Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers distributed in a sea lice treatment station, a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.

Abstract: A method includes receiving, from a multiscopic image capture system, a plurality of images depicting a scene. The method includes determining, by application of a neural network based on the plurality of images, a disparity map of the scene. The neural network includes a plurality of layers, and the layers include a rectification layer. The method include determining a matching error of the disparity map based on differences between corresponding pixels of two or more images associated with the disparity map. The method includes back-propagating the matching error to the rectification layer of the neural network. Back-propagating the matching error includes updating one or more weights applied to the rectification layer.

Abstract: Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.

Abstract: Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving, by one or more non-real-time processors, data defining a light illumination pattern for a robotic device. Generating, by the one or more non-real-time processors and based on the data, a spline that represents the light illumination pattern, where a knot vector of the spline defines a timing profile of the light illumination pattern. Providing the spline to one or more real-time processors of the robotic system. Calculating, by the one or more real-time processors, an illumination value from the spline at each of a plurality of time steps. Controlling, by the one or more real-time processors, illumination of a lighting display of the robotic system in accordance with the illumination value of the spline at each respective time step.

Abstract: Embodiments of techniques for inverse design of physical devices are described herein, in the context of generating designs for photonic integrated circuits (including a multi-channel photonic demultiplexer). In some embodiments, an initial design of the physical device is received, and a plurality of sets of operating conditions for fabrication of the physical device are determined. In some embodiments, the performance of the physical device as fabricated under the sets of operating conditions is simulated, and a total performance loss value is backpropagated to determine a gradient to be used to update the initial design. In some embodiments, instead of simulating fabrication of the physical device under the sets of operating conditions, a robustness loss is determined and combined with the performance loss to determine the gradient.

Abstract: Techniques for generating solutions from aural inputs include identifying, with one or more machine learning engines, a plurality of aural signals provided by two or more human speakers, at least some of the plurality of aural signals associated with a human-perceived problem; parsing, with the one or more machine learning engines, the plurality of aural signals to generate a plurality of terms, each of the terms associated with the human-perceived problem; deriving, with the one or more machine learning engines, a plurality of solution sentiments and a plurality of solution constraints from the plurality of terms; generating, with the one or more machine learning engines, at least one solution to the human-perceived problem based on the derived solution sentiments and solution constraints; and presenting the at least one solution of the human-perceived problem to the two or more human speakers through at least one of a graphical interface or an auditory interface.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

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: An example method includes determining, by a controller of an image capture system, a plurality of sets of exposure parameter values for one or more exposure parameters. The plurality of sets of exposure parameter values are determined at an exposure determination rate. The method further includes capturing, by an image capture device of the image capture system, a plurality of images. Each image of the plurality of images is captured according to a set of exposure parameter values of the plurality of sets of exposure parameter values. The method also includes sending, by the controller of the image capture system to an image processing unit, a subset of the plurality of images. Each subset of images is sent at a sampling rate, and the sampling rate is less than the exposure determination rate.

Abstract: A method includes receiving sensor data representative of surfaces in a physical environment containing an interaction point for a robotic device, and determining, based on the sensor data, a height map of the surfaces in the physical environment. The method also includes determining, by inputting the height map and the interaction point into a pre-trained model, one or more candidate positions for a base of the robotic device to allow a manipulator of the robotic device to reach the interaction point. The method additionally includes determining a collision-free trajectory to be followed by the manipulator to reach the interaction point when the base of the robotic device is positioned at a selected candidate position of the one or more candidate positions and, based on determining the collision-free trajectory, causing the base of the robotic device to move to the selected candidate position within the physical environment.

Abstract: A free-space optical communication device includes an optical fiber bundle and one or more processors. The optical fiber bundle includes a central fiber connected to a first photodetector, and a plurality of surrounding fibers, each surrounding fiber connected to a corresponding second photodetector. The one or more processors are in communication with the first photodetector and each second photodetector. The one or more processors are also configured to receive a current or voltage generated at the first photodetector and each second photodetector and to determine a pointing accuracy of a beam received at the optical fiber bundle based on the current or voltage generated at the second photodetectors.

