dataesr/scientific-paragraphs-categorization huggingface dataset:
Model approach We present numerical buoyancy-driven geodynamic subduction models using the code Underworld2  (Mansour et al. , 2019) , version 2. 7. The code is based on Underworld, developed by  Moresi et al. (2003 Moresi et al. ( , 2007)). In the older and newer versions of the code, distinct volumes such as the different layers of the tectonic plates, the sub-lithospheric upper mantle and lower mantle are represented by Lagrangian particles. The particles are embedded within a rectangular, Eulerian finite element mesh. The mesh discretises the problem, while the governing equations are solved on the nodes of the mesh. Underworld2 uses Python scripts to build the model setup (geometry, mesh dimensions, spatial mesh refinement, rheological functions) and to control the solver options. The models have been specifically designed to investigate subduction initiation in the overriding plate on the backarc side of a long-lived and wide (large trench-parallel extent) subduction zone (South American-Antarctic subduction zone).
Acknowledgements WPS would like to thank the editors of the Special Issue on plate tectonics for their formal invitation to submit a contribution to their Special Issue of Earth-Science Reviews. We would also like to thank editor  Gillian Foulger , who did all the editorial work for this paper, and three anonymous reviewers, who provided constructive comments on the paper. We would further like to thank  Louis Moresi ,  Mirko Velic ,  Julian Giordani ,  John Mansour  and  Owen Kaluza  for technical support with, and development of, the  Underworld2 code. This work has been funded by a  Vici Fellowship  ( 016. VICI. 170. 110 )from the  Dutch National Science Foundation (NWO)  awarded to WPS, and has been supported by computational resources from SURFsara, the Dutch national supercomputer facility, through project number  2019.
Code availability The Underworld2 code that was used to generate the numerical models is an open-source code and is available at https://www. underw orldcode. org and also at https://doi. org/10. 5281/zenodo. 3520250 (Mansour et al. , 2019 (Mansour et al. , , 2020)). The Gplates software that was used to build the reconstructions is available at https://www. gplates. org. All the data generated by the numerical models and the plate tectonic reconstructions that are necessary to evaluate this work are included in this published article. All the information that is required to reproduce the numerical model results are presented in this published article.
The Coco program: learning vs. phenotype To investigate the possible roles of proteins that are stable and that can interact with many partners in preserving the phenotype, we model the role of the interactions of a subset of macromolecules in determining the phenotype of the cell at any given time. In this modelling, we use a previously published, machine learning program, Coco  [28, 29] , that simulates, at a very simple level, how a cell evolves into a differentiated state (Figure  2 ). The idea here is to show that, during a learning task mimicking natural selection, conditions can exist that select a PPDP-like element. This program can have thousands of elements that can mimic macromolecules such as proteins; each element can be either active or inactive in determining the behaviour of the system in a time-step; an element is active if it is a member of the current state of the Active subset of elements (and inactive if it is not).
Simulation: A PPDP constrains phenotype space The acquisition of a subset of phenotypes or phenotype trajectory by a cell via natural selection during its evolution was simulated by Coco. This program learns to select its constituent elements (alias 'macromolecules') to be in the subset of elements that determines the system's behaviour in successive time-steps; this selection is based on the connections between elements; these connections result from reward and punishment strategies that strengthen or weaken these connections (cf. Methods). Coco was given the task of learning to respond correctly to the input sequence of (1, 2, 3, 2, 3) from the environment with an output sequence of (1000, 999, 999, 1000, 998) to that environment; in these input-output sequences, Coco has to learn to respond to the input of Element-1 with an output of Element-1000, this is followed by an input of Element-2 and a learnt output of Element-999 etc.
The idea that one function of the IDPs, or at least of the PPDPs, is to preserve the phenotype via their stabilizing properties is the key element of the Sherpa hypothesis as illustrated in Figure  1. The importance of the unfolded/misfolded proteins with non-conservative mutations in the evolution of protein networks has been demonstrated at the proteome level  [47]. Therefore, the opportunity for the preservation of the phenotype of the cells by PPDPs is reasonable, especially if they are associated with partners at very moment of their expression. The ongoing complexation of these proteins by the PPDPs is modelled in the Coco program by the many interactions possible for the PPDP elements and by the continued presence of the PPDPs (via the assumption of a zero Downtime i. e. , complete stability) for the PPDP elements themselves.
