dataesr/scientific-paragraphs-categorization huggingface dataset:
5), the party cannot be considered to even partially exhibit the outcome of adequate youth representation. The data on GDP per capita stems from the Maddison Project Database. As there are contradicting theoretical takes on whether a high or a low GDP per capita positively influences youth representation, we have operationalized the condition in the way in which it is easier to interpret: parties operating in a country with a GDP below 25. 000 USD are considered fully-out of the set, the cross-over point is set at 36. 000 USD, and parties in countries with a GDP per capita of 55. 000 USD and above are considered fully-in. The cross-over point and all other thresholds for this condition are based upon the overall data distribution in our sample, containing mostly affluent countries. They were set at points where a clear distinction between cases occurred.
The Youth Wing Index (Political Party Database, PPDB) is an additive index ranging from 0 to 1 and has not undergone calibration. The mentioning of a youth organization in the party statute is worth 0. 4 points, if the youth wing may send delegates to the party congress another 0. 3 points are added, and if the youth organization is entitled to seats on the party's highest executive body this is worth another 0. 3 points (see Appendix for more information). To measure the decentralization of selectorates we use the PPDB, which features several indicators covering different nomination levels. Following  Tuttnauer and Rahat (2023) , they have been combined to create a decentralization index (ranging from 0 to 1). Parties are considered fully-in at 0. 1, the cross-over is set at 0. 48 and fully-out at 0.
9. To measure the ideology of political parties, we use the society indicator provided by the Manifesto Project Database  (Lehmann et al. 2022) , mapping the parties' ideology on a dimension ranging from conservative to progressive. Parties are considered fully-in at 10, the cross-over is set at -8 and fully-out at -20. Data on party age stems from the Party Facts data set. Parties are considered fully-in the set if they are a maximum of three years old at the time of election. This benchmark relates to legislative periods mostly being between four and five years long. At three years of age, we can expect the party to not yet have incumbent candidates, which should benefit young candidates. The cross-over point is set at 19. 9 (roughly four to five legislatures) and parties are considered fully-out if they were founded 50 years or more before the time of election.
In our case selection, we have aimed to include as many cases from advanced democracies (V-Dem polyarchy-index > 0. 7) as possible. Fundamentally, the case selection has been data-driven: we selected cases where data was available for all conditions and the outcome variable. Data availability also determined the timeframe, as two of our central conditions stem from the Political Party Database Round 2, where data was collected between 2017 and 2019. We thus set the timeframe to include party parliamentary groups from elections between 2017 and 2020. 3 Finally, to run our QCA, we filtered for parties with at least ten MPs to make sure that the outcome variable is not biased by outliers. Our analysis is thus limited to larger (more successful) parties and to parties operating within parliaments where even smaller factions have more than ten members (large parliaments or majority systems).
Notes 1. The selectorate is defined as the decision-making body/bodies regarding candidate selection. 2. Replication Material can be found at Harvard Dataverse: https:// doi. org/ 10. 7910/ DVN/ CDZMPM 3. The timeframe led to the exclusion of parties from Bulgaria, as the last elections were held in 2016 and 2021. We also decided to exclude Israel, as MPs change too frequently between the parties for our theoretical framework to be applicable to the case. 4. All the other listed parties are either left(-leaning) or green parties. The case BEL-CD&V is therefore special, as it is Christian democratic. The CD&V's party manifesto for the 2019 elections scored high on the Party Manifesto Project's welfare indicator, which is why it is identified as a progressive party by the progcons indicator.
Notes on the Age Representation on Party-Level Data set To calculate our outcome variables, we used the age at the day of election and not the age at the beginning of the tenure, as the duration of the time between election and the beginning of a legislative period varies quite strongly over different countries and thus, using the time of election enhances comparability. As information on whether MPs were directly elected or entered parliament at a later point in time (as replacements) was not always available, both are included in our calculations. Hence in some cases, the number of MPs included in the calculation might not align with the number of seats the party held during the legislative period. For those MPs that entered parliament over the course of the legislative period, their age is still calculated for the time of election and thus will not correspond exactly with their age at the time of entering parliament.
Acknowledgements We would like to thank the panel participants and discussants at GraPa 2023, EPSA 2023, ECPR 2023 and especially the two anonymous reviewers for their valuable and very constructive feedback on earlier versions of the article. Additionally, we would like to thank  Megan Stewart ,  Malte Jennerjahn  and  João Marques Sampaio  for their indispensable help building the data set. Kira Renée Kurz  is receiving a scholarship from  Heinrich-Böll-Foundation.
