dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and Conclusion The BrainNetCNN graph-convolutional model is able to predict the brain states with a good reproducibility and accuracy. We noticed that the proposed network tends to shift the brain state transitions, explaining the observed differences between the accuracy metric and the AUCs. The gold standard is an empirical ground truth generated with the result of a k-means clustering. K-means clustering consists of linear decision boundaries, while the proposed network uses a cascade of many layers of nonlinear processing units for features extraction. This new latent space contains features that are likely to be linearly separable. The proposed network is able to learn a complex representation of the data that we think enables more accurate brain states prediction especially during transitions.
This grid can be seen as the reciprocal space of a set of plane waves, and its fineness is most conveniently parametrized by an energy cutoff (the "density" cutoff of plane-wave methods). There are limits to the softness of the functions that can be described with such a grid, so core electrons are not considered (although semi-core electrons usually are), and their effect is incorporated into pseudopotentials. The real-space grid is also used to solve the Poisson equation involved in the computation of the electrostatic potential from the charge density, through the use of a fast-fourier-transform method. This means that SIESTA uses periodic boundary conditions. Non periodic systems, such as molecules, tubes, or slabs, are treated using appropriate supercells. SIESTA is now a mature code with more than 20 years of existence.
B. New pseudopotential format for interoperability PSML (for PSeudopotential Markup Language)  21, 22  is a file format for norm-conserving pseudopotential data which is designed to encapsulate as much as possible the abstract concepts involved in the domain, and to provide appropriate metadata and provenance information. This extra level of formalization aims at removing the interoperability problems associated to bespoke pseudopotential formats, which usually were designed to serve the needs of specific generators and client codes, and thus contain implicit assumptions about the meaning of the data or lack information not considered relevant. PSML files can be produced by the ONCVPSP  23  and ATOM  24  pseudopotential generator programs, and are a download-format option in the Pseudo-Dojo database of curated pseudopotentials  25, 26.
(  7 ), the extrapolation propagation of Eq. (  13 ) and the two-step propagation of Eqs. (  18 ) and (  19 ), we compare their performance versus the P-TDDFT implementation  88  of the CPMD code  89  in the case of a double ionization of a uracil molecule in the gas phase  90, 91. Simulations of this type provide access to the ultrafast electronic dynamics that occurs at the atto and femto time-scales in the ionized genetic material (DNA and RNA) as a consequence of collisions with proton or carbon beams  92. This particular simulation addresses the fragmentation pattern of a doubly-ionized uracil molecule (its deepest Kohn-Sham orbital is empty) using the BLYP density functional  93, 94. The technical details for the CPMD simulation used as a reference can be found in Refs. 90 and 91. SIESTA calculations using the same density functional and the integrators described above use a DZP basis set.
M. Scripting and integration in external frameworks An ongoing trend in many areas of computational science is to move away from rigid and monolithic codes, favoring instead a more flexible approach in which the internal functionality of a program is somehow exposed to the outside world. If done in a proper and well-documented way, this can serve to enhance the interoperability of codes with different functionalities, playing to the relative strengths of each, and/or to implement new functionalities by combining the available basic blocks. In SIESTA we have followed two different but complementary routes to these ends: the development of an internal scripting framework based on the Lua language, which enables new functionality without code recompilation, and the implementation of a formal interface to the AiiDA platform.
Lua interface Lua  121  is an easy-to-learn and fast scripting language built for embedding. It is very lightweight (its memory footprint is less than 300 kB), and provides very simple ways to interface to the data structures and routines of a host program. A Lua script, interpreted by the Lua interpreter embedded in the program, can then control the flow of execution and the data. Different user-level scripts can implement new functionalities, without recompilation of the host code. The strategy we have followed in SIESTA is based on handling control to the Lua interpreter at specific relevant points in the program flow (e. g. at the beginning of a geometry step, at the end of a scf step, etc). Lua scripts implement handlers appropriate to the point they want to hook into, and can request access to specific data structures.
