With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution.The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of "In-Edge AI".

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The ethyl celluloses (ECs) modified with 5.0, 10.0, and 20.0 wt % polyaniline (PANI) (PANI/ECs) prepared by homogeneously mixing the EC and PANI formic acid solutions have demonstrated a superior hexavalent chromium (Cr­(VI)) removal performance to that of pure EC. Having an increased Cr­(VI) removal percentage with increased PANI loading, the PANI/ECs with 20.0% PANI loading were noticed to remove 2.0 mg/L Cr­(VI) completely within 5 min, much faster than the pristine EC (>1 h). A chemical redox of Cr­(VI) to Cr­(III) by the active functional groups of PANI/ECs was revealed from the kinetic study. Meanwhile, isothermal study demonstrated a monolayer adsorption behavior following the Langmuir model with a calculated maximum absorption capacity of 19.49, 26.11, and 38.76 mg/g for the 5.0, 10.0, and 20.0 wt % PANI/ECs, much higher than that of EC (12.2 mg/g). The Cr­(VI) removal mechanisms were interpreted considering the functional groups of both PANI and EC, the valence state fates of Cr­(VI), and the variation of solution acidity.

G-Quadruplex and i-motif are tetraplex structures that may form in opposite strands at the same location of a duplex DNA. Recent discoveries have indicated that the two tetraplex structures can have conflicting biological activities, which poses a challenge for cells to coordinate. Here, by performing innovative population analysis on mechanical unfolding profiles of tetraplex structures in double-stranded DNA, we found that formations of G-quadruplex and i-motif in the two complementary strands are mutually exclusive in a variety of DNA templates, which include human telomere and promoter fragments of hINS and hTERT genes. To explain this behavior, we placed G-quadruplex- and i-motif-hosting sequences in an offset fashion in the two complementary telomeric DNA strands. We found simultaneous formation of the G-quadruplex and i-motif in opposite strands, suggesting that mutual exclusivity between the two tetraplexes is controlled by steric hindrance. This conclusion was corroborated in the BCL-2 promoter sequence, in which simultaneous formation of two tetraplexes was observed due to possible offset arrangements between G-quadruplex and i-motif in opposite strands. The mutual exclusivity revealed here sets a molecular basis for cells to efficiently coordinate opposite biological activities of G-quadruplex and i-motif at the same dsDNA location.