dataesr/scientific-paragraphs-categorization huggingface dataset:
Our method applies the Gabor transform to an exemplar point pattern and extract multiresolution image-space features with a subsequent convolutional filtering step. The filtering step is performed using random filters which have shown success in reducing the feature space in neural networks [UBGB16; UVL18]. We then optimize a stochastic point set by using these features as target for by-example point pattern synthesis. We conduct qualitative and quantitative experiments on a large variety of patterns to demonstrate the applicability of our method for pattern creation and object placement. We further perform a spectral analysis for our filtering pipeline to interpret why random filters work. Our code is available at https://github. com/xchhuang/pps_gabor_random.
Qualitative Comparisons We compare our point pattern synthesis approach with Ma et al. Single-class point patterns. Fig. 5  shows comparisons on regular and irregular structures. Neighborhood-based methods [MWT11; RÖM*15] fail to synthesize the global structure accurately. PPS  [TLH19]  works well compared to other prior methods [MWT11; RÖM*15] but suffers from visible artifacts like holes or missing points (column b). PPS also does not capture well the regular structures in column (a) and (c). This may happen due to the fact that VGG-based filters are redundantly learned from the images' dataset and is not expressive enough to deal with point patterns. Our method handles well the regular structures and avoid any artifacts in stochastic distributions (Fig. 5b ). Please see the supplemental document for more comparisons with different point patterns.
The latter question is specifically addressed in our accompanying paper (Part B,  Reese et al. , 2022) , where long-term model simulations are used to assess whether present-day climate forcing has the potential to eventually lead to a collapse of major marine basins. The paper is structured as follows: In the following Sect. 2. 1 we present the common datasets and approaches used in our model initialisation. We then present details of the ice sheet model set-ups in Sect. 2. 2, and in Sect. 2. 3 we present the initial positions to perturbations in ice shelf buttressing (Sect. 2. 4). All three ice sheet models are initialised using slightly different procedures, the details of which are outlined in Sect. 2. 2. 1 and 2. 2. 2.
Common Approach To minimise the differences between the ice sheet initial states, we apply as many common features as possible in the model set-ups (Table  1 ), either in terms of model physics, input datasets or parameterisations. In principle, any remaining differences between the ice sheet reference states will be solely due to their individual initialisation procedures and implementation of model physics. A comparison of all three models is given in Table  1. PISM is a finite difference model with a regular 8-km grid, and is capable of simulating large ensembles on multi-millennial time-scales, whereas Elmer/Ice and Úa are finite element models with unstructured grids and are designed for decadal to century scale simulations. All models use ice density ρ i = 917 kg m -3 , sea water density ρ w = 1027 kg m -3 , and gravity g = 9.
81 m s -2. To use the most physics-based friction law all models impose a pressure-dependent friction law, which usually leads to more physically representative sliding close to the grounding line as compared to the Weertman sliding law  (Brondex et al. , 2019). The exact formulations of each law used are given in Appendices A1-A3. To initialise the models to present day conditions, we require a number of observational datasets. We use bedrock topography, ice surface elevation, and ice thickness from BedMachine Antarctica Version 2  (Morlighem et al. , 2020). To replicate the current ice flow, we take surface velocities from a recent snapshot in time (2015/16) from the MEaSUREs Annual Ice Velocity Maps Version 1 dataset  (Mouginot et al. , 2019) , which has a resolution of 1 km and good coverage across the entire ice sheet.
We define steady state as a state for which ice volume changes through time are as close as possible to zero. While the data assimilation approach is able to replicate present day ice velocities and grounding-line positions, once we run the models forward in time, we quickly drift away from these initial conditions, due to inconsistencies in the input datasets and uncertainties in model parameters. Similarly to  Price et al. (2011)  and  Goelzer et al. (2013) , and to overcome the challenges of model drift under control conditions, we apply a correction to the mass balance term ( ṁ) in the form ṁ = ḃRACMO -ḃPICO - dh dt t relax. (1) This seeks to bring rates of volume change as close as numerically possible to zero (steady state), by subtracting rates of ice sheet thickness change (dh/dt) needed to keep the model in balance.
