Abstract: A method of detecting three dimensional (3D) changes in sites, comprising receiving a plurality of images of a site captured from a plurality of viewpoints by image sensor(s) mounted on dynamic vehicle(s) scanning the site, registering at least some of the images to each other, creating one or more merged representations of at least part of the site based on the at least some registered images, comparing between the merged representation(s) and one or more corresponding previous merged representations created based on a plurality of previous images captured during one or more previous scans of the site, detecting, based on the comparison, one or more 3D changes indicative of a depth change exceeding a certain threshold in one or more 3D features at the site, and generating a change indication indicative of the one or more detected 3D changes.

Abstract: A method of detecting three dimensional (3D) changes in sites, comprising receiving a plurality of images of a site captured from a plurality of viewpoints by image sensor(s) mounted on dynamic vehicle(s) scanning the site, registering at least some of the images to each other, creating one or more merged representations of at least part of the site based on the at least some registered images, comparing between the merged representation(s) and one or more corresponding previous merged representations created based on a plurality of previous images captured during one or more previous scans of the site, detecting, based on the comparison, one or more 3D changes indicative of a depth change exceeding a certain threshold in one or more 3D features at the site, and generating a change indication indicative of the one or more detected 3D changes.

Abstract: A method of detecting three dimensional (3D) changes in sites, comprising receiving a plurality of images of a site captured from a plurality of viewpoints by image sensor(s) mounted on dynamic vehicle(s) scanning the site, registering at least some of the images to each other, creating one or more merged representations of at least part of the site based on the at least some registered images, comparing between the merged representation(s) and one or more corresponding previous merged representations created based on a plurality of previous images captured during one or more previous scans of the site, detecting, based on the comparison, one or more 3D changes indicative of a depth change exceeding a certain threshold in one or more 3D features at the site, and generating a change indication indicative of the one or more detected 3D changes.

Abstract: A system and a method for analyzing conversation text using a flow of query prompts, an ensemble of close ended questions and a neural network based language model. The method may be used as a tutor or examination bot, for mental coherency screening, data mining, clustering groups of trainees or customers according to training needs or interests, and the likes.

Abstract: There is provided a method of re-classifying a clinically significant feature of a medical image as an artifact, comprising: feeding a target medical image captured by a specific medical imaging sensor at a specific setup into a machine learning model, obtaining a target feature map as an outcome of the machine learning model, wherein the target feature map includes target features classified as clinically significant, analyzing the target feature map with respect to sample feature map(s) obtained as an outcome of the machine learning model fed a sample medical image captured by at least one of: the same specific medical imaging sensor and the same specific setup, wherein the sample feature map(s) includes sample features classified as clinically significant, identifying target feature(s) depicted in the target feature map having attributes matching sample feature(s) depicted in the sample feature map(s), and re-classifying the identified target feature(s) as an artifact.

Abstract: There is provided a method of re-classifying a clinically significant feature of a medical image as an artifact, comprising: feeding a target medical image captured by a specific medical imaging sensor at a specific setup into a machine learning model, obtaining a target feature map as an outcome of the machine learning model, wherein the target feature map includes target features classified as clinically significant, analyzing the target feature map with respect to sample feature map(s) obtained as an outcome of the machine learning model fed a sample medical image captured by at least one of: the same specific medical imaging sensor and the same specific setup, wherein the sample feature map(s) includes sample features classified as clinically significant, identifying target feature(s) depicted in the target feature map having attributes matching sample feature(s) depicted in the sample feature map(s), and re-classifying the identified target feature(s) as an artifact.

Abstract: Systems and methods for producing N-body entangling interactions to simplify n-gate operations in quantum computing applications are disclosed herein. According to at least some embodiments, an oscillator generates a two-tone field tuned to qubit resonance, first upper motion-induced sidebands, first lower motion-induced sidebands, second upper motion-induced sidebands, and second lower motion-induced sidebands.

Abstract: A system and method for protection of Web based applications are described. Anomalous traffic can be identified by comparing the traffic to a profile of acceptable user traffic when interacting with the application. Phishing and leeching are one type of anomalous traffic that is detected. The anomalous traffic, or security events, identified at the individual computer networks are communicated to a central security manager. Various responsive actions may be taken in response to detection of phishing or leeching.

Abstract: A system and method for protection of Web based applications are described. Anomalous traffic can be identified by comparing the traffic to a profile of acceptable user traffic when interacting with the application. Phishing and leeching are one type of anomalous traffic that is detected. The anomalous traffic, or security events, identified at the individual computer networks are communicated to a central security manager. Various responsive actions may be taken in response to detection of phishing or leeching.