Abstract: Provided is a system for detecting an anomaly in a multivariate time series that includes at least one processor programmed or configured to receive a dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, determine a set of target data instances based on the dataset, determine a set of historical data instances based on the dataset, generate, based on the set of target data instances, a true value matrix, a true frequency matrix, and a true correlation matrix, generate a forecast value matrix, a forecast frequency matrix, and a forecast correlation matrix based on the set of target data instances and the set of historical data instances, determine an amount of forecasting error, and determine whether the amount of forecasting error corresponds to an anomalous event associated with the dataset of data instances. Methods and computer program products are also provided.

Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

Abstract: Described are a method, system, and computer program product for generating robust graph neural networks using universal adversarial training. The method includes receiving a graph neural network (GNN) model and a bipartite graph including an adjacency matrix, initializing model parameters of the GNN model, initializing perturbation parameters, and sampling a subgraph of a complementary graph based on the bipartite graph. The method further includes repeating until convergence of the model parameters: drawing a random variable from a uniform distribution; generating a universal perturbation matrix based on the subgraph, the random variable, and the perturbation parameters; determining Bayesian Personalized Ranking (BPR) loss by inputting the bipartite graph and the universal perturbation matrix to the GNN model; updating the perturbation parameters based on stochastic gradient ascent; and updating the model parameters based on stochastic gradient descent.

Abstract: A method includes receiving a first data set comprising embeddings of first and second types, generating a fixed adjacency matrix from the first dataset, and applying a first stochastic binary mask to the fixed adjacency matrix to obtain a first subgraph of the fixed adjacency matrix. The method also includes processing the first subgraph through a first layer of a graph convolutional network (GCN) to obtain a first embedding matrix, and applying a second stochastic binary mask to the fixed adjacency matrix to obtain a second subgraph of the fixed adjacency matrix. The method includes processing the first embedding matrix and the second subgraph through a second layer of the GCN to obtain a second embedding matrix, and then determining a plurality of gradients of a loss function, and modifying the first stochastic binary mask and the second stochastic binary mask using at least one of the plurality of gradients.

Abstract: Embodiments are directed to novel techniques for performing domain adaptation on time series data. Using embodiments, labeled source time series data can be used in order to label unlabeled target time series data as either normal or anomalous. Embodiments can accomplish this using an anomaly detector system comprising an anomaly detector component and a context sampler component. The context sampler can determine source and target window sizes used to sample data from the source and target data sets respectively. These samples can be input into the anomaly detector, which can label a target data value corresponding to the target sample as normal or anomalous. The anomaly detector can additionally generate a state value, which can be used by the context sampler to adjust the source and target window sizes accordingly. In this way, embodiments can accurately and automatically perform domain adaptation.

Abstract: Processes for extracting lithium from a clay mineral and compositions thereof are described. The extraction process includes providing a clay mineral comprising lithium, mixing a cation source with the clay mineral, performing a high-energy mill of the clay mineral, and performing a liquid leach to obtain a lithium rich leach solution.

Abstract: Aromatic amines, for example, diarylamines such as diphenylamine, dinaphthylamine, N-phenyl-N-naphthyl amine etc., are alkylated by passing a mixture of the amine and an olefin, though a clay catalyst in a fixed bed reactor system. The process is conveniently run as a continued process, produces an alkylated aromatic amine in excellent purity and provides efficiencies in material and energy use.

Abstract: Aromatic amines, for example, diarylamines such as diphenylamine, dinaphthylamine, N-phenyl-N-naphthyl amine etc., are alkylated by passing a mixture of the amine and an olefin, though a clay catalyst in a fixed bed reactor system. The process is conveniently run as a continued process, produces an alkylated aromatic amine in excellent purity and provides efficiencies in material and energy use.

Abstract: A process for the catalytic coupling of aliphatic or alkyl branched alicyclic hydrocarbons with a functionalizing reagent under thermal conditions to selectively functionalize the hydrocarbon at its primary C—H site.

Abstract: A process for the catalytic coupling of cyclic hydrocarbons with a functionalizing reagent under thermal conditions to functionalize the cyclic hydrocarbon at its secondary or aromatic C—H site.