Abstract: Described is a video scene analysis system. The system includes a salience module that receives a video stream having one more pairs of frames (each frame having a background and a foreground) and detects salient regions in the video stream to generate salient motion estimates. The salient regions are regions that move differently than dominant motion in the pairs of video frames. A scene modeling module generates a sparse foreground model based on salient motion estimates from a plurality of consecutive frames. A foreground refinement module then generates a Task-Aware Foreground by refining the sparse foreground model based on task knowledge. The Task-Aware Foreground can then be used for further processing such as object detection, tracking or recognition.

Abstract: Described is a system for detecting cyber intrusions based on analysis of network traffic. During operation, the system performs a statistical analysis of message timing on network traffic to produce a temporal dependency matrix representative of temporal dependency between different message types in the network traffic. The sets of temporal dependency matrices are decomposed into component matrices, where at least one component matrix represents typical properties of these matrices and at least one other component matrix represents atypical properties of the matrices. A new temporal dependency matrix is generated based on new network traffic. Finally, anomalous behavior is detected in the new network traffic by comparing component matrices of the new temporal dependency matrix with component matrices of the temporal dependency matrices under normal operation conditions.

Abstract: Described is a system for predicting system trajectories toward critical transitions. The system transforms a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series. Then pair-wise time series of a transfer entropy (TE) measure are determined, wherein the TE measure quantifies the amount of information transfer from a source to a destination in the complex system. An associative transfer entropy (ATE) measure is determined which decomposes the pair-wise time series of TE to associative states of asymmetric, directional information flows, wherein the ATE measure is comprised of an ATE+ influence class and a ATE? influence class. The system estimates ATE+, TE, and ATE? trajectories over time, and at least one of the ATE+, TE, and ATE? trajectories is used to predict a critical transition in the complex system.

Abstract: A system and method for low power surveillance. The system receives a series of frames from a camera, each frame having a background and a foreground. A background template is generated. Thereafter, the system receives a new image frame of the scene, the new image frame having a background and a foreground. Potential regions of interest (ROI) are detected in the new image frame. Initial region descriptors are determined in the potential ROI in the foreground. The initial region descriptors are segmented to generate a segmented region. Region descriptors are re-determined from the segmented region. A contiguous sparse foreground is determined from the re-determined region descriptors, the contiguous sparse foreground being a contiguous ROI. The ROI is reconstructed using foveated compressive sensing to generate an image of an interesting object. Finally, the interesting object image is combined with the background template to reconstruct the foreground.

Abstract: Described is a system for inducing a desired behavioral effect using an electrical current stimulation. A brain monitoring subsystem includes monitoring electrodes for sensing brain activity, and a brain stimulation subsystem includes stimulating electrodes for applying an electrical current stimulation. Multi-scale distributed data is registered into a graphical representation. The system identifies a sub-graph in the graphical representation and maps the sub-graph onto concept features, generating a concept lattice which relates the concept features to a behavioral effect. Finally, an electrical current stimulation to be applied to produce the behavioral effect is determined.

Abstract: A method of and apparatus removing of a plurality of relatively narrow banded signals in a relatively wide banded input signal. The method involves and the apparatus provides for compressively sensing one relatively narrow banded signal in the relatively wide banded input signal and removing one relatively narrow banded signal from the relatively wide banded input signal before detecting and removing another relatively narrow banded signal in the relatively wide banded input signal, the step of and apparatus for compressing sensing occurring with respect to both (i) the input signal with the previously detected narrow banded signals removed therefrom and (ii) a frequency shifted version of (i).

Abstract: Described is a low power system for mobile devices that provides continuous, behavior-based security validation of mobile device applications using neuromorphic hardware. A mobile device comprises a neuromorphic hardware component that runs on the mobile device for continuously monitoring time series related to individual mobile device application behaviors, detecting and classifying pattern anomalies associated with a known malware threat in the time series related to individual mobile device application behaviors, and generating an alert related to the known malware threat. The mobile device identifies pattern anomalies in dependency relationships of mobile device inter-application and intra-applications communications, detects pattern anomalies associated with new malware threats, and isolates a mobile device application having a risk of malware above a predetermined threshold relative to a risk management policy.

Abstract: Described is a system for synthetic aperture radar (SAR) imaging. The system is adapted to reconstruct a set of images to generate a set of reconstructed SAR images, wherein at least some of the reconstructed SIR images have noise and contain glint. The reconstructed SAR images are then stacked into a matrix D, in which each column of the matrix is a reconstructed SAR image. Using sparse and low-rank decomposition on the matrix D, the system then extracts a clean background from the reconstructed SAR images and separates the noise and glint. Based on that, the system is operable to detect moving targets in sparse part S and issuing a notification of such a moving target.

Abstract: A method of and apparatus removing of a plurality of relatively narrow banded signals in a relatively wide banded input signal. The method involves and the apparatus provides for compressively sensing one relatively narrow banded signal in the relatively wide banded input signal and removing one relatively narrow banded signal from the relatively wide banded input signal before detecting and removing another relatively narrow banded signal in the relatively wide banded input signal, the step of and apparatus for compressing sensing occurring with respect to both (i) the input signal with the previously detected narrow banded signals removed therefrom and (ii) a frequency shifted version of (i).

Abstract: Described is a system for analyzing network activities. Each pair of node interactions between nodes in the network is represented with a tensor. For each pair of node interactions, a mesostructure is inferred using tensor decomposition of the tensor, resulting in inferred mesostructures. A temporal network structure representing each pair of node interactions is determined using a set of parameters generated from the tensor decomposition, resulting in temporal network structures. A future data cascade in the network is predicted using the temporal network structures.

