114,970 research outputs found
From Competition to Complementarity: Comparative Influence Diffusion and Maximization
Influence maximization is a well-studied problem that asks for a small set of
influential users from a social network, such that by targeting them as early
adopters, the expected total adoption through influence cascades over the
network is maximized. However, almost all prior work focuses on cascades of a
single propagating entity or purely-competitive entities. In this work, we
propose the Comparative Independent Cascade (Com-IC) model that covers the full
spectrum of entity interactions from competition to complementarity. In Com-IC,
users' adoption decisions depend not only on edge-level information
propagation, but also on a node-level automaton whose behavior is governed by a
set of model parameters, enabling our model to capture not only competition,
but also complementarity, to any possible degree. We study two natural
optimization problems, Self Influence Maximization and Complementary Influence
Maximization, in a novel setting with complementary entities. Both problems are
NP-hard, and we devise efficient and effective approximation algorithms via
non-trivial techniques based on reverse-reachable sets and a novel "sandwich
approximation". The applicability of both techniques extends beyond our model
and problems. Our experiments show that the proposed algorithms consistently
outperform intuitive baselines in four real-world social networks, often by a
significant margin. In addition, we learn model parameters from real user
action logs.Comment: An abridged of this work is to appear in the Proceedings of VLDB
Endowment (PVDLB), Vol 9, No 2. Also, the paper will be presented in the VLDB
2016 conference in New Delhi, India. This update contains new theoretical and
experimental results, and the paper is now in single-column format (44 pages
A new class of parallel data convolutional codes
We propose a new class of parallel data convolutional
codes (PDCCs) in this paper. The PDCC encoders inputs
are composed of an original block of data and its interleaved version.
A novel single self-iterative soft-in/soft-out a posteriori probability
(APP) decoder structure is proposed for the decoding of
the PDCCs. Simulation results are presented to compare the performance
of PDCCs
Optimal design application on the advanced aeroelastic rotor blade
The vibration and performance optimization procedure using regression analysis was successfully applied to an advanced aeroelastic blade design study. The major advantage of this regression technique is that multiple optimizations can be performed to evaluate the effects of various objective functions and constraint functions. The data bases obtained from the rotorcraft flight simulation program C81 and Myklestad mode shape program are analytically determined as a function of each design variable. This approach has been verified for various blade radial ballast weight locations and blade planforms. This method can also be utilized to ascertain the effect of a particular cost function which is composed of several objective functions with different weighting factors for various mission requirements without any additional effort
On the capacity and normalisation of ISI channels
[Abstract]: We investigate the capacity of various ISI channels with additive white Gaussian noise. Previous papers showed
a minimum Eb/N0 of −4.6 dB, 3 dB below the capacity of
a flat channel, is obtained using the water-pouring capacity
formulas for the 1 + D channel. However, these papers did
not take into account that the channel power gain can be
greater than unity when water-pouring is used. We present a
generic power normalization method of the channel frequency
response, namely peak bandwidth normalisation, to facilitate the fair capacity comparison of various ISI channels. Three types of ISI channel, i.e., adder channels, RC channels and magnetic recording channels, are examined. By using our channel power gain normalization, the capacity curves of these ISI channels are shown
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