Abstract: This disclosure enables various technologies for tracking various objects (e.g., mammals, animals, humans, pets) within various defined areas (e.g., rooms, apartments, residences, vehicles, tents) to determine whether those objects satisfy or do not satisfy various criteria, signatures, or thresholds, which may relate to health, safety, or security of those objects within those defined areas. These technologies may be enabled via various radars (e.g., time-of-flight radars, Doppler radars) positioned within those defined areas to track those objects therein. For example, some of such radars may operate in a Ku-band inclusively between about 12 GHz and about 18 GHz, a K-band inclusively between about 18 GHz and about 27 GHz, or a Ka-band inclusively between about 26.5 GHz and about 40 GHz, each of which has been unexpectedly found to be technologically beneficial for tracking those objects within those defined areas.

Abstract: In aspects, the disclosure provides a method of differentiating a malignant tumor from a non-malignant tumor in a mammal having a tumor comprising determining in a sample from the mammal the level of one or more of: (1) transthyretin (TTR) RNA or protein, (2) dual oxidase 2 (DUOX2) RNA or protein, and (3) G-protein coupled receptor 179 (GPR179) RNA or protein.

Abstract: A system schedules tasks using multiple processors. Examples of tasks include build tasks, training of machine learning models, and testing. The system groups the processors into a set of pools. A pool represents a set of processors associated with an affinity class. The system schedules execution of new tasks by determining an affinity class for a new build task based on characteristics of the new build task. The system identifies a pool matching the affinity class of the new build task. The system adds the new build task to the queue data structure of the pool matching the affinity class. The system adjusts the size of a pool based on a measure of workload associated with the pool. The system may determine the measure of workload as a weighted aggregate of various features describing the pool or using a machine learning model.

Abstract: This disclosure enables various technologies for tracking various objects (e.g., mammals, animals, humans, pets) within various defined areas (e.g., rooms, apartments, residences, vehicles, tents) to determine whether those objects satisfy or do not satisfy various criteria, signatures, or thresholds, which may relate to health, safety, or security of those objects within those defined areas. These technologies may be enabled via various radars (e.g., time-of-flight radars, Doppler radars) positioned within those defined areas to track those objects therein. For example, some of such radars may operate in a Ku-band inclusively between about 12 GHz and about 18 GHz, a K-band inclusively between about 18 GHz and about 27 GHz, or a Ka-band inclusively between about 26.5 GHz and about 40 GHz, each of which has been unexpectedly found to be technologically beneficial for tracking those objects within those defined areas.