Abstract: The present disclosure relates to a method for estimating a final rent estimate (FRE) of a property. The method enables receiving, at a computing device, a dwelling input from a user. A remote server acquires the received dwelling input from the computing device and analyses a property database to determine an initial rent estimate (IRE) based on each property located in a geological grid and each adjacent geological grid. The server identifies the multiple comparable properties based on the determined IRE, acquired dwelling input and at least one filter, which can be selected from a spatial filter, a temporal filter, a configuration filter and a variance filter. The FRE can be calculated based on the rental value of the identified multiple comparable properties, wherein a rental value contribution of each comparable property is inversely proportional to a distance from the location input. The calculated FRE is displayed at the computing device.

Abstract: A method for operating a neural network using an encoder-based model to provide a time series forecast, the method comprising: down sampling a time series dataset to generate an initial input having a first scale resolution, such that the first scale resolution is less than a scale resolution of the time series dataset; processing as a first iteration, using the model, the initial input to generate a first output; upsampling by an upsampling function the first output to generate a second input having a second scale resolution, the second scale resolution being higher than the first scale resolution, such that the second input is based on the first output; and processing as a second iteration, using the model, the second input to generate a second output; wherein the second output represents a time series forecast of the time series dataset.

Abstract: A method for preparing a trained complete selective classifier can be applied to a trained complete selective classifier having an existing trained selection mechanism. The trained selective classifier is modified to disregard the existing trained selection mechanism and use, as a basis for an alternate selection mechanism, at least one classification prediction value, for example the predictive entropy or the maximum predictive class logit. Optionally, before modifying the trained selective classifier, the method commences with an untrained selective classifier, which may be trained with a modified loss function to obtain the trained selective classifier. The modified loss function has at least one added term, relative to an original loss function, and the at least one added term decreases entropy.

Abstract: A multi-modal artificial neural network and a self-supervised learning method for training that network. The learning method involves processing, using a first modality simple Siamese network, a pair of first modality augmented views of an input; processing, using a second modality simple Siamese network, a pair of second modality augmented views of the input; determining at least one cross-modal loss between the first and second modality simple Siamese networks; determining a total loss from: (i) first and second modality losses respectively determined during the processing using the first and second modality simple Siamese networks; and (ii) the at least one cross-modal loss; and training the first and second modality simple Siamese networks based on the total loss. The trained network may be used to analyze multi-modal content such as video content that has an audio track. A Multi-Modal Multi-Head Network (M3HN) may also be trained to process modality-specific and modality-agnostic representations.

Abstract: Embodiments of a computer-implemented method of facilitating ultrasonic image analysis of a subject are disclosed. The method can involve receiving signals representing a set of ultrasound images of the subject, deriving one or more extracted feature representations from the set of ultrasound images, determining, based on the derived one or more extracted feature representations, a quality assessment value representing a quality assessment of the set of ultrasound images, determining, based on the derived one or more extracted feature representations, an image property associated with the set of ultrasound images, and producing signals representing the quality assessment value and the image property for causing the quality assessment value and the image property to be associated with the set of ultrasound images. Embodiments of a computer-implemented method of training one or more neural networks to facilitate ultrasonic image analysis is also disclosed.

Abstract: Embodiments of a computer-implemented method of facilitating ultrasonic image analysis of a subject are disclosed. The method can involve receiving signals representing a set of ultrasound images of the subject, deriving one or more extracted feature representations from the set of ultrasound images, determining, based on the derived one or more extracted feature representations, a quality assessment value representing a quality assessment of the set of ultrasound images, determining, based on the derived one or more extracted feature representations, an image property associated with the set of ultrasound images, and producing signals representing the quality assessment value and the image property for causing the quality assessment value and the image property to be associated with the set of ultrasound images. Embodiments of a computer-implemented method of training one or more neural networks to facilitate ultrasonic image analysis is also disclosed.

Abstract: An approach is provided for providing a calculated route using waypoints associated with a traveled route. A traveled route platform processes and/or facilitates a processing of one or more location logs to determine one or more waypoints, wherein the one or more location logs include, at least in part, one or more location samples for at least one device on a traveled route. The traveled route platform then processes and/or facilitates a processing of the one or more waypoints via one or more routing engines to determine a calculated route as a representation of the traveled route.

Abstract: An approach is provided for providing a calculated route using waypoints associated with a traveled route. A traveled route platform processes and/or facilitates a processing of one or more location logs to determine one or more waypoints, wherein the one or more location logs include, at least in part, one or more location samples for at least one device on a traveled route. The traveled route platform then processes and/or facilitates a processing of the one or more waypoints via one or more routing engines to determine a calculated route as a representation of the traveled route.

Abstract: An approach is provided for providing a calculated route using waypoints associated with a traveled route. A traveled route platform processes and/or facilitates a processing of one or more location logs to determine one or more waypoints, wherein the one or more location logs include, at least in part, one or more location samples for at least one device on a traveled route. The traveled route platform then processes and/or facilitates a processing of the one or more waypoints via one or more routing engines to determine a calculated route as a representation of the traveled route.