Participants that could not perform the Wii skateboard game up to level 6 were excluded from the later experiment. There were no other exclusion criteria. Originally, there were 19 participants that were recruited for the EEG experiment but five participants were excluded for reasons including the following: extremely noisy EEG data (three participants) and machine errors related to data acquisition (two participants). The experimental procedures were approved by the ATR Human Subject Review Committee (ethics approval number 158) and were carried out in accordance with the principles expressed in the WMA Declaration of Helsinki. The confidentiality rights of all participants were observed.
At the end of each game, when level 6 was finished, a final score is displayed to the participant. The participants are instructed to restart the game after level 6 until the audio task is finished. The Wii game should be continued to be played when the audio task finishes until the end of level 6. Then, the experiment is over. The button-press responses and the Wii Balance Board force sensor data were captured by lab streaming layer (LSL, UCSD, SCCN) for synchronization with other data streams. The auditory difference detection task required participants to push the B button on the bottom of the hand-held Wii mote controller when the current audio stimuli being played was different from the previous one played. There were two audio stimuli consisting of an upward chirp from 2 to 4 kHz (100 msec in duration) and a downward chirp from 4 to 2 kHz (100 msec in duration).
A total of 400 of each type of stimuli were presented in random order with an interstimulus interval randomly determined between 2 and 3 sec. The auditory difference detection task experiment was approximately 33 min long. MATLAB was used to present the audio stimuli and send triggers to LSL identifying which stimuli are presented. The low latency EPOS GTW 270 Bluetooth earbuds were used together with a low latency aptX Bluetooth transmitter to present the audio stimuli to the participants. Two cables were routed from the audio out of the computer. One cable was to the low latency aptX Bluetooth transmitter, and the other was to the Cognionics Trigger Box. The threshold on the trigger box was set to identify the onset of the audio stimuli. The audio level was set manually for each participant prior to the experiment to be as loud as they thought they could comfortably tolerate for the 30-to 40-min experiment.
Physiological recording and analysis The EEG data were measured using the Cognionics CGX Quick-32r dry-wireless EEG system (Cognionics, Inc. , San Diego). The sampling rate was 500 Hz with 24-bit analog-to-digital conversion. We used 29 electroencephalography EEG channels on the headset, with ground and reference electrodes located on the temporal bone behind the left and right ear respectively. The system utilizes active electrodes to minimize external noise pickup and artifacts. In addition to EEG data, the CGX Quick-32r headset also has accelerometer data in three axes. The EEG and accelerometer data were acquired wirelessly and streamed into LSL for recording and synchronization. The EEG data were processed using the EEGLAB toolbox  (Delorme and Makeig, 2004 ) using a similar pipeline as given in  Bigdely-Shamlo et al.
• Artifact subspace reconstruction (ASR) (see  Mullen et al. , 2013 and Chang et al. , 2018)  (Euclidian distance) was used to remove non-stationary high-variance signals from the EEG (standard deviation cutoff for removal of bursts = 20; windowed criterion = 0. 25). Two analyses were conducted: one in which the time windows were removed for which ASR did not repair completely; and another in which no time windows were removed. • Common average referencing of channels was conducted on the two datasets (time windows removed and time windows not removed) (An additional channel with all zeros was added so as to not lose 1 rank as a result of average referencing). • ICA using PCA reduction was used on the dataset in which the time windows were removed after ASR cleaning. The number of rejected channels determined the rank reduction by PCA.
• The weights of the ICA were then applied to the ASR results without the time windows removed. • Dipole fitting of the source for each independent component (IC) using Dipfit was conducted. • ICLabel (version 1. 1) was used to identify independent components (ICs) that are brain-related and artifact-related. Tonachini et al. , 2019). The criteria of selecting "Brain" ICs in our study were based on the percentage of "brain" categorization over 50%. • Brain-related ICs were retained, and all other ICs were removed from the dataset (only brain-related ICs were projected to the EEG electrodes). • The Bluetooth audio presentation system latency of approximately 45 msec was corrected. The audio events were extracted from -2 s before until 2 s after presentation of the audio stimulus based on the trigger from the audio output cable.
Source localization and statistical analysis LORETA Key software employing sLORETA  (Fuchs et al. , 2002; Pascual-Marqui, 2002; Jurcak et al. , 2007)  was used to determine source localization of brain-related activity on the surface of the cortex. The position of the electrodes of the CGX Quick-Cap 32r on the head was determined by reference to the 10-10 system within the LORETA Key software. Using the LORETA Key software, the event EEG data (-2 to 2 sec) were transformed from EEG to timefrequency cross-spectrum in the gamma frequency band (30. 5 to 50 Hz) and the alpha frequency band (8. 5 to 12 Hz), with a window width of 250 samples (continuous Gaussian window), and a delta T between running windows equal to 25 (50msec). The timevarying cross-spectra files were then converted to sLORETA files that localizes the activity across 6,239 voxels covering the cortex with 5-mm resolution.
