fundamental physical data acquisition and analysis€¦ · · 2016-08-04fundamental physical data...

Consortium for Verification Technology: Workshop - October 15th & 16th, 2015

FUNDAMENTAL PHYSICAL DATA ACQUISITION

AND ANALYSIS

ALFRED HERO

R. JAMISON AND BETTY WILLIAMS PROFESSOR OF ENGINEERING

CO-DIRECTOR OF MICHIGAN INSTITUTE FOR DATA SCIENCE

UNIVERSITY OF MICHIGAN


Outline I. Thrust 2 activities II. Overview of progress in data analytics III. Data-driven anomaly detection IV. Conclusion

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I. THRUST 2 ACTIVITIES


Thrust 2 co-Investigators • Alfred Hero (UM EECS/BME/STATS): Event correlation and anomaly

detection • John Fisher (MIT CSAIL): dynamic graphical models • Lawrence Carin (Duke ECE): compressive sensing and deep learning for

high-dimensional data • Sara Pozzi (UM NERS): physics of fission • John Mattingly (NCSU NE): High-throughput radiation detection systems

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Thrust 2 students and post-docs • Funded by CVT

– Elizabeth Hou (UM STATS) – Tony Van (UM STATS) – Charles Sosa (UM NERS) – David Carlson (Duke ECE) – Sue Zheng (MIT CSAIL) – Jack Linkous (NCSU Nuclear Engineering) – Yassin Yilmaz (UM EECS): post-doctoral fellow – Taposh Banerjee (UM EECS): post-doctoral fellow – Angela Di Fulvio (UM NERS), post-doctoral fellow – Xuejun Liao (Duke ECE), post-doctoral fellow – Jonathan Mueller (Duke Physics), post-doctoral fellow – Oren Freifeld (MIT CSAIL), post-doctoral fellow

• Funded from non-CVT sources

– Pin Yu Chen (UM EECS): PhD candidate (PNNL supported) – Matthew Marcath (UM NERS), Ph.D candidate – Tony Shin (UM NERS), M.S. student – Steve Ward (UM NERS), M.S. student – Andrew Stevens (Duke ECE), PhD student (PNNL employee) – Kyle Weinfurther (NCSU Nuclear Eng), PhD student (Sandia supported) – Pete Chapman (NCSU Nuclear Eng), Ph.D. student (supported by US Military Academy) – Rob Weldon (NCSU Nuclear Eng), Ph.D. student (supported by NCSU College of Engineering

fellowship)

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Thrust 2: Associated National Laboratories • Los Alamos National Laboratory (LANL)

– High dimensional anomaly detection project (LANL partner Earl Lawrence working w/ Hero) – Physics of fission experiments and theory (LANL partners Robert Haight and Patrick Talou

working with Pozzi) • Pacific Northwest National Laboratory (PNNL)

– Compressive sensing to transmission electron microscopy project (TEM) project (PNNL partners Andrew Stevens and Nigel Browning working with Carin)

– Heterogeneous network models for cyber-security project (PNNL partner Sutenay Choudhury working with Hero)

• Oak Ridge National Laboratory (ORNL) – Coded aperture fast neutron camera project (ORNL partners Jason Newby & Paul Hausladen

working witn Mattingly) – Application of deep-learning technology to airborne imagery (ORNL partner Thomas Potok

working with Carin) • Sandia National Laboratory (SNL)

– Single-volume neutron scatter camera (SVNSC) project (SNL partner Erik Brubaker working with Mattingly

• Lawrence Livermore National Laboratory (LLNL) – Large liquid scintillator array project (LLNL partner Les Nakae working with Mattingly) – FREYA Modeling of Fission Correlations (LLNL partner Ramona Vogt working with Pozzi)

• Lawrence Berkeley National Laboratory (LBNL) – FREYA Modeling of Fission Correlations (LBNL partner Jurgen Randrup working with Pozzi)

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Context: multi-layered data acquisition

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Facility

On site verification layer

Standoff verification layer

Emissions Utilities usage

Site visits

Context info • ISR • Public data • Shipping/Rcv Sensing modes • Reports • Newsfeeds • Event tagging • Social media • Blogs • Microblogs

MASINT • Satellite RS • Flyby sensors • Earthbased RS Sensing modes: • EO/SAR/IR • Hyperspectral • Radio Freq. • X-ray/gamma • Infrasound • Seismic

