Abstract: The system and methods of the present invention involve estimating model parameters for non-linear systems of Differential Equations (DEs). This includes receiving data from these systems and applying Weak-form Estimation of Nonlinear Dynamics methods to the data. These methods are robust to instances of large measurement noise. The system and methods also involve converting strong form representations of a model to weak forms, solving regression problems to perform parameter inference, and using Errors-In-Variables frameworks and iteratively reweighted least squares algorithms. The system and methods of the present invention can be applied to various models from different fields such as population biology, neuroscience, and biochemistry. The system and methods are highly robust and exhibit computational efficiency when used to estimate parameters in some common models including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, a Protein Transduction Benchmark model and Kuramoto-Sivashinsky.

Abstract: The invention relates to the novel use of photosynthetic microorganisms to allow for the generation of micron-scale optical output mechanical sensors. In one preferred embodiment, the invention includes systems, methods and compositions for the use of photosynthetic microbes as biologically-based micron-scale tunable, light/chemical-mechanical energy transducers, sensors, and/or actuators.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.

Abstract: Techniques related to vulnerability assessment based on machine inference are disclosed. A vulnerability assessment server may receive, from a client device, a set of metadata corresponding to a program stored on the client device. Further, the vulnerability assessment server may extract a program name from the set of metadata. Still further, the vulnerability assessment server may determine one or more vulnerabilities of the program based on searching for the program name in one or more storage systems that maintain sets of vulnerability data.