Publications

Refine Results

(Filters Applied) Clear All

Human-machine collaborative optimization via apprenticeship scheduling

September 1, 2018
  |
Journal Article
Author:
Matthew C. Gombolay
…
Published in:
J. Artif. Intell. Res., Vol. 63, 2018, pp. 1-49.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.
READ LESS

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale...

READ MORE
Human-machine collaborative optimization via apprenticeship scheduling

Valleytronics: opportunities, challenges, and paths forward

August 13, 2018
  |
Journal Article
Author:
Steven A. Vitale
…
Published in:
Small, 13 August 2018, 1801483.
Topic:
computing
R&D area:
R&D group:

Summary

A lack of inversion symmetry coupled with the presence of time-reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at the K and K' points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes, or excitons as a completely new paradigm in information processing. The opportunities and challenges associated with manipulation of the valley degree of freedom for practical quantum and classical information processing applications were analyzed during the 2017 Workshop on Valleytronic Materials, Architectures, and Devices; this Review presents the major findings of the workshop.
READ LESS

Summary

A lack of inversion symmetry coupled with the presence of time-reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at the K and K' points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes...

READ MORE
Valleytronics: opportunities, challenges, and paths forward

Fuel production systems for remote areas via an aluminum energy vector

July 29, 2018
  |
Journal Article
Author:
Eric R. Morgan
Published in:
Energy Fuels, Vol. 32, no. 9, 2018, pp. 9033-9042.
Topic:
energy systems
R&D area:
R&D group:

Summary

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid diesel fuels. On a volumetric basis, aluminum has more than twice the energy of diesel fuel, making it a superb structural energy vector for remote applications. Once aluminum is treated with gallium, water of nearly any purity is used to rapidly oxidize the aluminum metal which spontaneously evolves hydrogen and heat in roughly equal energetic quantities. The benign byproduct of the reaction could, in theory, be taken to an off-site facility and recycled back into aluminum using standard smelting processes or it could be left onsite as a high-value waste. The hydrogen can easily be used as a feedstock for diesel fuel, via Fischer-Tropsch (FT) reaction mechanisms, while the heat can be leveraged for other processes, including synthesis gas compression. It is shown that as long as a carbon source, such as diesel fuel, is already present, additional diesel can be made by recovering and recycling the CO2 in the diesel exhaust. The amount of new diesel that can be made is directly related to the fraction of available CO2 that is recovered, with 100% recovery being equivalent to doubling the diesel fuel. The volume of aluminum required to accomplish this is lower than simply bringing twice as much diesel and results in a 50% increase in volumetric energy density. That is, 50% fewer fuel convoys would be required for fuel delivery. Moreover, aluminum has the potential to be exploited as a structural fuel that can be used as pallets, containers, etc., before being consumed to produce diesel. Furthermore, FT diesel production via aluminum and CO2 can be achieved without sacrificing electrical power generation.
READ LESS

Summary

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid...

READ MORE
Fuel production systems for remote areas via an aluminum energy vector

Modeling and validation of a mm-wave shaped dielectric lens antenna

July 29, 2018
  |
Conference Paper
Author:
David C. Mooradd
…
Published in:
2018 Int. Applied Computational Electromagnetics Society Symp., ACES, 29 July - 1 August 2018.
Topic:
antennas
R&D area:
R&D group:

Summary

The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results as compared to measured far-field gain radiation patterns.
READ LESS

Summary

The modeling and validation of a 33 GHz shaped dielectric antenna design is investigated. The electromagnetic modeling was performed in both WIPL-D and FEKO, and was used to validate the antenna design prior to fabrication of the lens. It is shown that both WIPL-D and FEKO yield similarly accurate results...

READ MORE
Modeling and validation of a mm-wave shaped dielectric lens antenna

Colorization of H&E stained tissue using deep learning

July 17, 2018
  |
Conference Paper
Author:
Siddharth S. Samsi
…
Published in:
40th Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC, 17-21 July 2018.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Histopathology is a critical tool in the diagnosis and stratification of cancer. Digital Pathology involves the scanning of stained and fixed tissue samples to produce high-resolution images that can be used for computer-aided diagnosis and research. A common challenge in digital pathology related to the quality and characteristics of staining, which can vary widely from center to center and also within the same institution depending on the age of the stain and other human factors. In this paper we examine the use of deep learning models for colorizing H&E stained tissue images and compare the results with traditional image processing/statistical approaches that have been developed for standardizing or normalizing histopathology images. We adapt existing deep learning models that have been developed for colorizing natural images and compare the results with models developed specifically for digital pathology. Our results show that deep learning approaches can standardize the colorization of H&E images. The performance as measured by the chi-square statistic shows that the deep learning approach can be nearly as good as current state-of-the art normalization methods.
READ LESS

Summary

Histopathology is a critical tool in the diagnosis and stratification of cancer. Digital Pathology involves the scanning of stained and fixed tissue samples to produce high-resolution images that can be used for computer-aided diagnosis and research. A common challenge in digital pathology related to the quality and characteristics of staining...

