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This looks like that: deep learning for interpretable image recognition

Published in:
Neural Info. Process., NIPS, 8-14 December 2019.

Summary

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The algorithm thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, geologists, architects, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training, meaning that there are no labels for parts of images. We demonstrate the method on the CIFAR-10 dataset and 10 classes from the CUB-200-2011 dataset.
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Summary

When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network...

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On-demand forensic video analytics for large-scale surveillance systems

Published in:
2019 IEEE Intl. Symp. on Technologies for Homeland Security, 5-6 November 2019.

Summary

This work presents FOVEA, an add-on suite of analytic tools for the forensic review of video in large-scale surveillance systems. While significant investment has been made toward improving camera coverage and quality, the burden on video operators for reviewing and extracting useful information from the video has only increased. Daily investigation tasks (such as searching through video, investigating abandoned objects, or piecing together information from multiple cameras) still require a significant amount of manual review by video operators. In contrast to other tools which require exporting video data or otherwise curating the video collection before analysis, FOVEA is designed to integrate with existing surveillance systems. Tools can be applied to any video stream in an on-demand fashion without additional hardware. This paper details the technical approach, underlying algorithms, and effects on video operator performance.
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Summary

This work presents FOVEA, an add-on suite of analytic tools for the forensic review of video in large-scale surveillance systems. While significant investment has been made toward improving camera coverage and quality, the burden on video operators for reviewing and extracting useful information from the video has only increased. Daily...

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Toward technically feasible and economically efficient integration of distributed energy resources

Author:
Published in:
57th Annual Allerton Conf. on Communication, Control, and Computing, 24-27 September 2019.

Summary

This paper formulates the efficient and feasible participation of distributed energy resources (DERs) in complex electricity services as a centralized nonlinear optimization problem first. This problem is then re-stated using the novel energy/power transformed state space. It is shown that the DER dynamics in closed-loop can be made linear in this new state space. The decision making by the DERs then becomes a distributed model predictive control problem and it forms the basis for deriving physically implementable convex market bids. A multi-layered interactive optimization for clearing the distributed bids by higher layer decision makers, such as market aggregators, is posed and shown to lead to near-optimal system-level performance at the slower market clearing rates. A proof-of-concept example is illustrated involving close to one hundred heterogeneous controllable DERs with real consumption data of a distribution feeder in Texas, contributing to automatic generation control (AGC).
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Summary

This paper formulates the efficient and feasible participation of distributed energy resources (DERs) in complex electricity services as a centralized nonlinear optimization problem first. This problem is then re-stated using the novel energy/power transformed state space. It is shown that the DER dynamics in closed-loop can be made linear in...

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Introducing DyMonDS-as-a-Service (DyMaaS) for Internet of Things

Author:
Published in:
2019 IEEE High Performance Computing Conf., HPEC, 24-26 September 2019.

Summary

With recent trends in computation and communication architecture, it is becoming possible to simulate complex networked dynamical systems by employing high-fidelity models. The inherent spatial and temporal complexity of these systems, however, still acts as a roadblock. It is thus desirable to have adaptive platform design facilitating zooming-in and out of the models to emulate time-evolution of processes at a desired spatial and temporal granularity. In this paper, we propose new computing and networking abstractions, that can embrace physical dynamics and computations in a unified manner, by taking advantage of the inherent structure. We further design multi-rate numerical methods that can be implemented by computing architectures to facilitate adaptive zooming-in and out of the models spanning multiple spatial and temporal layers. These methods are all embedded in a platform called Dynamic Monitoring and Decision Systems (DyMonDS). We introduce a new service model of cloud computing called DyMonDS-as-a-Service (DyMaas), for use by operators at various spatial granularities to efficiently emulate the interconnection of IoT devices. The usage of this platform is described in the context of an electric microgrid system emulation.
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Summary

With recent trends in computation and communication architecture, it is becoming possible to simulate complex networked dynamical systems by employing high-fidelity models. The inherent spatial and temporal complexity of these systems, however, still acts as a roadblock. It is thus desirable to have adaptive platform design facilitating zooming-in and out...

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The Human Trafficking Technology Roadmap: A Targeted Development Strategy for the Department of Homeland Security(9.16 MB)

Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit pro fits annually. While agencies across the U.S. Government employ a diverse range of resources to combat human trafficking in the U.S. and abroad, trafficking operations remain challenging to measure, investigate, and interdict. Within the Department of Homeland Security, the Science and Technology Directorate is addressing these challenges by incorporating computational social science research into their counter-human trafficking approach. As part of this approach, the Directorate tasked an interdisciplinary team of national security researchers at the Massachusetts Institute of Technology's Lincoln Laboratory, a federally funded research and development center, to undertake a detailed examination of the human trafficking response across the Homeland Security Enterprise. The first phase of this effort was a government-wide systems analysis of major counter-trafficking thrust areas, including law enforcement and prosecution; public health and emergency medicine; victim services; and policy and legislation. The second phase built on this systems analysis to develop a human trafficking technology roadmap and implementation strategy for the Science and Technology Directorate, which is presented in this document.
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Summary

Human trafficking is a form of modern-day slavery that involves the use of force, fraud, or coercion for the purposes of involuntary labor and sexual exploitation. It affects tens of million of victims worldwide and generates tens of billions of dollars in illicit pro fits annually. While agencies across the U.S...

