also considerable flexibility in which measures of network position can be included as covariates; both the function and R package used can be specified within the function, with functionality for most common packages (e. g. sna:  (Butts, 2014) ; igraph:  (Csardi & Nepusz, 2006) ; tnet:  (Opsahl, 2009) ) incorporated.
Observation features The observation features sample the simulated grouping events and generate data for subsequent analyses. Data is generated in a variety of formats including GBI matrices for social network analysis and classic capture-recapture formats. There are two cap_and_obs() functions that generate an observed network dataset based on the sampling strategy and design. Of the two cap_and_obs2() has greater flexibility (see Supplementary Materials). Inputs include: a) data on true grouping events (the GBI and a vector indicating which behavioural timestep each group occurred in); b) vectors indicating behavioural timesteps to be sampled, indicated separately for captures and observations; c) the success of sampling including both the proportion of groups detected and the proportion of individuals in each sampled group detected; and d) a vector indicating which (if any) individuals had been captured previously.
The function then samples grouping events from each behavioural timestep indicated for captures and observations using a pre-defined probability (pcg and pmg respectively), and then individuals within these grouping events with a second pre-defined probability (pci and pmi respectively). Captures take precedence over observations in behavioural timesteps where both are indicated. Individuals can only be observed if they have previously been captured (although it is possible to provide additional information on previous captures using the argument pre_cap). The function returns GBIs for captured and observed groups and other related information. The cap_dat_gen() function transforms these network datasets into capture histories for both behavioural timesteps and demographic timesteps and the obs_net_checker() function provides comparisons between sampled networks and both 296 the network derived from grouping event data and the underlying population network.
5 on a logit scale) and position in the social network calculated from grouping events (see below) with a baseline survival probability of 0. 8 in females. We assumed no recruitment into the population (i. e. the population declined over the simulation). We assumed that all individuals in the population were marked or individuallyidentifiable prior to the start of the study. Captures and/or observations (which were functionally equivalent as all individuals were marked) took place in all behavioural timesteps (50 in total). Each group had either a 25%, 50% or 75% percent chance of being detected (parameter varied between simulation runs) with the detection probability of an individual in a detected group fixed at 0. 9. In total we generated 3240 simulated datasets, varying five parameters that influenced network dynamics (one parameter), network effects on survival (three parameters) and sampling (one parameter).
e. the population stayed roughly constant over each simulation). There was a 10% chance that a surviving individual rewired its underlying social connections after each demographic timestep, and if it did each connection had a 50% chance of changing. Edges were rewired using the same generative model used to create the initial network. The population was initially unmarked. Captures only occurred in the first behavioural timestep of each demographic timestep with 90% of groups sampled and a 0. 9 probability of individuals in a sampled group being detected. Sampling design and effort for subsequent observations depended on parameter choice (see below). In total we generated 2880 simulated datasets, varying five parameters that influenced network structure (one parameter), network effects on survival (two parameters) and sampling effort/design (two parameters).
(Gimenez et al. , 2019)   where different data sources could also be used to inform network structure itself. However, especially with improvements to imputation of latent network structures, there is also great potential to incorporate network effects within multi-state models more generally. Given the central role of social behaviour in mediating interactions between infectious disease dynamics and demographic processes  (Silk et al. , 2019; Silk & Fefferman, 2021) , extending multistate models to incorporate social network structure in this way could provide important new insights into wildlife disease ecology, to provide just one example. genNetDem can provide an ideal sandbox to refine these models for application to wild systems. Finally, we focus here on dyadic social networks, however many of the social interactions studied are non-dyadic and may include higher-order interactions  (Battiston et al.
Conclusions We introduce the R package genNetDem as a flexible tool for simulating combined social and demographic datasets. While we focus on the integration of social network and demographic models, the modular design of the package allows it to be an equally powerful tool for generating social network or capture-recapture datasets in their own right. It therefore provides a general tool for researchers interested in testing key methodological considerations in animal social network studies, especially as the field moves towards longitudinal analysis. It also helps researchers wishing to test the power of specific analyses or sampling designs in their own study systems. Silk, M. J. , Jackson, A. L. ,  Croft, D. P. , Colhoun, K. , & Bearhop, S. (2015). The consequences of unidentifiable individuals for the analysis of an animal social network.
Network features The network_generation_covariates() function provides a sophisticated way of generating underlying network structure (Fig. S12 ). Key data inputs to the function pre-specified number of groups independently from the population. This is computationally less challenging and may be accurate for some sampling strategies but is less effective for generating a true set of grouping events. Finally two network_checker() functions can be used to quantify and visualise how well the social network derived from simulated grouping events matches the underlying social network these grouping events were generated with. By default the network_checker_seq() function will: 1) plot the network generated from grouping event data; 2) plot relationships between a series of common centrality measures in the underlying network (degree, strength, betweenness and closeness) and the number of grouping events an individual is observed in (using interaction_generation_seq() function will typically result in a positive correlation between centrality and gregariousness); 3) conduct a basic matrix regression using the netlm function in the R package sna (using qapspp as the null hypothesis permutation test) to test how edge weights in the underlying network and those in the network generated from grouping events are related; and 4) plot relationships between commonly-used centrality measures calculated from the two different networks (again using degree, strength,.
betweenness and closeness). Users can also provide additional arguments to the function compare other individual-level measures between the underlying network and the one generated from grouping event data. The network_checker_simul() performs in the same way for interaction_generation_simul() function, sharing the same functionality aside from output 2as all individuals occur in the same number of grouping events. Observation featuresTwo main observation features are provided. There are two cap_and_obs() functions that generate an observed network dataset based on information provided to the function about sampling strategy and design. The cap_dat_gen() function transforms these network datasets into typical capture histories for use in capturerecapture analyses. It outputs capture histories for both behavioural timesteps and demographic timesteps (see Workflow section).
It works much the same way as the other network_checker functions, including exploiting the netlm function in sna and the ability to provide additional userspecified social network measures if preferred. The cap_dat_gen() function is a convenience function that can be used to transform the outputs of common workflows for genNetDem() to conventional capture history datasets for demographic modelling. Inputs provided are a) CG -the list of observed GBIs; b) SW_store -a list of all sampling windows as output from interaction_generation_simul(); c) full_indiv_dat -the full individuals dataset containing information on the ID, group and spatial behaviour of all individuals; d) inds_alive -a list of individuals recorded alive at each demographic timestep; and e) bs -the number of behavioural timesteps per demographic timestep.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 101023948. We thank  Sarah Clements ,  Qing Zhao ,  Mitch Weegman  and  Dave Hodgson  for insightful discussions related to this work. We also thank two reviewers for constructive comments that helped improve the paper.
Data/Code availability Data and code for the case studies are available at https://github. com/matthewsilk/NETDEM and the R package is available at https://github. com/NETDEM-project/genNetDem. An archived version of the R package associated with this publication is available at DOI: 10. 5281/zenodo. 7657423. An archived version of the datasets and code for the case studies is available at https://doi. org/10. 5061/dryad. m0cfxpp7s. Note that the case studies used an initial version of the R package available at https://github. com/matthewsilk/NETDEM/genNetDem.