GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Postoperative Radiotherapy and N2 Non-small Cell Lung Cancer Prognosis: A Retrospective Study Based on Surveillance, Epidemiology, and End Results Database

The purpose of this study is to clarify the significance of postoperative radiotherapy for N2 lung cancer. This study aimed to investigate the effect of postoperative radiotherapy on the survival and prognosis of patients with N2 lung cancer. Data from 12,000 patients with N2 lung cancer were extracted from the Surveillance, Epidemiology, and End Results database (2004-2012). Age at disease onset and 5-year survival rates were calculated. Survival curves were plotted using the Kaplan-Meier method. The univariate log-rank test was performed. Multivariate Cox regression were used to examine factors affecting survival. Patients’ median age was 67 years (mean 66.46 ± 10.03). The 5-year survival rate was 12.55%. Univariate analysis revealed age, sex, pathology, and treatment regimen as factors affecting prognosis. In multivariate analysis, when compared to postoperative chemotherapy, postoperative chemoradiotherapy was better associated with survival benefits (hazard ratio [HR]= 0.85, 95% confidence interval [CI]: 0.813-0.898, P <0.001). Propensity score matching revealed that patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy (HR=0.869, 95% CI: 0.817-0.925, P <0.001). Female patients and patients aged <65 years had a better prognosis than did their counterparts. Patients with adenocarcinoma had a better prognosis than did patients with squamous cell carcinoma. Moreover, prognosis worsened with increasing disease T stage. Patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy. Postoperative radiotherapy was an independent prognostic factor in this patient group

PO-224 Effect of high-fat diet on body weight and spontaneous physical activity of SD rats

Objective &nbsp;Excessive intake of high-energy foods and insufficient levels of physical activity are important causes of obesity. In addition, inadequate physical activity is also a major cause of cardiovascular disease and type 2 diabetes. Relevant data suggests that most adults fail to achieve the level of physical activity needed to improve their health. Therefore, understanding the reasons for the lack of physical activity levels is essential for developing a reduction in sedentary and thus preventing chronic acute illnesses. It is well known that physical activity is good for health, but little is known about the genetic and biological factors that may affect this complex behavior. Some studies have shown that diet-induced obesity may alter dopaminergic activity and thus reduce physical activity levels, suggesting that obesity and diet may be inversely related to dopamine signaling. Therefore, it is necessary to further study the correlation between obesity, dopamine and physical activity levels, and to explore the relationship between high-fat diet and body weight changes and physical activity levels. Methods &nbsp;Sixteen male Sprague-Dawley rats were randomly divided into two groups. The control group (n=8) was fed with basal diet for 8 weeks, and the high-fat group (n=8) was fed with high-fat diet for 8 weeks. To compare the difference in body weight and physical activity between SD rats fed with high-fat diet and normal diet, and the relationship between body weight and body activity level; in order to study the effect of obesity on exercise behavior, use the open field experimental recorder for each The movements of the rats in the group were recorded (autonomic activity for 30 min), and the correlation between the effects of high-fat diet on body weight and spontaneous activities of SD rats was analyzed. Results High-fat diet and normal-fed rats were in energy intake (high-fat group 4583.94±349.85; control group 3201±298.58), body weight (high-fat group 406.23±29.35; control group 306.66±31.44) and Lee's index (high-fat group 26.17 ± 0.57; control group 24.35 ± 0.97) were significantly different. There was a high correlation between energy intake and body weight in rats, correlation coefficient r=0.911 (p&lt;0.01); correlation coefficient between body weight and physical activity level r = 0.576 (p &lt; 0.05). In addition, by comparing the exercise time and average speed of rats in each group, the difference in exercise time between the two groups was not significant, and the average speed difference was significant (p&lt;0.05); exercise time was significantly correlated with physical activity level, r= 0.734 (p&lt;0.01); and the mean speed was also positively correlated with physical activity level, and the correlation coefficient was 0.660 (P&lt;0.01). Conclusions Obesity is greatly affected by dietary factors, and long-term high-fat diets lead to a decline in physical activity, which in turn promotes further deterioration of obesity. This interaction can create a vicious circle between obesity and physical activity. Further research on the mechanisms of obesity, lack of physical activity and their interaction may provide a theoretical basis for increasing the level of physical activity in obese people

Development and validation of questionnaire-based machine learning models for predicting all-cause mortality in a representative population of China

BackgroundConsidering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.MethodsTwo national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.ResultsIn the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80–0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77–0.87), 0.77 (95%CI: 0.75–0.79), and 0.79 (95%CI: 0.77–0.81), respectively, in predicting 2-, 9-, and 11-year mortality.ConclusionsIn this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population

New insights on hyperglycemia in 17-hydroxylase/17,20-lyase deficiency

ObjectiveThe adrenal glands of patients with 17-hydroxylase/17,20-lyase deficiency (17OHD) synthesize excessive 11-deoxycorticosterone(DOC) and progesterone, and produce less amount of sex steroid production. Mineralocorticoids and sex hormones play an important role in regulating glucose homeostasis. This study aimed to describe the glucose metabolism in 17OHD patients diagnosed at Peking Union Medical College Hospital (PUMCH).Design/methodsA total of 69 patients diagnosed with 17OHD after adolescence in PUMCH from 1995 to June in 2021. Among them 23 patients underwent a 3-hours oral glucose tolerance test (3hOGTT) after being diagnosed with 17OHD. Insulin response in patients with normal glucose tolerance (NGT) were further compared between the study two groups with different kalemia status. Another 19 patients were followed up to 30 years and older. All clinical data were obtained from the hospital information system of PUMCH.ResultsBaseline: (1) The average body mass index(BMI) of all patients at baseline was 20.3 ± 3.7kg/m2. Twenty-three patients underwent 3hOGTT, of whom three were diagnosed with diabetes mellitus, and one with impaired glucose tolerance (IGT). Positive correlation between the ratio of progesterone to upper limit of normal range (P times) and hyperglycaemia was exist(r=0.707, P=0.005). (2) In 19 NGT patients, the insulin concentrations at 0 minute, results of the homeostasis model assessment for β-cell function and insulin resistance were lower in the hypokalaemia group than in the normal kalemia group(7.0(5.8-13.2) vs 12.4(8.9-14.9) μIU/ml, P=0.017; 115.5(88.2-240.9) vs 253.1(177.2-305.8), P=0.048; 1.54(1.17-2.61) vs 2.47(1.91-2.98), P=0.022, respectively). Follow-up: Four patients had IGT, while seven patients had diabetes mellitus. Of the 19 patients,11 had hyperglycaemia. P times was significantly higher(7.6(5.0-11.0) vs 3.75(2.2-5.3), P=0.008) in hyperglycemia group than in the normal glucose group.ConclusionsAbnormal glucose metabolism was common in 17OHD patients, which was possibly associated with hypokalaemia and high progesterone levels. Routine monitoring on glucose metabolism in 17OHD patient should be conducted