dataesr/scientific-paragraphs-categorization huggingface dataset:
Practical advice or recommendations for enhancing the performance were provided only for reference. Brain science popularization. The enrolled participants and their guardians were regularly invited to attend talks popularizing brain science organized by the program staff. The talks were focused on providing an intuitive understanding of the personal development report and promoting extensive knowledge of brain science. During the progress, we emphasized the scientific significance of establishing longitudinal datasets for Chinese children and adolescents, with the aim of encouraging retention in the project. A featured program Localization of Frontiers for Young Minds articles (https://kids. frontiersin. org/articles) was launched in July 2019 with weekly neuroscience popularization articles promoted through various social media platforms, such as the WeChat Official Accounts Platform.
Participants who became adults in the longitudinal follow-up provided written consent once becoming 18 years old. experimental design. Detailed assessments are listed in Supplementary Table  S1. Data collection was accomplished by well-trained research assistants. Demographics & characteristics. Demographic information (e. g. , age, sex and handedness) and characteristics of both participants (e. g. , educational level) and their families (e. g. , number of children) were collected at the beginning of each wave through self-designed parental questionnaires. The hand preference of the participant was assessed by the Annett Hand Preference Questionnaire (AHPQ)  23  in the CGK Sample and was classified into 5 subgroups: strong right preference (RR), mixed with right tendencies (MR), mixed (M), mixed with left tendencies (ML), and strong left preference (LL).
Participants were instructed to judge whether the auditory word pronunciation matched the visual word form. RTs and accuracy were recorded. • Brief Affect Recognition Test This test was used to evaluate an individual's recognition of facial expressions  52, 53. Participants were first presented with a 200 ms fixation point in the centre of the screen, and then randomly presented with a picture of a model's emotional expression for 200 ms. Ten models (six women and four men) were selected from the Ekman database. Participants were asked to judge the expression presented from two options within the limited time (200 ms), or they would automatically skip to the next image. Failed to select an expression was marked as wrong. There were 30 sets of facial expressions made up of six different facial emotions (happiness, sadness, fear, disgust, surprise, and anger).
Data Records Dataset deposition. The devCCNP data has been publicly shared in the Chinese Color Nest Project (CCNP) -Lifespan Brain-Mind Development Data Community (https://ccnp. scidb. cn), which is a public platform supported by the National Science Data Bank (https://www. scidb. cn/en) for sharing CCNP-related data and promoting the cooperation of open neuroscience. To offer a better data acquisition, we only upload the MRI dataset on this platform, all the phenotypic data are sharing via deepneuro@bnu. edu. cn once the users' data access applications are approved by the Chinese Color Nest Consortium (CCNC). devCCNP Full. This release contains the full measurements of devCCNP protocol. MRI data have been deposited into the Science Data Bank 74 (https://doi. org/10. 57760/sciencedb. 07478).
The full dataset will be accessible upon requests submitted according to the instructions described below. A sample of the longitudinal data from Fig. 2  Example of the MRI raw data directory structure. Collected MRI raw data are structured within a hierarchy of folders according to the standard BIDS format. Under the toplevel project folder "devCCNP/", CKG (top) and PEK (bottom) Samples are organized separately. Each participant's folder "sub-CCNP*/" may contain several subfolders depending on how many waves have been completed to date (i. e. , if all waves are completed, the folder would include three "/ses-*" subfolders). Imaging data (". nii. gz") and metadata (". json") are organized into modality-specific directories "/anat/", "/func/" and "/dwi/". Note that in the PEK Sample, Diffusion Tension Imaging (DTI) data files are stored under "/dwi/" folders with datatype name "*_dwi.
*". a participant is fully accessible through FigShare (https://doi. org/10. 6084/m9. figshare. 22323691. v1) to demonstrate the data structure  75. Note that T2-tes/tirm in CKG Sample, which has only 30 slices used for detecting organic brain disease, was not uploaded. As it is not frequently used in scientific research. Any application for this part of data would be transferred case-by-case. devCCNP Lite. This release version contains only basic demographics (sex, age and handedness), T1-weighted MRI, rfMRI and diffusion tensor MRI data of devCCNP. No cognitive or behavioural information is included. The devCCNP Lite will be accessible upon the requests according to the instructions described below. Data have been deposited into the Science Data Bank 76 (https://doi. org/10. 57760/sciencedb.
