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Institution

Mitre Corporation

CompanyBedford, Massachusetts, United States
About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.


Papers
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Journal ArticleDOI
TL;DR: Pixel-DL as discussed by the authors employs pixel-wise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to reconstruct an image, achieving comparable or better performance to iterative methods and consistently outperformed other CNN-based approaches.
Abstract: Photoacoustic tomography (PAT) is a non-ionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pixel-wise deep learning (Pixel-DL) that first employs pixel-wise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to reconstruct an image. Simulated photoacoustic data from synthetic, mouse-brain, lung, and fundus vasculature phantoms were used for training and testing. Results demonstrated that Pixel-DL achieved comparable or better performance to iterative methods and consistently outperformed other CNN-based approaches for correcting artifacts. Pixel-DL is a computationally efficient approach that enables for real-time PAT rendering and improved image reconstruction quality for limited-view and sparse PAT.

125 citations

01 Jan 2012
TL;DR: A description of the potential ontologies and standards that could be utilized to extend the Cyber ontology from its initially constrained malware focus and some proposed next steps in the iterative evolution of the ontology development methodology are proposed.
Abstract: This paper reports on a trade study we performed to support the development of a Cyber ontology from an initial malware ontology. The goals of the Cyber ontology effort are first described, followed by a discussion of the ontology development methodology used. The main body of the paper then follows, which is a description of the potential ontologies and standards that could be utilized to extend the Cyber ontology from its initially constrained malware focus. These resources include, in particular, Cyber and malware standards, schemas, and terminologies that directly contributed to the initial malware ontology effort. Other resources are upper (sometimes called 'foundational') ontologies. Core concepts that any Cyber ontology will extend have already been identified and rigorously defined in these foundational ontologies. However, for lack of space, this section is profoundly reduced. In addition, utility ontologies that are focused on time, geospatial, person, events, and network operations are briefly described. These utility ontologies can be viewed as specialized super-domain or even mid-level ontologies, since they span many, if not most, ontologies -including any Cyber ontology. An overall view of the ontological architecture used by the trade study is also given. The report on the trade study concludes with some proposed next steps in the iterative evolution of the

125 citations

Proceedings ArticleDOI
17 Apr 2007
TL;DR: In this article, a matched filter coherently integrates the radar data even though the target scatterers move through many range resolution cells during the coherent integration time, which can produce simultaneous high range and high Doppler resolution matched filter outputs.
Abstract: We have developed and demonstrated the Keystone format to simultaneously remove linear range migration for all targets regardless of their velocities. Higher order motion and under sampling foldover can be removed by hypotheses. The authors present an approach to radar matched filtering which can produce simultaneous high range and high Doppler resolution matched filter outputs. The new matched filter coherently integrates the radar data even though the target scatterers move through many range resolution cells during the coherent integration time.

124 citations

Patent
30 Aug 2004
TL;DR: In this article, a system and method are provided that use explicitly and implicitly derived models of user information needs and content and presentation preferences together with relevancy feedback to expand a user's original query to the most related terms in a corpus and/or to allow the user to provide interactive feedback to enhance the relevance of selected news stories and display profile of the selected stories.
Abstract: A system and method are provided that use explicitly and implicitly derived models of user information needs and content and presentation preferences together with relevancy feedback to expand a user's original query to the most related terms in a corpus and/or to allow the user to provide interactive feedback to enhance the relevance of selected news stories and display profile of the selected news stories. By personalizing both the selection of stories and the form in which they are delivered, users are provided with tailored broadcast news.

123 citations

Posted Content
TL;DR: The use of machine learning is described on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task.
Abstract: A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.

122 citations


Authors

Showing all 4896 results

NameH-indexPapersCitations
Sushil Jajodia10166435556
Myles R. Allen8229532668
Barbara Liskov7620425026
Alfred D. Steinberg7429520974
Peter T. Cummings6952118942
Vincent H. Crespi6328720347
Michael J. Pazzani6218328036
David Goldhaber-Gordon5819215709
Yeshaiahu Fainman5764814661
Jonathan Anderson5719510349
Limsoon Wong5536713524
Chris Clifton5416011501
Paul Ward5240812400
Richard M. Fujimoto5229013584
Bhavani Thuraisingham5256310562
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
202210
202195
2020139
2019145
2018132