Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high SNR and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and develop an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans. Commo

Analytic Network Traffic Prediction Based on User Behavior Modeling

This paper proposes an interpretable user-behavior-based (UBB) network traffic prediction (NTP) method. Based on user behavior, a weekly traffic demand profile can be naturally sorted into three categories, i.e., weekday, Saturday, and Sunday. For each category, the traffic pattern is divided into three components which are mainly generated in three time periods, i.e., morning, afternoon, and evening. Each component is modeled as a normal-distributed signal. Numerical results indicate the UBB NTP method matches the practical wireless traffic demand very well. Compared with existing methods, the proposed UBB NTP method improves the computational efficiency and increases the predictive accuracy.Comment: This paper has been submitted to IEEE Networking Letters for possible publication

Object as Query: Lifting any 2D Object Detector to 3D Detection

3D object detection from multi-view images has drawn much attention over the past few years. Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detection, or employ object queries distributed in 3D space to localize objects. In this paper, we design Multi-View 2D Objects guided 3D Object Detector (MV2D), which can lift any 2D object detector to multi-view 3D object detection. Since 2D detections can provide valuable priors for object existence, MV2D exploits 2D detectors to generate object queries conditioned on the rich image semantics. These dynamically generated queries help MV2D to recall objects in the field of view and show a strong capability of localizing 3D objects. For the generated queries, we design a sparse cross attention module to force them to focus on the features of specific objects, which suppresses interference from noises. The evaluation results on the nuScenes dataset demonstrate the dynamic object queries and sparse feature aggregation can promote 3D detection capability. MV2D also exhibits a state-of-the-art performance among existing methods. We hope MV2D can serve as a new baseline for future research.Comment: technical repor

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence

Decoupled molecular and inorganic framework dynamics in CH3NH3PbCl3

The organic-inorganic lead halide perovskites are composed of organic molecules imbedded in an inorganic framework. The compounds with general formula CH$_{3}$NH$_{3}$PbX$_{3}$ (MAPbX$_{2}$) display large photovoltaic efficiencies for halogens $X$=Cl, Br, and I in a wide variety of sample geometries and preparation methods. The organic cation and inorganic framework are bound by hydrogen bonds that tether the molecules to the halide anions, and this has been suggested to be important to the optoelectronic properties. We have studied the effects of this bonding using time-of-flight neutron spectroscopy to measure the molecular dynamics in CH$_3$NH$_3$PbCl$_3$ (MAPbCl$_3$). Low-energy/high-resolution neutron backscattering reveals thermally-activated molecular dynamics with a characteristic temperature of $\sim$ 95\,K. At this same temperature, higher-energy neutron spectroscopy indicates the presence of an anomalous broadening in energy (reduced lifetime) associated with the molecular vibrations. By contrast, neutron powder diffraction shows that a spatially long-range structural phase transitions occurs at 178\,K (cubic $\rightarrow$ tetragonal) and 173\,K (tetragonal $\rightarrow$ orthorhombic). The large difference between these two temperature scales suggests that the molecular and inorganic lattice dynamics in MAPbCl$_3$ are actually decoupled. With the assumption that underlying physical mechanisms do not change with differing halogens in the organic-inorganic perovskites, we speculate that the energy scale most relevant to the photovoltaic properties of the lead-halogen perovskites is set by the lead-halide bond, not by the hydrogen bond.Comment: (10 pages, 5 figures, to be published in Physical Review Materials

Mycobacterium smegmatis Induces Neurite Outgrowth and Differentiation in an Autophagy-Independent Manner in PC12 and C17.2 Cells

Both pathogenic and non-pathogenic Mycobacteria can induce the differentiation of immune cells into dendritic cells (DC) or DC-like cells. In addition, pathogenic Mycobacteria is found to stimulate cell differentiation in the nerves system. Whether non-pathogenic Mycobacteria interacts with nerve cells remains unknown. In this study, we found that co-incubation with fast-growing Mycobacteria smegmatis induced neuron-like morphological changes of PC12 and C17.2 cells. Moreover, the M. smegmatis culture supernatant which was ultrafiltrated through a membrane with a 10 kDa cut-off, induced neurite outgrowth and differentiation in an autophagy-independent pathway in PC12 and C17.2 cells. Further analysis showed that IFN-γ production and activation of the PI3K-Akt signaling pathway were involved in the neural differentiation. In conclusion, our finding demonstrated that non-pathogenic M. smegmatis was able to promote neuronal differentiation by its extracellular proteins, which might provide a novel therapeutic strategy for the treatment of neurodegenerative disorders