University of Zurich

About: University of Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Medicine. The organization has 50842 authors who have published 124042 publications receiving 5304521 citations. The organization is also known as: UZH & Uni Zurich.

ILAE Commission Report. Mesial temporal lobe epilepsy with hippocampal sclerosis.

Abstract: Experts discussed the definition, natural history, pathologic features, pathogenesis, electroclinical, neurophysiological, neuropsychological, structural and functional imaging findings, as well as surgical outcome in mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). After a long-lasting consensus process the ILAE Commission Neurosurgery of epilepsy accepted the resulting conclusions as state-of-the art report on MTLE-HS. The majority of contributors considered MTLE-HS to represent a sufficient cluster of signs and symptoms to make up a syndromic diagnostic entity.

A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor

Abstract: Event-based dynamic vision sensors (DVSs) asynchronously report log intensity changes. Their high dynamic range, sub-ms latency and sparse output make them useful in applications such as robotics and real-time tracking. However they discard absolute intensity information which is useful for object recognition and classification. This paper presents a dynamic and active pixel vision sensor (DAVIS) which addresses this deficiency by outputting asynchronous DVS events and synchronous global shutter frames concurrently. The active pixel sensor (APS) circuits and the DVS circuits within a pixel share a single photodiode. Measurements from a 240×180 sensor array of 18.5 μm 2 pixels fabricated in a 0.18 μm 6M1P CMOS image sensor (CIS) technology show a dynamic range of 130 dB with 11% contrast detection threshold, minimum 3 μs latency, and 3.5% contrast matching for the DVS pathway; and a 51 dB dynamic range with 0.5% FPN for the APS readout.

A Pooled Analysis of Vitamin D Dose Requirements for Fracture Prevention

Abstract: BACKGROUND: The results of meta-analyses examining the relationship between vitamin D supplementation and fracture reduction have been inconsistent. METHODS: We pooled participant-level data from 11 double-blind, randomized, controlled trials of oral vitamin D supplementation (daily, weekly, or every 4 months), with or without calcium, as compared with placebo or calcium alone in persons 65 years of age or older. Primary end points were the incidence of hip and any nonvertebral fractures according to Cox regression analyses, with adjustment for age group, sex, type of dwelling, and study. Our primary aim was to compare data from quartiles of actual intake of vitamin D (including each individual participant's adherence to the treatment and supplement use outside the study protocol) in the treatment groups of all trials with data from the control groups. RESULTS: We included 31,022 persons (mean age, 76 years; 91% women) with 1111 incident hip fractures and 3770 nonvertebral fractures. Participants who were randomly assigned to receive vitamin D, as compared with those assigned to control groups, had a nonsignificant 10% reduction in the risk of hip fracture (hazard ratio, 0.90; 95% confidence interval [CI], 0.80 to 1.01) and a 7% reduction in the risk of nonvertebral fracture (hazard ratio, 0.93; 95% CI, 0.87 to 0.99). By quartiles of actual intake, reduction in the risk of fracture was shown only at the highest intake level (median, 800 IU daily; range, 792 to 2000), with a 30% reduction in the risk of hip fracture (hazard ratio, 0.70; 95% CI, 0.58 to 0.86) and a 14% reduction in the risk of any nonvertebral fracture (hazard ratio, 0.86; 95% CI, 0.76 to 0.96). Benefits at the highest level of vitamin D intake were fairly consistent across subgroups defined by age group, type of dwelling, baseline 25-hydroxyvitamin D level, and additional calcium intake. CONCLUSIONS: High-dose vitamin D supplementation (≥800 IU daily) was somewhat favorable in the prevention of hip fracture and any nonvertebral fracture in persons 65 years of age or older. (Funded by the Swiss National Foundations and others.) Copyright © 2012 Massachusetts Medical Society.

The China Shock: Learning from Labor-Market Adjustment to Large Changes in Trade

Abstract: China's emergence as a great economic power has induced an epochal shift in patterns of world trade. Simultaneously, it has challenged much of the received empirical wisdom about how labor markets adjust to trade shocks. Alongside the heralded consumer benefits of expanded trade are substantial adjustment costs and distributional consequences. These impacts are most visible in the local labor markets in which the industries exposed to foreign competition are concentrated. Adjustment in local labor markets is remarkably slow, with wages and labor-force participation rates remaining depressed and unemployment rates remaining elevated for at least a full decade after the China trade shock commences. Exposed workers experience greater job churning and reduced lifetime income. At the national level, employment has fallen in the US industries more exposed to import competition, as expected, but offsetting employment gains in other industries have yet to materialize. Better understanding when and where trade is ...

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

Abstract: Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs) However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition