Simultaneous Multiple Surface Segmentation Using Deep Learning

The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis. Recently, graph-based methods with a global optimization property have been developed and optimized for various medical imaging applications. Despite their widespread use, these require human experts to design transformations, image features, surface smoothness priors, and re-design for a different tissue, organ or imaging modality. Here, we propose a Deep Learning based approach for segmentation of the surfaces in volumetric medical images, by learning the essential features and transformations from training data, without any human expert intervention. We employ a regional approach to learn the local surface profiles. The proposed approach was evaluated on simultaneous intraretinal layer segmentation of optical coherence tomography (OCT) images of normal retinas and retinas affected by age related macular degeneration (AMD). The proposed approach was validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The experiments showed statistically significant improvement in accuracy for our approach compared to state-of-the-art graph based optimal surface segmentation with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used to learn the surfaces for both normal and diseased images. The mean unsigned surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI 2.02-2.60 voxels) was improved to $1.27$ voxels (95% CI 1.14-1.40 voxels) using our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB memory, which is much faster than the 2837.46 s and 6.87 GB memory required by the G-OSC method on the same computer system.Comment: 8 page

Drastic Reduction of Shot Noise in Semiconductor Superlattices

We have found experimentally that the shot noise of the tunneling current $I$ through an undoped semiconductor superlattice is reduced with respect to the Poissonian noise value $2eI$, and that the noise approaches 1/3 of that value in superlattices whose quantum wells are strongly coupled. On the other hand, when the coupling is weak or when a strong electric field is applied to the superlattice the noise becomes Poissonian. Although our results are qualitatively consistent with existing theories for one-dimensional mulitple barriers, the theories cannot account for the dependence of the noise on superlattice parameters that we have observed.Comment: 4 Pages, 3Figure

New Candidates for Topological Insulators : Pb-based chalcogenide series

Here, we theoretically predict that the series of Pb-based layered chalcogenides, Pb$_n$Bi$_2$Se$_{n+3}$ and Pb$_n$Sb$_2$Te$_{n+3}$, are possible new candidates for topological insulators. As $n$ increases, the phase transition from a topological insulator to a band insulator is found to occur between $n=2$ and 3 for both series. Significantly, among the new topological insulators, we found a bulk band gap of 0.40eV in PbBi$_2$Se$_4$ which is one of the largest gap topological insulators, and that Pb$_2$Sb$_2$Te$_5$ is located in the immediate vicinity of the topological phase boundary, making its topological phase easily tunable by changing external parameters such as lattice constants. Due to the three-dimensional Dirac cone at the phase boundary, massless Dirac fermions also may be easily accessible in Pb$_2$Sb$_2$Te$_5$

Liquid-liquid phase separation and morphology of internally mixed dicarboxylic acids/ammonium sulfate/water particles

Knowledge of the physical state and morphology of internally mixed organic/inorganic aerosol particles is still largely uncertain. To obtain more detailed information on liquid-liquid phase separation (LLPS) and morphology of the particles, we investigated complex mixtures of atmospherically relevant dicarboxylic acids containing 5, 6, and 7 carbon atoms (C5, C6 and C7) having oxygen-to-carbon atomic ratios (O:C) of 0.80, 0.67, and 0.57, respectively, mixed with ammonium sulfate (AS). With micrometer-sized particles of C5/AS/H_2O, C6/AS/H_2O and C7/AS/H_2O as model systems deposited on a hydrophobically coated substrate, laboratory experiments were conducted for various organic-to-inorganic dry mass ratios (OIR) using optical microscopy and Raman spectroscopy. When exposed to cycles of relative humidity (RH), each system showed significantly different phase transitions. While the C5/AS/H_2O particles showed no LLPS with OIR = 2:1, 1:1 and 1:4 down to 20% RH, the C6/AS/H_2O and C7/AS/H_2O particles exhibit LLPS upon drying at RH 50 to 85% and ~90%, respectively, via spinodal decomposition, growth of a second phase from the particle surface or nucleation-and-growth mechanisms depending on the OIR. This suggests that LLPS commonly occurs within the range of O:C < 0.7 in tropospheric organic/inorganic aerosols. To support the comparison and interpretation of the experimentally observed phase transitions, thermodynamic equilibrium calculations were performed with the AIOMFAC model. For the C7/AS/H_2O and C6/AS/H_2O systems, the calculated phase diagrams agree well with the observations while for the C5/AS/H_2O system LLPS is predicted by the model at RH below 60% and higher AS concentration, but was not observed in the experiments. Both core-shell structures and partially engulfed structures were observed for the investigated particles, suggesting that such morphologies might also exist in tropospheric aerosols

A novel route to a finite center-of-mass momentum pairing state; current driven FFLO state

The previously studied Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state is stabilized by a magnetic field via the Zeeman coupling in spin-singlet superconductors. Here we suggest a novel route to achieve non-zero center-of-mass momentum pairing states in superconductors with Fermi surface nesting. We investigate two-dimensional superconductors under a uniform external current, which leads to a finite pair-momentum of ${\bf q}_{e}$. We find that an FFLO state with a spontaneous pair-momentum of ${\bf q}_{s}$ is stabilized above a certain critical current which depends on the direction of the external current. A finite ${\bf q}_s$ arises in order to make the total pair-momentum of ${\bf q}_t(={\bf q}_s + {\bf q}_e)$ perpendicular to the nesting vector, which lowers the free energy of the FFLO state, as compared to the superconducting and normal states. We also suggest experimental signatures of the FFLO state.Comment: 4 pages, 5 figure

Gravitational Wave Background from Phantom Superinflation

Recently, the early superinflation driven by phantom field has been proposed and studied. The detection of primordial gravitational wave is an important means to know the state of very early universe. In this brief report we discuss in detail the gravitational wave background excited during the phantom superinflation.Comment: 3 pages, 2 eps figures, to be published in PRD, revised with published version, refs. adde

Self-management of context-aware overlay ambient networks

Ambient Networks (ANs) are dynamically changing and heterogeneous as they consist of potentially large numbers of independent, heterogeneous mobile nodes, with spontaneous topologies that can logically interact with each other to share a common control space, known as the Ambient Control Space. ANs are also flexible i.e. they can compose and decompose dynamically and automatically, for supporting the deployment of cross-domain (new) services. Thus, the AN architecture must be sophisticatedly designed to support such high level of dynamicity, heterogeneity and flexibility. We advocate the use of service specific overlay networks in ANs, that are created on-demand according to specific service requirements, to deliver, and to automatically adapt services to the dynamically changing user and network context. This paper presents a self-management approach to create, configure, adapt, contextualise, and finally teardown service specific overlay networks