Learning Perception-Aware Agile Flight in Cluttered Environments

Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minimum time. However, they are not perception aware, a crucial requirement in vision-based navigation due to the camera's limited field of view and the underactuated nature of a quadrotor. We propose a learning-based system that achieves perception-aware, agile flight in cluttered environments. Our method combines imitation learning with reinforcement learning (RL) by leveraging a privileged learning-by-cheating framework. Using RL, we first train a perception-aware teacher policy with full-state information to fly in minimum time through cluttered environments. Then, we use imitation learning to distill its knowledge into a vision-based student policy that only perceives the environment via a camera. Our approach tightly couples perception and control, showing a significant advantage in computation speed (10×faster) and success rate. We demonstrate the closed-loop control performance using hardware-in-the-loop simulation

Adipose-derived mesenchymal stem cells attenuate ischemic brain injuries in rats by modulating miR-21-3p/MAT2B signaling transduction

Aim To explore the mechanism underlying the protective effect of adipose-derived mesenchymal stem cells (ADMSCs) against ischemic stroke by focusing on miR-21-3p/ MAT2B axis. Methods Ischemic brain injury was induced in 126 rats by middle cerebral artery occlusion (MCAO). The effect of ADMSC administration on blood-brain barrier (BBB) condition, apoptosis, inflammation, and the activity of miR-21- 3p/MAT2B axis was assessed. The role of miR-21-3p inhibition in the function of ADMSCs was further validated in in vitro neural cells. Results ADMSCs administration improved BBB condition, inhibited apoptosis, and suppressed inflammation. It also reduced the abnormally high level of miR-21-3p in MCAO rats. Dual luciferase assays showed that miR-21-3p directly inhibited the MAT2B expression in neural cells, and miR-21 -3p inhibition by inhibitor or ADMSC-derived exosomes in neurons attenuated hypoxia/reoxygenation-induced impairments similarly to that of ADMSCs in vivo. Conclusion This study confirmed the protective effect of ADMSCs against ischemic brain injury exerted by suppressing miR-21-3p level and up-regulating MAT2B level

Communication-Efficient Topologies for Decentralized Learning with O(1)O(1) Consensus Rate

Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the agents' information is ``mixed'' by communication. We found that popular communication topologies either have large maximum degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and a network-size-independent consensus rate that is used to measure the mixing efficiency. In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve an $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, theoretically and empirically. Our code is implemented through BlueFog and available at \url{https://github.com/kexinjinnn/EquiTopo}Comment: NeurIPS 202