However, PISM, is also capable of locating most, if not all ice streams in their correct positions, and accurately replicating the observed ice thickness and surface velocities after the spin-up. To assess the sensitivity of Antarctic grounding lines to small changes in their position, we need to closely replicate the current observed position of the grounding line. Figure  2  shows that the initial grounding-line positions of all three models are in most parts in very good agreement with the observed position of the grounding line in the BedMachine dataset  (Morlighem et al. , 2020). We calculate the error in the initial grounding-line positions as the difference between simulated and observed grounded ice areas, divided by the simulated grounding-line length. The resulting grounding-line position errors are 90 m for Elmer/Ice, 562 m for Úa and 12.
3 km for PISM, which is below, or very close to, the minimum grid size used in each model. In addition, the good agreement of observed and modelled grounding-line positions can be seen in our individual glacier profile figures presented in the results. As expected, there is a smaller deviation between the observed and modelled grounding lines  flux across the grounding line, were calculated across the same 2 km resolution grid. Reference values for ice extents (total, floating, and grounded) were calculated using the BedMachine v2 dataset  (Morlighem et al. , 2020) , whereas ice mass values were taken from Table  S3  in the dataset paper  (Morlighem et al. , 2020)  (m SLE, metres sea-level equivalent). The total flux across the grounding line was calculated in each respective model. Observed grounding-line flux was taken from  Gardner et al.
and thus has a nearly complete spatial coverage but comes at the expense of the accuracy in areas where velocities have largely changed. The minimisation is then continued https://doi. org/10. 5194/tc-2022-104 Preprint. Discussion started: 21 June 2022 c Author(s) 2022. CC BY 4. 0 License. for 100 iterations using the 2015-2016 dataset to get closer to those observations while staying close to the initially inverted values in areas where observations are missing.
Code availability. Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem; Gagliardini et al. , 2013). All the simulations were performed with version 9. 0 (Rev: 242e4bb) of Elmer/Ice. PISM code is publicly available at https://github. com/ Data availability. Model output data will be made publicly available on Zenodo. DOI links to the repositories will be provided upon publication.
Mafic and ultramafic plutonic rocks are often considered to be crystal cumulates (i. e. , they are melt-depleted), but such a classification is much more contentious for intermediate to silicic granitoids (e. g. , tonalite, granodiorite, granite, and syenite). Whether or not a given plutonic rock has lost melt to feed shallower subvolcanic intrusive bodies or volcanic edifices has key implications for understanding igneous processes occurring within the crust throughout the evolution of the Earth. We use statistical analyses of a global volcanic and plutonic rock database to show that most mafic to felsic plutonic rock compositions can be interpreted as melt-depleted (i. e. , most of the minerals analyzed are more evolved than their bulk-rock compositions would allow). To illustrate the application of the method to natural samples (from the Tertiary Adamello Batholith in the southern Alps), we estimate the degree of melt depletion using a combination of magmatic textures, bulkrock chemistry, modal mineralogy, distributions of plagioclase composition (using scanning electron microscope phase mapping/electron microprobe analyses), and thermodynamic modeling.
Hence, finding ways to test whether a given plutonic rock lost melt (and potentially quantify the amount of melt loss) is fundamental to research focusing on crustal growth and evolution. It would also allow a better characterization of the volcanic/plutonic ratio in a given magmatic province, which is a key, albeit elusive, parameter in understanding secondary crustal formation and growth on rocky planets  (Taylor, 1989). In this study, we use the GEOROC geochemical database to determine which, if any, plutonic rocks are cumulative in nature, particularly those with a magmatic fabric (e. g. , foliation) and intermediate compositions (e. g. , granodiorite, tonalite, and syenite). However, samples from this large database typically lack petrographic/petrologic context and, therefore, present a challenge for determining the exact processes involved in generating the observed geochemical trends.