READ MORE
Colorization of H&E stained tissue using deep learning

Detecting intracranial hemorrhage with deep learning

July 17, 2018
  |
Conference Paper
Author:
Arjun Majumdar
…
Published in:
40th Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC, 17-21 July 2018.
Topic:
machine learning
R&D area:
R&D group:

Summary

Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our current work instead uses a deep convolutional neural network to simultaneously learn features and classification, eliminating the multiple hand-tuned steps. Performance is improved by computing the mean output for rotations of the input image. Postprocessing is additionally applied to the CNN output to significantly improve specificity. The database consists of 134 CT cases (4,300 images), divided into 60, 5, and 69 cases for training, validation, and test. Each case typically includes multiple hemorrhages. Performance on the test set was 81% sensitivity per lesion (34/42 lesions) and 98% specificity per case (45/46 cases). The sensitivity is comparable to previous results (on different datasets), but with a significantly higher specificity. In addition, insights are shared to improve performance as the database is expanded.
READ LESS

Summary

Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our...

READ MORE
Detecting intracranial hemorrhage with deep learning

Mission assurance: beyond secure processing

July 16, 2018
  |
Conference Paper
Author:
Mankuan Michael Vai
…
Published in:
18th IEEE Int. Conf. on Software Quality, Reliability, and Security, QRS 2018, 16-20 July 2018, pp. 593-8.
Topic:
autonomous systems
R&D area:
R&D group:

Summary

The processor of a drone runs essential functions of sensing, communications, coordination, and control. This is the conventional view. But in today's cyber environment, the processor must also provide security to assure mission completion. We have been developing a secure processing architecture for mission assurance. A study on state-of-the-art secure processing technologies has revealed that no one-size-fits-all solution can fully meet our requirements. In fact, we have concluded that the provision of a secure processor as a mission assurance foundation must be holistic and should be approached from a systems perspective. We have thus applied a systems analysis approach to create a secure base for the system. This paper describes our journey of adapting and synergizing various secure processing technologies into a baseline asymmetric multicore processing architecture. We will also describe a functional and security co-design environment, created to customize and optimize the architecture in a design space consisting of hardware, software, performance, and assurance.
READ LESS

Summary

The processor of a drone runs essential functions of sensing, communications, coordination, and control. This is the conventional view. But in today's cyber environment, the processor must also provide security to assure mission completion. We have been developing a secure processing architecture for mission assurance. A study on state-of-the-art secure...

READ MORE
Mission assurance: beyond secure processing

Adversarial co-evolution of attack and defense in a segmented computer network environment

July 15, 2018
  |
Conference Paper
Author:
Erik Hemberg
…
Published in:
Proc. Genetic and Evolutionary Computation Conf. Companion, GECCO 2018, 15-19 July 2018, pp. 1648-1655.
Topic:
cyber security
R&D area:
R&D group:

Summary

In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of attackers. We consider network segmentation, a widely recommended defensive strategy, deployed against the threat of serial network security attacks that delay the mission of the network's operator. We employ a simulation model to investigate the effectiveness over time of different defensive strategies against different attack strategies. For a set of four network topologies, we generate strong availability attack patterns that were not identified a priori. Then, by combining the simulation with a coevolutionary algorithm to explore the adversaries' action spaces, we identify effective configurations that minimize mission delay when facing the attacks. The novel application of co-evolutionary computation to enterprise network security represents a step toward course-of-action determination that is robust to responses by intelligent adversaries.
READ LESS

Summary

In computer security, guidance is slim on how to prioritize or configure the many available defensive measures, when guidance is available at all. We show how a competitive co-evolutionary algorithm framework can identify defensive configurations that are effective against a range of attackers. We consider network segmentation, a widely recommended...

READ MORE
Adversarial co-evolution of attack and defense in a segmented computer network environment

Curator: provenance management for modern distributed systems

July 11, 2018
  |
Journal Article
Author:
Warren W. Smith
…
Published in:
10th Intl. Workshop on Theory and Practice of Provenance, TaPP, 11-12 July 2018.
Topic:
resilient systems
R&D area:
R&D group:

Summary

Data provenance is a valuable tool for protecting and troubleshooting distributed systems. Careful design of the provenance components reduces the impact on the design, implementation, and operation of the distributed system. In this paper, we present Curator, a provenance management toolkit that can be easily integrated with microservice-based systems and other modern distributed systems. This paper describes the design of Curator and discusses how we have used Curator to add provenance to distributed systems. We find that our approach results in no changes to the design of these distributed systems and minimal additional code and dependencies to manage. In addition, Curator uses the same scalable infrastructure as the distributed system and can therefore scale with the distributed system.
READ LESS

Summary

Data provenance is a valuable tool for protecting and troubleshooting distributed systems. Careful design of the provenance components reduces the impact on the design, implementation, and operation of the distributed system. In this paper, we present Curator, a provenance management toolkit that can be easily integrated with microservice-based systems and...

READ MORE
Curator: provenance management for modern distributed systems

Airport Wind Observations Architectural Analysis(2.4 MB)

July 10, 2018
  |
Project Report
Author:
David A. Clark
…
Published in:
Project Report ATC-443, MIT Lincoln Laboratory
Topic:
weather
R&D area:
R&D group:

Summary

Airport wind information is critical for ensuring safe aircraft operations and for managing runway configurations. Airports across the National Airspace System (NAS) are served by a wide variety of wind sensing systems that have been deployed over many decades. This analysis presents a survey of existing systems and user requirements, identifies potential shortfalls, and offers recommendations for improvements to support the long-term goals of the FAA NextGen system.
READ LESS

Summary

Airport wind information is critical for ensuring safe aircraft operations and for managing runway configurations. Airports across the National Airspace System (NAS) are served by a wide variety of wind sensing systems that have been deployed over many decades. This analysis presents a survey of existing systems and user requirements...

READ MORE
Airport Wind Observations Architectural Analysis
291-300 of 2301