686 research outputs found
Current-voltage (I-V) characteristics of armchair graphene nanoribbons under uniaxial strain
The current-voltage (I-V) characteristics of armchair graphene nanoribbons
under a local uniaxial tension are investigated by using first principles
quantum transport calculations. It is shown that for a given value of
bias-voltage, the resulting current depends strongly on the applied tension.
The observed trends are explained by means of changes in the band gaps of the
nanoribbons due to the applied uniaxial tension. In the course of plastic
deformation, the irreversible structural changes and derivation of carbon
monatomic chains from graphene pieces can be monitored by two-probe transport
measurements.Comment: please see the published version at
http://prb.aps.org/abstract/PRB/v81/i20/e20543
Polarization Beam Splitter Based on Self-Collimation of a Hybrid Photonic Crystal
A photonic crystal polarization beam splitter based on photonic band gap and self-collimation effects is designed for optical communication wavelengths. The photonic crystal structure consists of a polarization-insensitive self-collimation region and a splitting region. TM- and TE-polarized waves propagate without diffraction in the self-collimation region, whereas they split by 90 degrees in the splitting region. Efficiency of more than 75% for TM- and TE-polarized light is obtained for a polarization beam splitter size of only 17 μm x 17 μm in a wavelength interval of 60 nm including 1.55 μm
Metal nanoring and tube formation on carbon nanotubes
The structural and electronic properties of aluminum covered single wall
carbon nanotubes (SWNT) are studied from first-principles for a large number of
coverage. Aluminum-aluminum interaction that is stronger than aluminum-tube
interaction, prevents uniform metal coverage, and hence gives rise to the
clustering. However, a stable aluminum ring and aluminum nanotube with well
defined patterns can also form around the semiconducting SWNT and lead to
metallization. The persistent current in the Al nanoring is discussed to show
that a high magnetic field can be induced at the center of SWNT.Comment: Submitted to Physical Review
Optoelectronic cooling of mechanical modes in a semiconductor nanomembrane
Optical cavity cooling of mechanical resonators has recently become a
research frontier. The cooling has been realized with a metal-coated silicon
microlever via photo-thermal force and subsequently with dielectric objects via
radiation pressure. Here we report cavity cooling with a crystalline
semiconductor membrane via a new mechanism, in which the cooling force arises
from the interaction between the photo-induced electron-hole pairs and the
mechanical modes through the deformation potential coupling. The optoelectronic
mechanism is so efficient as to cool a mode down to 4 K from room temperature
with just 50 uW of light and a cavity with a finesse of 10 consisting of a
standard mirror and the sub-wavelength-thick semiconductor membrane itself. The
laser-cooled narrow-band phonon bath realized with semiconductor mechanical
resonators may open up a new avenue for photonics and spintronics devices.Comment: 5 pages, 4 figure
Relational Reasoning Network (RRN) for Anatomical Landmarking
Accurately identifying anatomical landmarks is a crucial step in deformation
analysis and surgical planning for craniomaxillofacial (CMF) bones. Available
methods require segmentation of the object of interest for precise landmarking.
Unlike those, our purpose in this study is to perform anatomical landmarking
using the inherent relation of CMF bones without explicitly segmenting them. We
propose a new deep network architecture, called relational reasoning network
(RRN), to accurately learn the local and the global relations of the landmarks.
Specifically, we are interested in learning landmarks in CMF region: mandible,
maxilla, and nasal bones. The proposed RRN works in an end-to-end manner,
utilizing learned relations of the landmarks based on dense-block units and
without the need for segmentation. For a given a few landmarks as input, the
proposed system accurately and efficiently localizes the remaining landmarks on
the aforementioned bones. For a comprehensive evaluation of RRN, we used
cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system
identifies the landmark locations very accurately even when there are severe
pathologies or deformations in the bones. The proposed RRN has also revealed
unique relationships among the landmarks that help us infer several reasoning
about informativeness of the landmark points. RRN is invariant to order of
landmarks and it allowed us to discover the optimal configurations (number and
location) for landmarks to be localized within the object of interest
(mandible) or nearby objects (maxilla and nasal). To the best of our knowledge,
this is the first of its kind algorithm finding anatomical relations of the
objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table
The pathogen paradox: Evidence that perceived COVID-19 threat is associated with both pro- and anti-immigrant attitudes
COVID-19 pandemic, as a global threat to humanity, is likely to instigate a variety of collective responses in the society. We examined, for the first time, whether COVID-19 threat perception is related to attitudes towards Syrian immigrants in Turkey, theorizing a dual pathway whereby threat caused by the COVID-19 pandemic would relate to both pro- and anti-immigrant feelings. While drawing upon behavioral immune system theory, we expected that pathogen threat would lead to more exclusionary attitudes; relying on the common ingroup identity model, we predicted that pathogen threat would promote inclusionary attitudes through creating a common ingroup in the face of a global threat. Results from two studies using online search volume data at the province-level (N = 81) and self-report measures at the individual level (N = 294) demonstrated that perceived COVID-19 threat was directly associated with more positive attitudes towards immigrants (Study 1 and 2). Study 2 further revealed indirect positive (through a sense of common identity) and negative (through perceptions of immigrant threat) links between COVID-19 threat perception and attitudes towards immigrants. These results highlight the importance of integrating evolutionary and social identity perspectives when assessing pathogen-related threats. We draw attention to managing the public perceptions of COVID-19 threat which may mitigate the social aftermath of the pandemic.</p
Learning Optimal Deep Projection of F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
Several diseases of parkinsonian syndromes present similar symptoms at early
stage and no objective widely used diagnostic methods have been approved until
now. Positron emission tomography (PET) with F-FDG was shown to be able
to assess early neuronal dysfunction of synucleinopathies and tauopathies.
Tensor factorization (TF) based approaches have been applied to identify
characteristic metabolic patterns for differential diagnosis. However, these
conventional dimension-reduction strategies assume linear or multi-linear
relationships inside data, and are therefore insufficient to distinguish
nonlinear metabolic differences between various parkinsonian syndromes. In this
paper, we propose a Deep Projection Neural Network (DPNN) to identify
characteristic metabolic pattern for early differential diagnosis of
parkinsonian syndromes. We draw our inspiration from the existing TF methods.
The network consists of a (i) compression part: which uses a deep network to
learn optimal 2D projections of 3D scans, and a (ii) classification part: which
maps the 2D projections to labels. The compression part can be pre-trained
using surplus unlabelled datasets. Also, as the classification part operates on
these 2D projections, it can be trained end-to-end effectively with limited
labelled data, in contrast to 3D approaches. We show that DPNN is more
effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201
Reply to the Comment by B. Andresen
All the comments made by Andresen's comments are replied and are shown not to
be pertinent. The original discussions [ABE S., Europhys. Lett. 90 (2010)
50004] about the absence of nonextensive statistical mechanics with q-entropies
for classical continuous systems are reinforced.Comment: 5 pages. This is Reply to B. Andresen's Comment on the paper entitled
"Essential discreteness in generalized thermostatistics with non-logarithmic
entropy", Europhys. Lett. 90 (2010) 5000
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