Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of rother factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: A method can be implemented via execution of computing instructions configured to run at a processor and stored at a non-transitory computer-readable media. The method can include: determining one or more virtual categories associated with a search query by a user. The method can also include: classifying one or more search results for the search query into one or more category groups based on the one or more virtual categories. The method can further include: generating a respective user interface portion for each group of the one or more category groups. The method can additionally include: generating a user interface comprising the respective user interface portion for each group of the one or more category groups. The method can also include: transmitting, via a computer network, instructions to display the user interface on a user device for the user. Other embodiments are disclosed.

Abstract: There is no unified, comprehensive system for the governance of artificial intelligence (AI) agents. Accordingly, disclosed embodiments register a plurality of AI agents, potentially from disparate sources, into a centralized registry. Prior to activation, each AI agent is associated with a governance policy. After activation, each AI agent is monitored in real time, during execution, to determine whether or not the AI agent remains compliant with the associated governance policy. When an AI agent becomes non-compliant with the associated governance policy, corrective action may be taken, and the AI agent may be labeled as untrusted. The corrective action may comprise alerting a user, modifying the AI agent, adjusting an amount of each of one or more computational resources that is allocated to the AI agent, modifying an access of the AI agent to one or more systems, adjusting a communication control of the AI agent, and/or the like.

Abstract: Provided is a fluid delivery device for delivering at least two fluids, the device comprising: a first fluid pathway having a first upstream large volume and a first downstream small volume connected in fluid communication with the first upstream large volume, the first fluid pathway further comprising a first exit; a second fluid pathway having an second upstream large volume and a downstream second small volume connected in fluid communication with the second upstream large volume, the second fluid pathway further comprising a second exit; and an insertion member containing the first and second exits in a closely proximate relationship and configured to insert the first and second exits through tissue to a distribution location without prior mixing of the fluids. Also provided is a kit containing the fluid delivery device, methods of using the fluid delivery device, and methods of manufacturing the fluid delivery device.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of rother factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: An authentication request is received from a User Premises Equipment (UPE). The authentication request is responsive to expiration of a prior authentication certificate provided to the UPE. The authentication request comprises a set of metadata information elements. A subset of metadata information elements are selected from the set, comprising a static element descriptive of a UPE establishment event, and environmental elements indicative of a physical environment of the UPE. The static element is validated based on validation information. A trust score for the UPE is generated based on a comparison between the environmental elements and a UPE environmental profile descriptive of a known physical environment of a geographic area associated with the UPE. An authentication certificate is provided to the UPE based on the trust score being greater than a threshold trust score.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Abstract: A method can be implemented via execution of computing instructions configured to run at a processor and stored at a non-transitory computer-readable media. The method can include: determining one or more virtual categories associated with a search query by a user. The method can also include: classifying one or more search results for the search query into one or more category groups based on the one or more virtual categories. The method can further include: generating a respective user interface portion for each group of the one or more category groups. The method can additionally include: generating a user interface comprising the respective user interface portion for each group of the one or more category groups. The method can also include: transmitting, via a computer network, instructions to display the user interface on a user device for the user. Other embodiments are disclosed.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.

Abstract: The invention generally relates to plant cells and plants modified to increase resistance to necrotrophs or drought and methods of selecting and using the same. More specifically, the invention relates in part to plant cells and/or plants modified to eliminate or reduce as compared to control plants cell the NADPH oxidase activity or expression of certain respiratory burst oxidase homolog (RBOH) proteins and methods of selecting for and using the same.

Abstract: The invention generally relates to plant cells and plants modified to increase resistance to necrotrophs or drought and methods of selecting and using the same. More specifically, the invention relates in part to plant cells and/or plants modified to eliminate or reduce as compared to control plants cell the NADPH oxidase activity or expression of certain respiratory burst oxidase homolog (RBOH) proteins and methods of selecting for and using the same.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of rother factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: Methods, systems, and devices that support a dynamic screen refresh rate are described. An electronic device may dynamically (e.g., autonomously, while operating) adjust the rate at which a screen is refreshed, such as to balance considerations such as user experience and power consumption by the electronic device. For example, the electronic device may use an increased refresh rate when executing applications for which user experience is enhanced by a higher refresh rate and may use a decreased refresh rate when executing other applications. As another example, the electronic device may use different refresh rates while executing different portions of the same application, as some aspects of an application (e.g., more intense portions of a video game) may benefit more than others from a higher refresh rate. The electronic device may also account of other factors, such as battery level, when setting or adjusting the refresh rate of the screen.

Abstract: Methods and apparatuses relating to processing neural networks are described. In one embodiment, an apparatus to process a neural network includes a plurality of fully connected layer chips coupled by an interconnect; a plurality of convolutional layer chips each coupled by an interconnect to a respective fully connected layer chip of the plurality of fully connected layer chips and each of the plurality of fully connected layer chips and the plurality of convolutional layer chips including an interconnect to couple each of a forward propagation compute intensive tile, a back propagation compute intensive tile, and a weight gradient compute intensive tile of a column of compute intensive tiles between a first memory intensive tile and a second memory intensive tile.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instruction that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a training procedure, labels based at least in part on price band activity data from a time period; training, using the training procedure, an affinity prediction model of a machine learning architecture; analyzing, using the affinity prediction model of the machine learning architecture, as trained, the price band activity data indicating interactions of a user with items; and generating, using the labels and the affinity prediction model of the machine learning architecture, as trained, one or more price affinity predictions for one or more items for the user. Other embodiments are disclosed herein.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: evaluating, using a price band determination model, degrees of expensiveness of items relative to each other in item type categories; generating, using the price band determination model, price bands associated with item type categories; assigning each of the items to a respective one of the price bands associated with a respective one of the item type categories; and presenting, to one or more end-user applications, at least one other item corresponding to at least one of the price bands associated with at least one of the item type categories. Other embodiments are disclosed.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital recommendations including discounted items with associated savings. In some examples, a computing device obtains savings data including savings items. In response to obtaining the savings data, the computing device obtains a plurality of clusters. Each cluster of the plurality of clusters includes a set of product types. The computing device obtains user data associated with a user, and based on the savings data and the plurality of clusters, determines a set of initial items for recommendation. The set of initial items includes a subset of the savings items. The computing device also determines final recommendation items for the user based on the set of initial items and the user data.

Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transaction of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of churn scores, based on the set of features. In some examples, each churn score of the plurality of churn scores being associated with a particular user of the set of users and characterize a likelihood of a churn event of the corresponding user.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: determining price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.