One can argue that the depth of basins for these phenotypes is strongly determined by the expression/transformation of the two PPDP proteins; i. e. their intracellular concentrations in the course of their homeostasis. A model system has been established with SYN and TPPP, which are expressed endogenously in neurons and OLGs, respectively, in normal human brain (Figure  7 )  [34, [48] [49] [50]. The Coco program simulates certain related experimental phenomena, namely, the promotion of phenotype alterations by the transmission of SYN into OLGs or of TPPP into neuronal cells, which is due to the hetero-association of the disordered TPPP with another disordered protein, SYN  [17, [51] [52] [53] [54] [55]. In addition, the pathological overexpression of SYN could be considered as acting as a second PPDP resulting in SYN assembly with phenotypic alteration as illustrated by the Coco program.
Coco program In this version of Coco, a thousand elements are connected initially with one another in a pattern that has some structure. In essence, connections were made at random between the 36 elements in a group of elements (e. g. , between elements with addresses 50 to 86) for each of nine groups (with addresses 150 to 186, 250 to 286, 350 to 386 etc. ). Three of the thousand elements are chosen to be input elements (with addresses 1, 2 and 3), and three are chosen to be output elements (998, 999 and 1000). Coco learns to respond to repeated cycles of an input sequence of (1, 2, 3, 2, 3) with an output sequence of (1000, 999, 999, 1000, 998). Most elements have a Now and a Next eld that can vary in size from containing the address of just one element to containing the addresses of up to 8 different elements per eld (N.
We also give Element-10 large Now and Next elds of xed sizes that can each contain 60 addresses (corresponding to the many proteins with which an IDP might interact). These addresses run from 111 to 140 in Element-10's Now eld and from 141 to 170 in its Next eld; each of these addresses occurs twice. It is important to note that the addresses in Element-10's elds are xed at the start of the program and cannot be changed -in other words, they are exempt from the rewarding and punishing routines. To model the effects of a perturbation, we rst let Coco learn and, when this learning is stable, disable the rewarding and punishing routines along with those responsible for forced inputs from the environment (1, 2 and 3) and for forced outputs. Coco then continues with an unchanging cycle of states of the Active subset, which we take to represent a cell that has undergone natural selection then remaining in a differentiated state or phenotype (more accurately, a cycle of phenotypes) despite the absence of selective pressure.
At this stage, we subject Coco to a major perturbation. Firstly, in the time step 999, we double the size of the Active subset so that from then on a state of the Active subset contains 12 addresses. Secondly, in the next time step where there would otherwise be an input of 1, we create a new state of the Active subset; the addresses in this new state are those of elements that do not belong to those that are in the learnt sequence (i. e. , they are inactive elements). To model the effects of the introduction of a second IDP, we use a slightly different version of Coco. After the program has learnt, we create a second IDP using the inactive elements. The pattern of connections between the elements that are active in one of the last twenty-four states of the Active subset is recorded (which more than covers an entire learnt sequence of states).
Analysis of intractability of SYN and TPPP Interactability of human SYN (UniProt ID: P37840) and TPPP (UniProt ID: O94811) was analysed by the online database resource Search Tool for the Retrieval of Interacting Genes (STRING)  [68]. STRING generates a network of predicted associations based on predicted and experimentally-validated information on the interaction partners of a protein of interest  [68]. In the corresponding network, the nodes correspond to proteins, whereas the edges show predicted or known functional associations. Seven types of evidence are used to build the corresponding network, and are indicated by the differently coloured lines: a green line represents neighbourhood evidence; a red line -the presence of fusion evidence; a purple line -experimental evidence; a blue line -co-occurrence evidence; a light blue linedatabase evidence; a yellow line -text mining evidence; and a black line -co-expression evidence  [68].
