dataesr/scientific-paragraphs-categorization huggingface dataset:
■ ACKNOWLEDGMENTS This work was granted access to the HPC resources of TGCC and CINES under the allocation 22010 and 2020-A0090906018 made by  GENCI. The project leading to the work presented here has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement no. 755183. The views expressed here are the responsibility of the author(s) only. The  EU Commission  takes no responsibility for any use made of the information set out. ■ REFERENCES (1) Ferry, M. ; Roma, G. ; Cochin, F. ; Esnouf, S. ; Dauvois, V. ; Nizeyimana, F. ; Gervais, B. ;  Ngono-Ravache ,  Y. In Comprehensive Nuclear Materials , 2nd ed. ; Konings, R. J. , Stoller, R. E. , Eds. ; Elsevier: Oxford, 2020; pp  545-580. (2) Bolland, J. L. ; Gee,  G. Kinetic  studies in the chemistry of rubber and related materials.
The development of highly efficient, robust and scalable numerical algorithms lags behind the rapid increase in massive parallelism of modern hardware. We address this challenge with the accelerated pseudo-transient iterative method and present here a physically motivated derivation. We analytically determine optimal iteration parameters for a variety of basic physical processes and confirm the validity of theoretical predictions with numerical experiments. We provide an efficient numerical implementation of pseudo-transient solvers on graphical processing units (GPUs) using the Julia language. We achieve a parallel efficiency over 96% on 2197 GPUs in distributed memory parallelisation weak scaling benchmarks. 2197 GPUs allow for unprecedented terascale solutions of 3D variable viscosity Stokes flow on 4995 3 grid cells involving over 1.
In this study, we address (1) by presenting the accelerated PT method, resolving several types of basic physical processes. We consider (2) and (3) as challenges partly related to scientific software design and engineering; we address them using the emerging Julia language  (Bezanson et al. , 2017) , which solves the "two-language problem" and provides the missing tool towards making prototype and production code become one and breaking up the technically imposed hard division of the software stack into domain science tasks (higher levels of the stack) and computer science tasks (lower levels of the stack). The Julia applications featured in this study rely on recent Julia package developments undertaken by the authors to empower domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on massively parallel hardware such as latest GPU-accelerated supercomputers.
We assess the algorithmic and implementation performance and scalability of the 2D and 3D numerical Julia (multi-)GPU (non-)linear diffusion and visco-elastic Stokes flow implementations. We report scalability beyond terrascale number of DoFs on up to 2197 Nvidia Tesla P100 GPUs on the Piz Daint supercomputer at the Swiss National Supercomputing Centre, CSCS. We demonstrate the versatility and the robustness of our approach in handling nonlinear problems by applying the accelerated pseudo-transient method to resolve spontaneous strain localisation in elasto-viscoplastic media in 2D and 3D, and comparing time to solution with direct-sparse solvers in 2D. We further demonstrate the convergence of the method to be mostly insensitive to arbitrary distributions of viscous inclusions with viscosity contrasts up to 9 orders of magnitude in the incompressible viscous Stokes flow limit.
The latest versions of the open-source Julia codes used in this study are available from GitHub within the PTsolvers organisation at https://github. com/PTsolvers/PseudoTransientDiffusion. jl (last access: 9 December 2021) and https://github. com/PTsolvers/PseudoTransientStokes. jl (last access: 9 December 2021). Past and future versions of the software are available from a permanent DOI repository (Zenodo) at: https://doi. org/10. 5281/zenodo. 5764691 (Räss and Utkin, 2021a)  and https://doi. org/10. 5281/zenodo. 5764696 (Räss and Utkin, 2021b). The README files provide the instructions to get started reproducing majority of the presented results.
