dataesr/scientific-paragraphs-categorization huggingface dataset:
MPI collective operations can then be easily defined within a given group. Note that the ratio (time of calculation)/(time of communication) remains very large so that load imbalance issues are negligible at this stage. Figure  2  schematically illustrates the workflow of a single HFB calculation in this case. Most of the code execution is serial: all MPI processes involved perform exactly the same task. When the code enters a parallelized segment of the code, work is shared explicitly across available MPI processes. In both routines, the parallel segment involves nested loops: each MPI process handles different values of the index of the outermost loop. Some time within the parallel region, each MPI task may enter a multi-threaded region (OpenMP). At the end of the MPI parallel region, results from each MPI process are combined and broadcast back to each MPI process.
Note that different values of k i allow us to treat differences of the linear-momentum fluctuations along the three principal axes of a deformed nucleus. The expectation value of P 2 i in the HFB ground-state reads P 2 i = [Tr (P i ρ)] 2 + Tr P 2 i ρ -Tr [P i ρP i ρ] -Tr [P i κ (P i κ) * ] (114) where P i is the matrix of Pi in the good-simplex basis of hfodd, ρ is the one-body density matrix, and κ is the pairing tensor. The last term is non-zero only when pairing is active. Since operator P 2 i does not break time-reversal, simplex, parity, or T-simplex symmetries (see  [4]  for a definition of these symmetries), the code can maintain them during the calculation except when determining parameters k i by shifting wave functions. This is because the shifted wave functions may lose some of the symmetries.
Interface to a plotting program The hfodd review file FILREV, see Section II-3. 9  [2] , is a plain-text file that can be used to extract information necessary for creating various kinds of plots. As an example, the present distribution (v2. 73y) contains script getLevels. py, which reads the hfodd review file and prepares data files that can be used to plot single-particle energies or Routhians as functions of deformation (Q 20 moment) or rotational frequency (ω). In order to use the script, one has to run hfodd for a series of deformations/frequencies, corresponding to different values of QASKED (under the keyword MULTCONSTR in the hfodd input file) or OMEGAY (under keyword OMEGAY or OMEGA_XYZ). It should be remembered, however, that multipole moment q 20 of a converged solution may not be exactly equal to QASKED -discrepancies can be quite large, but, at least to some extend, can be controlled by adjusting stiffness parameter (STIFFQ under keyword MULTCONSTR in the hfodd's input data file).
Alternatively, one can use the ALM, see Section VI-2. 2. 2  [6]. Of course, the hfodd review file has to contain data describing single-particle levels, which means the parameter IREVIE (under the keyword REVIEW in the hfodd input file) must be set to at least 2. In the present distribution (v2. 73y), two examples of hfodd review files are provided: DyDef. rev corresponds to a series of deformations and DyOme. rev to a series of rotational frequencies. These two review files were obtained by running the code hfodd on input data files DyDef. dat and DyOme. dat, respectively. Having a hfodd review file prepared, one can run the script getLevels. py with three or four arguments: • The first (required) argument is the name of the hfodd review file produced by running the code hfodd -it should correspond to a series of results obtained from runs of the code hfodd with input data differing by the value of deformation (QASKED) or rotational frequency (OMEGAY or OMEGAX or OMEGAZ).
Each point of the curve is then represented by three space-separated elements: deformation (q 20 ), energy, and the Nilsson label of the leading component of a given state (the label will not contain any embedded spaces. ) Note that the label does not have to be the same for all points along a single curve. # 3/2 +1 -In the case of Routhians as functions of the rotational frequency: the form is similar, but the comment line specifies parity, signature and the curve number. The first number on each line now denotes the rotational frequency. # -1 +1 It may happen that states which belong to a given orbital are present at some deformations (frequencies) but are missing at other deformations, as they were too high to be calculated and/or output by hfodd. In this situation, the script adds "artificial" points to the curve with all quantum numbers in the Nilsson label set to zero and energy set to phonyEne.
Input data for serial mode The structure of the input data file has been described in Section II-3  [2] ; in version (v2. 73y) of the code hfodd this structure is exactly the same. All previous items of the input data file remain valid, and several new items were added, as described in Sections 3. 1. 1-3. 1. 7. For some pervious items, new features or new values of variables were added (Section 3. 1. 8), whereas some other items, although still active and allowed, have become obsolete and their further use is not recommended (Section 3. 1. 9).
