Abstract: An improved device and system for facilitating polymerase chain reaction including a light source, detector, waveguide, and filters that occupy minimal space and facilitate detection of stationary samples, reduced sample read time, and simultaneous reading of multiple light wavelengths.

Abstract: Provided herein are methods and systems for performing an automated tagging of features in digital micrographs representing slides with tissue samples. Automated tagging of features may include automated entry of metadata associated with whole slide images or regions of the whole slide images.

Abstract: Provided herein are methods and systems for performing an automated quality control analysis of digital micrographs representing slides with tissue samples. An automated quality control analysis may comprise analyzing digital micrographs of histology slides for gross errors and excessive regions of blurriness.

Abstract: Provided herein are methods and systems for performing an automated tagging of features in digital micrographs representing slides with tissue samples. Automated tagging of features may include automated entry of metadata associated with whole slide images or regions of the whole slide images.

Abstract: Techniques for implementing face-controlled liveness verification are provided. In one embodiment, a computing device can present, to a user, a sequential series of targets on a graphical user interface (GUI) of the computing device, where each target is a visual element designed to direct the user's attention to a location in the GUI. The computing device can further determine whether the user has successfully hit each target, where the determining comprises tracking movement of a virtual pointer controlled by the user's gaze or face pose and checking whether the user has moved the virtual pointer over each target. If the user has successfully hit each target, the computing device can conclude that the user is a live subject.

Abstract: A system and methods of diagnosing and monitoring neurological disorders in a patient utilizing an artificial intelligence based system. The system may comprise a plurality of sensors, a collection of trained machine learning based diagnostic and monitoring tools, and an output device. The plurality of sensors may collect data relevant to neurological disorders. The trained diagnostic tool will learn to use the sensor data to assign risk assessments for various neurological disorders. The trained monitoring tool will track the development of a disorder over time and may be used to recommend or modify the administration of relevant treatments. The goal of the system is to render an accurate evaluation of the presence and severity of neurological disorders in a patient without requiring input from an expertly trained neurologist.

Abstract: Techniques for implementing face-controlled liveness verification are provided. In one embodiment, a computing device can present, to a user, a sequential series of targets on a graphical user interface (GUI) of the computing device, where each target is a visual element designed to direct the user's attention to a location in the GUI. The computing device can further determine whether the user has successfully hit each target, where the determining comprises tracking movement of a virtual pointer controlled by the user's gaze or face pose and checking whether the user has moved the virtual pointer over each target. If the user has successfully hit each target, the computing device can conclude that the user is a live subject.

Abstract: Techniques for implementing face-controlled liveness verification are provided. In one embodiment, a computing device can present, to a user, a sequential series of targets on a graphical user interface (GUI) of the computing device, where each target is a visual element designed to direct the user's attention to a location in the GUI. The computing device can further determine whether the user has successfully hit each target, where the determining comprises tracking movement of a virtual pointer controlled by the user's gaze or face pose and checking whether the user has moved the virtual pointer over each target. If the user has successfully hit each target, the computing device can conclude that the user is a live subject.

Abstract: Techniques for implementing face-controlled liveness verification are provided. In one embodiment, a computing device can present, to a user, a sequential series of targets on a graphical user interface (GUI) of the computing device, where each target is a visual element designed to direct the user's attention to a location in the GUI. The computing device can further determine whether the user has successfully hit each target, where the determining comprises tracking movement of a virtual pointer controlled by the user's gaze or face pose and checking whether the user has moved the virtual pointer over each target. If the user has successfully hit each target, the computing device can conclude that the user is a live subject.

Abstract: Techniques for performing continuous enrollment for face verification are provided. In one embodiment, a computing device can receive, from a user, an indication that the user wishes to authenticate himself/herself with the computing device via face verification. In response to the indication, the computing device can capture, using a camera, a series of images of the user's face and can authenticate the user by evaluating each of the series of images against a face template for the user, where the user is authenticated based on an N-th image in the series. Once the user has been authenticated, the computing device can select one or more images from the series prior to the N-th image and can add the selected images to the user's face template.

Abstract: Techniques for performing continuous enrollment for face verification are provided. In one embodiment, a computing device can receive, from a user, an indication that the user wishes to authenticate himself/herself with the computing device via face verification. In response to the indication, the computing device can capture, using a camera, a series of images of the user's face and can authenticate the user by evaluating each of the series of images against a face template for the user, where the user is authenticated based on an N-th image in the series. Once the user has been authenticated, the computing device can select one or more images from the series prior to the N-th image and can add the selected images to the user's face template.