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Commuting is a key mechanism that governs the dynamics of cities. Despite its importance, very little is known of the properties and mechanisms underlying this crucial urban process. Here, we capitalize on $\sim$ 50 million individuals' smartphone data from 234 Chinese cities to show that urban commuting obeys remarkable regularities. These regularities can be generalized as two laws: (i) the scale-invariance of the average commuting distance across cities, which is a long-awaited validation of Marchetti's constant conjecture, and (ii) a universal inverted U-shape of the commuting distance as a function of the distance from the city centre within cities, indicating that the city centre's attraction is bounded. Motivated by such empirical findings, we develop a simple urban growth model that connects individual-level mobility choices with macroscopic urban spatial structure and faithfully explains both commuting laws. Our results further show that the scale-invariants of human mobility will ultimately lead to the polycentric transition in cities, which could be used to better inform urban development strategies.

China's demographic changes have important global economic and geopolitical implications. Yet, our understanding of such transitions at the micro-spatial scale remains limited due to spatial inconsistency of the census data caused by administrative boundary adjustments. To fill this gap, we manually collected and built a population census panel from 2010 to 2020 at both the county and prefectural-city levels. We show that the massive internal migration drives China's increasing population concentration and regional disparity, resulting in severe population aging in shrinking cities and increasing gender imbalance in growing cities.

The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that 1) people conserve a small number of repeatedly used routes, and 2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.

Reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems have aroused extensive research interests due to the controllable communication environment and the performance of combating multi-path interference. However, as the premise of RIS-assisted OFDM systems, the accuracy of channel estimation is severely degraded by the increased possibility of insufficient cyclic prefix (CP) produced by extra cascaded channels of RIS and the nonlinear distortion lead by imperfect hardware. To address these issues, an enhanced extreme learning machine (ELM)- based channel estimation (eELM-CE) is proposed in this letter to facilitate accurate channel estimation. Based on the model-driven mode, least square (LS) estimation is employed to highlight the initial linear features for channel estimation. Then, according to the obtained initial features, an enhanced ELM network is constructed to refine the channel estimation. In particular, we start from the perspective of guiding it to recognize the feature, and normalize the data after the network activation function to enhance the ability of identifying non-linear factors. Experiment results show that, compared with existing methods, the proposed method achieves a much lower normalized mean square error (NMSE) given insufficient CP and imperfect hardware. In addition, the simulation results indicate that the proposed method possesses robustness against the parameter variations., Comment: 5 pages, 3 figures

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise., Comment: 30 pages, 11 figures

The movements of individuals are fundamental to building and maintaining social connections. This pictorial presents Wanderlust, an experimental three-dimensional data visualization on the universal visitation pattern revealed from large-scale mobile phone tracking data. It explores ways of visualizing recurrent flows and the attractive places they implied. Inspired by the 19th-century art movement Impressionism, we develop a multi-layered effect, an impression, of mountains emerging from consolidated flows, to capture the essence of human journeys and urban spatial structure., Comment: IEEE VIS Arts Program (VISAP) 2021, pictorial, 14 pages