Abstract: Systems and methods for predicting differentiating features from tabular data for two different populations. In some aspects, the systems and methods provide for receiving first data entries and second data entries and generating a first graph based on a data entry from the first data entries and a second graph based on a data entry from the second data entries. A first node in the first graph is determined to correspond to a second node in the second graph. A first set of graph embeddings is generated based on the first graph. A second set of graph embeddings is generated based on the second graph. Using a machine learning model, the first set of graph embeddings and the second set of graph embeddings are processed to identify at least one feature indicative of a difference between the first data entries and the second data entries.

Abstract: Systems and methods for predicting change points in tabular data. In some aspects, the systems and methods provide for generating time-stamped graphs based on data entries and corresponding time stamps. Each graph of the time-stamped graphs corresponds to a data entry and is representative of one or more events associated with a time stamp corresponding to the data entry. The graph is independent of any events before or after the time stamp. For each graph of the time-stamped graphs, a set of graph embeddings is generated based on the graph and processed using a machine learning model to predict an occurrence of a change point in the data entries.

Abstract: Systems and methods for adjusting data processing components. In some aspects, the systems and methods include training a first machine learning model using a similarity graph generated based on training entries to predict whether a target system related to a node in the similarity graph will be non-operational within a future period of time, processing using the trained first machine learning model an updated similarity graph generated based on training and inference entries to predict for each node for the inference entries whether a target system related to the node will be non-operational within the future period of time, processing using a second machine learning model predictions and associated inference entries to predict that a target system related to a node for an entry will be non-operational within the future period of time, and adjusting data processing components related to the target system.

Abstract: In some aspects, a computing system may create different node embeddings (e.g., different instances of the same graph) and aggregate the node embeddings to form “multipurpose” node embeddings. As an example, the different node embeddings may include node embeddings generated using unsupervised machine learning and node embeddings generated using supervised machine learning. In this way, for example, a variety of machine learning models may use the aggregated node embeddings without the need for each machine learning model to generate separate node embeddings each time a machine learning task is performed. A machine learning model may use all or a portion of the features in the aggregated node embeddings as appropriate for the task the model is performing.

Abstract: One or more embodiments include a virtual personal assistant module executing on a virtual personal assistant system. The virtual personal assistant module obtains first sensor data from a first sensor included in a plurality of sensors. The virtual personal assistant module analyzes the first sensor data to generate a first result. The virtual personal assistant module obtains second sensor data from a second sensor included in the plurality of sensors. The virtual personal assistant module analyzes the second sensor data and the first result to generate a second result. The virtual personal assistant module outputs a natural language audio output to the user based on the second result.

Abstract: A child's walker includes pivotally connected first and second frame members which rotate with respect to one another to define an open position and a closed position. A third frame member has a tray on the front portion thereof and a rear portion which freely pivots on the first frame member. Height adjusting means are secured to the legs of the first frame member, these means supporting the legs of the second frame member when the walker is in the open position. The walker folds to the closed position for carrying and storage.