Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion neural network for mask aware image and typography editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from a base digital image. Moreover, the disclosed systems generate a mask-segmented image by combining a shape mask with the base digital image. In one or more implementations, the disclosed systems utilize noising steps of a diffusion noising model to generate a mask-segmented image noise map from the mask-segmented image. Furthermore, the disclosed systems utilize a diffusion neural network to create a stylized image corresponding to the shape mask from the base image embedding and the mask-segmented image noise map.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure include an image generation network configured to encode a plurality of abstract images using a style encoder to obtain a plurality of abstract style encodings, wherein the style encoder is trained to represent image style separately from image content. A clustering component clusters the plurality of abstract style encodings to obtain an abstract style cluster comprising a subset of the plurality of abstract style encodings. A preset component generates an abstract style transfer preset representing the abstract style cluster.

Abstract: In accordance with an embodiment, systems and methods described herein can be used, for example with a content management system, to provide recommendations to categorize/classify content into user-defined categories, which in turn provides an opportunity for content managers to place new content into accurate categories effortlessly, based on previously evaluated/categorized content. A recommendation system or tool can use artificial intelligence (AI) techniques to continuously learn from past data, and assist in placing content into a relevant category through automatic categorization/classification of newly created/edited content. The recommendation tool can be implemented and applied across diverse domains by generating feature vectors from contents, creating clusters in the feature space based on previously categorized content, and recommending a category for new content through feature space distance calculation from the clusters.

Abstract: A method for operating multiple output switching converters includes determining, by a pulse width modulation (PWM) duty split controller, a plurality of balanced reference signals including a first balanced reference signal and a second balanced reference signal, generating, by the PWM duty split controller, based on the plurality of balanced reference signals, a third balanced reference signal, the generating of the third balanced reference signal including limiting, using a limiter, the first balanced reference signal and the second balanced reference signal and accumulating the limited first balanced reference signal and the limited second balanced reference signal, using an accumulator, and operating, by the PWM duty split controller, a first switch and a plurality of second switches associated with the multiple output switching converters, based on the plurality of balanced reference signals, the third balanced reference signal, and one or more circuit parameters.

Abstract: A method includes receiving, by one or more processors of at least one server and via a connection between the at least one server and a client device, a request for a content item to be presented at the client device, receiving, by the one or more processors, data indicative of a network speed of the client device at which the content item is to be presented, selecting, by the one or more processors and based on the data indicative of the network speed, a first content item, and providing, by the one or more processors and for presentation at the client device, the first content item via the connection between the at least one server and the client device.

Abstract: A computer-implemented method can instantiate a net graph based on one or more existing bills of materials for one or more known entities. The net graph includes a plurality of interconnected nodes representing different objects included in the one or more known entities, and the one or more existing bill of materials define relationship between the objects. The method can determine carbon footprint values of the objects represented by the nodes, collect vectors of object features and carbon footprint values corresponding to selected nodes in the net graph, train a machine learning model using the collected vectors of object features and the carbon footprint values corresponding to the selected nodes, receiving a request associated with a target entity different from the one or more known entities, and responsive to the request, generate an estimated carbon footprint value for the target entity based on the machine learning model.

Abstract: A computer-implemented method can receive a message sent from a source entity, perform first pre-processing operations for verifying validity of the message, perform second pre-processing operations for determining a category of the message, extract metadata from the message, generate an enriched message comprising the metadata and the determined category, perform post-processing operations for classifying the enriched message into one of a plurality of event types, broadcast the enriched message to a message broker, and routing, by the message broker, the enriched message to one or more target entities registered an event type into which the message is classified.

Abstract: In accordance with an embodiment, systems and methods described herein can be used, for example with a content management system, to provide recommendations to categorize/classify content into user-defined categories, which in turn provides an opportunity for content managers to place new content into accurate categories effortlessly, based on previously evaluated/categorized content. A recommendation system or tool can use artificial intelligence (AI) techniques to continuously learn from past data, and assist in placing content into a relevant category through automatic categorization/classification of newly created/edited content. The recommendation tool can be implemented and applied across diverse domains by generating feature vectors from contents, creating clusters in the feature space based on previously categorized content, and recommending a category for new content through feature space distance calculation from the clusters.

Abstract: A code generator platform may receive source metadata and a target data model. The code generator platform may determine a parameter, of the target data model, that is associated with the attribute. The code generator platform may map, based on the attribute and the source metadata, the data to the parameter of the target data model. The code generator platform may generate, based on mapping the data to the parameter, data transformation code associated with the data and the target data model, wherein the data transformation code, when executed, generates target data that corresponds to the data according to the target data model. The code generator platform may perform an action associated with the data transformation code to permit the data transformation code to be executed in order to update a target database with the target data.

