Abstract: Using a first dataset of labeled data, a model is trained by adjusting a feature extractor parameter, a classifier parameter, and a discriminator parameter of the model. Using the discriminator parameter and a parametric function of the feature extractor parameter, a plurality of samples of a dataset of unlabeled data is scored. A subset of the scored plurality of samples is selected for labeling. Responsive to receiving a label of each of the selected subset of the scored plurality of samples, the first dataset of labeled data is augmented with the selected subset of the scored plurality of samples and the label of each of the selected subset of the scored plurality of samples. Using the augmented dataset of labeled data, the model is retrained. The retraining comprises further adjusting the feature extractor parameter, the classifier parameter, and the discriminator parameter of the model.

Abstract: A maintenance solution pipeline is automatically selected from a plurality of maintenance solution pipelines, based on obtained information. The maintenance solution pipeline is to be used in providing a physical asset maintenance solution for a plurality of physical assets. Code and model rendering for the maintenance solution pipeline automatically selected is initiated. Output from an artificial intelligence process is obtained. The output includes an automatically generated risk estimation relating to one or more conditions of at least one physical asset of the plurality of physical assets. Code and model rendering for the maintenance solution pipeline is re-initiated, based on the output from the artificial intelligence process. The maintenance solution pipeline automatically selected is reused.

Abstract: Methods, systems, and computer program products for a decision-improvement framework are provided herein. A computer-implemented method includes obtaining regression functions that predict an output of processes of a physical system based on inputs received at each process; automatically generating one or more constraints and one or more objective functions for a model for the physical system based on the regression functions and a representation of the physical system, where the representation specifies relationships between at least a portion of the processes; identifying a set of parameter values for controlling the physical system based on the model; generating a score, for the set of parameter values, based on a predicted improvement of the physical system relative to historical performance of the physical system; and in response to the generated score satisfying a threshold, causing the physical system to be configured in accordance with the set of parameter values.

Abstract: In example aspects of this disclosure, a method includes generating, by one or more computing devices, a parametric model that expresses condition states for each of a plurality of assets, and the probability of the assets transitioning between the condition states; generating, by the one or more computing devices, stochastic degradation predictions of a group of the assets, based on the condition states and the probability of transitioning between the condition states for at least some of the assets; and generating, by the one or more computing devices, a maintenance schedule based on: the stochastic degradation predictions of the group of the assets, costs of corrective maintenance for assets in a failed state, and costs of scheduled maintenance for the assets.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: A trajectory planning system can be used to select a trajectory for an autonomous vehicle. The trajectory planning system may generate multiple trajectories and extract features from the generated trajectories. The trajectory planning system may evaluate the trajectories based on the extracted features and select a trajectory for the vehicle based on the evaluation. The selected trajectory may be used to control the vehicle.

Abstract: Smart fertilizers and method of using and manufacturing are provided that include nanocarbon solution mixed with nanocomposite hydrogels at predetermined percentage weight or volume ratio (% w/w or % v/v). The resulted smart fertilizers are characterized in biosensors for detecting electricity conductance (EC) and/or pH degree of the surrounding soil caused by the release of ATPase H+ from the plant roots and in bioactuators for chemically reacting with the surrounding hydron ions to release the nutrients once the biodetectors detect the stimuli conditions.

Abstract: Provided are methods for augmenting data related to generation of vehicle trajectories, which include predicting, using a machine learning model, a first trajectory of a vehicle at a first time in an environment surrounding the vehicle and including at least one object, detecting a deviation of the predicted first trajectory at the first time from a first ground truth trajectory of the vehicle and determining that, at the first time, the deviation satisfies a threshold, predicting, using the machine learning model, a second trajectory of the vehicle based on the predicted first trajectory of the vehicle and a second ground truth trajectory of at least one object at a second time being subsequent to the first time, and generating a training dataset for training the machine learning model using the predicted first and second trajectories of the vehicle. Systems and computer program products are also provided.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: A substrate flipping device includes a substrate securing assembly, a gripping actuator, and a rotary actuator. The gripping actuator is configured to pneumatically cause the substrate securing assembly to be in an open position to receive a substrate and configured to pneumatically cause the substrate securing assembly to be in a closed position to secure the substrate. The rotary actuator is configured to pneumatically cause the substrate securing assembly to rotate to a flipped position and to pneumatically rotate to a non-flipped position.

Abstract: 1,8-naphthyridinone compounds as modulators of an adenosine receptor are provided. The compounds may find use as therapeutic agents for the treatment of diseases mediated through a G-protein-coupled receptor signaling pathway and may find particular use in oncology.

Abstract: In some embodiments, a method of determining a molecular structure of a protein is provided. A computing system receives voxel data representing electron density obtained via cryo-electron microscopy. The computing system uses one or more neural networks to predict one or more likelihoods for each voxel. The computing system determines a backbone structure based on the predicted likelihoods. The computing system maps amino acid sequences to the backbone structure based on the predicted likelihoods. Mapping the amino acid sequences to the backbone structure based on the predicted likelihoods includes conducting an alignment technique that uses a reward function and a gap penalty. The computing system determines locations of carbon, nitrogen, and oxygen atoms within the backbone structure based on the predicted likelihoods. The computing system determines side-chain atoms based on the predicted backbone structure and the amino acid sequences to complete the molecular structure.

Abstract: Various embodiments are described herein for the fabrication enzyme degradable hydrogels useful as payload delivery systems. More particularly, embodiments disclosed herein relate to enzyme-degradable hydrogel systems comprising a crosslinkable polymer, such as a chemically-modified biopolymer, for example, chemically-modified gelatin, the hydrogel formed by a method comprising sequential physical and chemical crosslinking steps, for delivery of various payloads. Enzymes may be selected and administered to tune the release profile of the hydrogel. The payload can be, but not limited to, drugs, markers, cells, or these members encapsulated within another drug delivery such as a nanoparticle, or liposome. The hydrogel system can also be combined with another device such as a contact lens or bandage for wound healing.

Abstract: Systems, apparatuses and methods may provide for a verification system for an aircraft. The verification system includes a verification function subsystem to automatically verify an operational condition of one or more components of an aircraft based on airflow related signals, hydraulic related signals, and electrical signals and associated impedances and present the operational condition via a display. The airflow related signals may be obtained from a plurality of airflow fittings, the hydraulic related signals may be obtained from a plurality of hydraulic fittings, and the electrical signals and associated impedances may be obtained from a plurality of electrical connectors. At least two of the airflow fittings, at least two of the hydraulic fittings, and at least two of the electrical connectors may be positioned in different aircraft sections before additional aircraft sections are assembled.

Abstract: A substrate flipping device includes a substrate securing assembly, a gripping actuator, and a rotary actuator. The gripping actuator is configured to pneumatically cause the substrate securing assembly to be in an open position to receive a substrate and configured to pneumatically cause the substrate securing assembly to be in a closed position to secure the substrate. The rotary actuator is configured to pneumatically cause the substrate securing assembly to rotate to a flipped position and to pneumatically rotate to a non-flipped position.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.