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

Hippocampal Long-Term Depression in the Presence of Calcium-Permeable AMPA Receptors

The GluA2 subunit of AMPA glutamate receptors (AMPARs) has been shown to be critical for the expression of NMDA receptor (NMDAR)-dependent long-term depression (LTD). However, in young GluA2 knockout (KO) mice, this form of LTD can still be induced in the hippocampus, suggesting that LTD mechanisms may be modified in the presence of GluA2-lacking, Ca2+ permeable AMPARs. In this study, we examined LTD at the CA1 synapse in GluA2 KO mice by using several well-established inhibitory peptides known to block LTD in wild type (WT) rodents. We showed that while LTD in the KO mice is still blocked by the protein interacting with C kinase 1 (PICK1) peptide pepEVKI, it becomes insensitive to the N-ethylmaleimide-sensitive factor (NSF) peptide pep2m. In addition, the effects of actin and cofilin inhibitory peptides were also altered. These results indicate that in the absence of GluA2, LTD expression mechanisms are different from those in WT animals, suggesting that there are multiple molecular processes enabling LTD expression that are adaptable to physiological and genetic manipulations

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

Metabolic Stability of New Mito-Protective Short-Chain Naphthoquinones

Short-chain quinones (SCQs) have been identified as potential drug candidates against mitochondrial dysfunction, which is largely dependent on their reversible redox characteristics of the active quinone core. We recently synthesized a SCQ library of > 148 naphthoquinone derivatives and identified 16 compounds with enhanced cytoprotection compared to the clinically used benzoquinone idebenone. One of the major drawbacks of idebenone is its high metabolic conversion in the liver, which significantly restricts its therapeutic activity. Therefore, this study assessed the metabolic stability of the 16 identified naphthoquinone derivatives 1–16 using hepatocarcinoma cells in combination with an optimized reverse-phase liquid chromatography (RP-LC) method. Most of the derivatives showed significantly better stability than idebenone over 6 hours (p < 0.001). By extending the side-chain of SCQs, increased stability for some compounds was observed. Metabolic conversion from the derivative 3 to 5 and reduced idebenone metabolism in the presence of 5 were also observed. These results highlight the therapeutic potential of naphthoquinone-based SCQs and provide essential insights for future drug design, prodrug therapy and polytherapy, respectively

Couleur de nuage

Préfacé, choisi, traduit et annoté par Marie LaureillardInternational audienceUne vingtaine d'essais antérieurs à 1949 composent ce recueil, auxquels font écho soixante dessins de l'auteur, au trait vif et expressif. Ce jeu de miroir entre texte et dessin révèle toute la richesse de cette œuvre qui porte un regard unique sur la Chine républicaine (1911-1949). Dans un style sobre, empreint de lyrisme et d'humour discret, Feng Zikai se livre à l'introspection, médite face à la fuite du temps, chante les vertus de l'amitié et s'emploie à regarder le monde qui l'entoure avec sagesse et modération. Feng Zikai (1898-1975) est le plus célèbre dessinateur chinois de la première moitié du XXe siècle. En 1925, la publication de ses dessins puis la parution de ses écrits lui confèrent d'emblée la renommée. Esthète de la simplicité et épris de compassion, il oscillera sa vie durant entre le rêve de se retirer du monde et la volonté d'être en prise avec son temps. La pensée bouddhique conjuguée à un certain idéal confucianiste sous-tend toute son oeuvre

Couleur de nuage

Préfacé, choisi, traduit et annoté par Marie LaureillardInternational audienceUne vingtaine d'essais antérieurs à 1949 composent ce recueil, auxquels font écho soixante dessins de l'auteur, au trait vif et expressif. Ce jeu de miroir entre texte et dessin révèle toute la richesse de cette œuvre qui porte un regard unique sur la Chine républicaine (1911-1949). Dans un style sobre, empreint de lyrisme et d'humour discret, Feng Zikai se livre à l'introspection, médite face à la fuite du temps, chante les vertus de l'amitié et s'emploie à regarder le monde qui l'entoure avec sagesse et modération. Feng Zikai (1898-1975) est le plus célèbre dessinateur chinois de la première moitié du XXe siècle. En 1925, la publication de ses dessins puis la parution de ses écrits lui confèrent d'emblée la renommée. Esthète de la simplicité et épris de compassion, il oscillera sa vie durant entre le rêve de se retirer du monde et la volonté d'être en prise avec son temps. La pensée bouddhique conjuguée à un certain idéal confucianiste sous-tend toute son oeuvre