Hence, to complement our statistical analysis of the GEOROC database, we consider a suite of samples from the well-studied Adamello Batholith (Tertiary Alpine Batholith in the southern Alps;  Hansmann and Oberli, 1991; Kagami et al. , 1991; Schoene et al. , 2012 , Fiedrich et al. , 2017)  for which we have clear geochemical, petrological, and textural context. Our analysis of samples from the Adamello Batholith follows a workflow based on a comprehensive analytical and thermodynamic modeling effort. Specifically, we compare the actual distribution of plagioclase anorthite (An) content in a given sample (determined through analysis of secondary electron microscope [SEM] chemical maps coupled with electron microprobe  [EMP]  analyses) to the modeled "ideal" distribution based on closed-system thermodynamics.
Statistics on the Global Plutonic Database We use a compilation of igneous data from GEOROC (http://georoc. mpch -mainz. gwdg. de / georoc/) in which the bulk-rock composition and associated mineral analyses were matched by  Ganne et al. (2018) ; see Supplemental Material File S1 1. Our data are filtered to exclude all rock 1 Supplemental Material. Includes database with matched compositions for bulk plutonic rocks and associated minerals; detailed tutorial for using iSpectra ® ; and more exhaustive set of simulations. Please visit https://doi. org /10. 1130 /GEOS. S. 19089920 to access the supplemental material, and contact editing@geosociety. org with any questions. samples with major oxide totals outside a 95-101. 5% range, assuming they underwent some type of alteration or were poorly analyzed.
The GEOROC database contains fewer mineral analyses for plutonic rocks (n = 615) than volcanic rocks (n = 5958). Additionally, the database is biased toward gabbroic and granitic compositions, which means that intermediate compositions are underrepresented  (Ganne et al. , 2018). We used GEOROC to evaluate disequilibrium among minerals and their respective bulk rocks across a range of compositions (cf. hereafter "Testing chemical equilibrium among bulk rocks and minerals"). As such, we rely on a procedure based on Monte Carlo analyses with bootstrap resampling to mitigate the sampling bias  (Keller et al. , 2015). The procedure was conducted as follows: (1) a subset of data was randomly selected so that the probability of inclusion in the resampled subset was directly proportional to the sample weight (calculated as (2) a synthetic data set for each sampled data point was drawn from a Gaussian distribution with a mean equal to the original value of the data point and the standard deviation equal to the estimated 1 standard uncertainty of the data point; (3) the resulting data were sorted into bins of composition (e.
Testing Chemical Equilibrium among Bulk Rocks and Minerals Following the same approach as  Ganne et al. (2018) , which focused on the extent and nature of mineral disequilibrium within their host rock in volcanic systems, we herein concentrate our investigation on the plutonic rocks recorded in the GEOROC database. The database with the matched compositions for bulk plutonic rocks and associated minerals is available in File S1; see footnote 1. We test for equilibrium among the minerals and a nominal, coexisting liquid by comparing observed and predicted Fe-Mg or An-Ab exchange values, or -.
Phase Mapping Phase mapping utilizes element maps via the SEM that are then processed using iSpectra ®. The SEM elemental maps were generated using a Thermo Fisher Ultradry energy-dispersive X-ray spectroscope (EDS) coupled to a Thermo Fisher Noran system 7 mounted on a Jeol JSM-6390LA to scan representative areas of a 20 nm carbon-coated thin section at the Department of Earth Sciences at ETH Zurich. The LaB 6 filament was set with an acceleration voltage of 15kV to measure the spectra of all major elements. Scanmode of the SEM operated 128 × 96 pixel maps with 10 frames per 60 s, yielding ~22,700 counts per second. The element maps were processed using iSpectra ®  (Liebske, 2015) , an add-on program of the software IGOR Pro ®. A detailed tutorial for using iSpectra ® is available in File S3 (see footnote 1).
Thermodynamic Simulations Applied to the Plagioclase Population of Adamello Samples We use the open source MELTS package provided online under the name xMELTS (https://gitlab. com /ENKI -portal /xMELTS) adapted to run in Matlab ® on Linux and Mac operating systems. We call the script MatMELTS, which can be provided on request. This script is capable of running a large number of calculations in a short amount of time. Different MELTS versions can be named (i. e. , rhyo-lite_MELTS v1. x. x, pMELTS) for the main calculation modes ("batch" for equilibrium crystallization, "fractionate solids" for fractional crystallization and others) and for setting intensive (temperature, pressure) and extensive (composition, H 2 O/CO 2 content, and oxygen fugacity) parameters. We will show a series of thermodynamic simulations with the MatMELTS script (using rhyo-lite_MELTS version 1.
