Data-driven pattern identification and outlier detection in time series

We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without the need for specifying user-defined parameters. From a data mining perspective, this opens up new ways of analyzing time series in a data-driven, bottom-up fashion. However, in order to get correct results, it is important to understand how the SVD-spectrum of a time series is influenced by various characteristics of the underlying signal and noise. In this paper, we have extended the work in earlier papers by initiating a more systematic analysis of these effects. We then illustrate our findings on some real-life data

Reconfigurable interconnects in DSM systems: a focus on context switch behavior

Recent advances in the development of reconfigurable optical interconnect technologies allow for the fabrication of low cost and run-time adaptable interconnects in large distributed shared-memory (DSM) multiprocessor machines. This can allow the use of adaptable interconnection networks that alleviate the huge bottleneck present due to the gap between the processing speed and the memory access time over the network. In this paper we have studied the scheduling of tasks by the kernel of the operating system (OS) and its influence on communication between the processing nodes of the system, focusing on the traffic generated just after a context switch. We aim to use these results as a basis to propose a potential reconfiguration of the network that could provide a significant speedup

Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis

Electrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas

Evidence for accretion rate change during type I X-ray bursts

The standard approach for time-resolved X-ray spectral analysis of thermonuclear bursts involves subtraction of the pre-burst emission as background. This approach implicitly assumes that the persistent flux remains constant throughout the burst. We reanalyzed 332 photospheric radius expansion bursts observed from 40 sources by the Rossi X-ray Timing Explorer, introducing a multiplicative factor $f_a$ to the persistent emission contribution in our spectral fits. We found that for the majority of spectra the best-fit value of $f_a$ is significantly greater than 1, suggesting that the persistent emission typically increases during a burst. Elevated $f_a$ values were not found solely during the radius expansion interval of the burst, but were also measured in the cooling tail. The modified model results in a lower average value of the $\chi^2$ fit statistic, indicating superior spectral fits, but not yet to the level of formal statistical consistency for all the spectra. We interpret the elevated $f_a$ values as an increase of the mass accretion rate onto the neutron star during the burst, likely arising from the effects of Poynting-Robertson drag on the disk material. We measured an inverse correlation of $f_a$ with the persistent flux, consistent with theoretical models of the disc response. We suggest that this modified approach may provide more accurate burst spectral parameters, as well as offering a probe of the accretion disk structure.Comment: 15 pages, 12 figures, 4 table

Human-triggered earthquakes and their impacts on human security

A comprehensive understanding of earthquake risks in urbanized regions requires an accurate assessment of both urban vulnerabilities and earthquake hazards. Socioeconomic risks associated with human-triggered earthquakes are often misconstrued and receive little scientific, legal, and public attention. However, more than 200 damaging earthquakes, associated with industrialization and urbanization, were documented since the 20th century. This type of geohazard has impacts on human security on a regional and national level. For example, the 1989 Newcastle earthquake caused 13 deaths and US$3.5 billion damage (in 1989). The monetary loss was equivalent to 3.4 percent of Australia’s national income (GDI) or 80 percent of Australia’s GDI per capita growth of the same year. This article provides an overview of global statistics of human-triggered earthquakes. It describes how geomechanical pollution due to large-scale geoengineering activities can advance the clock of earthquakes or trigger new seismic events. Lastly, defense-oriented strategies and tactics are described, including risk mitigation measures such as urban planning adaptations and seismic hazard mapping

Approximate Decoding Approaches for Network Coded Correlated Data

This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources