dataesr/scientific-paragraphs-categorization huggingface dataset:
43 Å) rather than the theoretical one (a th =5. 40 Å). Our calculations are performed by using the computational code ABINIT [91] over hundreds to thousands of processors  [71]. A norm-conserving pseudopotential is generated following the Troullier-Martins scheme  [92]  in the Kleinman-Bylander form  [93] , considering 4 electrons in the valence and a cutoff radius of 2. 1 bohr leading to a cutoff energy equal to 380 eV. Exchange and correlation energy was treated with the local density approximation LDA using the Teter parametrization  [94]. A careful treatment of the electronic density integration has been achieved with the use of a (2×2×2) Monkhorst-Pack mesh  [95]. Two important parameters drive the number of IFC coefficients really computed during the a-TDEP calculation : R cut2 and R cut3 , the cutoff radii at the 2 nd or 3 rd order, respectively.
Thermal stabilization of the Zr bcc phase In their original study, Hellman and coworkers  [1]  applied the TDEP method to the bcc phase of Zr. At 0 GPa, this structure is not stable at room temperature and all the calculations  [98, 99]  performed at 0 K show few soft modes in the phonon spectrum around the ω point (corresponding to q = (2/3, 2/3, 2/3) along the H-P high-symmetry line) and the Γ point. In the present work, AIMD simulations are performed with 128 atoms in the supercell, using the NVT ensemble, for two temperatures (300 and 1000 K) and two pressures (around 0 and 30 GPa) during several ps. Our calculations are performed by using the ABINIT code  [91]  over hundreds to thousands of processors  [71]. A projector augmented wave  [100, 101]  (PAW) atomic data is generated using the ATOMPAW code  [102]  in the XML format  [103] , considering 12 electrons in the valence and a cutoff radius of Γ H ω P Γ N -2 0 2 4 6 Frequency (THz) 300 K -0 GPa -30 GPa 1000 K -0 GPa -30 GPa Exp Figure  8 : (Color online) Phonon spectra of bcc zirconium computed using a-TDEP at two temperatures (300 and 1000 K) and around two pressures (0 and 30 GPa).
Failure of the QHA in α-U Uranium is well-know to be the only element in the periodic table in which a charge density wave (CDW) state is observed at room pressure  [105]. The phase transition at 50 K is accompanied by the softening of the longitudinal optic-like phonon mode in the  [100]  direction  [106, 107, 108]  and involves a doubling of the unit cell. In the present work, AIMD simulations are performed with 96 atoms in a 4 × 2 × 3 α-U supercell, using the NVT ensemble, for three temperatures  (   300 and 900 K) during several ps. Our calculations are performed by using the ABINIT code  [91]  over hundreds to thousands of processors  [71]. A PAW  [100, 101]  atomic data is generated using the ATOMPAW code  [102]  in the XML format  [103] , considering 14 electrons in the valence and a cutoff radius of 2.
Conclusion We have developed a post process code able to handle the anharmonic effects. This one is based on the TDEP method and is distributed in the ABINIT code under the GNU-GPL license. Many features can be computed using a-TDEP : phonon spectrum, elastic constants and moduli, Grüneisen parameters, thermal expansion, free energies. These capabilities have been checked on many materials (Si, U  [50, 52] , Zr, Pu  [53] , MgO  [55] , Ti  [51] , Fe  [54] ), in various thermodynamic situations (low/high temperature/pressure), and excellent agreements with experimental data are obtained everytime. This paper paves the way to have a complete reference support for the user of a-TDEP. It includes the theory needed to understand the TDEP method and to compute thermodynamic quantities, the algorithms used in this a-TDEP implementation, numerous references to works applying this method but also to other similar approaches and finally, several examples showing the capabilities of this code.
The choice to implement the a-TDEP code in ABINIT is definitively valuable since this post-process actually benefits of all the developments from the ABINIT ecosystem : the symmetries coming from the lattice or magnetic space group, the treatment of the dipole-dipole interaction  [10] , the input-output file formats (NetCDF, Yaml. ), the definition of Monkhorst-Pack grids of q-points used to computed the vDOS or other thermodynamic quantities, the stochastic generation of thermalized configurations  [90]. In the future, other interplay are expected, in particular the use of a-TDEP during the execution of ABINIT or MultiBinit  [109]. The present version of the a-TDEP code does not include several features. In particular, the 4 th order is not implemented  [57, 58, 59] , so the phonon lifetime, the frequency shift, the thermal conductivity, the spectral function and the anharmonic corrections to thermodynamic (free energy, heat capacity.