As a specific showcase of the power of the Lua embedding, we have developed a number of variations of the nudgedelastic band method (NEB)  123, 124  for transition-state search. Previously proposed implementations in SIESTA involved significant, hard to maintain code changes, and did not make into the mainstream version. With Lua, we have been able to implement, non-intrusively, not only the standard algorithm, but a Double Nudged Elastic Band (DNEB)  125  variation, and also another version which treats atomic coordinates and lattice variables on an equal footing (the variable-cell NEB, or VC-NEB, method  126  ). The integration of Lua functionality in SIESTA has been made possible by the development of an intermediate layer, FLOOK 127 , (for "fortran-Lua-hook"), which provides wrappers for access to Fortran data structures and subroutines.
AiiDA plugins and workflows The AiiDA framework  [128] [129] [130]  provides support for highthroughput computations in materials science, keeping full provenance of the calculations and facilitating data handling and sharing. The framework is open-source, written in Python, and designed to support arbitrary codes via a plugin interface. A plugin for SIESTA has been implemented and is distributed as the open-source package aiida-siesta  131. The plugin provides the basic operations of preparing the input files for a calculation using AiiDA-specific input objects, and parsing the results and generating AiiDA output objects. The AiiDA data are stored in a graph database that keeps a permanent record of the inputs and outputs of the calculation, and is fully searchable for, e. g. data analytics purposes.
AiiDA also provides robust support for the creation of workflows that incorporate all the necessary steps in the calculation of potentially complex properties, together with the proper heuristics and fail-safe features. The aiida-siesta package provides a base workflow and a few workflows for standard materials properties, such as band structures. Fig. 15  shows the execution graph of a workflow designed to generate a synthetic STM image from a given structure. Work is ongoing to implement more complex ones. In addition to an interface to the computational capabilities of the SIESTA code via the plugin and workflows, the aiida-siesta package also provides an implementation of basic objects representing pseudopotential files, notably one for PSML. Families of pseudopotentials can be uploaded to an AiiDA database and shared via the provided mechanisms for data export and import, facilitating the interoperability of different codes.
N. Utilities for post-processing and supplementary features SIESTA offers several features beyond the core functionality of solving the electronic structure problem and performing optional geometry relaxations and molecular dynamics runs. It is worth noting in particular that the atomic character of the basis set enables the use of a very intuitive suite of analysis tools, which take advantage of the fact that most of the concepts relating to chemical bonding use the language of atomic orbitals. The (partial) density of states, atomic and orbital populations, and other useful output can be obtained directly from the program. The SIESTA distribution includes also several tools in the Util directory for band-structure and wavefunction plotting, bonding analysis, etc. Beyond these, special tool packages that implement a specific feature that extends the functionality of the main program, or that provide extra options for visualization or post-processing in general, are available in alternate distribution points.
sisl SISL is a Python toolbox that was initially conceived to handle and manipulate SIESTA/TRANSIESTA output  103. It has since been extended to support other DFT codes, with the aim of offering equivalent operations for them. By reading the LCAO outputs from SIESTA one can postprocess the Hamiltonian and calculate e. g. Brillouin zone integrated DOS, wave functions expanded on grids, eigenvalues, band velocities and many more. SISL can process nearly all the SIESTA output files. In particular, it is also able to post-process data on the real-space grid. Its command line interface allows data format changes, e. g. conversion of SIESTA XV files to xyz/xsf files or SIESTA binary grid data (VH, VT,. ) to cube/xsf files. As it can process density matrices from SIESTA, one can also use SISL to prepare an input electronic-structure for new calculations, which may be helpful to reduce initial SCF steps.
Other post-processing and visualization utilities The body of utilities contributed by non-core developers and other SIESTA users has continued to expand. In particular, we feature in this section two suites of utilities, one dealing with alternate visualization tools for some SIESTA results, and another one specifically dealing with lattice dynamics. For structures, the xv2xsf and xv2vesta converters process data from the SIESTA. XV file into the native formats of XCrySDen  135  and VESTA  136  , respectively. Each of these two codes offers many options of graphical representation of structures, adding translations, clipping fragments etc. Threedimensional spatial functions (e. g. , charge density, local density of states integrated throughout the chosen energy range), computed by SIESTA on a real-space grid.