For each participant, the sLORETA-converted individual trials were submitted to statistical analyses using a two-sample unequal variances t-test for the following contrasts: Auditory change hits vs. misses for stimuli presented during levels 1 to 6 as well as during the transition period between levels. The resultant sLORETA files for these contrasts for each participant were used in random effects group level analyses using SnPM permutation analysis (5,000 randomizations) within the LORETA Key software. The random effects level analyses for data on levels 1 to 6 included single group analysis that the mean was not equal to zero as well as single group regression with the variable hit rate of levels 1 to 6 for the contrast of hits minus misses and the variable maximum Wii score across all games played during the experiment.
Hit rate was used as the correlation metric rather than D-prime because the false alarm rate was low and only hit and miss trials were used in the analyses of brain activity. The maximum Wii score was selected rather than other potential metrics such as mean score as it is commonly what is used to assess who is better in video game performance. To further assess the extent to which the results can be concluded to be both a product of performance and workload (thought to underlying processes related to inattentional deafness), random effects paired analyses (using SnPM permutation analysis (5,000 randomizations) within the LORETA Key software) were carried out for these same contrasts for skateboarding levels 1 to 6 (high dual-task workload) compared to the transition period between levels (low workload).
Fluid and melt transport in the solid mantle can be modeled as a two-phase flow in which the liquid flow is resisted by the compaction of the viscously deforming solid mantle. Given the wide impact of the liquid transport on the geodynamical and geochemical evolution of the Earth, the so-called "compaction equations" are more and more incorporated in geodynamical modeling studies. The implementation of these equations requires a regularization technique to handle the porosity singularity in dry mantle. Moreover, it is also common to enforce a positive porosity (liquid fraction) to avoid unphysical negative values of porosity. However, the effects of this "capped" porosity on the liquid transport and mass conservation have not been quantitatively evaluated. Here, we investigate these effects using a series of 1-and 2-dimensional numerical models using the commercial finite element package COMSOL Multiphysics®.
org/10. 5194/egusphere-2023-1719 Preprint. Discussion started: 4 August 2023 c Author(s) 2023. CC BY 4. 0 License. therefore difficult to handle numerically  (Arbogast et al. , 2017; Dannberg et al. , 2019). Thus, the equations are commonly regularized by imposing a small porosity in the entire model domain (e. g. ,  Wilson et al. , 2014; Cerpa et al. , 2017). Along with an assumption of small porosity, it is also required to "cap" the porosity field to avoid the development of negative porosity values which naturally arises from the governing equations. However, despite the capped porosity being broadly used in numerical models, its impacts on the liquid transport and mass conservation have not been quantitatively evaluated. In the present study, we investigate the effect of the regularization with the capped porosity on the liquid transport and mass conservation in the two-phase flow model for the Earth's mantle, using the commercial finite element package COMSOL Multiphysics® (COMSOL, hereafter).
Hereafter, these experiments are referred to as 'capped' and 'uncapped' experiments, respectively. For the Stokes equations (Eqs. 1 and 2), we use the Creeping Flow (CF) module in COMSOL with the quadratic and linear elements for the velocity and pressure, respectively. For the heat equation (Eq. 3), we use the Heat Transfer in Fluids (HT) module with the quadratic and continuous Galerkin finite elements. The standard stabilization methods of the streamline and crosswind diffusions are used for both the CF and HT modules. For solving the time-dependent equations (Eqs. 1-3), we use the generalized-alpha method with the direct fully-coupled PARDISO solver. Eq. 5 is solved using the Transport of Diluted Species (TDS) module with the stabilization method of the streamline diffusion. Eq. 6 is solved using the TDS module for benchmarking experiment against a semi-analytical solution for 1-and 2-D solitary waves (Section 3) and the generalized Coefficient Form of PDE (CFPDE) module for all the other experiments (Section 4) because the CFPDE module is more flexible with the boundary conditions that can be applied (e.
Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy.
To address these challenges, we present Deep-HP (HP stands for High-Performance), a multi-GPU MLP platform which is part of the Tinker-HP package and enables the coupling and development of MLPs with state-of-the-art many-body polarizable effects. Tinker-HP uses massive parallelization by means of 3D decomposition which is a particularly well suited strategy for MLPs that are oen developed by decomposing the total energy as a sum of atomic energy contributions. 15, 17 The platform theoretical scalability with MLPs is linear and allows scaling up to hundreds/thousands of GPUs on large systems. As the present code shares the Tinker-HP capabilities, it allows for invoking fast physics-based many-body energy contributions. We extensively test Deep-HP scalability and implementation on the ANI model, one of the most accurate MLPs to date for small organic molecules.
