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 transactional data 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 trial membership scores, based on the set of features. In some examples, each trial membership score of the plurality of trial membership scores are associated with a particular user of the set of users and characterize a likelihood of an acquisition event of the corresponding user changing from a non-member status to a trial member status.

Abstract: Examples described herein relate to hot page detection. Some examples include circuitry to provide a number of pages with access counts within a bucket of a histogram, wherein the bucket of the histogram is associated with a configured access count range; based on a distribution of access counts in the histogram being a first level, reduce the configured access count ranges of the different buckets of the histogram; determine a second level indicative of page access counts; and migrate data of pages from a far memory to a near memory based on the second level.

Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for simulation of electrical power grid behaviors. In some instances, a data model associated with an electrical power grid is obtained. The data model stores static data and dynamic data that can be continuously integrated into the data model based on data streams obtained from data sources associated with properties of the electrical power grid. A set of interfaces can be instantiated for querying data based on the data model. The querying is related to data from at least one of a planning analysis modeling domain or an operation analysis modeling domain from the data model related to the electrical power grid. A query associated with a first planning operation in the planning analysis modeling domain is executed. The query defines one or more nodes of the electrical power grid and relates to the planning analysis domain.

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: A microfluidic impedance cytometry apparatus, for position determination and impedance measurement of particle/s in a fluid carrying particles, comprising: a microfluidic impedance flow channel for allowing flow of said fluid; an upstream section; a downstream section; a sensing region to receive said channeled fluid, to sense one or more parameters of said fluid, said sensing region comprising one or more sets of pairs of electrodes, each pair forming a current path from an operative top to an operative bottom, each of said pairs being formed by an operative top electrode and an operative bottom electrode, electric potential being applied on said operative top electrode/s, each electrode for a particular pair being parallel-aligned and being symmetric, with respect to each other, same positive electric potential being applied on each of said top electrodes and each of said bottom electrodes is virtually grounded, for a pair; and a configuration of amplifiers.

Abstract: Systems and methods for generating top-k recommendation using latent space representations generated by deep joint variational autoencoder processes are disclosed. A user identifier is received and a set of prior interactions associated with the user identifier is obtained. A set of latent space representations of the set of prior interactions is generated using a trained inference model. The trained inference model includes a joint variational autoencoder model. A set of k-recommended items is generated based on a comparison of the set of latent space representations of the set of prior interactions and a set of latent space representations of one or more items. A user interface including the set of k-recommended items is generated.

Abstract: A session-specific conversion determination system can include a computing device configured to receive real-time signals of an event occurring from a user device. The real-time signals include interaction parameters. The computing device is also configured to obtain a set of historical data based on the interaction parameters and a set of facets and generate a probability affinity for each facet of the set of facets by implementing a machine learning model using the interaction parameters and the set of historical data as features. The computing device is also configured to adjust a display of a set of recommended items based on the probability affinity for each facet of the set of facets and transmit the display of the set of recommended items to a user interface of the user device.

Abstract: Systems and methods of generating an interface including elements related to a next best state prediction are disclosed. A request for an interface including a user identifier is received. A next state prediction engine receives a sequence unit set including at least one sequence unit associated with the user identifier and a set of features associated with the at least one sequence unit and generates at least one next state prediction using a trained sequential prediction model. The trained sequential prediction model is configured to receive the sequence unit set and the set of features for the at least one sequence unit and output at least one predicted next state for the sequence unit set. An interface generation engine generates an interface including at least one element related to the at least one predicted next state and transmits the interface to a user device associated with the user identifier.

Abstract: A method for handling voice input by voice assistant device are provided. The method includes receiving, by the voice assistant device, a voice input from a user; identifying, by the voice assistant device, at least one first user intent and a plurality of slots based on the voice input, wherein the plurality of slots may include at least one slot related to the at least one first user intent and at least one slot unrelated to the at least one first user intent; identifying, by the voice assistant device, at least one second user intent based on the voice input and the at least one slot unrelated to the at least one first user intent; and performing, by the voice assistant device, at least one first operation based on the at least one first user intent and at least one second operation based on the at least one second user intent.