The Julia multi-XPU implementation We use the Julia language  (Bezanson et al. , 2017)  to implement the suite of numerical experiments. Julia's high-level abstractions, multiple dispatch and meta-programming capabilities make it amenable to portability between backends (e. g. multi-core CPUs and Nvidia or AMD GPUs). Also, Julia solves the two-language problem making it possible to fuse prototype and production applications into a single one being both high-level and performance oriented -ultimately increasing productivity. We use the ParallelStencil. jl (Omlin et al. , 2021b)  and ImplicitGlobalGrid. jl (Omlin et al. , 2021a ) Julia packages we developed as building blocks to implement the diffusion and Stokes numerical experiments. ParallelStencil. jl permits to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs -here referred to as XPUs.
Performance similar to native CUDA C/C++ (Nvidia GPUs) or HIP (AMD GPUs) can be achieved. ParallelStencil. jl seamlessly composes with ImplicitGlobalGrid. jl, which allows for distributed parallelisation of stencil-based XPU applications on a regular staggered grid. In addition, ParallelStencil. jl enables hiding communication behind computation, where the communication package used can, a priori, be any package that lets the user control when communication is triggered. The communication and computation overlap approach splits local domain calculations in two regions, boundary regions and inner region, the latter containing majority of the local domain's grid cells. After successful completion of the boundary region computations, halo update (using e. g. point-to-point MPI) overlaps with inner point computations.
Selecting the appropriate width of the boundary region permits to fine-tune optimal hiding of MPI communication  (Räss et al. , 2019c; Alkhimenkov et al. , 2021b). In the present study, we focus on using ParallelStencil. jl with the CUDA. jl backend to target Nvidia GPUs  (Besard et al. , 2018 (Besard et al. , , 2019)) , and ImplicitGlobalGrid. jl which relies on MPI. jl  (Byrne et al. , 2021 ), Julia's MPI wrappers, to enable distributed memory parallelisation.
Results We here report the performance of the accelerated pseudo-transient Julia implementation of the diffusion and the Stokes flow solvers targeting Nvidia GPUs using ParallelStencil. jl's CUDA backend. For both physical processes, we analyse the iteration count as function of number of grid cells (i. e. the algorithmic performance), the effective memory throughput T eff [GB/s] (performing a single device (GPU) problem size scaling), and the parallel efficiency E (multi-GPU weak scaling). We report the algorithmic performance as the iteration count per number of physical time steps normalised by the number of grid cells in the x-direction. We do not normalise by the total number of grid cells in order to report the one-dimensional scaling even for 2D or 3D implementation. We motivate our choice as it permits a more accurate comparison to analytically derived results and leave it to the reader to appreciate the actual quadratic and cubic dependence of the normalised iteration count if using the total number of grid cells in 2D and 3D configurations, respectively.
Performance benefits for desktop-scale computing Besides the potential to tackle nonlinear multi-physics problem at supercomputer-scale, another critical aspect relies in the ability to solve smaller-scale nonlinear problems. Here we investigate wall-times for the simulation of the previously-described with the Cholesky factorisation of the symmetrised Jacobian  (Räss et al. , 2017). In the 2D pseudo-transient solver, written in Julia using the ParallelStencil. jl packages, the evaluation of nonlinearities is embedded in the pseudo-time integration loop. The timings reported for the DI and the PT schemes were produced on a 2. 9 GHz Intel Core i5 processor and on a single Nvidia Tesla V100 GPU, respectively. Each simulation resolves 100 physical (implicit) time steps. Models were run on resolutions involving 62 2 to 510 2 and up to 1022 2 grid cells for the DI and PT scheme , respectively.
High-resolution 3D results We here present preliminary 3D results of the spontaneous development of visco-plastic shear bands in pure shear deformation from an initial random cohesion field (Fig. 15 ). These 3D results further demonstrate the versatility of the pseudo-transient approach, enabling the seamless port of the 2D E-VP algorithm to 3D, extending recent work by  (Duretz et al. , 2019a)  to tackle three-dimensional configurations. We generate the 3D initial condition, a Gaussian random field with an exponential co-variance function, following the approach described in  (Räss et al. , 2019b) , available through the ParallelRandomFields. jl Julia package  (Räss and Omlin, 2021). We perform 100 implicit loading steps using the accelerated pseudo-transient method and execute the parallel Julia code relying on ParallelStencil.
