In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing perf...

APrIl 2017 IZA DP No. 10696 The Temporal Stability of Children’s Neighborhood Experiences: A Follow-up from Birth to Age 15 Despite increasing attention being paid to the temporal dynamics of childhood disadvantage, children’s neighborhood characteristics are often measured at a single point in time. Whether such cross-sectional measures serve as reliable proxies for children’s long-run neighbo...

Large-scale geospatial data pave the way for machine learning algorithms, and a good representation is related to whether model effective. Hence, it critical task learn effective feature data. In this paper, we construct spatial graph from locations propose contrastive method location representations. Firstly, skeleton in order preserve primary structure of solve positioning bias problem remote...

Establishing structural or functional relationship between sequences, for instance to infer the structural class of an unannotated protein, is a key task in biological sequence analysis. Recent computational methods such as profile and neighborhood mismatch kernels have shown very promising results for protein sequence classification, at the cost of high computational complexity. In this study ...

We propose a distributed control algorithm using a new idea of collaborative processing for networked control systems with limited computational resources. In a networked control system, the processing unit is required to complete multiple tasks (such as control, sensing, signal processing, etc.) in a timely manner. Due to limited computational resources that are available to the processing uni...

This paper presents a new dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a mini...

The use of neural networks to classify land-cover from remote sensing imagery relies on the ability to determine a winner from the candidate land-cover types based on the imagery information available. In the case of a “winnertakes-all” scenario, this does not allow us a measure of how much the prediction of each pixel’s land-cover can be trusted. We present a three-stage method where only winn...