dataesr/scientific-paragraphs-categorization huggingface dataset:
Neuroimaging HCP Acquisition Each participant underwent four resting-state fMRI sessions lasting 14 min 30 s with a repetition time (TR) of 0. 72 s, on a 3-T connectome Skyra scanner (Siemens) -two during the first day and two during the second day. The 2 fMRI sessions acquired on the same day differ only in the oblique axial acquisition phase encoding, one being from Left to Right (LR) and the other from Right to Left (RL). The acquisition and pre-processing of the data is fully described in detail at the HCP website https://www. humanconnectome. org/. Here, we used the fMRI data acquired on the first day of scanning. One subject was excluded because one session was missing. In total, two same-day resting-state fMRI sessions from 99 of the 100 unrelated subjects' sessions were used for the analysis.
Partition of Phase Space In order to achieve a state-based representation, the leading eigenvectors obtained from all 99 participants in the Left-Right (LR) fMRI scanning session -corresponding to a total of T = 118602 observations (99 × 1198 TRs) with N = 90 dimensions each -are partitioned into a set of discrete states. Importantly, we do not include in this partition the Right-Left (RL) fMRI scanning sessions from the same 99 participants recorded on the same day, which will serve to test the validity and consistency of the results, as described in the following section. Given the large number of observations in this dataset, clustering algorithms relying on the TxT similarity matrix had to be discarded because of limited computational resources (i. e. , computing our TxT matrix requires >100 GB of RAM).
Projection of the Validation Dataset Into the Same State Space We used the second fMRI scanning session from each of the 99 participants recorded on the same day as the primary datasetdiffering only in the oblique axial acquisition phase encoding, being Right to Left (RL) instead of Left to Right (LR) -to verify the validity and consistency of the partition performed in the previous session. To do so, we obtained all the 1xN leading eigenvectors from the the Right-Left (RL) scanning sessiontotaling 118602 observations (1198 TRs × 99 subjects) -using the same methodology as before, but instead of running the k-means algorithm, we compute the cosine distance between each 1xN eigenvector V 1 (t) and the k 1xN cluster centroids V cα obtained from the previous analysis, and define the trajectory vector x (t) by assigning each V 1 (t) to its closest cluster centroid V cα.
Effects of Low-Pass Temporal Filtering A typical step in the pre-procession of fMRI resting-state data is the application of a low-pass filter to exclude high frequency noise in the BOLD signal (typically < 0. 1 Hz). However, given that BOLD signals are already averaged over all voxels within each brain area -which should improve the signal-to-noise ratio-, and given the instantaneous nature of our dynamic analysis, we performed our first analysis directly on the unfiltered BOLD signals recorded at a TR of 0. 72 s, corresponding to a Nyquist frequency of fNq = 1/2TR = 0. 694 Hz. To verify whether the higher frequency components in the BOLD signal are meaningful for the dynamic analysis of functional networks, we apply a 2nd order Butterworth bandpass filter to the ROI-averaged BOLD signals -before computing LEiDA and clustering into k = 5 states -varying the low-pass cut-off frequency to 0.
Effect of the Temporal Filtering All the results shown so far were obtained directly from the ROI-averaged BOLD signals from the HCP dataset, without applying any temporal frequency filter. Temporal filtering is a typical pre-processing step in resting-state fMRI analysis to remove frequency components regarded as noise. In this section, we evaluate whether the inclusion of the higher frequency components in the BOLD signal improve the analysis of dynamic functional connectivity by evaluating its effects on the reliability (ICC) of the measures across sessions, which should be maximized if assuming stationarity in individual restingstate dynamics. As shown in Figures  7A, C , filtering has a crucial effect on the Dwell Times, with lower cut-off frequencies leading to longer Dwell Times for all states, and especially for state 1.