Cloud phase determination Layers identified as cloud then undergo a cloud-phase determination step. Here, the layer integrated linear depolarization ratio and attenuated backscatters are used in a manner similar to that used in CALIPSO data processing. Hu et al. (2009)  showed that this combination of parameters can effectively discriminate ice from liquid and horizontally oriented ice crystals. The liquid water relationship was calculated using Monte-Carlo lidar radiative transfer simulation results  (Hu et al. , 2007)  for a range of extinctions and particle sizes. For ATLID lidar, Monte-Carlo simulations have shown that the relationship between layer-integrated attenuated backscatter and layer depolarization are expected to be similar to those measured by CALIPSO  (Donovan et al. , 2015).
, 2013; Delanoë and Hogan, 2010,  https://web-backend. icare. univ-lille. fr/projects/dardar) which combines CloudSat and CALIPSO measurements. The AC-TC product will, however, incorporate the improvements in target classification provided by EarthCARE's new high spectral resolution lidar and Doppler radar capabilities, as shown below. The AC-TC processor assigns all points in an EarthCARE granule to the classifications defined in Table  4  by merging A-TC and C-TC products according to a synergistic decision matrix (Figure  2 ). Some preprocessing is needed for the input products, in order that the atmospheric profile obtained from each instrument has the same altitude sampling. ATLID and synergistic data products use a common joint standard grid (X-JSG;  Eisinger et al. , 2022) , which is defined by the vertical resolution of ATLID (≈ 103 meters), along-track according to CPR (1 km, corresponding to ≈ 2 radar pixels), with an across-track sampling size of 1 km.
A-TC and C-TC are defined different vertical grids: ATLID data are on the X-JSG by definition, while C-TC is at the native radar sampling. Both vertical grids have an approximate 100 meter sampling. The C-TC variables are regridded onto the X-JSG vertical grid using a nearest neighbour approach, which will result in relatively small vertical displacement (a maximum 50 m offset) that is not expected to result in any noticeable issues. The top of the cloud will be defined by lidar signals due to its higher vertical resolution. concentration, or a mixture of these. Regarding the radar, the reflectivity of its emitted signal provides information on optically thick ice clouds and on precipitation (snow or rain). Radar measurements take precedence over lidar for the identification of rain, drizzle and snow.
Moreover, such a modeling framework allows for capturing and modeling prosodic fea-tures  (Kharitonov et al. , 2021a) , as well as speaker identity  (Borsos et al. , 2022) , or even natural dialogues  (Nguyen et al. , 2022). This is in contrast to using textual features, as they do not encode such information. Unit-to-speech module converts the speech discrete units to a raw waveform. Lakhotia et al. (2021)  used a Tacotron2. 0  (Shen et al. , 2018)  based model followed by WaveGlow  (Prenger et al. , 2019 ) vocoder. Later, Polyak et al. (2021)  proposed a unit-based vocoder based on the HiFi-GAN architecture to convert units to speech directly. Such a paradigm seems to provide high-quality generations with better efficiency as it uses only one model rather than two. Kreuk et al. (2021)  and  Lee et al.
(2021)  additionally improved the unit based vocoder to include emotional tokens for speech emotion conversion tasks, and duration modeling for direct speech-to-speech translation. Zero-shot Evaluation. Evaluating such a complex pipeline comprised of several components is a challenging task. Lakhotia et al. (2021)  proposed a set of zero-shot evaluation tasks aiming for each of the modules. Overall the proposed tasks can be divided into four main groups: (i) acoustic encoding using ABX, bitrat, (ii) language encoding using sWUGGY, sBLIMP  (Nguyen et al. , 2020; Lakhotia et al. , 2021) , (iii) resynthesis using Phoneme/Word Error Rate; (iv) speech generation using VERT  (Lakhotia et al. , 2021) , Meaningfulness Mean Opinion Score.
Evaluation In the following, we study current state-of-theart representations for generative spoken language modeling using the proposed metric. The current popular quantization technique is a k-means model trained on top of a pre-trained encoder  (Lakhotia et al. , 2021). In our evaluation setup, we use a different number of clusters and encoder architectures. Our ablation study include quantizers with 50, 100, 200, and 500 clusters. We further investigate our metric on top of HuBERT  (Hsu et al. , 2021) , wav2vec2  (Baevski et al. , 2020) , and WavLM  (Chen et al. , 2022). For readability, throughout the paper, we report results for the Hu-BERT model while leaving the rest of the results in the Appendix material.