The WDCGG sites that show large disagreements are mostly located in coastal terrain areas, where our coarse-resolution model simplifies the coastline and thus cannot resolve the associated meteorology well (e. g. , land-sea breeze circulation)  (Palau et al. , 2005; Ahmadov et al. , 2007)  and possible local emission sources. Several sites at high northern latitudes also suggest relatively large modeling bias due to the lack of high-quality satellite data as an observational constraint. The modeled XCO with posterior fluxes agrees better than the prior results with the TCCON observations, especially for the rapidly declining trends between 2000 and 2017. The evaluation with measurement from WDCGG suggests that Inversion no. 3 gives a fair estimate of surface CO trends during 2010-2017 (NMB = -8 %, RMSE = 1.
Drivers of declining CO emissions We compare the inversion-based anthropogenic CO emissions to regional bottom-up inventories that are not used in our inversions (Fig. 8 ). Here we use the bottom-up inventory data from EPA for the USA (USEPA, 2018, Fig. 8a ), from MEIC v1. 3 for CHN  (Zheng et al. , 2018c, Fig. 8b ), from TNO-MACC III for EU  (Kuenen et al. , 2014, Fig. 8c ), from REAS v2. 1 for SAS  (Kurokawa et al. , 2013, Fig. 8d ), and from EDGAR v4. 3. 2 for EQAF  (Crippa et al. , 2018, Fig. 8e ), while our prior inventory is the CEDS global inventory (see Sect. 2. 2). These bottom-up emissions data are derived from detailed regional activity maps and sector-specific emission factors, except for EQAF, where we use the EDGAR v4. 3. 2 global inventory (up to 2012) due to the lack of regional inventories there.
Not only do we want to prevent congestion from occurring; we want to guarantee available bandwidth to allocated flows during their whole lifetime. This is achieved using bandwidth reservations -bandwidth that has been reserved cannot be used by another flow, even if it is not being used at the moment. There are several RINA implementations available: IRATI  [4]  and rlite  [5] , two full-stack implementations targeting Linux-based operating systems; Ouroboros  [6] , a RINA-inspired implementation; ProtoRINA [7], a Java-based academic prototype; and RINASim  [8] , a simulation framework for OMNeT++. After designing our distributed Flow Allocator policy, we have decided to implement and to evaluate it experimentally in the frame of the rlite stack. This paper contributes the design of a proactive flow allocation scheme based on bandwidth (or better would be "data-rate" in order to avoid misconception with terminology from physical layer) reservation.
Recursive InterNetwork Architecture We have designed and implemented our work in the context of the Recursive InterNetwork Architecture. As RINA uses concepts that often differ vastly from what is being used in TCP/IP, this section provides a brief introduction to the architecture. The fundamental idea RINA derives the rest of architecture from is that networking is IPC and only IPC  [28]. An Application Process (AP) is an instantiation of a program containing one or more tasks (Application Entities) and functions for managing allocated resources (processor, storage, IPC). A Processing System (PS) consists of hardware and software executing an Application Process capable of coordinating with a test-and-set instruction or its equivalent. A Computing System (CS) is a collection of Processing Systems under the same management domain.
The Design of Rlite Before describing how our policies have been implemented in rlite, we will first introduce rlite itself. Rlite is a free and open source RINA implementation for GNU/Linux systems. It consists of a set of kernel loadable modules (KLMs), and a set of user-space daemons (User-space IPCPs-UIPCPs) and tools, as shown in Figure  4. The tasks of kernel components is to provide: (i) a generic interface for user-space applications to transmit and receive PDUs; (ii) a set of management functions that allow the user-space components to manage the IPCP lifecycle, such as creation, configuration, inquiry and destruction; and (iii) functions to perform flow allocation and deallocation. Different types of IPCP types are supported, such as the normal IPCP, the shim IPCP over Ethernet, and the shim IPCP over UDP; each IPCP type is implemented as a separate kernel module.