Abstract: In some implementations, a provider edge device associated with a link aggregation group (LAG) may maintain, according to a link aggregation control protocol (LACP), a set of links that connect the PE device to a consumer edge device. The provider edge device may determine that the provider edge device and another provider edge device associated with the LAG are not receiving link aggregation control protocol data units (LACPDUs) from the consumer edge device. The provider edge device may cause the set of links to have a maintain LAG status, which causes the provider edge device to keep up the set of links and to cease maintaining the set of links according to the LACP. The provider edge device may route, based on causing the set of links to have the maintain LAG status, one or more packets to or from the consumer edge device via the set of links.

Abstract: A system can include 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: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. Other embodiments are disclosed herein.

Abstract: A system including 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: determining, by a distributed processing architecture and based on first transaction data of first transactions for first items, first micro-intents; grouping, by the distributed processing architecture, the first micro-intents into clusters; receiving, by a distributed memory architecture, second transaction data of a user transaction from a user interface of an electronic device of a user during a current browsing session of a website; determining, by the distributed processing architecture, one or more second micro-intents; determining, by the distributed processing architecture, that the user is expressing a current micro-intent; and transmitting an instruction to display a user interface element on the user interface of the electronic device. Other embodiments are described.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for an access controlled power grid model. A power grid model can include multiple regions. Access can be provided only to a subset of regions based on access privileges, and the user can be denied access to regions of the power grid model outside of the subset. A simulation can be executed using input from the user and can include simulation parameters for at least one of the regions in the subset. The simulation can be executed on the regions of the power grid model in the subset and at least one additional region that is not in the subset. The simulation can produce results that can include electrical values of components in the regions within the subset and values of components in at least one additional region. The output can include only the simulation results for regions within the subset.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: A system and method for recommending products based on characteristics of a customer's household. The system and method associates age dependent products with developmental stages on a universal developmental scale and determines a subset of age dependent products based on prior engagements by the customer's household. Using the development stages associated with the subset of age dependent products characteristics of the customer's household may determine specifically the number and ages of juveniles in the customer's household. Performing Gaussian mixture model or multivariate kernel density estimation on the developmental stages associated with the engagements of customer's household, the age(s) and number of juveniles respectively may be determined and recommendations of products and services to the customer or customer's household based upon these characteristics may be advantageously made.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform generating one or more item relational graphs for one or more items based on historical user purchases; transforming, using spectral filtering, the one or more item relational graphs into one or more frequency signals to remove noise from the one or more frequency signals; constructing, using a machine learning model, one or more item pair label classifications for one or more item pairs of the one or more items; generating a respective similarity score for each of the one or more item pairs; outputting a top k results for the one or more item pairs ranked by the respective similarity scores; and re-ranking, using a re-ranking algorithm, the top k results of the one or more item pairs based on a user preference for display on a user interface of an electronic device of a user. Other embodiments are disclosed.

Abstract: A method including training a logistic regression model to create a trained model to provide probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods. Input predictor variables of the logistic regression model include (i) user feature data including personal user features and online activity history for users in the multiple different time periods and (ii) email feature data including sent times and item category data for multiple different emails in the one or more email campaigns. Output dependent variables of the logistic regression model include responses by the users to the one or more email campaigns. The method also includes triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model. Other embodiments are disclosed.

Abstract: This disclosure describes a system and method for a central controller layer to monitor conditions and control operations of an electric grid. The central controller layer communicates with an intermediate controller layer that includes hubs to monitor operations of grid edge devices connected to different regions of the electric grid. The central controller layer obtains, from each hub, sensor data corresponding to measurements performed by the grid edge devices. The central controller layer determines, based on the sensor data and expected grid-wide operations, control strategies with expected electrical operating conditions for the respective region, and provides a respective control strategy to each hub. In response to receiving the respective control strategy for the hub, each hub generates operational parameters for at least one grid edge device that cause a grid edge device to adjust its operation based on the expected amount of power flow for the hub.

Abstract: Disclosed herein is a method of provisioning a virtual experience based on user preference. The method may include receiving an identity data associated with an identity of a user, retrieving a user profile data based on the identity data, analyzing the user profile data using a machine learning model, determining at least one preference data based on the analyzing, generating an interactive 3D model data, transmitting the interactive 3D model data to a user device configured to present the interactive 3D model data, receiving a reaction data from the user device. Further, the user device may include at least one sensor configured to generate the reaction data based on a behavioral reaction of a user consuming the interactive 3D model data.