The user should be aware that code may then diverge if during the iterations levels cross at the Fermi energy. IVACUM=1 is ignored unless IPAIRI=0 and is not yet implemented for IPNMIX=1. to saving files specific to these options have become obsolete, although they are still active and allowed. This concerns keywords YUKAWASAVE, LIPKINSAVE, REPYUKFILE, RECYUKFILE, REPLIPFILE, RECLIPFILE, and CONTYUKAWA. For IFCONT=1 (keyword CONTFIELDS), see Section VI-3. 8  [6] , when the code is restarted, the Yukawa or Lipkin-Nogami calculations can be automatically smoothly continued, and the contents of the Yukawa and Lipkin files is ignored. Then, for ILCONT=0 or 1 (keyword CONTLIPKIN), the Lipkin parameters are read from the input or record file, respectively. For IFCONT=0, by using the Yukawa or Lipkin files, a smooth continuation of the Yukawa or Lipkin-Nogami calculations, respectively, is still possible, but this option is not any more recommended.
In principle, these options provide enough flexibility to cover the majority of hfodd applications in parallel mode. The user interested in some specific option which could not be activated by one of the keywords above can still manually modify the routine PREDEF prior to compilation. This routine pre-defines all hfodd input data. • hfodd_hfbtho_200j. f90: hfbtho DFT solver based on version 200d published in Ref. [23]. • hfodd_interface_4. f90: Interface between the hfbtho and hfodd solvers. Contains the routine to transform the HFB matrix from the HO basis used in hfbtho (hfodd) to the basis used in hfodd (hfbtho). • hfodd_lipcorr_7c. f90: Toolkit for the Lipkin method. • hfodd_modules_17b. f: Definition of memory-consuming modules. Defines, among others, the matrices of the Bogoliubov transformation, the eigenvectors of the HF equations, etc.
• hfodd_mpiio_5b. f90: IO interface in MPI calculations. Contains the routine to read input data for parallel hfodd calculations. • hfodd_mpimanager_4b. f90: MPI toolkit. Defines the list of MPI tasks based on the data read in the parallel input file hfodd_mpiio. d. • hfodd_pnp_6. f90: Toolkit for particle number projection. • hfodd_shell_4b. f: Toolkit for the shell correction. • hfodd_sizes_2. f90: Static array size declaration. Contains all PARAMETER statements controlling the size of all the statically (and some of the dynamically allocated) arrays used in the code. • hfodd_SLsiz_3. f90: ScaLAPACK interface. Experimental module testing the ScaLA-PACK diagonalization routine for simplex-breaking HFB calculations. The FORTRAN source of version (v2. 73y) of the code hfodd contains numerous undocumented and untested features that are under development.
5 Acknowledgments Discussions with  Hai Ah Nam  and  Robert Parrish  are warmly acknowledged. X. B. Wang wishes to thank the  National Natural Science Foundation of China  for support under Grant Nos. 11505056  and  11605054. This work was supported in part by the  THEXO JRA  within the  EU-FP7 - IA  project  ENSAR  (No. 262010 ), by the  ERANET-NuPNET  grant SARFEN of the  Polish National Centre for Research and Development (NCBiR) , by the  Polish National Science Centre (NCN)  under Contracts Nos. 2014/15/N/ST2/03454  and  2015/17/N/ST2/04025 , by the  Academy of Finland  and  University of Jyväskylä  within the  FIDIPRO program , and by the  U. S. Department of Energy  under Contract Nos. DE-AC52-07NA27344  ( Lawrence Livermore National Laboratory ) and de-sc0008499 ( NUCLEI SciDAC Collaboration ).
Virtual loop contributions are numerically evaluated through the MadLoop module  [23]  and combined with the real-emission diagrams following the FKS subtraction method as implemented in MadFKS  [24, 25] , and the matching to parton showers is finally achieved according to the MC@NLO prescription  [26]. In Section 2, we detail how we have modified the modelindependent parameterisation of Ref. [17]  to make it suitable for NLO calculations in QCD matched to parton showers for vector-like quark processes. We also define a series of benchmark scenarios for the phenomenological studies performed in Section 3. We investigate vector-like quark pair production and single production (in association with either a jet or a weak gauge or Higgs boson), as well as diboson production. We summarise our work in Section 4.