Abstract: Technologies and solutions are provided for improving process efficiency/identifying efficient paths of process steps. A target outcome can be identified, which can be a particular status, such as a stage (or status/step) in a process, or a target outcome can be an identification of particular process statuses that can be reached, such as given a particular set of constraints. Proceeding between process steps involves the use of resources, where a process step can be reached, or having an increased chance of being reached, when the resources have been obtained. Various paths can exist for obtaining a resource, where some paths can be more efficient than others. Based on resource paths and paths between steps of a process, one or more paths can be suggested for reaching the target outcome, including providing information about the process step paths or the resources paths for reaching the target outcome.

Abstract: Described herein are systems and methods for providing a correlated content organization in a content management system based upon a training set. In accordance with an embodiment, the systems and methods described herein can build a training set based upon observations of received inputs to determine patterns that are used often in content merges. Once a pattern is established, the systems and methods can provide indications of proposed merges based upon the training set and rules established therefrom that fit the same, or similar (e.g., within a defined variant) of the pattern. The system can then receive an indication of whether the suggestion is accepted or rejected, and such decision can be fed back into the learning system. This way the accuracy of the content merge improves over time.

Abstract: Selection and serving of content items may include receiving data indicative of a status of an energy source of a device with a request for a content item. A first received content item may be associated with a first energy consumption level and a second received content item may be associated with a second energy consumption level. The accessed content items are responsive to the request for a content item. The first energy consumption level may be higher than the second energy consumption level. The first content item or the second content item may be selected based, at least in part, on the received data indicative of the status of the energy source of the device, and data to display the selected content item may be provided to the device.

Abstract: A device may include an amplifying transistor configured to amplify a radio frequency signal when powered by a supply signal and biased by a biasing signal. The device may include a biasing circuit configured to control the biasing signal based on a level of the supply signal, the biasing circuit including a reference transistor which is mirrored with the amplifying transistor to control a current flowing through the amplifying transistor such as to compensate a gain variation of the amplifier assembly.

Abstract: Described herein are novel LIF/LIFR inhibitors that exhibit improved cytotoxicity and bioavailability. These LIF/LIFR inhibitors are particularly useful for the treatment of tumors associated with overexpression of LIF.

Abstract: A method includes receiving, at a search toolbar, a search query from a machine in a network. The machine has an associated machine profile for participating in the network as an entity. The machine profile includes a machine identifier and machine metadata. A query type is determined from the search query. A search context for the machine is determined using a semantic graph of the network. From a set of services for the network, one or more relevant services to respond to the search query are identified based on the query type and the search context. The search query is applied to the one or more relevant services to obtain a set of responses. A set of relevant results for the search query is determined from the set of responses. The set of relevant results is transmitted to the machine.

Abstract: A method includes receiving a message query from an entity identifier participating in a social network. The message query specifies one or more entities, one or more requirements, and one or more constraints. A set of message query parameters is generated based on the message query. A set of queries for a semantic graph of the social network is generated based on the set of message query parameters. The set of queries is applied to the semantic graph to obtain a set of query results. A message context of the entity identifier is determined based on the set of query results and the set of message query parameters. A set of messages from a message repository is determined based on the message context. The set of messages can be presented on a client computer associated with the entity identifier.

Abstract: A computer-implemented method for building a machine learning (ML) model is provided. The method includes training a ML model using a set of input data, wherein the ML model includes a plurality of layers and each layer includes a plurality of filters, and wherein the set of input data includes class labels; obtaining a set of output data from training the ML model, wherein the set of output data includes class probabilities values; determining, for each layer in the ML model, by using the class labels and the class probabilities values, a working value for each filter in the layer; determining, for each layer in the ML model, a dominant filter, wherein the dominant filter is determined based on whether the working value for the filter exceeds a threshold; and building a subset ML model based on each dominant filter for each layer, wherein the subset ML model is a subset of the ML model.

Abstract: A computer-implemented method performed by a network node is provided. The method includes receiving a request for retrieving or executing a machine learning (ML) model or a combination of ML models. The request includes a first description of a specified output feature and specified input data type and distribution of input values for a ML model or combination of ML models. The method further includes obtaining an identification of a ML model, or a combination of ML models, having a second description that at least partially satisfies a match to the first description; identifying a candidate ML model, or combination of ML models, that produces the specified output feature of the first description based on a comparison of the first and second descriptions. The method further includes selecting a third description of the identified candidate ML model, or combination of ML models, based on a convergence.

Abstract: Systems, methods, and computer media for determining compatible users through machine learning are provided herein. Previous interactions between some users in a group can be used to determine a first set of user-to-user compatibility scores. Both the first set of compatibility scores and attributes for the users in the group can be provided as inputs to a machine learning model that can be used to determine a second set of user-to-user compatibility scores for user pairs who do not have an interaction history. Along with input constraints, the first and second sets of user-to-user compatibility scores can be used to select compatible user groups.

Abstract: Bioplastic compositions and methods of making are described herein. The various mechanical and physical properties of the bioplastic may be varied by altering the thermoforming or incorporating an additive depending on the desired mechanical and physical properties of the end product. The bioplastic may be made entirely of biomatter and be backyard compostable.