dataesr/scientific-paragraphs-categorization huggingface dataset:
We would like to mention also the software COMCOT, which is based on a modified leap-frog scheme discretizing NSWE in Cartesian and spherical coordinates  [91]. Finally, in the Institute of Computational Technologies (SB RAS) an in-house software MGC based on the finite difference MacCormack scheme in spherical coordinates was developed as well  [76]. A big boost in tsunami research happened after Tsunami Boxing day  [83]  due to the availability of real world data. After this Sumatra 2004 event, several studies questioned the importance of dispersive effects for trans-oceanic tsunami propagation  [22, 43, 69]. Their conclusions were supported by comparisons of numerical results with field and DART buoy data. We can only mention that the importance of dispersive effects has been already highlighted in 1982 by Mirchina and Pelinovsky  [68] : [.
Tables  5  and 6  respectively. All the parameters in these simulations were fixed and taken from Table  4  (except the parameter N , which was varied). The second order convergence can be observed in Table  5. In general, the results obtained on a moving grid are more accurate than those on a fixed one as it follows from the comparison of corresponding cells in Tables  5  and 6. In the last column of these tables we report also the CPU times measured on a computer equiped with an Intel Pentium CPU 3. 2 Ghz and 1Gb of RAM. The code was implemented in Fortran and compiled with Compaq Visual Fortran compiler (version 6. 6c). In particular, for the finest grid (N = 6 400) the overhead related to the grid motion represents less than 7% of the overall CPU time and the error (measured in the most stringent norm ⋅ ∞ ) is reduced by a factor of four.
Acknowledgments This research was supported by  RSCF  project No  14-17-00219. D. Dutykh  acknowledges the support of the  CNRS  under the  PEPS InPhyNiTi  project  FARA  and project  N ○ EDC26179 -"Wave interaction with an obstacle" as well as the hospitality of the Institute of Computational Technologies of SB RAS  during his visit in October 2015. D. Mitsotakis was supported by the  Marsden Fund  administered by the  Royal Society of New Zealand. We would like to thank  Prof. Laurent Gosse  ( CNR, Italy ) for insightful.
Each high resolution (1. 13 Å) cRED dataset was collected in ca 3 min, from which the unit cell parameters and space group were determined. The structure was solved and refined from the cRED data with the resolution of 1. 13 Å, and all framework atoms were located with high precision and reasonable agreement values (Supplementary Fig. 6b , Supplementary Table  3 ). It is to the best of our knowledge among the smallest ever used MOF crystals applying TEM electron diffraction for structure solution and refinement. Although the cRED data were clearly sufficient for determining the framework structure, it was not possible to locate water molecules in the pores. Therefore, structure refinement was successfully performed against PXRD data for a dry and a wet sample using the model from cRED as the starting model (Supplementary Figs.
4a , b, Supplementary Fig. 19 , Supplementary Tables  8 9 ). Le Bail fits of the PXRD data demonstrate persistent crystallinity of the compound. The water uptake capacities are very consistent and within the error limit of the measurement. Uptakes of 0. 393, 0. 389, and 0. 387 g H2O /g sorbent were recorded for the pure nanoscale powder and the coating before and after testing, respectively, while taking the amount of binder into account. The volumetric uptake capacity of a MOF/binder (83. 3/16. 7 wt%) composite was determined to be 0. 14 g H2O /cm³composite (composite density 0. 46 g/cm³, Supplementary Table  10 ). It should be kept in mind that although MOFs are intensively discussed for water adsorption applications, such long-term stability studies involving a few thousand cycles have rarely been reported  27, 33.
Belsorpmax at 77 K. The sample was activated at 150 °C for 16 h under reduced pressure (<0. 1 mbar). Single crystal electron diffraction using cRED. The sample of CAU-23 was crushed in a mortar and dispersed in ethanol. A few drops of hydrochloric acid (0. 1 mol/L) were added to break the agglomeration of the CAU-23 nanocrystals. The suspension was then treated by ultrasonication for about 30 s. Three droplets from the suspension were applied on a lacey carbon TEM grid (Okenshoji Co. , Ltd, Tokyo, Japan). cRED data were collected on a 200 kV JEOL JEM-2100 LaB 6 TEM at room temperature. The rotation speed of the goniometer, exposure time, spot size, and camera length were 0. 45°/s, 1 s, 2, 40 cm, respectively. Video frames of electron diffraction patterns were recorded by a high-speed hybrid electron camera (Timepix QTPX-262).