Inputs Outputs

Sensing modes • Power meter • Flow rates • Webcams

Sensing modes • Neutron det • 𝛾𝛾 imaging • Chemical det


Data acquisition and sampling

Feature representation

Statistical machine learning

HIGH DIMENSIONAL DATA

ANALYSIS/INFERENCE

Budget

Domain info

Shipping+Receiving Manifests

Physical Ingress/Egress

(Utilities/Emissions/Internet) Satellite imagery

Graph analytics

Error control 𝛼𝛼

Value of Info TSP‘11, Sensors ‘11,… TAES ‘06, TSP‘12, AISTATS ’13…

JASA ‘11, TIT ’12, NIPS ‘06,’11… TIT ‘13, PNAS 06, JCB ’09 …

Nuclear fuel cycle

Multimodal fusion Anomaly detection Event correlation Convolutional

Bayes networks w

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II. PROGRESS IN DATA ANALYTICS


Progress in data analytics

1. Data acquisition 1. Graphical models for emission detection (Fisher) 2. Quickest detection of nuclear fuel cycle diversions (Hero)

2. Feature representation: 1. Multimodal factor analysis (Hero, Carin) 2. Graph analytics for cyber-intrusion detection (Hero)

3. Statistical machine learning: 1. High dimensional anomaly detection (Hero) 2. Deep learning for count data (Carin)

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Progress in data analytics

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1. Progress presented in this talk • Graphical models for emission detection (Fisher) • Data-driven anomaly detection (Hero)

2. Additional work in progress that will be described in posters

• Elizabeth Hou, Earl Lawrence, Alfred Hero, Penalized Ensemble Kalman Filtering for High Dimensional Systems

• Yasin Yilmaz, Taposh Banerjee, Elizabeth Hou, Alfred Hero, Quickest Change Detection in Nuclear Fuel Cycles

3. Progress to be described next time • Yasin Yilmaz and Alfred O Hero, Multimodal factor analysis, Proc. of IEEE

Workshop on Machine Learning and Signal Processing, Boston, 2015. • Pin Yu Chen, Sutaney Choudhury, Alfred Hero, Multi-centrality graph spectral

decompositions and their application to cyber-intrusion detection, Submitted.


Airborne Detection of Localized Emissions (Fisher)

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Formulate sensing physics as probabilistic graphical model

Inference over events of interest (including uncertainty quantification)

Measurement model • material detector • platform location • wind-direction measurements Latent Model • platform dynamics • complex wind-field and propagation • background material model (low

SNR) • unknown number of emission

sources


Array detection of shielded material (Fisher)

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Determine optimal open loop array configuration

Comparison of Monte Carlo trials for sensor selection

Measurement model • NaI gamma-ray detectors Latent Model • MRF over material density • Exploit sub-modularity of

information rewards for near-optimal placement


III. DATA-DRIVEN ANOMALY DETECTION


Data-driven anomaly detection (Hero) The importance of anomaly detection in nuclear fuel cycle

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Taposh Banerjee, Hamed Firouzi and Alfred O Hero, Non-parametric Quickest Change Detection for Large Scale Random Matrices, Proc. IEEE Intl Symposium on Information Theory, Hong Kong, 2015.


Quickest Correlation Change Detection (QCCD)

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Challenges in high dimensional applications

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Background on QCD

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Conventional QCD tests: Limitations

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A High Dimensional QCCD test

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Order Statistic Thm yields QCCD test

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Implementation of QCCD test

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V. CONCLUSION


Conclusion 1. Thrust II co-Pis have made progress along multiple fronts 2. Progress described in this presentation: Data-driven anomaly detection

1. Taposh Banerjee, Hamed Firouzi and Alfred O Hero, Non-parametric Quickest Change Detection for Large Scale Random Matrices, Proc. IEEE Intl Symposium on Information Theory, Hong Kong, 2015.

3. Additional work in progress that will be described in posters 1. Elizabeth Hou, Earl Lawrence, Alfred Hero, Penalized Ensemble Kalman Filtering for

High Dimensional Systems 2. Yasin Yilmaz, Taposh Banerjee, Elizabeth Hou, Alfred Hero, Quickest Change Detection

in Nuclear Fuel Cycles

4. Progress that will be reported next time 1. Yasin Yilmaz and Alfred O Hero, Multimodal factor analysis, Proc. of IEEE Workshop on

Machine Learning and Signal Processing, Boston, 2015. 2. Pin Yu Chen, Sutaney Choudhury, Alfred Hero, Multi-centrality graph spectral

decompositions and their application to cyber-intrusion detection, Submitted.

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fundamental physical data acquisition and analysis€¦ · · 2016-08-04fundamental physical data...

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