228 research outputs found
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
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