dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we present Tensorpac, a cross-platform open-source Python toolbox, distributed under a BSD-3-Clause license, dedicated to the measurement of phase-amplitude relationships. This includes an array of functions to compute PAC and event-related PAC (ERPAC) alongside innovative features such as the estimation of the preferred-phase with polar plotting and exhaustive exploratory analysis across the full frequency space. Tensorpac also ships with additional tools in order to assess the reliability of the estimation such as power spectral density (PSD), Phase-Locking Value  [56]  and statistics. Crucially, what distinguishes Tensorpac even more from other available tools, is the combination of parallel computing and tensor-based implementation of the algorithms which drastically reduces computation time and opens up the possibility to compute PAC on large multidimensional arrays.
First, the raw data is respectively filtered with bandpass filters centered on 100hz and 10hz. Then, the complex analytic form of each signal is obtained using the Hilbert transform. The phase is extracted from the 10Hz signal (angle of analytic signal) and power from the 100Hz signal (amplitude of analytic signal). An uncorrected PAC measure is obtained from these two signals. To estimate the null distribution of the measure in the absence of any genuine coupling, the amplitude signal is split into two blocks at a random time point and the temporal order of those two blocks is swapped. Then, the PAC is estimated using this swapped version of amplitude and the originally extracted phase. By repeating this process and cutting at a random point, for example 200 times, we can obtain a distribution of surrogate values for which there is no genuine coupling.
Generating coupled signals For the implementation and validation of coupling methods, we used synthetic signals with controllable coupling frequencies. To this end, we included in the toolbox two ways to simulate synthetic signals that can be imported from tensorpac. signals : pac_signals_tort which is a method based on pure sines summation and modulation  [43]  and pac_signals_wavelet which extract the phase from a random distribution leading to more complex signals  [37]. Both methods, illustrated in Figure  2 , provide fine-grained control over the coupling frequency pair of (phase, amplitude), the amount of coupling and noise such as data length and sampling frequency. First row shows an example of a signal that contains a 5<->120 hz coupling defined as proposed by Tort et al. [44]. Bottom signal (B) also contains a 5<->120 hz coupling but defined as proposed in Dupré la Tour et al.
Extracting the instantaneous phase and the amplitude Since there is no consensus about whether the Hilbert or wavelet transforms constitutes a gold standard for extracting the phase and the amplitude, Tensorpac implements both. The Hilbert transform has to be applied on pre-filtered signals. For filtering, we implemented a Python equivalent to the two-way zero-phase lag finite impulse response (FIR) Least-Squares filter implemented in the EEGLAB toolbox  [54]. Filter orders are frequency dependent and are defined as a function of the number of cycles (by default 3 cycles are used for the phase and 6 cycles for the amplitude  [57] ). Extracting the phase and the amplitude using the absolute value and the phase of the Hilbert transform applied on these filtered signals. Both components can also be obtained by convolving with a Morlet's wavelets  [58]  with a default width of 7, a default value broadly used in electrophysiological data analyses.
Tensorpac provides functionalities that can help address these issues. First of all, a standard tool to compute the Power Spectrum Density (PSD) is available and adapted for standard electrophysiological data formats, i. e. datasets organized as an array with the number of epochs as rows and the number of time points as columns (See example in Fig  7A ). This can be used to identify prominents peaks either for the low or high frequency components. More importantly, in order to address the question of how to optimize the selection of the starting and ending frequencies for the intervals to use for PAC computation, Tensorpac allows for the possibility of defining triangular vectors and computing PAC for a range of starting and ending frequencies ((  ,   )). In addition, this triangular search can be used to find the best interval for the amplitude or for the phase.
Statistically tests of stationarity As mentioned earlier, stationary signals are a prerequisite for calculating phase-amplitude coupling. Tensorpac includes a function (tensorpac. stats. test_stationarity) to perform an Augmented Dickey-Fuller test (ADF)  [62]  to test the stationarity of time-series. The null hypothesis of the ADF test is that there is a unit root in the time-series. Said differently, H0 represents a nonstationary signal. Tensorpac uses the Statsmodels Python package  [63]  and returns a dataframe that contains, for each epoch, the p-values, a boolean if H0 has been accepted or rejected, the statistical test and critical values at 0. 05 and 0. 01.
