Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication

In the multiple unmanned aerial vehicle (UAV)- assisted downlink communication, it is challenging for UAV base stations (UAV BSs) to realize trajectory design and resource assignment in unknown environments. The cooperation and competition between UAV BSs in the communication network leads to a Markov game problem. Multi-agent reinforcement learning is a significant solution for the above decision-making. However, there are still many common issues, such as the instability of the system and low utilization of historical data, that limit its application. In this paper, a novel graph-attention multi-agent trust region (GA-MATR) reinforcement learning framework is proposed to solve the multi-UAV assisted communication problem. Graph recurrent network is introduced to process and analyze complex topology of the communication network, so as to extract useful information and patterns from observational information. The attention mechanism provides additional weighting for conveyed information, so that the critic network can accurately evaluate the value of behavior for UAV BSs. This provides more reliable feedback signals and helps the actor network update the strategy more effectively. Ablation simulations indicate that the proposed approach attains improved convergence over the baselines. UAV BSs learn the optimal communication strategies to achieve their maximum cumulative rewards. Additionally, multi-agent trust region method with monotonic convergence provides an estimated Nash equilibrium for the multi-UAV assisted communication Markov game.Comment: 13 page

Designing AI Interfaces for Children with Special Needs in Educational Contexts

The IDC research community has a growing interest in designing AI interfaces for children with special educational needs. Nonetheless, little research has explored the research and design issues, rationale, challenges, and opportunities in this field. Therefore, we propose to host a half-day workshop to bring together researchers and practitioners from the Learning & Education, Accessibility, and Intelligent User Interfaces sub-fields to discuss and identify existing design issues, challenges, and collaboration barriers, to establish consensus on the design of a pragmatic framework, as well as explore future innovation and research opportunities. We aim to foster mutual unders

DPPMask: Masked Image Modeling with Determinantal Point Processes

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks

Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task in which each client holds a locally heterogeneous dataset; if the datasets can be globally aggregated, they jointly exhibit a long-tailed distribution. Under such a setting, existing federated optimization and/or centralized long-tailed learning methods hardly apply due to challenges in (a) characterizing the global long-tailed distribution under privacy constraints and (b) adjusting the local learning strategy to cope with the head-tail imbalance. In response, we propose a method termed $\texttt{Fed-GraB}$, comprised of a Self-adjusting Gradient Balancer (SGB) module that re-weights clients' gradients in a closed-loop manner, based on the feedback of global long-tailed distribution evaluated by a Direct Prior Analyzer (DPA) module. Using $\texttt{Fed-GraB}$, clients can effectively alleviate the distribution drift caused by data heterogeneity during the model training process and obtain a global model with better performance on the minority classes while maintaining the performance of the majority classes. Extensive experiments demonstrate that $\texttt{Fed-GraB}$ achieves state-of-the-art performance on representative datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist.Comment: Accepted by NeurIPS 202

Efficacy and safety of pharmacotherapy for refractory or unexplained chronic cough: a systematic review and network meta-analysis