Tools are provided for interpolating the data from the SIESTA output grid (fixed by the unit cell dimensions and the MeshCutoff parameter) onto an arbitrarily cut (and possibly rotated or resampled) parallelepipedic box. XCrysDen provides a number of display options, including contour lines over grid planes, or isosurfaces. A special feature available in XCrySDen is plotting the Fermi surfaces. A special script, eig2bxsf, serves to analyze the list of k-points handled by SIESTA, expanding it onto a regular sequence, and writing the respective band energies in the necessary format. The tools concerning the lattice dynamics have been developed having in mind the Γ phonons calculated for a large enough supercell, that is a typical case in a simulation of molecular crystals or disordered substitutional alloys.
O. Software-engineering advances and partnerships The traditional development model for scientific codes in academic settings has been typically based on multiple contributions with various levels of programming competence, and with very little time to plan ahead in the face of pressing scientific demands. SIESTA has been no exception, and has grown in features and complexity over the years. It is very important to keep complexity under control, or else a project becomes un-maintainable and cannot survive. It is not simple, however, to balance the need of incorporation of new features, and the need to increase the computing performance in a landscape of constantly evolving hardware and programming models. One essential route is modularization, which allows the separation of concerns at various levels.
In the context of a code like SIESTA , this means that the scientific ideas and algorithms should be handled at a high level, calling on lower-level modules for specific functionality (domainspecific libraries, mathematical libraries, communication protocols, etc). These lower-level modules can hopefully be reused by different codes and, most importantly, can be focused on by highly-skilled programmers for optimization on relevant architectures. Another important method of taming complexity involves the streamlining of the data structures of the code. This is an ongoing process (see Sect. V), but has already taken a very significant step by the introduction of reference-counted data structures. They build on a well-known and not particularly advanced technique of memory-handling 157 , but in SIESTA they have enabled a much simpler bookkeeping of the data structures needed for a richer control of molecular-mechanics and scf iterations.
A number of modules from SIESTA have been turned into stand-alone libraries which now feature in the ESL: libGridXC for exchange and correlation calculations, libPSML as a handler of PSML files, xmlf90 for general purpose handling of XML files, etc. Conversely, SIESTA uses some of the libraries offered by the ESL, notably the ELSI library of electronic-structure solvers mentioned in Sect. III G 2, whose development, including its API design and internal data organization, has been in turn influenced by contributions and feedback from the SIESTA project, among others. There are also plans to incorporate the PSolver library 160 for the solution of the Poisson problem, a contribution to the ESL from the BigDFT project. We should mention that the renewed dynamism of SIESTA development and the advances made possible by the interaction with community initiatives are both a blessing and a challenge.
Our basic-development plans include also refactoring, apparently unexciting but essential to streamline the code base to enable further implementations. Also, we foresee a change in the release model, moving away from coexisting long-lived release branches whose maintenance takes up a lot of time, and offering instead more frequent and short-maintenance releases. We plan to exploit the idea of modularization, continuing the abstraction of relevant reusable pieces, but also dealing with a higher-level, exposing the core electronic-structure capabilities of SIESTA to other programs. It will be necessary to redesign some of the internal data structures to remove global variables and encapsulate them into objects or derived types associated to particular configurations and stages of the calculations.
This encapsulation will be matched by a streamlining of the input/output operations. This work will open the door to the creation of complex workflows leveraging the strengths of various codes. Accelerated hybrid architectures (including, for example, GPUs) are very likely going to feature prominently in the upcoming exascale machines. In the case of SIESTA, the data indirection associated to the handling of sparse matrices limits the acceleration possibilities of the section of the code that builds the Hamiltonian and overlap matrices, but the solver stage is more amenable to porting, and in fact several solver libraries used by SIESTA are being enhanced to offer GPU support, as mentioned in Sec. III G 2. Modularization and the use of new programming models cause an increase in the complexity of the building and deployment of the code.
