“A cancer in the minds of youth”? : A qualitative study of problematic smartphone use among undergraduate students

Aim : There is empirical evidence to suggest that problematic smartphone use (PSU) is associated with mental health problems including anxiety in educational settings. This qualitative study explored attitudes towards – and self-reported impacts of – smartphone use among British young adult students, as well as perceived causes of PSU. Methods : Free-response written accounts were gathered from 265 British undergraduates at an English university. Open-ended questions were asked about their attitudes towards smartphone use, their reasons for using their smartphones, and what they perceived as the consequences of their smartphone use. Narratives were analyzed using Framework Analysis and a thematic framework was identified. Results : The three main consequences of PSU described by participants were (i) uncontrolled frequent checking of smartphones, (ii) using smartphones late at night, and irrelevant use of smartphones in class. The main reported explanations for PSU were fear of missing messages, boredom in class, poor self-regulation, and external reasons (e.g., boring lectures). Smartphone use was reported to have both positive and negative impacts on young adults’ life satisfaction, social relationships, physical health and study. Many participants reported that they need to develop better self-regulation to address their PSU. Conclusions : Findings suggest that smartphone use can have benefits as well as potentially causing harm among university students. PSU can – in some cases – be understood as reflecting mental well-being issues, poor self-regulation, and social problems

Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region.Comment: 6 pages, 3 figures, accepted for IEEE Global Communication Conference (GLOBECOM) 2023 Workshop on Semantic Communication for 6

Safe Reinforcement Learning with Dual Robustness

Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either address safety requirements under the assumption of no adversary (e.g., safe RL) or only focus on robustness against performance adversaries (e.g., robust RL). Learning one policy that is both safe and robust remains a challenging open problem. The difficulty is how to tackle two intertwined aspects in the worst cases: feasibility and optimality. Optimality is only valid inside a feasible region, while identification of maximal feasible region must rely on learning the optimal policy. To address this issue, we propose a systematic framework to unify safe RL and robust RL, including problem formulation, iteration scheme, convergence analysis and practical algorithm design. This unification is built upon constrained two-player zero-sum Markov games. A dual policy iteration scheme is proposed, which simultaneously optimizes a task policy and a safety policy. The convergence of this iteration scheme is proved. Furthermore, we design a deep RL algorithm for practical implementation, called dually robust actor-critic (DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC achieves high performance and persistent safety under all scenarios (no adversary, safety adversary, performance adversary), outperforming all baselines significantly

On finite-time anti-saturated proximity control with a tumbling non-cooperative space target

For the challenging problem that a spacecraft approaching a tumbling target with non-cooperative maneuver, an anti-saturated proximity control method is proposed in this paper. First, a brand-new appointed-time convergent performance function is developed via exploring Bezier curve to quantitatively characterize the transient and steady-state behaviors of the pose tracking error system. The major advantage of the proposed function is that the actuator saturation phenomenon at the beginning can be effectively reduced. Then, an anti-saturated pose tracking controller is devised along with an adaptive saturation compensator. Wherein, the finite-time stability of both the pose and its velocity error signals are guaranteed simultaneously in the presence of actuator saturation. Finally, two groups of illustrative examples are organized and verify that the close-range proximity is effectively realized even with unknown target maneuver