Abstract: Embodiments are generally directed to extending artificial intelligence (AI) and machine learning (ML) techniques to generate content predicted to elicit a performance response from an intended recipient of a target audience. One method of generating content includes determining content generation information from a user prompt, the content generation information comprising a subject, an audience segment, and a performance indicator; and providing the content generation information to a content generation model to generate at least one item of audience-targeted content corresponding to the subject targeted to the audience segment to elicit a response defined by the performance indicator, wherein the content generation module comprises a natural language processing (NLP) model trained, via a content generation training module, using reinforcement learning based on a reward of a performance prediction determined by a performance prediction model based on historical performance data.

Abstract: Techniques for prompt-based image relighting and editing are described that support automatic generation of an edited digital image with high-fidelity and realistic lighting effects and background features. A processing device, for instance, receives as input a digital image that depicts a digital object, a lighting prompt, and a background prompt. The processing device generates a relit digital object that has a lighting condition specified by the lighting prompt applied to the digital object. The processing device further generates a background that includes a feature specified by the background prompt and the lighting condition. The processing device generates an edited digital object for output that includes the relit digital object and the background. The processing device further leverages a shadow synthesis model to edit shadows in the edited digital image. In this way, the techniques described herein preserve content details of the digital object when applying background and lighting effects.

Abstract: Some aspects relate to technologies for generating time series of sketches and using the time series of sketches for approximate query processing. In accordance with some aspects, tabular data is accessed that has a number of columns. Responsive to identifying a first column as comprising numerical data, sketches are generated for the numerical data for each of a number of time steps, and the sketches for the numerical data are stored as a first time series of sketches. Responsive to identifying a second column as comprising categorical data, sketches are generated for the categorical data for each of the time steps, and the sketches for the categorical data are stored as a second time series of sketches. When a query is received, a response to the query is provided using sketches from the first time series of sketches and/or the second time series of sketches.

Abstract: Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.

Abstract: A device includes a body having an outer surface. The device includes a plurality of pins in the body, a first pin of the plurality of pins configured to receive a first signal having a first rate, a second pin of the plurality of pins configured to receive a second signal having a second rate less than the first rate. The device includes a shield on a first portion of the outer surface to define an exposed second portion of the outer surface, wherein the second pin is positioned closer to the exposed second portion than the first pin.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

Abstract: Disclosed herein, among other things, are systems and methods for a hearing device antenna. One aspect of the present subject matter includes a hearing device configured to be worn in an ear of a wearer to perform wireless communication. The hearing device includes a housing, hearing electronics within the housing, and an inverted F antenna or loop antenna disposed at least partially in the housing and configured for performing 2.4 GHz wireless communication. In various embodiments, at least a portion of the antenna protrudes from an exterior of the housing.

Abstract: Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.

Abstract: In accordance with the described techniques, an image transformation system receives an input image and a text prompt, and leverages a generator network to edit the input image based on the text prompt. The generator network includes a plurality of layers configured to perform respective edits. A plurality of masks are generated based on the text prompt that define local edit regions, respectively, of the input image for respective layers of the generator network. Further, the generator network generates an edited image by editing the input image based on the plurality of masks, the respective edits of the respective layers, and the text prompt.

Abstract: In implementations of systems for generating image metadata using a compact color space, a computing device implements a color system to receive input data describing pixels of a digital image and corresponding RGB values of the pixels. The color system assigns a color of a compact color space to each of the pixels based on the corresponding RGB values of the pixels. The compact color space includes a subset of colors included in an RGB color space. The color system computes a histogram of colors of the compact color space and determines a particular color of the compact color space based on the histogram. The color system generates color metadata for the digital image describing a natural language name of the particular color of the compact color space.

Abstract: Systems and methods for document processing are provided. One aspect of the systems and methods includes identifying a theme and an input image of a product. Another aspect of the systems and methods includes generating an output image depicting the product and the theme based on the input image using an image generation model that is trained to generate images consistent with a brand. Another aspect of the systems and methods includes generating text based on the product and the theme using a text generation model. Another aspect of the systems and methods includes generating custom content consistent with the brand and the theme based on the output image and the text.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Disclosed herein, among other things, are systems and methods for a hearing device antenna. One aspect of the present subject matter includes a hearing device configured to be worn in an ear of a wearer to perform wireless communication. The hearing device includes a housing, hearing electronics within the housing, and an inverted F antenna or loop antenna disposed at least partially in the housing and configured for performing 2.4 GHz wireless communication. In various embodiments, at least a portion of the antenna protrudes from an exterior of the housing.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that control bias in machine learning models by utilizing a fairness deviation constraint to learn a decision matrix that modifies machine learning model predictions. In one or more embodiments, the disclosed systems generate, utilizing a machine learning model, predicted classification probabilities from a plurality of samples comprising a plurality of values for a data attribute. Moreover, the disclosed systems determine utilizing a decision matrix and the predicted classification probabilities, that the machine learning model fails to satisfy a fairness deviation constraint with respect to a value of the data attribute. In addition, the disclosed systems generate a modified decision matrix for the machine learning model to satisfy the fairness deviation constraint by selecting a modified decision threshold for the value of the data attribute.

Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

Abstract: Disclosed herein, among other things, are systems and methods for a hearing device antenna. One aspect of the present subject matter includes a hearing device configured to be worn in an ear of a wearer to perform wireless communication. The hearing device includes a housing, hearing electronics within the housing, and an inverted F antenna or loop antenna disposed at least partially in the housing and configured for performing 2.4 GHz wireless communication. In various embodiments, at least a portion of the antenna protrudes from an exterior of the housing.

Abstract: In some examples, a prototype model that includes a representative subset of data points (e.g., inputs and output classifications) of a machine learning model is analyzed to efficiently interpret the machine learning model's behavior. Performance metrics such as a critic fraction, local explanation scores, and global explanation scores are determined. A local explanation score capture an importance of a feature of a test point to the machine learning model determining a particular class for the test point and is computed by comparing a value of a feature of a test point to values for prototypes of the prototype model. Using a similar approach, global explanation scores may be computed for features by combining local explanation scores for data points. A critic fraction may be computed to quantify a misclassification rate of the prototype model, indicating the interpretability of the model.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.