Publications

Refine Results

(Filters Applied) Clear All

Estimation of Causal Peer Influence Effects

Author:
Published in:
International Conference on Machine Learning, 17-19 June 2013

Summary

The broad adoption of social media has generated interest in leveraging peer influence for inducing desired user behavior. Quantifying the causal effect of peer influence presents technical challenges, however, including how to deal with social interference, complex response functions and network uncertainty. In this paper, we extend potential outcomes to allow for interference, we introduce welldefined causal estimands of peer-influence, and we develop two estimation procedures: a frequentist procedure relying on a sequential randomization design that requires knowledge of the network but operates under complicated response functions, and a Bayesian procedure which accounts for network uncertainty but relies on a linear response assumption to increase estimation precision. Our results show the advantages and disadvantages of the proposed methods in a number of situations.
READ LESS

Summary

The broad adoption of social media has generated interest in leveraging peer influence for inducing desired user behavior. Quantifying the causal effect of peer influence presents technical challenges, however, including how to deal with social interference, complex response functions and network uncertainty. In this paper, we extend potential outcomes to...

READ MORE

Wind-shear detection performance study for multifunction phased array radar (MPAR) risk reduction

Published in:
MIT Lincoln Laboratory Report ATC-409

Summary

Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the TDWR, and the change in operational frequency from C band to S band are analyzed. Results from the 2012 MPAR Wind-Shear Experiment (WSE) are presented, with microburst and gust-front detection statistics for the Oklahoma City TDWR and the National Weather Radar Testbed (NWRT) phased array radar, which are located 6 km apart. The NWRT has sensitivity and beamwidth similar to a conceptual terminal MPAR (TMPAR), which is a scaled-down version of a full-size MPAR. The microburst results show both the TDWR probability of detection (POD) and the estimated NWRT POD exceeding the 90% requirement. For gust fronts, however, the overall estimated NWRT POD was more than 10% lower than the TDWR POD. NWRT data is also used to demonstrate that rapid-scan phased array radar has the potential to enhance microburst prediction capability.
READ LESS

Summary

Multifunction phased array radars (MPARs) of the future that may replace the current terminal wind-shear detection systems will need to meet the Federal Aviation Administration's (FAA) detection requirements. Detection performance issues related to on-airport siting of MPAR, its broader antenna beamwidth relative to the TDWR, and the change in operational...

READ MORE

Sector workload model for benefits analysis and convective weather capacity prediction

Published in:
10th USA/Europe Air Traffic Management Research and Development Sem., ATM 2013, 10-13 June 2013.

Summary

En route sector capacity is determined mainly by controller workload. The operational capacity model used by the Federal Aviation Administration (FAA) provides traffic alert thresholds based entirely on hand-off workload. Its estimates are accurate for most sectors. However, it tends to over-estimate capacity in both small and large sectors because it does not account for conflicts and recurring tasks. Because of those omissions it cannot be used for accurate benefits analysis of workload-reduction initiatives, nor can it be extended to estimate capacity when hazardous weather increases the intensity of all workload types. We have previously reported on an improved model that accounts for all workload types and can be extended to handle hazardous weather. In this paper we present the results of a recent regression of that model using an extensive database of peak traffic counts for all United States en route sectors. The resulting fit quality confirms the workload basis of en route capacity. Because the model has excess degrees of freedom, the regression process returns multiple parameter combinations with nearly identical sector capacities. We analyze the impact of this ambiguity when using the model to quantify the benefits of workload reduction proposals. We also describe recent modifications to the weather-impacted version of the model to provide a more stable normalized capacity measure. We conclude with an illustration of its potential application to operational sector capacity forecasts in hazardous weather.
READ LESS

Summary

En route sector capacity is determined mainly by controller workload. The operational capacity model used by the Federal Aviation Administration (FAA) provides traffic alert thresholds based entirely on hand-off workload. Its estimates are accurate for most sectors. However, it tends to over-estimate capacity in both small and large sectors because...

READ MORE

Efficient anomaly detection in dynamic, attributed graphs: emerging phenomena and big data

Published in:
ISI 2013: IEEE Int. Conf. on Intelligence and Security Informatics, 4-7 June 2013.

Summary

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that an approximation to the exact model yields an exploitable structure in the edge probabilities, allowing for efficient scaling of a spectral framework for anomaly detection through analysis of graph residuals, and a fast and simple procedure for estimating the model parameters. In simulation, we demonstrate that taking into account both attributes and dynamics in this analysis has a much more significant impact on the detection of an emerging anomaly than accounting for either dynamics or attributes alone. We also present an analysis of a large, dynamic citation graph, demonstrating that taking additional document metadata into account emphasizes parts of the graph that would not be considered significant otherwise.
READ LESS

Summary

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For...

READ MORE

Single event transients in digital CMOS - a review

Published in:
IEEE Trans. Nucl. Sci., Vol. 60, No. 3, June 2013, pp. 1767-90.