Data licence. To access data, investigators must complete the application file Data Use Agreement on Chinese Color Nest Project (DUA-CCNP) located at: http://deepneuro. bnu. edu. cn/?p=163 and have it reviewed and approved by CCNC. Compliance with all terms specified by the DUA-CCNP is required. Meanwhile, the baseline CKG Sample on brain imaging is available to researchers via the International Data-sharing Neuroimaging Initiative (INDI) through the Consortium for Reliability and Reproducibility (CoRR)  77. More information about CCNP can be found at: http://deepneuro. bnu. edu. cn/?p=163 or https://github. com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by CCNC; please read the Data Use Agreement and contact us via deepneuro@bnu. edu. cn.
technical Validation Sample composition. A total of 479 participants completed baseline visits, 247 (51. 6%) completed the second wave data collection, and 138 (28. 9%) completed the third wave (i. e. , final protocol) as of December 2022. There were 648 (75. 0%) measurements completed by participants twelve years old or younger. The number of participants who completed visits in each age cohort are shown in Table  4 , and age and sex composition are presented in Fig. 1c, d. Demographic and enrolment data for both the CKG Sample (enrolled in 2013-2017) and the PEK (enrolled in 2018-2022) Sample are listed in Table  5. As mentioned above, the overall design has a longitudinal follow-up interval of 15 months, to which the CKG Sample consistently adhered; however, during the PEK sampling, the intervals were prolonged.
For instance, inevitable practical situations affected community-based recruitment, primarily the COVID-19 pandemic. Please note that during COVID-19, data collection was suspended from January to August 2020. We designed questionnaires to assess participants' learning and daily life status  12. Each participant's sampling age and corresponding intervals are presented in Fig. 1e-h. For all of the measurement intervals, 122 (32. 1%) were achieved by design. Quality assessment. Phenotypic data. All of the psychological and behavioural data were made available to users regardless of data quality. We provided all the information on situations that may affect the quality of data within the "json" file. This can guide investigators decisions regarding inclusion of the result data. To verify whether the measured distributions obey a normal distribution, we performed preliminary statistical analysis of several core behaviour measures in the dataset (Fig.
We obscured the facial information using the face-masking tool customized with the Chinese paediatric templates  16. The anonymized images were then denoised by spatially adaptive nonlocal means and corrected for intensity normalization in the Connectome Computation System (CCS)  78. To extract individual brains, we trained a deep learning method using a small set of semiautomatically extracted brains in the CKG Sample, and then applied it to all the devCCNP samples. The preprocessed brain volumes were all in the native space and fed into the FreeSurfer (version 6. 0) pipeline to obtain general morphological measurements of different brain morphometry. All the preprocessing are accomplished through Connectome Computation System (module H1), scripts can be found at github (https://github. com/zuoxinian/CCS/tree/master/H1).
We visually inspected the quality of the T1-weighted images, and two raters were trained to rate the quality using a 3-class framework  79  , with "0" denoting images that suffered from gross artefacts and were considered unusable, "1" with some artefacts, but that were still considered usable, and "2" free from visible artefacts. Images with an average score lower than "2" across the two raters were excluded. A total of 761 (91. 9%) images passed the quality control, with 436 (94. 8%) images in the CKG Sample and 325 (88. 3%) images in the PEK Sample. The intra-class correlation coefficient of the two raters was 0. 532. Functional MR imaging. Resting state fMRI(rs-fMRI) data preprocessing  78  included the following steps: (1) dropping the first 10 s (5 TRs) for the equilibrium of the magnetic field; (2) correcting head motion; (3) slice timing; (4) despiking for the time series; (5) estimating head motion parameters; (6) aligning functional images to high resolution T1 images using boundary-based registration; (7) mitigating nuisance effects such as ICA-AROMA-derived, CSF and white matter signals; (8) removing linear and quadratic trends of the time series; (9) projecting volumetric time series to fsaverage5 cortical surface space; and (10) 6-mm spatial smoothing (we also provide a version of preprocessing results without smoothing).
