International Business Machines Corporation

University College Dublin

Sergiy Zhuk

Robert Shorten

Tigran Tchrakian

Randall Lee Cogill

Martin M Rufli

Rudi Verago

Zubair Nabi

Chungmok Lee

Fabian Wirth

Olivier Gallay

Stephen Michael Moore

Jonathan P Epperlein

Yassine Lassoued

Julien Monteil

Emanuele Ragnoli

Rahul Nair

Martin Mevissen

Rodrigo Hernan Ordonez-Hurtado

Pol MacAonghusa

Joe Naoum-Sawaya

Giovanni Russo

Francesco Fusco

Fearghal O'Donncha

Frank Suits

Jakub Marecek

Kerry J Halupka

Seshu Tirupathi

Bradley Eck

Wynita M Griggs

Jia Yuan Yu

Timothy Michael Lynar

John Michael Wagner

Jeffrey L Rogers

Christine Schieber

Bo Wen

Sean A McKenna

Michele Gazzetti

Darcy James Beurle

Claudio Gambella

Mykhaylo Zayats

Nigel Hinds

Andrew Rawlinson

Albert Akhriev

Anton Dekusar

Colin Compas

Melanie E Roberts

Pol Mac Aonghusa

Sergio Cabrero Barros

Yasmin Blunck

Nicole A Taheri

Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Three-dimensional delineation of tumor boundaries via supervised machine learning algorithms and augmented reality

Embodiments for identifying stochastic models representing the individual decision makers in a computing environment by a processor. One or more non-deterministic (stochastic, probabilistic) models may be identified according to a sequence of outcomes from decisions of each of a plurality of decision makers.

Identification of non-deterministic models of multiple decision makers

Embodiments for implementing intelligent risk detection and mitigation in a transport network by a processor. Data gathered from a plurality of data sources relating to an entity and a selected region of interest may be analyzed. Behavior of an entity, in relation to a risk event, may be learned and interpreted based on a plurality of identified contextual factors, geographical data, current data, historical data, a learned risk event model, or a combination thereof. One or more mitigation actions may be performed to mitigate risk of occurrence or a possible negative impact of the risk event caused at least in part by the behavior of the entity.

Detecting and mitigating risk in a transport network

In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

GPU accelerated perfusion estimation from multispectral videos

Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

Intelligent monitoring of a health state of a user engaged in operation of a computing device

A technique relates to coronary reconstruction. Angiogram data associated with cardiac catheterization of an artery for a subject is received, where a contrasting agent is used during the cardiac catheterization. Frames that match from the angiogram data are selected, the frames being from different views. Two-dimensional ordered point clouds for the frames are formed. A three-dimensional ordered point cloud of a reconstructed coronary artery for the subject from the two-dimensional ordered point clouds of the frames is formed. Observed contrast motion caused by the contrasting agent onto the reconstructed coronary artery is mapped, the reconstructed coronary artery thereby providing a three-dimensional model of the artery of the subject during the cardiac catheterization.

Optimum treatment planning during coronary intervention by simultaneous simulation of a continuum of outcomes

Embodiments for implementing intelligent boundary delineation of a region of interest of an organism in two spatial dimensions in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels.

Intelligent boundary delineation of regions of interest of an organism from multispectral video streams using perfusion models

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

Mitigating risk behaviors

A method for imaging a coronary arterial system of an individual includes releasing, using an actuator, pulses of a radio-opaque dye into a coronary arterial tree of the individual. The method further includes obtaining, using an image capture device, a sequence of invasive coronary x-ray angiogram images over time of the pulses of the radio-opaque dye. The method also includes tracking, using a processor, the pulses through the sequence of invasive coronary x-ray angiogram images and locating the pulses on a three dimensional (3D) structural model of the coronary arterial system to generate a three dimensional (3D) functional model of the coronary arterial system that shows a trajectory of the dye as it flows through different arterial branches.

Fluid-injector for a simultaneous anatomical and fluid dynamic analysis in coronary angiography

Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. The one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models, representing spatio-temporal behavior of the contrast agent reflected by the time series data, and by using a machine learning operation.

Intelligent classification of regions of interest of an organism from multispectral video streams using perfusion models

A method for estimating a fluid flow velocity may include receiving, with a processing device, a plurality of observations corresponding to a concentration of a constituent of a flowing fluid mixture, and computing a final estimate of an average velocity of the flowing fluid mixture based at least in part on the observations, wherein the constituent is undergoing a chemical reaction and the computing implements a reactive transport model.

Computer based fluid flow velocity estimation from concentrations of a reacting constituent for products and services

Embodiments for testing embedded systems and their applications in an Internet of Things (IoT) environment by a processor, denoted as a Hardware-in-the-Loop as a Service (HiLaaS). In a simulated environment, one or more simulated entities and one or more real entities in a networked system may be tested in real-time according to received control parameters, for a price. The price is estimated by the system, based on other parameters, and offered to the user to accept or reject. Alternatively, the user may specify the price, the system estimates control parameters, and the user can accept or reject the control parameters. One or more properties of the one or more entities, the network system, or combination thereof may be estimated based on the testing of the one or more simulated entities, when the price and control parameters are accepted.

Testing embedded systems and application using hardware-in-the-loop as a service (HILAAS)

A mechanism is provided in a data processing system for minimizing uncertainty envelopes in trajectories of evolving ensemble members. The mechanism generates a trajectory forecast of each member object of an ensemble based on an initial state-space and a model for predicting trajectories of the member objects to generate a plurality of trajectory forecasts. Each of the plurality of trajectory forecasts has an individual uncertainty envelope. The mechanism applies a classification algorithm on the plurality of trajectory forecasts to identify at least one group of member objects having similar trajectory forecasts, generates a reduced ensemble of member objects including the identified group of member objects, and reconfigures the state-space and the model for predicting trajectories. The mechanism generates an updated trajectory forecast of each member object of the reduced ensemble based on the reconfigured state-space and the reconfigured model for predicting trajectories of the member objects.

Minimizing uncertainty envelopes in trajectories of evolving ensemble members

A method and system are provided. The method includes performing, by at least a computer processing system having a hardware processor, a particulate mapping process to predict particulate exposure at a target location based on an estimated particulate source, an estimated particulate source output, and a fine-grained weather forecast for the target location. The performing step includes estimating the target location using a fine-grained weather hindcast and inverse modelling. The performing step further includes generating observations of particulate exposure for one or more specific particulates, using a set of particulate sensors.

Personalized particulate matter exposure management using fine-grained weather modeling and optimal control theory

A system for posterior estimation of variables. Receiving a set of data inputs. Determining a first model of the water distribution network based on the set of data inputs. Determining a second model of the water distribution network based on the set of data inputs, and the first model.

Posterior estimation of variables in water distribution networks

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Dublin, Ireland

Sergiy Zhuk