dataesr/scientific-paragraphs-categorization huggingface dataset:
During the texture computation, the mosaicking of the 41 original images was performed using an average of the values on each polygon of the 3-D model. Figure  4  shows different views of the textured 3-D model reconstructed by the photogrammetric software. The astronauts documented approximatively one-half of the boulder. The 41 positions and orientations of the camera computed by Metashape are indicated at their exact location (annotated squares) around the perspective view in the lower left of Figure  4. The inset (white rectangle) shows the level of detail achieved during the reconstruction process. Arrows point, for example, to a veinlet of dark very fine-grained melt-breccia that stands out on the model and that can be traced throughout various parts of the boulder. It presents potential similarities with impact pseudotachylites  [29].
Four samples were collected across the layering (samples 72255, 72215, 72235, and 72275). We used a set of 20 images labeled AS17-137-20900 to AS17-137-20909 and AS17-138-21029 to AS17-138-21042 to generate the textured 3-D model shown in Figure  8. The second part of the data set, covering the right part of the boulder, is in black and white with slightly overexposed images that we had to homogenize. This does not seem to affect the photogrammetric treatment. The subtle difference in color between the left and right parts of the boulder should, however, not be considered. The alignment generated a sparse point cloud of 88,000 tie-points. A dense point cloud of about 13 million points allowed the generation of a mesh downsampled to 400,000 polygons. This boulder appears much less coherent than the tan-gray or blue-gray breccia types  [30].
Publication of the Models on a Web-Based Platform The Sketchfab 3-D meshes repository platform (www. sketchfab. com) provides a digital access and intuitive basic controls to interact with online 3-D objects of various scales, from centimeter-scale samples to large digital outcrop models, without the need for specific software and hardware. The visualization can be done on either a traditional computer screen, on a smartphone, or using a virtual reality headset, directly online. When using a smartphone or a tablet, the 3-D models can also be viewed in augmented reality. Contributors are allowed to upload models using a web-based interface, and to define custom rendering options, such as lighting, material properties, and the size.
Publication of the Models on a Web-Based Platform The Sketchfab 3-D meshes repository platform (www. sketchfab. com) provides a digital access and intuitive basic controls to interact with online 3-D objects of various scales, from centimeter-scale samples to large digital outcrop models, without the need for specific software and hardware. The visualization can be done on either a traditional computer screen, on a smartphone, or using a virtual reality headset, directly online. When using a smartphone or a tablet, the 3-D models can also be viewed in augmented reality. Contributors are allowed to upload models using a web-based interface, and to define custom rendering options, such as lighting, material properties, and the size and orientation of the model with respect to the VR rendering.
Integration into a Game Engine Compared to web-based platforms, game engines such, as Unity 3D or Unreal, developed for and by the game industry, provide a much more versatile way to integrate, manipulate, and merge a large quantity of planetary orbital and in situ data (e. g. ,  [9, 10, 14] ). For our project, we selected the Unity 3D platform (version 2019. 2. 15 f1), which is thoroughly documented through user manuals, Figure  11. Snapshots of the reconstructed boulder models that can be viewed online on the web-based Sketchfab platform (e. g. , https://sketchfab. com/LPG-3D). Any user equipped with a virtual reality headset can stand in front of these boulders and investigate them in detail at a true scale in virtual reality). The character is 1. 80 m tall. The rendering in VR allows in particular the user to investigate the morphology of the boulders at real scales.
Various components can be added to the scene, such as a skybox with stars, a 3-D model of the lunar module, in situ 360° panoramas, or sound recordings of the actual astronauts' conversations, which are triggered when the user is approaching relevant elements, such as the boulders. A user interface control panel is available to switch between different textures (i. e. , Kaguya TC versus Clementine) or to directly go to points of interest. The 3-D models of the outcrops can then be directly added to the scene by using a simple drag and drop function on the scene hierarchy and placed at the relevant location with the right orientation and scaling. We used the Autodesk FilmBoX (. fbx) file export format from Metashape, associated with corresponding jpeg textures, for this purpose. The advantage of the.