High resolution powder X-ray diffraction measurements. The high resolution powder X-ray diffraction data were recorded at beamline I11 at Diamond Light Source Didcot, UK  51. A 0. 5 mm capillary was filled, evacuated and heated to 100 °C for 2 h and then sealed. The measurements were performed on a setup consisting of 9 Dectris ® Mythen 2 detectors with an overall opening angle of 90°2θ at room temperature. Rietveld refinement. Rietveld refinement was subsequently performed by TOPAS 6 against synchrotron PXRD data 52. All atoms were refined with isotropic displacement parameters. The ligand was modeled using a Z-matrix to constrain some of the geometry. Aluminum-oxygen distances and oxygen-aluminum-oxygen angles were allowed to be refined with bond distance and angle restraints. The positions of water molecules in the pores were established by simulated annealing using antibump restraints, followed by refinement with restraints.
Triple-ζ plus valence polarized Gaussian-type basis sets (TZVP-MOLOPT) were considered for all atoms, except for the Al centers, where short ranged double-ζ plus valence polarization functions (DZVP-MOLOPT) were employed  56. The interaction between core electrons and valence shells of the atoms were described by the pseudopotentials derived by Goedecker, Teter, and Hutter (GTH)  [57] [58] [59]. The auxiliary plane wave basis sets were truncated at 400 Ry. Single point energy calculations were performed to extract the atomic partial charges of CAU-23 applying the Restrained Electrostatic Potential (RESP) fitting strategy for the periodic system as implemented in the CP2K code (Supplementary Fig. 16  for atom types). The DFT geometry optimized model of CAU-23 was further employed in Grand Canonical Monte Carlo (GCMC) calculations to determine the water adsorption properties of the MOF at 25 °C using the Complex Adsorption and Diffusion Simulation Suite (CADSS) code  60.
Acknowledgements We thank  Dr. Helge Reinsch  for his support during the synthesis development. The authors gratefully acknowledge  Dr. Michael Wharmby  for the possibility to collect high resolution PXRD data at Diamond Light Source beam line I11 ( EE16502 ) and  Prof. Chiu C. Tang  for support during the experiment. S. -J. E. gratefully acknowledges the financial support of the  Federal German Ministry of Education and Research (BMBF)  under grant no. 03SF0492A. A. K. I. is grateful for support from the  Swedish Foundation for Strategic Research (SSF)  and the  Knut and Alice Wallenberg Foundation  ( KAW 2016. 0072 ).
A central philosophy of the NetKet framework is to provide tools that are as simple as possible to use for the end user. Given the huge popularity of the Python programming language and of the many accompanying tools gravitating around the Python ecosystem, we have built NetKet as a full-fledged Python library. This simplicity of use however does not come at the expense of performance. With this efficiency requirement in mind, all critical routines and components of NetKet have been written in C++11.
A. Software architecture The core of NetKet is implemented in C++. For ease of use and in order to facilitate the integration with other frameworks, a Python interface is provided, which exposes all high-level functionality from the C++ core via pybind11  [16]  bindings. Use of the Python interface is recommended for users building on the library for research purposes, while the C++ code should be modified for extending the NetKet library itself. NetKet is divided into several submodules. The modules graph, hilbert, and operator contain the classes necessary for specifying the structure of the many-body Hilbert space, the Hamiltonian, and other observables of a quantum system. The core component of NetKet is the machine module, which provides different variational representations of the quantum wavefunction, particularly in the form of NQS.
The dynamics module provides a basic Runge-Kutta ODE solver which is used for the exact imaginary time propagation. The utility modules output, stats, and util contain some additional functionality for output and statistics that is used internally in other parts of NetKet. An overview of the most important modules and their dependencies is given in Figure  1. A more detailed description of the module contents will be given in the next section. NetKet uses the Eigen 3 library  [21]  for linear algebra routines. In the Python interface, Eigen datatypes are transparently converted to and from NumPy  [22]  arrays by pybind11. The NetKet driver classes provide methods to directly write the simulation output to JSON files, which is done with the help of the nlohmann/json library for C++  [23]. Parallelization is implemented based on the Message Passing Interface (MPI), allowing to substantially decrease running time.
