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Liquid crystal uncooled thermal imager development

Published in:
SPIE, Vol. 9974, Infrared Sensors, Devices, and Applications VI, 28 August 2016.

Summary

An uncooled thermal imager is being developed based on a liquid crystal (LC) transducer. Without any electrical connections, the LC transducer pixels change the long-wavelength infrared (LWIR) scene directly into a visible image as opposed to an electric signal in microbolometers. The objectives are to develop an imager technology scalable to large formats (tens of megapixels) while maintaining or improving the noise equivalent temperature difference (NETD) compared to microbolometers. The present work is demonstrating that the LCs have the required performance (sensitivity, dynamic range, speed, etc.) to enable a more flexible uncooled imager. Utilizing 200-mm wafers, a process has been developed and arrays have been fabricated using aligned LCs confined in 20-20-um cavities elevated on thermal legs. Detectors have been successfully fabricated on both silicon and fused silica wafers using less than 10 photolithographic mask steps. A breadboard camera system has been assembled to test the imagers. Various sensor configurations are described along with advantages and disadvantages of component arrangements.
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Summary

An uncooled thermal imager is being developed based on a liquid crystal (LC) transducer. Without any electrical connections, the LC transducer pixels change the long-wavelength infrared (LWIR) scene directly into a visible image as opposed to an electric signal in microbolometers. The objectives are to develop an imager technology scalable...

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The AFRL-MITLL WMT16 news-translation task systems

Published in:
Proc. First Conf. on Machine Translation, Vol. 2, 11-12 August 2016, pp. 296-302.

Summary

This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation.
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Summary

This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation.

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Matching community structure across online social networks

Author:
Published in:
arXiv, 3 August 2016.

Summary

The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecting the community structure. In this paper, we explore several approaches for community detection across online social networks with limited knowledge of username alignment across the networks. We refer to the known alignment of usernames as seeds. We investigate strategies for seed selection and its impact on networks with a different fraction of overlapping vertices. The goal is to study the interplay between network topologies and seed selection strategies, and to understand how it affects the detected community structure. We also propose several measures to assess the performance of community detection and use them to measure the quality of the detected communities in both Twitter-Twitter networks and Twitter-Instagram networks.
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Summary

The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across...

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Analytical models and methods for anomaly detection in dynamic, attributed graphs

Published in:
Chapter 2, Computational Network Analysis with R: Applications in Biology, Medicine, and Chemistry, 2017, pp. 35-61.

Summary

This chapter is devoted to anomaly detection in dynamic, attributed graphs. There has been a great deal of research on anomaly detection in graphs over the last decade, with a variety of methods proposed. This chapter discusses recent methods for anomaly detection in graphs,with a specific focus on detection within backgrounds based on random graph models. This sort of analysis can be applied for a variety of background models, which can incorporate topological dynamics and attributes of vertices and edges. The authors have developed a framework for anomalous subgraph detection in random background models, based on linear algebraic features of a graph. This includes an implementation in R that exploits structure in the random graph model for computationally tractable analysis of residuals. This chapter outlines this framework within the context of analyzing dynamic, attributed graphs. The remainder of this chapter is organized as follows. Section 2.2 defines the notation used within the chapter. Section 2.3 briefly describes a variety of perspectives and techniques for anomaly detection in graph-based data. Section 2.4 provides an overview of models for graph behavior that can be used as backgrounds for anomaly detection. Section 2.5 describes our framework for anomalous subgraph detection via spectral analysis of residuals, after the data are integrated over time. Section 2.6 discusses how the method described in Section 2.5 can be efficiently implemented in R using open source packages. Section 2.7 demonstrates the power of this technique in controlled simulation, considering the effects of both dynamics and attributes on detection performance. Section 2.8 gives a data analysis example within this context, using an evolving citation graph based on a commercially available document database of public scientific literature. Section 2.9 summarizes the chapter and discusses ongoing research in this area.
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Summary

This chapter is devoted to anomaly detection in dynamic, attributed graphs. There has been a great deal of research on anomaly detection in graphs over the last decade, with a variety of methods proposed. This chapter discusses recent methods for anomaly detection in graphs,with a specific focus on detection within...

