Disclosed are systems and methods relating to providing intersection metrics based on road network data and telematic data.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

Aspects relate to providing a counterfactual inference management technique capable of providing increased flexibility to allow users to select an appropriate counterfactual inference and offering scalability for handling tabular data and image data in a single configuration. A counterfactual inference management device comprising a classifier unit trained to determine whether a set of input data that includes a set of data features achieves a predetermined target and a counterfactual inference unit for generating a set of transformed data in which a subset of the set of data features are modified to counterfactual features. The classifier unit processes the set of transformed data to determine whether it achieves the predetermined target and calculates a counterfactual loss. The counterfactual inference unit is trained to reduce the counterfactual loss and generate a set of transformed data including counterfactual features that achieve the predetermined target.

Described herein are software and systems for analyzing videos and/or images. Software and systems described herein are configured in different embodiments to carry out different types of analyses. For example, in some embodiments, software and systems described herein are configured to locate an object of interest within a video and/or image.

Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.

Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

A system for categorizing seeds of plants into hybrid and non-hybrid categories. Seeds sorted according to the disclosed system are also disclosed.

Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit (“MNL”) models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.

A system and a method for detecting animals in a region of interest are disclosed. An image that captures a scene in the region of interest is received. The image is fed to an animal detection model to produce a group of probability maps for a group of key points and a group of affinity field maps for a group of key point sets. One or more connection graphs are determined based on the group of probability maps and the group of affinity field maps. Each connection graph outlines a presence of an animal in the image. One or more animals present in the region of interest are detected based on the one or more connection graphs.