2,984 research outputs found
Positional Order and Diffusion Processes in Particle Systems
Nonequilibrium behaviors of positional order are discussed based on diffusion
processes in particle systems. With the cumulant expansion method up to the
second order, we obtain a relation between the positional order parameter
and the mean square displacement to be with a reciprocal vector and the dimension of the system .
On the basis of the relation, the behavior of positional order is predicted to
be when the system involves normal diffusion
with a diffusion constant . We also find that a diffusion process with
swapping positions of particles contributes to higher orders of the cumulants.
The swapping diffusion allows particle to diffuse without destroying the
positional order while the normal diffusion destroys it.Comment: 4 pages, 4 figures. Submitted to Phys. Rev.
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
We present a simple and effective framework for simultaneous semantic
segmentation and instance segmentation with Fully Convolutional Networks
(FCNs). The method, called BiSeg, predicts instance segmentation as a posterior
in Bayesian inference, where semantic segmentation is used as a prior. We
extend the idea of position-sensitive score maps used in recent methods to a
fusion of multiple score maps at different scales and partition modes, and
adopt it as a robust likelihood for instance segmentation inference. As both
Bayesian inference and map fusion are performed per pixel, BiSeg is a fully
convolutional end-to-end solution that inherits all the advantages of FCNs. We
demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.Comment: BMVC201
Approximate Methods for Solving Chance Constrained Linear Programs in Probability Measure Space
A risk-aware decision-making problem can be formulated as a
chance-constrained linear program in probability measure space.
Chance-constrained linear program in probability measure space is intractable,
and no numerical method exists to solve this problem. This paper presents
numerical methods to solve chance-constrained linear programs in probability
measure space for the first time. We propose two solvable optimization problems
as approximate problems of the original problem. We prove the uniform
convergence of each approximate problem. Moreover, numerical experiments have
been implemented to validate the proposed methods
最適化の数理と応用
Open House, ISM in Tachikawa, 2013.6.14統計数理研究所オープンハウス(立川)、H25.6.14ポスター発
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