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The TESS camera: modeling and measurements with deep depletion devices

Summary

The Transiting Exoplanet Survey Satellite, a NASA Explorer-class mission in development, will discover planets around nearby stars, most notably Earth-like planets with potential for follow up characterization. The all-sky survey requires a suite of four wide field-of-view cameras with sensitivity across a broad spectrum. Deep depletion CCDs with a silicon layer of 100 um thickness serve as the camera detectors, providing enhanced performance in the red wavelengths for sensitivity to cooler stars. The performance of the camera is critical for the mission objectives, with both the optical system and the CCD detectors contributing to the realized image quality. Expectations for image quality are studied using a combination of optical ray tracing in Zemax and simulations in Matlab to account for the interaction of the incoming photons with the 100 um silicon layer. The simulations include a probabilistic model to determine the depth of travel in the silicon before the photons are converted to photo-electrons, and a Monte Carlo approach to charge diffusion. The charge diffusion model varies with the remaining depth for the photo-electron to traverse and the strength of the intermediate electric field. The simulations are compared with laboratory measurements acquired by an engineering unit camera with the TESS optical design and deep depletion CCDs. In this paper we describe the performance simulations and the corresponding measurements taken with the engineering unit camera, and discuss where the models agree well in predicted trends and where there are differences compared to observations.
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Summary

The Transiting Exoplanet Survey Satellite, a NASA Explorer-class mission in development, will discover planets around nearby stars, most notably Earth-like planets with potential for follow up characterization. The all-sky survey requires a suite of four wide field-of-view cameras with sensitivity across a broad spectrum. Deep depletion CCDs with a silicon...

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Cross-domain entity resolution in social media

Published in:
4th Int. Workshop on Natural Language Processing for Social Media, SocialNLP with IJCAI, 11 July 2016.

Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%.
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Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution...

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Apprenticeship scheduling: learning to schedule from human experts

Published in:
Proc. of the Int. Joint Conf. Artificial Intelligence (IJCAI), 9-15 July 2016.

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, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state-space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on both a synthetic data set incorporating job-shop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem.
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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...

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Charting a security landscape in the clouds: data protection and collaboration in cloud storage

Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for comparing secure storage approaches based on their residual vulnerabilities: attack surfaces against which an approach cannot protect. Our categorization therefore ranks approaches from the weakest (the most residual vulnerabilities) to the strongest (the fewest residual vulnerabilities). In addition to the security provided by each approach, we also consider their inherent costs and limitations. This report can therefore help an organization select a cloud data protection approach that satisfies their enterprise infrastructure, security specifications, and functionality requirements.
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Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers. We propose a metric for...

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Directly-deposited blocking filters for high-performance silicon x-ray detectors

Published in:
SPIE, Vol. 9905, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray, July 2016, 99054C.

Summary

Silicon X-ray detectors often require blocking filters to mitigate noise and out-of-band signal from UV and visible backgrounds. Such filters must be thin to minimize X-ray absorption, so direct deposition of filter material on the detector entrance surface is an attractive approach to fabrication of robust filters. On the other hand, the soft (E < 1 keV) X-ray spectral resolution of the detector is sensitive to the charge collection efficiency in the immediate vicinity of its entrance surface, so it is important that any filter layer is deposited without disturbing the electric field distribution there. We have successfully deposited aluminum blocking filters, ranging in thickness from 70 to 220nm, on back-illuminated CCD X-ray detectors passivated by means of molecular beam epitaxy. Here we report measurements showing that directly deposited filters have little or no effect on soft X-ray spectral resolution. We also find that in applications requiring very large optical density (> OD 6) care must be taken to prevent light from entering the sides and mounting surfaces of the detector. Our methods have been used to deposit filters on the detectors of the REXIS instrument scheduled to fly on OSIRIS-ReX later this year.
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Summary

Silicon X-ray detectors often require blocking filters to mitigate noise and out-of-band signal from UV and visible backgrounds. Such filters must be thin to minimize X-ray absorption, so direct deposition of filter material on the detector entrance surface is an attractive approach to fabrication of robust filters. On the other...

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Resonance fluorescence from an artificial atom in squeezed vacuum

Published in:
Phys. Rev. X, Vol. 6, No. 3, July-September 2016, 031004.

Summary

We present an experimental realization of resonance fluorescence in squeezed vacuum. We strongly couple microwave-frequency squeezed light to a superconducting artificial atom and detect the resulting fluorescence with high resolution enabled by a broadband traveling-wave parametric amplifier. We investigate the fluorescence spectra in the weak and strong driving regimes, observing up to 3.1 dB of reduction of the fluorescence linewidth below the ordinary vacuum level and a dramatic dependence of the Mollow triplet spectrum on the relative phase of the driving and squeezed vacuum fields. Our results are in excellent agreement with predictions for spectra produced by a two-level atom in squeezed vacuum [Phys. Rev. Lett. 58, 2539 (1987)], demonstrating that resonance fluorescence offers a resource-efficient means to characterize squeezing in cryogenic environments.
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Summary

We present an experimental realization of resonance fluorescence in squeezed vacuum. We strongly couple microwave-frequency squeezed light to a superconducting artificial atom and detect the resulting fluorescence with high resolution enabled by a broadband traveling-wave parametric amplifier. We investigate the fluorescence spectra in the weak and strong driving regimes, observing...

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