All preprocessing scripts of the above steps are available on github (https://github. com/zuoxinian/CCS/tree/master/H1)  78. Scans with a mean FD greater than 0. 5 were excluded. A total of 452 (98. 3%) scans in the CKG Sample and 328 (92. 4%) scans in the PEK Sample had at least one rfMRI passed the quality control in each session.
Brain growth charts. Growth charts on height, weight and head circumference are a cornerstone of paediatric health care. A similar tool has been recently generated for lifespan development of human brain morphology  18  by LBCC (https://github. com/brainchart/lifespan). While promising for characterizing the neurodevelopmental milestones and neuropsychiatric disorders  18, 80  , these charts need more diverse samples to enhance their utility in practice  81. Here, we employed the devCCNP Sample and the NKI-Rockland Sample (NKI-RS) for Longitudinal Discovery of Brain Development Trajectories  82  to upgrade the LBCC charts. All the preprocessed T1-weighted MRI images from devCCNP and NKI-RS were subjected to the same manual quality control procedure from the same raters at each site. Specifically, the maximum likelihood method was used to estimate sample-specific or site-specific statistical offsets (random effects, i.
According to the lifespan WMV trajectory from the LBCC seminal work  18  (i. e. , rapid growth from mid-gestation to early childhood, peaking in young adulthood at 28. 7 years), we presented the growth curves of WMV for devCCNP-CKG, deveCCNP-PEK and NKI-RS (Fig. 4 ). These curves indicated rapid increases in WMV from childhood to adolescence consistent with the LBCC findings. To better illustrate the growth curve differences between populations, we depicted site-and sex-specific (adjusted) growth curves of WMV in Fig. 4  (top). WMV is made up of the connections between neurons for cortical communications via neural information flow, and thus, its growth reflects underlying microstructural plasticity during school-age neurodevelopment  84  (e. g. , language performance and training effects during learning  85  ).
visualization purposes. Details of NV are listed in Supplementary Table  S2  and S3 according to their ranking orders. Individual differences in growth charts of cortical volumes between devCCNP and NKI-RS are much larger than those between CKG and PEK. Such differences are spatially ranked in a consistent order among for detailed values. Note that only the NV values and rank of female participants are shown here, as brain charts are modelled sex-specifically  18. The left hemisphere is plotted here purely for visualization purposes. See Figure  S6  for results relating to male participants. populations, indicating more diverse growth curves among individuals in high-order associative (frontoparietal or cognitive control, ventral attention, default mode and language) areas than those in primary areas.
Usage Notes Part of this dataset has been successfully used in our previous publications. Two review articles (in Chinese  15  and English 12 ) were published to summarize the devCCNP protocol for experimental design, sample selection, data collection, and preliminary key findings in stages. With the baseline brain imaging data from devCCNP-CKG, we previously reported that children exhibited similar region-specific asymmetry of the dorsal anterior cingulate cortex (dACC) as adults, and further revealed that dACC functional connectivity with the default, frontoparietal and visual networks showed region-specific asymmetry  87. Head motion data during mock scanning from devCCNP-PEK were used to demonstrate frequency-specific evidence to support motion potentially as a developmental trait in children and adolescents by the development of a neuroinformatic tool DREAM  88.