Tensor-based implementation Traditionally, phase-amplitude coupling measures are implemented in a vector fashion where only a single time-series is processed at a time. This is the most straightforward implementation and the easiest to read, however computing PAC on relatively large datasets can be dramatically slow, especially when the number of dimensions increases (i. e the number of trials, phases and amplitudes). In Tensorpac, all of the implemented PAC methods have been adapted in order to support multidimensional computations and thereby decrease computing time. This was made possible by using the NumPy function einsum which uses the Einstein summation convention in order to perform linear algebraic array operations. In order to illustrate the gains in terms of computing time, we compared the vector-based and the tensor-based implementations on simulated data (100 trials and 3000 time points).
We then computed a comodulogram with 26 phases and 24 amplitudes. Figure  8  presents the comparison of computing time per method (Fig 8A ) and the ratio (Fig  8B ). The tensor-based implementation is between 6 times to more than 12 times faster than the vector-based one depending on the PAC method that is used. This gain in terms of computation time would be obviously even larger when considering an increasing number of phases and amplitudes or if the permutations for assessing statistics have to be measured. We first generate a relatively small dataset composed of 100 trials of 3000 time points each. We then evaluated the comodulogram by extracting 26 phases and 24 amplitudes. The comodulogram is then either computed using onedimensional time-series (vector-based) or directly using multidimensional arrays (tensor-based).
Documentation and API provision Tensorpac is a Python 3 package and is distributed under a BSD-3-Clause license. This package relies on NumPy  [64] , SciPy  [65] , Joblib for parallel computing and Matplotlib for plotting. In addition, Pandas  [66]  and Statsmodels  [63]  is required if the stationarity test is to be performed. We also provide full documentation for the package (https://etiennecmb. github. io/tensorpac) which is automatically built using sphinx. This documentation also explains how to install Tensorpac. An API tab is accessible from this documentation and describes the most up-to-date implemented functions and descriptions (using NumPy doc convention). It also feature a gallery of examples, built with sphinx-gallery, which illustrate all classes and functions of Tensorpac.
For beginners or non-python users that want to cross the open-source bridge, we added a public Gitter chat for answering questions. Finally, the code follows PEP8 and Flake8 guidelines for code readability. We also added a suite of unit-tests that are systematically launched on Linux and Windows systems. we also presented some unique features such as the preferred phase or an exhaustive research of frequency bounds. As spurious coupling can be observed in many scenarios, we also provide additional tools and statistics to control both the reliability of an estimation. The latest version of Tensorpac is hosted on Github (https://github. com/EtienneCmb/tensorpac) but can also be installed via the pip command from a regular terminal. Tensorpac comes with an online documentation that describes installation options, also for contributors, the functionalities and illustrative examples.
Figure1: Estimation process of a corrected 10<->100 hz PAC. For illustration, here simulated raw data contains a coupling between a 10 hz phase and a 100 hz amplitude. First, the raw data is respectively filtered with bandpass filters centered on 100hz and 10hz. Then, the complex analytic form of each signal is obtained using the Hilbert transform. The phase is extracted from the 10Hz signal (angle of analytic signal) and power from the 100Hz signal (amplitude of analytic signal). An uncorrected PAC measure is obtained from these two signals. To estimate the null distribution of the measure in the absence of any genuine coupling, the amplitude signal is split into two blocks at a random time point and the temporal order of those two blocks is swapped. Then, the PAC is estimated using this swapped version of amplitude and the originally extracted phase.
