CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback

The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication. With the growing complexity of CSI, CSI feedback in massive MIMO system has become a bottleneck problem, the traditional compressive sensing based CSI feedback approaches have limited performance. Recently, numerous deep learning based CSI feedback approaches demonstrate their efficiency and potential. However, most existing methods improve accuracy at the cost of computational complexity and the accuracy decreases significantly as the CSI compression rate increases. This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41% in both outdoor and indoor scenarios with average 24.1% less computational overhead. Codes are available at GitHub.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

THE ECONOMIC IMPACT OF THE FIRST GREAT MIGRATION

This dissertation studies the first Great Migration of African Americans from the rural South to Urban areas in the northern United States. While most existing research has focused on the experiences of the migrants themselves, I am focused on how this influx of rural black migrants impacted outcomes for African Americans who were already living in the north and had already attained a modicum of economic success. Common themes throughout this dissertation involve the use of the complete-count U.S. population census to link records across years. In the first chapter, I linked northern-born blacks from 1910 to 1930 to study how the arrival of new black residents affected the employment outcomes of existing northern-born black residents. I find that southern black migrants served as both competitors and consumers to northern-born blacks in the labor market. In the second chapter, my co-authors and I study the role of segregated housing markets in eroding black wealth during the Great Migration. Building a new sample of matched census addresses from 1930 to 1940, we find that racial transition on a block was associated with both soaring rental prices and declines in the sales value of homes. In other words, black families paid more to rent housing and faced falling values of homes they were able to purchase. Finally, the third chapter compares the rates of intergenerational occupational mobility by both race and region. I find that racial mobility difference in the North was more substantial than it was in the South. However, regional mobility difference for blacks is greater than any gap in intergenerational mobility by race in prewar American. Therefore, the first Great Migration helped blacks successfully translate their geographic mobility into economic mobility

A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information

Time series forecasting represents a significant and challenging task across various fields. Recently, methods based on mode decomposition have dominated the forecasting of complex time series because of the advantages of capturing local characteristics and extracting intrinsic modes from data. Unfortunately, most models fail to capture the implied volatilities that contain significant information. To enhance the prediction of contemporary diverse and complex time series, we propose a novel time series forecasting paradigm that integrates decomposition with the capability to capture the underlying fluctuation information of the series. In our methodology, we implement the Variational Mode Decomposition algorithm to decompose the time series into K distinct sub-modes. Following this decomposition, we apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the volatility information in these sub-modes. Subsequently, both the numerical data and the volatility information for each sub-mode are harnessed to train a neural network. This network is adept at predicting the information of the sub-modes, and we aggregate the predictions of all sub-modes to generate the final output. By integrating econometric and artificial intelligence methods, and taking into account both the numerical and volatility information of the time series, our proposed framework demonstrates superior performance in time series forecasting, as evidenced by the significant decrease in MSE, RMSE, and MAPE in our comparative experimental results

The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology Analysis

House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people’s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people’s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people’s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ‘enclosure’ and ‘comfort’ are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales

Associations between smoking status and infertility: a cross-sectional analysis among USA women aged 18-45 years

BackgroundAlthough many studies have proven the harmful effects of smoking on human health, the associations between smoking status and infertility are limited in large epidemiologic studies. We aimed to investigate the associations between smoking status and infertility among child-bearing women in the United States of America (USA).MethodsA total of 3,665 female participants (aged 18-45) from the National Health and Nutrition Examination Survey (NHANES) (2013-2018) were included in this analysis. All data were survey-weighted, and corresponding logistic regression models were performed to investigate the associations between smoking status and infertility.ResultsIn a fully adjusted model, the risk of infertility was found to be increased by 41.8% among current smokers compared to never smokers (95% CI: 1.044-1.926, P=0.025). In the subgroup analysis, the odds ratios (95% CI) of the risk of infertility for current smokers were 2.352 (1.018-5.435) in the unadjusted model for Mexican American, 3.675 (1.531-8.820) in the unadjusted model but 2.162 (0.946-4.942) in fully adjusted model for people aged 25-31, 2.201 (1.097-4.418) in the unadjusted model but 0.837 (0.435-1.612) in fully adjusted model for people aged 32-38.ConclusionCurrent smokers was associated with a higher risk of infertility. The underlying mechanism of these correlations still needs more research. Our findings indicated that quitting smoking may serve as a simple index to reduce the risk of infertility

Analysis of Multiple Scattering Characteristics of Cable-Stayed Bridges with Multi-band SAR

Accurate localization of multi-scattering features of cable-stayed bridges in multi-band Synthetic Aperture Radar (SAR) imagery is crucial for intelligent recognition of bridge targets within images, as well as for precise water level extraction. This study focuses on the Badong Yangtze River Bridge, utilizing Unmanned Aerial Vehicle (UAV) LiDAR data of the bridge, and analyzes the multi-scattering characteristics of different bridge structural targets based on Geometric Optics (GO) methods and the Range-Doppler principle. Furthermore, the study integrates LiDAR data of the bridge's cable-stays to examine their multi-scattering phenomena, finding that the undulations of the Yangtze River's surface waves significantly contribute to the pronounced double scattering features of the bridge's cable-stays. Additionally, statistical analysis of multi-source SAR data indicates that this phenomenon is not directly correlated with radar wavelength, implying no direct connection to surface roughness. Utilizing LiDAR point cloud data from the bridge's street lamps, this paper proposes a novel method for estimating water level elevation by identifying the center position of spots formed by double scattering from lamp posts. The results show that using TerraSAR ascending and descending orbit images, this method achieves a water level elevation accuracy of approximately 0.2 meters