arXiv research

MEC-IP uses IP to efficiently find MECs in BNs from observational data.

Paper proposes FMore to incentivize edge nodes in federated learning with MEC.

MEC improves efficiency and robustness in semi-supervised inference.

A fast algorithm for counting Markov equivalent DAGs and designing experiments.

MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and o…

The application of standard sufficient dimension reduction methods for reducing the dimension space of predictors without losing regression information requires inverting the covariance matrix of the predictors. This has posed a number of challenges especially when analyzing high-dimensional data sets in which the numb…

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. Nevertheless, by considering a MEC system consisting of mu…

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computat…

This paper tackles risk-aware energy scheduling for MEC networks with microgrids.

Characterizes Bayesian networks up to unconditional equivalence.

New method tests causal relationships from data without needing to learn the entire graph.

This work sets a universal lower bound for learning causal DAGs with atomic interventions.

Deep learning approach for efficient IoT task scheduling in MEC networks.

Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.

CodedFedL speeds up federated learning in MEC networks by 15x.

A new method for efficient causal structure learning at scale.

Coded Federated Learning speeds up training in edge computing networks.

Reframed GES uses a neural conditional dependence measure for consistent causal structure learning.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wir…

New measures assess differences in causal graphs' separations.

AdaDEM decouples EM into two parts to improve class overlap and uncertainty.

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, reso…

Scheduling and power allocation improve federated learning efficiency in NOMA networks.

Hexagon grid patterns emerge from conformal isometry in grid cell neural networks.

We consider the problem $\mathbf{P_{curve}}$ of minimizing $\int \limits_0^L \sqrt{ξ^2 + κ^2(s)} \, {\rm d}s$ for a curve $\mathbf{x}$ on $\mathbb R$ with fixed boundary points and directions. Here the total length $L\geq 0$ is free, $s$ denotes the arclength parameter, $κ$ denotes the absolute curvature of $\mathbf{x}…

Federated learning optimizes task and resource allocation in balloon networks.