Publications

Refine Results

(Filters Applied) Clear All

Influence estimation on social media networks using causal inference

Published in:
Proc. IEEE Statistical Signal Processing (SSP) Workshop, 10-13 June 2018.

Summary

Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative propagation over the network using a network causal inference framework applied to data arising from graph sampling and filtering. This causal framework infers the difference in outcome as a function of exposure, in contrast to existing approaches that attribute impact to activity volume or topological features, which do not explicitly measure nor necessarily indicate actual network influence. Cramér-Rao estimation bounds are derived for parameter estimation as a step in the causal analysis, and used to achieve geometrical insight on the causal inference problem. The ability to infer high causal influence is demonstrated on real-world social media accounts that are later independently confirmed to be either directly affiliated or correlated with foreign influence operations using evidence supplied by the U.S. Congress and journalistic reports.
READ LESS

Summary

Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing...

READ MORE

Rapid Quantitative Analysis of Multiple Explosive Compound Classes on a Single Instrument via Flow-Injection Analysis Tandem Mass Spectrometry

Summary

A flow-injection analysis tandem mass spectrometry (FIA MSMS) method was developed for rapid quantitative analysis of 10 different inorganic and organic explosives. Performance is optimized by tailoring the ionization method (APCI/ESI), de-clustering potentials, and collision energies for each specific analyte. In doing so, a single instrument can be used to detect urea nitrate, potassium chlorate, 2,4,6-trinitrotoluene, 2,4,6-trinitrophenylmethylnitramine, triacetone triperoxide, hexamethylene triperoxide diamine, pentaerythritol tetranitrate, 1,3,5-trinitroperhydro-1,3,5-triazine, nitroglycerin, and octohy-dro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine with sensitivities all in the picogram per milliliter range. In conclusion, FIA APCI/ESI MSMS is a fast (<1 min/sample), sensitive (~pg/mL LOQ), and precise (intraday RSD < 10%) method for trace explosive detection that can play an important role in criminal and attributional forensics, counterterrorism, and environmental protection areas, and has the potential to augment or replace several of the existing explosive detection methods.
READ LESS

Summary

A flow-injection analysis tandem mass spectrometry (FIA MSMS) method was developed for rapid quantitative analysis of 10 different inorganic and organic explosives. Performance is optimized by tailoring the ionization method (APCI/ESI), de-clustering potentials, and collision energies for each specific analyte. In doing so, a single instrument can be used to...

READ MORE

On large-scale graph generation with validation of diverse triangle statistics at edges and vertices

Published in:
2018 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW, 21 May 2018.

Summary

Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with efficiently calculable ground-truth solutions to the desired output. Reproducibility for current generators used in benchmarking are somewhat lacking in this respect due to their randomness: the output of a desired graph analytic can only be compared to expected values and not exact ground truth. Nonstochastic Kronecker product graphs meet these design criteria for several graph analytics. Here we show that many flavors of triangle participation can be cheaply calculated while generating a Kronecker product graph.
READ LESS

Summary

Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with efficiently calculable ground-truth solutions to the desired output. Reproducibility for current generators used in benchmarking are somewhat lacking in this respect due to...

READ MORE

Optical Nondestructive Dynamic Measurements of Wafer-Scale Encapsulated Nanofluidic Channels

Published in:
Applied Optics, vol. 57, no. 15

Summary

Nanofluidic channels are of great interest for DNA sequencing, chromatography, and drug delivery. However, metrology of embedded or sealed nanochannels and measurement of their fill-state have remained extremely challenging. Existing techniques have been restricted to optical microscopy, which suffers from insufficient resolution, or scanning electron microscopy, which cannot measure sealed or embedded channels without cleaving the sample. Here, we demonstrate a novel method for accurately extracting nanochannel cross-sectional dimensions and monitoring fluid filling, utilizing spectroscopic ellipsometric scatterometry, combined with rigorous electromagnetic simulations. Our technique is capable of measuring channel dimensions with better than 5-nm accuracy and assessing channel filling within seconds. The developed technique is, thus, well suited for both process monitoring of channel fabrication as well as for studying complex phenomena of fluid flow through nanochannel structures.
READ LESS