Acknowledgments EC and AB were supported by the  French National Agency  ( ANR-18-CE28-0016-01 )). EC was also supported by funding via a  Natural Sciences and Engineering Research Council of Canada (NSERC). JLPS acknowledge support from the  Brazilian Ministry of Education  (CAPES grant  1719-04-1 ) and the  Fulbright Commission  to  J. L. P. Soto. KJ was supported by funding from the  Canada Research Chairs program  and a  Discovery Grant  ( RGPIN-2015-04854 ) from  NSERC (Canada) , a  New Investigators Award  from  FQNT  ( 2018-NC-206005 ) and an  IVADO-Apogée fundamental research project grant. This research is also supported in part by the  FRQNT Strategic Clusters Program  ( 2020-RS4-265502  - Centre UNIQUE -Union Neurosciences & Artificial Intelligence -Quebec  ). TC acknowledges support through the  Centre de Recherches Mathématiques (CRM).
An implicit, staggered elastoplastic version of the phase-field approach was implemented in the commercial finite element code Abaqus through the UEL option. By means of simple examples we show that localized ductile deformations first increase both resistance and toughness. Then, after a maximum value, the resistance starts to decrease with a significant increment in energy dissipation. By favoring shear deformation over tensile failure the fracture pattern changes. First the branching disappears, then the crack propagation angle changes and becomes a shear band. Finally, we observed the increment of the instantaneous dynamic stress intensity factor during the acceleration stage of the crack without introducing a rate dependent critical fracture energy. We explained this phenomenon with the increasing roughness of the fracture surface.
Only two implementations  [61, 62]  exist with details on the development of the theory and some examples. Despite the benefits, the use of phase-fields is still limited in practice because none of the commonly available finite element software provide it as a built-in option. Recently Liu et al. [63]  published a dynamic phase-field model using Abaqus, however their subroutine is still reserved for private use. A smart implementation was proposed recently by Azinpour et al. [64] , who used the built-in thermal module of the software and used the temperature field as damage. Furthermore, hydrogen induced cracking  [65]  and a coupled cohesive zone model  [35]  was implemented in Abaqus using either UMATs or UELs. Both authors provide an open-source version of their codes. Crack propagates in an unstable or dynamic manner if the energy release rate increases with crack growth.
Finally, with the help of the newly formulated element we study the impact of plasticity on the fragmentation patterns, the crack velocity and the macroscopic fracture toughness in dynamic crack propagation. As a summary, we propose a robust and versatile open-source subroutine for practical engineers as well as research scientists in a widely available commercial finite element code Abaqus. The implementation can be used to study both brittle and ductile static and dynamic fracture. The paper is structured as follows: Section 2 presents the theory of the elasto-plastic dynamic phase-field formulation. Section 3 asses the effect of the yield parameters in the quasi-static case. While section 4 shows 2D and 3D dynamic crack topologies, and section 5 presents the instantaneous fracture toughness obtained using the Abaqus implementation.
Staggered time-integration algorithm There is an ongoing discussion whether the monolithic or the staggered scheme should be used in practice. In the case of unstable crack propagation, the monolithic solution  [31]  tends to become numerically unstable as the algorithm needs to find the fully formed crack path in only one iteration, which is of-course impossible. There are various techniques, such as viscous regularization  [40] , path following algorithms  [41] , or even globalization  [34] , which can improve the solution. However, none of them are suitable for the code implemented in Abaqus. Therefore, present the paper focuses on the staggered scheme with the history variable proposed by Miehe et al. [30]. This method solved the topological problem and provided incredible robustness to the technique.
Conclusion This study is set out to investigate the effect of plasticity on dynamic fracture propagation. An elastoplastic version of the phase-field approach was implemented in the framework of a staggered implicit dynamic time integration scheme. We chose to use the commercial finite element code Abaqus with the UEL option because it is widely available in both industrial workspaces and in academic laboratories. Therefore, this 2D and 3D implementation enables practicing engineers and scientists to simulate easily not only static but dynamic and ductile crack propagation with the same UEL subroutine. With the help of this novel approach we studied the fracture patterns, the crack tip velocity and the instantaneous stress intensity factor (K ID ) during dynamic fracture for various elastic and ductile cases.