The physics we selected for the numerical experiments represent key building blocks to further tackle various multiphysics coupling, usually the combination of mechanical and diffusive processes. Our systematic results on the multi-inclusion setup with huge viscosity contrasts provides some preliminary results assessing the robustness of the accelerated PT method, which we further employ to resolve shear bad formation in 3D as a result of plastic yielding in elasto-viscoplastic materials. Our study paves the way for resolving coupled and nonlinear multi-physics applications in natural sciences and engineering on extremely high resolutions on next generation of exascale-capable supercomputers, revamping elegant iterative techniques and implementing them with the portable Julia language. https://doi.
Author contributions. LR Original study design, code and algorithm development, Julia at scale implementations, scaling and performance benchmarks, figure creation, manuscript edition. IU Code and algorithm development, dispersion analysis, parametric study, figure creation, for continuous technical support. LR, IU and SO acknowledge financial support from the Swiss University Conference and the Swiss Council of Federal Institutes of Technology through the Platform for Advanced Scientific Computing (PASC) program. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID c23, obtained via the PASC project GPU4GEO. IU and YP acknowledge the financial support from the Russian Ministry of Science and Higher Education (project No. 075-15-2019-1890).
Code availability. The various software developed and used in the scope of this study is licensed under MIT License. The latest versions of the code is available from GitHub at: https://github. com/PTsolvers/PseudoTransientDiffusion. jl and https://github. com/PTsolvers/ PseudoTransientStokes. jl (last access: 9 December 2021). Past and future versions of the software are available from a permanent DOI repository (Zenodo) at: https://doi. org/10. 5281/zenodo. 5764691 (Räss and Utkin, 2021a) and https://doi. org/10. 5281/zenodo. 5764696 (Räss   and Utkin, 2021b). Both the PseudoTransientDiffusion. jl and the PseudoTransientStokes. jl repositories contain most Julia multi-XPU code examples implementing the accelerated PT method and can be readily used to reproduce the figures of the paper. The codes are written using the Julia programming language.
We introduce a method for measuring the variability of membership coefficients of individuals in a predefined group, making use of an analogy between variability across individuals in membership coefficient vectors and variation across populations in allele frequency vectors. We show that in a model in which membership coefficient vectors in a population follow a Dirichlet distribution, the measure increases linearly with a parameter describing the variance of a specified component of the membership vector. We apply the approach, which makes use of a normalized F ST statistic, to data on inferred population structure in three example scenarios. We also introduce a bootstrap test for equivalence of two or more groups in their level of membership coefficient variability. Our methods are implemented in the R package FSTruct.
(2019) , researchers seeking to quantify variability in membership coefficients across individuals or to compare this variability between two or more groups often do so visually or informally. Here, we introduce a statistical method to measure variability in membership coefficients inferred Measuring ancestry variation with FSTruct by model-based clustering, and to compare this variability across populations. We apply the method to examples from real and simulated data. The method is implemented in the R package FSTruct.
For example, if individuals were mislabeled prior to analysis with methods such as Structure or Admixture, then our measure would be affected. Further, Q matrices generated by Structure and Admixture do not contain information about the magnitude of the difference between ancestral clusters; our measure only captures variation in ancestry with respect to the clusters that such programs infer. The new measure, which we have implemented in the R package FSTruct, contributes to a body of methods for quantitative analysis of inferred membership coefficients. This collection of methods includes computations useful for analyzing the level of support observed for different numbers of clusters K  (Evanno et al. , 2005)  and methods of aligning the clustering solutions observed in replicate analyses  (Jakobsson and Rosenberg, 2007; Kopelman et al.
Acknowledgments. We acknowledge support from  NIH  grant  R01 HG005855. MLM acknowledges support from a  National Science Foundation Graduate Research Fellowship  and the Anne T. and  Robert M. Bass   Stanford Graduate Fellowship. We thank  P. Verdu ,  M. Antonio , and  M. Edge  for assistance with data sets from their studies,  H. Moots  and  J. Pritchard  for helpful comments on the method, and  D. Cotter  and  J. Mooney  for suggestions for the software. Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization.