Abstract: A computer-implemented method for designing an image processing device includes defining a loss function within a simulation space composed of a plurality of voxels; defining an initial structure for one or more physical features of a metasurface and one or more architectural features of a neural network in the simulation space; determining, using a computer system, values for at least one structural parameter, and/or at least one functional parameter for the one or more physical features and at least one architectural parameter for the one or more architectural features, using a numerical solver to solve Maxwell's equations so that a loss determined according to the loss function is within a threshold loss; defining a final structure of the metasurface based on the values for the one or more structural parameters; and defining a final structure of the neural network based on the values for the at least one architectural parameter.

Abstract: The subject matter of this specification generally relates to modular vehicles including separable pod and base units. In some implementations, a computing system installed in a vehicle base identifies a vehicle pod that is detachably connected to a chassis on the vehicle base. In response to identifying that the vehicle pod is detachably connected to the chassis on the vehicle base, a communications link can be established between the computing system installed in the vehicle base and a computing system installed in the vehicle pod. Based on information obtained through the communications link, the computing system installed in the vehicle base can determine a particular configuration of the vehicle pod that is detachably connected to the chassis. The computing system can then verify that the vehicle base can safely transport the vehicle pod while the vehicle pod is detachably connected.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, can be implemented to perform certain actions. The actions can include maintaining biological data related to multiple biological systems in a first format in a data repository, receiving a first selection of a biological system of the multiple biological systems for constructing a model that simulates a behavior of the biological system, retrieving a subset of data of the biological data that is associated with the first selection of the biological system, receiving a second selection of a modeling technique of the multiple modeling techniques for constructing the model, compiling the subset of biological data into configuration data of a second format that is specific to the modeling technique and that is different from the first format, and generating the model using the modeling technique and the configuration data.

Abstract: The disclosure provides a communication system that includes sensors, a plurality of components, and processors. The sensors receive measurements related to a state of the communication system. The processors receive an indication of an amount of received power at a remote communication system and estimate a state of the plurality of components based on the received one or more measurements and the received indication. Using the indication and the estimated state, the processors determine whether the amount of received power is likely to fall below a minimum received power within a given time interval. When it is likely, the processors select an adjustment technique of a plurality of adjustment techniques for adjusting a data rate of the outbound signal and adjust a given component of the communication system using the selected adjustment technique to change the data rate of the outbound signal.

Abstract: The present disclosure relates to leveraging explainable machine learning methods and feature importance mechanisms as a mechanism for gene discovery and furthermore leveraging the outputs of the gene discovery to recommend ideal gene expression profiles and the requisite genome edits that are conducive to a desired phenotype. Particularly, aspects of the present disclosure are directed to obtaining gene expression profiles for a set of genes measured in a tissue sample of a plant, inputting the gene expression profiles into a prediction model constructed for a task of predicting a phenotype as output data, generating, using the prediction model, the prediction of the phenotype for the plant, analyzing, by an explainable artificial intelligence system, decisions made by the prediction model to predict the phenotype, and identifying a set of candidate gene targets for the phenotype as having a largest contribution or influence on the prediction based on the analyzing.

Abstract: A method includes receiving a first depth map that includes a plurality of first pixel depths and a second depth map that includes a plurality of second pixel depths. The first depth map corresponds to a reference depth scale and the second depth map corresponds to a relative depth scale. The method includes aligning the second pixel depths with the first pixel depths. The method includes transforming the aligned region of the second pixel depths such that transformed second edge pixel depths of the aligned region are coextensive with first edge pixel depths surrounding the corresponding region of the first pixel depths. The method includes generating a third depth map. The third depth map includes a first region corresponding to the first pixel depths and a second region corresponding to the transformed and aligned region of the second pixel depths.

Abstract: A method includes receiving sensor data representing a first object in an environment and generating, based on the sensor data, a first state vector that represents physical properties of the first object. The method also includes generating, by a first machine learning model and based on the first state vector and a second state vector that represents physical properties of a second object previously observed in the environment, a metric indicating a likelihood that the first object is the same as the second object. The method further includes determining, based on the metric, to update the second state vector and updating, by a second machine learning model configured to maintain the second state vector over time and based on the first state vector, the second state vector to incorporate into the second state vector information concerning physical properties of the second object as represented in the first state vector.