9,796 research outputs found
Sequential anomaly detection in the presence of noise and limited feedback
This paper describes a methodology for detecting anomalies from sequentially
observed and potentially noisy data. The proposed approach consists of two main
elements: (1) {\em filtering}, or assigning a belief or likelihood to each
successive measurement based upon our ability to predict it from previous noisy
observations, and (2) {\em hedging}, or flagging potential anomalies by
comparing the current belief against a time-varying and data-adaptive
threshold. The threshold is adjusted based on the available feedback from an
end user. Our algorithms, which combine universal prediction with recent work
on online convex programming, do not require computing posterior distributions
given all current observations and involve simple primal-dual parameter
updates. At the heart of the proposed approach lie exponential-family models
which can be used in a wide variety of contexts and applications, and which
yield methods that achieve sublinear per-round regret against both static and
slowly varying product distributions with marginals drawn from the same
exponential family. Moreover, the regret against static distributions coincides
with the minimax value of the corresponding online strongly convex game. We
also prove bounds on the number of mistakes made during the hedging step
relative to the best offline choice of the threshold with access to all
estimated beliefs and feedback signals. We validate the theory on synthetic
data drawn from a time-varying distribution over binary vectors of high
dimensionality, as well as on the Enron email dataset.Comment: 19 pages, 12 pdf figures; final version to be published in IEEE
Transactions on Information Theor
Cavity Control of a Single-Electron Quantum Cyclotron:\\Measuring the Electron Magnetic Moment
Measurements with a one-electron quantum cyclotron determine the electron
magnetic moment, given by , and the fine structure
constant, . Brief
announcements of these measurements are supplemented here with a more complete
description of the one-electron quantum cyclotron and the new measurement
methods, a discussion of the cavity control of the radiation field, a summary
of the analysis of the measurements, and a fuller discussion of the
uncertainties
SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
While deep learning approaches have shown remarkable performance in many
imaging tasks, most of these methods rely on availability of large quantities
of data. Medical image data, however, is scarce and fragmented. Generative
Adversarial Networks (GANs) have recently been very effective in handling such
datasets by generating more data. If the datasets are very small, however, GANs
cannot learn the data distribution properly, resulting in less diverse or
low-quality results. One such limited dataset is that for the concurrent gain
of 19 and 20 chromosomes (19/20 co-gain), a mutation with positive prognostic
value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for
the mutation to streamline the extensive and invasive prognosis pipeline. Since
this mutation is relatively rare, i.e. small dataset, we propose a novel
generative framework - the Sequential Attribute GEnerator (SAGE), that
generates detailed tumor imaging features while learning from a limited
dataset. Experiments show that not only does SAGE generate high quality tumors
when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN
with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers
accurately
Tiresias: Predicting Security Events Through Deep Learning
With the increased complexity of modern computer attacks, there is a need for
defenders not only to detect malicious activity as it happens, but also to
predict the specific steps that will be taken by an adversary when performing
an attack. However this is still an open research problem, and previous
research in predicting malicious events only looked at binary outcomes (e.g.,
whether an attack would happen or not), but not at the specific steps that an
attacker would undertake. To fill this gap we present Tiresias, a system that
leverages Recurrent Neural Networks (RNNs) to predict future events on a
machine, based on previous observations. We test Tiresias on a dataset of 3.4
billion security events collected from a commercial intrusion prevention
system, and show that our approach is effective in predicting the next event
that will occur on a machine with a precision of up to 0.93. We also show that
the models learned by Tiresias are reasonably stable over time, and provide a
mechanism that can identify sudden drops in precision and trigger a retraining
of the system. Finally, we show that the long-term memory typical of RNNs is
key in performing event prediction, rendering simpler methods not up to the
task
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