dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009; Yi et al. , 2018]. 3. Normal Error ("Err"): 1 N N -1 i=0 arccos(|n T pi n * pi |), where n pi and n * pi are ground truth and predicted unit normal, respectively. 4. Inference Time ("Time"): The inference time on the whole test dataset. 5. Average Primitive Number ("Num"): The predicted average number of primitives on the whole test data set. We record these evaluation metrics in table  1  and 2   2  shows visual depictions of the results. Our results show the best performance regarding primitive type accuracy, normal fitting error, and inference time. Our method is much faster for inference because it uses a general formula for different primitive types, and the embedding module is free of iterations. Other methods treat primitives with different equations, and ParSeNet and HPNet need a mean-shift step.
Ablation Studies We first conduct experiments to verify the usefulness of the soft voting regularizer. The soft voting regularizer favors point primitive type consistency for each primitive instance, i. e. , points assigned to the same primitive instance should have the same primitive type. From our experiment, we find that the soft voting regularizer not only improves the primi- tive type accuracy but also accelerates training relaxed IOU. Please refer to figure  3  and the last two rows of table  3. We also verify the functionalities of each module. If we only use the decomposition module, the result is not good even though the "Acc" and "RI" are slightly higher because the decomposition module ignores the fitting, limiting the segmentation applicable to specific datasets. The reconstruction module reduces the "Err" significantly compared to the fitting module because the reconstruction module controls how "well-fitted" a predicted Bézier primitive is to the input point clouds.
Stress Tests To test whether our algorithm can work in real-world scenarios, we show more results from the real-scan data from the Aim@Shape dataset  [Falcidieno, 2004]. The sampling is non-uniform, with missing data and measurement noise compared to the ABC dataset. Besides, We cannot train the network on those data directly because they lack groundtruth labels. Instead, we use the models trained on the ABC dataset and test the performance on real-scan data. Our algorithm still works, while other methods are sensitive. Another positive aspect is that our algorithm could decompose the axis-symmetric free-form point clouds with much smoother boundaries of different patches. Please refer to figure 4. We also test the performance of our network by adding Gaussian white noise. Specifically, we apply different scales of Gaussian white noise to the point coordinates after normalizing them into a unit sphere.
Applications We can reconstruct the full Bézier model from the Bézier primitive segmentation. We do not follow ParSeNet to pretrain a model that outputs a fixed control point size. Instead, we reuse the rational Bézier patch to refit the canonical Bézier patch. We treat the degrees of the canonical Bézier patch the same as the rational Bézier patch. As a result, we fetch the segmentation and degrees of each patch predicted from the network. Then, we use the parameterization  [Lévy et al. , 2002]  to recompute uv parameters and least squares to refit control points for each patch. Each patch is expanded by enlarging the uv domain to guarantee intersections with its adjacent patches. After that, we use the CGAL co-refinement package  [CGAL, 2009]  to detect intersecting polylines for adjacent tessellated patches and trim the tessellated patch with the intersected polylines.
Acknowledgements This research is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 860843. The work of  Pierre Alliez  is also supported by the  French government , through the  3IA Côte d'Azur Investments  in the Future project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002.
Model input datasets Inputs related to atmospheric CO2 concentration (  [CO2] ), land-use, wood harvest, N-fertilizer and nitrogen deposition are those used for the historical and the different SSP CMIP6-related experiments and stored on input4MIPs nodes (https://esgfnode. llnl. gov/projects/input4mips/). Land-use, wood harvest and N-fertilizer input data are produced by the Land-Use Harmonization 2 (LUH2) project  (Hurtt et al. , 2020). Land use information from LUH2 consists of fractions of grid cell area at 0. 25° resolution for cropland (five sub-categories), managed pasture, rangeland, urban, primary and secondary forested and non-forested land. The procedure needed for translating the original data for land-use into the fifteen land classes of ORCHIDEE is described in  Lurton et al. (2020).