Future surface photogrammetric work could also be carried out with the full-resolution scans of the Apollo Image Archive. However, with ~300 MB per image, this would require a very powerful computing system, for an improvement which is still to be evaluated, depending often on the focus quality of the views. These original high-resolution scans also need to be centered and cropped in a consistent manner before being usable in an automated photogrammetric pipeline. It could also be interesting to further work on the lighting conditions in the reconstructed virtual world, in order to merge orbital data with ground-based data taken with a solar azimuth and elevation as close as possible. Figure  12  uses the LRO image M129086118LR for this purpose. Another optimization of the final products could be the generation of normal maps to realistically render the micro relief on the boulders without overloading the graphic processor.
Additional evidence in that direction is displayed in Figure  1b , where we show the proportion of correct responses to stimuli seen for the first time after the first correct response in an episode. This quantity was determined for the last third of the session, when the subjects had already experienced several times the three reoccurring task-sets within the recurrent session. If the subjects had perfectly learned the task-sets, they could directly infer the correct response to the newly seen stimulus from a single correct response to another stimulus. The data shows that indeed some subjects perfectly learned full tasksets, so that their performance was maximal after the first correct trial. The performance averaged over all subjects was significantly higher in the recurrent session compared to the open-ended session (T-test on related samples, second stimulus: 0.
Neural correlates of task-set inference Using the model fitted to individual subjects, we next aimed to identify the neural correlates of taskset inference based on blood-oxygen-level-dependent (BOLD) signal recorded from functional magnetic resonance imaging (40 subjects, Experiment 2  [Donoso et al. , 2014] , see Figure  5 -figure supplement 1a for model fits on this dataset). We specifically examined the correlations between the task-set inference and the BOLD signal at the time of feedback, while controlling for trial difficulty and prediction error (see Materials and methods). In the recurrent session (Figure  7 -source For each episode switch, and subject by subject, we compute the probability of making a correct choice after the first correct trial, for a different stimulus. Trials are classified from a model-based criterium as 'chunked' or 'independent', respectively depending on the presence or absence of an inference from the task-set network to the associative network.
This analysis showed a difference of neural activity in the dorsal system corresponding to the necessity of learning and using (recurrent session) or not (open-ended session) the model of the task. Additional controls were performed using ROIs selected from several meta-analyses  (Lancaster et al. , 1997; Yarkoni et al. , 2011; Shirer et al. , 2012; Glasser et al. , 2016 , see Materials and methods and Figure  7 -source data 2). Altogether, they confirmed dorsolateral and dorsomedial prefrontal cortex were specifically recruited in the recurrent session and correlated with task-set inference. Since our model predicted the specific trial at the beginning of an episode where task-set inference should be maximal, we finally compared the BOLD activity on that trial with two trials immediately before and after it.
The Bayesian information criterion (Figure  5a  and Figure  5 -figure supplement 1) uses the likelihood computed by MLE but introduces a penalty term for model complexity depending on the size of the observed sample. Following Figure  2  of  Collins and Koechlin (2012) , the BIC was computed as the opposite of equation (4. 139) of  Bishop (2007). We also compared the AIC (Akaike information criterion) for both models and reached identical conclusions (see Figure  5 source data 1). A larger log-likelihood and lower BIC and AIC correspond to best model fits. We combined a grid search on initial parameters values with a gradient descent algorithm from the SciPy optimization toolbox. Parameters were estimated subject by subject. On average over subjects (see Figure  5 -source data 1), the learning rate in the associative network (a ¼ 0:35, s ¼ 0:0073) is twice the learning rate in the task-set network (Q P ¼ 0:17, s ¼ 0:0070).
