Abstract: An autonomous vehicle comprises a sensor and one or more processors. The one or more processors can be configured to receive a set of object location data from the sensor, the set of object location data indicating an object; generate a measured representation of the object based on the set of object location data; execute a tracking protocol using data of the measured representation of the object as input to generate a virtual representation of the object, the virtual representation of the object comprising a virtual point that is nearest to a defined location of the autonomous vehicle; and translate the virtual point of the virtual representation of the object to a measured point of the measured representation of the object.

Abstract: Handguns are disclosed including improved safety mechanisms, improved magazine catch mechanisms, improved slide stop mechanisms, and improved optics mounts. Selected embodiments may include one or more than one of such improvements. Embodiments may be particularly suited to semi-automatic handguns, including 1911-style handguns and/or 2011-style handguns. Other embodiments may also be implemented in the context of different types of handguns.

Abstract: Handguns are disclosed including improved safety mechanisms, improved magazine catch mechanisms, improved slide stop mechanisms, and improved optics mounts. Selected embodiments may include one or more than one of such improvements. Embodiments may be particularly suited to semi-automatic handguns, including 1911-style handguns and/or 2011-style handguns. Other embodiments may also be implemented in the context of different types of handguns.

Abstract: Handguns are disclosed including improved safety mechanisms, improved magazine catch mechanisms, improved slide stop mechanisms, and improved optics mounts. Selected embodiments may include one or more than one of such improvements. Embodiments may be particularly suited to semi-automatic handguns, including 1911-style handguns and/or 2011-style handguns. Other embodiments may also be implemented in the context of different types of handguns.

Abstract: An autonomous vehicle comprises a sensor and one or more processors. The one or more processors can be configured to receive a set of object location data from the sensor, the set of object location data indicating an object; generate a measured representation of the object based on the set of object location data; execute a tracking protocol using data of the measured representation of the object as input to generate a virtual representation of the object, the virtual representation of the object comprising a virtual point that is nearest to a defined location of the autonomous vehicle; and translate the virtual point of the virtual representation of the object to a measured point of the measured representation of the object.

Abstract: Techniques described herein include selecting experience data for use when training or retraining a model. In one example, this disclosure describes a method that includes generating a plurality of trajectories, each comprising a contiguous sequence of instances of experience data, where each instance of experience data in the contiguous sequence has an error value associated that instance of experience data; determining, for each of the trajectories, a sorted order of the instances of experience data, wherein the sorted order is based on the error value associated with each of the instances of experience data; selecting, based on a distribution function applied to the sorted order of the instances of experience data in at least one of the trajectories, a subset of instances of the experience data; and retraining a reinforcement learning model, using the subset of instances of experience data, to predict an optimal action to take in a state.

Abstract: Techniques described herein include selecting experience data for use when training or retraining a model in a reinforcement learning environment. In one example, this disclosure describes a method that includes generating a plurality of instances of episode data, each comprising a sequence of instances of experience data; storing each of the plurality of instances of episode data in a buffer; compiling statistics associated with each of the plurality of instances of episode data; selecting, based on the statistics associated with each of the plurality of instances of episode data, instances of the experience data from the buffer; and training a model, using the selected instances of experience data, to predict an optimal action to take in a reinforcement learning model, wherein the optimal action is an action expected to result in a maximum future reward.

Abstract: The present specification describes a medicine dispensing system for automatically dispensing medication at a predetermined time. The system includes a cartridge that carries a strip of medicine pouches. When the cartridge is dropped into a dispensing unit, it automatically aligns with the dispensing unit without requiring any installation work from the user. The automatic alignment is enabled because of self-aligning features manufactured in the cartridge housing.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with sensor output segmentation are provided. For example, sensor data can be accessed. The sensor data can include sensor data returns representative of an environment detected by a sensor across the sensor's field of view. Each sensor data return can be associated with a respective bin of a plurality of bins corresponding to the field of view of the sensor. Each bin can correspond to a different portion of the sensor's field of view. Channels can be generated for each of the plurality of bins and can include data indicative of a range and an azimuth associated with a sensor data return associated with each bin. Furthermore, a semantic segment of a portion of the sensor data can be generated by inputting the channels for each bin into a machine-learned segmentation model trained to generate an output including the semantic segment.

Abstract: A furnace stave comprising a plurality of internal channels or conduits for circulating cooling fluid through the stave; an inlet and an outlet channel associated with each internal channel or conduit; wherein one of the internal channels or conduits is disposed in a protrusion from the stave.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with sensor output segmentation are provided. For example, sensor data can be accessed. The sensor data can include sensor data returns representative of an environment detected by a sensor across the sensor's field of view. Each sensor data return can be associated with a respective bin of a plurality of bins corresponding to the field of view of the sensor. Each bin can correspond to a different portion of the sensor's field of view. Channels can be generated for each of the plurality of bins and can include data indicative of a range and an azimuth associated with a sensor data return associated with each bin. Furthermore, a semantic segment of a portion of the sensor data can be generated by inputting the channels for each bin into a machine-learned segmentation model trained to generate an output including the semantic segment.

Abstract: During operation, a system may apply a polarizing field and an excitation sequence to a sample. Then, the system may measure a signal associated with the sample for a time duration that is less than a magnitude of a relaxation time associated with the sample. Next, the system may calculate the relaxation time based on a difference between the measured signal and a predicted signal of the sample, where the predicted signal is based on a forward model, the polarizing field and the excitation sequence. After modifying at least one of the polarizing field and the excitation sequence, the aforementioned operations may be repeated until a magnitude of the difference is less than a convergence criterion. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform on the measured signal.