50. 1. 2 Neural network potentials. Feed-forward neural network (FFNN) is a machine learning model that uses as building blocks connected layers of nodes (i. e. , neurons) each associated with their weights and bias. The output of each neuron is computed through a function of the output of the previous layer. Each weight is the strength associated with a specic node connection and they are updated during the training process. The depth (i. e. , number of layers) of the FFNN is related to its exibility and the complexity of the training dataset. Through careful optimization of hyperparameters, weights, biases and architecture, the FFNN can learn high dimensional non-linear functions such as potential energy surfaces. For HDNNP, the FFNN maps molecular structures to potential energy. The original HDNNP, introduced by Behler and Parrinello, expresses the total energy of a system E T as a sum of atomic contributions E i.
2. 2 Deep-HP: a multi-GPU MLP platform within Tinker-HP 2. 2. 1 A general machine learning platform. ] [61]  Conversely, most of the MD codes (CHARMM, GROMACS, Tinker-HP,. )  7, 62  are oen written using compiled languages such as Fortran or C/C++. To allow for the simultaneous execution of both Python-based MLP codes and Tinker-HP we implemented an interface that allows for efficient data exchanges between environments while maintaining Tinker-HP as the master process which, punctually, calls the MLP code. Identied by Tinker-HP as another computational subroutine, the MLP code should be therefore provided as a Python API. We have implemented such functionality using the C Foreign Function Interface (cffi) for Python which allows for efficient API embedding, within a dynamic library to be linked with.
Technically, within such a framework we can now call Python frozen codes from C using such cffi embedding features, thus enabling the use of various MLP codes within Tinker-HP. In that context, the recent GPU-accelerated version of Tinker-HP  17  offers the opportunity to build an overall very efficient hybrid MD/MLP code as both applications are running on the same GPU platform. To do so, we need to design a Python/C interface in a way that avoids any substantial data transfers between Python and C environments. In practice, the cffi module is not natively designed to interface data structures from device memory: its dictionary can only process host addresses on array datatype or scalar data structures. Based on these constraints, our code would be forced to perform two host-device data transfers in order to communicate through Fortran/C and Python interface.
To overcome this issue that would be detrimental to the global performance, we directly send generic memory addresses through the interface as scalar values and use the PyCUDA python module to manually cast these addresses into Tensor type that can actually be used by MLP codes. Fortunately, PyCUDA and PyTorch provide such casting routines. Thus, calling Python codes from Fortran/C with device data among the calling arguments can be done independent of the size of those arguments. Furthermore, we built the interface of the MLP code in order to keep Tinker-HP model-agnostic. In practice, Tinker-HP provides positions and neighbor lists and gets energies and forces in return. Adding a new MLP to the platform then becomes an easy task, especially if it was developed using the PyTorch or TensorFlow libraries.
17 Naturally, we wanted to preserve this property with the MLP code interface despite the fact that TorchANI is not designed to run on multiple GPUs. Using the DD method from Tinker-HP, we can isolate the local atoms of a domain and its neighbors and send the information to an MLP code instance through the interface for calculation. We also bypass the implemented neighbor list within TorchANI, and use the one of Tinker-HP. Indeed, we veried that the TorchANI neighbor list algorithm scales as OðN 2 Þ (N being the number of atoms), both in execution time and memory, which limits its applicability to small systems. For instance, a 12 000 atom water box on a Quadro GV100 GPU card supported by 32 GB memory already caused a memory overow. Because TorchANI requires a pair list of indices as a data structure, we adapted the highly GPU-optimized linked-cell method, thoroughly described in ref.
17. In practice, the list is built by partitioning the box into smaller ones and resorting to an adjacency matrix and a ltering process. Finally, the complexity of the neighbor list generation outperforms the original TorchANI implementation, thus signicantly reducing both the computational cost and memory footprint and allowing the handling of much larger systems. For example, systems made of more than 100 000 atoms are now manageable on a single 32 GB GV100 GPU. On top of that, we also noticed a constant memory allocation from Python (especially when running in parallel) which happens to be detrimental to the performance and, on some occasions, can lead to a crash. This issue has been solved by resorting to an upstream bounded buffer reservation whose size is proportional to the number of atoms in the system.
Looking in detail at the free energy acceleration strategy, we were overall able to use a RESPA approach on 12 of the 15 (14 + C5) tested ligands. The integrator was not stable enough for the C2, C3 and C4 compounds (see Fig. 3  and ESI, Table  S9  †). This is due to different reasons. First, C2 and C4 exhibited notably higher differences between the ANI-2X and AMOEBA potentials compared to other ligands. This can be easily understood when considering that C2 and C4 are actually associated with the two largest AMOEBA dataset deviations from the experimental reference values (errors of 3. 14 and 2. 94 kcal mol -1 , for C2 and C4 respectively). Since our initial choice was to not perform any specic AMOEBA re-parametrization or ANI-2X dataset modication, the strategy required to either use a tighter, but computationally inefficient Verlet/0.