Summary

The creation of soft errors due to the propagation of single event transients (SETs) is a significant reliability challenge in modern CMOS logic. SET concerns continue to be exacerbated by Moore's Law technology scaling. This paper presents a review of digital single event transient research, including: a brief historical overview of the emergence of SET phenomena, a review of the present understanding of SET mechanisms, a review of the state-of-the-art in SET testing and modelling, a discussion of mitigation techniques, and a discussion of the impact of technology scaling trends on future SET significance.
READ LESS

Summary

The creation of soft errors due to the propagation of single event transients (SETs) is a significant reliability challenge in modern CMOS logic. SET concerns continue to be exacerbated by Moore's Law technology scaling. This paper presents a review of digital single event transient research, including: a brief historical overview...

READ MORE

An assessment of the operational utility of a GOES lightning mapping sensor

Published in:
MIT Lincoln Laboratory Report NOAA-18A

Summary

This report evaluates the incremental operational benefits of a proposed Lightning Mapping Sensor (LMS) for NOAA's Geostationary Operational Environmental Satellites (GOES). If deployed, LMS would provide continuous, real-time surveillance of total lightning activity over large portions of the North and South American continents and surrounding oceans. In contrast to the current National Lightning Detection Network, LMS would monitor total lightning activity, including the dominant intracloud component which is estimated to occur with order of magnitude greater frequency than cloud-to-ground lightning and may occur ten minutes or more in advance of a storm's first ground flash.
READ LESS

Summary

This report evaluates the incremental operational benefits of a proposed Lightning Mapping Sensor (LMS) for NOAA's Geostationary Operational Environmental Satellites (GOES). If deployed, LMS would provide continuous, real-time surveillance of total lightning activity over large portions of the North and South American continents and surrounding oceans. In contrast to the...

READ MORE

Link prediction methods for generating speaker content graphs

Published in:
ICASSP 2013, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 25-31 May 2013.

Summary

In a speaker content graph, vertices represent speech signals and edges represent speaker similarity. Link prediction methods calculate which potential edges are most likely to connect vertices from the same speaker; those edges are included in the generated speaker content graph. Since a variety of speaker recognition tasks can be performed on a content graph, we provide a set of metrics for evaluating the graph's quality independently of any recognition task. We then describe novel global and incremental algorithms for constructing accurate speaker content graphs that outperform the existing k nearest neighbors link prediction method. We evaluate those algorithms on a NIST speaker recognition corpus.
READ LESS

Summary

In a speaker content graph, vertices represent speech signals and edges represent speaker similarity. Link prediction methods calculate which potential edges are most likely to connect vertices from the same speaker; those edges are included in the generated speaker content graph. Since a variety of speaker recognition tasks can be...

READ MORE

Sparse volterra systems: theory and practice

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-31 May 2013.

Summary

Nonlinear effects limit analog circuit performance, causing both in-band and out-of-band distortion. The classical Volterra series provides an accurate model of many nonlinear systems, but the number of parameters grows extremely quickly as the memory depth and polynomial order are increased. Recently, concepts from compressed sensing have been applied to nonlinear system modeling in order to address this issue. This work investigates the theory and practice of applying compressed sensing techniques to nonlinear system identification under the constraints of typical radio frequency (RF) laboratories. The main theoretical result shows that these techniques are capable of identifying sparse Memory Polynomials using only single-tone training signals rather than pseudorandom noise. Empirical results using laboratory measurements of an RF receiver show that sparse Generalized Memory Polynomials can also be recovered from two-tone signals.
READ LESS

Summary

Nonlinear effects limit analog circuit performance, causing both in-band and out-of-band distortion. The classical Volterra series provides an accurate model of many nonlinear systems, but the number of parameters grows extremely quickly as the memory depth and polynomial order are increased. Recently, concepts from compressed sensing have been applied to...

READ MORE

Probabilistic threat propagation for malicious activity detection

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-31 May 2013.

Summary

In this paper, we present a method for detecting malicious activity within networks of interest. We leverage prior community detection work by propagating threat probabilities across graph nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints which remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of Probabilistic Threat Propagation on the task of detecting malicious web destinations.
READ LESS

Summary

In this paper, we present a method for detecting malicious activity within networks of interest. We leverage prior community detection work by propagating threat probabilities across graph nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints which remove the adverse effect of cyclic...

READ MORE

Large-scale community detection on speaker content graphs

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-31 May 2013.

Summary

We consider the use of community detection algorithms to perform speaker clustering on content graphs built from large audio corpora. We survey the application of agglomerative hierarchical clustering, modularity optimization methods, and spectral clustering as well as two random walk algorithms: Markov clustering and Infomap. Our results on graphs built from the NIST 2005+2006 and 2008+2010 Speaker Recognition Evaluations (SREs) provide insight into both the structure of the speakers present in the data and the intricacies of the clustering methods. In particular, we introduce an additional parameter to Infomap that improves its clustering performance on all graphs. Lastly, we also develop an automatic technique to purify the neighbors of each node by pruning away unnecessary edges.
READ LESS

Summary

We consider the use of community detection algorithms to perform speaker clustering on content graphs built from large audio corpora. We survey the application of agglomerative hierarchical clustering, modularity optimization methods, and spectral clustering as well as two random walk algorithms: Markov clustering and Infomap. Our results on graphs built...

READ MORE