International Business Machines Corporation

Massachusetts Institute of Technology

Giacomo Domeniconi

Eun Kyung Lee

Alessandro Morari

Jie Chen

Yoonho Park

Toyotaro Suzumura

Ruhong Zhou

Charles E Leiserson

Bruce David D'Amora

Guojing Cong

Tengfei Ma

Chih-Chieh Yang

Aldo Pareja

Jong Yoon Lee

Leili Zhang

Timothy Kaler

Tao B Schardl

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.

Optical flow based forecasting of binding states in molecular dynamic simulations

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.

Job-launch time reduction by node pre-configuration

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.

Evolving graph convolutional networks for dynamic graphs

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.

NLP-based context-aware log mining for troubleshooting

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.

Selective diagnostics for computing systems

Growing community of inventors

White Plains, NY, United States of America

Giacomo Domeniconi