Acknowledgements FB was funded by  Ecole des Neurosciences de Paris Ile-de-France  and  Region Ile de France (DIM Cerveau and Pensee). SP is supported by an  ATIP-Avenir grant  ( R16069JS )  Collaborative Research in Computational Neuroscience ANR-NSF  grant ( ANR-16-NEUC-0004 ), the  Programme Emergence(s) de la Ville de Paris , the  Fondation Fyssen  and the  Fondation Schlumberger pour l'Education et la Recherche. SO is funded by the  Ecole des Neurosciences de Paris Ile-de-France , the  Programme Emergences of City of Paris ,  Agence Nationale de la Recherche  grants  ANR-16-CE37-0016-01  and  ANR-17-ERC2-0005-01. This work was supported by the  program 'Investissements d'Avenir ' launched by the  French Government  and implemented by the  ANR , with the references  ANR-10-LABX-0087 IEC  and  ANR11-IDEX-0001-02 PSL Research University.
Software All simulations were done with Python 2. 7 (using numpy and scipy, and the scikit-learn package;  Pedregosa et al. , 2011). The fMRI analysis was done with Matlab and SPM12  (Ashburner et al. , 2014). Code is available at https://github. com/florapython/ TemporalChunkingTaskSets  (Bouchacourt, 2020 ; copy archived at https://github. com/elifesciencespublications/TemporalChunkingTaskSets).
Frozen Density Embedding (FDE) represents a versatile embedding scheme to describe the environmental effect on the electron dynamics in molecular systems. The extension of the general theory of FDE to the real-time time-dependent Kohn-Sham method has previously been presented and implemented in plane-waves and periodic boundary conditions (Pavanello et al. J. Chem. Phys. 142, 154116, 2015). In the current paper, we extend our recent formulation of real-time time-dependent Kohn-Sham method based on localized basis set functions and developed within the Psi4NumPy framework to the FDE scheme. The latter has been implemented in its "uncoupled" flavor (in which the time evolution is only carried out for the active subsystem, while the environment subsystems remain at their ground state), using and adapting the FDE implementation already available in the PyEmbed module of the scripting framework PyADF.
The implementation was facilitated by the fact that both Psi4NumPy and PyADF, being native Python API, provided an ideal framework of development using the Python advantages in terms of code readability and reusability. We employed this new implementation to investigate the stability of the time propagation procedure, which is based an efficient predictor/corrector second-order midpoint Magnus propagator employing an exact diagonalization, in combination with the FDE scheme. We demonstrate that the inclusion of the FDE potential does not introduce any numerical instability in time propagation of the density matrix of the active subsystem and in the limit of weak external field, the numerical results for low-lying transition energies are consistent with those obtained using the reference FDE calculations based on the linear response TDDFT.
24 Many of them rely on the real space grid methodology  24  with Siesta and Octopus as the most recent ones. 25, 26 Alternative approaches employ plane waves such as in Qbox  27  or QUANTUM ESPRESSO  28, 29  and analytic atom centered Gaussian basis implementations (i. e. , Gaussian,  30, 31  NWChem,  32  Q-Chem 33,34 ) also have gained popularity. The scheme has been also extended to include relativistic effects at the highest level. Repisky et al. proposed the first application and implementation of relativistic TDDFT to atomic and molecular systems 35 based on the four-component Dirac hamiltonian and almost simultaneously Goings et al. 36  published the development of X2C Hamiltonianbased electron dynamics and its application to the evaluation of UV/vis spectra. Very recently, some of us presented a rt-TDDFT implementation  37, 38  based on state-of-the-art software engineering approaches (i.
Note that also in this uncoupled version the embedding potential is time-dependent and needs to be recomputed and updated during the time propagation. However, the propagation scheme is restricted to the active subsystem and the approach is promising to include environmental effects in the real-time simulation. Numerous applications within the context of the linear response TDDFT showed that an uncoupled FDE is sufficient for reproducing supermolecular results with good accuracy even in the presence of hydrogen bonds as long as there are no couplings in the excitations between the systems. In this work we extend rt-TDDFT based on localized basis functions to the FDE scheme in its uncoupled version (uFDE-rt-TDDFT), taking advantage of modern software engineering and code reusability offered by the Python programming language.
