dataesr/scientific-paragraphs-categorization huggingface dataset:
II. TECHNICAL OVERVIEW The yambo package is released under the GNU GPL (v2) license and is hosted on GitHub in a set of public and private repositories at https://github. com/ yambo-code. Snapshots of major releases are also available for direct download through the yambo website  [28]. The general structure of the yambo software is laid out in Fig. 2. The software consists of three kinds of executable that generally reflect the order in which the code is run. First, the output from standard DFT codes are converted into NetCDF 'database' files (ns. db1 and ns. wf) within a SAVE directory using the a2y and p2y routines (see Section II C below). Second, the main calculations ('runlevels') of linear response, GW, and BSE are performed using the standard executable yambo or the project-specific executables.
These include yambo rt for real-time propagation (Section VII A), yambo nl for nonlinear optics (Section VII B), and yambo ph for electronphonon simulations (Section VI). Running these codes results in the reading and writing of further databases (SAVE/ndb. *), as well as generation of text files for reading or plotting. Third, post-processing routines (ypp and runlevel specific ypp nl or ypp rt executables) are used to manipulate and analyze the computed quantities stored in the databases. In special cases ypp, ypp nl or ypp rt executables are needed as pre-processing tools to further manipulate the core databases (i. e. to remove some symmetries before real time simulations) or to create new databases (i. e. an ndb containing a mapping between core databases on two different k-grids), before actually running the main calculation.
A. Installation & projects yambo is compiled using the standard autotools procedure:. /configure; make all will generate the main executables listed in Fig. 2. Since the first release the configure script has been wholly upgraded to reflect the widespread availability of high performance software libraries and to aid portability across a wider range of system architectures. By running. /configure; make, the list of possible executables is returned [all projects] all [project-related suite] project (core, rt-project,. ) [core] yambo [core] ypp [core] a2y [core] p2y [ph-project] yambo_ph [ph-project] ypp_ph [rt-project] yambo_rt [rt-project] ypp_rt [nl-project] yambo_nl [nl-project] ypp_nl [kerr-project] yambo_kerr While yambo and ypp are the main code components, a series of projects appear in the form of yambo PJ/ypp PJ with P J being the specific project identifier (ph,rt,nl,kerr).
Spin is now fully supported both in collinear and noncollinear frameworks. For example, the use of magnetic symmetries allows to take advantage of composite symmetries, i. e. which contain time-reversal, in systems which are not invariant under pure time-reversal. Work is in progress to extend the support of ultra-soft pseudopotentials (USPP). Other important changes were carried out to optimize the interface, first of all with an improved parallelization (implemented over the writing of wavefunction fragments). Moreover the Kleinman-Bylander (KB) form factors are now converted in a yambo-like database, while the calculation of the commutator [r, V nl ], which was previously done at the p2y level, is now more efficiently done by yambo while computing the dipoles.
ypp is the yambo postprocessing and preprocessing tool. It has several capabilities which can be used to prepare yambo simulations (preprocessing) or subsequently analyse (postprocessing) the outcome. As one of the preprocessing options, ypp can generate random grids of k-points to be used as input for a DFT code to compute the corresponding KS energies. The same ypp can then generate an auxiliary database with a map linking the KS energies on the random grid to the uniform grid used to compute, for example, spectral properties. The approach is useful to speed up convergence as discussed in Sec. V A 1. Another preprocessing option is the removal of a specific set of symmetries and thus the expansion of the wave-functions from the IBZ associated to the full set of symmetries to the resulting IBZ.
This is needed to perform real-time simulations as described in Sec. VII. Finally preprocessing can also be used to map DFT calculations with and without spin-orbit coupling (SOC) to compute absorption spectra with SOC corrections included in a perturbative way as described in the supplemental material of Ref. [43]. Most of the postprocessing features involve data analysis. ypp can prepare readable ascii files to plot several single-particle properties such as wave-functions, charge density, density of states (DOS), magnetization, current and band structures. In particular, it can be used to obtain the QP-DOS and to interpolate QP-energies to plot the resulting band-structure along high-symmetry paths. A mixed feature (i. e. which can be used both for prepro-cessing and for postprocessing) is the ability of ypp to manipulate QP-databases (ndb.
Wannier interpolation of GW band structures requires two sets of inputs: on one side quantities computed at the DFT level such as KS eigenvalues, overlaps between different KS states, and orbital and spin projections of KS states, that are imported from Quantum ESPRESSO, and on the other side the QP corrections computed by yambo. In fact, wannier90 works with uniform coarse meshes on the whole BZ, while yambo uses symmetries to compute quantities on the IBZ. In addition, converging the GW self-energy typically requires denser meshes with respect to what is needed for the charge density or Wannier interpolation. To address this issue, ypp allows one to unfold the QP corrections from the IBZ to the whole BZ, as required by wannier90 for interpolation purposes  [71]. Finally the wannier90 code yields a GW-corrected Wannier Hamiltonian and interpolates the GW band structure.
