Abstract: A batting glove for baseball or softball may include a glove base and a reinforcement layer set. The glove base may include a palmar region, a dorsal region located on an opposite exterior side of the batting glove from the palmar region, a plurality of finger regions, a wrist region at a bottom of the glove base, and an ulnar border region. The ulnar border region may be located along an outermost edge region of the glove base following a length of the little finger region along an outermost side of the little finger region. The reinforcement layer set may be connected to and exterior of the glove base in a manner that covers at least a portion of each of (a) the palmar region, (b) the ulnar border region, and (c) the dorsal region.

Abstract: A system and method for generating a decision tree having a plurality of nodes, arranged hierarchically as parent nodes and child nodes, comprising: generating a node including: receiving i) training data including data instances, each data instance having a plurality of attributes and a corresponding label, ii) instance weightings, iii) a valid domain for each attribute generated, and iv) an accumulated weighted sum of predictions for a branch of the decision tree; and associating one of a plurality of binary prediction of an attribute with each node including selecting the one of the plurality of binary predictions having a least amount of error; in accordance with a determination that the node includes child nodes, repeat the generating the node step for the child nodes; and in accordance with a determination that the node is a terminal node, associating the terminal node with an outcome classifier; and displaying the decision tree including the plurality of nodes arranged hierarchically.

Abstract: A batting glove for baseball or softball may include a glove base and a reinforcement layer set. The glove base may include a palmar region, a dorsal region located on an opposite exterior side of the batting glove from the palmar region, a plurality of finger regions, a wrist region at a bottom of the glove base, and an ulnar border region. The ulnar border region may be located along an outermost edge region of the glove base following a length of the little finger region along an outermost side of the little finger region. The reinforcement layer set may be connected to and exterior of the glove base in a manner that covers at least a portion of each of (a) the palmar region, (b) the ulnar border region, and (c) the dorsal region.

Abstract: Exemplary embodiments described in this disclosure are generally directed to systems and methods for assigning parking spots to autonomous vehicles based on data transfer throughput and other considerations. In one exemplary method, a server computer receives from a first autonomous vehicle, information regarding a size of a first dataset available for uploading from the first autonomous vehicle into the server computer. The server computer may further receive from a second autonomous vehicle, information regarding a size of a second dataset that the second autonomous vehicle has available for uploading into the server computer. The server computer may then assign parking spots to the two autonomous vehicles based on evaluating various factors such as the size of one or both datasets, characteristics of wireless links for carrying out data transfer in the parking area, characteristics of various access points in the parking area, and priorities associated with the data transfer.

Abstract: Exemplary embodiments described in this disclosure are generally directed to systems and methods for assigning parking spots to autonomous vehicles based on data transfer throughput and other considerations. In one exemplary method, a server computer receives from a first autonomous vehicle, information regarding a size of a first dataset available for uploading from the first autonomous vehicle into the server computer. The server computer may further receive from a second autonomous vehicle, information regarding a size of a second dataset that the second autonomous vehicle has available for uploading into the server computer. The server computer may then assign parking spots to the two autonomous vehicles based on evaluating various factors such as the size of one or both datasets, characteristics of wireless links for carrying out data transfer in the parking area, characteristics of various access points in the parking area, and priorities associated with the data transfer.

Abstract: Systems and methods are disclosed for an external vehicle wireless connection. Example methods may include: determining a condition associated with a vehicle; determining, based on the condition, a switching state between a first antenna external to the vehicle and a second antenna internal to the vehicle associated with the vehicle; transmitting, based on the switching state and via the first antenna, a first signal on a first frequency and on a first network or on a second network; and transmitting, based on the switching state and via the second antenna, a second signal on a second frequency on the first network.

Abstract: Systems and methods are disclosed for an external vehicle wireless connection. Example methods may include: determining a condition associated with a vehicle; determining, based on the condition, a switching state between a first antenna external to the vehicle and a second antenna internal to the vehicle associated with the vehicle; transmitting, based on the switching state and via the first antenna, a first signal on a first frequency and on a first network or on a second network; and transmitting, based on the switching state and via the second antenna, a second signal on a second frequency on the first network.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: Systems and methods are disclosed for integrated antennas in vehicles and corresponding techniques for use in connection with location determination and wireless communication protocols. Example methods may include determining a condition associated with a vehicle; and determining, based on the condition, to switch from a first antenna associated with wireless communication, or a second antenna associated with location determination, to a backup antenna associated with the vehicle. Moreover, the backup antenna may have a wireless communication capability and a location determination capability.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

Abstract: A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.