Abstract: Systems and methods for uncovering a conservation law in physical systems including evaluating collected data by a representation learning system. Analyzing the collected data by a topological learning analysis system, transferring the data into at least two displayable trajectories. Generating a distance matrix by computing so-called Wasserstein distances between the trajectories, with the distance matrix showing a shape space between the trajectories. Embedding the shape space of the distance matrix into lower dimensions by using a so-called uniform manifold approximation and projection (UMAP). Determining at least two scores for different embeddings to choose a score, representing the shape space, and/or representing a minimal dimensionality. Using a symbolic regression to identify a closed form of the integrals of motion.

Abstract: Systems and methods for detecting malicious activity on an endpoint, the endpoint having executing processes, including tracking behavior of executing processes, generating a provenance graph to group the behavior events, transforming the provenance graph into a sequence of behavior events, training a sequence classification machine learning model based on the sequence of behavior events, processing a sequence of test behavior events using the sequence classification machine learning model to generate a probability of maliciousness, and alerting for malicious activity when the probability of maliciousness for the sequence of test behavior events is greater than a threshold.

Abstract: A method for predicting at least one property of a crystal of a material, with the crystal exhibiting at least one point defect. At least one module samples structures of materials and point defects of the crystal. The method comprises: providing a neural network. The neural network comprises: receiving as input a structure of the material and an ideal crystal unit cell structure. The neural network also comprises: outputting at least one target quantity and using a generated set of data and representing the point defect. The point defect represents a set of coordinates and a type of the point defect and a crystal unit cell structure. The neural network further comprises: receiving as input at least one of a cloud of defect points and a global state vector. The neural network additionally comprises outputting a vector.

Abstract: There is disclosed a method and system for operating a storage area network (SAN) simulator. The method comprises generating training data representative of a SAN system. The method also comprises generating a SAN simulator corresponding to the SAN system, where the SAN simulator outputs a predicted metric of at least one component of the SAN system. The method then comprises using the training data to train a machine learning algorithm (MLA) to determine adjustments for parameters of the SAN simulator.