Differently, the acceptability judgment test (sBLIMP) requires detecting the syntactically correct sentence from a pair of sentences, one of which is syntactically correct and the other is wrong. The detection is based on the perplexity of the language model. As presented in Table  2 , our method enables improvement in all the investigated setups for both the spot-the-word and acceptability judgment tests. This is especially noticeable for a larger number of units. For instance, when considering 200 or 500 units, the absolute improvement of the sWUGGY score is 4. 17 and 3. 21, respectively.
B Extended Experimental Setup Models. We study our method using the base versions of HuBERT, wav2vec2, and WavLM. Similar to prior work, for HuBERT and WavLM, we use the ninth and sixth layers for wav2vec2. For readability, we report results for HuBERT in the main paper. The results for wav2vec2 and WavLM are presented in Appendix C. In our quantizer learning process, we use a learning rate of 0. 0001, a batch size of 32, and Adam optimizer (Kingma and Ba, 2014). Our quantizer is composed of three fully connected layers with LeakyReLU activation between them. The dimensions of those layers are determined by the division floor of the difference between the upstream dimension to the number of units. We train our quantizer using a single NVIDIA V100 GPU. Datasets. To match the current k-means popular training set, we use the Librispeech-100h to learn our quantizer  (Panayotov et al.
, 2015). We analyze our metric using the 'clean' and 'other' development sets from Librispeech. The augmentations in all setups include time stretch, pitch shift, reverberation, and noise injection (exact parameters are detailed in Section 3. 2. 1). For the sWUGGY and sBLIMP evaluations, we use the 'big' transformer language model from  Lakhotia et al. (2021). This appendix begins with a detailed explanation on the Levenshtein distance (Section A). Then, in Section C, we present additional results. We report results on two additional state-of-the-art selfsupervised speech representations. We show that our method is indeed effective for those representations as well as shown in the main paper.
The main goal of the present paper is to establish the connection between the small-scale (∼30-500 pc) supersonic motions of the ionised gas (in the form of turbulence or expanding superbubbles) and the stellar population for a sample of nearby (D < 20 Mpc) star-forming spiral galaxies. We investigate 19 galaxies from the PHANGS survey 1 for which both integral-field spectroscopy (IFS) with the Multi Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT;  Emsellem et al. 2022 ) and multi-band imaging with the Hubble Space Telescope (HST;  Lee et al. 2022)  are available. With this dataset in hand, we can simultaneously analyse the properties of the ionised gas and the young stellar associations, and link their properties to each other. In particular, after identifying the regions dominated by non-circular supersonic motions of the ionised gas via their elevated velocity dispersion, we can compare their kinetic energy with the energy input produced by massive stars to judge the importance of stellar feedback in powering the regions of high velocity dispersion and estimate the efficiency of the mechanical stellar feedback.
Description of the data cubes and their analysis The important advantage of MUSE is the simultaneous combination of high angular resolution (PSF is given in Table  1  for each galaxy), small pixel size (0. 2), and large field of view  (1 × 1 ). PHANGS-MUSE data cubes represent the mosaics of three to 15 MUSE fields for 19 nearby star-forming galaxies and cover a large part of their discs (extending to at least half of their optical radii, R 25 ). In the present paper, we use the publicly available 2 data cubes and maps ('copt' dataset with homogenised PSFs) with the distribution of properties of the ionised gas. The maps were obtained with a data analysis pipeline (Dap 3 ; based on the Gist 4 code by  Bittner et al. 2019 ), which we also apply further to analyse the extracted spectra of the regions of interest.
