Joint Computation and Communication Cooperation for Mobile Edge Computing

This paper proposes a novel joint computation and communication cooperation approach in mobile edge computing (MEC) systems, which enables user cooperation in both computation and communication for improving the MEC performance. In particular, we consider a basic three-node MEC system that consists of a user node, a helper node, and an access point (AP) node attached with an MEC server. We focus on the user's latency-constrained computation over a finite block, and develop a four-slot protocol for implementing the joint computation and communication cooperation. Under this setup, we jointly optimize the computation and communication resource allocation at both the user and the helper, so as to minimize their total energy consumption subject to the user's computation latency constraint. We provide the optimal solution to this problem. Numerical results show that the proposed joint cooperation approach significantly improves the computation capacity and the energy efficiency at the user and helper nodes, as compared to other benchmark schemes without such a joint design.Comment: 8 pages, 4 figure

Single-cell analysis reveals specific neuronal transition during mouse corticogenesis

Background: Currently, the mechanism(s) underlying corticogenesis is still under characterization.Methods: We curated the most comprehensive single-cell RNA-seq (scRNA-seq) datasets from mouse and human fetal cortexes for data analysis and confirmed the findings with co-immunostaining experiments.Results: By analyzing the developmental trajectories with scRNA-seq datasets in mice, we identified a specific developmental sub-path contributed by a cell-population expressing both deep- and upper-layer neurons (DLNs and ULNs) specific markers, which occurred on E13.5 but was absent in adults. In this cell-population, the percentages of cells expressing DLN and ULN markers decreased and increased, respectively, during the development suggesting direct neuronal transition (namely D-T-U). Whilst genes significantly highly/uniquely expressed in D-T-U cell population were significantly enriched in PTN/MDK signaling pathways related to cell migration. Both findings were further confirmed by co-immunostaining with DLNs, ULNs and D-T-U specific markers across different timepoints. Furthermore, six genes (co-expressed with D-T-U specific markers in mice) showing a potential opposite temporal expression between human and mouse during fetal cortical development were associated with neuronal migration and cognitive functions. In adult prefrontal cortexes (PFC), D-T-U specific genes were expressed in neurons from different layers between humans and mice.Conclusion: Our study characterizes a specific cell population D-T-U showing direct DLNs to ULNs neuronal transition and migration during fetal cortical development in mice. It is potentially associated with the difference of cortical development in humans and mice

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions