775 research outputs found
Online Influence Maximization in Non-Stationary Social Networks
Social networks have been popular platforms for information propagation. An
important use case is viral marketing: given a promotion budget, an advertiser
can choose some influential users as the seed set and provide them free or
discounted sample products; in this way, the advertiser hopes to increase the
popularity of the product in the users' friend circles by the world-of-mouth
effect, and thus maximizes the number of users that information of the
production can reach. There has been a body of literature studying the
influence maximization problem. Nevertheless, the existing studies mostly
investigate the problem on a one-off basis, assuming fixed known influence
probabilities among users, or the knowledge of the exact social network
topology. In practice, the social network topology and the influence
probabilities are typically unknown to the advertiser, which can be varying
over time, i.e., in cases of newly established, strengthened or weakened social
ties. In this paper, we focus on a dynamic non-stationary social network and
design a randomized algorithm, RSB, based on multi-armed bandit optimization,
to maximize influence propagation over time. The algorithm produces a sequence
of online decisions and calibrates its explore-exploit strategy utilizing
outcomes of previous decisions. It is rigorously proven to achieve an
upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and
real-world datasets, which demonstrates that our algorithm outperforms previous
stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio
Rapid Determination of Isomeric Benzoylpaeoniflorin and Benzoylalbiflorin in Rat Plasma by LC-MS/MS Method
Benzoylpaeoniflorin (BP) is a potential therapeutic agent against oxidative stress related Alzheimer’s disease. In this study, a more rapid, selective, and sensitive liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was developed to determine BP in rat plasma distinguishing with a monoterpene isomer, benzoylalbiflorin (BA). The method showed a linear response from 1 to 1000 ng/mL (r>0.9950). The precision of the interday and intraday ranged from 2.03 to 12.48% and the accuracy values ranged from −8.00 to 10.33%. Each running of the method could be finished in 4 minutes. The LC-MS/MS method was validated for specificity, linearity, precision, accuracy, recovery, and stability and was found to be acceptable for bioanalytical application. Finally, this fully validated method was successfully applied to a pharmacokinetic study in rats following oral administration
Online VNF Scaling in Datacenters
Network Function Virtualization (NFV) is a promising technology that promises
to significantly reduce the operational costs of network services by deploying
virtualized network functions (VNFs) to commodity servers in place of dedicated
hardware middleboxes. The VNFs are typically running on virtual machine
instances in a cloud infrastructure, where the virtualization technology
enables dynamic provisioning of VNF instances, to process the fluctuating
traffic that needs to go through the network functions in a network service. In
this paper, we target dynamic provisioning of enterprise network services -
expressed as one or multiple service chains - in cloud datacenters, and design
efficient online algorithms without requiring any information on future traffic
rates. The key is to decide the number of instances of each VNF type to
provision at each time, taking into consideration the server resource
capacities and traffic rates between adjacent VNFs in a service chain. In the
case of a single service chain, we discover an elegant structure of the problem
and design an efficient randomized algorithm achieving a e/(e-1) competitive
ratio. For multiple concurrent service chains, an online heuristic algorithm is
proposed, which is O(1)-competitive. We demonstrate the effectiveness of our
algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure
Coherent heteronuclear spin dynamics in an ultracold spin-1 mixture
We report the observation of coherent heteronuclear spin dynamics driven by
inter-species spin-spin interaction in an ultracold spinor mixture, which
manifests as periodical and well correlated spin oscillations between two
atomic species. In particular, we investigate the magnetic field dependence of
the oscillations and find a resonance behavior which depends on {\em both} the
linear and quadratic Zeeman effects and the spin-dependent interaction. We also
demonstrate a unique knob for controlling the spin dynamics in the spinor
mixture with species-dependent vector light shifts. Our finds are in agreement
with theoretical simulations without any fitting parameters.Comment: 13 pages including the supplementary materia
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