We devised an unified framework based on Python in which the high interoperability allowed the concerted and efficient use of the recent rt-TDDFT procedure, which some of us have implemented in the framework of the Psi4Numpy API, 37,38 and the PyADF API. 86 The rt-TDDFT procedure has served as the main interface where the PyADF methods, which gave direct access to the key quantities necessary to devise the FDE scheme, can be accessed within a unified framework. Since in this work we introduced a new flavor of the rt-TDDFT Psi4Numpy-based program, to avoid confusions, from now on, we will refer to the aforementioned rt-TDDFT based on Psi4Numpy as Psi4-rt, while its extension to the FDE subsystem framework will be referred as Psi4-rt-PyEmbed. In Section 2 we review the fundamentals of FDE and its extension to rt-TDDFT methodology.
In Section 3 computational details are given with a specific focus on the interoperability of the various codes we merged and used: Psi4Numpy, 39 XCFun 87 and PyADF,  86  including the PyEmbed module recently developed by some of us. In Section 4 we report and comment the results of the calculations we performed on excitation transitions for different molecular systems, including: a water-ammonia complex, a water cluster and a more extend acetone-in-water cluster case. Finally, we give some preliminary results about the applicability and numerical stability of the method in presence of intense external field inducing strong non-linear effects as High Harmonic Generation (HHG) in the active system. Concluding remarks and perspectives are finally given in Section 5.
Computational Details and Implementation In this section we outline the computational strategy we adopted to implement the uFDErt-TDDFT scheme. We devised a multi-scale approach where we take advantage of the real-time TDDFT reference procedure, recently implemented within Psi4Numpy framework (i. e. Psi4-RT program), 37,38 while the FDE computational core relies on PyADF  86, 105  and makes use of its PyEmbed module, which some of us have recently developed. 106, 107 PyEmbed provides a Python implementation for computing the interaction energy (Eq. 4) and embedding potential (Eq. 7) from FDE on user-defined integration grids, while using the XCFun library  18, 87  to evaluate non-additive xc and kinetic energy contributions. With PyEmbed, quantum chemistry codes require only minimal changes: functionality to provide electron densities and its derivatives, as well as the electrostatic potential, over the grid, as well as to read in the embedding potential, and add it as a one-electron operator in the Fock matrix.
91 e PyADF scripting framework provides all the necessary tools to manage various computational tasks and manipulate the relevant quantities for electronic-structure methods. The resulting Python code, referred as Psi4-rt-PyEmbed, is available under GPLv3 license at Ref. 108. A data set collection of computational results, including numerical data and parameters used to obtain the absorption spectra of Sections 4. 2, 4. 3 and 4. 5, is available in the Zenodo repository and can be freely accessed at Ref. 109.
Rapid prototyping and implementation Psi4Numpy 39,110 and PyADF,  86, 105  both provide a Python interface, which greatly simplifies the computational work-flow from input data to the results. PyADF is a quantum chemistry scripting framework that provides mechanisms for both controlling the execution of different computational tasks and for managing the communication between these tasks using Python object-oriented programming techniques. As we already mentioned, its built-in classes permit to handle different aspects involved in the work-flow as a single unit. All the advantages coming from object-oriented programming (i. e extensibility and inheritance) are readily available and allow us to incorporate third-party scientific code and directly manipulate quantities coming from different codes (Psi4Numpy) in our case.
The Python HLL (High-Level Language), among others, permits to formally express complex algorithms in comparatively few lines of codes. This makes rather straightforward to let PyADF interact with Psi4Numpy native Python API. For the sake of completeness we want to finally mention that, to accomplish our goal, we firstly had to port some of the frameworks (specifically XCFun, PyADF and PyEmbed) to the new Python 3. 0 standard (i. e. , we used a private branch of the cited packages, available at Refs. 111,112). As an explicit example of the interoperativity achieved between different codes we report in Algorithm 1 some basic directives used to compute those key quantities necessary for our uFDE-rt-TDDFT. The electron density of an active system is obtained via Psi4Numpy while the electron density, the Coulomb potential and non-additive terms of the environment are managed using PyADF.
