Abstract: Triggering a prioritized alert and provisioning an action may include receiving historical data associated with a set of projects, the historical data spanning multiple consecutive time periods. A hierarchical data structure is generated that includes occurrences of performance factors in the historical data. Based on the hierarchical data structure, Bayesian scores associated with the performance factors are derived, the Bayesian scores representing likelihood of the performance factors occurring in a given project. The performance factors are ranked based on the Bayesian scores. Based on ranking, an alert and an action may be automatically triggered.

Abstract: Techniques facilitating tree-based associative data augmentation are provided. In one example, a computer-implemented method comprises: generating, by a device operatively coupled to a processor, a model probability distribution for one or more measured samples based on conditional probabilities for respective nodes of a tree structure associated with dimensions of the one or more measured samples; and producing, by the device, synthetic samples by drawing from the model probability distribution for at least one of the one or more measured samples.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.

Abstract: A computer-implemented method according to one embodiment includes identifying principal components for a dataset defined by data instances and features corresponding to the data instances, identifying, for at least one of the data instances, at least some of the principal components, wherein the identified principal components are determined to be salient for said at least one data instance, and determining, for said at least one of the data instances, one or more salient features corresponding to the identified salient principal components.

Abstract: Techniques facilitating tree-based associative data augmentation are provided. In one example, a computer-implemented method comprises: generating, by a device operatively coupled to a processor, a model probability distribution for one or more measured samples based on conditional probabilities for respective nodes of a tree structure associated with dimensions of the one or more measured samples; and producing, by the device, synthetic samples by drawing from the model probability distribution for at least one of the one or more measured samples.

Abstract: A computer-implemented method according to one embodiment includes identifying principal components for a dataset defined by data instances and features corresponding to the data instances, identifying, for at least one of the data instances, at least some of the principal components, wherein the identified principal components are determined to be salient for said at least one data instance, and determining, for said at least one of the data instances, one or more salient features corresponding to the identified salient principal components.

Abstract: Triggering a prioritized alert and provisioning an action may include receiving historical data associated with a set of projects, the historical data spanning multiple consecutive time periods. A hierarchical data structure is generated that includes occurrences of performance factors in the historical data. Based on the hierarchical data structure, Bayesian scores associated with the performance factors are derived, the Bayesian scores representing likelihood of the performance factors occurring in a given project. The performance factors are ranked based on the Bayesian scores. Based on ranking, an alert and an action may be automatically triggered.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: A method of operating an image detection device includes receiving an image, dividing the image into a plurality of patches, grouping ones of the plurality of patches, generating a set of saccadic paths through the plurality of patches of the image, generating a cluster-direction sequence for each saccadic path, generating a policy function for identifying an object in a new image using a combination of the cluster-direction sequences, and operating the image detection device using the policy function to identify an object in the new image.

Abstract: In one embodiment, a computer-implemented method includes identifying a plurality of origin-destination (OD) pairs within a transportation network. A plurality of ranges for price elasticities may be calculated, where the ranges include at least one price elasticity range for each of the OD pairs, and where, for each OD pair, the associated range of the price elasticity spans multiple values. A robustness parameter may be received. A pricing scheme may be calculated, by a computer processor, based at least in part on the robustness parameter, where the pricing scheme includes a price for each of the OD pairs in the network and is at least partially based on the robustness parameter and the plurality of ranges for the price elasticities.

Abstract: According to an exemplary embodiment, a computer-implemented method for attempting to optimize a supply chain network (SCN) includes forecasting demand in the SCN based on a set of demand data. One or more time-dependent reorder points (ROPs) deemed to optimize the SCN are generated by a computer processor, based on the demand forecast, where each time-dependent ROP represents an ROP that changes over time. A simulation of operations of the SCN is performed, using the time-dependent ROPs.

Abstract: According to an exemplary embodiment, a computer-implemented method for attempting to optimize a supply chain network (SCN) includes forecasting demand in the SCN based on a set of demand data. One or more time-dependent reorder points (ROPs) deemed to optimize the SCN are generated by a computer processor, based on the demand forecast, where each time-dependent ROP represents an ROP that changes over time. A simulation of operations of the SCN is performed, using the time-dependent ROPs.