An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

Storage management is performed. For example, a computing device may determine that a fault belongs to one of a plurality of predefined fault categories based on description information of the fault of a storage system. Then, the computing device may determine at least one fault cause associated with the fault category at a first level of a hierarchical structure of predetermined fault causes. Further, the computing device may determine a first fault cause that causes the fault among the at least one fault cause. After that, the computing device may determine a target fault cause at the deepest level that causes the fault based on the first fault cause. As a result, the root cause of a fault of a storage system may be accurately and efficiently determined, thereby providing the possibility of fundamentally eliminating the fault.

A system and method for identifying ground types from one or more interpolated covariates. The method proceeds by accessing soil composition information for plots of land, in which the soil composition information includes measured soil sample results, environmental results, soil conductivity results or any combination thereof. The method continues by identifying covariates from the soil composition information. Subsequently, the method interpolates covariates associated with different locations with an interpolation training model. Voxels are generated that are each associated with interpolated covariates having a corresponding geographical location. The method trains a random forest training model with the interpolated covariates. The voxels traverse the trained random forest model to identify clusters of voxels that are co-associated. The method identifies a ground type by combining the co-associated clusters. Each ground type is associated with a crop zone, a soil fertility, or a farm management recommendation.

A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.

A learning device is configured to perform learning of a decision tree. The learning device includes a branch score calculator, and a scaling unit. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The scaling unit is configured to perform scaling on a value related to the cumulative sum used for calculating the branch score by the branch score calculator to fall within a numerical range with which the branch score is capable of being calculated.

A vision sensor includes a pixel array comprising pixels arranged in a matrix, an event detection circuit, an event rate controller, and an interface circuit. Each pixel is configured to generate an electrical signal in response to detecting a change in incident light intensity. The event detection circuit detects whether a change in incident light intensity has occurred at any pixels, based on processing electrical signals received from one or more pixels, and generates one or more event signals corresponding to one or more pixels at which a change in intensity of incident light is determined to have occurred. The event rate controller selects a selection of one or more event signals corresponding to a region of interest on the pixel array as one or more output event signals. The interface circuit communicates with an external processor to transmit the one or more output event signals to the external processor.

Provided are an object detection system and an object detection method. An object detection system may include a feature map extraction module configured to receive an image for object detection and extract a feature map having multiple resolutions for the image; a bounding box detection module configured to classify a bounding box by applying a first group of convolutional layers to the feature map, and predict the bounding box by applying a second group of convolutional layers to the feature map; and a mask generation module configured to generate a mask for the shape of the object in the bounding box using the feature map.