On the use of sensitivity tests in seismic tomography

ACKNOWLEDGEMENTS This work was partly supported by ARC Discovery Project DP120103673 and by the Research Council of Norway through its Centres of Excellence funding scheme, project number 223272. We thank Maximilliano Bezada and an anonymous referee for constructive comments which improved the original version of the manuscript. We also thank the Editor, A. Morelli, for providing additional helpful comments.Peer reviewedPublisher PD

Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the `positive' case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the `positive' and `negative' samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation.Comment: 15 pages, 13 figures, accepted by Benelearn 2018 conferenc

Mining Network Events using Traceroute Empathy

In the never-ending quest for tools that enable an ISP to smooth troubleshooting and improve awareness of network behavior, very much effort has been devoted in the collection of data by active and passive measurement at the data plane and at the control plane level. Exploitation of collected data has been mostly focused on anomaly detection and on root-cause analysis. Our objective is somewhat in the middle. We consider traceroutes collected by a network of probes and aim at introducing a practically applicable methodology to quickly spot measurements that are related to high-impact events happened in the network. Such filtering process eases further in- depth human-based analysis, for example with visual tools which are effective only when handling a limited amount of data. We introduce the empathy relation between traceroutes as the cornerstone of our formal characterization of the traceroutes related to a network event. Based on this model, we describe an algorithm that finds traceroutes related to high-impact events in an arbitrary set of measurements. Evidence of the effectiveness of our approach is given by experimental results produced on real-world data.Comment: 8 pages, 7 figures, extended version of Discovering High-Impact Routing Events using Traceroutes, in Proc. 20th International Symposium on Computers and Communications (ISCC 2015

Time-lapse geophysical investigations over a simulated urban clandestine grave

A simulated clandestine shallow grave was created within a heterogeneous, made-ground, urban environment where a clothed, plastic resin, human skeleton, animal products, and physiological saline were placed in anatomically correct positions and re-covered to ground level. A series of repeat (time-lapse), near-surface geophysical surveys were undertaken: (1) prior to burial (to act as control), (2) 1 month, and (3) 3 months post-burial. A range of different geophysical techniques was employed including: bulk ground resistivity and conductivity, fluxgate gradiometry and high-frequency ground penetrating radar (GPR), soil magnetic susceptibility, electrical resistivity tomography (ERT), and self potential (SP). Bulk ground resistivity and SP proved optimal for initial grave location whilst ERT profiles and GPR horizontal "time-slices" showed the best spatial resolutions. Research suggests that in complex urban made-ground environments, initial resistivity surveys be collected before GPR and ERT follow-up surveys are collected over the identified geophysical anomalies