Abstract: A method for deconvolution of bulk RNA sequencing data is provided. Input comprising single-cell RNA sequencing (RNA-seq) data is obtained, and diverse datasets are generated based on a principle of same generating mixture probability such that each of the diverse datasets has a same cell type mixture proportion. The generated diverse datasets are used as input datasets for training a prediction model using machine learning, including creating a causal prediction model in which virtual samples are generated from the generated diverse datasets, and performing contrastive learning on the causal prediction model, wherein a contrastive loss is used for the learning of invariant features with respect to a measurement mechanism by which the RNA-seq datasets have been generated. The trained prediction model is used to predict the mixture of cell type quantities in the bulk RNA sequencing data. It can also contribute to predictive optimization regarding patient-specific risks.

Abstract: A method for measuring relatedness between prediction tasks includes receiving data for a first prediction task. The method further includes measuring the relatedness of the first prediction task to at least one previous prediction task as a difference between divergence of conditional probabilities of the tasks. The method can be advantageously applied in artificial intelligence or continual learning systems.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.

Abstract: A method for modular-based techniques for continual learning applications includes training a neural network based on learning a plurality of parameters associated with the neural network using input data associated with a current task. The neural network comprises a plurality of layers. A first layer, of the plurality of layers, comprises a plurality of nodes. Modularization of the neural network is performed to group the plurality of nodes of the first layer into at least two separate groups.

Abstract: A method for measuring dependence and independence of variables includes collecting data for the variables, computing sample Gram matrices for the variables, and computing element-wise products of the sample Gram matrices. Normalized mutual information of the variables is then computed using Renyi's ?-order entropy function based on the element-wise products as an independence measure of the variables.

Abstract: Systems and methods for graph approximation include computing an incident matrix based on an original graph, defining a cost function of a new graph, the cost function including an entropy of the new graph, a graph distance and a number of edges and/or nodes, wherein the graph distance includes a value representing a distance between the new graph and the original graph, determining a reduced cost function by, iteratively: a) computing a gradient of the cost function for the new graph, and b) modifying the new graph by adding an edge to, or removing an edge from, the new graph; and outputting an approximated graph, the approximated graph corresponding to the modified new graph having a minimum of the cost function.

Abstract: A method for measuring relatedness between prediction tasks includes receiving data for a first prediction task. The method further includes measuring the relatedness of the first prediction task to at least one previous prediction task as a difference between divergence of conditional probabilities of the tasks. The method can be advantageously applied in artificial intelligence or continual learning systems.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.

Abstract: A method and system is disclosed for validating honesty of a student taking an online test. A camera is used to detect and monitor the test taker's eye movements and eye gaze positions during the taking of the test to ensure the test taker is looking only at the computer display showing the questions and answer choices to the test taker. A microphone is incorporated into, placed on, or in the vicinity of the computer display and the system compares audio received to recorded voice of the test taker to detect another voice. Other voices and eye movement beyond the display screen may be flagged as indicative of dishonest test taking.