We will leverage the ESL bundle, created to facilitate the use of the modules in the ESL collection, to streamline SIESTA's building process, and explore containerization as an option for deployment of the code. The "pseudopotential barrier to entry" has been lowered by the availability of curated databases supporting the PSML format. Basis sets are a perennial challenge, but new tools and ideas are being explored to provide users with appropriate basis sets: High-throughput workflows for optimization; "tiers" of quality/cost, but perhaps not just of a simple "periodic table" form, as offered by other codes (e. g. , FHI-aims  13  ), but with a possible dependence on an approximate characterization of the chemical environment in which a given atom finds itself. Complementary to the underlying basis-set optimization that focuses on providing an adequate variational freedom, an on-the-fly contraction of the basis set, which results in a set of lower-cardinality adapted to the description of the occupied subspace can be exploited for increased efficiency.
ACKNOWLEDGMENTS SIESTA development has been historically supported by different  Spanish National Plan  projects:  MEC-DGES-PB95-0202 , MCyT- BFM2000-1312 ,  MEC-BFM2003-03372 ,  FIS2006-12117 ,  FIS2009-12721 ,  FIS2012-37549 ,  FIS2015-64886-P , and  RTC-2016-5681-7 , the latter one together with  Simune Atomistics Ltd. Currently , we thank financial support from the  Spanish Ministry of Science, Innovation and Universities  through the grant No. PGC2018-096955-B. We acknowledge the  Severo Ochoa Centers of Excellence Program  under Grants No. SEV-2015-0496  (ICMAB), and  SEV-2017-0706 (ICN2 ), the  GenCat  Grant No. 2017SGR1506 , and the  European Union MaX Center of Excellence  ( EU-H2020 Grant No. 824143 ). P. G. -F. acknowledges support from  Ramón y Cajal  Grant No. RyC-2013-12515. J.
Segmentation of the images and morphological analysis The second step is to segment pores and the solid phase in the SEM images. In the present case, a classical thresholding is not well adapted. Indeed, images do not correspond to serial sections and there is an important depth of field: this would result in "shine-through" artifacts  [20]. Hence, segmentation by learning, using the free software Ilastik  [27] , was performed on the 600 2D images of the stack, in a short CPU time after a brief training phase (Fig. 3 ). Ilastik has the capability to perform the segmentation process in a full 3D manner. Yet, in our work, the segmentation process has been performed slice by slice, i. e. using only 2D information. Each expected phase is initially informed using a rough manual labelling (see e.
To do this, a home-made Python script was developed on purpose. After a hole-filling morphological operation, the 3D image was labelled. In order to analyze each entity separately, a watershed operation is performed  [28]. The Sub-Network of the Oversegmented Watershed (SNOW) method published by Gostick  [29] , was employed together with the "SciPy"  [30]  and "SciKit-Image"  [31]  Python modules. The SNOW method has the advantage of eliminating useless maxima of the distance image; moreover, it allows working with labels during watershed segmentation. After border cleaning (deletion of the unexploitable zones such as platinum coating and incomplete spheres), removal of small volumes (connected subsets with a volume less than a given critical value, corresponding to the third of the average volume of the labelled entities), and relabeling the remaining subsets, the digitized image is considered as ready for subsequent statistical analysis.
ACKNOWLEDGEMENTS This work was financially supported by  ANR  ( French National Agency for Research)  in the framework of the  MOMA (Modeling of porous electrodes for an Optimized MAterial design)  project ( ANR-17-CE08-0005 ) through a  Post-doc grant  to A. B. The authors are grateful to  C. Josse  from the  Institute of Micro-characterization Raymond Castaing in Toulouse , for FIB-SEM images acquisition.