Shim IPCPs act as a compatibility layer, allowing deployments of RINA networks over existing Ethernet/IP infrastructure. The Data Transfer and Data Transfer Control functions of an IPCP (i. e. , the DTP and DTCP protocols that manage data flow between DIF members) are implemented as a part of the normal IPCP module. Communication with the kernel modules is achieved through two special character devices exposed by the kernel: one serves as a packet I/O interface, the other provides management and control functionality. The main part of the user-space toolkit, the UIPCP daemon, implements most of the management functionality of the normal IPCP. Functions such as routing, flow allocation, application registration and enrollment are the responsibility of distinct components within the daemon. The actual behavior of these components is configurable via run-time replaceable policies.
Querying the Interface Bandwidth To properly manage a resource -in our case the network bandwidth -it is necessary to have up-to-date information about the resource and its state. We have selected the physical link speed as our ground truth; the assumption is that there are no rate-limiting mechanisms in place and every link can operate at their reported speed. We use the fact that shim over Ethernet DIFs are created over an Ethernet interface, and are usually the lowest DIFs in a system. During DIF creation, we request the interface speed for the associated interface from the kernel. As rlite targets Linux-based systems, this is achieved using the ioctl() system call with the SIOCETHTOOL command. The reported interface speed is stored as a property of the newly created IPCP, to be used by other components.
Propagating the Interface Bandwidth to IPCPs within a DIF A shim IPCP over Ethernet therefore knows its own link speed; this information is propagated to a N+1 IPCP during the enrollment process. To share bandwidth information with IPCPs within the same DIF, we use the messages exchanged as part of the routing process. Rlite implements a link-state routing mechanism, using a Lower-Flow DataBase (LFDB). This shared database contains all the necessary information to provide a holistic view of the DIF topology to each IPCP, as required by link-state routing to facilitate proper operation. As such, it is necessary that its state is kept up to date. We can easily distribute bandwidth information about links to other IPCPs by extending the LFDB. Routing update messages will ensure the information is propagated through neighbors to all the IPCPs participating in the DIF.
Materials and Methods The source code for our implementation is available at https://github. com/autrimpo/rlite, tagged with the computers20 tag. Instructions how to build rlite are in the included README. md file. Unit tests for the graph algorithm are defined in the file user/uipcps/lfdb-bw-test. cpp and can be ran using build/user/uipcps/lfdb-bw-test. Each test prints out information about the graph, the requested flow, as well as the found path, if any. The bandwidth use measurements were done on a virtual QEMU network created using the included demonstrator tool. The demonstrator tool, located in demo/demo. py, takes a configuration file describing the desired network. Configuration files used in this paper are available at http: //www. stud. fit. vutbr. cz/~koutenmi/Computers20/multipath.
conf for Figure  9 , http://www. stud. fit. vutbr. cz/~koutenmi/Computers20/stress-10. conf for Figure  10 , and http://www. stud. fit. vutbr. cz/ ~koutenmi/Computers20/stress-20. conf for Figure  12. The second and third configurations already come with the bw-res policy set; in the case of the first one, manual adjustment of the running network is required. Before creating the network, it is necessary to build a custom buildroot image using https: //github. com/rlite/buildroot. The process is documented in the update. sh script: it requires proper configuration of the package/rlite/rlite. mk file to use the local rlite source code, setting the matching output path in the update script and running the script itself. The demonstrator has been run with the following parameters:. /demo. py -b udp -e full-mesh -c <CONF_FILE>.