The baseline imaging data of the CKG Sample has been released as part of the CoRR 77 and the IPCAS 7 site (https://doi. org/10. 15387/fcp_indi. corr. ipcas7), which has been listed as one of the existing, ongoing large-scale developmental dataset  91. As part of an international consortium recently initiated for the generation of human lifespan brain charts  18  , CCNP contributes to the largest worldwide MRI samples (N > 120,000) for building normative brain charts for the human lifespan (0-100 years). The full set of devCCNP data is increasingly appreciated by collaborative studies on school-aged children and adolescents. All data obtained freely from the INDI-CoRR-IPCAS7 or CCNC, can only be used for scientific research purposes. The users of this dataset should acknowledge the contributions of the original authors, properly cite the dataset based on the instructions on the Science Data Bank website (https://doi.
acknowledgements CCNP receives funding support from the  National Basic Science Data Center  " Chinese Data-sharing Warehouse for In-vivo Imaging Brain " ( NBSDC-DB-15 ), the  Key-Area Research and Development Program of Guangdong Province  ( 2019B030335001 ), the  Start-up Funds for Leading Talents   at Beijing Normal University , the  Beijing Municipal Science and Technology Commission  ( Z161100002616023 ,  Z181100001518003 ), the  Major Project of National Social Science Foundation of China  ( 20&ZD296 ), the  CAS-NWO Programme  ( 153111KYSB20160020 ), the  Guangxi BaGui Scholarship  ( 201621 ), the  National Basic Research  ( 973) Program  ( 2015CB351702 ), the  Major Fund for International Collaboration of National Natural Science Foundation of China  ( 81220108014 ), the  Chinese Academy of Sciences Key Research Program  ( KSZD-EW-TZ-002 ) and the  National Basic Research Program  ( 2015CB351702 ).
Aims. A linear scaling of the mixed third-order moment of the magnetohydrodynamic (MHD) fluctuations is used to estimate the energy transfer rate of the turbulent cascade in the expanding solar wind. Methods. In 1976, the Helios 2 spacecraft measured three samples of fast solar wind originating from the same coronal hole, at different distances from the Sun. Along with the adjacent slow solar wind streams, these intervals represent a unique database for studying the radial evolution of turbulence in samples of undisturbed solar wind. A set of direct numerical simulations of the MHD equations performed with the Lattice-Boltzmann code FLAME was also used for interpretation. Results. We show that the turbulence energy transfer rate decays approximately as a power law of the distance and that both the amplitude and decay law correspond to the observed radial temperature profile in the fast wind case.
g. ,  Dong et al. 2014; Montagud-Camps et al. 2020 , 2022). In this work, we use the lattice Boltzmann (LB) code FLAME  (Foldes et al. 2022)  to integrate the quasiincompressible MHD equations in a 3D periodic domain that is not expanding. In the LB approach, the volume discretization is operated on a gas of particles distributed on a lattice, rather than on a grid. The dynamics of the particles devel-ops in the frame of the kinetic theory, the temporal evolution of the plasma being achieved through the recursive application of simple collision and streaming operations. The macroscopic MHD fields (e. g. , fluid velocity u, density ρ, and magnetic field B) are then obtained through integration of the statistical moments of the particle distribution functions. We note that the particles described here are not plasma particles; rather, they exist on the level of the numerical scheme and are instrumental to the LB approach to obtain (in the case of FLAME) the fields of the simulated MHD plasma (see details in  Foldes et al.
Conclusions We used Helios 2 measurements of the solar wind emitted from a steady coronal source and without interactions with coronal or heliospheric structures, collected at different distances from the Sun. We have shown that the turbulence energy transfer rate decays approximately as a power law of the distance, and we provided measured decay exponents that may be used to constrain models of solar wind expansion. It should be pointed out that the linear scaling range becomes narrow at larger distance from the Sun. In the slow wind, for example, such a range covers slightly less than a decade, which stands in contrast to the observed broad spectral inertial range. This is likely due to the limited statistics provided by the relatively low resolution Helios data and, more generally, to the difficult observation of signed third-order scaling laws.
Acknowledgements. L. S. -V. and E. Y. were supported by  SNSA  grants  86/20  and  145/18. L. S. -V. is supported by the  Swedish Research Council  (VR) Research Grant  N. 2022-03352. R. M. and R. F. acknowledge support from the project " EVENTFUL " ( ANR-20-CE30-0011 ), funded by the  French "Agence Nationale de la Recherche" -ANR  through the program  AAPG-2020. This research was supported by the  International Space Science Institute (ISSI) in Bern , through  ISSI International Team  project # 556  ( Cross-scale energy transfer in space plasmas ).
