Publications
Tagged As
XLab: early indications & warning from open source data with application to biological threat
January 3, 2018
Conference Paper
Published in:
Proc. 51st Hawaii Int. Conf. on System Sciences, HICSS 2018, pp. 944-953.
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Summary
XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics—including link estimation, entity disambiguation, and event detection—to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario.
Summary
XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper...
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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data
November 13, 2017
Journal Article
Published in:
bioRxiv preprint doi: https://doi.org/10.1101/218818, 13 November 2017.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with highresolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states...
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Use of mass spectrometric vapor analysis to improve canine explosive detection efficiency
June 9, 2017
Journal Article
Published in:
Anal. Chem., Vol. 89, 9 June 2017, 6482-90.
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Summary
Canines remain the gold standard for explosives detection in many situations, and there is an ongoing desire for them to perform at the highest level. This goal requires canine training to be approached similarly to scientific sensor design. Developing a canine training regimen is made challenging by a lack of understanding of the canine's odor environment, which is dynamic and typically contains multiple odorants. Existing methodology assumes that the handler's intention is an adequate surrogate for actual knowledge of the odors cuing the canine, but canines are easily exposed to unintentional explosive odors through training material cross-contamination. A sensitive, real-time (~1 s) vapor analysis mass spectrometer was developed to provide tools, techniques, and knowledge to better understand, train, and utilize canines. The instrument has a detection library of nine explosives and explosive-related materials consisting of 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), 2,4,6-trinitrotoluene (TNT), nitroglycerin (NG), 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), pentaerythritol tetranitrate (PETN), triacetone triperoxide (TATP), hexamethylene triperoxide diamine (HMTD), and cyclohexanone, with detection limits in the parts-per-trillion to parts-per-quadrillion range by volume. The instrument can illustrate aspects of vapor plume dynamics, such as detecting plume filaments at a distance. The instrument was deployed to support canine training in the field, detecting cross-contamination among training materials, and developing an evaluation method based on the odor environment. Support for training material production and handling was provided by studying the dynamic headspace of a nonexplosive HMTD training aid that is in development. These results supported existing canine training and identified certain areas that may be improved.
Summary
Canines remain the gold standard for explosives detection in many situations, and there is an ongoing desire for them to perform at the highest level. This goal requires canine training to be approached similarly to scientific sensor design. Developing a canine training regimen is made challenging by a lack of...
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Aircraft laser strike geolocation system
June 5, 2017
Conference Paper
Published in:
17th AIAA Aviation Technology, Integration, and Operations Conf., 5-9 June 2017.
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Laser strikes against aircraft are increasing at an alarming rate, driven by the availability of cheap powerful lasers and a lack of deterrence due to the challenges of locating and apprehending perpetrators. Although window coatings and pilot goggles effectively block laser light, uptake has been low due to high cost and pilot reluctance. This paper describes the development and testing of a proof-of-concept ground based sensor system to rapidly geolocate the origin of a laser beam in a protected region of airspace and disseminate this information to law enforcement to allow a timely and targeted response. Geolocation estimates with accuracies of better than 20 m have been demonstrated within 30 seconds of an event at a range of 8.9 nmi with a 450 mW laser. Recommendations for an operational prototype at an airport are also described.
Summary
Laser strikes against aircraft are increasing at an alarming rate, driven by the availability of cheap powerful lasers and a lack of deterrence due to the challenges of locating and apprehending perpetrators. Although window coatings and pilot goggles effectively block laser light, uptake has been low due to high cost...
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Germanium CCDs for large-format SWIR and x-ray imaging
May 25, 2017
Journal Article
Published in:
J. Instrum., Vol. 12, No. 5, May 2017, C05014.
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Germanium exhibits high sensitivity to short-wave infrared (SWIR) and X-ray radiation, making it an interesting candidate for imaging applications in these bands. Recent advances in germanium processing allow for high-quality charge-coupled devices (CCDs) to be realized in this material. In this article, we discuss our evaluation of germanium as an absorber material for CCDs via fabrication and analysis of discrete devices such as diodes, metal-insulator-semiconductor capacitors, and buried-channel metal-oxide-semiconductor field-effect transistors (MOSFETs). We then describe fabrication of our first imaging device on germanium, a 32 x 1 x 8.1 um linear shift register. Based on this work, we find that germanium is a promising material for CCDs imaging in the SWIR and X-ray bands.
Summary
Germanium exhibits high sensitivity to short-wave infrared (SWIR) and X-ray radiation, making it an interesting candidate for imaging applications in these bands. Recent advances in germanium processing allow for high-quality charge-coupled devices (CCDs) to be realized in this material. In this article, we discuss our evaluation of germanium as an...
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Detecting virus exposure during the pre-symptomatic incubation period using physiological data
March 3, 2017
Journal Article
Published in:
Science Translational Medicine (submitted), 3 March 2017.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Using high-resolution physiological data from non-human primate studies of Ebola and Marburg viruses, we pre-processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm. In most subjects detection is achieved well before the onset of fever; subject cross-validation lead to 52±14h mean early detection (at >0.90 area under the receiver-operating characteristic curve). Cross-cohort tests across pathogens and exposure routes also lead to successful early detection (28±16h and 43±22h, respectively). We discuss which physiological indicators are most informative for early detection and options for extending this capability to lower data resolution and wearable, non-invasive sensors.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during...