113 We mention here that PyADF, despite its name, is not specific to this program, but works with a number of different quantum chemistry codes. The density and Coulomb potential resulting from this calculation, that are represented on a common numerical grid, are obtained using get_density() and get_potential() methods (Line 25,27) respectively. The PyEmbed module has all the methods needed to manage the density of both the reference system and environment to finally compute the non-additive embedding potential. Indeed, the embed_eval object is instantiated (Line 34) and the non-additive embedding potential is evaluated on the numerical grid using get_nad_pot (Line 36), once the density of both the active system and of the environment has been provided. Algorithm 1 has well illustrated how we can utilize the classes provided by PyADF to obtain a very simple workflow in which we are able to manipulate quantities coming from Psi4Numpy.
The evaluation of ρ I (r) on the numerical grid is efficiently accomplished using the molecular orbitals (MO), which requires the valuation the localized basis functions at the grid points. Finally, the PyEmbed module comes into play (last block of the out-of-loop section), the real-space electron densities ρ I and ρ II serve as input for the get_nad_pot() method. Thus the non-additive kinetic and exchange potential are obtained. The embedding potential is then calculated from its constituents (i. e. the environment electrostatic and nuclear potential and the non-additive contribution as detailed in Eq. 7) and evaluated at each grid point, v emb (r k ). The embedding potential matrix representation in the active subsystem basis set, V emb , is calculated numerically on the grid as V emb µν = k χ µ (r k )χ ν (r k )v emb (r k )w k (20) where χ µ (r k ) are the Gaussian-type basis set functions employed in the active systems (used in Psi4Numpy) evaluated at the grid point, r k.
Results and Discussion In the present section we report a series of results mainly devoted to assess the correctness of the uFDE-rt-TDDFT scheme. To the best of our knowledge, this implementation is the first available for localized basis sets. Since our implementation relies on the embedding strategies implemented in PyADF, it appears natural and appropriate to choose as a useful reference the uncoupled FDE-TDDFT scheme, based on the linear response  75, 115  and implemented in the ADF program package. 116.
We mention that the results obtained using our Psi4-rt implementation perfectly agree with those obtained using the TDDFT implementation based on linear response implemented in the NWChem code, which uses the same Gaussian type basis set (see Table  S1  in SI). Thus, we conclude that most of the deviations from the ADF-LR values can be ascribed to unavoidable basis set differences. A qualitatively similar pattern of differences is to be expected when including the environment effect within the FDE framework. Table  1 : Excitation energies (in eV) corresponding to the first five low-lying transitions of the isolated water molecule. Data obtained using TDDFT based on linear response implemented in ADF (ADF-LR) and the our real-time TDDFT implemented (Psi4-rt). The labels (D, T, Q) correspond to data obtained using the Gaussian-type basis sets aug-cc-pVXZ (X = D,T,Q) and Slater-type basis sets AUG-X (X = DZP,TZ2P,QZ4P) which are used in the Psi4-rt and ADF-LR codes, respectively (see text for details).
In each case, we use 9000 time steps of propagation which corresponds to a simulation of ≈ 22 fs (time step of 0. 1 a. u. ). The corresponding dipole strength functions (S z (w) = 2w/(3π)Im[α zz (w)]) along the z-direction are reported in Fig. 3. Upon the increase of the cluster dimension, the lowest-lying transition shifts within a range of about 1 eV and no spectra display cusps or irregular behavior. These results give confidence in the numerical stability of the propagation when the number of molecules in the environment is increased. The systematic increase of the size of the environment makes it possible to also consider the actual computational scaling of the Psi4-rt-PyEmbed code for this case. To show this scaling, we carried out a single time step of the real-time propagation and broke down the computational cost into those of the different steps in the work-flow, as reported in Fig.