Model specification NetKet currently supports lattice models with a finite Hilbert space of the form H = N i=1 H local where N denotes the number of lattice sites. The system is defined on a graph G = (V, E) with a set of sites V and a set of edges (also called bonds) E between sites. The graph structure is used to help with the definition of operators on the lattice and to encode the spatial structure of the model, which is necessary, e. g. , to work with convolutional neural networks (CNNs). NetKet provides the predefined Hypercube and Lattice graphs. Furthermore, CustomGraph supports arbitrary edge-colored graphs, where each edge is associated with an integer label called its color. This color can be used to describe different types of bonds. Several predefined Hamiltonians are provided, such as spin models (transverse field Ising, Heisenberg models) or bosonic models (Bose-Hubbard model).
Variational quantum states The purpose of variational states is to provide a compact and computationally efficient representation of quantum states. Since generally only a subset of the full many-body Hilbert space will be covered by a given vari- ational ansatz, the aim is to use a parametrization that captures the relevant physical states for a given problem. The variational wavefunctions supported by NetKet are provided as part of the machine module, which currently includes NQS but also Jastrow wavefunctions  [24, 25]  and matrix-product states (MPS)  [26] [27] [28]. Broadly, there are two main types of NQS available in NetKet: restricted Boltzmann machines (RBM)  [29]  and feed-forward neural networks (FFNN)  [8, 9, 30, 31]. Both types of networks are fully complex, i. e. , with both complex-valued parameters and output.
Unsupervised learning NetKet also allows to carry out unsupervised learning of unknown probability distributions, which in this context corresponds to quantum state tomography  [38]. Given an unknown quantum state, a neural network can be trained on projective measurement data to discover an approximate reconstruction of the state  [4]. In NetKet, this functionality is contained within the unsupervised. Qsr class. For some given target quantum state |Ψ tar , the training dataset D consists of a sequence of projective measurements σ b in different bases b, with underlying probability distribution P (σ b ) = |Ψ tar (σ b )| 2. The quantum reconstruction of the target state translates into minimizing the statistical divergence between the distribution of the measurement outcomes and the distribution generated by the NQS.
This corresponds, up to a constant dataset entropy contribution, to maximizing the log-likelihood of the network distribution over the measurement data L = σ b ∈D log π(σ b ) , (3) where π denotes the probability distribution  2 transverse field Ising model with 10 sites from ED data, using an RBM with 20 hidden units. The blue line shows the overlap between the RBM wavefunction and the exact wavefunction for each iteration. π(σ) = |Ψ NN (σ)| 2 σ |Ψ NN (σ )| 2. (4 The network parameters are updated according to the gradient of the log-likelihood L. This can be computed analytically, and it requires expectation values over both the training data points and the network distribution π(σ). While the first is trivial to compute, the latter should be approximated by a Monte Carlo average over configurations sampled from a Markov chain.
Variational Monte Carlo Finally, NetKet supports ground state searches for a given many-body quantum Hamiltonian Ĥ. In this context, the task is to optimize the parameters of a variational wavefunction Ψ in order to minimize the energy Ĥ. The variational. Vmc driver class contains the main logic to optimize a variational wavefunction given a Hamiltonian, a sampler, and an optimizer. The energy of a wavefunction Ψ(σ) = σ|Ψ can be estimated as Ĥ = σ,σ Ψ * (σ) σ| Ĥ |σ Ψ(σ ) σ |Ψ(σ)| 2 = σ σ σ| Ĥ |σ Ψ(σ ) Ψ(σ) |Ψ(σ)| 2 σ |Ψ(σ )| 2 ≈ σ σ| Ĥ |σ Ψ(σ ) Ψ(σ) σ ( 5 ) where in the last line • σ denotes a stochastic expectation value taken over a sample of configurations {σ} drawn from the probability distribution corresponding to the variational wavefunction (  4 ). This sampling is performed by classes from the sampler module, which generate Markov chains of configurations using the Metropolis algorithm  [39]  to ensure detailed balance.