(2017). The mean value of σ LSF (Hα) is 48. 9 km s -1 after averaging over all sample galaxies. We do not correct σ(Hα) for thermal and fine structure broadening, as their contribution is negligible compared to the values measured in this paper. The maps of the stellar mass surface density Σ measured from the stellar continuum spectra and provided in the internal data release DR2. 2 are used for the analysis in Sect. 5. 2. We also analysed the integrated spectrum of each region with locally elevated σ(Hα). For this purpose, we ran the 3 https://gitlab. com/francbelf/ifu-pipeline 4 https://abittner. gitlab. io/thegistpipeline Dap on the extracted integrated spectra for each of these regions with the same settings as for the main PHANGS-MUSE products. As a result, we obtained information on the fluxes of the main diagnostic emission lines used in our later analysis (Hβ, [O iii] 5007 Å, [S ii] 6717, 6731 Å, Hα, [N ii] 6584 Å), and their velocity dispersion (corrected for instrumental broadening in the same way as described above).
Our analysis further relies on the catalogue of nebulae detected within PHANGS-MUSE galaxies (hereinafter -the PHANGS-MUSE nebular catalogue;  Groves et al. 2023). It was created by identifying H ii regions using the HIIphot code  (Thilker et al. 2000) , and includes derived physical properties of these regions. In the present paper, we use the information on the electron density (derived from the ratio of the [S ii] 6717/6731 Å lines using Pyneb;  Luridiana et al. 2015) , size, total reddeningcorrected Hα flux of the region, and gas-phase metallicity (traced by oxygen abundance 12 + log(O/H) measured using the Scalibration from  Pilyugin & Grebel 2016). We consider only those nebulae classified as photoionised H ii regions (based on the diagnostic BPT diagrams;  Baldwin et al. 1981). Details of the PHANGS-MUSE nebular catalogue compilation and the parameters are provided in  Groves et al.
Qualitatively, these distributions agree with what was obtained by  Law et al. (2021b)  from a similar test for MaNGA data. As can be seen, we can trace the velocity dispersion in the Hα line with MUSE with a precision better than 30% (median uncertainty is ∼12%) starting from σ(Hα) ∼ 40 km s -1 for a S /N = 30 or higher, and increasing the S/N above 30 does not improve the precision significantly even for much higher values of σ(Hα). 5 The mosaics in PHANGS data were obtained from individual exposures obtained at 4 different orientations in order to minimise the instrumental artefacts and spatial variations of the LSF. The individual exposures in the deep field from  Bacon et al. (2017)  were obtained at 8 different orientations. 6 Law et al. (2021b) compared the results of fitting the simple Gaussian and more realistic inverse gamma error distribution to the modelled line profiles and found a small ( 10%) difference in σ(Hα) when both σ(Hα) and S/N are low, and negligible difference at high S/N.
PHANGS-HST All 19 galaxies considered in this paper have publicly available HST observations obtained as part of the PHANGS-HST survey (PI: Lee). Some of the data 7  were obtained within the LEGUS (Legacy ExtraGalactic Ultraviolet Survey) programme  (Calzetti et al. 2015). Images in 5 broad-band filters were obtained with Wide Field Camera 3 (WFC3) or Advanced Camera for Surveys (ACS) in bands roughly corresponding to the NUV, U, B, V, and I bandpasses. A complete description of the PHANGS-HST survey and details on the data reduction are provided in  Lee et al. (2022). At the HST resolution (PSF ∼ 0. 08 = 2. 0-7. 6 pc at the distance of our galaxies), stellar associations can be identified by morphologically associating neighbouring young (NUV-bright) sources. Larson et al. (2023)  have constructed a catalogue of stellar associations, which were defined as local peaks in the NUV band, together with location masks derived by applying a watershed algorithm.
(2023). The catalogues of stellar associations and compact star clusters provide information on their mass and age, computed from fitting the observed spectral energy distribution (SED) in all 5 bands with Cigale  (Boquien et al. 2019 ) assuming theoretical models appropriate for a single stellar population from  Bruzual & Charlot (2003). The details of the SED fitting of the PHANGS-HST data are given in  Turner et al. (2021). In the present analysis, we use the measurements of the total stellar mass and the luminosity-weighted age of each association (or star cluster), and the corresponding stellar association masks. We note that both catalogues (stellar associations and compact star clusters) are used for the analysis, although for brevity we sometimes refer to stellar associations or star clusters only.