arXiv subjects

Yue Han

Publications and source records attributed to Yue Han.

39 records · Page 3Linked to original sources

arXiv2021

Dynamics in Coded Edge Computing for IoT: A Fractional Evolutionary Game Approach

Recently, coded distributed computing (CDC), with advantages in intensive computation and reduced latency, has attracted a lot of research interest for edge computing, in particular, IoT applications, including IoT data pre-processing and data analytics. Nevertheless, it can be challenging for edge infrastructure providers (EIPs) with limited edge resources to support IoT applications performed in a CDC approach in edge networks, given the additional computational resources required by CDC. In this paper, we propose coded edge federation, in which different EIPs collaboratively provide edge resources for CDC tasks. To study the Nash equilibrium, when no EIP has an incentive to unilaterally alter its decision on edge resource allocation, we model the coded edge federation based on evolutionary game theory. Since the replicator dynamics of the classical evolutionary game are unable to model economic-aware EIPs which memorize past decisions and utilities, we propose fractional replicator dynamics with a power-law fading memory via Caputo fractional derivatives. The proposed dynamics allow us to study a broad spectrum of EIP dynamic behaviors, such as EIP sensitivity and aggressiveness in strategy adaptation, which classical replicator dynamics cannot capture. Theoretical analysis and extensive numerical results justify the existence, uniqueness, and stability of the equilibrium in the fractional evolutionary game. The influence of the content and the length of the memory on the rate of convergence is also investigated.

cs.DC↗

arXiv2018

QuickIM: Efficient, Accurate and Robust Influence Maximization Algorithm on Billion-Scale Networks

The Influence Maximization (IM) problem aims at finding k seed vertices in a network, starting from which influence can be spread in the network to the maximum extent. In this paper, we propose QuickIM, the first versatile IM algorithm that attains all the desirable properties of a practically applicable IM algorithm at the same time, namely high time efficiency, good result quality, low memory footprint, and high robustness. On real-world social networks, QuickIM achieves the $Ω(n + m)$ lower bound on time complexity and $Ω(n)$ space complexity, where $n$ and $m$ are the number of vertices and edges in the network, respectively. Our experimental evaluation verifies the superiority of QuickIM. Firstly, QuickIM runs 1-3 orders of magnitude faster than the state-of-the-art IM algorithms. Secondly, except EasyIM, QuickIM requires 1-2 orders of magnitude less memory than the state-of-the-art algorithms. Thirdly, QuickIM always produces as good quality results as the state-of-the-art algorithms. Lastly, the time and the memory performance of QuickIM is independent of influence probabilities. On the largest network used in the experiments that contains more than 3.6 billion edges, QuickIM is able to find hundreds of influential seeds in less than 4 minutes, while all the state-of-the-art algorithms fail to terminate in an hour.

cs.SI↗

arXiv2015

Bio-Inspired Aggregation Control of Carbon Nanotubes for Ultra-Strong Composites

High performance nanocomposites require well dispersion and high alignment of the nanometer-sized components, at a high mass or volume fraction as well. However, the road towards such composite structure is severely hindered due to the easy aggregation of these nanometer-sized components. Here we demonstrate a big step to approach the ideal composite structure for carbon nanotube (CNT) where all the CNTs were highly packed, aligned, and unaggregated, with the impregnated polymers acting as interfacial adhesions and mortars to build up the composite structure. The strategy was based on a bio-inspired aggregation control to limit the CNT aggregation to be sub 20--50 nm, a dimension determined by the CNT growth. After being stretched with full structural relaxation in a multi-step way, the CNT/polymer (bismaleimide) composite yielded super-high tensile strengths up to 6.27--6.94 GPa, more than 100% higher than those of carbon fiber/epoxy composites, and toughnesses up to 117--192 MPa. We anticipate that the present study can be generalized for developing multifunctional and smart nanocomposites where all the surfaces of nanometer-sized components can take part in shear transfer of mechanical, thermal, and electrical signals.

cond-mat.mtrl-sci↗