357 research outputs found
Steady-state bifurcation of FHN-type oscillator on a square domain
The Turing patterns of reaction-diffusion equations defined over a square region are more complex because of the D4-symmetry of the spatial region. This leads to the occurrence of multiple equivariant Turing bifurcations. In this paper, taking the FHN model as an example, we give a explicit calculation formula of normal form for the simple and double Turing bifurcation of the reaction-diffusion equation with Dirichlet boundary conditions and defined on a square space, and we also obtain a method for the calculation of the existence of spatially inhomogeneous steady-state solutions. This paper provides a theoretical basis for exploring and predicting the pattern formation of spatial multimode interaction
Alice Benchmarks: Connecting Real World Object Re-Identification with the Synthetic
For object re-identification (re-ID), learning from synthetic data has become
a promising strategy to cheaply acquire large-scale annotated datasets and
effective models, with few privacy concerns. Many interesting research problems
arise from this strategy, e.g., how to reduce the domain gap between synthetic
source and real-world target. To facilitate developing more new approaches in
learning from synthetic data, we introduce the Alice benchmarks, large-scale
datasets providing benchmarks as well as evaluation protocols to the research
community. Within the Alice benchmarks, two object re-ID tasks are offered:
person and vehicle re-ID. We collected and annotated two challenging real-world
target datasets: AlicePerson and AliceVehicle, captured under various
illuminations, image resolutions, etc. As an important feature of our real
target, the clusterability of its training set is not manually guaranteed to
make it closer to a real domain adaptation test scenario. Correspondingly, we
reuse existing PersonX and VehicleX as synthetic source domains. The primary
goal is to train models from synthetic data that can work effectively in the
real world. In this paper, we detail the settings of Alice benchmarks, provide
an analysis of existing commonly-used domain adaptation methods, and discuss
some interesting future directions. An online server will be set up for the
community to evaluate methods conveniently and fairly.Comment: 9 pages, 4 figures, 4 table
Biological Factor Regulatory Neural Network
Genes are fundamental for analyzing biological systems and many recent works
proposed to utilize gene expression for various biological tasks by deep
learning models. Despite their promising performance, it is hard for deep
neural networks to provide biological insights for humans due to their
black-box nature. Recently, some works integrated biological knowledge with
neural networks to improve the transparency and performance of their models.
However, these methods can only incorporate partial biological knowledge,
leading to suboptimal performance. In this paper, we propose the Biological
Factor Regulatory Neural Network (BFReg-NN), a generic framework to model
relations among biological factors in cell systems. BFReg-NN starts from gene
expression data and is capable of merging most existing biological knowledge
into the model, including the regulatory relations among genes or proteins
(e.g., gene regulatory networks (GRN), protein-protein interaction networks
(PPI)) and the hierarchical relations among genes, proteins and pathways (e.g.,
several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has
the ability to provide new biologically meaningful insights because of its
white-box characteristics. Experimental results on different gene
expression-based tasks verify the superiority of BFReg-NN compared with
baselines. Our case studies also show that the key insights found by BFReg-NN
are consistent with the biological literature
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