Abstract: Described is system for feature transformation of neural activity using sparse and low-rank (SLR) decomposition. A set of neural activity signals associated with different stimuli are obtained, and a neural feature is extracted or each stimuli from the set of neural activity signals using SLR decomposition. The neural feature is then used to generate a classification of the stimuli. The neural activity signals may include functional magnetic resonance imaging (fMRI) signals, fMRI blood-oxygen-level dependent (BOLD) signals, electroencephalography (EEG) signals, functional near-infrared spectroscopy (fNIRS) signals, or magnetoencephalography (MEG) signals. The system according to the principles of the present invention will he an important component of any neural activity based classification system.

Abstract: Described is a system for predicting temporal evolution of contagions on multilayer networks. The system determines a functional epidemic threshold for disappearance of a contagion on a multilayer network model according to a score value s=??/?, where ? corresponds to an adjacency matrix of the first layer of the multilayer network model, ? represents a spread rate of the contagion, and ? represents a recovery rate. A prediction of future behavior of the contagion on the multilayer network model using the functional epidemic threshold is output and utilized to inform decisions regarding connectivity within a multilayer network in order to prevent spread of the contagion on a multilayer network.

Abstract: Systems and methods according to one or more embodiments are provided for mapping and registration of synthetic aperture raw radar data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory including executable instructions and a processor adapted to receive phase history data associated with observation views of a scene. The processor further converts the received phase history data associated with the observation views to a range profile of the scene. The range profile is compared to a range profile template of the scene to estimate a geometric transformation of the scene encoded in the received phase history data with respect to a reference template.

Abstract: Described is a system for multiscale monitoring. During operation, the system receives surveillance data of a scene having a plurality of zones. The surveillance data includes an object flow tensor V indicating a number of objects flowing from one zone to another zone at time t and an object communication tensor C indicating a number of communications sending from one zone to another zone at time t. The system then determines a cluster membership of the plurality of zones. Dependency links between communications and flows are then determined. At least one cluster of one or more zones is designated as a region of interest based on the dependency links, which allows the system to control a device based on the designated region(s) of interest.

Abstract: Described is a system for inducing a desired behavioral effect using an electrical current stimulation. A brain monitoring subsystem includes monitoring electrodes for sensing brain activity, and a brain stimulation subsystem includes stimulating electrodes for applying an electrical current stimulation. Multi-scale distributed data is registered into a graphical representation. The system identifies a sub-graph in the graphical representation and maps the sub-graph onto concept features, generating a concept lattice which relates the concept features to a behavioral effect. Finally, an electrical current stimulation to be applied to produce the behavioral effect is determined.

Abstract: Described is a system for determining reliability of nodes in a mobile wireless network. The system is operable for receiving an Exploitation Network (Xnet) database. The Xnet database has an Xnet structure formed of a physical node layer (NetTopo), a network dependent (NetDep) layer, and an application dependent (AppDep) layer. The NetTopo layer includes NetTopo graphs reflecting connectivity between the nodes. The NetDep layer includes NetDep graphs reflecting connectivity dependencies amongst the nodes, and the AppDep layer includes Appdep graphs reflecting software application dependencies amongst the nodes. An Xnet Analytics Engine is run that monitors and evaluates reliability of each node in the mobile wireless network to provide a reliability estimate of each node.

Abstract: Network of networks (NoN) structure reconstruction employs compressed sensing with multivariate time series data and graph partitioning to reconstruct a node-to-node connection structure of an NoN. The NoN structure reconstruction includes determining an adjacency matrix of the NoN from the multivariate time series data using the compressed sensing. Partitioning a graph representing the determined adjacency matrix into subgraphs provides the reconstruction of the node-to-node connection structure.

Abstract: Described is a system for feature selection for formal concept analysis (FCA). A set of data points having features is separated into object classes. For each object class, the data points are convolved with a Gaussian function, resulting in a class distribution curve for each known object class. For each class distribution curve, a binary array is generated having ones on intervals of data values on which the class distribution curve is maximum with respect to all other class distribution curves, and zeroes elsewhere. For each object class, a binary class curve indicating for which interval a performance of the known object class exceeds all other known object classes is generated. The intervals are ranked with respect to a predetermined confidence threshold value. The ranking of the intervals is used to select which features to extract from the set of data points in FCA lattice construction.

Abstract: Described is a system for using social media data to supplement survey data for discrete choice analysis. Survey data from consumers is segmented into demographic groups. Individual demographic attributes and consumer product attribute preferences are extracted from a set of social media data. Consumer product attribute preferences are determined for each demographic group using the set of social media data. Consumers' preference coefficients are generated for each demographic group. Finally, individualized incentives for a target consumer product are determined using the consumers' preference coefficients.

Abstract: Described is a system for predicting system trajectories toward critical transitions. The system transforms a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series. Then pair-wise time series of a transfer entropy (TE) measure are determined, wherein the TE measure quantifies the amount of information transfer from a source to a destination in the complex system. An associative transfer entropy (ATE) measure is determined which decomposes the pair-wise time series of TE to associative states of asymmetric, directional information flows, wherein the ATE measure is comprised of an ATE+ influence class and a ATE? influence class. The system estimates ATE+, TE, and ATE? trajectories over time, and at least one of the ATE+, TE, and ATE? trajectories is used to predict a critical transition in the complex system.