Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning

To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique. By collaboratively training a shared learning model on edge devices, raw data transmission and storage are replaced by the exchange of the local computed parameters/gradients in FL, which thus helps address latency and privacy issues. However, the number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to perform one-shot model aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a performance-oriented design scheme that directly minimizes the optimality gap of the loss function is proposed to accelerate the convergence of AirComp-based FL. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline approaches to tackle the resulting design problem. Simulation results demonstrate that such a performance-oriented design strategy can achieve higher test accuracy than the conventional isolated mean square error (MSE) minimization approach in FL.Comment: This work has been submitted to the IEEE for possible publicatio

Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing

The Digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. This paper proposes the network-wide integration of DT and MEC, then demonstrates the necessity of DT for achieving an optimal performance in DITEN systems through analysis and numerical results

Interaction Effects of Life Events and Hair Cortisol on Perceived Stress, Anxiety, and Depressive Symptoms Among Chinese Adolescents: Testing the Differential Susceptibility and Diathesis-Stress Models

The differential susceptibility model and the diathesis-stress model on the interaction effect between the individuals’ traits and environmental factors will be conducive to understand in depth whether the psychophysiological traits are the risk factors of child development. However, there is no study focusing on the activity of the hypothalamic-pituitary-adrenal (HPA) axis. We examined whether the HPA activity serves as a physiological marker of the differential susceptibility model or the diathesis-stress model by exploring the interactive effect of life events and hair cortisol on perceived stress, anxiety, and depressive symptoms among Chinese adolescents. The participants were 324 students in senior high school. They reported their psychological states with questionnaires in their first semester after a 3-month adaptation period; 2 weeks later, they provided 1-cm hair segments closest to the scalp. We measured hair cortisol concentration as a biomarker of HPA activity using high-performance liquid chromatography–tandem mass spectrometry. There was a significant interaction effect of academic events and hair cortisol on adolescents’ perceived stress, anxiety, and depression symptoms. We also observed a significant interaction between interpersonal events and hair cortisol on adolescents’ anxiety symptoms. Looking at the region of significance, proportion of interaction index, and proportion affected index, we found that adolescents with higher cortisol levels had a tendency to experience higher perceived stress and anxiety symptoms when they had high academic events scores, but lower perceived stress and anxiety symptoms when they had lower academic events scores. By contrast, adolescents with higher cortisol levels had a greater risk of experiencing high depressive symptoms only when they had higher academic events scores. Adolescents with higher cortisol levels also tended to have lower anxiety symptoms when they had higher interpersonal events scores, but greater anxiety symptoms when they had lower interpersonal events scores. These results suggested that HPA activity might serve as a biomarker of the differential susceptibility model for perceived stress and anxiety symptoms, while for depressive symptoms, it might serve as a marker of the diathesis-stress model

A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization

The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the “intrinsic edges” to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases

Bayesian updating for ground surface settlements of shield tunneling

Accurate prediction of ground surface settlements induced by shield construction is of great significance for ensuring the safety of shield construction. This paper proposes a ground surface settlement prediction method for shield tunneling based on Bayesian updating. The sequential observation data during the advance of excavation is utilized to update the key soil parameters, leading to a more accurate settlement prediction for the subsequent excavation stages. Response surfaces are constructed to replace the finite element model as the forward models for higher computational efficiency. A tunnel excavation project in Hangzhou, China, is selected to illustrate the effectiveness of the proposed method. The shield excavation face passes through four soil layers, and two soil parameters (i.e., Young’s modulus and friction angle) of these soil layers are selected as random variables to be updated. The results show that the soil parameters can be effectively updated based on the observation data at multiple points and various excavation stages. The predictions of ground surface settlements are improved by using the updated soil parameters. The prediction accuracy of the proposed method increases as more stages of observation data are sequentially obtained and incorporated

State feedback predictive control for nonlinear hydro-turbine governing system

The present work introduces a novel predictive control strategy for the analysis of the dynamic performance of hydro-turbine governing systems based on fuzzy logic. Firstly, a six-dimensional nonlinear dynamic model of the system is defined. The defined model is applied to a realistic case-study, aiming to investigate the dynamic behavior of the system. In order to deal effectively with the nonlinearity of the system under study, the Takagi–Sugeno fuzzy approach is adopted. The results demonstrated through the use of the discrete Lyapunov function and Schur complements of matrices suggest that the closed-cycle control system can achieve a global asymptotic stability state. The second part focuses on the quantification of the impact of sudden changes in operating conditions on the overall performance. The numerical results indicate that the proposed predictive control method can ensure the performance of the system to be reliable and robust to external inferences. In addition to this, the approach proposed has unquestionable advantages over the traditional proportional–integral–derivative and model predictive controllers with regard to nonlinear systems applications

Response of glacial-lake outburst floods to climate change in the Yarkant River basin on northern slope of Karakoram Mountains, China

Based on the glacial flood events and climate change in the Yarkant River basin during the past 50 years, the study investigated the long-term change of temperature and precipitation, the characteristics of glacial floods, the origin of sudden flood release, the suggested flood mechanism of glacial lakes and the relationship between glacial floods and climate change. Results showed that there was an obvious increase in the temperature of the basin since 1987. Specifically in the mountainous area, the significantly increasing temperature in the summer and autumn seasons accelerated the melting rate of glaciers and caused glacial-lake burst. Sudden flood release occurred frequently. The frequency of glacial-lake outburst floods was 0.4 times/a during the period 1959-1986 and increased to 0.7 times/a during 1997-2006. Peak discharge also increased. There were seven floods with peak discharge over 4000 m(3)/s from 1959-2006, and three occurred after 1997. The increasing frequency and magnitude of glacial outburst floods mirrored the effect of climate warming on glaciers. (C) 2010 Elsevier Ltd and INQUA. All rights reserved

Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China

Based on the hydrologic and meteorological data in the Tarim River basin from 1958 to 2004, the trend, characteristics and spatial variation of climate change in the upper reaches of the Tarim River were examined in the study. The long-term trend of climate change and hydrological variations were determined by using both Mann-Kendall and Mann-Whitney nonparametric tests. The results showed that the temperature and precipitation had significantly increased in the drainage basin in the mid-1980s. The climate was the warmest in 1990s among the recent 50 years. The increase of temperature in the tributaries of the Aksu River and Kaidu-Kongque River is higher than that in the tributaries of the Yarkand River and Hotan River. The streamflow at Aksu River showed a significant increasing monotonic trend. The annual runoff in the Aksu River had increased by 10.9% since 1990. The independence test of temperature and precipitation with chi(2) of the El Nino event reveals that there is no significant effect of the El Nino and La Nina events on the annual temperature and annual precipitation in the drainage basin. (C) 2008 Elsevier Ltd and INQUA. All rights reserved