73,405 research outputs found
Group Minds and the Case of Wikipedia
Group-level cognitive states are widely observed in human social systems, but
their discussion is often ruled out a priori in quantitative approaches. In
this paper, we show how reference to the irreducible mental states and
psychological dynamics of a group is necessary to make sense of large scale
social phenomena. We introduce the problem of mental boundaries by reference to
a classic problem in the evolution of cooperation. We then provide an explicit
quantitative example drawn from ongoing work on cooperation and conflict among
Wikipedia editors, showing how some, but not all, effects of individual
experience persist in the aggregate. We show the limitations of methodological
individualism, and the substantial benefits that come from being able to refer
to collective intentions, and attributions of cognitive states of the form
"what the group believes" and "what the group values".Comment: 21 pages, 6 figures; matches published versio
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
Cloud Compute-and-Forward with Relay Cooperation
We study a cloud network with M distributed receiving antennas and L users,
which transmit their messages towards a centralized decoder (CD), where M>=L.
We consider that the cloud network applies the Compute-and-Forward (C&F)
protocol, where L antennas/relays are selected to decode integer equations of
the transmitted messages. In this work, we focus on the best relay selection
and the optimization of the Physical-Layer Network Coding (PNC) at the relays,
aiming at the throughput maximization of the network. Existing literature
optimizes PNC with respect to the maximization of the minimum rate among users.
The proposed strategy maximizes the sum rate of the users allowing nonsymmetric
rates, while the optimal solution is explored with the aid of the Pareto
frontier. The problem of relay selection is matched to a coalition formation
game, where the relays and the CD cooperate in order to maximize their profit.
Efficient coalition formation algorithms are proposed, which perform joint
relay selection and PNC optimization. Simulation results show that a
considerable improvement is achieved compared to existing results, both in
terms of the network sum rate and the players' profits.Comment: Submitted to IEEE Transactions on Wireless Communication
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