dataesr/scientific-paragraphs-categorization huggingface dataset:
Discretization effect in age and metallicity The simulations produce stellar particles with ages and metallicities that span continuous ranges (constrained only by numerical limits). When the mock datacubes are produced, the particles are. Three simulated galaxies with stellar masses 10 9 M , 10 10 M , and 10 11 M show the mocking process. Panels on the left-hand side show the stellar mass density of the galaxies, while the central panels show their spectra at different radii (centre in blue, 0. 5R e in red, and 1R e in yellow). Squared panels on the right-hand side show the stellar properties derived directly from the particles (top row for each galaxy) and recovered from the spectral cubes with pyPipe3D (bottom row for each galaxy). From left to right, these panels show the V-band reconstructed image (mass density for the particle maps), luminosity-weighted age, luminosity-weighted metallicity, LsOS velocity, and velocity dispersion.
Origin Name Description TNG50 Intrinsic 2D intrinsic property maps of the simulated galaxies, as extracted from the particle information. Same spatial resolution as MaNGA. TNG50 Assigned Age and metallicity stellar maps of the simulated galaxies, after the stellar particles are assigned a spectrum from the SSP template. Same spatial resolution as MaNGA. TNG50 Recovered Stellar and kinematic maps of simulated galaxies recovered with the pyPipe3D spectral fit. MaNGA Recovered Stellar and kinematic maps of observed galaxies recovered with the pyPipe3D spectral fit. Notes. See Sect. 3 for more details. assigned to a limited number of SSPs (  273 ) with discrete distributions in age and metallicity. The way the SSP grid is defined will determine how well the continuous values are represented by the template.
Recovering physical properties with PYPIPE3D Determining age, metallicity, and extinction from a spectrum is a highly degenerate problem. Even in the most favourable case where the input spectrum is a linear combination of spectra from the SSP template, recovering ages and metallicities through template fitting is a challenging task. Therefore, to see how well these properties are disentangled by pyPipe3D, we compare the stellar population maps recovered with pyPipe3D (recovered) with a set of maps that were produced directly from the stellar A23, page 9 of 22 Fig. 6. Difference between the intrinsic and assigned mean LW-age and metallicity (top and bottom, respectively, both in log-scale) within 1R e per galaxy (dots), calculated from the respective 2D maps. The yellow solid and dashed lines indicate mean and standard deviation of the differences of the complete set of galaxies.
While ages are slightly younger after being assigned, metallicities show a greater negative deviation from the intrinsic values. particle properties after being assigned the nearest ages and metallicities from the template's discrete grid (assigned maps, see Sect. 3. 2). The latter maps are produced with the same spatial properties as the mock datacubes so that the resolution is comparable. Additionally, the age and metallicity maps are luminosityweighted and averaged in log-scale, like those recovered by pyPipe3D. Qualitatively, except for differences due to the binning in the recovered maps, we find that the spatially resolved properties are properly recovered, as seen in the examples in Fig. 5  (right panels). As the degeneracy in the determination of ages and metallicities is case-sensitive, it is worth analysing how the age and metallicity estimations depend on the galaxy properties.
We calculate the difference between the average values within 1 R e of the recovered and assigned maps for each galaxy (age and metallicity in log-scale). In Fig. 7  we see that the estimated values of extinction tend to be lower than the real ones as their values grow (bottom right panel). This is a well-known problem of any inversion fitting code: the code can only measure the stellar properties of the observed light within the optical depth, and cannot recover the information of the obscured light beyond it. Therefore, the derivation of the recovered extinction is limited to the observable light, while the intrinsic extinction is estimated from all the particles along the line of sight assuming an ideal and completely transparent galaxy. This produces a systematic underestimation of the recovered extinction as the intrinsic extinction grows (see pyPipe3D.
The difference ∆ is calculated as recovered-assigned for age and metallicity [dex] and as recovered-intrinsic for Av. The panels show the number density of galaxies of the differences across each parameter. The solid and dashed cyan lines show the mean and standard deviation of the differences, respectively, while the dashed magenta lines show 2σ deviations from the mean. An age-metallicity degeneracy is seen as pyPipe3D tends to recover systematically younger ages and higher metallicities than expected. However, the recovered values are within the expected errors of 0. 1 dex. As expected, extinction is increasingly underestimated as its value grows. Although this means that the recovered spectra are redder than the original ones, this is not evidently compensated by an older or metal-rich estimation.
Compared to the observations, the simulations form more metal-rich bulges in low-to intermediate-mass galaxies. As seen before, there is some degeneracy when recovering age and metallicity with pyPipe3D; however, the discrepancy between observations and simulations is still evident when we compare these values with those obtained from the particle maps (dashed lines in Fig. 11 ). As pointed out in the previous section, pyPipe3D tends to predict lower ages than the real ones. This effect is greater in the outer regions of the galaxies, suggesting that the code's performance might be affected by low S/N.
For these reasons, we stick to a simpler approach to dust emission that is less computationally demanding. However, because the modelling of the dust in our mocks is similar to the inverse analysis carried out by pyPipe3D, the performance of the code might be biased to more accurate results than in real observations where the nature of the extinction is not fully known. To build IFU datacubes, a complex pipeline is needed. While this work, B&H, and I-M reproduce MaNGA fibre observations to form the spatial grid of the datacubes, N21 and N22a mimic this effect by convolving a high-resolution datacube with a comparable PSF in the spatial axes. By generating the fibre spectra, the spatially correlated noise of the datacubes is reproduced, as the noise is introduced by the spectrograph's detector and propagated to the datacubes during the recombination of the fibres.
While it is possible to combine the output of an RT code with the RealSim code used by B&H to fully imitate MaNGA's pipeline, our approach (and I-M's) is considerably less time-consuming because the spectra are built per fibre, which translates to building up to 381 spectra per datacube (the largest IFU has 127 and three dithering positions) instead of producing a factor of ten more spectra to form the spatial grid that covers the IFU's FOV with MaNGA's spatial sampling (70 × 70 pixels of 0. 5 × 0. 5 arcsec 2 ). The stellar population templates used in each case are different as they either use different stellar libraries or assume different stellar formation models (e. g. IMF, isochrones; see references in Table  3 ). As in our work, the approaches of I-M and N22a are intended to perform a stellar population analysis on the mock datacubes.
Therefore, the SSP templates of choice are the same as those used to process the MaNGA observations with the different stellar population analysis pipelines. This work and N22a, N22b use SSPs based on the MaStar library. The spectral fitting code used in N21, N22a, and N22b is ppxf  (Cappellari & Emsellem 2004 , Cappellari 2016) , which groups the spaxels in Voronoi bins  (Voronoi 1908a,b)  to achieve a minimal S/N per bin defined by the user. In contrast, the binning scheme used in the fitting codes Pipe3D and PyPipe3D (used in I-M and this work, respectively) is CS-binning, which produces bins that not only target a minimal S/N, but also minimize the flux variation within the bin  (Sánchez et al. 2016b). Therefore, the shapes of the CS-bins align with the galaxy's isophotes. In N22a and I-M only emission lines due to star-burst regions are simulated.
e. accretion history, black hole growth), but can be easily extracted from the simulations. Therefore, this dataset can be used to constrain the cause of observables and infer the history of the observed galaxies. The sample is also well suited for machine learning applications given its large volume and realistic observational features. The sample, mock datacubes, and derived maps are made publicly available with the publication of this paper. While only stellar mass, size, and redshift were matched, the overall trends in age, metallicity, and velocity dispersion are in good agreement with observations. The sample was obtained with a one-to-one match to MaNGA galaxies, but the dataset presented in this work is intended to be used in a statistical manner as other key parameters have not been matched (e.
