Abstract: In one embodiment, a system generates an image using either a first or a second image signal processing (ISP) algorithm, where the first or second ISP algorithm is applied to raw image data of a camera of an autonomous driving vehicle (ADV) to generate the image. The system applies a machine learning model to the image to identify a representation of an obstacle, where the machine learning model is generated by a few shots learning algorithm that contrasts labeled data of a positive training sample from images corresponding to the first and second ISP algorithms to labeled data of a negative training sample from images corresponding to the first and second ISP algorithm. The system determines a classification and a location of the obstacle based on the representation of the obstacle. The system plans a motion control of the ADV based on the classification and location of the detected object.

Abstract: Methods and systems for cluster-based historical data are disclosed. In one embodiment, a method includes accessing historical data associated with a plurality of users and, based on the historical data, constructing a plurality of clusters. Constructing each cluster involves selecting a subset of comparable users and including in the cluster historical data associated with the subset of comparable users. The operations further include storing the cluster, receiving, from a new user, a request for a recommendation, and accessing historical data associated with the new user. The operations further include, based on the historical data associated with the new user, selecting a cluster for which the subset of comparable users is comparable to the new user, based on the selected cluster, determining a recommendation, and providing the recommendation to the new user.

Abstract: Methods and systems for cluster-based historical data are disclosed. In one embodiment, a method includes accessing historical data associated with a plurality of users and, based on the historical data, constructing a plurality of clusters including historical data associated with a subset of the users. The method further includes, for each cluster, identifying users in the subset of users that are historically improving users, and, for each historically improving user, determining, based on the historical data associated with the historically improving user, a positive predictive attribute indicative of historical improvement. The method further includes receiving, from a new user, a request for a recommendation, accessing historical data associated with the new user, based on the historical data associated with the new user, selecting a cluster, determining the recommendation based on the positive predictive attribute for the selected cluster, and providing the recommendation to the new user.

Abstract: An apparatus and method for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, includes obtaining aircraft fleet data, obtaining data on independent variables, determining a cycle indicator, determining the operation type of the aircraft, estimating the model using certain estimation steps using the aircraft fleet data, examining and updating the model based on changes in the independent variables, and forecasting on a year-to-year basis by age and operation cohort.

Abstract: An apparatus and method for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, includes obtaining aircraft fleet data, obtaining data on independent variables, determining a cycle indicator, determining the operation type of the aircraft, estimating the model using certain estimation steps using the aircraft fleet data, examining and updating the model based on changes in the independent variables, and forecasting on a year-to-year basis by age and operation cohort.

Abstract: Provided are, among other things, devices for and methods for performing thermal signature assays on a two or more samples in an array, using active/control base thermopiles, the method comprising: [a] performing a heat transfer to the two or more samples in each of a two or more containers, using at least one base thermopile in thermal communication with the two or more containers; and [b] determining a total heat transferred to the samples by the base thermopile in step [a]; and [c] sensing in real time a temperature difference between a first sample and a second sample of the two or more samples resulting from performing step [a].