2 vertex, respectively. Moreover, an explicit summation upon q, Q and f implies a summation over all quark species, the extra quark species and the Standard Model quark species, respectively. In the phenomenological study undertaken in Section 3, the virtual one-loop contributions to the NLO predictions are evaluated with the MadLoop module  [23] , and then combined with the real contributions by means of the FKS subtraction method  [24]  as implemented in the Mad-FKS package  [25]. Both MadLoop and MadFKS being part of MadGraph5 aMC@NLO  [18] , the entire calculation is entirely automated from the knowledge of the bare Lagrangian of Eq. (  1 ) and the specification of the process of interest  [30]. Technically, the translation of the model Lagrangian into a UFO library  [22]  that contains ultraviolet and R 2 counterterms and that could be used by MadGraph5 aMC@NLO is automatically performed with the FeynRules  [19]  and NLOCT  [20]  packages, the latter program taking care of the calculation of the one-loop ingredients of the model files.
This, however, goes beyond the scope of this work, so that in order to maintain automation from the user standpoint, we have instead released a public script that should be called for diagram generation in order to prevent MadGraph5 aMC@NLO from discarding any loop diagram that would be tagged as an electroweak correction to a QCD Born diagram. More precisely, the script allows for the inclusion of all box diagrams containing at least two strong interaction vertices, but it removes weak-boson or Higgs-boson loop contributions that consist of an electroweak correction to a QCD Born process. Additionally, diagrams exhibiting the t-channel exchange of a weak or Higgs boson but with an additional gluon are kept. Not using the script instead yields the removal of several necessary box diagram contributions.
The related MadGraph5 aMC@NLO command allowing for generating interference events is, again for the example of T T production, generate p p > tp tp~QCD^2=2 QED^2=2 which is a standard command for LO event generation in MadGraph5 aMC@NLO. All MadGraph5 aMC@NLO scripts necessary for differential and total cross-section calculations and event generation are available from the webpage http://feynrules. irmp. ucl. ac. be/wiki/NLOModels, together with the UFO and FeynRules models. In Figure  2  and Table  1 , we present LO and NLO total cross sections for the production of a pair of vector-like T quarks for the different scenarios introduced in Section 2. 2, and we depict the dependence of the cross sections on the vector-like quark mass. We first focus on the pure QCD contribution that is independent of the vector-like quark nature (diagrams of the upper line of Figure  1 ).
In our calculations, we have made use of the MadSpin  [40]  and MadWidth  [41]  programs for automatically taking care of the heavy quark decays in a way in which both off-shell and spin correlation effects are retained, matched the fixed-order calculcation with the parton-shower and hadronisation infrastructure as modelled by the Pythia 8 package  [42] , and we have reconstructed all final-state jets by means of the anti-k T algorithm  [43]  with a radius parameter set to 0. 5 as implemented in FastJet  [44]. As shown on Figure  3  and Figure  4 , our predictions confirm the total cross-section findings of Table  4  (see Appendix A). The contributions of the electroweak diagrams are, respectively, negligible and significant for light and heavy vector-like quarks. Moreover, the parton density uncertainties dominate for setups exhibiting a large M B value, rendering the theoretical predictions barely reliable.
As for electroweak contributions to vector-like quark pair production, mixed QCD and electroweak loop diagrams appear at the NLO level and must be treated consistently for getting ultravioletfinite results. This step being not automated, we provide scripts that steer the event generation process on the UFO model webpage. In Figure  6  and Table  2 , we present LO and NLO total cross section for single B quark production for the BZ1, BZ2, BW1 and BW2 scenarios introduced in Section 2. 2 for which single vector-like B quark production occurs via Z-boson or W -boson exchanges. This consists of the first NLO predictions for a single vector-like quark production process. Although all depicted total cross sections exhibit a similar order of magnitude, a small hierarchy is observed. It is driven by an interplay of the parton densities that enhance mechanisms involving first generation quarks and of the new physics coupling strengths that are much larger for vector-like quark mixings with second generation (κ Q = 0.
58 in at least one of the 53 tissues, were retained. This cutoff was determined as the optimal expression cutoff (maximizing the sum of specificity and sensitivity) in a ROC curve experiment between the pre-filtering lncRNA-protein interaction prediction dataset (∼243 million interaction predictions) and a set of 2438 experimentally detected CLIP interactions taken from StarBase v2. 0  (18)  with at least 100 mapped reads (area under the ROC = 0. 71). The expression metric used ('paired expression') was calculated for each protein-RNA pair as the lowest RPKM expression between the protein and RNA for each tissue, to which the maximum RPKM value among tissues for that protein-RNA pair is then withdrawn, i. e. E (Protein, RNA) = max t ∈ tissues (min (E t (Protein) , E t (RNA))) where E(Protein, RNA) denotes the 'paired expression' for each protein-RNA pair and E t denotes the RPKM expression in tissue t (RPKM values were log 10 -transformed).
