Camera winch control for dynamic monitoring

    公开(公告)号:US11089227B1

    公开(公告)日:2021-08-10

    申请号:US16785252

    申请日:2020-02-07

    Abstract: A method for controlling a sensor subsystem, the method including receiving one or more metrics representing one or more characteristics of livestock, including one or more livestock objects, contained in an enclosure and monitored by one or more sensors coupled to a winch subsystem. The method further includes determining a position to move the one or more sensors based on the metrics and determining an instruction that includes information related to a movement of the one or more sensors. The method further includes sending the instruction to the winch subsystem to change the position of the one or more sensors.

    PARTIAL HRTF COMPENSATION OR PREDICTION FOR IN-EAR MICROPHONE ARRAYS

    公开(公告)号:US20210211810A1

    公开(公告)日:2021-07-08

    申请号:US17203589

    申请日:2021-03-16

    Abstract: In some embodiments, an ear-mounted sound reproduction system is provided. The system includes an ear-mountable housing that sits within the pinna of the ear and occludes the ear canal. In some embodiments, the ear-mountable housing includes a plurality of external-facing microphones. Because the external-facing microphones may be situated within the pinna of the ear but outside of the ear canal, the microphones will experience some, but not all, of the three-dimensional acoustic effects of the pinna. In some embodiments, sound is reproduced by an internal-facing driver element of the housing using a plurality of filters applied to the signals received by the plurality of external-facing microphones to preserve three-dimensional localization cues that would be present at the eardrum in the absence of the housing, such that the housing is essentially transparent to the user. In some embodiments, techniques are provided for deriving the plurality of filters.

    TRAINING ARTIFICIAL NEURAL NETWORKS BASED ON SYNAPTIC CONNECTIVITY GRAPHS

    公开(公告)号:US20210201158A1

    公开(公告)日:2021-07-01

    申请号:US16731331

    申请日:2019-12-31

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output.

    TRANSFORMATION MODE SWITCHING FOR A REAL-TIME ROBOTIC CONTROL SYSTEM

    公开(公告)号:US20210197373A1

    公开(公告)日:2021-07-01

    申请号:US16730864

    申请日:2019-12-30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing transformation mode switching in a robotics control system. One of the methods includes receiving data representing a state machine that defines one or more portions of a robotics task; executing a first control loop corresponding to a first node of the state machine, wherein executing the first control loop comprises providing commands to the robotic components computed from a first coordinate transformation process; determining, based on one or more status messages, that an exit condition for the first node has been satisfied; performing a mode switch between the first coordinate transformation process and a different second coordinate transformation process; and executing a second control loop corresponding to a second node of the state machine, wherein executing the second control loop comprises providing commands to the robotic components computed from the second coordinate transformation process.

    MAGNETOENCEPHALOGRAPHY
    226.
    发明申请

    公开(公告)号:US20210196177A1

    公开(公告)日:2021-07-01

    申请号:US17138696

    申请日:2020-12-30

    Abstract: A magnetoencephalography apparatus includes: a lead configured to be secured to a user's head; a first magnetic field sensor attached to the lead, the first magnetic field sensor including a substrate, and an electron spin defect layer on the substrate, the electron spin defect layer including at least one lattice defect, in which a first spin energy level of the at least one lattice defect splits upon exposure to a microwave; and cabling, in which the cabling includes a first microwave transmission line arranged to provide a first microwave field to the electron spin defect layer and in which the cabling includes an optical fiber arranged to provide, from a first end of the optical fiber, a first light signal to the electron spin defect layer and to receive, at the first end of the optical fiber, a second light signal emitted by the electron spin defect layer.

    Automated identification of code changes

    公开(公告)号:US11048482B2

    公开(公告)日:2021-06-29

    申请号:US16523363

    申请日:2019-07-26

    Abstract: Implementations are described herein for automatically identifying, recommending, and/or automatically effecting changes to a source code base based on updates previously made to other similar code bases. Intuitively, multiple prior “migrations,” or mass updates, of complex software system code bases may be analyzed to identify changes that were made. More particularly, a particular portion or “snippet” of source code—which may include a whole source code file, a source code function, a portion of source code, or any other semantically-meaningful code unit—may undergo a sequence of edits over time. Techniques described herein leverage this sequence of edits to predict a next edit of the source code snippet. These techniques have a wide variety of applications, including but not limited to automatically updating of source code, source code completion, recommending changes to source code, etc.

    CLOSED LOOP CONTINUOUS APTAMER DEVELOPMENT SYSTEM

    公开(公告)号:US20210189385A1

    公开(公告)日:2021-06-24

    申请号:US17126842

    申请日:2020-12-18

    Inventor: Ivan Grubisic

    Abstract: The present disclosure relates to a closed loop aptamer development system that identifies one or more aptamers observed experimentally and implements machine-learning models to identify other aptamers not observed experimentally. Particularly, aspects of the present disclosure are directed to receiving a query concerning one or more targets, acquiring a library of aptamers that potential satisfy the query, identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query, obtaining sequence data for the first set of aptamers, generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers, validating the third set of aptamers that substantially or completely satisfy the query, and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.

    REDUCING MOTION BLUR FOR ROBOT-MOUNTED CAMERAS

    公开(公告)号:US20210187747A1

    公开(公告)日:2021-06-24

    申请号:US16724883

    申请日:2019-12-23

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for deblurring an image captured by a robot-mounted camera. One of the methods comprises capturing, using a camera that is attached to an arm of a robot, an image at a first time, wherein the image exhibits motion blur, and wherein the exhibited blur was caused by movement of the arm of the robot at the first time; receiving, from a robot control system of the robot, motion data characterizing the movement of the arm of the robot at the first time; generating a motion kernel using the received motion data; and generating a deblurred image by processing the image using the motion kernel.

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