Abstract: A feature extractor and novel training objective are provided for content-based image retrieval. For example, a computer-implemented method includes applying a query image and a search image to a neural network of a feature extraction network of a computing device, the query image indicating an object to be searched for in the search image. The feature extraction network includes the neural network, a spatial feature neural network receiving a first output of the neural network pertaining to the search image, and an embedding network receiving a second output of the neural network pertaining to the query image. The method includes generating spatial search features from the spatial feature neural network, generating a query feature from the embedding network, applying the query feature to an artificial neural network (ANN) index, and determining an optimal matching result of an object in the search image based on an operation using the ANN index.

Abstract: Generating trajectories from an implicit neural representation (INR) model to predict human mobility in uncertain traffic conditions includes receiving geocoordinate data representing vehicle motion observations of a traffic pattern; receiving a road network based on the geocoordinate data; training the INR model to learn continuous, latent fields of stochastic traffic properties over space and time based on the geocoordinate data; utilizing the INR model to extract spatio-temporal speed distributions from the geocoordinate data; applying a near-shortest-path, heuristic algorithm, weighted by predictions of the INR model, to produce real-world routing choices for traversing the road network; generating trajectories for transportation between an origin and destination in the road network using the algorithm and the predictions of the INR model, wherein the trajectories reflect non-deterministic and diverse route choices in the road network; and outputting generated trajectories to improve routing choices for a

Abstract: A feature extractor and novel training objective are provided for content-based image retrieval. For example, a computer-implemented method includes applying a query image and a search image to a neural network of a feature extraction network of a computing device, the query image indicating an object to be searched for in the search image. The feature extraction network includes the neural network, a spatial feature neural network receiving a first output of the neural network pertaining to the search image, and an embedding network receiving a second output of the neural network pertaining to the query image. The method includes generating spatial search features from the spatial feature neural network, generating a query feature from the embedding network, applying the query feature to an artificial neural network (ANN) index, and determining an optimal matching result of an object in the search image based on an operation using the ANN index.

Abstract: Techniques performed by a data processing system for operating a personalized oncology system herein include accessing a first histopathological image of a histopathological slide of a sample taken from a first patient; analyzing the first histopathological image using a first machine learning model configured to extract first features from the first histopathological image; searching a histological database that includes a plurality of second histopathological images and corresponding clinical data for a plurality of second patients to generate search results; analyzing the plurality of third histopathological images and the corresponding clinical data associated with the plurality of third histopathological images using statistical analysis techniques to generate associated statistics and metrics associated with mortality, morbidity, time-to-event, or a combination thereof for the plurality of third patients associated with the third histopathological images; and presenting an interactive visual representat

Abstract: Techniques for alerting a human operator of a predicted side collision of a vehicle with a detected pedestrian or bicyclist herein including obtaining sensor data indicative of a trajectory of the vehicle, determining a predicted trajectory of the vehicle based on the sensor data, estimating a likelihood of collision between the detected pedestrian or bicyclist and the first side of the vehicle based on at least the first position of the pedestrian or bicyclist determined by the fusion module and the predicted trajectory of the vehicle, determining that a warning should be presented based on at least the estimated likelihood of collision, and presenting an alert to a human operator of the vehicle in response to the determination that the warning should be presented.

Abstract: Systems and methods for alerting a human operator of a predicted side collision of a transit bus with a pedestrian or bicyclist, including receiving range scanner data from a range scanner located at a first side of the bus covering a first area on the first side of the bus, receiving image data from a sensor located at the first side of the bus and covering an area along the first side of the bus that overlaps the first area, detecting and tracking the pedestrian or bicyclist based on the range scanner data and the image data, estimating a likelihood of collision between the pedestrian or bicyclist and the first side of the bus based on the tracking of the pedestrian or bicyclist, and presenting an alert to the human operator in response to a determination that a warning should be presented based on the estimated likelihood of collision.