Nguyen - Science of Information, Computation and Fusion - Spring Review 2013

1DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution15 February 2013
Integrity  Service  Excellence
Tristan Nguyen
Program Officer
AFOSR/RTC
Air Force Research Laboratory
Science of Information,
Computation and Fusion
Date: 06 03 2013

2DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution
2013 AFOSR SPRING REVIEW
NAME: Tristan Nguyen
BRIEF DESCRIPTION OF PORTFOLIO:
 Research new techniques that enable or facilitate extracting, assembling, and
understanding of information collected from multiple sources.
 Challenges:
1. Dealing with information at different levels of abstraction
2. Mechanizing patterns of reasoning in terms of computation.
LIST SUB-AREAS IN PORTFOLIO:
Sub-Areas Objectives
Bottom-up Low-level Data
Analytics
• Discover structures in data and shape them into information
• Formulate models to describe different data sources
• Find efficient and provable computational algorithms
Top-down High-level
Information Processing
• Develop expressive, computable representation of information
• Synthesize contextual information with observed data through
reasoning

Bottom-up Data-driven
Focus On:
 New Data Structures
 Information Extraction Procedures
 Constructive Computational Algorithms
 Provable Performance Guarantees
Stay Away From:
 Data Provenance
 Information Management
 Cloud Computing
 Radar, Communications, Signal Processing
Important but being
funded elsewhere
NEW TRENDS
(Dealing with Big Data)
Few data samples in high
dimensions
Nonlinear high-dimensional data
Fast approximation algorithms
Integration of multiple models/
techniques

Top-down Concept-driven
Focus On:
 Construction of rich data types
 Models of computation
 New programming language
 Connection with data analytics
Stay Away From
 Cognitive modeling
 Decision analysis and modeling
 Current semantic technologies
 Various database models
NEW TRENDS
Higher-order structures
Constructive techniques
Synthesis of reasoning & computing
Merging qualitative & quantitative
models
Important but being
funded elsewhere

Similar Programs But with Different
Emphases and Approaches
Mathematics of Sensing, Exploitation, and Execution
 Network-based Hard/Soft Information Fusion
 Value-centered Information Theory for Adaptive Learning,
Inference, Tracking, and Exploitation
 Revolutionizing High-Dimensional Microbial Data Integration
 Information Integration and Informatics
 EarthCube
 Algorithms for Threat Detection (NSF-DTRA-NGA)
 Information Integration
 Intelligent and Autonomous Systems

Collaborations & Transitions
 NSF-DTRA-NGA – Algorithms for Threat Detection
 OSD/AFRL/RH – Autonomy
 DARPA/AFRL/RY – Mathematics of Sensing,
Exploitation and Execution (MSEE)
 ASD R&E/JCTD – Advanced Mathematics for DoD
Battlefield Challenges
 STTR – Space-time Signal Processing for Detecting and
Classifying Distributed Attacks in Networks, Numerica
Corporation

Air Force Relevance
Technological
Applications:
 Information Triage
 Automated Reasoning
 Human-Machine Interface
 Formal Verification
Autonomy
Intelligence, Surveillance,
Reconnaissance
Cyber Domain
Space Situational
Awareness

Highlights of Research

Image Articulation Manifolds
R. Baraniuk et al., Rice University
Motivation: Data are “sparsely”
collected by a moving platform
Challenges:
 How to integrate the motion information
with data
 Lack of mathematical tools
Objective: To develop a mathematical
foundation for Image Articulation
Manifolds
New Techniques:
 Generalization of group action
 Generalization of many ideas in
differential geometry
Applications: Beyond videos

Bayesian Nonparametric Modeling of Data
E. Fox et al. – University of Washington, Seattle
Objective: Relating multiple time series for analysis and fusion
Main Theme: The time-series collection encodes shared dynamic
behaviors (features)
Notes:
 This generative model provides a probabilistic, top-down model for data
sources.
 The PI is collaborating with Ed Zelnio’s group in AFRL/RY.
New Idea: Using Beta and
Bernoulli processes to
 Allow for infinitely many
features
 Induce sparsity with
shared features.

Graph-structured Activation
A. Singh et al., Carnegie Mellon University
Objectives: Detection and localization of weak
structured patterns in large graphs from a small
number of compressive measurements.
Achievements:
 Determined the minimum number of
measurements and weakest SNR required for
detection and localization in lattices.
 Adaptivity of measurements and structure of
activation can improve localization but not
detection.
Future Research: Consider more general
structures of activation on any graphs.

