1,363 research outputs found
Time Dependent Saddle Node Bifurcation: Breaking Time and the Point of No Return in a Non-Autonomous Model of Critical Transitions
There is a growing awareness that catastrophic phenomena in biology and
medicine can be mathematically represented in terms of saddle-node
bifurcations. In particular, the term `tipping', or critical transition has in
recent years entered the discourse of the general public in relation to
ecology, medicine, and public health. The saddle-node bifurcation and its
associated theory of catastrophe as put forth by Thom and Zeeman has seen
applications in a wide range of fields including molecular biophysics,
mesoscopic physics, and climate science. In this paper, we investigate a simple
model of a non-autonomous system with a time-dependent parameter and
its corresponding `dynamic' (time-dependent) saddle-node bifurcation by the
modern theory of non-autonomous dynamical systems. We show that the actual
point of no return for a system undergoing tipping can be significantly delayed
in comparison to the {\em breaking time} at which the
corresponding autonomous system with a time-independent parameter undergoes a bifurcation. A dimensionless parameter
is introduced, in which is the curvature
of the autonomous saddle-node bifurcation according to parameter ,
which has an initial value of and a constant rate of change . We
find that the breaking time is always less than the actual point
of no return after which the critical transition is irreversible;
specifically, the relation is analytically obtained. For a system with a small , there exists a significant window of opportunity
during which rapid reversal of the environment can save the system from
catastrophe
Processes on the emergent landscapes of biochemical reaction networks and heterogeneous cell population dynamics: differentiation in living matters.
The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat. The theory is equally applicable not only to intracellular dynamics of biochemical regulatory networks within an individual cell but also to tissue dynamics of heterogeneous interacting cell populations. The landscape for an individual cell, applicable to a population of isogenic non-interacting cells under the same environmental conditions, is defined on the counting space of intracellular chemical composition
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution
Since the number of incident energies is limited, it is difficult to directly
acquire hyperspectral images (HSI) with high spatial resolution. Considering
the high dimensionality and correlation of HSI, super-resolution (SR) of HSI
remains a challenge in the absence of auxiliary high-resolution images.
Furthermore, it is very important to extract the spatial features effectively
and make full use of the spectral information. This paper proposes a novel HSI
super-resolution algorithm, termed dual-domain network based on hybrid
convolution (SRDNet). Specifically, a dual-domain network is designed to fully
exploit the spatial-spectral and frequency information among the hyper-spectral
data. To capture inter-spectral self-similarity, a self-attention learning
mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid
structure is applied to increase the acceptance field of attention, which
further reinforces the feature representation ability of the network. Moreover,
to further improve the perceptual quality of HSI, a frequency loss(HFL) is
introduced to optimize the model in the frequency domain. The dynamic weighting
mechanism drives the network to gradually refine the generated frequency and
excessive smoothing caused by spatial loss. Finally, In order to better fully
obtain the mapping relationship between high-resolution space and
low-resolution space, a hybrid module of 2D and 3D units with progressive
upsampling strategy is utilized in our method. Experiments on a widely used
benchmark dataset illustrate that the proposed SRDNet method enhances the
texture information of HSI and is superior to state-of-the-art methods
Imaging through multimode fibres with physical prior
Imaging through perturbed multimode fibres based on deep learning has been
widely researched. However, existing methods mainly use target-speckle pairs in
different configurations. It is challenging to reconstruct targets without
trained networks. In this paper, we propose a physics-assisted, unsupervised,
learning-based fibre imaging scheme. The role of the physical prior is to
simplify the mapping relationship between the speckle pattern and the target
image, thereby reducing the computational complexity. The unsupervised network
learns target features according to the optimized direction provided by the
physical prior. Therefore, the reconstruction process of the online learning
only requires a few speckle patterns and unpaired targets. The proposed scheme
also increases the generalization ability of the learning-based method in
perturbed multimode fibres. Our scheme has the potential to extend the
application of multimode fibre imaging
Підходи до тлумачення сутності понять "конкурентоспроможність продукції" та "конкурентоспроможність підприємства"
Super-resolution imaging through a multimode fiber: the physical upsampling of speckle-driven
Following recent advancements in multimode fiber (MMF), miniaturization of
imaging endoscopes has proven crucial for minimally invasive surgery in vivo.
Recent progress enabled by super-resolution imaging methods with a data-driven
deep learning (DL) framework has balanced the relationship between the core
size and resolution. However, most of the DL approaches lack attention to the
physical properties of the speckle, which is crucial for reconciling the
relationship between the magnification of super-resolution imaging and the
quality of reconstruction quality. In the paper, we find that the
interferometric process of speckle formation is an essential basis for creating
DL models with super-resolution imaging. It physically realizes the upsampling
of low-resolution (LR) images and enhances the perceptual capabilities of the
models. The finding experimentally validates the role played by the physical
upsampling of speckle-driven, effectively complementing the lack of information
in data-driven. Experimentally, we break the restriction of the poor
reconstruction quality at great magnification by inputting the same size of the
speckle with the size of the high-resolution (HR) image to the model. The
guidance of our research for endoscopic imaging may accelerate the further
development of minimally invasive surgery
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