Statistical Analysis of the GEOROC Database for Melt-Crystal Disequilibrium For the plutonic rocks, the GEOROC database provides 5958 mineral analyses within 615 plutonic bulk-rock samples. The occurrence and extent of disequilibrium between each mineral analysis with its respective bulk-rock host is tested using the methods described earlier (cf. , Methods section). The global statistical result of the equilibrium test for all kinds of minerals explored (Fig. 2 ) indicates that 65% of the tabulated plutonic rocks in the database have over 90% of their respective mineral assemblages in disequilibrium. This result indicates that most of the plutonic rocks in the GEOROC database have either more evolved or less evolved minerals (see definitions in Testing Chemical Equilibrium between Bulk Rocks and Minerals section) than the host plutonic bulk-rock equilibrium compositions.
Evaluation of the Equilibrium among the GEOROC Plagioclase Population and Bulk Plutonic and Volcanic Compositions We evaluate the discrepancies between volcanic and plutonic data by using a modified version of Rhodes diagrams for the plutonic and volcanic plagioclase-bulk rock (considered to be initial melt compositions) pairs in the GEOROC database using the -KD Ca Na Min Liq / of plagioclase and bulk rocks (Fig. 5 ). These diagrams reveal the amount of mineral disequilibrium as well as the magnitude and direction of disequilibrium of each data point. When combined into bootstrapped equilibrium values (red points in Fig. 5 ) for each range of bulk-rock compositions [e. g. , CaO/(CaO +Na 2 O)], an equilibrium line can be drawn for the entire range of bulk-rock compositions for the plutonic and volcanic mineral host pairs.
Similarly, bootstrapped disequilibrium values (black points in Fig. 5 ) display the variations of magnitude and direction of disequilibrium in the entire range of bulk-rock compositions in both systems from which a disequilibrium line can be drawn. The main observation is that the plutonic plagioclase population obtained with bootstrapping is more evolved relative to the equilibrium curve whereas, for the volcanic database, the plagio clase population encompasses both more and less evolved compositions relative to the equilibrium curve (Fig. 5 ). These observations highlight that there are major differences between the plutonic and volcanic mineral assemblages in terms of amount and direction of disequilibrium along a wide range of bulk-rock compositions. The magnitude and direction of disequilibrium between minerals and the host bulk-rock compositions suggest an important influence from open-system processes.
In the case of the volcanic compositions in the database, the less evolved nature of the mineral assemblage of the intermediate rock compositions (e. g. , andesites/dacites) suggests frequent primitive recharge, which leads to mixing and mingling among different batches of magma (Fig. 5 ;  Ganne et al. , 2018). In the case of the plutonic compositions in the database, the more evolved nature of the mineral assemblage of all bulk-rock compositions suggests that some melt loss may have fed shallower systems (hypabyssal intrusions, porphyries, and/or volcanic units) or remained close to the host magma source. Alternatively, the more evolved nature of the minerals can also be partly explained by melt evolution under closed-system conditions (leading to normal zoning and evolved rims on the minerals).
However, if a closed system dominates, the initial bulk-rock composition at the origin of a plagioclase population would be preserved. For an open system, melt depletion at some instance during the life of a magma changes the bulk-rock composition and inhibits the preservation of the initial bulk composition (see annotated diagram for the plutonic database in Fig. 5 ). Our statistical investigation using GEOROC shows that the volcanic and plutonic rock/mineral compositions have fundamental differences in terms of equilibrium/disequilibrium and indicates that plutonic rocks may, indeed, be mainly affected by melt extraction mechanisms. However, the lack of a petrological context for each sample in these databases precludes a detailed analysis of how much melt a given sample might have lost.