A. Yambopy Yambopy is a community project to develop Python classes and scripts to express, automate, and analyze calculations with the yambo code. A typical yambo workflow involves a few steps: generating the KS states with a DFT code, preparing the yambo databases and then running the yambo code. These workflows become more complicated when performing convergence tests or when repeating the same calculations for multiple materials. With yambopy the user can express these yambo workflows on a python script that can then be shared and reproduced among different users. We currently provide python classes to read and write the input files as well as the output of the yambo code. A lightweight python interface for the Quantum ESPRESSO Suite is also provided in the qepy package distributed along with yambopy.
For more comprehensive python interfaces for the DFT codes see ASE  [138] , Abipy [139] for the Abinit code, or AiiDA  [137]. The YamboIn class provided by yambopy is used to read and write the base yambo input file generated by yambo and modify it in a programmatic way. With this tool it is possible, for example, to create a set of input files by changing a single variable inside a for loop. We also provide classes to read the yambo NetCDF databases in Python (for a complete list see the on-line documentation [140]). These classes provide methods to manipulate and represent the data using the matplotlib  [141]  library giving great flexibility for the interpretation and analysis of the results. Running these workflows on a HPC context requires to write job submission scripts for different job schedulers, this can be done using the schedulerpy package also accompanying yambopy.
For quick access to some features from the command line, we provide the yambopy shell command. The script is automatically installed with yambopy in such a way that some functionalities of yambopy are directly callable from the command line. This script has features to plot the convergence tests for GW and BSE calculations, excitonic wave functions, and dielectric functions among others. To ensure software quality and usability we provide yambopy as an open-source code along with documentation and automatic testing. A detailed documentation of the classes, features, and a tutorial are available on the yambopy website  [140]. We keep a public git repository hosted on Github where the users can download the latest version of the code as well as contribute with patches, features, and workflows.
B. yambo within the AiiDA platform The AiiDA platform is a materials' informatics infrastructure which implements the so-called ADES model (Automation, Data, Environment and Sharing) for computational science  [137]. The AiiDA plugins and workflows for yambo are publicly available on GitHub [143], while online documentation and tutorials are available on Read the Docs [144]. 1. The yambo-AiiDA plugin Input parameters and scheduler settings are stored as code-agnostic AiiDA data-types in a database, then converted by the yambo-AiiDA plugin into yambo input files and transferred to a computational unit (e. g a remote workstation or an HPC cluster). The AiiDA daemon submits, monitors and eventually retrieves the output files of the yambo calculation, the relevant information is then parsed and stored by the plugin.
While the relevant data is properly stored in a suitable database, the raw input and output files are also stored locally in a repository. Therefore inputs, calculations and outputs are all stored as nodes of a database connected by directional links, preserving the full data provenance and ensuring reproducibility. The yambo-AiiDA plugin currently supports calculations of quasi-particle corrections (e. g. at the COH-SEX or GW level) and optical properties (e. g. IP-RPA). Quantum ESPRESSO, one of the main DFT codes interfaced with yambo, is also strongly supported with specific plugins and workflows for AiiDA  [145]. Some of the parsing functionalities of the plugin are powered by the yambopy package  [140]. Different types of calculations can be performed, either starting from Quantum ESPRESSO or from p2y or from a previous (possibly unfinished) yambo run.
AiiDA workflows: automated GW The yambo-AiiDA package provides automated workflows that capture the knowledge of an experienced user in performing e. g. GW calculations within the plasmon-pole approximation, accepting minimal inputs such as a DFT calculation or a crystal structure, and returning as outputs a set of quasi-particle corrections. The yambo-AiiDA plugin repository hosts four AiiDA workflows  [137]  of increasing complexity and abstraction: YamboRestart, YamboWf, YamboConvergence and YamboFullConvergence, that perform different but mutually interdependent tasks, with the latter depending on the former in the listed order. YamboRestart is a low level AiiDA workflow that takes a DFT calculation (or a prior yambo calculation) as input, performs GW or BSE calculations, and returns the results.