Many examples of rockfall simulation software provide great flexibility to the user at the expense of a hardly achievable parameter unification. With sensitive site-dependent parameters that are hardly generalizable from the literature and case studies, the user must properly calibrate simulations for the desired site by performing back-calculation analyses. Thus, rockfall trajectory reconstruction methods are needed. For that purpose, a computer-assisted videogrammetric 3D trajectory reconstruction method (CAVR) built on earlier approaches is proposed. Rockfall impacts are visually identified and timed from video footage and are manually transposed on detailed high-resolution 3D terrain models that act as the spatial reference. This shift in reference removes the dependency on steady and precisely positioned cameras, ensuring that the CAVR method can be used for reconstructing trajectories from witnessed previous records with nonoptimal video footage.
Introduction Many examples of rockfall simulation software provide great flexibility to the user at the expense of a hardly achievable parameter unification, as highlighted by  Berger and Dorren (2006) ,  Berger et al. (2011) ,  Volkwein et al. (2011) ,  Jarsve (2018) ,  Garcia (2019) ,  Bourrier et al. (2021) ,  and Noël et al. (2021). Even when using the same rebound model, though it may be implemented in a different piece of software, the results using the same parameters may vary, as shown previously in  Noël et al. (2021)  when comparing CRSP 4  (Pfeiffer and Bowen, 1989; Jones et al. , 2000)  with RocFall 8  (Stevens, 1998; Rocscience Inc. , 2022). The settings of the rebound model parameters are often specific to the model, rockfall software, and version used. Thus, it is difficult to transpose them from experimental results, such as the apparent coefficient of restitution from impact experiments.
, 2020) , referred to with the acronym "SLF 2020", are compared to newer footage captured with a narrower FOV and manual tracking allowed by the CAVR method as acquisition examples (Fig. 5 ). The WSL Institute for Snow and Avalanche Research SLF performed novel rockfall experiments with instrumented rocks at the Chant Sura test site (Fig. 6 ) that involved a 5 m tall by 60 m long 2000 kJ ROCCO flexible barrier from Geobrugg  (Caviezel et al. , 2019 (Caviezel et al. , , 2020 (Caviezel et al. , , 2021;; Sanchez and Caviezel, 2020). They opened their experiment to the public with Geobrugg at the GEO summit 2019 conference and publicly shared part of the acquired data in  Caviezel et al. (2020). The transparency of such an action toward open science should be emphasized. The accessible data can be very helpful to the geohazard community when assessing the sensitivity of current rockfall simulation software and for finding the right simulation parameters to be used for similar sites.
Cell Characteristics and Test Equipment One commercial lithium-ion cell was chosen to perform the experiments. The cell is the Samsung INR18650-25R5 cylindrical 18650. This cell is made of a graphite negative electrode and a LiNi 0. 8 Al 0. 15 Co 0. 05 O 2 (nickel-cobalt-aluminum, NCA) positive electrode. It has a rated nominal capacity of 2. 5 A. h, and an operating voltage window between 2. 5 V and 4. 2 V. The tests were performed on the Cacyssée platform of the IMS laboratory. All the tests were performed inside a controlled thermal environment with a CLIMATS climatic chamber. Measurements were performed using the 4-wire method and recorded by a BioLogic BCS-815 potentiostat connected to a computer and controlled through the BT-Lab software. Data points were recorded every second.