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New software helps users build resilient, cost-effective energy architectures

Published in:
Lincoln Laboratory News
R&D group:

Summary

The Energy Resilience Analysis tool lets mission owners and energy managers balance the needs of critical missions on military installations with affordability when they design energy resilience solutions.
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Summary

The Energy Resilience Analysis tool lets mission owners and energy managers balance the needs of critical missions on military installations with affordability when they design energy resilience solutions.

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A framework for evaluating electric power grid improvements in Puerto Rico

Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It is well recognized that the existing system needs to be both hardened and further enhanced by deploying Distributed Energy Resources (DERs), solar photovoltaics (PV) in particular, and local reconfigurable microgrids to manage these newly deployed DERs. While deployment of microgrids and DERs has been advocated by many, there is little fundamental understanding how to operate Puerto Rico's electrical system in a way that effectively uses DERs during both normal operations and grid failures. Utility companies' traditional reliability requirements and operational risk management practices rely on excessive amounts of centralized reserve generation to anticipate failures, which increases the cost of normal operations and nullifies the potential of DERs to meet loads during grid failures. At present, no electric power utility has a ready-to-use framework that overcomes these limitations. This report seeks to fill this void.
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Summary

This report is motivated by the recognition that serving highly distributed electric power load in Puerto Rico during extreme events requires innovative methods. To do this, we must determine the type and locations of the most critical equipment, innovative methods, and software for operating the electrical system most effectively. It...

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Feature forwarding for efficient single image dehazing

Published in:
IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, CVPRW, 16-17 June 2019.

Summary

Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore the dependency of dehazed image quality on parameter count and model design. The first two variants presented, a small and big version, make use of a single efficient encoder–decoder convolutional feature extractor. The final variant utilizes a pair of encoder-decoders for atmospheric light and transmission map estimation. Each variant ends with an image refinement pyramid pooling network to form the final dehazed image. For the big variant of the single-encoder network, we demonstrate state-of-the-art performance on the NYU Depth dataset. For the small variant, we maintain competitive performance on the superresolution O/I-HAZE datasets without the need for image cropping. Finally, we examine some challenges presented by the Dense-Haze dataset when leveraging CNN architectures for dehazing of dense haze imagery and examine the impact of loss function selection on image quality. Benchmarks are included to show the feasibility of introducing this approach into real-time systems.
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Summary

Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore...

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AI enabling technologies: a survey

Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together in order to provide capabilities that can be used by decision makers, warfighters and analysts. These pieces include data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming. While much of the popular press today surrounds advances in algorithms and computing, most modern AI systems leverage advances across numerous different fields. Further, while certain components may not be as visible to end-users as others, our experience has shown that each of these interrelated components play a major role in the success or failure of an AI system. This article is meant to highlight many of these technologies that are involved in an end-to-end AI system. The goal of this article is to provide readers with an overview of terminology, technical details and recent highlights from academia, industry and government. Where possible, we indicate relevant resources that can be used for further reading and understanding.
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Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together...

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Banshee distribution network benchmark and prototyping platform for hardware-in-the-loop integration of microgrid and device controllers

Summary

This article provides a unique benchmark to integrate and systematically evaluate advanced functionalities of microgrid and downstream device controllers. The article describes Banshee, a real-life power distribution network. It also details a real-time controller hardware-in-the-loop (HIL) prototyping platform to test the responses of the controllers and verify decision-making algorithms. The benchmark aims to address power industry needs for a common basis to integrate and evaluate controllers for the overall microgrid, distributed energy resources (DERs), and protective devices. The test platform will accelerate microgrid deployment, enable standard compliance verification, and further develop and test controllers' functionalities. These contributions will facilitate safe and economical demonstrations of the state-of-the-possible while verifying minimal impact to existing electrical infrastructure. All aspects of the benchmark and platform development including models, configuration files, and documentation are publicly available via the electric power HIL controls collaborative (EPHCC).
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Summary

This article provides a unique benchmark to integrate and systematically evaluate advanced functionalities of microgrid and downstream device controllers. The article describes Banshee, a real-life power distribution network. It also details a real-time controller hardware-in-the-loop (HIL) prototyping platform to test the responses of the controllers and verify decision-making algorithms. The...

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