8,555 research outputs found
Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games
Enforcing cooperation among substantial agents is one of the main objectives
for multi-agent systems. However, due to the existence of inherent social
dilemmas in many scenarios, the free-rider problem may arise during agents'
long-run interactions and things become even severer when self-interested
agents work in collusion with each other to get extra benefits. It is commonly
accepted that in such social dilemmas, there exists no simple strategy for an
agent whereby she can simultaneously manipulate on the utility of each of her
opponents and further promote mutual cooperation among all agents. Here, we
show that such strategies do exist. Under the conventional repeated public
goods game, we novelly identify them and find that, when confronted with such
strategies, a single opponent can maximize his utility only via global
cooperation and any colluding alliance cannot get the upper hand. Since a full
cooperation is individually optimal for any single opponent, a stable
cooperation among all players can be achieved. Moreover, we experimentally show
that these strategies can still promote cooperation even when the opponents are
both self-learning and collusive
Blind Demixing for Low-Latency Communication
In the next generation wireless networks, lowlatency communication is
critical to support emerging diversified applications, e.g., Tactile Internet
and Virtual Reality. In this paper, a novel blind demixing approach is
developed to reduce the channel signaling overhead, thereby supporting
low-latency communication. Specifically, we develop a low-rank approach to
recover the original information only based on a single observed vector without
any channel estimation. Unfortunately, this problem turns out to be a highly
intractable non-convex optimization problem due to the multiple non-convex
rankone constraints. To address the unique challenges, the quotient manifold
geometry of product of complex asymmetric rankone matrices is exploited by
equivalently reformulating original complex asymmetric matrices to the
Hermitian positive semidefinite matrices. We further generalize the geometric
concepts of the complex product manifolds via element-wise extension of the
geometric concepts of the individual manifolds. A scalable Riemannian
trust-region algorithm is then developed to solve the blind demixing problem
efficiently with fast convergence rates and low iteration cost. Numerical
results will demonstrate the algorithmic advantages and admirable performance
of the proposed algorithm compared with the state-of-art methods.Comment: 14 pages, accepted by IEEE Transaction on Wireless Communicatio
Holographic RG flows with nematic IR phases
We construct zero-temperature geometries that interpolate between a Lifshitz
fixed point in the UV and an IR phase that breaks spatial rotations but
preserves translations. We work with a simple holographic model describing two
massive gauge fields coupled to gravity and a neutral scalar. Our construction
can be used to describe RG flows in non-relativistic, strongly coupled quantum
systems with nematic order in the IR. In particular, when the dynamical
critical exponent of the UV fixed point is z=2 and the IR scaling exponents are
chosen appropriately, our model realizes holographically the scaling properties
of the bosonic modes of the quadratic band crossing model.Comment: 19 pages, 2 figures. References added. Expanded discussion on nematic
orde
NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data
RNA-sequencing (RNA-Seq) has become a powerful technology to characterize
gene expression profiles because it is more accurate and comprehensive than
microarrays. Although statistical methods that have been developed for
microarray data can be applied to RNA-Seq data, they are not ideal due to the
discrete nature of RNA-Seq data. The Poisson distribution and negative binomial
distribution are commonly used to model count data. Recently, Witten (2011)
proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson
assumption may not be as appropriate as negative binomial distribution when
biological replicates are available and in the presence of overdispersion
(i.e., when the variance is larger than the mean). However, it is more
complicated to model negative binomial variables because they involve a
dispersion parameter that needs to be estimated. In this paper, we propose a
negative binomial linear discriminant analysis for RNA-Seq data. By Bayes'
rule, we construct the classifier by fitting a negative binomial model, and
propose some plug-in rules to estimate the unknown parameters in the
classifier. The relationship between the negative binomial classifier and the
Poisson classifier is explored, with a numerical investigation of the impact of
dispersion on the discriminant score. Simulation results show the superiority
of our proposed method. We also analyze four real RNA-Seq data sets to
demonstrate the advantage of our method in real-world applications
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