Publications

Refine Results

(Filters Applied) Clear All

A secure cloud with minimal provider trust

Summary

Bolted is a new architecture for a bare metal cloud with the goal of providing security-sensitive customers of a cloud the same level of security and control that they can obtain in their own private data centers. It allows tenants to elastically allocate secure resources within a cloud while being protected from other previous, current, and future tenants of the cloud. The provisioning of a new server to a tenant isolates a bare metal server, only allowing it to communicate with other tenant's servers once its critical firmware and software have been attested to the tenant. Tenants, rather than the provider, control the tradeoffs between security, price, and performance. A prototype demonstrates scalable end-to-end security with small overhead compared to a less secure alternative.
READ LESS

Summary

Bolted is a new architecture for a bare metal cloud with the goal of providing security-sensitive customers of a cloud the same level of security and control that they can obtain in their own private data centers. It allows tenants to elastically allocate secure resources within a cloud while being...

READ MORE

Lessons learned from a decade of providing interactive, on-demand high performance computing to scientists and engineers

Summary

For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively develop and share code projects across the globe. As the HPC community faces the challenges associated with guiding researchers from disciplines using high productivity interactive tools to effective use of HPC systems, it seems appropriate to revisit the assumptions surrounding the necessary skills required for access to large computational systems. For over a decade, MIT Lincoln Laboratory has been supporting interactive, on demand high performance computing by seamlessly integrating familiar high productivity tools to provide users with an increased number of design turns, rapid prototyping capability, and faster time to insight. In this paper, we discuss the lessons learned while supporting interactive, on-demand high performance computing from the perspectives of the users and the team supporting the users and the system. Building on these lessons, we present an overview of current needs and the technical solutions we are building to lower the barrier to entry for new users from the humanities, social, and biological sciences.
READ LESS

Summary

For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively...

READ MORE

Learning network architectures of deep CNNs under resource constraints

Published in:
Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW, 18-22 June 2018, pp. 1784-91.

Summary

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyperparameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully automated model learning approach that (1) treats architecture selection as part of the learning process, (2) uses a blend of broad-based random sampling and adaptive iterative refinement to explore the solution space, (3) performs optimization subject to given memory and computational constraints imposed by target deployment scenarios, and (4) is scalable and can use only a practically small number of GPUs for training. We present results that show graceful model degradation under strict resource constraints for object classification problems using CIFAR-10 in our experiments. We also discuss future work in further extending the approach.
READ LESS

Summary

Recent works in deep learning have been driven broadly by the desire to attain high accuracy on certain challenge problems. The network architecture and other hyperparameters of many published models are typically chosen by trial-and-error experiments with little considerations paid to resource constraints at deployment time. We propose a fully...

READ MORE

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