17,368 research outputs found
Trees with Maximum p-Reinforcement Number
Let be a graph and a positive integer. The -domination
number \g_p(G) is the minimum cardinality of a set with
for all . The -reinforcement
number is the smallest number of edges whose addition to results
in a graph with \g_p(G')<\g_p(G). Recently, it was proved by Lu et al.
that for a tree and . In this paper, we
characterize all trees attaining this upper bound for
Rayleigh-Schroedinger-Goldstone variational perturbation theory for many fermion systems
We present a Rayleigh-Schroedinger-Goldstone perturbation formalism for many
fermion systems. Based on this formalism, variational perturbation scheme which
goes beyond the Gaussian approximation is developed. In order to go beyond the
Gaussian approximation, we identify a parent Hamiltonian which has an effective
Gaussian vacuum as a variational solution and carry out further perturbation
with respect to the renormalized interaction using Goldstone's expansion.
Perturbation rules for the ground state wavefunctional and energy are found.
Useful commuting relations between operators and the Gaussian wavefunctional
are also found, which could reduce the calculational efforts substantially. As
examples, we calculate the first order correction to the Gaussian
wavefunctional and the second order correction to the ground state of an
electron gas system with the Yukawa-type interaction.Comment: 11pages, 1figur
Network-Coded Multiple Access
This paper proposes and experimentally demonstrates a first wireless local
area network (WLAN) system that jointly exploits physical-layer network coding
(PNC) and multiuser decoding (MUD) to boost system throughput. We refer to this
multiple access mode as Network-Coded Multiple Access (NCMA). Prior studies on
PNC mostly focused on relay networks. NCMA is the first realized multiple
access scheme that establishes the usefulness of PNC in a non-relay setting.
NCMA allows multiple nodes to transmit simultaneously to the access point (AP)
to boost throughput. In the non-relay setting, when two nodes A and B transmit
to the AP simultaneously, the AP aims to obtain both packet A and packet B
rather than their network-coded packet. An interesting question is whether
network coding, specifically PNC which extracts packet (A XOR B), can still be
useful in such a setting. We provide an affirmative answer to this question
with a novel two-layer decoding approach amenable to real-time implementation.
Our USRP prototype indicates that NCMA can boost throughput by 100% in the
medium-high SNR regime (>=10dB). We believe further throughput enhancement is
possible by allowing more than two users to transmit together
Block Belief Propagation for Parameter Learning in Markov Random Fields
Traditional learning methods for training Markov random fields require doing
inference over all variables to compute the likelihood gradient. The iteration
complexity for those methods therefore scales with the size of the graphical
models. In this paper, we propose \emph{block belief propagation learning}
(BBPL), which uses block-coordinate updates of approximate marginals to compute
approximate gradients, removing the need to compute inference on the entire
graphical model. Thus, the iteration complexity of BBPL does not scale with the
size of the graphs. We prove that the method converges to the same solution as
that obtained by using full inference per iteration, despite these
approximations, and we empirically demonstrate its scalability improvements
over standard training methods.Comment: Accepted to AAAI 201
The effects of optically induced non-Abelian gauge field in cold atoms
We show that degenerate dark states can be generated by coupling
-fold degenerate ground states and a common excited state with laser
fields. Interferences between light waves with different frequencies can
produce laser fields with time-dependent amplitudes, which can induce not only
U(N) non-Abelian vector fields but also the scalar ones for the adiabatic
motion of atoms in such laser fields. As an example, a time-periodic gauge
potential is produced by applying specific laser fields to a tripod system.
Some features of the Landau levels and the ground-state phase diagram of a
rotating Bose-Einstein condensate for a concrete gauge field are also
discussed.Comment: Revtex 6 pages, 2 figures, version to be published in PR
Virtual to Real Reinforcement Learning for Autonomous Driving
Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data
A Vertical Channel Model of Molecular Communication based on Alcohol Molecules
The study of Molecular Communication(MC) is more and more prevalence, and
channel model of MC plays an important role in the MC System. Since different
propagation environment and modulation techniques produce different channel
model, most of the research about MC are in horizontal direction,but in nature
the communications between nano machines are in short range and some of the
information transportation are in the vertical direction, such as transpiration
of plants, biological pump in ocean, and blood transportation from heart to
brain. Therefore, this paper we propose a vertical channel model which
nano-machines communicate with each other in the vertical direction based on
pure diffusion. We first propose a vertical molecular communication model, we
mainly considered the gravity as the factor, though the channel model is also
affected by other main factors, such as the flow of the medium, the distance
between the transmitter and the receiver, the delay or sensitivity of the
transmitter and the receiver. Secondly, we set up a test-bed for this vertical
channel model, in order to verify the difference between the theory result and
the experiment data. At last, we use the data we get from the experiment and
the non-linear least squares method to get the parameters to make our channel
model more accurate.Comment: 5 pages,7 figures, Accepted for presentation at BICT 2015 Special
Track on Molecular Communication and Networking (MCN). arXiv admin note: text
overlap with arXiv:1311.6208 by other author
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