dataesr/scientific-paragraphs-categorization huggingface dataset:
ecmwf. int/en/forecasts/dataset/ecmwf-reanalysis-v5). We thus consider a very long CMIP6 vector consisting of four successive components: x ¼ ðG all ; R all ; R ghg ; RE nat Þ ð2Þ where each element is an entire 1850-2099 timeseries of the forced response, G and R stand for global mean surface temperature and a regional variable, respectively. "all", "ghg" or "nat" are the subsets of external forcings considered. Similarly, we define an observed vector made of two components: y ¼ ðG obs ; R obs Þ ð 3Þ i. e. , only observed timeseries are used in y. The length of these timeseries depends on the selected dataset: 1850-2020 for HadCRUT5 and 1950-2020 for ERA5 (we have noticed that ERA5 has been recently updated from 1940 onwards, but we feel that the 1940s will not provide a notable additional constraint within KCC given the limited GHG influence in the mid-20th century).
Note the HadCRUT5 dataset merges near-surface air temperature over land and sea ice, but sea surface temperature over the free ocean. This blended global mean surface temperature (GMST) is compared with the global mean near-surface air temperature (GSAT) from climate models. This approximation was discussed in details in former studies  31, 32  and does not represent as a major issue compared to the other sources of uncertainty considered in the present study. The limited number of near-surface observations over sea ice and the coarse resolution (5°by 5°) of HadCRUT5 are, however, an incentive to also estimate the observed Arctic warming or other recent changes in the Arctic climate using ERA5 at a much higher resolution (here 0. 5°b y 0. 5°). All attribution or projection diagnoses presented below can be derived from the posterior distribution p(x | y).
Code availability The full KCC statistical package is freely accessible at https://gitlab. com/saidqasmi/KCC or under a GNU General Public License, version 3 (GPLv3), at https://doi. org/10. 5281/ zenodo. 5233947. The additional related R scripts which have been used to plot Fig. 3  and 4 are available at https://doi. org/10. 5281/zenodo. 8004439. The other figures are not specific to KCC and can be easily reproduced with your favorite graphic tools.
However, these last methods can be used to generate a rich and consistent database to train a fast AI accelerated by the recent advances in machine learning procedures. In this work, we aim to merge the physical consistency of classical simulation methods with the speed of realtime physics engines using a deep learning approach. Although the formulation is general for a wide variety of dynamical systems, in this work we focus on nonlinear solid mechanics. The results are consistent with the laws of thermodynamics by construction and are able to achieve real-time performance in general load cases which were not previously seen by the network. In Section 2, we explore some of the related work involving real-time reality simulators and physics-informed deep learning. In Section 3 we introduce the thermodynamically-informed graph neural networks together with the vision and visualization system used to record the demo videos.
The graph features are processed with a message-passing graph neural network. The step prediction is performed using the GENERIC integration scheme in Eq. (  3 ), which is repeated iteratively to get the complete rollout of the simulation. a vector f i ∈ R F f. We employ an encode-process-decode scheme  [37] , built upon multilayer perceptrons (MLPs) shared between all the nodes and edges of the graph. We use this architecture to learn the GENERIC structure of the evolution in time of the variables governing the virtual system  [38, 39]. It consists on splitting the system into a conservative and dissipative contribution. The conservative dynamics are defined using an energy potential E and a symplectic Poisson matrix L, which recover the Hamiltonian formalism, whereas the dissipative dynamics are described by the entropy potential S and the dissipative or friction matrix M, which accounts for the non-reversible dynamics.
The computational cost of the high fidelity FEM simulations used for the training of the neural network is 15 s per simulation, up to 795 s for the whole dataset, and 243 Mb in memory storage. Conversely, the training time of the neural network is 4. 5 h and the mean evaluation time is 9 ms with a memory storage of 12. 3 Mb. Thus, the use of a deep learning approach allows a drastic reduction of the online computation time with the inconvenient of larger offline training time. It is also worth noting that the network parameters are more than 10 times lighter in terms of memory storage.
