27,471 research outputs found
On the Outage Probability of Localization in Randomly Deployed Wireless Networks
This paper analyzes the localization outage probability (LOP), the
probability that the position error exceeds a given threshold, in randomly
deployed wireless networks. Two typical cases are considered: a mobile agent
uses all the neighboring anchors or select the best pair of anchors for
self-localization. We derive the exact LOP for the former case and tight bounds
for the LOP for the latter case. The comparison between the two cases reveals
the advantage of anchor selection in terms of LOP versus complexity tradeoff,
providing insights into the design of efficient localization systems
Nonlinear theory of transverse beam echoes
Transverse beam echoes can be excited with a single dipole kick followed by a
single quadrupole kick. They have been used to measure diffusion in hadron
beams and have other diagnostic capabilities. Here we develop theories of the
transverse echo nonlinear in both the dipole and quadrupole kick strengths. The
theories predict the maximum echo amplitudes and the optimum strength
parameters. We find that the echo amplitude increases with smaller beam
emittance and the asymptotic echo amplitude can exceed half the initial dipole
kick amplitude. We show that multiple echoes can be observed provided the
dipole kick is large enough. The spectrum of the echo pulse can be used to
determine the nonlinear detuning parameter with small amplitude dipole kicks.
Simulations are performed to check the theoretical predictions. In the useful
ranges of dipole and quadrupole strengths, they are shown to be in reasonable
agreement.Comment: 32 pages, 11 figure
Active Sampling of Pairs and Points for Large-scale Linear Bipartite Ranking
Bipartite ranking is a fundamental ranking problem that learns to order
relevant instances ahead of irrelevant ones. The pair-wise approach for
bi-partite ranking construct a quadratic number of pairs to solve the problem,
which is infeasible for large-scale data sets. The point-wise approach, albeit
more efficient, often results in inferior performance. That is, it is difficult
to conduct bipartite ranking accurately and efficiently at the same time. In
this paper, we develop a novel active sampling scheme within the pair-wise
approach to conduct bipartite ranking efficiently. The scheme is inspired from
active learning and can reach a competitive ranking performance while focusing
only on a small subset of the many pairs during training. Moreover, we propose
a general Combined Ranking and Classification (CRC) framework to accurately
conduct bipartite ranking. The framework unifies point-wise and pair-wise
approaches and is simply based on the idea of treating each instance point as a
pseudo-pair. Experiments on 14 real-word large-scale data sets demonstrate that
the proposed algorithm of Active Sampling within CRC, when coupled with a
linear Support Vector Machine, usually outperforms state-of-the-art point-wise
and pair-wise ranking approaches in terms of both accuracy and efficiency.Comment: a shorter version was presented in ACML 201
- …