A Scale-Separated Dynamic Mode Decomposition From Observations of the Ionospheric Electron Density Profile

We present a method for modeling a time series of ionospheric electron density profiles using modal decompositions. Our method is based on the Dynamic Mode Decomposition (DMD), which provides a means of determining spatiotemporal modes from measurements alone. DMD-derived models can be easily updated as new data is recorded and do not require any physics to inform the dynamics. However, in the case of ionospheric profiles, we find a wide range of oscillations, including some far above the diurnal frequency. Therefore, we propose nontrivial extensions to DMD using multiresolution analysis (MRA) via wavelet decompositions. We call this method the Scale-Separated Dynamic Mode Decomposition (SSDMD) since the MRA isolates fluctuations at different scales within the time series into separated components. We show that this method provides a stable reconstruction of the mean plasma density and can be used to predict the state of the vertical profile at future time steps. We demonstrate the SSDMD method on data sets covering periods of high and low solar activity.Comment: 26 pages, 16 figure

Non-parametric belief propagation for mobile mapping sensor fusion

© 2016 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Many different forms of sensor fusion have been proposed each with its own niche. We propose a method of fusing multiple different sensor types. Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional (3D) point clouds. We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors. This technique allows continuous variables to be used, is trivially parallel making it suitable for modern many-core processors, and easily accommodates varying types and combinations of sensors. By defining the relationships between common sensors, a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors. This allows the use of unreliable sensors which firstly, may start and stop providing data at any time and secondly, the integration of new sensor types simply by defining their relationship with existing sensors. These features allow a flexible framework to be developed which is suitable for many tasks. Using an abstract algorithm, we can instead focus on the relationships between sensors. Where possible we use the existing relationships between sensors rather than developing new ones. These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network. In this paper, we present the initial results from this technique and the intended course for future work

Survey propagation at finite temperature: application to a Sourlas code as a toy model

In this paper we investigate a finite temperature generalization of survey propagation, by applying it to the problem of finite temperature decoding of a biased finite connectivity Sourlas code for temperatures lower than the Nishimori temperature. We observe that the result is a shift of the location of the dynamical critical channel noise to larger values than the corresponding dynamical transition for belief propagation, as suggested recently by Migliorini and Saad for LDPC codes. We show how the finite temperature 1-RSB SP gives accurate results in the regime where competing approaches fail to converge or fail to recover the retrieval state

Modeling the Measurements of Cochlear Microcirculation and Hearing Function after Loud Noise

Objective: Recent findings support the crucial role of microcirculatory disturbance and ischemia for hearing impairment especially after noise-induced hearing loss (NIHL). The aim of this study was to establish an animal model for in vivo analysis of cochlear microcirculation and hearing function after a loud noise to allow precise measurements of both parameters in vivo. Study Design: Randomized controlled trial. Setting: Animal study. Subjects and Methods: After assessment of normacusis (0 minutes) using evoked auditory brainstem responses (ABRs), noise (106-dB sound pressure level [SPL]) was applied to both ears in 6 guinea pigs for 30 minutes while unexposed animals served as controls. In vivo fluorescence microscopy of the stria vascularis capillaries was performed after surgical exposure of 1 cochlea. ABR measurements were derived from the contralateral ear. Results: After noise exposure, red blood cell velocity was reduced significantly by 24.3% (120 minutes) and further decreased to 44.5% at the end of the observation (210 minutes) in contrast to stable control measurements. Vessel diameters were not affected in both groups. A gradual decrease of segmental blood flow became significant (38.1%) after 150 minutes compared with controls. Hearing thresholds shifted significantly from 20.0 ± 5.5 dB SPL (0 minutes) to 32.5 ± 4.2dB SPL (60 minutes) only in animals exposed to loud noise. Conclusion: With regard to novel treatments targeting the stria vascularis in NIHL, this standardized model allows us to analyze in detail cochlear microcirculation and hearing function in vivo

Critical phenomena in complex networks

The combination of the compactness of networks, featuring small diameters, and their complex architectures results in a variety of critical effects dramatically different from those in cooperative systems on lattices. In the last few years, researchers have made important steps toward understanding the qualitatively new critical phenomena in complex networks. We review the results, concepts, and methods of this rapidly developing field. Here we mostly consider two closely related classes of these critical phenomena, namely structural phase transitions in the network architectures and transitions in cooperative models on networks as substrates. We also discuss systems where a network and interacting agents on it influence each other. We overview a wide range of critical phenomena in equilibrium and growing networks including the birth of the giant connected component, percolation, k-core percolation, phenomena near epidemic thresholds, condensation transitions, critical phenomena in spin models placed on networks, synchronization, and self-organized criticality effects in interacting systems on networks. We also discuss strong finite size effects in these systems and highlight open problems and perspectives.Comment: Review article, 79 pages, 43 figures, 1 table, 508 references, extende

C60: the first one-component gel?

Until now, gels have been formed of multicomponent soft matter systems, consisting of a solvent and one or more macromolecular or colloidal species. Here we show that, for sufficient quench rates, the Girifalco model of C60 can form gels which we identify by their slow dynamics and long-lived network structure. These gels are stable at room temperature, at least on the simulation timescale up to 100 ns. At moderate temperatures around 1000 K, below the bulk glass transition temperature, C60 exhibits crystallisation and phase separation proceeds without the dynamical arrest associated with gelation, in contrast to many colloidal systems.Comment: Accepted by J. Phys. Chem. C. special issue 'Clusters in complex fluids