Selection of Dominant Characteristic Modes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The theory of characteristic modes is a popular physics based deterministic approach which has found several recent applications in the fields of radiator design, electromagnetic interference modelling and radiated emission analysis. The modal theory is based on the approximation of the total induced current in an electromagnetic structure in terms of a weighted sum of multiple characteristic current modes. The resultant outgoing field is also a weighted summation of the characteristic field patterns. Henceforth, a proper modal measure is an essential requirement to identify the modes which play a dominant role for a frequency of interest. The existing literature of significance measures restricts itself for ideal lossless structures only. This paper explores the pros and cons of the existing measures and correspondingly suggests suitable alternatives for both radiating and scattering applications. An example is presented in order to illustrate the proposed modal method for approximating the shielding response of a slotted geometry

An HMM-based Comparative Genomic Framework for Detecting Introgression in Eukaryotes

One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on a new comparative genomic framework for detecting introgression in genomes, called PhyloNet-HMM, which combines phylogenetic networks, that capture reticulate evolutionary relationships among genomes, with hidden Markov models (HMMs), that capture dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detects a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgression regions. Based on our analysis, it is estimated that about 12% of all sites withinchromosome 7 are of introgressive origin (these cover about 18 Mbp of chromosome 7, and over 300 genes). Further, our model detects no introgression in two negative control data sets. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism

Multiobjective synchronization of coupled systems

Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications

Addition of X-ray fluorescent tracers into polymers, new technology for automatic sorting of plastics : proposal for selecting some relevant tracers

A description of a new technology for automatic sorting of plastics, based on X-ray fluorescence detection of tracers, added in such materials is presented. This study describes the criteria for the selection of tracers, and concluded that the most adapted for XRF are some rare earth oxides. The plastics chosen for tracing and identification are the ones contained in ELV and WEEE from which discrimination is difficult for the existing sorting techniques due to their black colour.A description of a new technology for automatic sorting of plastics, based on X-ray fluorescence detection of tracers, added in such materials is presented. This study describes the criteria for the selection of tracers, and concluded that the most adapted for XRF are some rare earth oxides. The plastics chosen for tracing and identification are the ones contained in ELV and WEEE from which discrimination is difficult for the existing sorting techniques due to their black colour

Optimizing Ranking Measures for Compact Binary Code Learning

Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest---multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.Comment: Appearing in Proc. European Conference on Computer Vision 201