Hidden Symmetry Protection and Topology in Surface Maxwell Waves

Since the latter half of the 20th century, the use of metal in optics has become a promising plasmonics field for controlling light at a deep subwavelength scale. Surface plasmon polaritons localized on metal surfaces are crucial in plasmonics. However, despite the long history of plasmonics, the underlying mechanism producing the surface waves is not fully understood. This study unveils the hidden symmetry protection that ensures the existence of degenerated electric zero modes. These zero modes are identified as physical origins of surface plasmon polaritons, and similar zero modes can be directly excited at a temporal boundary. In real space, the zero modes possess vector-field rotation related to surface impedance. Focusing on the surface impedance, we prove the bulk-edge correspondence, which guarantees the existence of surface plasmon polaritons even with nonuniformity. Lastly, we extract the underlying physics in the topological transition between metal and dielectric material using a minimal circuit model with duality. The transition is considered the crossover between electric and magnetic zero modes.Comment: 26 pages, 19 figures, minor correction

Dusty ERO Search behind Two Massive Clusters

We performed deep K'-band imaging observations of 2 massive clusters, MS 0451.6-0305 at z = 0.55 and MS 0440.5+0204 at z = 0.19, for searching counterparts of the faint sub-mm sources behind these clusters, which would provide one of the deepest extremely red object(ERO) samples. Comparing our near-infrared images with optical images taken by the Hubble Space Telescope and by the Subaru Telescope, we identified 13 EROs in these fields. The sky distributions of EROs are consistent with the previous results, that there is a sign of strong clustering among detected EROs. Also, the surface density with corrected lensing amplification factors in both clusters are in good agreement with that derived from previous surveys. We found 7 EROs and 3 additional very red objects in a small area (\sim 0.6 arcmin^2) of the MS 0451.6-0305 field around an extended SCUBA source. Many of their optical and near-infrared colors are consistent with dusty star-forming galaxies at high redshifts(z \sim 1.0-4.0), and they may be constituting a cluster of dusty starburst galaxies and/or lensed star-forming galaxies at high redshift. Their red J-K' colors and faint optical magnitudes suggest they are relatively old massive stellar systems with ages(>300 Mega years) suffering from dust obscuration. We also found a surface-density enhancement of EROs around the SCUBA source in the MS 0440.5+0204 field.Comment: 19 pages, 11 figures, Latex(using pasj00.cls). To be published in PASJ vol 55, No. 4(Aug 2003

Homotopy on spatial graphs and generalized Sato-Levine invariants

Edge-homotopy and vertex-homotopy are equivalence relations on spatial graphs which are generalizations of Milnor's link-homotopy. Fleming and the author introduced some edge (resp. vertex)-homotopy invariants of spatial graphs by applying the Sato-Levine invariant for the constituent 2-component algebraically split links. In this paper, we construct some new edge (resp. vertex)-homotopy invariants of spatial graphs without any restriction of linking numbers of the constituent 2-component links by applying the generalized Sato-Levine invariant.Comment: 16 pages, 13 figure

The Atomic and Electronic structure of 0{\deg} and 60{\deg} grain boundaries in MoS2

We have investigated atomic and electronic structure of grain boundaries in monolayer MoS2, where relative angles between two different grains are 0 and 60 degree. The grain boundaries with specific relative angle have been formed with chemical vapor deposition growth on graphite and hexagonal boron nitride flakes; van der Waals interlayer interaction between MoS2 and the flakes restricts the relative angle. Through scanning tunneling microscopy and spectroscopy measurements, we have found that the perfectly stitched structure between two different grains of MoS2 was realized in the case of the 0 degree grain boundary. We also found that even with the perfectly stitched structure, valence band maximum and conduction band minimum shows significant blue shift, which probably arise from lattice strain at the boundary

Integrating Preprocessing Operations into Deep Learning Model: Case Study of Posttreatment Visual Acuity Prediction

Designing a deep neural network model that integrates clinical images with other electronic medical records entails various preprocessing operations. Preprocessing of clinical images often requires trimming of parts of the lesions shown in the images, whereas preprocessing of other electronic medical records requires vectorization of these records; for example, patient age is often converted into a categorical vector of 10-year intervals. Although these preprocessing operations are critical to the performance of the classification model, there is no guarantee that the preprocessing step chosen is appropriate for model training. The ability to integrate these preprocessing operations into a deep neural network model and to train the model, including the preprocessing operations, can help design a multi-modal medical classification model. This study proposes integration layers of preprocessing, both for clinical images and electronic medical records, in deep neural network models. Preprocessing of clinical images is realized by a vision transformer layer that selectively adopts the parts of the images requiring attention. The preprocessing of other medical electrical records is performed by adopting full-connection layers and normalizing these layers. These proposed preprocessing-integrated layers were verified using a posttreatment visual acuity prediction task in ophthalmology as a case study. This prediction task requires clinical images as well as patient profile data corresponding to each patient's posttreatment logMAR visual acuity. The performance of a heuristically designed prediction model was compared with the performance of the prediction model that includes the proposed preprocessing integration layers. The mean square errors between predicted and correct results were 0.051 for the heuristic model and 0.054 for the proposed model. Experimental results showed that the proposed model utilizing preprocessing integration layers achieved nearly the same performance as the heuristically designed model