Abstract: Methods and systems for customizing the characteristic of an electronic device (in the Internet of Things (IoT) environment based on at least one user's physiological state are provided. The method includes identifying context of the electronic device in response to receiving at least one event by the electronic device, wherein the at least one context includes at least one current user activity and an environmental context of a user. The method includes determining the change in a health parameter of the user and re-calibrates the characteristics of an electronic device through the magnitude of change in health parameter from the learning module. The method includes identifying current user activity and an environment context of the user on receiving the event from the electronic device).

Abstract: The present invention provides fire-retarded rigid polyurethane foam comprising the reaction product of polyol and isocyanate foam forming components and a dialkyl phosphorus-containing compound, namely a reactive mono-hydroxyl-functional dialkyl phosphinates, as flame retardant, serving as highly efficient reactive flame retardant in said rigid polyurethane foam.

Abstract: This disclosure includes technologies for image processing, particularly for image generation and editing in a configurable semantic direction. A generative adversarial network is trained with an auxiliary network with an auxiliary task that is designed to disentangle the latent space of the generative adversarial network. Resultantly, a new type of GAN is created to improve image generation or editing in both conditional and unconditional settings.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a classification neural network to classify digital images in few-shot tasks based on self-supervision and manifold mixup. For example, the disclosed systems can train a feature extractor as part of a base neural network utilizing self-supervision and manifold mixup. Indeed, the disclosed systems can apply manifold mixup regularization over a feature manifold learned via self-supervised training such as rotation training or exemplar training. Based on training the feature extractor, the disclosed systems can also train a classifier to classify digital images into novel classes not present within the base classes used to train the feature extractor.

Abstract: Embodiments are disclosed for training a neural network classifier to learn to more closely align an input image with its attribution map. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a training image comprising a representation of one or more objects, the training image associated with at least one label for the representation of the one or more objects, generating a perturbed training image based on the training image using a neural network, and training the neural network using the perturbed training image by minimizing a combination of classification loss and attribution loss to learn to align an image with its corresponding attribution map.

Abstract: Stabilizing a container-based application includes determining a health of a container. Based on the container health, a most recent stable version of an image for the container is identified. A container image is considered stable if containers spawned from the image have a relatively high MTTF and relatively low MTTR compared to other versions of same image. The container is then deployed using the most recent stable version of the image for the container.

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.

Abstract: Stabilizing a container-based application includes determining a health of a container. Based on the container health, a most recent stable version of an image for the container is identified. A container image is considered stable if containers spawned from the image have a relatively high MTTF and relatively low MTTR compared to other versions of same image. The container is then deployed using the most recent stable version of the image for the container.

Abstract: Stabilizing a container-based application includes determining a health of a container. Based on the container health, a most recent stable version of an image for the container is identified. A container image is considered stable if containers spawned from the image have a relatively high MTTF and relatively low MTTR compared to other versions of same image. The container is then deployed using the most recent stable version of the image for the container.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training a classification neural network to classify digital images in few-shot tasks based on self-supervision and manifold mixup. For example, the disclosed systems can train a feature extractor as part of a base neural network utilizing self-supervision and manifold mixup. Indeed, the disclosed systems can apply manifold mixup regularization over a feature manifold learned via self-supervised training such as rotation training or exemplar training. Based on training the feature extractor, the disclosed systems can also train a classifier to classify digital images into novel classes not present within the base classes used to train the feature extractor.

Abstract: The present invention provides fire-retarded rigid polyurethane foam comprising the reaction product of polyol and isocyanate foam forming components and a dialkyl phosphorus-containing compound, namely a reactive mono-hydroxyl-functional dialkyl phosphinates, as flame retardant, serving as highly efficient reactive flame retardant in said rigid polyurethane foam.

Abstract: The present invention provides dialkyl phosphorus-containing compounds, namely reactive mono-hydroxyl-functional dialkyl phosphinates, serving as highly efficient reactive flame retardants in flexible polyurethane foams. The invention further provides fire-retarded polyurethane compositions comprising said the reaction product of the mono-hydroxyl-functional dialkyl phosphinates with polyol and isocyanate foam forming components.

Abstract: The present invention provides novel cyclic phosphorus-containing compounds, namely hydroxyl-functional phospholene-1-oxides, serving as highly efficient reactive flame retardants in urethane systems, particularly in flexible polyurethane foams, semi-rigid and rigid polyurethane and polyisocyanurate foams. The invention further provides fire-retarded polyurethane compositions comprising said hydroxyl-functional phospholene-1-oxides.

Abstract: The present invention provides novel cyclic phosphorus-containing compounds, namely hydroxyl-functional phospholene-1-oxides, serving as highly efficient reactive flame retardants in urethane systems, particularly in flexible polyurethane foams, rigid polyurethane foams and rigid polyisocyanurate foams. The invention further provides fire-retarded polyurethane compositions comprising said hydroxyl-functional phospholene-1-oxides.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating trained neural network with increased robustness against adversarial attacks by utilizing a dynamic dropout routine and/or a cyclic learning rate routine. For example, the disclosed systems can determine a dynamic dropout probability distribution associated with neurons of a neural network. The disclosed systems can further drop neurons from a neural network based on the dynamic dropout probability distribution to help neurons learn distinguishable features. In addition, the disclosed systems can utilize a cyclic learning rate routine to force copy weights of a copy neural network away from weights of an original neural network without decreasing prediction accuracy to ensure that the decision boundaries learned are different.

Abstract: Stabilizing a container-based application includes determining a health of a container. Based on the container health, a most recent stable version of an image for the container is identified. A container image is considered stable if containers spawned from the image have a relatively high MTTF and relatively low MTTR compared to other versions of same image. The container is then deployed using the most recent stable version of the image for the container.

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).