Abstract: Applicant discloses herein relate to systems, methods, apparatuses, and non-transitory computer readable media for generating physiological signals datasets, analyzing physiological signals in the physiological signals datasets, extract fractional dynamics signatures specific to Chronic Obstructive Pulmonary Disease (COPD) medical records, and identifying, using a deep neural network (DNN), a COPD stage.

Abstract: The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space. Described herein is a multiwavelet-based neural operator learning scheme that compresses the associated operator's kernel using fine-grained wavelets. The system embeds the inverse multiwavelet filters to learn the projection of the kernel onto fixed multiwavelet polynomial bases. The projected kernel is trained at multiple scales derived from using repeated computation of multiwavelet transform. This allows learning the complex dependencies at various scales and results in a resolution-independent scheme. These techniques exploit the fundamental properties of the operator's kernel, which enables numerically efficient representation. These techniques show significantly higher accuracy in a large range of datasets.

Abstract: A system includes one or more processors, coupled with memory, to generate, according to a model corresponding to a state of a network of neurons of a brain of a predetermined patient, a first matrix indicative of activity among a plurality of neurons of the brain during an event of the brain corresponding to epilepsy, modify, according to the model, one or more elements of the first matrix into a second matrix indicative of mitigation of the event for the plurality of neurons, and provide, to the patient to mitigate the event according to the second matrix, a therapeutic response having a physical property based on the first matrix and the second matrix.

Abstract: A method includes generating topological clusters and network communities, relating each cluster and each community to a pharmacological property or pharmacological action, identifying, within each topological cluster or modularity class community, a subset of drugs that are not compliant with the cluster or community label, validating indicated repositionings, and analyzing molecular docking parameters for previously unaccounted repositionings.

Abstract: Computer systems and computer implemented methods are presented for designing and making vaccines to pathogens, particular viral pathogens. Vaccine compositions for COVID-19 are also disclosed, as well as method of using the same.

Abstract: With increasing demand for distributed intelligent physical systems performing big data analytics on the field and in real-time, processing-in-memory (PIM) architectures integrating 3D-stacked memory and logic layers could provide higher performance and energy efficiency. Towards this end, the PIM design requires principled and rigorous optimization strategies to identify interactions and manage data movement across different vaults.

Abstract: Methods, systems, devices and apparatuses for reconstructing a network. The network reconstruction system includes a processor. The processor is configured to determine an unknown sub-network of a network. The unknown sub-network includes multiple unknown nodes and multiple unknown links. The processor is configured to determine the unknown sub-network based on a known sub-network that has multiple known nodes and multiple known links, a network model and an attacker's statistical behavior to reconstruct the network. The processor is configured to determine one or more network parameters of the network. The network processor is configured to provide a probability of an outcome of an input or observation into the network or into a second network that has the one or more network parameters of the network.

Abstract: A self-optimizing and self-programming computing system (SOSPCS) design framework that achieves both programmability and flexibility and exploits computing heterogeneity [e.g., CPUs, GPUs, and hardware accelerators (HWAs)] is provided. First, at compile time, a task pool consisting of hybrid tasks with different processing element (PE) affinities according to target applications is formed. Tasks preferred to be executed on GPUs or accelerators are detected from target applications by neural networks. Tasks suitable to run on CPUs are formed by community detection to minimize data movement overhead. Next, a distributed reinforcement learning-based approach is used at runtime to allow agents to map the tasks onto the network-on-chip-based heterogeneous PEs by learning an optimal policy based on Q values in the environment.

Abstract: With increasing demand for distributed intelligent physical systems performing big data analytics on the field and in real-time, processing-in-memory (PIM) architectures integrating 3D-stacked memory and logic layers could provide higher performance and energy efficiency. Towards this end, the PIM design requires principled and rigorous optimization strategies to identify interactions and manage data movement across different vaults.

Abstract: Method and system for non-linear modeling of physiological behavior, such as R-R intervals, in implantable devices, such as a rate responsive pacemakers, comprising a comprehensive modeling and optimization methodology based on fractional calculus and constrained finite horizon optimal control theory that allows for allows for fine-grain optimization of pacemaker response to heart rate variations; and the theoretical basis on which a hardware implementation of the fractional optimal controller that can respond to changes in the heart rate dynamics. Present invention describes a fractal approach to pacemaker control based on the constrained finite horizon optimal control problem. This is achieved by modeling the heart rate dynamics via fractional differential equations. Also, by using calculus of variations, the invention describes how the constrained finite horizon optimal control problem can be reduced to solving a linear system of equations.

Abstract: Method and system for non-linear modeling of physiological behavior, such as R-R intervals, in implantable devices, such as a rate responsive pacemakers, comprising a comprehensive modeling and optimization methodology based on fractional calculus and constrained finite horizon optimal control theory that allows for allows for fine-grain optimization of pacemaker response to heart rate variations; and the theoretical basis on which a hardware implementation of the fractional optimal controller that can respond to changes in the heart rate dynamics. Present invention describes a fractal approach to pacemaker control based on the constrained finite horizon optimal control problem. This is achieved by modeling the heart rate dynamics via fractional differential equations. Also, by using calculus of variations, the invention describes how the constrained finite horizon optimal control problem can be reduced to solving a linear system of equations.