Redundancy (engineering)

About: Redundancy (engineering) is a research topic. Over the lifetime, 28243 publications have been published within this topic receiving 400653 citations. The topic is also known as: redundant system & engineered redundancy.

Compressing Neural Networks with the Hashing Trick

Abstract: As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket share a single parameter value. These parameters are tuned to adjust to the HashedNets weight sharing architecture with standard backprop during training. Our hashing procedure introduces no additional memory overhead, and we demonstrate on several benchmark data sets that HashedNets shrink the storage requirements of neural networks substantially while mostly preserving generalization performance.

Exploitation of spectral redundancy in cyclostationary signals

Abstract: It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described. >

Abnormal Event Detection at 150 FPS in MATLAB

Abstract: Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140-150 frames per second on average when computing on an ordinary desktop PC using MATLAB.

Evaluation of distributed recovery in large-scale storage systems

Abstract: Storage clusters consisting of thousands of disk drives are now being used both for their large capacity and high throughput. However, their reliability is far worse than that of smaller storage systems due to the increased number of storage nodes. RAID technology is no longer sufficient to guarantee the necessary high data reliability for such systems, because disk rebuild time lengthens as disk capacity grows. We present fast recovery mechanism (FARM), a distributed recovery approach that exploits excess disk capacity and reduces data recovery time. FARM works in concert with replication and erasure-coding redundancy schemes to dramatically lower the probability of data loss in large-scale storage systems. We have examined essential factors that influence system reliability, performance, and costs, such as failure detections, disk bandwidth usage for recovery, disk space utilization, disk drive replacement, and system scales, by simulating system behavior under disk failures. Our results show the reliability improvement from FARM and demonstrate the impacts of various factors on system reliability. Using our techniques, system designers will be better able to build multipetabyte storage systems with much higher reliability at lower cost than previously possible.

Quality management on Amazon Mechanical Turk

Abstract: Crowdsourcing services, such as Amazon Mechanical Turk, allow for easy distribution of small tasks to a large number of workers. Unfortunately, since manually verifying the quality of the submitted results is hard, malicious workers often take advantage of the verification difficulty and submit answers of low quality. Currently, most requesters rely on redundancy to identify the correct answers. However, redundancy is not a panacea. Massive redundancy is expensive, increasing significantly the cost of crowdsourced solutions. Therefore, we need techniques that will accurately estimate the quality of the workers, allowing for the rejection and blocking of the low-performing workers and spammers.However, existing techniques cannot separate the true (unrecoverable) error rate from the (recoverable) biases that some workers exhibit. This lack of separation leads to incorrect assessments of a worker's quality. We present algorithms that improve the existing state-of-the-art techniques, enabling the separation of bias and error. Our algorithm generates a scalar score representing the inherent quality of each worker. We illustrate how to incorporate cost-sensitive classification errors in the overall framework and how to seamlessly integrate unsupervised and supervised techniques for inferring the quality of the workers. We present experimental results demonstrating the performance of the proposed algorithm under a variety of settings.