Data access and additional products The data products are available with the publication of this work 6  This includes the catalogue of TNG50 simulated galaxies; the RSS files from which the MaNGA-like datacubes are easily derivable; and the intrinsic, assigned, and recovered stellar maps. The code is made available here 7  and designed in a block chain style, as described in Sect. 3 and shown in Fig. 3. The intermediate files and final data products are as follows: -Particle file: Files with particle information from the simulation: position, velocity, mass, age, and metallicity of stellar particles and gas cells in the FOV. -RSS: FITS file comprising the stacked fibre spectra and their corresponding coordinates in the FOV. The running time for producing a RSS file scales with the number of fibres and IFU used, the number of stellar particles and gas cells, SSP age, and metallicity sampling.
This can take from a few minutes (<10 min) up to 12 hours for massive galaxies. This process can be distributed over several CPUs to reduce computing times. -MaNGA-like datacube: FITS file with the spectral datacube. It takes less than 15 min on one CPU to produce the datacube from the RSS file. -pyPipe3D SSP output: FITS file comprising the 2D maps with recovered stellar properties. -Control maps: 2D maps with the intrinsic and assigned stellar properties of the simulated galaxies. Together with the data, we release a set of masks to obtain the different bundles. Alternatively, the row-stacked spectra data is also available and it is possible to recombine the fibres for the different bundles with the code available. by the Spanish Supercomputing Network (Red Española de Supercomputación), as well as the computer resources used: the LaPalma Supercomputer, located at the Instituto de Astrofísica de Canarias.
Acknowledgements. We thank  Kevin Bundy  for coming up with the name of the MaNGIA sample, as well as envisioning MaNGA in the first place. R. S. thanks the  Max-Planck-Institut für Astronomie  for the hospitality during a visit. We thank  Ignacio Martín Navarro  for the interesting discussions on galaxy properties and how they are affected by different evolution channels. We acknowledge financial support from the  State Research Agency (AEIMCINN) of the Spanish Ministry of Science and Innovation  under the grants " The structure and evolution of galaxies and their central regions " with reference  PID2019-105602GBI00/10. 13039/501100011033  and " Galaxy Evolution with Artificial Intelligence " with reference  PGC2018-100852-A-I00 , from the  ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de Canarias  and the  European Regional Development Fund (ERDF)  under grants with reference  PROID2021010044  and  PROID2020010057 , and from  IAC  projects  P/300724  and  P/301802 , financed by the  Ministry of Science and Innovation , through the  State Budget  and by the  Canary Islands Department of Economy, Knowledge and Employment , through the  Regional Budget of the Autonomous Community.
From definition (Sect. 3. 1) Building MaNGA-like datacubes (Sect. 3. 3) From spectra to stellar population maps (Sect. 3. 4) The cubes are analyzed with the pyPIPE3D (Sánchez+ 2016 a, Sánchez+ 2016 b, Lacerda+ 2022), using the MaStar_sLOG template. Output: maps of age, metallicity and kinematics comparable to those derived from the MaNGA observed galaxies (Sánchez+ 2022). Each stellar particle is associated to a synthetic spectrum from the MaStar_sLOG template (see Sánchez+ 2022) and linearly combined to form the fiber spectrum. A simple screen model accounts for dust attenuation (Ibarra-Medel+ 2019). Output: RSS file.
Implementation Submissions to the ZPrize "Accelerating MSM on Mobile" division must run on Android 12 (API level 32) and are tested on the Samsung Galaxy A13 5G (Model SM-A136ULGDXAA with SoC MediaTek Dimensity 700 (MT6833)). The MSM must be an instance of 2 16 G 1points on the BLS12-377 curve. The baseline is the arkworks [aC22] MSM implementation in Rust (the bucket-list method), a widely used library in SNARK projects. Submissions must beat this baseline by at least 10% in order to be eligible for the prize. We implemented our algorithm in Go language using the gnark-crypto bigint library [BPH + ]. The ZPrize judges have chosen this mobile device as a representative Android device, with specifications similar to older higher-end devices and new budget devices, and as a result represents the kind of hardware that is common today in wealthy markets, and will become common over the next 3-5 years in middle income markets.