Notably, when reaching up to the Nyquist frequency, the mean Dwell Times of states 2 to 5 approach the duration of 2TRs consistently for all subjects and in both LR and RL fMRI sessions, while state 1 lasts for slightly longer periods. When evaluating the Dwell Times ICC (Figure  7E ), we find that the ICC is maximal for states 1, 2, and 3 when the high frequency components of the BOLD signal are included. However, it is important to take into account that the accurate estimation of Dwell Times is limited by the temporal resolution of the current fMRI dataset, minimizing the difference between subjects and hence affecting the ICC estimation. Regarding the Fractional Occupancy of the states, it appears from Figures 7B,D that filtering does not affect the overall values estimated across subjects and in the different sessions.
DISCUSSION The challenge of describing dynamic functional connectivity for a mechanistic understanding of the brain processing as well as for its potential use in clinical research, has been of great interest to the neuroimaging community  (Hutchison et al. , 2013; Preti et al. , 2016). With the advent of open multimodal neuroimaging data, it is possible to address and validate these approaches in representative datasets  (Van Essen et al. , 2013; Poldrack and Gorgolewski, 2014). In this work, we apply, formalize and validate the Leading Eigenvector Dynamics Analysis (LEiDA) to a large cohort of 99 healthy unrelated HCP subjects  (Glasser et al. , 2013). We describe brain activity during rest as a time evolving trajectory in a low-dimensional state space, where states are defined according to characteristic whole-brain configurations of BOLD phase-locking.
This indicates that restingstate activity also involves connectivity to subcortical areas, which appears particularly important for the study of psychiatric disorders, such as anxiety-related disorders involving the basal ganglia. Following previous LEiDA studies, we chose here a coarse parcellation into N = 90 brain areas and did not include the BOLD signal detected in the cerebellum. The Anatomic Automatic Labeling Atlas has been validated in many studies and has shown consistency in the LEiDA results across datasets  (Cabral et al. , 2017b; Figueroa et al. , 2019; Lord et al. , 2019). However, it is based on an anatomic definition of the brain regions and as such might not generalize adequately to the dynamic functional connectivity analysis. We expect to extend to finer-grained and fMRI-derived parcellations in future studies, potentially including other substructures such as the cerebellum, in order to gain a wider insight into the network configurations observed in brain activity at the macroscopic scale  (Cammoun et al.
000013 and  NORTE-01-0145-FEDER-000023 , supported by the  Northern Portugal Regional Operational Programme  ( NORTE 2020 ), under the Portugal 2020 Partnership Agreement, through the  European Regional Development Fund (FEDER). JC was supported by  Portuguese Foundation for Science and Technology   CEECIND/03325/2017 , Portugal. GD acknowledges funding from the  European Union 's  Horizon 2020 FET Flagship Human Brain Project  under Grant Agreement  785907 HBP SGA2 , the  Spanish Ministry  Project  PSI2016-75688-P (AEI/FEDER ) and the Catalan Research Group Support 2017 SGR 1545. MK was supported by the  European Research Council  Consolidator Grant: CAREGIVING (615539),  Pettit Foundation ,  Carlsberg Foundation and Center for Music in the Brain , funded by the  Danish National Research Foundation  ( DNRF117 ).
Sample collection For the genetic analysis, a total of N g = 215 (Table  1 ) adult common sole was sampled on the spawning grounds in French, off the rivers Seine (SW) and Somme (NE), and UK waters (Fig 1 ) in April and May 2017 and 2018 from commercial fishing vessels during tagging experiments  [22]. An exception was made in July 2018, when individuals from the English part of the EEC were collected during the UK Beam Trawl Survey. Each fish total length was measured (in cm) and sex was determined by visual inspection of the gonads. A caudal fin clip was sampled and stored in pure ethanol for genetic analysis. For each fish, paired sagittal otoliths were removed and photographed. The right otolith was then used for age determination. spatial differences in these parameters. The first 99 elliptical Fourier harmonics of each otolith were extracted from the scanned image via TNPC 7 software.
