In this paper, a conventional multiuser space-time line code (MSTLC) transmission scheme is enhanced to a new enhanced MSTLC (EMSTLC). By introducing the balancing factors at receivers’ antennas to adjust the achievable rates among users, the EMSTLC precoder and balancing factors are designed in a unified minimum mean square error framework to maximize either sum rate or rate balancing. Numerical results verify that the proposed EMSTLC systems achieve a higher sum rate and better minimum user rate than baseline multiuser transmission methods, including the conventional MSTLC scheme. Furthermore, the designed low-complexity algorithms can significantly reduce the computational complexity of the EMSTLC system.

Abstract In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms.

Traffic prediction is one of the key elements to ensure the safety and convenience of citizens. Existing traffic prediction models primarily focus on deep learning architectures to capture spatial and temporal correlation. They often overlook the underlying nature of traffic. Specifically, the sensor networks in most traffic datasets do not accurately represent the actual road network exploited by vehicles, failing to provide insights into the traffic patterns in urban activities. To overcome these limitations, we propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns. Despite making minimal modifications to the conventional graph convolutional recurrent networks and graph convolutional transformer architectures, our approach achieves state-of-the-art performance without introducing excessive computational overhead.

Commercial gentrification refers to the replacement of low-value businesses like small local stores into high-value businesses like boutiques and chain stores. A handful of research efforts have been made to identify gentrification and their change by leveraging social media. However, their approaches lack in inferring how much commercial gentrification is developed in a target area and how long it has taken for the area to get to that phase. In this paper, we propose a novel scheme to estimate the commercial gentrification status of a target area and its development in terms of time and geographic dispersion using Instagram data. For this, we define our commercial gentrification phase criteria based on the conceptual model from the urban study. Then, we extract social features from both images and texts of Instagram posts, and leverage regression models to infer the commercial gentrification phase of a target area at the monthly timestamp. We also measure how geographical dispersion of geo-tagged Instagram posts matches the boutiques, which is the physical variable that has the strongest correlation with the commercial gentrification. Evaluation results show that our method yields a good quality of estimation compared to the ground truth. This assures that our method could be a meaningful tool for urban planners and policymakers to investigate and manage commercial gentrification.