Due to computing time resources, we limited our sensitivity study to four SSP markers among the eight available and for each of these four SSPs, we used the land-use and nitrogen inputs trajectories of this set of four SSP markers to assess their impacts on CLCS. The selected SSPs were SSP1-1. 9, SSP3-7. 0, SSP4-3. 4 and SSP5-8. 5. We selected these four SSPs because 1/ they encompass a large spread of CO2 level in 2100 ranging from 394 to 1135 ppm; and 2/ the inter-IAM spreads for land-use but also N-related input data trajectories from this selection are comparable to those from the eight SSPs. Based on the IAM output data produced for CMIP6 available on the SSP Database (https://tntcat. iiasa. ac. at/SspD), we showed that the inter-selected SSP markers spread of the forested global land area in 2100 is narrower than the inter-IAM spread for six out of eight SSPs (Fig.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement N°  101003536  ( ESM2025 -Earth   System Models for the Future ). This work was granted access to the HPC resources of GENCI-TGCC under the allocation  A0130106328. JARS acknowledges for support from the  Commissariat à l'Energie Atomique et aux Energies Alternatives ( CFR grant ).
https://doi. org/10. 5194/esd-2023-31 Preprint. Discussion started: 6 November 2023 c Author(s) 2023. CC BY 4. 0 License. increasing atmospheric N deposition, with the objective of quantifying the co-effects of atmospheric CO2 and N deposition increases. Unfortunately, only three modelling groups performed these two additional experiments and no study made use of them so far. In the Land Use Model Intercomparison Project (Lawrence et al. , 2016), the two experiments ssp370-ssp126Lu and ssp126-ssp370Lu, based on the ScenarioMIP ssp370 and ssp126 experiments but swapping their land-use datasets (Hurtt et al. , 2020), aim at quantifying the specific contribution from land-use change on the climate and carbon cycle over the 21 st century. With this set of 2x2 experiments, Ito et al. (2020) quantified the impact of land-use change on the total soil carbon stock (cSoil) simulated by seven ESMs.
Although we showed that the inter-marker spread and the inter-IAM spread for a given SSP were of the same order for the land-use trajectories but also for the N emissions trajectories globally, the two spreads are not strictly similar for each diagnostic variable by the IAMs or for each SSP. In this respect, there is a need for delivering harmonized and downscaled information about land-use changes, N emissions and N atmospheric deposition trajectories simulated by all IAMs for each SSP and not only by the marker IAMs. Performing sensitivity ESM or land-only experiments with these extra datasets is the only way to accurately assess the specific IAMrelated uncertainty impacts on the carbon cycle and the climate system. While many GHG mitigation strategies rely more and more on land-based solutions, this calls for facilitating the communication and evaluation between IAM and ESM modelling frameworks.
Literature search The literature search was conducted from December 10 until February 28, 2022, and updated to June 9, 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements for systematic reviews and meta-analyses  (Page et al. , 2021). PubMed, Scopus, Web of Science and PsycINFO databases were independently searched by AP, GB and CF, using the following keyword strings: 1) (''EEG" OR ''MEG" OR ''electroencephal*" OR ''magnetoencephal*") AND (''connectivity" OR ''synchronization") AND (''Alzheimer" OR ''MCI" OR ''mild cognitive impairment" OR ''aging") NOT ''child*" (Table  S1a ) and, from June 22, 2023 2) All Fields (eeg OR meg OR electroencephal* OR magnetoencephal*) AND (connectivity OR synchronization OR coherence OR phase locking value OR phase lag index OR synchronization likelihood OR phase amplitude coupling OR amplitude envelope correlation) AND (Alzheimer OR mci OR "mild cognitive impairment'' OR aging) NOT ("child*) (Table  S1b ).
Data extraction To avoid imbalance in the sample sizes for the various contrasts, we decided to analyze the HC > MCI contrast only (12 studies per condition; k(YA > HC) = 4 and k(MCI > AD) = 4). The following data were extracted from each study: (1) Basic bibliographic information (i. e. first author's name, year of publication, journal); (2) EEG parameters such as montage, number of electrodes (spanned from 16 to 128), placement of the analyzed electrodes (Table  2 ) and frequency band considered; (3) Functional connectivity metrics implemented; (4) sample demographic data (i. e. samples size, age); (5) paradigm and type of the task or (6) resting state modality (eyes open (EO) or eyes closed (EC)); (  7 ) the effect size of the between groups contrast quantifying any change in functional connectivity.