Stanford bunny The second example is a bunny mesh from the Stanford 3D scanning repository, which is a more complex geometry as the one shown in the previous example. It is a standard reference model in computer graphics research. The model is represented in Fig. 5. The finite element mesh from which data are obtained consisted of N e = 4941 tetrahedral linear elements and N = 1352 nodes. The constitutive parameters of the hyperelastic strain energy potential are C 10 = 2. 6 • 10 -1 and D 1 = 4. 9 • 10 -2. Similarly as the previous case, a concentrated load of F = 1 is applied in 100 different positions with a perpendicular direction to the solid surface. The body was fixed to the ground plane by disabling displacements and rotations at the lower model nodes. Each simulation is composed of N T = 20 time increments of ∆t = 5 • 10 -2 s.
The graph neural network vertex and edge MLPs have two layers of F h = 100 neurons each, with 10 message passing sequential blocks. The training was performed for N epoch = 1800 epochs and learning rate l r = 10 -4. Fig. 6  shows a real-time video sequence generated using the bunny model, also with a minimum framerate of 30 frames per second. In this case the stress field errors reported in Fig. 7b  are higher due to the stress peaks at the single-node force application, which causes that the elasticity phenomena remain very local in space. In this case, the computational cost of the high fidelity FEM simulations is 27 s per simulation, up to 2700 s for the whole dataset and 833 Mb in memory storage. The dataset and edge count is much larger in this example, which increases the training time of the neural network to 20 h and the evaluation time to 11 ms with the same memory storage as the previous example.
Sequence mapping: Burrows-Wheeler Aligner (BWA, version 0. 5. 10-evan. 9-1-g44db244), PCR duplicate removal: bam-rmdup (https://github. com/mpieva/biohazard-tools/tree/v0. 2-knowngood), Nucleotide substitution model selection: jModelTest (version 2. 1), Identification of primate sequences: Kraken (version 1), Genotype calling: bam-caller version 0. 2 (https://github. com/bodkan/bam-caller, 369) For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e. g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Article to material typically used for osseous artefact production, and submerged them in several reagents previously used in ancient DNA extraction, as well as in water for comparison. These included (1) a guanidinium thiocyanate-containing reagent previously suggested for non-destructive DNA extraction 12 , (2) an ethylenediaminetetraacetate (EDTA) solution, which is a decalcifier commonly used in ancient DNA extraction  [14] [15] [16]  , (3) a sodium hypochlorite (bleach) solution, which is an oxidizing reagent used to remove surface-exposed contaminant DNA  11, 17  , and (4) a sodium phosphate buffer supplemented with detergent  11  , which has been recently shown to enable temperature-controlled DNA release from powdered bone samples  18. Mapping of the microtopography using quantitative 3D surface texture analysis  19, 20  before and after the treatments revealed substantial surface alterations on all objects exposed to either the guanidinium thiocyanate reagent or EDTA (Extended Data Fig.
DNA was then extracted from the sediment clumps, the water was used for washing and the sediment particles were collected in this process ('sediment pellets'), as well as directly from the artefacts using the non-destructive method described above. Ancient mammalian mtDNA was detected in all fractions that were analysed, except for some of the water washes and the associated sediment pellets (Fig. 2 ). The trajectories of DNA released from the four artefacts were similar in that the highest yield of mammalian mtDNA was obtained at 90 °C in the phosphate-based DNA extraction (up to 734, 6,614, 456 and 77,910 mtDNA fragments for BKP1, BKP2, BKP3 and DCP1, respectively; Supplementary Data 1). However, library preparation efficiencies were low for the Bacho Kiro Cave material (less than 10% for many fractions), presumably due to the co-extraction of inhibitory substances (Fig.
