International Business Machines Corporation

University of California

Agency for Science, Technology and Research

Simone Bianco

Vito Paolo Pastore

Mark Kunitomi

James L Hedrick

Yi Yan Yang

Xuan Liu

Ajay Ashok Deshpande

Yi Zhou

Chuan Yang

Nathalie Baracaldo Angel

Xin Ding

Ali Anwar

Zhen Chang Liang

Vishrawas Gopalakrishnan

James H Kaufman

Raman Srinivasan

Pan Ding

Paola Florez De Sessions

Balamurugan Periaswamy

Sayali Navalekar

Kun Hu

Nimrod Megiddo

Thomas Guthrie Zimmerman

Luisa Dominica Bozano

Andrea Fasoli

Jacob O Miller

Aminat Adebiyi

Mohammed Abdi

Alberto Mannari

George Sirbu

Ujwal Reddy Moramganti

Rebecca M McGillivary

Wallace F Marshall

Sarah Kefayati

Provided is a deep learning algorithm that analyzes fragments of biological sequences. The input for the deep learning algorithm is a biological sequence fragment of unknown origin and the output is the closest known biological genome that could share phenotypic properties with the biological species of unknown origin. The workflow thus has application for genomic classification, identification of mutations within known genomes, and the identification of the closest class for unknown species.

Identification of unknown genomes and closest known genomes

Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

Hypothetical scenario evaluation in infectious disease dynamics based on similar regions

Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Controlling compartmental flows in epidemiological modeling based on mobility data

Techniques regarding treating one or more microbe infections with combination therapy are provided. For example, one or more embodiments described herein can comprise a method, which can comprise enhancing an antimicrobial activity of an antibiotic by a combination therapy. The combination therapy can comprise the antibiotic and a polycarbonate polymer functionalized with a guanidinium functional group.

Utilizing polymers and antibiotics to enhance antimicrobial activity and inhibit antibiotic resistance

Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Volatile organic compound detection and classification

A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.

Semantic learning in a federated learning system

A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.

Semi-supervised classification of microorganism

A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.

A computer-implemented method according to one embodiment includes identifying a plurality of two-dimensional (2D) images illustrating a subject performing a rotation, selecting a representative image of the subject, cropping the plurality of 2D images, utilizing the representative image of the subject, to create a cropped plurality of 2D images, aligning each of the cropped plurality of 2D images, thereby creating an aligned plurality of 2D images, selecting a subset of the aligned plurality of 2D images that illustrate a predetermined amount of rotation of the subject, and creating a three-dimensional (3D) point cloud of the subject, utilizing the subset of the aligned plurality of 2D images.

Creating a three-dimensional model from a sequence of images

Growing community of inventors

San Francisco, CA, United States of America

Simone Bianco