Abstract: A system for generating a permutation invariant representation of a graph is provided. The system assembles a dataset including a graph having a plurality of nodes and a number of features per node and generates a first matrix and a second matrix based on the plurality of nodes and the number of features per node. The system determines a set of node embeddings by a graph convolutional network based on the first matrix and the second matrix and determines a permutation invariant representation of the graph by a permutation invariant mapping based on the set of node embeddings. The system determines a universal attribute of the graph by a fully connected network based on the permutation invariant representation of the graph.

Abstract: Computer vision systems and methods for optimized computer vision using deep neural networks and Lipschitz analysis are provided. The system receives signals or data related to visual imagery, such as data from a camera, and feed-forwards the signals/data through the multiple layers of a convolutional neural network (CNN). At one or more layers of the CNN, the system determines at least one Bessel bound of that layer. The system then determines a Lipschitz bound based on the one or more Bessel bounds. The system then applies the Lipschitz bound to the signals. Once the Lipschitz bound is applied, the system can feed-forward the signals to other processes of the layer or to a further layer.

Abstract: Computer vision systems and methods for optimized computer vision using deep neural networks and Lipschitz analysis are provided. The system receives signals or data related to visual imagery, such as data from a camera, and feed-forwards the signals/data through the multiple layers of a convolutional neural network (CNN). At one or more layers of the CNN, the system determines at least one Bessel bound of that layer. The system then determines a Lipschitz bound based on the one or more Bessel bounds. The system then applies the Lipschitz bound to the signals. Once the Lipschitz bound is applied, the system can feed-forward the signals to other processes of the layer or to a further layer.

Abstract: A computer-implemented method for blind-source separation includes capturing a mixed source signal by two or more sensors, transforming the mixed source signal from a time domain into a frequency domain, and estimating a mixing parameter of the mixed source signal. The method further includes determining a plurality of parameters of a source signal in the mixed source signal, separating the source signal from the mixed source signal under a sparsity constraint, transforming a separated source signal from the frequency domain into the time domain, and outputting the separated source signal.

Abstract: Disclosed is an apparatus for and a method of filtering noise from a mixed sound signal to obtained a filtered target signal, comprising the steps of inputting (100) the mixed signal through a pair of microphones (10) into a first channel (15a) and a second channel (15b), separately Fourier transforming (110) each said mixed signal into the frequency domain, computing (130) a signal short-time spectral amplitude |?| from said transformed signals, computing (140) a signal short-time spectral complex exponential ei arg(S) from said transformed signals, where arg(S) is the phase of the target signal in the frequency domain, computing (150) said target signal S in the frequency domain from said spectral amplitude and said complex exponential.

Abstract: A system and method for nonlinear signal enhancement is provided. The method comprises: performing a linear transformation on a measured signal comprising a source component and a noise component; determining a modulus of the linear transformed signal; estimating a noise-free part of the linear transformed signal; and reconstructing the source component of the measured signal using the noise-free part of the linear transformed signal.

Abstract: A system that reconstructs independent signals from degenerate mixtures estimates independent Auto Regressive (AR) processes from their sum. The system includes an identification system and an estimator. A mixture of two signals is inputted into the system and through the identifying and filtering processes, two estimates of the original signals are outputted. The identification system includes an ARMA identifier, a computation of autocovariance coefficients, an initializer and a gradient descent system. The estimator includes filtering.