For each frequency band and pair of brain areas, differences in the corresponding functional connectivity between groups were collected and converted to t-values on https://www. sdmproject. com/utilities/?show=Statistics. Some of the selected studies did not report any values for significant effects (''non-statistically significant unreported effects" [NSUEs]). To include these studies with NSUEs in this metaanalysis, we used the ''MetaNSUE" package  (Albajes-Eizagirre et al. , 2019; Radua et al. , 2015)  implemented in R (https://cran. rproject. org/). This novel method estimates the bounds where NSUEs fall and transforms them to unbiased effect sizes. The first step consists in using the following formula Eq. (  1 ) implemented in the function ''smd_from_t" to convert two-sample t-values into standard mean difference: y i ¼ J df 1 ð ÞÁ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n i;1 þ 1 n i;2 s Á t i ð1Þ where J is the exact form of the Hedge's correction factor, df 1 are the degrees of freedom and n i;1 and n i;2 are respectively the size of the first and the second sample.
Study selection The keywords filled in each one of the databases queried returned a total of 4685 articles, of which 1220 from PubMed, 838 from Scopus, 2219 from Web of Science and 408 from Psy-chInfo, while 730 were retrieved by cross-reference methods. From the research of June 2023 (with different keywords) a total of 4109 articles were found, of which 112 from PubMed, 979 from Scopus, 2578 from Web of Science and 440 from Psychinfo. After removing duplicates, title and abstract of 3852 articles were independently screened and then cross-checked by GB and CF. This first skimming step allowed us to proceed with the full-text screening of 398 articles, 367 of which were excluded for the following reasons: they did not allow open access (n = 20), the study design did not match with our research question  [(n = 166) , due to the contrast analyzed (n = 140) or the employment of an active task (n = 32)], they investigated other diseases (n = 6), they carried out within-group contrasts (n = 11) or data were incomplete (n = 3).
Sample description Sample characteristics are reported in Table  3. Demographic data of both HC (N = 327, 180 women; mean age 68. 7 years, standard deviation (sd) 5. 1 years; mean education 9. 9 years, sd 1. 3 years) and MCI patients (N = 379; 220 women; mean age 70. 9 years, sd 4. 1 years; mean education 8. 6 years, sd 1. 5 years) were included in the meta-analysis. The cognitive state of the sample was assessed by the Mini-Mental State Examination (MMSE)  (Folstein et al. , 1983)  both for HC (mean score 28. 6, sd 0. 8) and MCI patients (mean score 25, sd 3. 5). By applying independent sample t-tests between groups, the sample was found to be comparable for age (p = 0. 283) and education (p = 0. 376), but significant differences were found in MMSE score (p < 0. 001). The diagnosis of MCI was based on NIA-AA  (Jack et al.
2017a). Combining peculiar velocities from SNe Ia, Tully-Fisher and Fundamental Plane can set the best constraints on the growth-rate at z < 0. 1 and will allow us to constrain alternatives to GR  (Kim & Linder 2020; Lyall et al. 2022). In this work we study the possibility for a first growthrate measurement using uniquely SN Ia data from ZTF. In preparation for the analysis of these data, we produced realistic simulations of ZTF SN Ia light-curves, including selection effects and instrumental noise, and we performed the analysis required to derive f σ 8 , based on the maximum likelihood method  (Johnson et al. 2014; Howlett et al. 2017a). This article is organized as follows. In Sect. 2 we describe the pipeline to produce ZTF simulations of SN Ia observations. In Sect. 3 we present the method used to estimate f σ 8.
ZTF simulations This section describes our framework to produce realistic sets of simulated SN Ia light-curves from the Zwicky Transient Facility, including peculiar velocities and multiple possible observational effects. Our pipeline goes through the following steps: 1) we extract host catalogs from a suitable N-body simulation; 2) we generate SN Ia events with positions drawn from the host catalog and random dates; 3) we generate their true light-curve parameters; 4) we simulate ZTF-like light-curves based on real observations (cadence, filters, noise); 5) we introduce ZTF-like spectroscopic selection effects; 6) we apply ZTF-like quality cuts on selected observations. Each of these steps are described in detail below. This pipeline, named Snsim 1 , was implemented in python language and is publicly available.
Generating SN Ia events The next step is to generate type-Ia supernova events. We used the latest estimates for the rate of explosions, r v,P20 (z = 0) = (2. 35 ± 0. 24) × 10 -5 Mpc -3 yr -1 , (9) measured from ZTF data  (Perley et al. 2020). We rescaled this rate to our fiducial H 0 value with r v = r v,P20 (h/0. 70) 3. We also account for the time dilation at z > 0 by scaling the rate by 1/(1 + z). From this rate we computed the average number of SNe Ia given the volume and the duration of our survey. We then drew the number of SNe Ia in a given realization using a Poisson law. These SN Ia events were spatially assigned to the halos positions from the N-body simulation. The velocity of the halos were also directly assigned to their corresponding SN Ia. This procedure neglects the velocity contribution from the relative velocity between the SN Ia and its host, which would simply add extra intrinsic scatter to their velocities.
