29,957 research outputs found
Impact of delay on HIV-1 dynamics of fighting a virus with another virus
In this paper, we propose a mathematical model for HIV-1 infection with
intracellular delay. The model examines a viral-therapy for controlling
infections through recombining HIV-1 virus with a genetically modified virus.
For this model, the basic reproduction number are identified
and its threshold properties are discussed. When , the
infection-free equilibrium is globally asymptotically stable. When
, becomes unstable and there occurs the
single-infection equilibrium , and and exchange their
stability at the transcritical point . If , where is a positive constant explicitly depending on the model
parameters, is globally asymptotically stable, while when , loses its stability to the double-infection equilibrium .
There exist a constant such that is asymptotically stable if
, and and exchange their stability at the
transcritical point . We use one numerical example to
determine the largest range of for the local stability of
and existence of Hopf bifurcation. Some simulations are performed to support
the theoretical results. These results show that the delay plays an important
role in determining the dynamic behaviour of the system. In the normal range of
values, the delay may change the dynamic behaviour quantitatively, such as
greatly reducing the amplitudes of oscillations, or even qualitatively changes
the dynamical behaviour such as revoking oscillating solutions to equilibrium
solutions. This suggests that the delay is a very important fact which should
not be missed in HIV-1 modelling
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
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