19 research outputs found
Complex Projective Synchronization in Drive-Response Stochastic Complex Networks by Impulsive Pinning Control
The complex projective synchronization in drive-response stochastic coupled networks with complex-variable systems is considered. The impulsive pinning control scheme is adopted to achieve complex projective synchronization and several simple and practical sufficient conditions are obtained in a general drive-response network. In addition, the adaptive feedback algorithms are proposed to adjust the control strength. Several numerical simulations are provided to show the effectiveness and feasibility of the proposed methods
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Recently, deep hashing with Hamming distance metric has drawn increasing
attention for face image retrieval tasks. However, its counterpart deep
quantization methods, which learn binary code representations with
dictionary-related distance metrics, have seldom been explored for the task.
This paper makes the first attempt to integrate product quantization into an
end-to-end deep learning framework for face image retrieval. Unlike prior deep
quantization methods where the codewords for quantization are learned from
data, we propose a novel scheme using predefined orthonormal vectors as
codewords, which aims to enhance the quantization informativeness and reduce
the codewords' redundancy. To make the most of the discriminative information,
we design a tailored loss function that maximizes the identity discriminability
in each quantization subspace for both the quantized and the original features.
Furthermore, an entropy-based regularization term is imposed to reduce the
quantization error. We conduct experiments on three commonly-used datasets
under the settings of both single-domain and cross-domain retrieval. It shows
that the proposed method outperforms all the compared deep hashing/quantization
methods under both settings with significant superiority. The proposed
codewords scheme consistently improves both regular model performance and model
generalization ability, verifying the importance of codewords' distribution for
the quantization quality. Besides, our model's better generalization ability
than deep hashing models indicates that it is more suitable for scalable face
image retrieval tasks
Learning to Construct 3D Building Wireframes from 3D Line Clouds
Line clouds, though under-investigated in the previous work, potentially
encode more compact structural information of buildings than point clouds
extracted from multi-view images. In this work, we propose the first network to
process line clouds for building wireframe abstraction. The network takes a
line cloud as input , i.e., a nonstructural and unordered set of 3D line
segments extracted from multi-view images, and outputs a 3D wireframe of the
underlying building, which consists of a sparse set of 3D junctions connected
by line segments. We observe that a line patch, i.e., a group of neighboring
line segments, encodes sufficient contour information to predict the existence
and even the 3D position of a potential junction, as well as the likelihood of
connectivity between two query junctions. We therefore introduce a two-layer
Line-Patch Transformer to extract junctions and connectivities from sampled
line patches to form a 3D building wireframe model. We also introduce a
synthetic dataset of multi-view images with ground-truth 3D wireframe. We
extensively justify that our reconstructed 3D wireframe models significantly
improve upon multiple baseline building reconstruction methods. The code and
data can be found at https://github.com/Luo1Cheng/LC2WF.Comment: 10 pages, 6 figure
Outer Synchronization of Drive-Response Complex-Valued Complex Networks via Intermittent Pinning Control
This paper is concerned with the outer exponential synchronization of the drive-response complex dynamical networks subject to time-varying delays. The dynamics of nodes is complex valued, the interactions among of the nodes are directed, and the two coupling matrices in the drive system and the response system are also different. The intermittent pinning control is proposed to achieve outer exponential synchronization in the aperiodical way. Some novel sufficient conditions are derived to guarantee outer exponential synchronization of the considered complex-valued complex networks by using the Lyapunov functional method. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed control protocols
Integrated brain on a chip and automated organâonâchips systems
Abstract The nervous system plays an irreplaceable role in maintaining homeostasis and coordinating with the external environment. However, the incidence of neurological diseases is high and increasing year by year. Long drug development cycles, low efficacy, improper models and other bottlenecks restrict the prevention and treatment of diseases. Organâonâchips (OOCs), as in vitro constructed organ microsystems, have made remarkable progress in recent years. The bloodâbrain barrier chip, neurovascular unit chip, nerve signal transduction chip, and other chips related to brain function have been widely studied. However, in vitro modeling of complex biological systems remains a major challenge for OOCs. The future development goal of OOC is to realize automatic culture, organ function simulation, and realâtime monitoring of physiological and biochemical indicators. In this paper, a strategy for optimizing the structure and functional interface of cellâderived modules is presented, and a specific model of the automated integration system is proposed. It aims to build standardized and commercial chips related to brain functions and systems by integrating multidisciplinary strengths. In addition, it will drive the progress of life science research, disease modeling, and drug research and promote the development of related industries
The pth Moment Exponential Synchronization of Drive-Response Memristor Neural Networks Subject to Stochastic Perturbations
In this paper, the pth moment exponential synchronization problems of drive-response stochastic memristor neural networks are studied via a state feedback controller. The dynamics of the memristor neural network are nonidentical, consisting of both asymmetrically nondelayed and delayed coupled, state-dependent, and subject to exogenous stochastic perturbations. The pth moment exponential synchronization of these drive-response stochastic memristor neural networks is guaranteed under some testable and computable sufficient conditions utilizing differential inclusion theory and Filippov regularization. Finally, the correctness and effectiveness of our theoretical results are demonstrated through a numerical example