Replicating ZTF observations The Zwicky Transient Facility (ZTF) is conducting a photometric survey using the Samuel Oschin 48-inch (1. 2-m) Schmidt Telescope at the Palomar observatory  (Graham et al. 2019). It covers the entire northern visible sky in the g, r and i bands with 30-second exposures pointing a fixed grid of fields with minimal dithering  (Bellm et al. 2019). The ZTF camera hosts 16 charge coupled devices (CCD) containing a total of 0. 6 gigapixels with an effective field of view of 47 deg 2. We use the focal plane dimensions, including inter-CCD gaps from Dekany et al. (2020) (Table  3 ). The data processing pipelines are managed by Infrared Processing and Analysis Center (IPAC,  Masci et al. 2019) , which provides metadata tables. In order to replicate ZTF observations we query these metadata using the public code Ztfquery 4.
The essential quantities of our simulations are: the dates of observations; the filters used; the limiting magnitude at 5-σ of the observations m 5σ , which defines the magnitude at which the signal-to-noise ratio (S/N) is equal to 5; the CCD gain G of each observation in units of electrons per analog-to-digital unit (ADU); the zero point ZP of each observation which gives the magnitude of an object that produces a flux of 1 ADU during an exposure. We also account for the Milky-Way dust extinction using the CCM89 model  (Cardelli et al. 1989 ) and computing each object EBV from the  Schlegel et al. (1998)  dust map implemented in the pyhton package Sfdmap 5. We obtained true fluxes for each epoch of ZTF observations using Sncosmo package 6   (Barbary et al. 2016). Since the flux noise is dominated by the sky background at highmagnitude, we compute an effective sky noise using the limiting magnitude: σ sky = 1 5 10 -0.
4(m 5σ -ZP). (13) A random noise is added to the true flux of each epoch. This noise is drawn from a Gaussian distribution with standard deviation given by σ 2 F = F G + σ 2 sky + ln(10) 2. 5 F 2 σ 2 ZP , (14) where we set σ ZP = 0. 01 as a calibration uncertainty. This calibration uncertainty will be refined in further works, together with taking into account host background, brighter-fatter effect, point spread function effects and calibration uniformity. Figure  2  displays examples of simulated light-curves from ZTF SN Ia events, at three redshifts. We note that since i-band observations are not performed over the whole sky, some supernovae have few or no i-band observations as the one shown in the bottom panel of Fig. 2. We simulated a 6-year data sample, similar to the full ZTF survey duration.
We used observation metadata from 2018-06-19 to 2022-08-31, to follow realistic observing conditions. To obtain a 6-year simulated survey, we artificially increased the SN Ia rate. The photometric completeness is computed relative to the parent sample while the spectroscopic one is relative to those detected in photometry, using the full redshift range of the simulation, that is 0 < z < 0. 14. Different ranges are used for the color scales.
The parent sample (blue dots) uniformly covers the northern sky at Dec > -30 deg, by construction. Photometry and spectroscopy preferentially select SN Ia events outside the Galactic plane. We estimated the sky coverage of each sample and defined their completeness. The sky coverage is calculated by assigning SNe Ia of 27 mock realizations to an angular mesh provided by the Healpix 7  software  (Górski et al. 2005; Zonca et al. 2019) , which yields pixels of equal area. Using 12,288 pixels (n side = 32), we estimated that the parent sample covers uniformly an area of 31537. 3 deg 2. The photometric and spectroscopic samples cover respectively 30698. 0 and 28700. 5 deg 2. Since photometric and spectroscopic samples are subsamples of the parent sample, we can estimate their completeness by computing the ratio of the number of SNe Ia in each sample to the same number in the parent sample, in each angular pixel.