9% for each of them. The number of elliptical Fourier descriptors was further reduced using a principal component analysis (PCA) with the prcomp function of the stats R package. The number of principal components (PC) was then chosen so that 99% of the variance was explained. The matrix of chosen principal components (S) thus represents the otolith shape matrix. The differences in otolith shape between subunits was visualized using the mean otolith shape of each subunit formed by the outline reverse Fourier transform of the first n = max(n j ) normalized harmonics. This visualization gave a first insight into spatial otolith shape variations. Then, spatial (Subunit), total fish length (Length), otolith side (left or right, Side), sex (Sex) and sampling year (Year) effects were tested on the otolith shape matrix (S) using a redundancy analysis (RDA) with the rda function of the vegan R package  [72]  (Eq 2): ðSÞ � Subunit þ Length þ Side þ Sex þ Year ð2Þ Permutation tests using the anova.
Discriminatory power of genetic and otolith shape approaches The discriminatory power of genetic and otolith shape approaches was investigated using the assignPOP R package. This package allows to analyze genetic, non-genetic and integrated data and is thus particularly suited to compare the discriminatory powers of genetic and otolith shape tracers. The package provides a machine learning framework whose principle is to assign individuals from different source populations by dividing the entire data set into training (i. e. baseline) and test data sets (i. e. unknown individuals) and building a machine learning classification function  [75]. The predictive model is then applied to all unknown individuals (i. e. individuals that are not in the baseline) to assign them to their population of origin probabilistically.
In practice, we applied a K-fold crossvalidation procedure. The K-fold cross-validation method divided the whole data set in K subsets that were alternatively used as a training (i. e. baseline) or testing dataset (i. e. the remaining individuals). In our case, best accuracies were obtained by dividing each subunit into K = 3 groups. This procedure prevents from unbalanced training data sets among source populations  [75]. Assignments of fish corresponded to the highest membership probability across the tests. An individual was correctly assigned if the predicted membership corresponded to the sampling subunit. Here, the predictive model was built using the Linear Discriminant Analysis (LDA) of the MASS R package  [76]. The assign. matrix function of the assignPOP R package was used to compute a pairwise assignment matrix with mean and standard deviation of assignment accuracies across all assignment tests for each data type (i.
e. genetic and otolith shape) and sampling year (i. e. 2017 and 2018). This method was used as a direct comparison of the discriminatory power of genetic and otolith shape approaches. Prior to the assignment analyses, genetic and otolith shape data sets were pre-computed. Regarding the genetic approach, all loci were used to build the predictive model. Before the LDA was computed, a PCA was automatically applied on the genetic data for reducing the dimensions. Individual genotypes of the 2017 and 2018 data sets were clustered independently, since libraries were built separately. Regarding the otolith shape approach, the predictive model was built using the more discriminant otolith shape descriptors among Fourier and shape indices or both (see 3. 2). The potentially confounding factors on otolith shape (i.
Indeed, only two shape indices upon five (i. e. the circularity and form coefficient) allowed to detect spatial variations and those two shape indices were highly correlated. Moreover, the assignPOP framework is designed to assign both genetic and non-genetic data sets in an homogeneous manner  [75]. This method allowed to compare the discriminatory power of genetic and otolith shape approaches. The assignPOP R package is supposed to limit the bias in assignment due to unbalanced sample size between sources  [94]. We chose the K-fold cross-validation method to limit such a bias. However, whatever the tracer or year data sets used; assignment results suggested that individuals were mostly assigned into the subunits where sample size was the highest. Then, the unbalanced sample size between locations probably contributed to the low discriminatory power of genetic and otolith shape approaches.
Acknowledgments The authors acknowledge  B. Hellemans  and  S. Delerue-Ricard  ( Laboratory of Biodiversity and Evolutionary Genomics, KU Leuven, Leuven, Belgium ) for preparing the genetic libraries and sequencing. We are grateful to the team of the  Sclerochronology Centre  and  A. Morell  ( Ifremer, Boulogne-sur-Mer, France ), who largely contributed to the compilation of the otolith shape database.
This research was part of the  SMAC (Sole de Manche Est Amelioration des Connaissances pour une meilleure gestion du stock) research program , supported by the fisheries organization " France Filière Pêche ", the  French Ministry of Fisheries « Direction des Pêches Maritimes et de l'Aquaculture ». This study was carried out with support of the  European Marine Biological Resource Centre (EMBRC) Belgium  funded by the  Research Foundation -Flanders  (project  I001219N ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
high-throughput automation frameworks), as well as the implementation of modern software engineering practices (unit testing, continuous integration, and automatic source-code documentation). These new features, capabilities, and code development model aim to further sustain and expand the community uptake and range of applicability, that nowadays spans complex and accurate dielectric, electronic, magnetic, optical, topological and transport properties of materials. * Members of the Wannier Developers Group, who are responsible for the long-term maintenance and sustainability of Wannier90.
