Abstract: A data processing system may generate a plurality of probability distributions, each probability distribution corresponding to a different profile characteristic regarding transactions performed by an entity. The data processing system may receive a first profile characteristic configuration for each of the plurality of probability distributions and a corresponding first start time. The data processing system may adjust each of the plurality of probability distributions according to the first profile characteristic configuration for the probability distribution and the first start time to generate a first set of adjusted probability distributions. The data processing system may sample each of the first set of adjusted probability distributions to generate transaction data for one or more first transactions. The data processing system may generate a record comprising the generated transaction data.

Abstract: A method comprises generating a first feature vector comprising a plurality of values for a plurality of transactions, each of the plurality of transactions corresponding to an account and performed within a defined time period, and a second feature vector comprising an attribute value of an attribute of the account; inserting the first feature vector into a first encoder of a machine learning model to generate a transaction embedding and the second feature vector into a second encoder of the machine learning model to generate an attribute embedding; concatenating the transaction embedding and the attribute embedding to generate a concatenated embedding; and generating an account prediction value by propagating the concatenated embedding into a set of prediction layers of the machine learning model.

Abstract: Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.

Abstract: A method and system for generating a map that shows subsurface structures includes the use of machine learning to develop a trained classifier that associates features in data with types of subsurface structures.

Abstract: A system includes a plurality of graph convolutional networks corresponding to a plurality of time steps, each network modelling a graph including nodes and edges, and in turn including a plurality of graph convolution units; an evolving mechanism; and an output layer. Each of the units, for a given one of the time steps, takes as input a graph adjacency matrix, a node feature matrix, and a parameter matrix for a current layer, and outputs a new node feature matrix for a next highest layer. The mechanism takes as input a parameter matrix for a prior time step updates the input parameter matrix, and outputs the parameter matrix for the given time step. The output layer obtains, as input, output of each of the units for a final time step, and based on the output of each of the units for the final time step, outputs a graph solution.

Abstract: A method for context-aware data mining of a text document includes receiving a list of words parsed and preprocessed from an input query; computing a related distributed embedding representation for each word in the list of words using a word embedding model of the text document being queried; aggregating the related distributed embedding representations of all words in the list of words to represent the input query with a single embedding, by using one of an average of all the related distributed embedding representations or a maximum of all the related distributed embedding representations; retrieving a ranked list of document segments of N lines that are similar to the aggregated word embedding representation of the query, where N is a positive integer provided by the user; and returning the list of retrieved segments to a user.

Abstract: A computer-implemented method for executing a computation task in a molecular dynamic simulation includes identifying a bonding target on a ligand; constructing a protein structure; rendering an image of the ligand; subsampling data pertaining to the constructed protein structure and the image of the ligand at a particular frequency; rendering a two-dimensional image of the constructed protein structure relative to the ligand from a plurality of viewpoints; computing optical flows of the protein structure relative to the ligand based on the two-dimensional image; analyzing the optical flows to determine a displacement of atoms; simulating a binding state outcome of the protein structure relative to the ligand for each of the plurality of viewpoints; and predicting a probability of the protein structure binding with the ligand, based on the predicted binding state outcome for each of the plurality of viewpoints.

Abstract: Altering protein-ligand structures by generating molecular trajectory data for a protein-ligand structure, determining a molecular level binding affinity according to the molecular trajectory data, determining an atom level binding affinity for a first atom of the protein-ligand structure according to the molecular trajectory data, determining a correlation between the atom level and the molecular level binding affinities, and altering the protein-ligand structure according to the correlation.

Abstract: Method and systems for generating an adjacency matrix A and a directional relation graph representing a relationship between one or more diagnostics. The adjacency matrix with all zero's is initialized. One or more entries in the adjacency matrix A are revised by, for each source diagnostic s, decreasing a corresponding count A(s,d) in the adjacency matrix A to decrease a corresponding directional relation in response to the diagnostic s failing and one or more other diagnostics d passing; increasing the corresponding count A(s,d) in the adjacency matrix A to increase the corresponding directional relation in response to the diagnostic s failing and one or more other diagnostics d failing; and maintaining a current value of the corresponding count A(s,d) in the adjacency matrix A in response to the diagnostic s passing or having no corresponding data. The directional relation graph is generated based on the adjacency matrix A.

Abstract: A system includes a plurality of graph convolutional networks corresponding to a plurality of time steps, each network modelling a graph including nodes and edges, and in turn including a plurality of graph convolution units; an evolving mechanism; and an output layer. Each of the units, for a given one of the time steps, takes as input a graph adjacency matrix, a node feature matrix, and a parameter matrix for a current layer, and outputs a new node feature matrix for a next highest layer. The mechanism takes as input a parameter matrix for a prior time step updates the input parameter matrix, and outputs the parameter matrix for the given time step. The output layer obtains, as input, output of each of the units for a final time step, and based on the output of each of the units for the final time step, outputs a graph solution.

Abstract: Based on historic job data, a computer processor can predict a configuration of a computer node for running a future computer job. The computer processor can pre-configure the computer node based on the predicted configuration. Responsive to receiving a submission of a job, the computer processor can launch the job on the pre-configured computer node.

Abstract: Method and systems for generating an adjacency matrix A and a directional relation graph representing a relationship between one or more diagnostics. The adjacency matrix with all zero's is initialized. One or more entries in the adjacency matrix A are revised by, for each source diagnostic s, decreasing a corresponding count A(s,d) in the adjacency matrix A to decrease a corresponding directional relation in response to the diagnostic s failing and one or more other diagnostics d passing; increasing the corresponding count A(s,d) in the adjacency matrix A to increase the corresponding directional relation in response to the diagnostic s failing and one or more other diagnostics d failing; and maintaining a current value of the corresponding count A(s,d) in the adjacency matrix A in response to the diagnostic s passing or having no corresponding data. The directional relation graph is generated based on the adjacency matrix A.

Abstract: A method for context-aware data mining of a text document includes receiving a list of words parsed and preprocessed from an input query; computing a related distributed embedding representation for each word in the list of words using a word embedding model of the text document being queried; aggregating the related distributed embedding representations of all words in the list of words to represent the input query with a single embedding, by using one of an average of all the related distributed embedding representations or a maximum of all the related distributed embedding representations; retrieving a ranked list of document segments of N lines that are similar to the aggregated word embedding representation of the query, where N is a positive integer provided by the user; and returning the list of retrieved segments to a user.