Light-curve parameter adjustment and quality cuts Each simulated SN Ia light-curve is fit using the same framework (SALT2) that was used to generate them with the Sncosmo package implementation. For each light-curve, we fit for the stretch x 1 , color c, peak-magnitude m B , and time of peakbrightness t 0. The redshift z obs of host galaxies is assumed to be provided by an external spectroscopic survey. We fixed it to its true value during the fit as we assume the error to be negligible. The covariance matrix C SALT,i of the fit is defined by C SALT,i =           σ 2 m B ,i Cov m B x 1 ,i Cov m B c,i Cov m B x 1 ,i σ 2 x 1 ,i Cov x 1 c,i Cov m B c,i Cov x 1 c,i σ 2 c,i           , (15) and will be used when fitting for standardization parameters in Sect. 3. 1. 1. Some of our fits did not converge during the first attempt for at least two reasons.
6 ± 0. 5 h -1 Mpc) In Sect. 5. 1 we further discuss the impact of this choice of prior. 12 The averaged uncertainty on f σ 8 is computed on the 27 mocks as σ 2 f σ 8 ( f σ 8 ) fid. A197, page 10 of 18  We recover f σ 8 /( f σ 8 ) fid = 1. 036 ± 0. 031 with a bias smaller than 2σ. However this bias is undetectable for one single mock realization since the average uncertainty is 0. 185. This is shown in the middle of Fig. 8. We then proceeded to a global fit letting all parameters free (p and p HD ) as one would ideally do in the analysis. Using the 6-year dataset, we verified that the f σ 8 value is not biased, as can be seen in Fig. 8. However, on the bottom panel of Fig. 9  we can notice that one of our 27 mocks has low fit values of f σ 8 and that its fit became unstable for the smallest redshift range leading to null value of f σ 8 with extremely large errors.
Comparison with previous measurements Our baseline analysis of the ZTF 6-years SN Ia sample yields an uncertainty of 19% on the growth rate of structures f σ 8 (Eq. (  40 )) when considering the N ∼ 1660 SNe Ia distributed over more than 28k deg 2 and 0. 02 < z < 0. 06. We considered only velocity-velocity correlations in this work. It is interesting to compare our predictions to past measurements of f σ 8 using peculiar velocity data. Figure  11  compares our estimate from ZTF simulations to previous measurements, all of them using Tully-Fisher or Fundamental Plane distances (except  Johnson et al. 2014; Boruah et al. 2020  who include SNe Ia from several compilations). Most datasets also include a galaxy survey in order to cross-correlate density and velocity field, so their information source is larger than in our case where we just use velocities.
ZTF DR2 forecast The next data release for ZTF (DR2) is expected to publish supernovae lightcurves for a survey of thirty-months. We sim-  5. 4. Impact of a bias correction beyond z = 0. 06 With our cut at z = 0. 06, we are left with N ∼ 1660 SNe Ia. This number represents only half of the spectroscopically typed sample. In Fig. 9  we show f σ 8 results as a function of the redshift upper bound z max. In the middle and bottom panel of Fig. 9 , we show the impact of this sample bias on the f σ 8 fit compared A197, page 13 of 18 to the fit using true velocities of the top pannel. After z > 0. 06 the bias begins to grow up to ∼180 percent at z = 0. 12. This bias cannot be corrected simply by using the methods introduced in  Betoule et al. (2014)  or in  Kessler & Scolnic (2017). A correction in redshift bins will shift the velocity of an entire redshift shell with the same factor, leading to fake velocity correlation.
Dupuy et al. 2019he growth-rate of structures f σ 8 from peculiar velocity and galaxy survey data. Error bars with lighter shade are those including quoted systematic errors (except forDupuy et al. 2019, where the extra contribution is from cosmic variance). Our prediction is from Eq. (40) and only considers the spectroscopically classified sample of ZTF SNe Ia between 0. 02 < z < 0. 06. This work Johnson et al. 2014 Huterer et al. 2017 Howlett et al. 2017 Adams & Blake 2020 Lai et al. 2023 Qin et al. 2019 Nusser 2017 Dupuy et al. 2019 Turner et al. 2023 Davis et al. 2011 Carrick et al. 2015 Boruah et al. 2020 Said et al. 2020 Authors 2M++, 6dFGSv, SDSS-FP 2M++, 2MTF, SFI++, A2 2MRS, SFI++ 2MRS, SFI++ 6dFGSz, 6dFGSv CF3 CF3, 2MRS 6dFGSv, 2MTF SDSS-FP 6dFGSz, 6dFGSv 2MTF 6dFGSv 6dFGSv ZTF SNIa (simulations) Dataset Max-likelihood Compressed 2pt Density vs velocity 0.