KD Ca Na Min Liq / as used in the statistical analysis of the GEOROC database. We also use recently published experimental data from Adamello Batholith starting compositions to show the range of plagioclase compositions in equilibrium with a rhyolitic melt (<An 50-55 ;  Marxer and Ulmer, 2019). As additional visual support, we also display the plagioclase population in disequilibrium from AV1a and AF15 in the modified Rhodes diagram (Fig. 5 ). We observe that in our AV1a plagioclase EMP database (n = 460), ~80% of the plagioclase crystals are in disequilibrium with the bulk rock (Figs. 5  and 9 ; using the -KD Ca Na Min liq / of 0. 27 ± 0. 11;  Putirka, 2008) , including ~70% of the disequilibrium plagioclase compositions being less An-rich (more sodic) than the equilibrium range despite the presence of inherited high-An cores.
Note that, if we compare the histograms provided by the plagioclase compositional maps (Fig. 6C ) with the EMP histogram (Fig. 9 ), we can see that the high-An cores peak is absent in the plagioclase compositional maps, which suggests that these high-An cores are only a tiny amount of the mapped surface of the thin section. Thus, the high-An cores signal results from a sampling bias that is not representative of the sample. The latter indicates that using plagioclase compositional maps significantly improves the representation of the dominant plagioclase compositional range in the rock. In contrast, our AF15 EMP plagioclase database (n = 411) only shows disequilibrium for ~20% of the plagioclase crystals analyzed, which implies crystallization from melts that were closer to the composition of the bulk rock.
■ DISCUSSION AND PERSPECTIVES The results presented here follow up on recent observations that inter mediate to silicic plutonic rocks are partly cumulative  (Barnes et al. , 2016; Barnes et al. , 2020; Bertolett et al. , 2019; Fiedrich et al. , 2017; Garibaldi et al. , 2018; Hartung et al. , 2017; Holness, 2018; Keller et al. , 2015; Laurent et al. , 2020; Lee and Morton, 2015; Tavazzani et al. , 2020; Webber et al. , 2015; Werts et al. , 2020; Wolff, 2017). As most of these studies focused on single plutonic systems, a global assessment of how pervasive melt loss is in plutons worldwide appears to be an essential step forward. Our investigation into the equilibrium among minerals and bulk-rock pairs in a compiled database (i. e. , for plutonic and volcanic rocks) using geochemical tests for mineralmelt equilibria  (Rhodes et al.
ACKNOWLEDGMENTS We thank  Guest Associate Editor Guilherme Gualda  and reviewers  Calvin Barnes  and  Eva Hartung , as well as an anonymous reviewer, for their constructive efforts to improve this manuscript. O. Bachmann  and  J. Cornet  were supported by  Swiss National Fund (SNF)  grant no. IZSAZ2_173428 ,  Alina Fiedrich  was supported by  SNF  grant no. 200021_178928 , and  Christian Huber  was supported by  National Science Foundation  grant no. 1760004. Jérôme Ganne  was supported by the  Institute of Research for Development (IRD) research funds (GDRI LithoSud ).
Introduction Translational Magnetic Resonance Imaging (MRI) of the brain is becoming increasingly popular, as evidenced by the growing number of "MRI", "Translational" and "Brain" publications in various research databases such as Scopus and PubMed. Rodents are often used as starting points for various complex studies, e. g. , in the field of neuroscience. The ability to obtain multimodal information in the same animal, e. g. , anatomy, function, and metabolism, makes MRI a powerful imaging modality for neuroscience research in humans, as it allows non-invasive in vivo studies from disease diagnosis to treatment monitoring, the study of anatomy, functions and metabolism, and has no adverse effects  [1, 2]. The use of MRI allows a simplified transfer of results from animal models to humans, as the same acquisition scheme can be used to generate datasets in both humans and animals.
Conventional data augmentation does not fill all existing gaps in the dataset, and some generative models, for example, AutoEncoder and Variational AutoEncoder (VAE), cannot produce sufficiently realistic scans, and they are characterised by blur and low detail in each structure  [17]. To deal with this situation,  Goodfellow et al. (2014)   [18]  introduced a new approach to improve generative models, which is called Generative Adversarial Networks (GANs). GANs are a type of deep neural architecture with two simultaneously trained networks, one generating the image and the other specialised in discrimination. The first aims to generate fake images from an input vector and to fool the discriminator, which is a classifier that evaluates whether the images generated by the generator are realistic.