Summary

Nanofluidic channels are of great interest for DNA sequencing, chromatography, and drug delivery. However, metrology of embedded or sealed nanochannels and measurement of their fill-state have remained extremely challenging. Existing techniques have been restricted to optical microscopy, which suffers from insufficient resolution, or scanning electron microscopy, which cannot measure sealed...

READ MORE

CoSPA and Traffic Flow Impact Operational Demonstration for the 2017 Convective Season(4.48 MB)

Published in:
Project Report ATC-441, MIT Lincoln Laboratory

Summary

MIT Lincoln Laboratory personnel conducted field observations of the Consolidated Storm Prediction for Aviation (CoSPA) 8-hr deterministic convective forecast, and the decision support tool, Traffic Flow Impact (TFI), from 6 June to 31 October 2017. Four field observations were performed during the demonstration period.
READ LESS

Summary

MIT Lincoln Laboratory personnel conducted field observations of the Consolidated Storm Prediction for Aviation (CoSPA) 8-hr deterministic convective forecast, and the decision support tool, Traffic Flow Impact (TFI), from 6 June to 31 October 2017. Four field observations were performed during the demonstration period.

READ MORE

Polarimetric observations of chaff using the WSR-88D network

Published in:
J. Appl. Meteor. Climatol., Vol. 57, No. 5, 1 May 2018, pp. 1063-1081.

Summary

Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D radars can appear prominently on radar users displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for automated algorithms to do the same. The WSR-88D network provides dual-polarimetric capabilities across the United States, leading to the collection of a large database of chaff cases. The database is analyzed to determine the characteristics of chaff in terms of the reflectivity factor and polarimetric variables on large scales. Particular focus is given to the dynamics of differential reflectivity (ZDR) in chaff and its dependence on height. Contrary to radar data observations of chaff for a single event, this study is able to reveal a repeatable and new pattern of radar chaff observations. A discussion regarding the observed characteristics is presented, and hypotheses for the observed ZDR dynamics are put forth.
READ LESS

Summary

Chaff is a radar countermeasure typically used by military branches in training exercises around the United States. Chaff within view of the S-band WSR-88D radars can appear prominently on radar users displays. Knowledge of chaff characteristics is useful for radar users to discriminate between chaff and weather echoes and for...

READ MORE

Quantification of radar QPE performance based on SENSR network design possibilities

Published in:
2018 IEEE Radar Conf., RadarConf, 23-27 April 2018.

Summary

In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial use. As part of this goal, the participating agencies have developed preliminary performance requirements that not only assume minimum capabilities based on legacy radars, but also recognize the need for enhancements in future radar networks. The relatively low density of the legacy radar networks, especially the WSR-88D network, had led to the goal of enhancing low-altitude weather coverage. With multiple design metrics and network possibilities still available to the SENSR agencies, the benefits of low-altitude coverage must be assessed quantitatively. This study lays the groundwork for estimating Quantitative Precipitation Estimation (QPE) differences based on network density, array size, and polarimetric bias. These factors create a pareto front of cost-benefit for QPE in a new radar network, and these results will eventually be used to determine appropriate tradeoffs for SENSR requirements. Results of this study are presented in the form of two case examples that quantify errors based on polarimetric bias and elevation, along with a description of eventual application to a national network in upcoming expansion of the work.
READ LESS

Summary

In 2016, the FAA, NOAA, DoD, and DHS initiated a feasibility study for a Spectrum Efficient National Surveillance Radar (SENSR). The goal is to assess approaches for vacating the 1.3- to 1.35-GHz radio frequency band currently allocated to FAA/DoD long-range radars so that this band can be auctioned for commercial...