C. Test-suite and benchmark-suite A new important tool introduced to improve and stabilize the development of the yambo code is the test-suite. The yambo test-suite is stored in a dedicated repository (yambo-tests) on GitHub and contains a series of tests which can be run in an automated manner. The repository is freely accessible after registering as a "yambo user" on the GitHub. While the test-suite is mainly aimed at developers, users can also benefit from accessing its input and reference files and automatically checking if their compiled version of yambo works properly. The test-suite is governed by using a Perl script, driver. pl. This script uses internal Perl modules to perform several tasks: it automatically compiles the yambo code (a precompiled version can also be used), it runs the code and checks the output against reference files stored in the test-suite repository.
The code can be run in serial, parallel with OpenMP threads and checking parallel I/O and/or parallel linear algebra. At least two different groups of tests are available: smaller (and faster) tests which are run on a daily basis and longer tests which are used for a deeper testing of the code before a release. The same driver can also be used to run yambo benchmarks. Benchmarks tests are a particular group of materials that, describing complex nano-structures (a 1D polymer or carbon-based ribbon) or a water cell, require a large number of reciprocal space vectors and/or k-points. As a consequence these systems are suitable to be executed using a large number of cores on parallel machines. In this case the test-suite can collect the results and loop on different parallel configurations testing their performances.
X. CONCLUSIONS AND PERSPECTIVES This paper describes the main development lines of the yambo project since the 2009 reference paper  [4]. Yambo is a scientific code supported and continuously developed by a collaborative team of researchers. The long list of authors of this work attests to the involvement of numerous experienced and young developers in addition to the four founders  [4]. The yambo team currently comprises a balance of renowned scientists, with long-standing experience in abinitio approaches, and young researchers. We welcome students and post-docs with new ideas. This combination makes possible the growth of a software suite which is formally rigorous and able to address topics at the frontiers of materials science. By exploiting the power of many body perturbation theory at equilibrium and out-of-equilibrium within a state-of-the-art ab initio framework, the code is able to make predictions of the electronic and optical properties of novel materials, and moreover to provide interpretation of cutting-edge experiments ranging from ultrafast electron dynamics to nonlinear optics.
The involvement of parallel computing experts (two members of the Italian National Supercomputing Center CINECA co-authored this paper, for example) ensures that the code is also efficient and portable to the latest supercomputing architectures. As a result, new features added to the code immediately benefit from the native parallelized environment. The modular structure of the code and the interface to external supporting software (AiiDA and Yambopy) complete the picture providing the end-user with a wealth of tools that cover the actual preparation, calculation and post-processing of data. The yambo suite thus provides all the ingredients for an advanced and computationally powerful approach to theoretical and computational material science. Indeed, despite being born as a code for MBPT, thus tailored for sophisticated calculations on simple materials, yambo can nowadays be used to study complex materials and interfaces as well.
This means in practice that, while the first versions of the code were designed to run on unit cells containing very few atoms (like bulk silicon), nowadays yambo can be easily used to study unit cells with 10 to 20 atoms and can be pushed on HPC centers up to hundreds of atoms  [147] [148] [149]. The number of atoms which can be dealt is, thus, approximately one order of magnitude less than advanced DFT codes. The exact limit is mainly imposed by the power of HPC facilities. We are also working on a dedicated section on the yambo web-site with detailed information on time scaling of different runlevels across the releases of the code. What lies in yambo's future? We expect that the future development of yambo will be driven by the need to interpret new experiments. This will be achieved through the implementation of advanced computational algorithms and physical methodologies and will increasingly exploit interoperability with other software.
XI. ACKNOWLEDGMENTS The yambo team acknowledges financial support from a number of sources, notably including H2020 funding, as follows. This work was in part supported by the project MaX -MAterials at the eXascale -by the European Union H2020-EINFRA-2015-1 and H2020-INFRAEDI-2018-1 programs (Grant No. 676598 and No. 824143, respectively). This work was also supported by the European Union H2020-INFRAIA-2014-2015 initiative (Grant No. 654360, project NFFA -Nanoscience Foundries and Fine Analysis). We also would like to acknowledge support from cost action CA17126. We thank CECAM and Psi-K network for financial and practical support related to the organization of yambo training and tutorial events. yambo developers have benefit from direct interactions with personnel from INTEL (Hans Pabst) and NVIDIA (Massimiliano Fatica, Everett Phillips, Josh Romero), especially concerning parallel performance and porting.
Telomerase is the specific enzyme that allows such elongation, both by providing the template and catalytic unit. It is composed of the hTERT (human telomerase reverse transcriptase) catalytic subunit and the hTR (human telomerase RNA), a RNA component serving as a primer and template Figure  1. Telomere length decreases with age. Initial telomere variability comes from the parental telomere length and is modulated both by the father's age at birth and paternal birth year, a correction factor allowing comparison between different datasets  [38]. During development, telomere length decreases abruptly and reaches a steady slow rate of erosion after birth. Telomere attrition is confronted to a critical size corresponding to the senescence stage of the cell. Beyond senescence, cells undergo crisis with continuous telomere length decrease leading to activation of ATM/ATR checkpoint and DNA Damage Response (DDR) pathway activation.
