Abstract: To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios.

Abstract: To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios.

Abstract: According to an aspect of an embodiment, a method may include obtaining a data set that includes categories (or features), and a target criteria. The method may further include obtaining a first decision tree model using the data set. The method may further include ranking the categories based on the first decision tree model and removing low-ranking categories from the data set. The method may further include generating a second decision tree model using the data set. The second decision tree model may include branch nodes. Each of branch nodes may represent a branch criteria. The method may further include pruning a branch node. The method may further include designating a remaining branch nodes as a rule node. The method may further include generating a rule based on the branch criteria of the rule node and presenting the rule in a graphical user interface.

Abstract: A method may include directing display of a dataset menu listing datasets representative of graphs. The method may include identifying features in the datasets as corresponding to nodes and edges. The method may include selecting local or global mapping to map categorical feature values to numeric values. Local mapping may be selected in response to a distribution of feature values not corresponding across different graphs. Global mapping may be selected in response to a distribution of the feature values corresponding across different graphs. The method may include directing display of configuration settings that indicate the selection between local and global mapping for training a classification model. The method may include obtaining selected configuration settings. The method may include providing the selected configuration settings and datasets to a machine learning backend, which may utilize the machine learning algorithm, datasets, and selected configuration settings to train the classification model.

Abstract: In the present disclosure, operations may include obtaining a parking area constraint, a time constraint, and one or more mobile machine parameters. The operations may include determining a first parking position within the parking area during the time period. The operations may include identifying one or more shade-providing objects. The operations may include determining, based on the one or more shade-providing objects, one or more shadow profiles. The operations may include determining, based on the time constraint, the parking area constraint, and the one or more shadow profiles, a first shadow-position relationship that indicates shade provided at the first parking position during the time period. In addition, the operations may include selecting the first parking position based on the first shadow-position relationship and the one or more mobile machine parameters. The operations may further include, in response to the selecting, causing the mobile machine to park at the first parking position.

Abstract: A method may include directing display of a dataset menu listing datasets representative of graphs. The method may include identifying features in the datasets as corresponding to nodes and edges. The method may include selecting local or global mapping to map categorical feature values to numeric values. Local mapping may be selected in response to a distribution of feature values not corresponding across different graphs. Global mapping may be selected in response to a distribution of the feature values corresponding across different graphs. The method may include directing display of configuration settings that indicate the selection between local and global mapping for training a classification model. The method may include obtaining selected configuration settings. The method may include providing the selected configuration settings and datasets to a machine learning backend, which may utilize the machine learning algorithm, datasets, and selected configuration settings to train the classification model.

Abstract: A method may include identifying multiple evaluation functions related to operation of a machine learning system as a first set of inputs for a computing process configured to generate a Pareto set of solutions of the evaluation functions. The method may also include identifying a set of initial search points for the computing process, each of the search points including a potential solution of the evaluation functions, each of the potential solutions including values for a set of variables affecting the evaluation functions and an associated weight for each of the variables. The method may also include performing the computing process using both the evaluation functions and the set of initial search points such that the computing process varies the potential solutions over a potential solution space, thereby identifying the Pareto set of solutions that improve or hold steady a performance score of each of the evaluation functions.

Abstract: To identify physiological states that are predictive of a person's performance, a system provides physiological and behavioral interfaces and a data processing pipeline. Physiological sensors generate physiological data about the person while performing a task. The behavioral interface generates performance data about the person while performing the task. The pipeline collects the physiological and performance data along with reference data from a population of people performing the same or similar tasks. In various implementations, the physiological states are brain states. In one implementation, the pipeline computes bandpower ratios.

Abstract: In the present disclosure, operations may include obtaining a parking area constraint, a time constraint, and one or more mobile machine parameters. The operations may include determining a first parking position within the parking area during the time period. The operations may include identifying one or more shade-providing objects. The operations may include determining, based on the one or more shade-providing objects, one or more shadow profiles. The operations may include determining, based on the time constraint, the parking area constraint, and the one or more shadow profiles, a first shadow-position relationship that indicates shade provided at the first parking position during the time period. In addition, the operations may include selecting the first parking position based on the first shadow-position relationship and the one or more mobile machine parameters. The operations may further include, in response to the selecting, causing the mobile machine to park at the first parking position.

Abstract: According to an aspect of an embodiment, a method may include obtaining a data set that includes categories (or features), and a target criteria. The method may further include obtaining a first decision tree model using the data set. The method may further include ranking the categories based on the first decision tree model and removing low-ranking categories from the data set. The method may further include generating a second decision tree model using the data set. The second decision tree model may include branch nodes. Each of branch nodes may represent a branch criteria. The method may further include pruning a branch node. The method may further include designating a remaining branch nodes as a rule node. The method may further include generating a rule based on the branch criteria of the rule node and presenting the rule in a graphical user interface.

Abstract: Described is a bio-inspired vision system for object recognition. The system comprises an attention module, an object recognition module, and an online labeling module. The attention module is configured to receive an image representing a scene and find and extract an object from the image. The attention module is also configured to generate feature vectors corresponding to color, intensity, and orientation information within the extracted object. The object recognition module is configured to receive the extracted object and the feature vectors and associate a label with the extracted object. Finally, the online labeling module is configured to alert a user if the extracted object is an unknown object so that it can be labeled.