This generates several shell scripts that can be used to start (up. sh), stop (down. sh), and access (access. sh) the virtual machines. Server rinaperf instances have been started with rinaperf -l -z <UNIQUE_NAME> and the clients used rinaperf -z <SERVER_NAME> -B 500000 -D 1000000. Each client running requires its own server instance. Data has been gathered by dumping the RIB on a node participating in the Raft cluster with rlite-ctl dif-rib-show. Author Contributions: Conceptualization, M. K. and V. M. ; methodology, M. K. ; software, M. K. and V. M. ; validation, M. K. ; formal analysis, M. K. ; investigation, M. K. ; resources, M. K. , V. V. and V. M. ; data curation, M. K. ; writing-original draft preparation, M. K. , V. V. and V. M. ; writing-review and editing, M. K. , V. V. and V. M.
below. Lower-Flow Database: Local: n1. b. IPCP, Remote: n1. c. IPCP, Total: 100 Mbps, Free: 20 Mbps Local: n1. b. IPCP, Remote: n1. a. IPCP, Total: 100 Mbps, Free: 20 Mbps Local: n1. a. IPCP, Remote: n1. b. IPCP, Total: 100 Mbps, Free: 20 Mbps Local: n1. c. IPCP, Remote: n1. b. IPCP, Total: 100 Mbps, Free: 20 Mbps Local: n1. c. IPCP, Remote: n1. d. IPCP, Total: 100 Mbps, Free: 20 Mbps Local: n1. d. IPCP, Remote: n1. c. IPCP, Total: 100 Mbps, Free: 20 Mbps Supported flows (Src/Dst <Appl,IPCP,port>): [R], Src=<rinaperf-data|client,n1. a. IPCP,2> Dst=<rinaperf-data|server,n1. d. IPCP,2> Connections: [<SrcCep=1, DstCep=1, QosId=0> ] [R], Src=<rinaperf-data|client,n1. a. IPCP,1> Dst=<rinaperf-data|server,n1. d. IPCP,1> Connections: [<SrcCep=0, DstCep=0, QosId=0> ] Flow reservation table: n1. a.
Methods Phylogenetic and spatial data. We focused on the woody plants of sub-Saharan Africa, and sample all the major lineages of southern African trees, following recent taxonomic rearrangements (notably, the quintessentially African genus Acacia is now recognised as polyphyletic and African Acacia are included in our dataset as two separate genera, Senegalia and Vachellia, following ref. 42 ). Phylogenetic relationships and divergence times were extracted from the dated phylogenetic tree of Charles-Dominique and colleagues  39  , sampling over 1800 of the ca. 2000 woody taxa of sub-Saharan Africa. This phylogenetic hypothesis was generated using DNA sequences for the core plant barcodes matK and rbcL using maximum likelihood (ML) and Bayesian inference. The ML analysis was run in raxmlGUI  43  , enforcing topological constraint based on an APG III backbone in Phylomatic  44.
Branch lengths were made proportional to time using BEAST  45  assuming an uncorrelated lognormal distribution for the molecular clock model and 20 secondary calibration points obtained from ref. 46  as minimum age constraints. Full details are provided in refs. 38 and  39. Species range maps were obtained from ref. 38  , and represent outputs from MaxEnt 47 species distribution models fitted to point data from the African plants database (https ://www. ville -ge. ch/cjb/) supplemented by records from the Naturalis Biodiversity Center (https ://www. natur alis. nl/) and the Global Biodiversity Information Facility (GBIF; https ://www. gbif. org). The MaxEnt presence-absence maps were estimated from the 19 WorldClim 48 bioclimatic variables using the equal training sensitivity and specificity threshold  49  and imposing spatial filters to account for geographically restricted ranges, following ref.
(2012)  to locate weak-amplitude subsurface sources, and  Chmiel et al. (2015)  used it for microseismic data denoising. Also,  Seydoux et al. (2017)  and  Moreau et al. (2017)  showed that the SVD-based approach improves the convergence of NCC towards the GF estimate. Here, we combine MFP and SVD in order to remove spurious arrivals in NCC caused by the moulin located within the GIS array and thus improve the GF estimate emergence.
MFP is a forward propagation process. It places a "trial source" at each point of a search grid, computes the model-based GF on the receiver array, and then calculates the phase match between the frequency-domainmodeled GF and the Fourier transform of time-windowed data. The optimal noise source location is revealed by the grid points with maximum signal coherence across the array. In order to calculate the MFP output, we use 24 h data of continuous recordings on 27 July which encompass the moulin tremor. We calculate a daily estimate of the CSDM by using 5 min long time segments in the frequency band between 2. 5 and 6 Hz, which gives in total M = 288 of segments for 1 d. This ensures a robust, full-rank estimation of the CSDM (M N). The modeled GFs are computed over the two horizontal spatial components (easting and northing) using a previously optimized Rayleigh wave velocity of c = 1680 m s -1 corresponding to the propagation of Rayleigh waves within the array obtained by  Walter et al.