Implementation The consensus-based rescoring functions are implemented in python code accessible on GitHub, which operates directly on ZDOCK output files, and allows to treat rapidly thousands of docking poses (typically a few seconds for 2,000 poses, up to 1 min for 54,000 poses). In addition, the code allows to compute statistics on a given set of poses and re-score another of structures (see "Results" section). All the scripts necessary to reproduce the results shown in this article are available at: https://github. com/ MMSB-MOBI/CHOKO.
Combining Clusters Clustering is classically used to improve the performance, by reducing the structural redundancy of docking solutions  (Kozakov et al. , 2005; Hwang et al. , 2010b; Koukos et al. , 2020). Here, we used the BSAS clustering algorithm, which takes into account the scores, to cluster poses by their ligand center of mass. When applied to ZDOCK results, independently of rescoring, we obtained an improvement in terms of number of successes: 34 successes instead of 29, reflecting a structural redundancy of the ZDOCK set. We first analyzed the overlap between ZDOCK results and the results of each rescoring function when using structural clustering, see Figure  2. As shown in Figure  2 , the results of ZDOCK and rescoring functions are partially overlapping also after structural clustering.
ACKNOWLEDGMENTS This manuscript has been released as a pre-print at bioRxiv (Launay G,  Ohue M ,  Prieto Santero J, Matsuzaki Y, Hilpert C, Uchikoga N , et al. Rescoring ensembles of proteinprotein docking poses using consensus approaches. bioRxiv. 2020;  2020. 04. 24. 059469. doi:  10. 1101/2020. 04. 24. 059469 ) (Launay et al. , 2020). We would like to thank  Robinson Martin Minard  for his support during this work.
As the topic of HAT is multidisciplinary in nature, similar to  [33] , we included all the articles found without considering the journal or discipline they belong to. In step 2 we downloaded all the records from the WoS portal to conduct a bibliographic analysis and performed a co-citation analysis (CCA) in the VosViewer software, as cocitation analysis provides the theoretical background for the intended topic  [32]. "Cocitation is defined as the frequency with which two documents are cited together"  [34, p. 269 ]. Before using the records for creating CCA analysis it is important to ensure that the documents included for conducting the analysis are relevant to our study and match the criteria. First, we looked for any duplicate records in the filtered n=789 documents and we found two duplicate records and removed them.
One of the limitation of this study is the breadth of our search which is based on the Ergonomics domain in Web of Science (WoS)  [32]. Broadening the search to include other domains, might bring to light studies that might have not been published in our selected database. Other scholars could develop a bibliographic coupling analysis (BCA) in order to highlight future research trends to cross it with the SHAMT concept. All in all, our study recommends that future research should focus on all the elements of a maritime ecosystem to optimize the effectiveness of autonomous human-machine teammates incorporating environmental, social and human needs for interactions with autonomous machines and AI. The ideas developed in this research may also be useful in other transportation industries such as aviation  [16]  space exploration, SAR (Search and Rescue) operations, UAV (Unmanned Aerial Vehicles) UGV(Unmanned Ground Vehicles) enabling both researchers and practitioners to perform tasks in extreme environments and thus improve disaster management without putting human operators at risk  [67].
Measurements During Norte #33 there were several instruments deployed in the TL to measure physical variables as part of the JEST project. The location of the Acoustic Doppler Current Profilers (ADCPs) and temperature-pressure (TP) sensors is shown in Figure  1. Of the temperature-pressure sensors that were installed, the TP-01 was lost at the end of January 2010, whereby the data series at that location starts on July 5, 2010. Also, during the Norte, one of the safety loops of the TP-06 sensor broke due to the strength of the waves, and data collected after the Norte event were noisy. Therefore, those measurements were discarded. The data from the other four TP sensors (TP-02, TP-03, TP-04, and TP-05) were used in the analysis, along with two current profilers (CP-00 and CP-07). All these sensors collected data every 10 min.
