Hrl Laboratories, LLC

Toyota Motor Engineering & Manufacturing North America, Inc.

Fujitsu Corporation

Optios, Inc.

Gm Global Technolgoy Operations, Inc.

United States of America As Represented by the Administrator of Nasa

Carnegie Mellon University

Suhas E Chelian

Rajan Bhattacharyya

Narayan Srinivasa

Michael D Howard

Matthias Ziegler

James Benvenuto

Vincent De Sapio

Rashmi N Sundareswara

Matthew E Phillips

Yuri Owechko

Deepak Khosla

Heiko Hoffmann

David W Payton

Swarup S Medasani

Steven Lamont Miller

David L Allen

Ajay Chander

Shinko Yuanhsien Cheng

Paul Sajda

Paul DeGuzman

Jacek Dmochowski

Will McBurnett

Amy Kruse

Thomas F Nugent, Iii

Douglas Alan Moore

James W Wells

Charles W Wampler, Ii

David J Huber

Douglas Martin Linn

Yaser Sheikh

Tiffany L Chen

Christopher P Lee

Kevin R Martin

Lyndon Bridgwater

Allison Thackston

Howard Neely, Iii

Keith J Wells

Robert D McClain

Giorgio A Ascoli

Jeffrey Lawrence Krichmar

Stephanie E Goldfarb

Zhe Cao

Jeffrey Fischer

Neil David Mc Kay

Vivek Lakshmanan

Qi Zhu

Shanka R Rao

Christopher Kanan

Li Deng

David Bach

Randall C O'Reilly

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. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data—for example, clusters of correlations of decomposed data envelopes—grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.

Identifying and strengthening physiological/neurophysiological states predictive of superior performance

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. In another implementation, the pipeline decomposes the physiological data into frequency-banded components, identifies brain states derived from the decomposed data&#x2014;for example, clusters of correlations of decomposed data envelopes&#x2014;grades the performance data, compares the graded performance data to the brain states, and identifies statistical relationships between the brain states and levels of performance.

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.

Accessible machine learning

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.

Accessible machine learning backends

Described is a system for selecting among intelligence elements of a neural model. An intelligence element is selected from a set of intelligence elements which change group attack probability estimates and processed via multiple operations. A semantic memory component learns group probability distributions and rules based on the group probability distributions. The rules determine which intelligence element related to the groups to select. Given an environment of new probability distributions, the semantic memory component recalls which rule to select to receive a particular intelligence element. An episodic memory component recalls a utility value for each information element A procedural memory component recalls and selects the information element considered to have the highest utility. A list of intelligence elements is published to disambiguate likely attackers.

Method and apparatus for decision making in a neural model including semantic, episodic, and procedural memory components

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.

Automated exploitation of shade-giving structures

Described is a system for proactive and reactive cognitive control using a neural module. The system calculates, for each hypothesis of a set of hypotheses, a probability that an event will occur. The neural module comprises a plurality of neurons and includes the PC module, a prefrontal cortex (PFC) module, an anterior cingulate cortex (ACC) module, a locus coeruleus (LC) module, and a basal forebrain (BF) module. The set of hypotheses are related to tasks to be performed by a plurality of groups, each group having a corresponding hypothesis. For each probability, the system calculates a conflict value across all hypotheses with the ACC module, compares each conflict value to a predetermined threshold using the BF and LC modules. A determination is made whether to directly output the calculated probability or perform an additional probability calculation and output an updated probability.

Method and apparatus for proactive and reactive cognitive control using models of the anterior cingulate cortex and the neuromodulatory system

A vehicle for transporting and communicating with a robot includes a vehicle body and a robot dock mechanically coupled to the vehicle body and configured to mechanically couple the robot to the vehicle body. The vehicle also includes an input/output port configured to be electrically coupled to the robot. The vehicle also includes an electronic control unit (ECU) coupled to the input/output port and configured to transmit a signal to the robot via the input/output port to at least one of program the robot or control the robot.