ShuFeng JieDu capsule (SFJDC), a traditional Chinese medicine, has been recommended for the treatment of COVID-19 infections. However, the pharmacological mechanism of SFJDC still remains vague to date. The active ingredients and their target genes of SFJDC were collected from TCMSP. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). NCP-related target genes were collected from GeneCards database. The ingredients-targets network of SFJDC and PPI networks were constructed. The candidate genes were screened by Venn diagram package for enrichment analysis. The gene-pathway network was structured to obtain key target genes. In total, 124 active ingredients, 120 target genes of SFJDC and 251 NCP-related target genes were collected. The functional annotations cluster 1 of 23 candidate genes (CGs) were related to lung and Virus infection.
Screening of active Ingredients in SFJDC We identified the active ingredients of SFJDC from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP http://tcmspw. com/tcmsp. php) [26]. TCMSP is a unique herbal pharmacology platform that captures the relationship between drugs, target genes and diseases. The database includes the detection of natural compounds such as chemical, target and drug target networks. ADME is pharmacokinetics, which refers to the absorption, distribution, metabolism and excretion of exogenous chemicals by myosome. The four key parameters of ADME were blood-brain barrier (BBB), oral bioavailability (OB), Caco-2 permeability (Caco-2) and drug-likeness (DL)  [27]. In this study select candidate compounds which has OB≥30%, DL≥ 0. 18, Caco-2≥-0.
Identification of SFJDC putative target genes This study used the TCMSP platform to obtain the putative target genes of active ingredients of SFJDC. The Uniprot (https://www. uniprot. org/) [28]  database provides a comprehensive, high quality and freely available source of protein sequence and function information. The putative target information corresponding to the active ingredients were input into UniProt database to obtain the standard name of the action target genes.
PPI (Protein-Protein Interaction) network construction of SFJDC putative and NCP related target genes The PPI network of SFJDC putative and NCP related targets would be obtained from STRING (https://string-db. org/ ver11. 0, update Jan 2019)  [30]. Active interaction sources were set as follows: Text-mining, Co-expression, Neighborhood, Experiments, Databases, Gene Fusion and Co-occurrence. The required minimum interaction score was set at 0. 4 in PPI network of SFJDC related targets, PPI network of NCP was set at 0. 9. The barplot were generated by the R software (https://www. r-project. org/ver 3. 6. 2) based on counts value.
Construction of SFJDC ingredient-target network Perl (https://www. perl. org/get. html) is a programming language suitable for writing simple scripts as well as complex applications. We used Strawberry Perl 5. 30. 1. 1 to prepare the ingredienttarget network. Cytoscape is a universal open source software for large-scale integrated development of molecular interaction networks working data. Then the ingredients-targets network of SFJDC was constructed using Cytoscape 3. 7. 2 software  [31].
Identification of candidate genes (CGs) and enrichment analysis of CGs The CGs were filtered with R software using the Venn Diagram package (https://cran. r-project. org/ web/packages/VennDiagram/index. html). The CGs would be used for Gene Ontology (GO) analysis (including biological processes (BP), molecular functions (MF), and cellular components (CC)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO and KEGG pathway analyses results were processed by the "enrichplot" (http://www. bioconductor. org/packages/release/bioc/html/enric hplot. html) "clusterProfiler" (http://www. bioconductor. org/packages/release/bioc/html/clust erProfiler. html) and "ggplot2" packages by R software. A P value of less than 0. 05 was used regarded as statistically significant. At the same time, we input CGs into DAVID (https://david.
Putative target genes of each herb in SFJDC and NCP related target genes The 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table  3 ). And 251 NCP related target genes were identified from Gene Cards database (Table  4 ).
PPI network analysis of SFJDC against NCP PPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure  4A. The average degree of all nodes was 44. 5692 and we selected the nodes with more than 44. 5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure  4B ). The average value of BC was 711. 9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure  4C ). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.