Finite temperature effects on the neutrino decoupling in the early Universe

Leading finite temperature effects on the neutrino decoupling temperature in the early Universe have been studied. We have incorporated modifications of the dispersion relation and the phase space distribution due to the presence of particles in the heat bath at temperature around MeV. Since both the expansion rate of the Universe and the interaction rate of a neutrino are reduced by finite temperature effects, it is necessary to calculate thermal corrections as precisely as possible in order to find the net effect on the neutrino decoupling temperature. We have performed such a calculation by using the finite temperature field theory. It has been shown that the finite temperature effects increase the neutrino decoupling temperature by 4.4%, the largest contribution coming from the modification of the phase space due to the thermal bath.Comment: 18 pages, LaTeX (uses RevTeX), 6 figures added as PS files, submitted to Phys.Rev.

Effect of excited states and applied magnetic fields on the measured hole mobility in an organic semiconductor

Copyright 2010 by the American Physical Society. Article is available at

On the transport and thermodynamic properties of quasi-two-dimensional purple bronzes A0.9_{0.9}Mo6_6O17_{17} (A=Na, K)

We report a comparative study of the specific heat, electrical resistivity and thermal conductivity of the quasi-two-dimensional purple bronzes Na$_{0.9}$Mo$_6$O$_{17}$ and K$_{0.9}$Mo$_6$O$_{17}$, with special emphasis on the behavior near their respective charge-density-wave transition temperatures $T_P$. The contrasting behavior of both the transport and the thermodynamic properties near $T_P$ is argued to arise predominantly from the different levels of intrinsic disorder in the two systems. A significant proportion of the enhancement of the thermal conductivity above $T_P$ in Na$_{0.9}$Mo$_6$O$_{17}$, and to a lesser extent in K$_{0.9}$Mo$_6$O$_{17}$, is attributed to the emergence of phason excitations.Comment: 8 pages, 6 figures, To appear in Physical Review

q-Deformation of W(2,2) Lie algebra associated with quantum groups

An explicit realization of the W(2,2) Lie algebra is presented using the famous bosonic and fermionic oscillators in physics, which is then used to construct the q-deformation of this Lie algebra. Furthermore, the quantum group structures on the q-deformation of this Lie algebra are completely determined.Comment: 12 page

Recent advances in human respiratory epithelium models for drug discovery

The respiratory epithelium is intimately associated with the pathophysiologies of highly infectious viral contagions and chronic illnesses such as chronic obstructive pulmonary disorder, presently the third leading cause of death worldwide with a projected economic burden of £1.7 trillion by 2030. Preclinical studies of respiratory physiology have almost exclusively utilised non-humanised animal models, alongside reductionistic cell line-based models, and primary epithelial cell models cultured at an air-liquid interface (ALI). Despite their utility, these model systems have been limited by their poor correlation to the human condition. This has undermined the ability to identify novel therapeutics, evidenced by a 15% chance of success for medicinal respiratory compounds entering clinical trials in 2018. Consequently, preclinical studies require new translational efficacy models to address the problem of respiratory drug attrition. This review describes the utility of the current in vivo (rodent), ex vivo (isolated perfused lungs and precision cut lung slices), two-dimensional in vitro cell-line (A549, BEAS-2B, Calu-3) and three-dimensional in vitro ALI (gold-standard and co-culture) and organoid respiratory epithelium models. The limitations to the application of these model systems in drug discovery research are discussed, in addition to perspectives of the future innovations required to facilitate the next generation of human-relevant respiratory models

Wetting and bonding characteristics of selected liquid-metals with a high power diode laser treated alumina bioceramic

Changes in the wettability characteristics of an alumina bioceramic occasioned by high power diode laser (HPDL) surface treatment were apparent from the observed reduction in the contact angle. Such changes were due to the HPDL bringing about reductions the surface roughness, increases in the surface O2 content and increases in the polar component of the surface energy. Additionally, HPDL treatment of the alumina bioceramic surface was found to effect an improvement in the bonding characteristics by increasing the work of adhesion. An electronic approach was used to elucidate the bonding characteristics of the alumina bioceramic before and after HPDL treatment. It is postulated that HPDL induced changes to the alumina bioceramic produced a surface with a reduced bandgap energy which consequently increased the work of adhesion by increasing the electron transfer at the metal/oxide interface and thus the metal-oxide interactions. Furthermore, it is suggested that the increase in the work of adhesion of the alumina bioceramic after HPDL treatment was due to a correlation existing between the wettability and ionicity of the alumina bioceramic; for it is believed that the HPDL treated surface is less ionic in nature than the untreated surface and therefore exhibits better wettability characteristics

A bayesian scene-prior-based deep network model for face verification

Face recognition/verification has received great attention in both theory and application for the past two decades. Deep learning has been considered as a very powerful tool for improving the performance of face recognition/verification recently. With large labeled training datasets, the features obtained from deep learning networks can achieve higher accuracy in comparison with shallow networks. However, many reported face recognition/verification approaches rely heavily on the large size and complete representative of the training set, and most of them tend to suffer serious performance drop or even fail to work if fewer training samples per person are available. Hence, the small number of training samples may cause the deep features to vary greatly. We aim to solve this critical problem in this paper. Inspired by recent research in scene domain transfer, for a given face image, a new series of possible scenarios about this face can be deduced from the scene semantics extracted from other face individuals in a face dataset. We believe that the “scene” or background in an image, that is, samples with more different scenes for a given person, may determine the intrinsic features among the faces of the same individual. In order to validate this belief, we propose a Bayesian scene-prior-based deep learning model in this paper with the aim to extract important features from background scenes. By learning a scene model on the basis of a labeled face dataset via the Bayesian idea, the proposed method transforms a face image into new face images by referring to the given face with the learnt scene dictionary. Because the new derived faces may have similar scenes to the input face, the face-verification performance can be improved without having background variance, while the number of training samples is significantly reduced. Experiments conducted on the Labeled Faces in the Wild (LFW) dataset view #2 subset illustrated that this model can increase the verification accuracy to 99.2% by means of scenes’ transfer learning (99.12% in literature with an unsupervised protocol). Meanwhile, our model can achieve 94.3% accuracy for the YouTube Faces database (DB) (93.2% in literature with an unsupervised protocol)

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201