Wannier90 is an open-source computer program for calculating maximally-localised Wannier functions (MLWFs) from a set of Bloch states. It is interfaced to many widely used electronicstructure codes thanks to its independence from the basis sets representing these Bloch states. In the past few years the development of Wannier90 has transitioned to a community-driven model; this has resulted in a number of new developments that have been recently released in Wannier90 v3. 0. In this article we describe these new functionalities, that include the implementation of new features for wannierisation and disentanglement (symmetry-adapted Wannier functions, selectivelylocalised Wannier functions, selected columns of the density matrix) and the ability to calculate new properties (shift currents and Berry-curvature dipole, and a new interface to many-body perturbation theory); performance improvements, including parallelisation of the core code; enhancements in functionality (support for spinor-valued Wannier functions, more accurate methods to interpolate quantities in the Brillouin zone); improved usability (improved plotting routines, integration with.
I. INTRODUCTION Wannier90 is an open-source code for generating Wannier functions (WFs), in particular maximallylocalised Wannier functions (MLWFs), and using them to compute advanced materials properties with high efficiency and accuracy. Wannier90 is a paradigmatic example of interoperable software, achieved by ensuring that all the quantities required as input are entirely independent of the underlying electronic-structure code from which they are obtained. Most of the major and widely used electronic-structure codes have an interface to Wannier90, including Quantum ESPRESSO, 1 ABINIT, 2 VASP,  [3] [4] [5]  Siesta, 6 Wien2k,  7  Fleur 8 and Octopus. 9 As a consequence, once a property is implemented within Wannier90, it can be immediately available to users of all codes that interface to it.
V. NEW POST-PROCESSING FEATURES Once the electronic bands of interest have been disentangled and wannierised to obtain well-localised WFs, the Wannier90 software package includes a number of modules and utilities that use these WFs to calculate various electronic-structure properties. Much of this functionality exists within postw90. x, an MPI-parallel code that forms an integral part of the Wannier90 package. In v2. x of Wannier90, postw90. x included functionality for computing densities of states and partial densities of states, energy bands and Berry curvature along specified lines and planes in k-space, anomalous Hall conductivity, orbital magnetisation and optical conductivity, Boltzmann transport coefficients within the relaxation time approximation, and band energies and derivatives on a generic user-defined list of k-points.
Some further functionality exists in a set of utilities that are provided as part of the Wannier90 package, including a code (w90pov. F90) to plot WFs rendered using the Persistence of Vision Raytracer (POV-Ray)  34  code and to compute van der Waals interactions with WFs (w90vdw. F90). In addition, there are a number of external packages for computing advanced properties based on WFs and which interface to Wannier90. These include codes to generate tight-binding models such as pythTB  35  and tbmodels,  36  quantum transport codes such as sisl,  37  gollum, 38 omen 39 and nanoTCAD-ViDES,  40  the EPW 41 code for calculating properties related to electron-phonon interactions and WannierTools 42 for the investigation of novel topological materials. Below we describe some of the new post-processing features of Wannier90 that have been introduced in the latest version of the code, v3.
[70] [71] [72]  The sensitivity of both the Berry curvature and the intrinsic orbital moment to the details of the electronic structure, together with the need to sample them on a dense mesh of k-points, calls for the development of accurate and efficient ab initio methodologies for this class of problems. Building on existing Wannier-interpolation schemes for calculating the spontaneous intrinsic AHC and orbital magnetisation,  32, 84  the corresponding methodology for gyrotropic effects was presented in Ref. 85, where it was applied to p-doped trigonal tellurium (in that work, only the intraband contribution to the CPGE was considered). The resulting computer code has been incorporated in Wannier90 as the gyrotropic. F90 module. The central task of that module is to evaluate response tensors such as the "Berry-curvature dipole" 68 D ab = BZ dk (2π) 3 n ∂ nk ∂k a Ω b nk - ∂f 0 ∂ = nk , (56) where f 0 is the equilibrium occupation factor and Ω nk = ∇ k ×A nnk is the Berry curvature of the Bloch bands (the curl of the band-diagonal Berry connection A nnk introduced in Sec.