The SR approach is closely related to the natural gradient descent method used in machine learning  [43]. In the NetKet implementation, SR is performed using either an exact or an iterative linear solver, the latter being recommended when the number of variational parameters is large. Information on the optimization run (sampler acceptance rates, energy, energy variance, expectation of additional observables, and the current variational parameters) for each iteration can be written to a log file in JSON format. Alternatively, they can be accessed directly inside the simulation loop in Python to allow for more flexible output.
III. ILLUSTRATIVE EXAMPLES NetKet is available as a Python package and can be obtained from the Python package index (PyPI)  [44]. Assuming a properly configured Python environment, NetKet can be installed via the shell command pip install netket which will download, compile, and install the package. A working MPI environment is required to run NetKet. In case multiple MPI installations are present on the system and in order to avoid potential conflicts, we recommend to run the installation command as CC=mpicc CXX=mpicxx pip install netket with the desired MPI environment loaded in order to perform the build with the correct compiler. After a successful installation, the NetKet module can be imported in Python scripts. Alternatively to installing NetKet locally, NetKet also uses the deployment of BinderHub from mybinder.
A. One-dimensional Heisenberg model As a first example, we present a Python script for obtaining a variational RBM representation of the ground state of the spin- 1  2 Heisenberg model on a onedimensional chain with periodic boundary conditions. The code for this example is shown in Listing 1. Figure  2  shows the evolution of the energy expectation value over the course of the optimization run. We see that for a small chain of 20 sites and an RBM with 20 hidden units, the energy converges to a relative error of the order 10 -5 within about 100 iteration steps.
B. Supervised learning As a second example, we use the supervised learning module in NetKet to optimize an RBM to represent the ground state of the transverse field Ising model. The example script is shown in Listing 2. The exact ground state wavefunction is first obtained by exact diagonalization and then used for training the RBM state by minimizing the overlap loss (1). Figure  3  shows the evolution of the overlap over the training iterations.
IV. IMPACT Given the flexibility of NetKet, we envision several potential applications of this library both in data-driven 1 im po rt n e t k e t a s nk  u s e i t e r a t i v e=True , method= ' Sr ' )  34  35 # Run t h e VMC s i m u l a t i o n f o r 1000 i t e r a t i o n s 36 # and s a v e t h e ou tp ut i n t o f i l e s with p r e f i x " t e s t " 37 # The machine p a r a m e t e r s a r e s t o r e d i n " t e s t. wf " 38 # w h i l e t h e measurements a r e s t o r e d i n " t e s t. l o g " 39 g s. run ( o u t p u t p r e f i x= ' t e s t ' , n i t e r =1000) Listing 1. Example script for finding the ground state of the one-dimensional spin- 1  2 Heisenberg model using an RBM ansatz. experimental research and in more theoretical, problemdriven research on interacting quantum many-body systems.
For example, several important theoretical and practical questions concerning the expressibility of NQS, the learnability of experimental quantum states, and the efficiency at finding ground states of k-local Hamiltonians, can be directly addressed using the current functionality of the software. Moreover, having an easy-to-extend set of tools to work with NQS-based applications can propel future research in the field, without researchers having to pay a significant cost of entry in terms of algorithm implementation and testing. Since its early release in April 2017, NetKet has already been used for research purposes by several groups worldwide. We also hope that, building upon a common set of tools, practices like publishing accompanying codes to research papers, largely popular in the ML community, can become standard practice also for ML applications in quantum physics.
V. CONCLUSIONS AND FUTURE DIRECTIONS We have introduced NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. Central to this framework are variational parameterizations of many-body wavefunctions in the form of artificial neural networks. NetKet is a Python framework implemented in C++11, designed with efficiency as well as ease of use in mind. Several examples, tutorials, and notebooks are provided with our software in order to reduce the learning curve for newcomers. The NetKet project is meant to continuously evolve in future releases, welcoming suggestions and contributions from its users. For example, future versions may provide a natural interface with general ML frameworks such as 1 im po rt n e t k e t a s nk 2 from numpy imp ort l o g 3 4 # 1D L a t t i c e 5 g = nk.