To improve the OCV approximation, the discharge rate can be reduced, although it was kept constant here for all temperatures to keep the experimental protocol identical. Incremental capacity data presented in the rest of this article are obtained in the following way. First, high frequency noise on the measured voltage of low-current discharge data is filtered. Then, both the voltage and the capacity derivatives are computed using the gradient function in MATLAB ®. Finally, Equation (  1 ) is used to obtain the IC. Concerning the procedure used to parameterize the compared models, two different MATLAB ® functions were used. For the proposed logistic model, the function lsqcurvefit is used to solve this nonlinear curve-fitting problem in the least-squares sense. For the two polynomial models, the function polyfit is used to find the best polynomial coefficients, also in the least squares sense.
com, "polyfit, Polynomial curve fitting". URL = "https://fr. mathworks. com/ help/matlab/ref/polyfit. html", accessed on 7 April 2022). () of Equation (  7 ) depending on the current working data. As the three compared models are adaptative, they do not contain a fixed num parameters. Hence, a comparison of their accuracy against experimental data as a fu of the number of model parameters was first completed. Figure  3  shows the evolu the coefficient of determination ² and the root-mean-square error (RMSE)  w number of parameters for the three compared models and for both voltage vs. ca data and IC vs. voltage data. Based on the ² criterion, the three compared model show a similar good fit t age vs. capacity data for  = 6 or more parameters (Figure  3a ). However, the pro model shows clear superiority when looking at IC vs.
com, "polyfit, Polynomial curve fitting". URL = "https://fr works. com/help/matlab/ref/polyfit. html". Accessed on 7 April 2022. ). Based on the results presented in Figure  3 , the proposed model shows significative improvements to fit both the capacity and its derivative. This also allows us to determine how many peaks can be kept without significantly reducing the accuracy of the model. For both sets of experimental data and for both fit criteria (R 2 and ), the logistic model shows already a very good fit to experimental data for N = 15 parameters, while adding more parameters do not significantly improve the fit. Thus, a number of 15 parameters is selected for the rest of the study. For the proposed model, this means 5 IC peaks are considered for this specific cell. For the polynomial models, this corresponds to a 14th order polynomial.
Standard Approach Fails to Distinguish Sleep From Freezing As a case study, we analyzed data recorded from rats performing both freezing and sleeping behaviors. The rats underwent a multiday fear conditioning and extinction protocol, and rest sessions were recorded in a familiar non-anxiogenic environment before and after each training session (Figure  1A ). We first used standard methods to detect freezing and sleep. Freezing is typically defined as all immobility periods longer than a particular threshold (0. 5-2 s; e. g. ,  Bagur et al. , 2018; Moberly et al. , 2018; Gründemann et al. , 2019; Jercog et al. , 2021; Ressler et al. , 2021). Nonetheless, standard sleep-scoring algorithms assume that all such immobility corresponds to rest states. Therefore, these techniques label most immobility periods as both sleep and freezing.
Indeed, while in some cases immobility periods correspond to sleep (see the left part of Figure  1B ; Supplementary Video 1), a freezing period may also last for long periods of time (right part of Figure  1B ; Supplementary Video 2). This makes the standard approach to sleep detection unsuitable for data involving freezing. Our recordings included the local field potential (LFP) from the hippocampus (HPC) and medial prefrontal cortex (mPFC). As expected, theta and delta oscillations displayed a marked difference between REM and SWS epochs (Figure  2C ). However, the theta/delta ratio did not help separate SWS from freezing, because in both of these epochs delta power dominated over theta power (Figure  1B ). This is likely due to the overlap between the delta frequency band and the frequency band of the respiratorydriven cortical oscillation around 4 Hz associated with freezing  (Ciatipis et al.
Effective Detection of Sleep and Freezing To validate our scoring pipeline, we analyzed data from a multiday fear conditioning and extinction dataset. The results of the algorithm were consistent with behavior to be expected in our protocol. Regardless of the day, in rest sessions animals spent most of the time sleeping, and freezing levels were low (Figures  3A, D ). In habituation sessions, in the first 3 min (baseline) of being placed in context A, animals tended to actively explore the environment (Supplementary Video 3), and freezing and sleep were mostly absent (Figure  3B ). Conversely, in the last 3 min of these sessions, after the animal had remained there for some time (each training session lasted 35 min), sleep was not uncommon (Figure  3C ; Supplementary Video 4). Following fear conditioning in context A, animals tended to freeze there during baseline (Supplementary Video 2).