SOCIAL STATUS INSECURITY AND BODY IMAGE RELATED HEALTH OUTCOMES: TESTING A MODERATED MEDIATION MODEL

Body dissatisfaction has been found to be associated with a wide range of maladjustment outcomes and negative behavioral consequences. To identify the sources leading to body dissatisfaction, the present study proposed that an aspect of social cognition specifically regarding peer status, known as social status insecurity, may function as a precursor of body dissatisfaction. This study further examined the associations between social status insecurity and body-image-related health outcomes by focusing on the mediation effects of body dissatisfaction in a sample of 308 Chinese adolescents (117 girls, 191 boys). Furthermore, this study examined whether these mediation processes were moderated by social status (i.e., popularity status, social preference) and gender, with each type of social status (e.g., popularity) moderating the corresponding type of social status insecurity (e.g., popularity status insecurity). Results from path analyses generally demonstrated that feeling insecured about one’s status among peers is directly or indirectly associated with maladaptive eating behaviors, worse health conditions, social anxiety, and depressive symptoms, depending on the attained status and/or gender. Findings from this study can inform researchers, educators, and clinicians of peer status related vulnerabilities that likely induce adolescents’ disordered eating behaviors and physical, mental health problems, as well as inform them of some new directions for interventions aiming at reducing these negative outcomes

Relationship Between Noninterest Income and Bank Valuation: Evidence from the U.S. Bank Holding Companies

This paper investigates the impact of noninterest income on bank valuation using 625 U.S. Bank Holding Companies over the period 2003-2015. We use two measures of valuation: Tobin’s q and the market-to-book ratio. Using the whole sample, we find a positive relation between noninterest income and valuation. We then divide banks in our sample into three groups based on size, and the sample period into three sub-periods. We find that noninterest income is positively related to valuation (1) for large banks in each sub-period, (2) for medium-sized banks during and after the financial crisis of 2007-2009, and (3) for small banks after the financial crisis

A Novel PTS Scheme for PAPR Reduction of Filtered-OFDM Signals without Side Information

In this paper, a novel partial transmit sequence (PTS) scheme is proposed for reducing the peak-to-average power ratio (PAPR) of filtered orthogonal frequency division multiplexing (f-OFDM) systems. The PTS method is modified such that no side information (SI) transmission is needed. The data and pilot recovery are accomplished by a simple detector, making use of the correlation property of the Hadamard sequence and the transparency property of the pilot signal and an iterative phase detection is further added in a fading channel. Simulation results show that the modified solution provides a higher correct detection probability without increasing the system complexity nor affecting the PAPR suppression performance

Tackling Visual Control via Multi-View Exploration Maximization

We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks. To the best of our knowledge, MEM is the first approach that combines multi-view representation learning and intrinsic reward-driven exploration in reinforcement learning (RL). More specifically, MEM first extracts the specific and shared information of multi-view observations to form high-quality features before performing RL on the learned features, enabling the agent to fully comprehend the environment and yield better actions. Furthermore, MEM transforms the multi-view features into intrinsic rewards based on entropy maximization to encourage exploration. As a result, MEM can significantly promote the sample-efficiency and generalization ability of the RL agent, facilitating solving real-world problems with high-dimensional observations and spare-reward space. We evaluate MEM on various tasks from DeepMind Control Suite and Procgen games. Extensive simulation results demonstrate that MEM can achieve superior performance and outperform the benchmarking schemes with simple architecture and higher efficiency.Comment: 21 pages, 9 figure

Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of Procgen games and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.Comment: 23 pages, 16 figure

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