Vehicle docking and control systems for robots

Described is a system for explaining how the human brain represents conceptual knowledge. A semantic model is developed, and a behavioral exam is performed to assess a calibration subject into a cohort and reveal semantic relationships to modify a personalized semantic space developed by the semantic model. Semantic features are extracted from the personalized semantic space. Neural features are extracted from neuroimaging of the human subject. A neuroceptual lattice is created having nodes representing attributes by aligning the semantic features and the neural features. Structures in the neuroceptual lattice are identified to quantify an extent to which the set of neural features represents a target concept. The identified structures are used to interpret conceptual knowledge in the brain of a test subject.

Method and system to predict and interpret conceptual knowledge in the brain

Systems and methods for articulated pose estimation are provided. Some embodiments include training a convolutional neural network for object pose estimation, which includes receiving a two-dimensional training image of an articulated object that has a plurality of components and identifying, from the two-dimensional training image, at least one key point for each of the plurality of components. Some embodiments also include testing the accuracy of the object pose estimation, which includes visualizing a three or more dimensional pose of each of the plurality of components of the articulated object from a two-dimensional testing image and providing data related to the visualization for output.

Systems and methods for articulated pose estimation

A device for providing feedback includes an imaging device configured to capture an image of a subject, a feedback device, and a controller communicatively coupled to the imaging device and the feedback device. The controller includes at least one processor and at least one memory storing computer readable and executable instructions that, when executed by the processor, cause the controller to determine one or more identifying characteristics of the subject, set one or more parameters for determining a facial expression of the subject in the image based on the one or more identifying characteristics of the subject, determine the facial expression of the subject in the image based on the one or more parameters, and provide the feedback with the feedback device based on the determined facial expression.

Smart necklace for social awareness

A system for improving safety while providing privacy for a vehicle. The system includes a display screen located within the vehicle, configured to alternate between a displaying state and a non-displaying state. The system includes a window configured to alternate between an opaque state and a transparent state. The system includes an electronic control unit (ECU) configured to determine whether the display screen is powered on. The ECU is configured to alternate the display screen between the displaying state and the non-displaying state at a predetermined frequency when the display screen is powered on. The ECU is configured to alternate the window between the opaque state and the transparent state at the predetermined frequency, the display screen being in the displaying state when the window is in the opaque state and the display screen being in the non-displaying state when the window is in the transparent state.

Vehicle display screen safety and privacy system

Methods, systems, and apparatus for controlling air filtration, circulation, climate control and/or other adjustable components. The air control system includes an air filtration or circulation unit for filtering or circulating air. The air control system includes a navigation unit for providing at least one of a navigational map information or environment information including a current location of the vehicle. The air control system includes an electronic control unit connected to the air filtration or circulation unit. The electronic control unit is configured to determine an air control event based on at least one of static air quality information or dynamic air quality information. The electronic control unit is configured to determine that the air control event is within a range of the current location and is approaching. The electronic control unit is configured to activate the air filtration or circulation unit before the air control event reaches the vehicle.

Automatic air quality monitoring and improvement systems

Described is a system for modeling probability matching in human subjects. Features related to probability matching are extracted from a set of human subject responses from behavioral tasks. Neural network model instances are trained on the set of features, resulting in a set of trained neural network model instances. A set of model parameters are derived from the set of trained neural network instances, and the set of derived model parameters are used to emulate human performance on novel data.

Method and apparatus for modeling probability matching human subjects in n-arm bandit tasks

Described is system and method for cognitive recognition. The system receives a multi-dimensional scene array as input data. A foveation module divides the multi-dimensional scene array into a plurality of sub-arrays and outputs contents of a currently selected sub-array. The contents are clustered with a hierarchical clustering module to generate a spatially invariant hierarchical cluster of the contents comprising a plurality of components which are based on a statistical distribution of co-occurrence of features across the currently selected sub-array. Task-relevant components are selectively gated and robustly maintained into a component memory location of a pattern bank with a working memory module with an input gating module. If the task-relevant components activate an abstract category module based on pattern matching, then a category recognition label is generated for the contents of the currently selected sub-array with an executive control module.

Hierarchical clustering method and apparatus for a cognitive recognition system based on a combination of temporal and prefrontal cortex models

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Encino, CA, United States of America

Suhas E Chelian