Equations (  58 ) are very similar to the Kubo formula for the AHC, except for the replacement of a velocity matrix element by a spin-current matrix element. As mentioned in the previous two subsections, Wannierinterpolation techniques are very efficient at calculating such quantities. A Wannier-interpolation method scheme for evaluating the intrinsic SHC was developed in Ref. 88 (see also Ref. 89 for a related but independent work). The required quantities from the underlying ab initio calculation are the spin matrix elements S (0) mnk,a = ψ (0) mk |s a |ψ (0) nk , the Hamiltonian matrix elements H (0) mnk = ψ (0) mk |H|ψ (0) nk = (0) mk δ mn , and the overlap matrix elements of Eq. (  17 ). Since the calculation of all these quantities has been previously implemented in pw2wannier90. x (the interface code between pwscf and Wannier90), this advantageous interpolation scheme can be readily used while keeping to a minimum the interaction between the ab initio code and Wannier90.
We find that for well-known systems SCDM-k is typically already capable of selecting a smooth manifold and no further subspace selection is needed. This method is now implemented as part of the pw2wannier90. x interface code to Quantum ESPRESSO. We have decided to implement the algorithm in the interface code(s) rather than in Wannier90 itself, because the SCDM method requires knowledge of the wavefunctions ψ nk , which are only available in the ab initio code. In Wannier90 only a single new input parameter auto projections is required. This disables the check on the number of projections specified in the input file (as we rely on SCDM to provide us with the initial guesses) and adds a new entry to the <seedname>. nnkp file (which is read by pw2wannier90. x in order to compute the quantities required by Wannier90) that specifies the number of Wannier functions required.
VII. AUTOMATION AND WORKFLOWS: AIIDA-WANNIER90 PLUGIN AiiDA 12 (Automated Interactive Infrastructure and Database for Computational Science) is an informatics infrastructure that helps researchers in managing, automating, storing and sharing their computations and results. AiiDA automatically tracks the entire provenance of every calculation to ensure full reproducibility, which is also stored in a tailored database for efficient querying of previous results. Moreover, it provides a workflow engine, allowing researchers to implement high-level workflows to automate sequences of tedious or complex calculation steps. AiiDA supports simulation codes via a plugin interface, and over 30 different plugins are available to date. 105 mong these, the AiiDA-Wannier90 plugin provides support for the Wannier90 code.
Users interact with the code (to submit calculations and retrieve the results) via the high-level python interface provided by Ai-iDA rather than directly creating the Wannier90 input files. AiiDA will then handle automatically the various steps involved in submitting calculations to a cluster computer, retrieving and storing the results, and parsing them into a database. Furthermore, using the Ai-iDA workflow system users can chain pre-processing and post-processing calculations automatically (e. g. , the preliminary electronic structure calculation with an ab initio code). These scientific workflows, moreover, can encode in a reproducible form the scientific knowledge of expert computational researchers in the field on how to run the simulations, choose the numerical parameters and recover from potential errors.
More detailed information on all inputs can be found in the AiiDA-Wannier90 package documentation. 106 fter the Wannier90 execution is completed, the AiiDA-Wannier90 plugin provides parsers that are able to detect whether the convergence was successful and retrieve key parameters including the centres of the Wannier functions and their spread, as well as the different components of the spread (Ω I , Ω D , Ω OD and Ω), and (if computed) the maximum imaginary/real ratio of the Wannier functions and the interpolated band structure. The whole simulation is stored in the form of a graph, representing explicitly the provenance of the data generated including all inputs and outputs of the codes used in the workflow. An example of a provenance graph, automatically generated by AiiDA when running a Quantum ESPRESSO calculation followed by a Wannier90 calculation, is shown in Fig.