Phylogenetic analyses and dating On the basis of the branch length of the DCP1 consensus sequence in a tree with other present-day and ancient human mtDNA genomes (Fig. 3a ), we estimated its age to 18. 5 thousand years (kyr), with a 95% For nuclear DNA analysis, hybridization capture was performed using libraries from the second and third 90 °C phosphate fractions, targeting sites in the human genome that are known to be polymorphic in modern or archaic humans and that are located in regions of high sequence divergence between humans and other mammals  8. Sequence information was obtained for 336,429 of these sites (71. 5% of the sites targeted), with estimates of present-day human and faunal contamination both below 1%. Comparisons with present-day human populations 26 using ƒ3-statistics and D-statistics  27, 28  show high affinities to Native Americans (Extended Data Fig.
Taxonomic assignment of mtDNA sequences Sequences resulting from mammalian or human mtDNA capture were assigned to mammalian taxa on the biology family level using a previously published computational pipeline  6  based on BLAST and MEGAN (version 0. 0. 12)  51 , with modifications in data filtering detailed in Vernot et al. (  2021 )  8  (Supplementary Data 1 and Supplementary Information 4). In brief, as described in the latter study, false identifications of taxa were minimized by requiring at least three unique sequences (covering at least 105 positions in the reference genome) to be assigned to a family, and by requiring these sequences to represent at least 1% of all taxonomically identified sequences. The presence of ancient DNA was determined individually for the sequences from each family (and each library) by computing the frequency of terminal C-to-T substitutions as a proxy for the deamination rate at the molecule ends.
Human and cervid mtDNA analysis Present-day human contamination in the fractions from DCP1 that yielded ancient human mtDNA was estimated using the software tool AuthentiCT (version 1. 0. 0)  52 and a near-complete consensus sequence called for the second 90 °C fraction from DCP1 using positions with at least tenfold coverage of the mtDNA genome. This consensus sequence was then used for haplogroup assignment with Haplogrep 2 (version2. 4. 0)  53  and to identify 'diagnostic' positions in the mtDNA genome, which were used to determine the support for the consensus.
GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data.
, 1997 (Heenan et al. , , 2006) )  as well as some at reactor sources  (Kampmann et al. , 2006; Dewhurst et al. , 2016; Sokolova et al. , 2019). TOF instruments index the incoming neutron wavelength by the time of arrival of neutrons at the detector and, in the case of neutron instruments designed for structural material investigations, typically ignore effects of inelastic energy or wavelength change by interaction with the sample. When TOF data are histogrammed in slices of time of arrival (or wavelength), this third dimension also needs to be considered in the data treatment. The additional third dimension available in GRASP lends itself usefully to the treatment and analysis of time (wavelength)-histogrammed TOF SANS data. Software tools such as GRASP are key in efficiently processing and cross referencing large quantities of scattering data, and offer a 'toolbox' of utilities to optimize extraction of the quantities of interest.
GRASP history and software package Since its first release in 2001, GRASP has attracted significant attention and support, both within the user community and from facility instrument responsibles of similar multidetectorbased instruments at other neutron facilities, X-ray facilities and some laboratory-based X-ray small-angle scattering instruments. Some of these instruments are catered for within GRASP, including instruments at the ILL; SINQ, PSI, Switzerland; Helmholtz-Zentrum, Germany; Laboratoire Leon Brillouin, France; Oak Ridge National Laboratory, USA; NIST Centre for Neutron Research, USA; the Australian Nuclear Science and Technology Organisation, Australia; and Maier-Leibnitz Zentrum, Germany. Modern graphical-userinterface-based packages such as MATLAB  (MathWorks, 2023)  offer a high-level and system-independent computing language with easy matrix manipulation, comprehensive graphics and interface-building tools.