It is also widely available and relatively inexpensive. We achieved a speedup of 78% (cf. Table  4 ). The source code is available under MIT or Apache-2 licenses at: https://github. com/gbotrel/zprize-mobile-harness The speedup against the baseline/arkworks comes from the algorithmic optimizations discussed in this paper and the bigint arithmetic optimizations in gnark-crypto aimed at the arm64 target. We use a Montgomery CIOS variant to handle the field multiplication (Details of the algorithms and proofs are in Appendix A). On x86 architectures, gnark-crypto leverages the ADX and BMI2 instructions to efficiently handle the interleaved carry chains in the algorithm. For arm64 architecture, we "untangled" the carry propagation in the pure Go code to ensure the carry chains were uninterrupted.
The butterfly performance impact was ∼ 5%, as it speeds up the unified (mixed) addition in the tEd form. However, the large gap cannot be justified by these facts only. The target device SoC can run 32-bit and 64-bit instruction sets. The stock firmware runs a 32-bit ARM architecture (armv7, 32-bit) on which the baseline implementation is benchmarked by the ZPrize judges. For the sake of the competition, we performed a static build targeting a 64-bit ARM architecture (armv8-a, 64-bit), which allowed us without a complicated build process to run the 64-bit code on the target device. That is said, for a fair comparison, we perform the same architecture hack on the baseline implementation. We report in Figure  3  a comparison of our code to the baseline. We report timings of several MSM instances of different sizes and with different curve parameterizations (SW in extended Jacobians vs.
Acknowledgement The two co-authors of this paper are also co-authors of the gnark-crypto library. We thank the other co-authors  Thomas Piellard ,  Ivo Kubjas ,  Arya Pourtaba Tabaie  and  Gus Gutoski  for their contributions to gnark-crypto, which allowed this work. We also acknowledge that the appendix of this paper is a reprint of the material as it appears in a blog note we shared in 2020 on hackmd. io. This note was never published in an academic paper.
The poor treatment, or complete omission, of water vapor transport has been identified as a major limitation suffered by currently available snowpack models. Vapor and heat fluxes being closely intertwined, their mathematical representation amounts to a system of non-linear and tightly-coupled partial differential equations, which is particularly challenging to solve numerically. The choice of the numerical scheme and the representation of couplings between processes is crucial to ensure an accurate and robust solution that guarantees mass and energy conservation, while allowing time steps in the order of 15 minutes. To explore the numerical treatments fulfilling these requirements, we have developed a highly-modular finite-element program. The code is written in python. Every step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations.
To this end, we unify previous model developments within a comprehensive, stand-alone, finite-element core written in python. Contrary to  Schürholt et al. (2022) , each step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations which relies on the standard numpy linear algebra library. In this way, we have a complete control on every detail of the numerical recipe, and can thus explore various solving strategies. We demonstrate the improvements within established benchmark scenarios and carve out the origin of errors in the numerical solution of the conservation laws. We also discuss the treatment of boundary conditions (BCs) on vapor. The paper is organised as follows. In Sect. 2, we introduce the mathematical models derived by  Calonne et al.
Acknowledgements. We thank  Clément Cancès  for fruitful discussions on numerical methods. We thank  Louis Le Toumelin  for his help in managing the git repository. CNRM/CEN is part of  Labex OSUG  ( ANR10 LABX56 ). JB, KF, MD, and FT have received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( IVORI  (grant no. 949516 )).
Code availability. The source files of the code are provided at https://doi. org/10. 5281/zenodo. 7941767 to guarantee the permanent reproducibility of results. However, we recommend that potential future users and developers access the code from its Git repository (https: //github. com/jbrondex/ivori_model_homemadefem, last access: 16 May 2023) to benefit from the last versions of the code. The version used in this work is tagged as v0. 1. 0. For setting up the environment and running the simulations, please follow the instructions described in the README file present in the GitHub repository.