Protein complex and network module datasets We collected protein complex information from the (i) Bio-Plex publication  (19)  Supplementary Table  S3 , which includes 354 complexes; (ii) list of conserved protein complexes from Wan et al. (20) , Supplementary Table  S4 , which includes 981 complexes; (iii) list of non-redundant CORUM (21) complexes from Havugimana et al. (22) , Supplementary Table  S3 , which includes 324 complexes, referred to as 'non-redundant CORUM complexes'. Protein network modules were extracted from a human interactome as described in  (23). See Supplementary Material for details.
Since many lncRNAs have only been found to be expressed at very low levels and often in a tissue-specific manner (26), we only retained 6. 02 million protein-lncRNA interactions between molecules co-present in at least one out of the 53 human tissues from the GTEx RNA-seq dataset  (17)  (see Materials and Methods). Globally, the 6. 02 million predicted interactions occur between 12629 proteins and 2799 lncRNAs (Figure  2 ), i. e. between 80% of the tested proteins and 18% of our initial set of lncRNAs. Individual proteins are predicted to interact with up to 2. 5% of the lncRNAs on average (Supplementary Figure  S1 ). When considering only RBPs (Supplementary Material), we predict them to interact with 4. 14% of the lncRNAs on average, in the same A global lncRNA-protein interaction network.
Predicted protein-lncRNA interaction network composed by more than 6 millions interactions (grey circle) between 12629 proteins (pink circle) and 2799 lncRNAs (blue circle). The size of the network is compared to the human binary protein-protein interaction network (see Supplementary Methods). All circles are proportional to their components. range as eCLIP results on 82 RBPs  (14) , which interact with 7. 98% of the lncRNAs from the same dataset. On the lncRNA side, their median number of protein interactions is 1267 (Supplementary Figure  S1 ), a higher number than suggested by current RNA pull-down studies that report between 126 and 852 interacting proteins per lncRNA  (27, 28). As evident in recent high-throughput screenings, the complexity of biological systems challenges interpretation of experimental results due to the specific interactions occurring in different contexts  (12, 14 ).
Yet our predictions, based on molecular physicochemical properties, represent a set of possible interactions between co-expressed proteins and RNA, independent of the cellular sub-localization and the cellular states. Our predictions therefore cover a larger spectrum of conditions in which protein-RNA interactions may occur, compared to the ones assessed in specific in vivo studies. This allows us to detect, for example, lncRNAs acting exclusively upon DNA damage and other stress conditions, or interactions restricted to a few cell types. Despite all this, 9414 of our predicted interactions are found in the relatively small set of eCLIP experiments, a highly significant overlap considering the 82 proteins and 7381 transcripts present in both eCLIP and catRAPID datasets (Pvalue < 2. 2e-271, OR = 1.
Interactions between lncRNAs and protein complexes or network modules To assess our capacity to computationally predict lncRNA interactions with protein complexes, we studied the possible association between a recently discovered evolutionarilyconserved and muscle-restricted lncRNA, lnc-405  (29) , and the Pur␣-Pur␤-YBX1 protein complex, implicated in gene regulation of muscle cells  (30). The catRAPID omics algorithm predicts the interaction of human lnc-405 with Pur␣, Pur␤ and YBX1 with moderate to high scores  (38. 56, 44. 05, 67. 84, respectively). To determine if catRAPID correctly predicted the interactions of the lncRNA to the protein complex in a cellular context, we performed endogenous lnc-405 RNA pull-down from nuclear extracts of C2C12 mouse myotubes followed by a mass spectrometry (MS) analysis.
Moreover, a GSEA experiment shows that the top interactors of lnc-405 predicted by catRAPID are enriched in proteins identified in the MS experiment (Figure  3 , P-value = 0. 017). These results remarkably show that catRAPID is able to correctly predict interactions between lncRNAs and proteins (whether in a complex or not), in line with good catRAPID performances observed for other ncRNAs and reported in previous articles  (24, 25, 31). We thus proceeded with the exploration of our catRAPID predicted lncRNA-protein interaction network, aiming to test the hypothesis that lncRNAs frequently scaffold known protein complexes through protein-RNA interaction. For this, we investigated three public datasets of human macromolecular complexes. Briefly, we used the (i) non-redundant dataset of 326 CORUM complexes  (21)  collected by Havugimana et al.
