Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

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.

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.

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.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, mass, and health of fish are described. A pair of stereo cameras may be utilized to obtain off-axis images of fish in a defined area. The images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the fish model to determine characteristics of the fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Abstract: An example method includes receiving, by a computational assistant executing at one or more processors, a representation of an utterance spoken at a computing device; identifying, based on the utterance, a task to be performed; determining a capability level of a first party (1P) agent to perform the task; determining capability levels of respective third party (3P) agents of a plurality of 3P agents to perform the task; responsive to determining that the capability level of the 1P agent does not satisfy a threshold capability level, that a capability level of a particular 3P agent of the plurality of 3P agents is a greatest of the determined capability levels, and that the capability level of the particular 3P agent satisfies the threshold capability level, selecting the particular 3P agent to perform the task; and performing one or more actions determined by the selected agent to perform the task.

Abstract: Methods, systems, and apparatus for receiving, by a voice action system, data specifying trigger terms that trigger an application to perform a voice action and a context that specifies a status of the application when the voice action can be triggered. The voice action system receives data defining a discoverability example for the voice action that comprises one or more of the trigger terms that trigger the application to perform the voice action when a status of the application satisfies the specified context. The voice action system receives a request for discoverability examples for the application from a user device having the application installed, and provides the data defining the discoverability examples to the user device in response to the request. The user device is configured to provide a notification of the one or more of the trigger terms when a status of the application satisfies the specified context.

Abstract: Example aspects of the present disclosure are directed to processing voice commands or utterances. For instance, data indicative of a voice utterance can be received. A device topology representation can be accessed. The device topology representation can define a plurality of smart devices associated with one or more structures. The device topology representation can further define a location of each of the plurality of devices within the associated structures. A transcription of the voice utterance can be determined based at least in part on the device topology representation. One or more selected devices and one or more actions to be performed by the one or more selected devices can be determined based at least in part on the determined transcription and the device topology representation.

Abstract: In some implementations, (i) audio data representing a voice command spoken by a speaker and (ii) a speaker identification result indicating that the voice command was spoken by the speaker are obtained. A voice action is selected based at least on a transcription of the audio data. A service provider corresponding to the selected voice action is selected from among a plurality of different service providers. One or more input data types that the selected service provider uses to perform authentication for the selected voice action are identified. A request to perform the selected voice action and (i) one or more values that correspond to the identified one or more input data types are provided to the service provider.

Abstract: Methods, systems, and apparatus for receiving, by a voice action system, data specifying trigger terms that trigger an application to perform a voice action and a context that specifies a status of the application when the voice action can be triggered. The voice action system receives data defining a discoverability example for the voice action that comprises one or more of the trigger terms that trigger the application to perform the voice action when a status of the application satisfies the specified context. The voice action system receives a request for discoverability examples for the application from a user device having the application installed, and provides the data defining the discoverability examples to the user device in response to the request. The user device is configured to provide a notification of the one or more of the trigger terms when a status of the application satisfies the specified context.

Abstract: Example aspects of the present disclosure are directed to processing voice commands or utterances. For instance, data indicative of a voice utterance can be received. A device topology representation can be accessed. The device topology representation can define a plurality of smart devices associated with one or more structures. The device topology representation can further define a location of each of the plurality of devices within the associated structures. A transcription of the voice utterance can be determined based at least in part on the device topology representation. One or more selected devices and one or more actions to be performed by the one or more selected devices can be determined based at least in part on the determined transcription and the device topology representation.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.

Abstract: A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.

Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computerized travel services. One of the methods includes identifying photographs using an index of photographs, the photographs being identified from the index as photographs geographically related to a point of interest or destination and having a creation timestamp corresponding to a time of the year; determining for each of the photographs, a relevancy score based at least in part on: selection success data of the photograph for image queries referring to the point of interest or destination, and references to the point of interest or destination in documents associated with the photograph; and selecting a selected photograph from the photographs based at least in part on a respective visual quality score and the respective relevancy scores, the visual quality score representing a degree of visual quality of the respective photographs.

Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computerized travel services. One of the methods includes identifying photographs using an index of photographs, the photographs being identified from the index as photographs geographically related to a point of interest or destination and having a creation timestamp corresponding to a time of the year; determining for each of the photographs, a relevancy score based at least in part on: selection success data of the photograph for image queries referring to the point of interest or destination, and references to the point of interest or destination in documents associated with the photograph; and selecting a selected photograph from the photographs based at least in part on a respective visual quality score and the respective relevancy scores, the visual quality score representing a degree of visual quality of the respective photographs.

Abstract: A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.