Projection-embedded BYY learning algorithm for Gaussian mixture-based clustering

Abstract On learning the Gaussian mixture model, existing BYY learning algorithms are featured by a gradient-based line search with an appropriate stepsize. Learning becomes either unstable if the stepsize is too large or slow and gets stuck in a local optimal solution if the stepsize is too small. An algorithm without a learning stepsize has been proposed with expectation-maximization (EM) like two alternative steps. However, its learning process may still be unstable. This paper tackles this problem of unreliability by a modified algorithm called projection-embedded Bayesian Ying-Yang learning algorithm (pBYY). Experiments have shown that pBYY outperforms learning algorithms developed from not only minimum message length with Jeffreys prior (MML-Jef) and Variational Bayesian with Dirichlet-Normal-Wishart (VB-DNW) prior but also BYY with these priors (BYY-Jef and BYY-DNW). pBYY obtains the superiority with an easy implementation, while DNW prior-based learning algorithms suffer a complicated and tedious computation load. The performance of pBYY has also been demonstrated on the Berkeley Segmentation Dataset for the topic of unsupervised image segmentation. The resulted performances of semantic image segmentation have shown that pBYY outperforms not only MML-Jef, VB-DNW, BYY-Jef, and BYY-DNW but also three leading image segmentation algorithms, namely gPb-owt-ucm, MN-Cut, and mean shift.</jats:p

Effect of magnetic field on the spin resonance in FeTe(0.5)Se(0.5) as seen via inelastic neutron scattering

Inelastic neutron scattering and susceptibility measurements have been performed on the optimally-doped Fe-based superconductor FeTe(0.5)Se(0.5), which has a critical temperature, Tc of 14 K. The magnetic scattering at the stripe antiferromagnetic wave-vector Q = (0.5,0.5) exhibits a "resonance" at ~ 6 meV, where the scattering intensity increases abruptly when cooled below Tc. In a 7-T magnetic field parallel to the a-b plane, Tc is slightly reduced to ~ 12 K, based on susceptibility measurements. The resonance in the neutron scattering measurements is also affected by the field. The resonance intensity under field cooling starts to rise at a lower temperature ~ 12 K, and the low temperature intensity is also reduced from the zero-field value. Our results provide clear evidence for the intimate relationship between superconductivity and the resonance measured in magnetic excitations of Fe-based superconductors.Comment: 4 pages, 3 figure

Direct observation of magnon-phonon coupling in yttrium iron garnet

The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic transition temperature of $\sim$560 K has been widely used in microwave and spintronic devices. Anomalous features in the spin Seeback effect (SSE) voltages have been observed in Pt/YIG and attributed to the magnon-phonon coupling. Here we use inelastic neutron scattering to map out low-energy spin waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic field. By comparing the zero and 9.1 T data, we find that instead of splitting and opening up gaps at the spin wave and acoustic phonon dispersion intersecting points, magnon-phonon coupling in YIG enhances the hybridized scattering intensity. These results are different from expectations of conventional spin-lattice coupling, calling for new paradigms to understand the scattering process of magnon-phonon interactions and the resulting magnon-polarons.Comment: 5 pages, 4 figures, PRB in pres