Abstract: Systems and methods for enhancing anomaly detection using a pattern dictionary are disclosed. An example method includes receiving, from a wearable device, physiological data of the user, and parsing the physiological data into a set of parsed phrases having a number of parsed phrases by applying a pattern dictionary encoder using a pattern dictionary. Each parsed phrase represents a respective subsequence of the physiological data. The example method includes determining a codelength corresponding to the physiological data based on the set of parsed phrases, and comparing (i) the number of parsed phrases to a parsed phrase threshold, and (ii) the codelength to a codelength threshold using an anomaly detection model. Responsive to the number of parsed phrases exceeding the parsed phrase threshold or the codelength exceeding the codelength threshold, the example method includes generating an alert for display on a user interface indicating that the physiological data is anomalous.

Abstract: The lack of robustness of Deep Neural Networks (DNNs) against different types of attacks is problematic in adversarial environments. The long-standing and arguably most powerful natural defense system is the mammalian immune system, which has successfully defended the species against attacks by novel pathogens for millions of years. This disclosure proposes a Robust Adversarial Immune-inspired Learning System (RAILS) inspired by the mammalian immune system. The RAILS approach is demonstrated using adaptive immune system emulation to harden Deep k-Nearest Neighbor (DkNN) architectures against evasion attacks. Using evolutionary programming to simulate new B-cell generation that occurs in natural immune systems, e.g., B-cell flocking, clonal expansion, and affinity maturation, it is shown that the RAILS learning curve exhibits similar learning behavior as observed in in-vitro experiments on B-cell affinity maturation. The life-long learning mechanism allows RAILS to evolve and defend against diverse attacks.

Abstract: Methods of identifying infectious disease infection prior to presentation of symptoms, assays for identifying genomic markers of infectious disease, and methods for diagnosing the underlying etiology of infectious disease.

Abstract: Techniques identify origins of ventricular arrhythmias (e.g., ventricular tachycardia or premature ventricular complexes) including exit sites or other sites using a single or multi-lead electrocardiogram (ECG) assembly. The ECG assembly is used to map an organ into a series of different three-dimensional (3D) regions. Pace maps or ventricular arrhythmia signals are used in form of ECG signals along with a supervised learning methods to pinpoint the potential origin of VT, i.e., exit sites, in the various regions.

Abstract: Methods of identifying infectious disease infection prior to presentation of symptoms, assays for identifying genomic markers of infectious disease, and methods for diagnosing the underlying etiology of infectious disease.

Abstract: A computer implemented method is provided for clustering and visualization of an n-dimensional space in a Euclidean space. The method includes: collecting a plurality of multi-dimensional data sets; estimating a probability density function from each data set; approximating a dissimilarity between every pair of said probability density functions and recording every dissimilarity in a dissimilarity matrix; embedding the dissimilarity matrix into a Euclidean space having a dimensionality of three or less using a multi-dimensional scaling method; and graphically displaying relationships between data sets using data in the Euclidean space on a display of a computing device.

Abstract: A method determines alignment of images in high dimensional feature space. The method comprises registering a source image of a reference modality to a target image of a second modality with an algorithm based upon a measure of information affinity present in both of the source and target image to create a registered image. Next, a plurality of feature vector are extracted from the registered image for each of the source and target images and attributes of the joint distribution of feature vector are captured using an entropic graph spanning the features. Edge lengths are between proximal feature vectors are extracted from the entropic graph and a similarity measure of one of an ?-divergence estimate or an ?-affinity estimate is constructed based upon these edge lengths to quantify whether the source and target image are sufficiently registered.

Abstract: A computer implemented method is provided for clustering and visualization of an n-dimensional space in a Euclidean space. The method includes: collecting a plurality of multi-dimensional data sets; estimating a probability density function from each data set; approximating a dissimilarity between every pair of said probability density functions and recording every dissimilarity in a dissimilarity matrix; embedding the dissimilarity matrix into a Euclidean space having a dimensionality of three or less using a multi-dimensional scaling method; and graphically displaying relationships between data sets using data in the Euclidean space on a display of a computing device.

Abstract: A method determines alignment of images in high dimensional feature space. The method comprises registering a source image of a reference modality to a target image of a second modality with an algorithm based upon a measure of information affinity present in both of the source and target image to create a registered image. Next, a plurality of feature vectors are extracted from the registered image for each of the source and target images and attributes of the joint distribution of feature vectors are captured using an entropic graph spanning the features. Edge lengths are between proximal feature vectors are extracted from the entropic graph and a similarity measure of one of an ?-divergence estimate or an ?-affinity estimate is constructed based upon these edge lengths to quantify whether the source and target image are sufficiently registered.