220,909 research outputs found
Spin-Boson Model with Diagonal and Off-Diagonal Coupling to Two Independent Baths: Ground-State Phase Transition in the Deep Sub-Ohmic Regime
We investigate a spin-boson model with two boson baths that are coupled to
two perpendicular components of the spin by employing the density matrix
renormalization group method with an optimized boson basis. It is revealed that
in the deep sub-Ohmic regime there exists a novel second-order phase transition
between two types of doubly degenerate states, which is reduced to one of the
usual type for nonzero tunneling. In addition, it is found that expectation
values of the spin components display jumps at the phase boundary in the
absence of bias and tunneling.Comment: 4 pages, 4 figure
Distributed Flow Scheduling in an Unknown Environment
Flow scheduling tends to be one of the oldest and most stubborn problems in
networking. It becomes more crucial in the next generation network, due to fast
changing link states and tremendous cost to explore the global structure. In
such situation, distributed algorithms often dominate. In this paper, we design
a distributed virtual game to solve the flow scheduling problem and then
generalize it to situations of unknown environment, where online learning
schemes are utilized. In the virtual game, we use incentives to stimulate
selfish users to reach a Nash Equilibrium Point which is valid based on the
analysis of the `Price of Anarchy'. In the unknown-environment generalization,
our ultimate goal is the minimization of cost in the long run. In order to
achieve balance between exploration of routing cost and exploitation based on
limited information, we model this problem based on Multi-armed Bandit Scenario
and combined newly proposed DSEE with the virtual game design. Armed with these
powerful tools, we find a totally distributed algorithm to ensure the
logarithmic growing of regret with time, which is optimum in classic
Multi-armed Bandit Problem. Theoretical proof and simulation results both
affirm this claim. To our knowledge, this is the first research to combine
multi-armed bandit with distributed flow scheduling.Comment: 10 pages, 3 figures, conferenc
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