Bathymetric and topographic information The same bathymetric data described by Contreras et al. (2014)  was used. This bathymetry was obtained from the digitization of nautical charts of the Mexican Navy and echo sounding campaigns during the monitoring period. For the terrain elevation data, LIDAR data of 5 m resolution from INEGI (2012) was processed. Due to limited computing resources, we sub-sampled data at a resolution of 25 m, which was considered enough for the flood model resolution.
The time series of wind speed and surface pressure of the WRF hourly outputs were extracted for the grid points closest to the locations of the two meteorological stations (MS-01 and MS-03) to compare with the observations. The WRF model was able to capture the Norte events occurred during the simulation period. There is a good representation of the wind speed intensification during the occurrence of the Norte, as well as for the cycle of sea-land breezes. In Figure  2 , the wind speed and direction are compared with observations from the meteorological station at Aguada Island (MS-03). The MS-01 station has not data during the Norte, but for the rest of the measurement period the data for this station shows less directional variability compared to the MS-03 station. Not all the wind variability is reproduced due, in part, to the effect on the wind direction generated by some tall buildings near the meteorological station.
Model setup The creation of unstructured meshes is a complex task, but there are many free software and commercial tools to accomplish this task. One of the most valuable adopted tools is MESH2D  (Engwirda, 2014) , which is a MATLAB/OCTAVE-based unstructured mesh-generator for two-dimensional polygonal geometries, providing a range of relatively simple, yet effective two-dimensional meshing algorithms. The unstructured mesh created for this study (Fig. 4 ), has 178 716 nodes and 357 218 cells or triangular elements. The length of the smallest elements is about 30 to 50 m along the coastline and the largest is about 10 km in the area farthest from the coast. Isla Aguada station WRF model output The mesh has nodes in sea and land to study the floods produced by intense extreme events and to make easier the identification and quantification of flooded areas.
The bathymetry and topography data were interpolated to the mesh nodes using an Inverse Distance Weighted function (gIDW) implemented in MATLAB  (Langella, 2010). The FVCOM model was configured to run in 2D barotropic mode instead of the full 3D baroclinic mode  (Weisberg and Zheng, 2008; Zheng et al. , 2013). This was decided after some tests to evaluate the performance of the model to reproduce the measurements of the sensors and comparing the flood areas obtained with both modes. The tests showed no significant differences, but the computation time using the 2D barotropic mode was 40 to 45% less compared to the 3D baroclinic mode. Also, the flooding process involves the transport of small water columns, for which a 2D barotropic mode is considered appropriate. For the storm surge simulations, the model was forced by wind speed and atmospheric pressure acting on the surface, and tide elevation at the open boundaries.
The 10-m zonal and meridional wind components (U10, V10) and surface pressure (PSFC) variables were extracted from the WRF simulation. The tidal elevation consists of eight major tidal constituents (M 2 , S 2 , N 2 , K 2 , K 1 , O 1 , P 1 , and Q 1 ) obtained from the TPXO7. 2 database  (Egbert and Erofeeva, 2002)  and interpolated to the mesh nodes at the open boundary. To avoid artificial numerical modes from being generated by a sudden impulse of the initial condition  (Chen et al. , 2006) , the tidal forcing was increased from zero to its full value over two M 2 tidal cycles. The time step used for the external mode was 2 s, and the output of the model is hourly. Several test simulations were performed with different spatial-varying bottom roughness coefficients to better reproduce the tidal signal inside the TL.
Conclusion The water level and environmental conditions during an intense Norte event occurred in February 2010 were analyzed based on tide gauge and weather station data and by numerical simulations using a calibrated hydrodynamic model. The FVCOM model turned out to be a suitable tool to study changes in storm surge impacts due to sea level rise because of its adequate representation of water level variations during the event. The model simulated wet and dry cells, tidal propagation, and the response to atmospheric forcing. WRF wind was used for the surface momentum forcing. The simulations performed for this study covered the period  Results from the case study showed that an intense Norte could generate a significant storm surge in the TL area flooding large zones, including Carmen Island where the CdC city is located.