The architecture and configuration flexibility of GRASP combined with the continuously developing capabilities of the professionally maintained MATLAB environment have allowed continued development of GRASP, with it undergoing approximately ten major architectural overhauls over its 22-year lifetime. MATLAB can be compiled into standalone licence-free runtime code for Windows, Linux and Macintosh OSX platforms, allowing GRASP to be distributed to users freely without obligation to buy the MATLAB package. On the other hand, many users choose to use the source m-code for GRASP directly in MATLAB, contributing to bug fixes, customized analysis routines  (Holmes, 2014)  and incorporation of user modules. The source m-code and compiled packages are freely available and distributed via the GRASP website hosted by the ILL at https://www.
Main interface Fig. 1  shows the main interface of GRASP on the left alongside the MATLAB command/output window or the text output window for the compiled version of GRASP on the right. The main interface hosts the 2D graphical display of data and principal GUI control elements while the output window provides a detailed text update of parameters and procedures executed during the data treatment. The 2D displayed image properties are controlled by GUI elements on the upper-right-hand side of the main interface such as logarithmic intensity display, grouped and manual intensity scale, rendering style, contour, and colour-map options. Data entry into GRASP and control of the displayed scattering data and background subtractions are achieved using the worksheet selectors below the main display.
Right-clicking on many of the checkboxes, push buttons or edit boxes enables rapid access to additional options otherwise found within the main menus. The command/output text window provides a live and detailed summary of the latest analysis processes, and reporting of a great many instrument-, data-and treatment-related parameters. Running live under MATLAB, the command window also serves for custom access to GRASP raw and treated data as well as program configuration variables, and allows the development of custom user operations. The command/output window is an incredibly valuable tool and is useful to keep visible at all times during operation of GRASP. The main interface features of GRASP are summarized as follows: (a) 2D graphical display -display of raw, corrected or calibrated detector(s) data depending on the program state and the selected normalization, correction and calibration options through the data-treatment process.
Menu and toolbar features The menu and tool items in the main GRASP interface allow access to control program behaviour, input-output, instrument and data configurations, and the wide variety of data extraction and reduction and analysis modules. The mainmenu features of GRASP are summarized as follows, while some of these items can also be found in the toolbar, main and spreadsheet interfaces: (i) File -general project tools including loading/saving GRASP projects, definition of data sources, import and export of data, masks, detector efficiencies, print and export of images, and program preferences. (ii) Display -control and options for the main 2D display, GRASP interface and text output window including imagedisplay render, colour maps and palette manipulation, contour options, display options, and axis options.
The resolution control centre allows users to specifically tailor and inspect contributions to the overall calculated instrument resolution Q(Q). (vi) User modules. The already comprehensive suite of data-inspection and analysis tools can be enhanced or customized by the user contribution of user modules, fitting functions or GRASP script routines. Submitted and validated user modules are available under the user-modules menu, with current science-specific examples being tools for the analysis of diffraction from the vortex lattice in type-II superconductors; a rheological scattering anisotropy calculator; plotting, extraction and analysis of parameters from non-SANS in situ techniques; and SANS instrumentation calculators such as the TOF calculator and the D33 chopper timedistance calculator.
(vii) GRASP Script -allows access to the GRASP Script Editor, running of GRASP scripts and GRASP Script help. GRASP Script allows MATLAB and GRASP functions to be executed as a script of commands in order to automate data processing in GRASP. Scripting and execution of MATLAB commands are even possible in the compiled version exploiting the eval functionality of the software. Automation is easily achieved using standard MATLAB commands, via a dedicated set of macro-commands using the gs command, and through the globally accessible variables parameterizing the interface features and data-treatment options. (viii) Help -access to documentation including the GRASP manual, notes on polarization and analysis corrections, and GRASP developer notes.
