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 18^{18}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 $^{18}$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