Objective: Develop reasoning and computing mechanisms across different
domains of information to support information fusion.
Key Ingredients:
 Reasoning - Making inference, generating hypothesis, verifying hypothesis
based on observed data
 Computing - Manipulating data or its mathematical structures and connecting
with bottom-up data analytics
Motivation: Dependent Type Theory was recently applied (2012) to
information fusion and situation awareness. Types allows for expressive data
structures and properties (logical, mathematical, etc.)
Technical Approach: Develop Homotopy Type Theory to mechanize
reasoning, computing, and constructing data types in the same framework.
Homotopy Type Theory for Reasoning &
Computing
S. Awodey, R. Harper, J. Avigad, Carnegie Mellon University

Directed Information & Graphical Models
N. Kiyavash – University of Illinois, Urbana
Objective: To model networks of
coupled random processes with
causal dependence structures.
Technical Approaches:
 Discovered two new equivalent graphical models –
Minimum Generative
Model Graph
Directed Information
Graph
 Constructed efficient algorithms to identify these graphs.
Future Research: Can the time-invariance hypothesis on the causal dependence
structures be weakened or removed?

Sensor Scheduling for Tracking
Resident Space Objects
I. Clarkson, University of Queensland
Objective: To improve the current tracking system, Tasking Autonomous Sensors in
a Multiple Application Network (TASMAN).
Current Technical Approaches: Unscented Kalman Filter for updating objects’
states and setting up scheduling.
Challenge: Objects can be out of field of view in a scheduling period
New Approach: Integrated searching and
tracking via particle-filtering (in collaboration with
AFRL’s AMOS)
Simulated Results

Some Intramural Projects
 J. Culbertson (RY) & K. Sturtz (Universal Mathematics)
o Categorical Formulation of Probability and Bayesian
o Collaborating with D. Koditschek (U Penn) and MURI Team
 W. Sakla (RY) & T. Klausutis (RW)
o Manifold Learning and Sparse Representation
o Will collaborate with a new PI
 R. Ilin & L. Perlovsky (RY)
o Integration of text with sensor data via parametric models
 W. Curtis (RW)
o Map-based Particle Filtering for Target Tracking

FY10 MURI - Control of Information
Collection and Fusion
Objective: To formulate a new perspective on
the joint control of heterogeneous information
sources to simultaneously achieve quantified
informational and physical objective.
• RCA.1 Unified Mathematical Representation
– for sensor, control, mission objectives
– incorporating multiple scales of resolution and
uncertainty
• RCA.2 Joint Physical-Information State Descriptors
– capturing physical state of the information gathering
system and the state of the information
– include formal expression of constraints limiting state
transitions
• RCA.3 Control-Information Linkage to:
– robustly link control actions to information states
– support feedback to enable simultaneous control of
physical and information states
• Jadbabaie (control) + 1PhD
• Koditschek (robotics) + 2PhD + 1.5PD
• Kumar (robotics) + 2PhD
• Ribeiro (sig. proc.) + 3PhD
Berkeley
• Ramachandran (info. thry) + 1PhD
• Sastry (control) + 1PhD
• Tomlin (control) + 1PhD + 1PD
Illinois
• Baryshnikov (math) + 1PhD
Minnesota
• Giannakis (sig. proc.) + 4PhD
• Roumeliotis (robotics) + 4PhD
Melbourne
• Howard (comm., radar)
• Moran (appl. math)

Consistent Vision-aided Inertial Navigation System
(VINS) – S. Roumeliotis et al. (U Minnesota)
 Challenge: VINS is a nonlinear estimation problem;
Linearized estimators (e.g., Extended Kalman filter (EKF),
Unscented (U)-KF) become inconsistent.
 Solution:
o Determined unobserved directions of the nonlinear
system using finitely many Lie derivatives
o Linearized states using the observability matrix
o Identified cause of inconsistency
o Used the computed unobserved directions to improve
consistency and accuracy

Multi-robot Team To Find Targets and Avoid Hazardous
Areas – V. Kumar et al. (U Penn)
Challenge: Sensing, communication, and coordination are
coupled.
Solution:
 Distributed algorithms for detection and multi-target detection
and localization using
o a recursive filter based on Finite Set statistics
o approximated gradient of mutual information between
sensor readings & target locations.
 Complexity reduction by
o clustering robots into groups
o adding access points connected to a central server.
Next Step: Empirical validation and merge with VINS in complex
environments.

Summary
 Bottom-up data-driven analysis can discover
structures in data.
 Top-down conceptually driven processing
can integrate these structures.
 These two directions may not align nicely.
So recursion may require.
 There are several layers of abstraction in
information processing.
 Different technical tools are needed to treat
various layers of abstraction.
Symbols,
Magnitudes, etc.
Semantics,
Logics, etc.
MoreAbstract

Science of Information, Computation and Fusion

Nguyen - Science of Information, Computation and Fusion - Spring Review 2013

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Nguyen - Science of Information, Computation and Fusion - Spring Review 2013