The concept of S value implicitly refers to model-based dosimetry, where tables of precomputed S values are calculated for anthropomorphic dosimetric models. Codes like the wellknown OLINDA/EXM implements this model-based approach  [47, 48]. Interestingly, S values can be adapted when the mass of organs/tissues differ from that of the reference model  [49]. A new generation of voxel-based and more realistic human models was proposed by various authors  [50]  and implemented in more recent software like OLINDA/EXM 2. 0  [51]  or IDAC  [52]. The recently created OpenDose collaboration aims at providing open access resources (including S values) for nuclear medicine dosimetry  [53]. At the same time, research work and clinical needs have stimulated the emergence of commercial software enabling to directly compute patient-specific, image-based absorbed dose (rate) maps at the voxel level, from anatomic/functional data derived from hybrid imaging technologies.
WENGAN was benchmarked in its three assembly modes, namely WENGANM (MINIA3  [6] ), WENGANA (ABYSS2  [7] ) and WENGAND (DISCOVARDENOVO  [8] ). We compared WENGAN to six state-of-theart assemblers (Table  1 ). The list is composed of 5 long-read-only assemblers  [4, 5, 16, 18, 19]  and a hybrid assembler  [31]  (MASURCA, Table  1 ). All benchmarked genome assemblies were generated by the developer of the respective assembler (Supplementary Table  3 ). In particular, the SHASTA assemblies were generated using an independent Nanopore dataset  [19] , with a genome coverage of ~60X, and including at least 6X coverage of ultra-long-reads (>100kb). For NA12878 (Table  1 ), WENGAN produced the most contiguous assemblies, with contig NG50s of 17. 24, 25. 99, and 35. 31 Mb for WENGANM, WENGANA and WENGAND, respectively.
Moreover, the WENGAN approach also provides a natural framework to combine long-read with linked-read data, and/or Sanger size short-reads  [39] , and/or optical maps (BIONANO), which may lead to the assembly of 'telomere-2-telomere' scaffolds without the need for extra polishing and finishing methods. Therefore, WENGAN should facilitate the democratization of de novo assembly of human genomes, enabling high-quality genome-assembly for many applications. The WENGAN assembler is available at GITHUB (https://github. com/adigenova/wengan) and CODE OCEAN (https://doi. org/10. 24433/CO. 9469612. v1).
Assembly validation. Genome assemblies generated by WENGAN and other assemblers were assessed by whole genome alignment to the human reference genome using the QUAST  [37]  (Version: 5. 0. 2) tool. QUAST was run with the options "large -min-identity 80 -fragmented" using the GRCh38 (patch 19) reference (autosomes plus X and Y). Additionally, we ran a QUAST analysis using as reference the curated CHM13 assembly (chm13. draft_v0. 7, 2. 9384 Gb) generated by the T2T consortium  [30]  for all the CHM13 assemblies (Supplementary Table  4 ). Several assembly metrics (i. e. NG50, NGA50, largest alignment block, indels per 100kb, genome fraction, and others) were collected from the QUAST report. QUAST assembly errors overlapping centromeric regions or segmental duplications annotated in GRCh38 were excluded from the analysis using the script and annotation files provided by Shafin et al.
Data availability All sequence datasets and de novo genome assemblies described in the manuscript are publicly available through the corresponding repositories. Specific hyperlinks for the four human datasets are provided in the Supplementary Material: Supplementary Table  1  provides hyperlinks for all the long-read datasets; Supplementary Table  2  provides hyperlinks for all the short-read datasets; Supplementary Table  3  provides hyperlinks for all the de novo assemblies used in the benchmark; Supplementary.
Aims. Currently, the global characteristics and evolution of super star clusters (SSCs) are not well understood, due to the large distances to their host galaxies. We aim to study the population of SSCs in IRAS 17138-1017, a luminous infrared galaxy (LIRG), in terms of age, extinction, mass, and luminosity distribution. Methods. We analyzed imaging data in the near-infrared from the GeMS/GSAOI instrument on the Gemini telescope and generated simulations with the radiative transfer code MontAGN. The extraction of SSCs from the images and their photometry in J, H, and K s allowed us to derive color-color and color-magnitude diagrams. Comparison with a theoretical stellar evolutionary track gives a first hint into the extinction towards each SSC, as well as their ages, despite some degeneracy between those two quantities.