A personal route prediction system based on trajectory data mining

This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method

Low-Complexity Non-uniform Constellation Demapping Algorithm for Broadcasting System

This paper presents a novel low-complexity soft demapping algorithm for  two-dimensional non-uniform spaced constellations (2D-NUCs) and massive order one-dimensional NUCs (1D-NUCs). NUCs have been implemented in a wide range of new broadcasting systems to approach the Shannon limit further, such as DVB-NGH, ATSC 3.0 and NGB-W. However, the soft demapping complexity is extreme due to the substantial distance calculations. In the proposed scheme, the demapping process is classified into four cases based on different quadrants. To deal with the complexity problem, four groups of reduced subsets in terms of the quadrant for each bit are separately calculated and stored in advance. Analysis and simulation prove that the proposed demapper only introduces a small penalty under 0.02dB with respect to Max-Log-MAP demapper, whereas a significant complexity reduction ranging from 68.75\% to 88.54\% is obtained

Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that either equally divides the transmission time slots among users or maximizing some ratios without physcial meanings, we propose to use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness. Since it is difficult to directly optimize 5TUDR, we first cast the problem into the stochastic game framework and subsequently propose a Multi-Agent Reinforcement Learning (MARL)-based algorithm to perform distributed optimization on the resource block group (RBG) allocation. Furthermore, each MARL agent is designed to take information measured by network counters from multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the input states while the RBG allocation as action with a proposed reward function designed to maximize 5TUDR. Extensive simulation is performed to show that the proposed MARL-based scheduler can achieve fair scheduling while maintaining good average network throughput as compared to conventional schedulers.Comment: 30 pages, 13 figure

Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19

Background: Several studies have described the clinical characteristics of patients with novel coronavirus (SARS-CoV-2) infected pneumonia (COVID-19), indicating severe patients tended to have higher neutrophil to lymphocyte ratio (NLR). Whether baseline NLR could be an independent predictor of in-hospital death in Chinese COVID-19 patients remains to be investigated. Methods: A cohort of patients with COVID-19 admitted to the Zhongnan Hospital of Wuhan University from January 1 to February 29 was retrospectively analyzed. The baseline data of laboratory examinations, including NLR, were collected. Univariate and multivariate logistic regression models were developed to assess the independent relationship between the baseline NLR and in-hospital all-cause death. A sensitivity analysis was performed by converting NLR from a continuous variable to a categorical variable according to tertile. Interaction and stratified analyses were conducted as well. Results: 245 COVID-19 patients were included in the final analyses, and the in-hospital mortality was 13.47%. Multivariate analysis demonstrated that there was 8% higher risk of in-hospital mortality for each unit increase in NLR (Odds ratio [OR] = 1.08; 95% confidence interval [95% CI], 1.01 to 1.14; P = 0.0147). Compared with patients in the lowest tertile, the NLR of patients in the highest tertile had a 15.04-fold higher risk of death (OR = 16.04; 95% CI, 1.14 to 224.95; P = 0.0395) after adjustment for potential confounders. Notably, the fully adjusted OR for mortality was 1.10 in males for each unit increase of NLR (OR = 1.10; 95% CI, 1.02 to 1.19; P = 0.016). Conclusions: NLR is an independent risk factor of the in-hospital mortality for COVID-19 patients especially for male. Assessment of NLR may help identify high risk individuals with COVID-19