Abstract: The present disclosure is directed to disclosed compounds that modulate e.g., address underlying defects in cellular processing of CFTR activity.

Abstract: In a method for a cooktop, in particular for producing and/or operating the cooktop, which has at least one variable cooking surface, the cooking surface is partitioned in an operating mode along a partitioning direction into a plurality of heating zones to which at least one heating parameter is assigned in each case in a location-dependent manner in order to heat a cooking utensil that is deposited on the heating zone. In order to ensure flexible production and/or flexible operation of the cooktop, during partitioning of the cooking surface into the heating zones in at least one peripheral region of the cooking surface, at least one cooking utensil characteristic is taken into account.

Abstract: An aspect of the disclosure provides for an optical-phased array (OPA) comprising a star coupler coupled to a plurality of grating couplers, wherein the star coupler comprises multiple inputs and multiple outputs. In some embodiments, each output of the star coupler is coupled to a grating coupler of the plurality of grating couplers. In some embodiments, the star coupler is coupled to the plurality of grating couplers by a plurality of optical delay lines. In some embodiments each output of the star coupler is coupled to a grating coupler of the plurality of grating couplers via an optical delay line of the plurality of optical delay lines. In some embodiments, the OPA further includes an optical switch coupled to the multiple inputs of the star coupler, and at least one tunable laser. Another aspect of the disclosure provides a method for steering light using the OPA.

Abstract: The technical field of engineering chains, specifically chains for the transport of raw material is provided, which purpose is to reduce the number of moving parts usually used in engineering chains with outboard wheels, without sacrificing optimal performance. To this end, it provides a straight plate chain mechanism with external and internal pitch, with a ratchet mechanism between the pitches, making the chain self-supporting and allowing it to only rotate in the internal direction when it has direct interaction with the sprocket, thus considerably reducing the possible catenaries.

Abstract: A check valve assembly includes a first flapper, a second flapper, an internal passage formed by the first flapper and the second flapper, and a nozzle disposed within the internal passage and configured to generate an internal flow in the internal passage. In response to a fluid flow through the check valve assembly, the first flapper and the second flapper are configured to move between an open position allowing the internal flow through the internal passage and an outer flow outside the internal passage, and a closed position preventing flow through the internal passage. When the first flapper and the second flapper are operably in the open position, the internal flow has a lower pressure than the outer flow outside of the internal passage, such that the first flapper and the second flapper are maintained in the open position.

Abstract: A method may include obtaining lidar data comprising a plurality of lidar returns from an environment of an autonomous vehicle. The lidar data may be processed with a machine learning model to generate, for the plurality of lidar returns, a plurality of first outputs that each identify a respective lidar return as belonging to an object or non-object and a plurality of second outputs that identify lidar returns belonging to objects as harmful or non-harmful to the autonomous vehicle. A subset of the lidar returns identified as belonging to objects that (i) do not correspond to any of a plurality of pre-classified objects and (ii) were identified as harmful to the autonomous vehicle may be determined. The autonomous vehicle may be controlled based at least in part on the subset of lidar returns.

Abstract: A check valve assembly includes a first flapper, a second flapper, an internal passage formed by the first flapper and the second flapper, and a nozzle disposed within the internal passage and configured to generate an internal flow in the internal passage. In response to a fluid flow through the check valve assembly, the first flapper and the second flapper are configured to move between an open position allowing the internal flow through the internal passage and an outer flow outside the internal passage, and a closed position preventing flow through the internal passage. When the first flapper and the second flapper are operably in the open position, the internal flow has a lower pressure than the outer flow outside of the internal passage, such that the first flapper and the second flapper are maintained in the open position.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: A method may include obtaining sensor data that include a plurality of sensor returns from an environment of an autonomous vehicle. A first set of features may be extracted from the sensor data. The first set of features may be processed with a machine learning model to generate, for at least a subset of the plurality of sensor returns, a first output that classifies a respective sensor return as corresponding to an object or non-object and a second output that indicates a property of the object. The sensor returns classified as corresponding to objects may be compared to a plurality of pre-classified objects to generate one or more generic object classifications. The autonomous vehicle may be controlled based at least in part on the one or more generic object classifications.

Abstract: The technical field of engineering chains, specifically chains for the transport of raw material is provided, which purpose is to reduce the number of moving parts usually used in engineering chains with outboard wheels, without sacrificing optimal performance. To this end, it provides a straight plate chain mechanism with external and internal pitch, with a ratchet mechanism between the pitches, making the chain self-supporting and allowing it to only rotate in the internal direction when it has direct interaction with the sprocket, thus considerably reducing the possible catenaries.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: Automatic watering systems for hydrating a cut tree or the like from a reservoir of standing water are described herein. Automatic watering systems may include a water source comprising a tank and a supply-water reservoir within the tank. Water may be automatically delivered from the supply-water reservoir to a tree-receiving reservoir which may be associated with a tree stand. Water in the tree-receiving reservoir may be taken up by the tree to keep the tree branches green and supple. A controller detects water levels in the supply-water reservoir and tree-receiving reservoir to control operation of a pump which pumps water to the tree-receiving reservoir for continuous tree hydration. The tank and other components of the system can be made to have the decorative appearance of a gift package to enhance the appearance of the tree and surrounding decor.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.