ChildNet is capable of generating multiple child images per parent pair input, while providing a way to control the image generation variability. Additionally, we introduce a mechanism to control the dominant parent image. Finally, to facilitate the task of kinship face synthesis, we introduce a new kinship dataset, called Next of Kin. This dataset contains 3690 high-resolution face images with a diverse range of ethnicities and ages. We evaluate ChildNet in comprehensive experiments against three competing kinship face synthesis models, using two kinship datasets. The experiments demonstrate the superior performance of ChildNet in terms of identity similarity, while exhibiting high perceptual image quality. The source code for the model is publicly available at: https://github. com/MartinPernus/ChildNet.
Based on experiments with two kinship datasets, ChildNet achieves higher facial similarity with real child images than competing methods, while exhibiting high degree of perceptual image quality. A critical problem in kinship face synthesis research is the lack of appropriate datasets to train a high-quality synthesis model. Specifically, kinship face synthesis models are trained on datasets whose primary goal is kinship recognition, which typically contain facial images with highly variable image quality and tightly cropped facial images. This is in contrast to modern datasets used for training facial image generation models  [6] ,  [7]. These datasets typically contain high-resolution, high-quality face images with consistent positioning. Therefore, we present a new kinship dataset, named Next of Kin (NoK), that includes 3690 512×512 highquality, high-resolution facial images of 553 subjects that correspond to 161 families, i.
e. , parents and their children. In summary, we make the following main contributions in this paper: • We propose ChildNet, a state-of-the-art model for kinship face synthesis. ChildNet is based on a novel latent-space manipulation scheme designed around the StyleGAN2 model and leads to convincing child synthesis results with photorealistic, high-resolution and artifact-free synthesized images. • We introduce several synthesis control mechanisms that involve age and gender manipulation, image variability control, and determination of the dominant parental image. These mechanisms facilitate conditional child synthesis and attribute control for the synthesized images. • We present the Next of Kin (NoK) dataset, a dataset of high-resolution face images with various metadata, e. g. , kinship relation, age, ethnicity, gender and emotion.
II. RELATED WORK In this section we present prior work closely related to our paper. We discuss GANs with a focus on their use in image processing. The GAN model represents a core synthesis component in our paper due to its ability to synthesize highresolution, high-quality facial images. Next, we focus on existing kinship datasets, discussing their characteristics and limitations. This highlights the necessity for a dataset tailored specifically for synthesis purposes. Finally, we examine the image kinship research, which mostly focused on the task of kinship recognition, with a recent focus towards the task of kinship synthesis.
2) GAN-INVERSION GAN-inversion methods are concerned with inverting an image into the latent space of a pre-trained GAN model before performing image editing operations. Abdal et al. [17]  first introduced an iterative latent code inversion technique within the extended latent space of StyleGAN, demonstrating its significant image reconstruction capabilities. This work was further extended in  [18] , which enhanced the inversion algorithm. The pSp model  [19]  defined an image encoder that directly predicted GAN latent codes without the need of iterative latent code optimization. E4e model  [20]  proposed an encoder that focused not only on image reconstruction but also on preserving the original latent code distribution, resulting in improved image editing. ReStyle  [21]  introduced an iterative encoder refinement of the latent code.
3) GAN-INVERSION BASED IMAGE EDITING Recently, a considerable number of methods have emerged that perform image manipulation based on a pre-trained GAN. Abdal et al. [17]  performed various image manipulation techniques, e. g. , face morphing, style transfer and expression transfer. Their follow-up work  [18]  demonstrated additional manipulations, e. g. , image crossover, local image editing and inpainting. Shen et al. [25]  proposed fitting a linear support vector machine to the StyleGAN latent space based on face attribute labels of the corresponding images. The computed directions were then used to edit images in a manner that displayed the desired facial attribute. Some methods focused on unsupervised discovery of semantic latent directions in the latent space  [26] ,  [27]. MaskFaceGAN  [28]  introduced several constraints during the latent code optimization for face editing that achieved disentangled face editing.