, 1990). A more complete method of calculating the instrument resolution is to use the componentsmearing-function shapes and widths directly to numerically evaluate the final 'real-shape' resolution  (Nelson & Dewhurst, 2014). This is the default method within GRASP. Under regrouping of data, the real-shape resolution kernels are also averaged to give the final resolution or smearing function describing the data in each 1D Q bin. At present, this realshape kernel exists only internally within GRASP and is used in subsequent data fitting within the program. This is primarily because of the lack of software within the small-angle scattering community that can deal directly with the more detailed real-shape kernels. Upon export of the 1D reduced I versus |Q| data, the real-shape resolution kernel is reduced into standard deviation with an assumed Gaussian profile for simplicity and compatibility with other existing codes.
In this example, the sample was simulated as monodisperse spherical particles in solution of radius 60 A ˚, contrast of 6 Â 10 À6 A ˚À2 and with a fractional concentration of 0. 01. The simulated instrument was set up with an incoming wavelength of 6 A ˚and a beam collimation of 5. 3 m, and with the two detector trolleys at 4 m (rear central detector) and 1. 2 m (front panel detectors). Importantly, the 'SANS instrument model' also reproduces the effects of instrument resolution. Fig. 3  shows how the main GRASP interface is set up with all background components subtracted, transmissions and beam centre calculated, and calibration options enabled, as described in Section 4. 1. The 'Averaging' analysis module has been opened, allowing the user to produce the final 1D isotropic scattering curve in absolute units of cross section, as indicated by the output 'GRASP_Plot' window.
The white points show data from the rear detector, while the red and green points show data from the side and top/bottom front panel detectors, respectively. Fig. 4  shows the fitting of the reduced 1D scattered intensity to the same spherical-particle model used in the instrument simulation. The 1D curve fit module can be opened from the 'GRASP_Plot' menus, allowing least-squares fitting of data to various user-defined model functions. The 'GRASP_Plot' window in Fig. 4.
Polarizer and spin-flipper efficiencies are measured experimental parameters and should be adjusted during the datatreatment process in the 'Polarisation Analysis' modules in GRASP. Analysis of the scattered neutron spin is most usefully achieved on SANS instruments using polarized 3 He gas as a spin analyser. Polarized 3 He gas is typically produced on-site using optical pumping techniques and can reach 3 He gas polarizations of $80%  (Andersen et al. , 2005). Polarization of the 3 He gas is maintained on the instrument using a 'magic box' highly uniform magnetic field cavity, and polarization of the neutron beam can reach values much greater than that of the 3 He gas depending on the gas pressure and cell thickness. Nevertheless, the polarization of the 3 He analyser gas decays exponentially as a function of time (with a half-life of 10s to 100s of hours) with corresponding varying analysing efficiency.
GRASP_Plot 'GRASP_Plot' is the 1D graph output window, as highlighted in Figs. 3 4 5 6 7. As well as providing a standardized interface for reduced data, 'GRASP_ Plot' also provides a number of useful tools for manipulation of 1D data produced by GRASP. These are: (1) Display options -the usual graph-inspection tools such as zooming, panning, control of axis limits, axis scaling, legends and grids. (2) Save 'GRASP_Plot'. 'GRASP_Plots' can be saved as regular MATLAB figures and reopened directly from the main GRASP interface or the existing 'GRASP_ Plot'. Open 'GRASP_Plots' are automatically saved with the saving of a GRASP project from the main interface. (3) Image export -graphical export of figures in the usual image formats such as JPG, BMP and PNG, or in vector formats such as Adobe PDF or AI (Illustrator).
SANS instrument model GRASP contains a rather useful data-simulation tool, the 'SANS instrument model', giving users of the software the ability to simulate scattering from a few typical sample and background models, for example a sphere form factor with user-definable parameters such as particle radius, polydispersity, contrast and concentration. The software also aims to reproduce most optical features of the instrument, instrument resolution, neutron flux and counting statistics in order to provide realistic looking 2D simulated data, as if it had come from the real instrument and as shown in Figs. 3 4 5. Simulated data can then be corrected and reduced to 1D in GRASP just as for real scattering data. The instrument optical model is analytic while scattering at the sample is simulated using the Monte Carlo method.