ACKNOWLEDGEMENTS We acknowledge support from the  Flatiron Institute of the Simons Foundation. J. E. T. S. gratefully acknowledges support from a fellowship through The  Molecular Sciences Software Institute  under  NSF  Grant  ACI1547580. H. T. is supported by a grant from the  Fondation CFM pour la Recherche. S. E. and I. G. are supported by an  ERC   Advanced Grant QENOCOBA  under the  EU Horizon2020 program  (grant agreement  742102 ) and the  German Research Foundation (DFG)  under Germany's Excellence Strategy through Project No. EXC-2111 -390814868  (MCQST). This project makes use of other open source software, namely pybind11 [16], Eigen [21], ALPS IETL [17,18], nlohmann/json [23], and NumPy [22]. We further acknowledge discussions with, as well as bug reports, comments, and more from  S.
2015). The Viticulture Data Journal*4 (VDJ) was created to respond to the major technological and sociological changes that have influenced the entire process of scholarly communication towards Open Science. VDJ is an innovative, open access, peer-reviewed journal which facilitates the publication of various research outcomes along the research cycle in the area of viticulture: data, models, methods, software, data analytics pipelines and visualisation methods in viticultural research. Viticultural research covers a wide range of topics, from genetic research, food safety of viticultural products to climate change adaptation of grapevine varieties through grapespecific research. The journal will consider manuscripts for publication related (but not limited) to the following topics:.
• Phenotyping and genotyping VDJ is published on the ARPHA-XML*5 journal platform, which supports the full life cycle of a manuscript, from writing through submission, peer review, publication and dissemination within a single online collaborative platform. A key feature of VDJ is that the manuscripts should be written in the ARPHA Writing Tool*6 (AWT), where the authors can use a set of pre-defined, but flexible manuscript templates covering those research outcomes considered for publication in the journal, for example: • Data Paper • Methods • Emerging Techniques • Applied Study • Software Description • R Package • Commentary Within the ARPHA Writing Tool, authors can also invite external contributors, such as mentors, reviewers, linguistic and copy editors, or just colleagues, who may correct and comment on the manuscript submission to the journal.
Once an article is published, it can also be the subject of a post-publication peer review and/or comments via both the inbuilt ARPHA commenting tool and an integrated hypothes. is*7 plugin. One of the most advanced features of ARPHA is the "living article" function, which allows authors to convert published papers back into editing mode at the click of a button; the manuscript can then be revised and re-published in a new version under a different DOI, linked to previous versions via CrossMark*8. During the AGINFRA+*9 project, ARPHA has been extended to be used from the AGINFRA+ Virtual Research Environment (VRE)*10, which would allow the authors to use the VRE as an additional gate to the AWT and the journal, as well as to benefit from the integration of AWT with several other services offered by the AGINFRA+ platform  (Ballis et al.
2018 ). The AGINFRA+ platform has been designed as a Gateway*11 providing online access through a one-stop endpoint to services (for list and description of services see:  Ballis et al. 2018 , Filter et al. 2019 ), aiming at the integration of the traditional narrative of research articles with their underlying data, software code and workflows. We sincerely hope that the viticultural research community will embrace the opportunity to publish their unconventional, though valuable, research outcomes in the Viticulture Data Journal!.
Application Design The resulting Android application data flow is depicted in Fig. 1. Upon opening, our application sends a geo-located request to our server. The server interacts with necessary APIs to obtain the data: the current physical activity progress rate from the fitness tracker, e. g. 90 %; the weather 2 h forecast, e. g. rain,and current temperature, e. g. 26 • C; and the current air quality index (AQI), e. g. 140. From the profile stored in the database, information about one's personal condition is retrieved: asthma, e. g no; disability, e. g. yes. All the information is injected into a semantic graph structure using an ontology model in which the parameters fit: the knowledge base. The reasoning process applies our set of rules on this knowledge base. The rules keep producing new knowledge until a "desirability" of modelled mobility solutions is obtained.
Reasoner Once the description of our universe (knowledge base) and its laws (rules) is ready, we apply a reasoner. A reasoner is an application that takes our triplets in input, along with the rules to be applied and a query that defines a selection of the knowledge base that is of interest for us. The reasoner we use is called "eye" shorthand for "Euler yet another proof Engine". The choice of this tool is discussed in  [6]. Eye is accessed via a Javascript interface inside our NodeJS application. Components of our application can add, remove and update the knowledge base.