Award Data

Interpretation of video imagery is the quintessential goal of computer vision. The ability to group moving pixels into regions and then associate semantic labels with those regions has long been studied by the vision community. Only recently have thecomponent technologies matured sufficiently to make this goal attainable for well-defined scenarios. We propose a system for semantic interpretation o ...

Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated ...

On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

CONCUR develops a computational framework for assessing and characterizing urban environments stability or fragility in response to volatility and stress, identifying specific weaknesses as well as key tipping points which could lead to rapid systemic failure. CONCUR explicitly models urban environments as emergent complex systems, focusing attention on the critical triggers that could lead to rap ...

The unique scale, population density, complexity, and connectedness of megacities requires new tools for detecting and assessing risks related to civil unrest, rule of law, terrorism, and other sources of instability, and for understanding the underlying dynamics. In addition, gray zone operations pose a new and strategically important class of threats to the stability of nation states and cities ...

Boston Fusion Corp. and Arizona State University will research and develop REsilience & Stability in DENse Terrains (RESIDENT), a multi-model, multi-scale framework for assessing indicators of stability and resilience in dense urban environments. Our team consists of subject matter experts in the Social and Computer Sciences providing the bedrock on which to build accurate mathematical models of u ...

Metalenses, with their ability to arbitrarily control the amplitude and phase of light across a band of wavelengths, have the potential to disrupt imaging and communication systems which rely on traditional lenses to focus, collimate, and otherwise manipulate optical signals, and are under increasing pressure to operate with reduced size and weight. We propose to design, develop, and demonstrate a ...

We propose to design, build and test genetic circuits and DNA constructs that will be expressed in human cells and that will ultimately have applications for the health of warfighters. The circuits will have physiological inputs representing, for example, (1) infection/sepsis, (2) altitude sickness or blood loss, and (3) radiation exposure; which will be mediated through signal transduction pathwa ...

Software and hardware flaws can be exploited to make programs perform unintended computations or leak sensitive data. We propose to counter these threats by isolating libraries and other program units inside a single process. The developer will insert source-level annotations that i) map code and data units to compartments and ii) capture how each compartment is intended to interact with others, i ...