13,897 research outputs found
A Novel A Priori Simulation Algorithm for Absorbing Receivers in Diffusion-Based Molecular Communication Systems
A novel a priori Monte Carlo (APMC) algorithm is proposed to accurately
simulate the molecules absorbed at spherical receiver(s) with low computational
complexity in diffusion-based molecular communication (MC) systems. It is
demonstrated that the APMC algorithm achieves high simulation efficiency since
by using this algorithm, the fraction of molecules absorbed for a relatively
large time step length precisely matches the analytical result. Therefore, the
APMC algorithm overcomes the shortcoming of the existing refined Monte Carlo
(RMC) algorithm which enables accurate simulation for a relatively small time
step length only. Moreover, for the RMC algorithm, an expression is proposed to
quickly predict the simulation accuracy as a function of the time step length
and system parameters, which facilitates the choice of simulation time step for
a given system. Furthermore, a rejection threshold is proposed for both the RMC
and APMC algorithms to significantly save computational complexity while
causing an extremely small loss in accuracy.Comment: 11 pages, 14 figures, submitted to IEEE Transactions on
NanoBioscience. arXiv admin note: text overlap with arXiv:1803.0463
Open-Category Classification by Adversarial Sample Generation
In real-world classification tasks, it is difficult to collect training
samples from all possible categories of the environment. Therefore, when an
instance of an unseen class appears in the prediction stage, a robust
classifier should be able to tell that it is from an unseen class, instead of
classifying it to be any known category. In this paper, adopting the idea of
adversarial learning, we propose the ASG framework for open-category
classification. ASG generates positive and negative samples of seen categories
in the unsupervised manner via an adversarial learning strategy. With the
generated samples, ASG then learns to tell seen from unseen in the supervised
manner. Experiments performed on several datasets show the effectiveness of
ASG.Comment: Published in IJCAI 201
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