Developmental differences in the structure of executive function in middle childhood and adolescence

Although it has been argued that the structure of executive function (EF) may change developmentally, there is little empirical research to examine this view in middle childhood and adolescence. The main objective of this study was to examine developmental changes in the component structure of EF in a large sample (N = 457) of 7–15 year olds. Participants completed batteries of tasks that measured three components of EF: updating working memory (UWM), inhibition, and shifting. Confirmatory factor analysis (CFA) was used to test five alternative models in 7–9 year olds, 10–12 year olds, and 13–15 year olds. The results of CFA showed that a single-factor EF model best explained EF performance in 7–9-year-old and 10–12-year-old groups, namely unitary EF, though this single factor explained different amounts of variance at these two ages. In contrast, a three-factor model that included UWM, inhibition, and shifting best accounted for the data from 13–15 year olds, namely diverse EF. In sum, during middle childhood, putative measures of UWM, inhibition, and shifting may rely on similar underlying cognitive processes. Importantly, our findings suggest that developmental dissociations in these three EF components do not emerge until children transition into adolescence. These findings provided empirical evidence for the development of EF structure which progressed from unity to diversity during middle childhood and adolescence

Mega-NeRF++: An Improved Scalable NeRFs for High-resolution Photogrammetric Images

Over the last few years, implicit 3D representation has attracted more and more research endeavors, typified by the so-called Neural Radiance Fields (NeRF). The original NeRF and some relevant variants mostly address on small-scale scene (such as, indoor or tiny toys), which already show good novel views rendering results. It still remains challenging when dealing with wide coverage area that is captured by large number of high-resolution images, the time efficiency and rendering quality is generally limited. To cope with large-scale scenario, recently, Mega-NeRF was proposed to divide the area into several overlapping sub-area and train corresponding sub-NeRFs, respectively. Mega-NeRF adopts the method of parallel training of multiple sub-modules, which means sub-modules are absolutely independent of each other, which might in principle not be an optimal solution, as two sub-NeRFs of adjacent sub-models obtained by parallel training are likely to get different rendering results for the overlapping area, and the final rendering result is supposed to be negative affected. Therefore, we present Mega-NeRF++, and our goal is to improve Mega-NeRF by implementing extra sub-models optimization that alleviate the rendering discrepancy of overlapping sub-NeRFs. More specifically, we further fine tune the original Mega-NeRFs by considering the consistency of adjacent overlapping area, which means the training data used in the optimization are only from the overlapping region, and we also proposed a novel loss, so that it not only takes into account the difference between the prediction of each sub-model and the true value, but also considers the consistency of the predicted results between various adjacent sub-modules in the overlapping region. The experimental results show that, for the overlapping area, our Mega-NeRF++ can qualitatively render better images with higher fidelity and quantitively have higher PNSR and SSIM compare to original Mega-NeRF

Variational operator learning: A unified paradigm for training neural operators and solving partial differential equations

Based on the variational method, we propose a novel paradigm that provides a unified framework of training neural operators and solving partial differential equations (PDEs) with the variational form, which we refer to as the variational operator learning (VOL). We first derive the functional approximation of the system from the node solution prediction given by neural operators, and then conduct the variational operation by automatic differentiation, constructing a forward-backward propagation loop to derive the residual of the linear system. One or several update steps of the steepest decent method (SD) and the conjugate gradient method (CG) are provided in every iteration as a cheap yet effective update for training the neural operators. Experimental results show the proposed VOL can learn a variety of solution operators in PDEs of the steady heat transfer and the variable stiffness elasticity with satisfactory results and small error. The proposed VOL achieves nearly label-free training. Only five to ten labels are used for the output distribution-shift session in all experiments. Generalization benefits of the VOL are investigated and discussed.Comment: 35 pages, 22 figure

Bootstrap Motion Forecasting With Self-Consistent Constraints

We present a novel framework for motion forecasting with Dual Consistency Constraints and Multi-Pseudo-Target supervision. The motion forecasting task predicts future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of DCMS is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during the training stage. In addition, we design a novel self-ensembling scheme to obtain accurate pseudo targets to model the multi-modality in motion forecasting through supervision with multiple targets explicitly, namely Multi-Pseudo-Target supervision. Our experimental results on the Argoverse motion forecasting benchmark show that DCMS significantly outperforms the state-of-the-art methods, achieving 1st place on the leaderboard. We also demonstrate that our proposed strategies can be incorporated into other motion forecasting approaches as general training schemes

Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China

260-270The southern coastal area of Laizhou Bay is subjected to severe soil salinization due to saline groundwater. The degree of spatial variability is strongly affected by seasonal changes during an annual cycle. In this paper, the spatio-temporal variability of soil salinity in Laizhou Bay, China, was examined to ascertain the current situation of soil salinization in the study area and to reveal the characteristics of seasonal variation of soil salinity. The classical statistical methods and geostatistical methods were applied to soil salinity data collected from four soil layers, i.e., 0-30, 30-60, 60-90, and 0-100 cm, during summer and autumn in 2014. The results indicated that the variation of soil salinity of all the soil layers in summer and autumn was moderate. The soil salinity in the 0-30 cm layer showed a moderate spatial autocorrelation, whereas the spatial autocorrelations of soil salinity in other layers were strong. The overall spatial distribution of soil salinity showed a clear banding distribution and the degree of salinization in the eastern area was lower than that in the western and northern regions.A high ratio of evaporation/precipitation is one of the important reasons for the soil salinity in July is significantly higher than that in November. The rank of soil salinity under different land-use types was: salt pan > orchard > weeds > soybean > woods > cotton > maize > ginger > sweet potato. The research findings can provide theoretical guidance for accurate assessment and soil partition management of regional soil salinization