Numerical Simulation of Converter Fed Squirrel Cage Induction Motors

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The mammalian rod synaptic ribbon is essential for Cav channel facilitation and ultrafast fusion of the readily releasable pool of vesicles

Rod photoreceptors (PRs) use ribbon synapses to transmit visual information. To signal ‘no light detected’ they release glutamate continually to activate post-synaptic receptors, and when light is detected glutamate release pauses. How a rod’s individual ribbon enables this process was studied here by recording evoked changes in whole-cell membrane capacitance from wild type and ribbonless (RIBEYE-ko) rods. Wild type rods created a readily releasable pool (RRP) of 92 synaptic vesicles (SVs) that emptied as a single kinetic phase with a τ < 0.4 msec. Lowering intracellular Ca2+-buffering accelerated Cav channel opening and facilitated release kinetics, but RRP size was unaltered. In contrast, ribbonless rods created an RRP of 24 SVs, and lacked Cav channel facilitation; however, Ca2+ channel-release coupling remained tight. The release deficits caused a sharp attenuation of rod-driven light responses measured from RIBEYE-ko mice. We conclude that the synaptic ribbon facilitates Ca2+-influx and establishes a large RRP of SVs

Generator of Time Series of Rain Attenuation: Results of Parameter Extraction

Rain attenuation has a significant impact on the availability of millimeter wave communication systems. In order to dynamically simulate such radio systems, several generators of artificial time series of rain attenuation have been developed. This paper briefly describes the DLR channel model and presents the results of model parameter extraction from time series measured on terrestrial microwave paths in the Czech Republic

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A space X is called s-point finite refinable (ds-point finite refinable) provided every open cover of X has an open refinement such that, for some (closed discrete) C⫅X

Visual on-line learning in distributed camera networks

Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge network traffic. Thus, the goal of this paper is to overcome these problems, which is realized by a person detection system, that is based on distributed smart cameras (DSCs). Assuming that we have a large number of cameras with partly overlapping views, the main idea is to reduce the model complexity of the detector by training a specific detector for each camera. These detectors are initialized by a pre-trained classifier, that is then adapted for a specific camera by co-training. In particular, for co-training we apply an on-line learning method (i.e., boosting for feature selection), where the information exchange is realized via mapping the overlapping views onto each other by using a homography. Thus, we have a compact scenedependent representation, which allows to train and to evaluate the classifiers on an embedded device. Moreover, since the information transfer is reduced to exchanging positions the required network-traffic is minimal. The power of the approach is demonstrated in various experiments on different publicly available data sets. In fact, we show that on-line learning and applying DSCs can benefit from each other. Index Terms — visual on-line learning, object detection, multi-camera networks 1

Resolving the molecular architecture of the photoreceptor active zone with 3D-MINFLUX

Cells assemble macromolecular complexes into scaffoldings that serve as substrates for catalytic processes. Years of molecular neurobiology research indicate that neurotransmission depends on such optimization strategies. However, the molecular topography of the presynaptic active zone (AZ), where transmitter is released upon synaptic vesicle (SV) fusion, remains to be visualized. Therefore, we implemented MINFLUX optical nanoscopy to resolve the AZ of rod photoreceptors. This was facilitated by a novel sample immobilization technique that we name heat-assisted rapid dehydration (HARD), wherein a thin layer of rod synaptic terminals (spherules) was transferred onto glass coverslips from fresh retinal slices. Rod ribbon AZs were readily immunolabeled and imaged in 3D with a precision of a few nanometers. Our 3D-MINFLUX results indicate that the SV release site in rods is a molecular complex of bassoon–RIM2–ubMunc13-2–Cav1.4, which repeats longitudinally on both sides of the ribbon

Global first-passage times of fractal lattices

The global first passage time density of a network is the probability that a random walker released at a random site arrives at an absorbing trap at time T. We find simple expressions for the mean global first passage time for five fractals: the d-dimensional Sierpinski gasket, T fractal, hierarchical percolation model, Mandelbrot-Given curve, and a deterministic tree. We also find an exact expression for the second moment and show that the variance of the first passage time, Var(T), scales with the number of nodes within the fractal N such that Var(T)similar to N(4/d), where d is the spectral dimension

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral

Existence of a Meromorphic Extension of Spectral Zeta Functions on Fractals

We investigate the existence of the meromorphic extension of the spectral zeta function of the Laplacian on self-similar fractals using the classical results of Kigami and Lapidus (based on the renewal theory) and new results of Hambly and Kajino based on the heat kernel estimates and other probabilistic techniques. We also formulate conjectures which hold true in the examples that have been analyzed in the existing literature