Furthermore, this study advocates for a limited impact of environmental factors over telomere erosion (if one consider that twins likely lived in different environments over time). Respective chromosomal telomeres within cells are highly heterogeneous. Nevertheless, length distribution between chromosomes arms is conserved between cell types in a given individual  [33, 55, 56]. More recently, parental contribution to inherited telomere length has been proposed. A meta-analysis on nearly 20,000 participants revealed strong correlation between paternal age at conception (PAC) and offspring telomere length. Sperm telomere length increases with age in humans. As a result older fathers' Cells 2019, 8, 30 5 of 19 offspring inherit longer telomeres  [57]. In a more recent study, using 3 of the largest available datasets, a group refined the calculations, and incorporated the paternal birth year as a corrective parameter allowing comparison of different datasets since paternal birth year (PBY) was collinear with age and father's age at birth of the offspring in the three datasets analyzed (e.
The elaboration of the model is important, but the application of models is indispensable also. In the literature, the focus is mainly on the implementation of the behaviors models in the building programs simulations. As we mention, one of the main issues is the lack of the accuracy in occupant behavior in the Building Program Simulation, which provide an incomplete prediction for the building energy performance. In this context, the implementation of models that predict the behaviors with more precisely is one an effective solutions to overcome this inconsideration of the occupant behaviors. Several tools (obXML, obFMU, occupancy simulator) were developed to realize the implementation of the quantitative models on the BPS programs in order to increase the analysis and evaluation of the impact of occupant behavior on building energy performance( [7][5][21][19][2][17]).
Discussion and conclusion This study propose an overall perspective of the topic of the occupant behavior with the identification of the important aspects in this field. The occupant behavior as explained is not a supplement axis to study for the evaluation of the energy building performance, but a main and critical factor that should not be neglected. For our study, three key main approaches were identified for the treatment of the subject of occupant behavior. At first, the monitoring approach is essential for the modeling and the identification of different aspect of human behavior. The data collection is developing more and more by the development of sensors and communication technologies, and provide a huge and delicate data to analyze. However, the main question after the collection part is how we should analyze the database in order to extract the information needed from the research.
In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles. artificial intelligence | machine learning | diauxic shift S ystems biology presents an extreme challenge to the tradi- tional human-based scientific method  (1, 2).
All the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for execution. The resulting improved model is relevant to understanding cancer, the immune system, and aging. poorly understood, and existing systems biology models of this transformation could be greatly improved. We combined system biology software for data analysis, model formation, experiment generation, experiment execution, model refinement, systems biology modeling, bioinformatics, laboratory robotic control, and semantic web techniques to execute three cycles of diauxic shift model improvement (Fig. 1A ). The wide range of software and tools required to achieve this are shown in Fig. 2. (CoRegNet and CoRegFlux are available in bioconductor. All of the other software is available on request at LIPN GitLab).
3A ). Mz is of high quality in terms of dependencies recall (small number of false positive links), but it is relatively incomplete (missing links). Mz is also optimistic in that it predicts the occurrence of diauxic shifts for almost all strains with gene/protein deletions in the model. In the first cycle of model improvement, the initial step was application of the bioinformatic program CoRegNet (9, 10) to identify genes potentially involved in control of the diauxic shift. CoRegNet integrates information from microarray experiments, regulatory interactions from the YEASTRACT database, and the S. cerevisiae Kinase and Phosphatase Interactome resource. CoRegNet uses a cooperative network based on shared transcription factor targets to identify coregulatory relationships from gene expression data (SI Appendix 4).
We then applied a two-step model refinement process to its output (Fig. 3B ): (i) We applied the ensemble network inference algorithm ELSA (Ensemble Learning of Spanning Arborescences)  (11)  to the Brauer microarray dataset (4), with Mz as a learning prior on the model space composed of the union of the Mz regulatory genes, the top 40 transcription factors identified by CoRegNet, and the top 40 kinases identified by CoRegNet (SI Appendix 5); (ii) We then applied a forward selection step to add to Mz edges that improve gene state predictions on the Brauer microarray dataset, using leave 1 out cross-validation. This generated model M1 (Fig. 1B ). At the start of the second cycle, we used tools to design experiments to provide the maximum amount of information to optimize the improvement of Mz to form M1, see Fig.