375 research outputs found
Symbolic bisimulation for quantum processes
With the previous notions of bisimulation presented in literature, to check
if two quantum processes are bisimilar, we have to instantiate the free quantum
variables of them with arbitrary quantum states, and verify the bisimilarity of
resultant configurations. This makes checking bisimilarity infeasible from an
algorithmic point of view because quantum states constitute a continuum. In
this paper, we introduce a symbolic operational semantics for quantum processes
directly at the quantum operation level, which allows us to describe the
bisimulation between quantum processes without resorting to quantum states. We
show that the symbolic bisimulation defined here is equivalent to the open
bisimulation for quantum processes in the previous work, when strong
bisimulations are considered. An algorithm for checking symbolic ground
bisimilarity is presented. We also give a modal logical characterisation for
quantum bisimilarity based on an extension of Hennessy-Milner logic to quantum
processes.Comment: 30 pages, 7 figures, comments are welcom
Unsupervised Generative Adversarial Cross-modal Hashing
Cross-modal hashing aims to map heterogeneous multimedia data into a common
Hamming space, which can realize fast and flexible retrieval across different
modalities. Unsupervised cross-modal hashing is more flexible and applicable
than supervised methods, since no intensive labeling work is involved. However,
existing unsupervised methods learn hashing functions by preserving inter and
intra correlations, while ignoring the underlying manifold structure across
different modalities, which is extremely helpful to capture meaningful nearest
neighbors of different modalities for cross-modal retrieval. To address the
above problem, in this paper we propose an Unsupervised Generative Adversarial
Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for
unsupervised representation learning to exploit the underlying manifold
structure of cross-modal data. The main contributions can be summarized as
follows: (1) We propose a generative adversarial network to model cross-modal
hashing in an unsupervised fashion. In the proposed UGACH, given a data of one
modality, the generative model tries to fit the distribution over the manifold
structure, and select informative data of another modality to challenge the
discriminative model. The discriminative model learns to distinguish the
generated data and the true positive data sampled from correlation graph to
achieve better retrieval accuracy. These two models are trained in an
adversarial way to improve each other and promote hashing function learning.
(2) We propose a correlation graph based approach to capture the underlying
manifold structure across different modalities, so that data of different
modalities but within the same manifold can have smaller Hamming distance and
promote retrieval accuracy. Extensive experiments compared with 6
state-of-the-art methods verify the effectiveness of our proposed approach.Comment: 8 pages, accepted by 32th AAAI Conference on Artificial Intelligence
(AAAI), 201
Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction
Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies
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