Introduction Large Language Models (LLMs) have proved effective for a variety of applications, but adapting them to a task or a specialized domain remains a major challenge. In recent years, prompting Generative LLMs has become a dominant paradigm as a first approach to solving various downstream tasks  [1]. However, few studies have focused on the task of rhetorical role prediction using generative approaches, in particular in legal cases  [2]. Kalamkar et al. showed that labelling sentences of legal cases with rhetorical roles, such as Facts, Arguments or Analysis, improve performance on the tasks of summarization and legal judgment prediction  [3]. This research paper focuses on evaluating the potential of generative pre-trained transformers (GPT), specifically OpenAI's GPT-3. 5-turbo  [1, 4] , to autonomously conduct rhetorical analysis on sentences extracted from legal cases.
The best system  [24] , denoted AntContentTech, equipped SciBERT-HSLN with domain-adaptive pretraining, data augmentation strategies, as well as auxiliary-task learning techniques. On the LegalEval test dataset, SciBERT-HSLN obtained a weighted F1 score of 0. 79 while AntContentTech obtained a score of 0. 8593. For an indicative comparison,  [5]   2  reported a score of 0. 65 with a simple BERT fine-tuned for single sentence classification (hereinafter denoted as BERT), and between 0. 75-0. 77 with architecture adaptations to take into account the local context of the sentence to label (denoted as BERT+local context).
Data We utilized the data provided by Sub-task A, "Rhetorical Roles Prediction," of the SemEval 2023 Task 6, "LegalEval -Understanding Legal Texts" challenge  [3]   5. This dataset consists of Indian legal data extracted from court judgments, featuring 13 distinct rhetorical roles (RRs). and averaging 117. 31 sentences per document. To prepare for our experiments, we randomly selected 10 documents (1,101 sentences) from the validation data to manage costs associated with using the GPT API. We observed that there was less variation between the original LegalEval dataset and the segments chosen for our experimentation (See Table  1 ).
Model parameters We experimented the gpt-3. 5-turbo model (-0613 snapshot from June 13th 2023) which extends text-davinci-003 (175B GPT-3 LLM trained on code-completion tasks and fine-tuned on natural language instruction tasks) with optimization for chat  6. Its maximum input length is 4,096 tokens. To ensure that its completion was deterministic, we set the temperature for all experiments to 0. Other parameters were set to their default values (Top P=1, Frequency penalty = 0, Presence penalty = 0). The cost of all experiments was 68 euros. Measures The performance of the NLP models for the rhetorical roles task is assessed using Weighted-Precision ( w P), Weighted-Recall ( w R), Accuracy (A), Weighted-F1 ( w F1) and Macro F1 ( M F1) scores based on the hidden test set. The weighted F1 score considers both precision and recall, and it is calculated by taking into account the class-wise F1 scores weighted by the number of samples in each class.
The United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM) established a working group to develop a global geodetic road map that addresses key elements relating to the development and sustainability of the Global Geodetic Reference Frame (GGRF). Several space missions in order to reach the GGOS accuracy and stability goals were proposed in the past, such as GRASP  (Bar-Sever et al. 2009 ), E-GRIP  (Jetzer et al. 2017 ) and E-GRASP/Eratosthenes  (Biancale et al. 2017). Today, the GENESIS mission is timely considering the large number of long-term scientific undertakings, where many different space data need to be analyzed together, as, for example, for the quantification of the mass-loss in the polar regions, where altimetry missions (ICESat-2, Cryo-Sat-2, Sentinel-6) and gravity field missions (GRACE and GRACE-FO) need to be jointly exploited.
The strong statements by international bodies underline that the GENESIS mission is highly needed and timely, being the only mission to cover this topic, worldwide. A very large and strong scientific community involved in the worldwide networks and data and analysis centers of the four geometrical IAG Services supports the GEN-ESIS mission, a component of the FutureNAV program of the European Space Agency (ESA). Clearly, GENESIS will deliver exploratory results across many Earth's science disciplines, mainly where precise positioning, surface motions and mass movement are critical. The mission is thus supported by an active and broad community, and the downstream science and policy-making users will continuously benefit from the improvements in the TRF and of its link to the CRF.