Abstract: A split-inverter heat pump system includes a continuously variable speed compressor motor operably connected to a refrigerant compressor of a condenser unit of the heat pump system, a compressor motor controller, a network device, and a thermostat. The compressor motor controller is configured to generate a compressor power signal corresponding to electrical power consumed by the compressor motor in operating the refrigerant compressor. The compressor motor controller is configured to control a rotational speed of the compressor motor according to an electrical power level within a range of electrical power levels based on a compressor control signal. The network device is operably connected to the compressor motor controller and is configured to receive the compressor power signal from the compressor motor controller. The network device is configured to transmit the compressor power signal to a remote computer over the Internet. The thermostat is configured to receive an ambient temperature signal.

Abstract: A system includes a machine learning network input interface configured to receive input data from a sensor, one or more processors collectively programmed to receive an input data from the sensor, wherein the input data is indicative of image of a scene that includes a perturbation from a black-box attack with a physical perturbation at the scene, display an adversarial pattern at the scene, determine an objective function utilizing at least the adversarial pattern and a target classification of the machine-learning network, randomly select a plurality of data points associated with the adversarial pattern and the objective function, wherein the data points are associated with a number of queries of the objective function, obtain a machine-learning model output utilizing the data points displayed in the scene, and in response to meeting a criteria associated with the adversarial pattern and model output, identify a successful attack pattern.

Abstract: A method includes receiving a plurality of paired input images, wherein the paired images includes a first set of images from a first modality and a second set of images from a second modality, outputting a list of bounding boxes and labels in response to running an image-based object detection model, mapping each bounding box to a region of interest that is corresponding to the bounding box and associated with the second set of images, cropping the region of interest from the first and second set of images to generate a cropped first and second set of images, sending the cropped first set of images to a first encoder and a cropped second set of images to a second encoder, wherein the first encoder is configured for the first modality and the second encoder is configured for the second modality, outputting object-level embeddings for both the cropped first and second set of images utilizing encoders, identifying a loss function associated with the images, and in response to when a threshold is met, outputting

Abstract: A method and system for training a target neural network using a foundation model having a source neural network that has been pre-trained to operate on a source modality. Inputting source data to the foundation model. The source neural network of the foundation model having at least one source encoder having a source weights which has been pre-trained to compute source features which are computable within the source data of the source modality. Inputting target data to a target neural network operating on a target modality. The target neural network including at least one target encoder having target weights for computing target features within the target data of the target modality. Training the target weight by pairing the target data with the source data and freezing the source weights of the source neural network for a pre-determined epoch.

Abstract: A system includes a machine learning network input interface configured to receive input data from a sensor, one or more processors collectively programmed to receive an input data from the sensor, wherein the input data is indicative of image of a scene that includes a perturbation from a black-box attack with a physical perturbation at the scene, display an adversarial pattern at the scene, determine an objective function utilizing at least the adversarial pattern and a target classification of the machine-learning network, randomly select a plurality of data points associated with the adversarial pattern and the objective function, wherein the data points are associated with a number of queries of the objective function, obtain a machine-learning model output utilizing the data points displayed in the scene, and in response to meeting a criteria associated with the adversarial pattern and model output, identify a successful attack pattern.

Abstract: The systems and methods described herein may include one or more processors configured to receive a command from a user related to a subject; access a representation space associated with the command; receive a first dataset related to the command, a second dataset related to the subject, and a third dataset which includes subjects related to the command; update the representation space based on at least one of the first, second, and third dataset; generate a goal representation based on the representation space; receive, from a plurality of sensors, a sensor data of a current environment; generate a first and a second series of steps based on the goal representation and the current environment; annotate the sensor data based on performance of the first series of steps to generate an annotated senor data; and update the second series of steps based on the annotated sensor data.

Abstract: A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.

Abstract: A computer-implemented system and method includes obtaining a plurality of tasks from a first domain. A machine learning system is trained to perform a first task. A first set of prototypes is generated. The first set of prototypes is associated with a first set of classes of the first task. The machine learning system is updated based on a first loss output. The first loss output includes a first task loss, which takes into account the first set of prototypes. The machine learning system is trained to perform a second task. A second set of prototypes is generated. The second set of prototypes is associated with a second set of classes of the second task. The machine learning system is updated based on a second loss output. The second loss output includes a second task loss, which takes into account the second set of prototypes. The machine learning system is updated based on the second loss output. The machine learning system is fine-tuned with a new task from a second domain.

Abstract: A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.