(22)  (hereafter referred to as 'non-redundant CORUM'), (ii) a set of 981 metazoan-conserved complexes produced by Wan et al. (20)  through biochemical fractionation with quantitative MS (hereafter referred to as 'Wan 2015'), as well as (iii) the BioPlex dataset  (19)  of 354 complexes detected through affinity purification, MS experiments and interaction network analysis. Moreover, the human cell contains groups of functionally-related proteins that interact more transiently but may nevertheless be assembled or gathered together by lncRNA scaffolds to participate in metabolic or signaling pathways. For these reasons, we also used a dataset of 874 functional modules identified   (69)  of catRAPID predictions between lnc-405 lncRNA and 1459 human RBPs (Supplementary Material), using the RBPs identified as interactors in the MS experiment as a gene set.
Note that only RBPs with catRAPID predictions (within size restrictions) were considered. P-value = 0. 017 (10 000 simulations), normalized enrichment score = 1. 59). in the human interactome using OCG, an algorithm that decomposes a network into overlapping modules, based on modularity optimization  (32). These modules (hereafter referred to as 'Network modules') are groups of highly interacting proteins, which tend to be involved in the same cellular processes, metabolic or signalling pathways  (33)  (Supplementary Table  S2 ). Using these datasets of protein groups and our protein-lncRNA interaction predictions, we identified lncRNAs that may scaffold complexes or modules by assessing first, for each lncRNA, the enrichment of the lncRNA's interacting proteins among those proteins composing each complex or network module (hypergeometric test, Benjamini-Hochberg corrected FDR 5%) (Figure  1B ).
Remarkably, we also predict that ∼56% of the known protein complexes and 66% of the network modules are scaffolded by at least one lncRNA (Supplementary Table  S3 ). These results suggest that lncRNAs scaffolding complexes and modules are highly prevalent. Moreover, as the set of predicted complexes and modules found to be scaffolded by lncRNAs are involved in most cellular biological processes (Supplementary Figure  S3 ), the scaffolding function of lncRNAs appears therefore to be a general feature and not restricted to specific cellular processes. Although current experimental protein-lncRNA interaction datasets are largely incomplete and limited to 148 RBPs  (14, 15, 18) , we find that 832 out of 6186 lncRNAprotein-group interactions including at least one of the 148 RBPs contain one or more known experimental interactions (Supplementary Table  S4 ).
Global analysis of scaffolded complexes and modules In order to analyse the patterns of predicted interactions between lncRNAs and protein groups, we represent them as a clustered matrix (Figure  4 ). Clusters of protein groups with similar enrichment profiles often share proteins, while clusters of lncRNAs with similar enrichment profiles are largely composed of transcript isoforms from the same or paralog genes. While some protein groups and lncR-NAs interact specifically, others--protein groups as well as lncRNAs--do so more promiscuously, and this occurs for each of the four protein group datasets used. Indeed, we observe that some lncRNAs are predicted to interact with 1 to 98 protein groups, according to the dataset, i. e. at most 54 (16. 7% of total) non-redundant CORUM complexes, 35 (9.
9%) in BioPlex, 98 (10. 0%) in Wan 2015, 68 (7. 8%) in network modules (Figure  4 ; Supplementary Figure  S5A ). Likewise, protein complexes are predicted to interact with 1 to 401 lncRNAs i. e. at most 401 lncRNAs (2. 6% of total tested) in non-redundant CORUM, 17 (0. 1%) in BioPlex, 248 (1. 6%) in Wan 2015, 115 (0. 7%) in network modules (Figure  4 ; Supplementary Figure  S5B ). Interestingly, some of our predictions corroborate and further extend the current knowledge of protein-RNA complexes. For instance, the polycomb repressive complex 2 (PRC2 complex), previously found associated with lncR-NAs (4), is predicted to be scaffolded by 101 different lncR-NAs in our analysis. Indeed, the PRC2 complex and some of its constituent proteins have previously been found to bind hundreds of lncRNAs, presumably as a part of its targeted gene repression mechanism or its regulation by decoy lncR-NAs  (4, 34).