8,479 research outputs found
Correspondence Networks with Adaptive Neighbourhood Consensus
In this paper, we tackle the task of establishing dense visual
correspondences between images containing objects of the same category. This is
a challenging task due to large intra-class variations and a lack of dense
pixel level annotations. We propose a convolutional neural network
architecture, called adaptive neighbourhood consensus network (ANC-Net), that
can be trained end-to-end with sparse key-point annotations, to handle this
challenge. At the core of ANC-Net is our proposed non-isotropic 4D convolution
kernel, which forms the building block for the adaptive neighbourhood consensus
module for robust matching. We also introduce a simple and efficient
multi-scale self-similarity module in ANC-Net to make the learned feature
robust to intra-class variations. Furthermore, we propose a novel orthogonal
loss that can enforce the one-to-one matching constraint. We thoroughly
evaluate the effectiveness of our method on various benchmarks, where it
substantially outperforms state-of-the-art methods.Comment: CVPR 2020. Project page: https://ancnet.avlcode.org
On fast-slow consensus networks with a dynamic weight
We study dynamic networks under an undirected consensus communication
protocol and with one state-dependent weighted edge. We assume that the
aforementioned dynamic edge can take values over the whole real numbers, and
that its behaviour depends on the nodes it connects and on an extrinsic slow
variable. We show that, under mild conditions on the weight, there exists a
reduction such that the dynamics of the network are organized by a
transcritical singularity. As such, we detail a slow passage through a
transcritical singularity for a simple network, and we observe that an exchange
between consensus and clustering of the nodes is possible. In contrast to the
classical planar fast-slow transcritical singularity, the network structure of
the system under consideration induces the presence of a maximal canard. Our
main tool of analysis is the blow-up method. Thus, we also focus on tracking
the effects of the blow-up transformation on the network's structure. We show
that on each blow-up chart one recovers a particular dynamic network related to
the original one. We further indicate a numerical issue produced by the slow
passage through the transcritical singularity
Extremism propagation in social networks with hubs
One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission
Noise in Coevolving Networks
Coupling dynamics of the states of the nodes of a network to the dynamics of
the network topology leads to generic absorbing and fragmentation transitions.
The coevolving voter model is a typical system that exhibits such transitions
at some critical rewiring. We study the robustness of these transitions under
two distinct ways of introducing noise. Noise affecting all the nodes destroys
the absorbing-fragmentation transition, giving rise in finite-size systems to
two regimes: bimodal magnetisation and dynamic fragmentation. Noise Targeting a
fraction of nodes preserves the transitions but introduces shattered
fragmentation with its characteristic fraction of isolated nodes and one or two
giant components. Both the lack of absorbing state for homogenous noise and the
shift in the absorbing transition to higher rewiring for targeted noise are
supported by analytical approximations.Comment: 20 page
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Diffusion lms strategy over wireless sensor network
The mess with distributed detection, where nodes arranged in certain topology are obliged to decideamong two speculations focused around accessible estimations.We look for completely appropriated and versatile usage, where all nodes make singular constant-choices by putting crosswise over with their quick neighbours just, and no combination focus is vital. The proffered distributed detection algorithms are based on a concept of extension of strategies that are employed for diffusion mechanism in a distributed network topology. After a large-scale systematic plan or arrangement for attaining some particular object or putting a particular idea into effect detection using diffusion LMS are fascinating in the context of sensor networksbecause of their versatility, enhanced strength to node and connection disappointment as contrasted with unified frameworks and their capability to convey vitality and correspondence assets. The proposed algorithms are inherently adaptive and can track changes in the element speculation.We examine the operation of the suggested algorithms in terms of their chances of detection and false alarm, and provide simulation results comparing with other cooperation schemes, including centralized processing and the case where there is no cooperation. In the context of digital signal processing and communication, the role of adaptive filters is very vital. In day to daywork where practical requirement is necessary,the computational complexities is the most considerable parameter in context of an adaptive filter. As it tells us about reliability of any system, agility to real time environment least mean squares (LMS) algorithm is generally utilized in light of its low computational multifaceted nature (O(N)) and easier in implementation.
Adaptive sliding mode observation in a network of dynamical systems
This paper considers the problem of reconstructing state information in all the nodes of a complex network of dynamical systems. The individual nodes comprise a known linear part and unknown but bounded uncertainties in certain channels of the system. A supervisory adaptive sliding mode observer configuration is proposed for estimating the states. A linear matrix inequality (LMI) approach is suggested to synthesise the gains of the sliding mode observer. Although deployed centrally to estimate all the states of the complex network, the design process depends only on the dynamics of an individual node of the network. The methodology is demonstrated by considering a network of Chua oscillators
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