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Characterization of nitrated sugar alcohols by atmospheric-pressure chemical-ionization mass spectrometry
February 28, 2017
Journal Article
Published in:
Rapid Commun. Mass Spectrom., Vol. 33, 2017, pp. 333-43.
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RATIONALE: The nitrated sugar alcohols mannitol hexanitrate (MHN), sorbitol hexanitrate (SHN) and xylitol pentanitrate (XPN) are in the same class of compounds as the powerful military-grade explosive pentaerythritol tetranitrate (PETN) and the homemade explosive erythritol tetranitrate (ETN) but, unlike for PETN and ETN, ways to detect MHN, SHN and XPN by mass spectrometry (MS) have not been fully investigated. METHODS: Atmospheric-pressure chemical-ionization mass spectrometry (APCI-MS) was used to detect ions characteristic of nitrated sugar alcohols. APCI time-of-flight mass spectrometry (APCI-TOF MS) and collision-induced dissociation tandem mass spectrometry (CID MS/MS) were used for confirmation of each ion assignment. In addition, the use of the chemical ionization reagent dichloromethane was investigated to improve sensitivity and selectivity for detection of MHN, SHN and XPN. RESULTS: All the nitrated sugar alcohols studied followed similar fragmentation pathways in the APCI source. MHN, SHN and XPN were detectable as fragment ions formed by the loss of NO2, HNO2, NO3, and CH2NO2 groups, and in the presence of dichloromethane chlorinated adduct ions were observed. It was determined that in MS/MS mode, chlorinated adducts of MHN and SHN had the lowest limits of detection (LODs), while for XPN the lowest LOD was for the [XPN-NO2]- fragment ion. Partially nitrated analogs of each of the three compounds were also present in the starting materials, and ions attributable to these compounds versus those formed from in-source fragmentation of MHN, SHN, and XPN were distinguished and assigned using liquid chromatography APCI-MS and ESI-MS. CONCLUSIONS: The APCI-MS technique provides a selective and sensitive method for the detection of nitrated sugar alcohols. The methods disclosed here will benefit the area of explosives trace detection for counterterrorism and forensics.
Summary
RATIONALE: The nitrated sugar alcohols mannitol hexanitrate (MHN), sorbitol hexanitrate (SHN) and xylitol pentanitrate (XPN) are in the same class of compounds as the powerful military-grade explosive pentaerythritol tetranitrate (PETN) and the homemade explosive erythritol tetranitrate (ETN) but, unlike for PETN and ETN, ways to detect MHN, SHN and XPN...
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Biomimetic sniffing improves the detection performance of a 3D printed nose of a dog and a commercial trace vapor detector
December 1, 2016
Journal Article
Published in:
Scientific Reports, Vol. 6 , art. no. 36876, December 2016. DOI: 10.1038/srep36876.
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Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog's nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the "aerodynamic reach" for inspiration of otherwise inaccessible odors. Chemical sampling and detection experiments quantified two modes of operation with the artificial nose-active sniffing and continuous inspiration-and demonstrated an increase in odorant detection by a factor of up to 18 for active sniffing. A 16-fold improvement in detection was demonstrated with a commercially-available explosives detector by applying this bio-inspired design principle and making the device "sniff" like a dog. These lessons learned from the dog may benefit the next-generation of vapor samplers for explosives, narcotics, pathogens, or even cancer, and could inform future bio-inspired designs for optimized sampling of odor plumes.
Summary
Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog's nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the "aerodynamic reach" for inspiration of...
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Development of a high-throughput microwave imaging system for concealed weapons detection
October 18, 2016
Conference Paper
Published in:
6th Int. Symp. on Phased Array Systems and Technology, PAST 2016, 18-21 October 2016.
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A video-rate microwave imaging aperture for concealed threat detection can serve as a useful tool in securing crowded, high foot traffic environments. Realization of such a system presents two major technical challenges: 1) implementation of an electrically large antenna array for capture of a moving subject, and 2) fast image reconstruction on cost-effective computing hardware. This paper presents a hardware-efficient multistatic array design to address the former challenge, and a compatible fast imaging technique to address the latter. Prototype hardware which forms a partition of an imaging aperture is discussed. Using this hardware, it is shown that the proposed array design can be used to form high-fidelity 3D images, and that the presented image reconstruction technique can form an image of a human-sized domain in ≤ 0.1s with low cost computing hardware.
Summary
A video-rate microwave imaging aperture for concealed threat detection can serve as a useful tool in securing crowded, high foot traffic environments. Realization of such a system presents two major technical challenges: 1) implementation of an electrically large antenna array for capture of a moving subject, and 2) fast image...
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Directly-deposited blocking filters for high-performance silicon x-ray detectors
July 1, 2016
Conference Paper
Published in:
SPIE, Vol. 9905, Space Telescopes and Instrumentation 2016: Ultraviolet to Gamma Ray, July 2016, 99054C.
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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.
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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