READ MORE

Multi-layered interactive energy space modeling for near-optimal electrification of terrestrial, shipboard and aircraft systems

Author:
Published in:
Annual Reviews in Control, no. 45, 2018, pp. 52-75.
R&D group:

Summary

In this paper, we introduce a basic multi-layered modeling framework for posing the problem of safe, robust and efficient design and control that may lend itself to ripping potential benefits from electrification. The proposed framework establishes dynamic relations between physical concepts such as stored energy, useful work, and wasted energy, on one hand; and modeling, simulation, and control of interactive modular complex dynamical systems, on the other. In particular, our recently introduced energy state-space modeling approach for electric energy systems is further interpreted using fundamental laws of physics in multi-physical systems, such as terrestrial energy-systems, aircrafts and ships. The interconnected systems are modeled as dynamically interacting modules. This approach is shown to be particularly well-suited for scalable optimization of large-scale complex systems. Instead of having to use simpler models, the proposed multi-layered modeling of system dynamics in energy space offers a promising basic method for modeling and controlling inter-dependencies across multi-physics subsystems for both ensuring feasible and near-optimal operation. It is illustrated how this approach can be used for understanding fundamental physical causes of inefficiencies created either at the component level or are a result of poor matching of their interactions.
READ LESS

Summary

In this paper, we introduce a basic multi-layered modeling framework for posing the problem of safe, robust and efficient design and control that may lend itself to ripping potential benefits from electrification. The proposed framework establishes dynamic relations between physical concepts such as stored energy, useful work, and wasted energy...

READ MORE

Highly Efficient All-Optical Beam Modulation Utilizing Thermo-optic Effects

Summary

Suspensions of plasmonic nanoparticles can diffract optical beams due to the combination of thermal lensing and self-phase modulation. Here, we demonstrate extremely efficient optical continuous wave (CW) beam switching across the visible range in optimized suspensions of 5-nm Au and Ag nanoparticles in non-polar solvents, such as hexane and decane. On-axis modulation of greater than 30 dB is achieved at incident beam intensities as low as 100 W/cm2 with response times under 200 μs, at initial solution transparency above 70%. No evidence of laser-induced degradation is observed for the highest intensities used. Numerical modeling of experimental data reveals thermo-optic coefficients of up to −1.3 × 10−3 /K, which, to our knowledge, is the highest observed to date in such nanoparticle suspensions.
READ LESS

Summary

Suspensions of plasmonic nanoparticles can diffract optical beams due to the combination of thermal lensing and self-phase modulation. Here, we demonstrate extremely efficient optical continuous wave (CW) beam switching across the visible range in optimized suspensions of 5-nm Au and Ag nanoparticles in non-polar solvents, such as hexane and decane...

READ MORE

Hybrid mixed-membership blockmodel for inference on realistic network interactions

Published in:
IEEE Trans. Netw. Sci. Eng., Vol. 6, No. 3, July-Sept. 2019.

Summary

This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as mixed-membership and popularity. A rigorous inference procedure is developed for estimating the parameters of this model through iterative Bayesian updates, with targeted initialization to improve identifiability. For the estimation of mixed-membership parameters, the Cramer-Rao bound is derived by quantifying the information content in terms of the Fisher information matrix. The effectiveness of the proposed inference is demonstrated in simulations where the estimates achieve covariances close to the Cramer-Rao bound while maintaining good truth coverage. We illustrate the utility of the proposed model and inference procedure in the application of detecting a community from a few cue nodes, where success depends on accurately estimating the mixed-memberships. Performance evaluations on both simulated and real-world data show that inference with HMMB is able to recover mixed-memberships in the presence of challenging community overlap, leading to significantly improved detection performance over algorithms based on network modularity and simpler models.
READ LESS

Summary

This work proposes novel hybrid mixed-membership blockmodels (HMMB) that integrate three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as...

READ MORE