Background: Refractory chronic cough (RCC) has a significant impact on patient's health-related quality of life and represents a challenge in clinical management. However, the optimal treatment for RCC remains controversial. This study aimed to investigate and compare the efficacy and safety of the current pharmacological therapeutic options for RCC. Methods: A systematic review was performed by searching PubMed, Web of Science, Embase, and Ovid databases from January 1, 2008 to March 1, 2023. All randomised control trials (RCTs) reporting outcomes of efficacy or/and safety were included in the Bayesian network meta-analysis. Here, we compared the effects on Leicester Cough Questionnaire (LCQ), Visual Analogue Scale (VAS), and objective cough frequency of patients with RCC. Besides, we also compared the incidence of adverse events (AEs) for analysis of safety. PROSPERO registration: CRD42022345940. Findings: 19 eligible RCTs included 3326 patients and 7 medication categories: P2X3 antagonist, GABA modulator, Transient Receptor Potential (TRP) modulator, NK-1 agonist, opioid analgesic, macrolide, and sodium cromoglicate. Compared with placebo, mean difference (MD) of LCQ and 24 h cough frequency for P2X3 antagonist relief were 1.637 (95% CI: 0.887–2.387) and −11.042 (P = 0.035). Compared with placebo, effect sizes (MD for LCQ and cough severity VAS) for GABA modulator were 1.347 (P = 0.003) and −7.843 (P = 0.003). In the network meta-analysis, gefapixant is the most effective treatment for patients with RCC (The Surface Under the Cumulative Ranking Curves (SUCRA) is 0.711 in LCQ, 0.983 in 24 h cough frequency, and 0.786 in cough severity VAS). Lesogaberan had better efficacy than placebo (SUCRA: 0.632 vs. 0.472) in 24 h cough frequency. Eliapixant and lesogaberan had better efficacy than placebo in cough severity VAS. However, TRP modulator had worse efficacy than placebo. In the meta-analysis of AEs, the present study found P2X3 antagonist had a significant correlation to AEs (RR: 1.129, 95% CI: 1.012–1.259), especially taste-related AEs (RR: 6.216, P < 0.05). Interpretation: In this network meta-analysis, P2X3 antagonist showing advantages in terms of efficacy is currently the most promising medication for treatment of RCC. GABA modulator also showed potential efficacy for RCC but with AEs of the central system. Nevertheless, the role of TRP modulator needed to be revisited. Further research is needed to determine the potential beneficiary population for optimizing the pharmacological management of chronic cough. Funding: National Natural Science Foundation of China ( 81870079), Guangdong Science and Technology Project ( 2021A050520012), Incubation Program of National Science Foundation for Distinguished Young Scholars ( GMU2020-207)

Longitudinal white-matter abnormalities in sports-related concussion: A diffusion MRI study

Objective To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non–contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non–contact-sport controls. Conclusions Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery tim

Average trapping time on weighted directed Koch network

Numerous recent studies have focused on random walks on undirected binary scale-free networks. However, random walks with a given target node on weighted directed networks remain less understood. In this paper, we first introduce directed weighted Koch networks, in which any pair of nodes is linked by two edges with opposite directions, and weights of edges are controlled by a parameter θ . Then, to evaluate the transportation efficiency of random walk, we derive an exact solution for the average trapping time (ATT), which agrees well with the corresponding numerical solution. We show that leading behaviour of ATT is function of the weight parameter θ and that the ATT can grow sub-linearly, linearly and super-linearly with varying θ . Finally, we introduce a delay parameter p to modify the transition probability of random walk, and provide a closed-form solution for ATT, which still coincides with numerical solution. We show that in the closed-form solution, the delay parameter p can change the coefficient of ATT, but cannot change the leading behavior. We also show that desired ATT or trapping efficiency can be obtained by setting appropriate weight parameter and delay parameter simultaneously. Thereby, this work advance the understanding of random walks on directed weighted scale-free networks

Controlling the trapping efficiency in a family of scale-free tree networks

Efficiently controlling the trapping process is very significant in the study of trapping problem in diverse dynamic processes. In this paper, we explore the trapping efficiency on a family of scale-free tree networks with a deep trap positioned at an initial node, which is controlled by three different strategies. In the first technique, the transition probability is modified by an edge weight parameter. In the second method, the transition probability is controlled by a delay parameter. In the third approach, we use the delay parameter and weight parameter simultaneously to control the trapping process. For all the three control methods, the analytical results of average trapping time (ATT) exactly agree with the numerical results. The result of the first control strategy shows that the average trapping time can scale sublinearly, linearly or superlinearly by modifying the weight parameter. The analytic expression of the ATT in the second method shows that the delay parameter can only modify the main coefficient of ATT, but cannot change the dominant behavior of trapping efficiency. The explicit expression of average trapping time when random walk on scale-free tree network is controlled by the third method shows that it is a fine control. We can get desired trapping efficiency by changing the weight parameter and the delay parameter simultaneously. This work provides a better understanding of controlling the trapping process in a family of scale-free tree networks