Abstract: An example server includes: a memory storing an account database for a centralized reward hub; a processor interconnected with the memory, the processor configured to: receive a new hub account request for a new hub account at the centralized reward hub; obtain user parameters associated with the new hub account request; filter a list of merchants based on the user parameters; provide the filtered list of merchants for presentation at a client device; receive a selection of a merchant from the filtered list; send a new merchant account request to create a merchant account for the merchant; and create the new hub account in the account database for the centralized reward hub.

Abstract: An example server includes: a memory storing an account database for a centralized reward hub; a processor interconnected with the memory, the processor configured to: provide a centralized reward hub interface for display at a client device associated with a client account, the centralized reward hub providing multiple merchants for selection; receive a selection of a merchant from the client device; redirect the client device to a merchant site for the selected merchant; provide, to a discount module integrated with the merchant site, a point balance for a client account; receive, from the discount module, an updated point balance for the client account after application of a portion of the point balance to apply a discount at the merchant site; and update the point balance for the client account in the account database.

Abstract: An example server includes: a memory storing an account database for a centralized reward hub; a processor interconnected with the memory, the processor configured to: receive purchase data for a purchase by a customer, the purchase data including a customer identifier; determine whether account database includes an existing entry corresponding to the customer identifier; when the account database does not include an existing entry corresponding to the customer identifier, create a new entry for the customer identifier; send the customer a login link for the centralized reward hub based on the new entry or the existing entry corresponding to the customer identifier.

Abstract: The present disclosure provides a novel approach, recomSELEX, that highly integrate mutually supportive recombination and computational methods for aptamer selection and identification. The recomSELEX approach comprises a recombinatorial SELEX platform for aptamer screening that exponentially increases the sequence space that is explored by incorporation of a DNA shuffling step that allows recombination between aptamers. Subsequently, the recombinatorial SELEX platform can also be employed to develop new and optimize already existing aptamers. The recomSELEX further comprises a computational SELEX platform with a constrained genetic algorithm (GA) to identify potential aptamers that are stable and have the desired specificity and affinity of a target.

Abstract: An example system includes an e-commerce platform to: detect a predictor of a purchase of an item by a customer on an e-storefront serviced by the e-commerce platform; and a loyalty rewards system in communication with the e-commerce platform, the loyalty rewards system to: receive, from the e-commerce platform, a customer identifier identifying the customer; determine, in response to detection of the predictor, whether a point balance associated with the customer identifier satisfies a threshold condition; when the point balance satisfies the threshold condition, select a reward option; and send a preview including the reward option and a discounted price for the item computed based on the selected reward option for presentation to the customer at the e-commerce platform.

Abstract: An example system includes an e-commerce platform to: detect a predictor of a purchase by a customer on an e-storefront serviced by the e-commerce platform; and a loyalty rewards system in communication with the e-commerce platform, the loyalty rewards system to: receive a customer identifier from the e-commerce platform, the customer identifier identifying the customer; determine, in response to detection of the predictor, whether a point balance associated with the customer identifier is within a threshold distance of a rewards level; when the point balance is within the threshold distance of the rewards level, select an activity associated with a designated number of points for presentation by the e-commerce platform to the customer; and in response to completion of the activity, update the point balance based on the designated number of points.

Abstract: An example e-commerce system includes an e-commerce platform to: receive a purchase order from a guest user, the purchase order including a guest user identifier representing a means of communication for the guest user; match the guest user identifier to a customer identifier; send a confirmation of the purchase order to the guest user via the means of communication for the guest user; and a loyalty rewards system to communicate with the e-commerce platform, the loyalty rewards system to: associate the customer identifier with a guest user account of the loyalty rewards system; and track reward points from the purchase order in association with the guest user account.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables where the cost functions have a relationship with the variables and receiving a landmark indicating a point that an agent is to visit while moving, a cost being associated with ignoring the landmark. The method includes generating a first message for the cost functions for the corresponding variable based upon the relationship and a second message for each of the variables for the corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes repeating steps (c), (d), and (e) until a consensus is formed between the first and second messages until the optimization solution is determined based upon the consensus.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables in which each of the cost functions has a predetermined relationship with select ones of the variables. The variables comprise a sub-solution of a spline indicative of a curved path along an estimated trajectory. The method includes generating a first message for each of the cost functions for each corresponding variable based upon the relationship and a second message for each of the variables for each corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes forming a consensus between the first and second messages until the optimization solution is determined.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables where the cost functions have a relationship with the variables and receiving a landmark indicating a point that an agent is to visit while moving, a cost being associated with ignoring the landmark. The method includes generating a first message for the cost functions for the corresponding variable based upon the relationship and a second message for each of the variables for the corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes repeating steps (c), (d), and (e) until a consensus is formed between the first and second messages until the optimization solution is determined based upon the consensus.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables in which each of the cost functions has a predetermined relationship with select ones of the variables. The method includes generating a first message for each of the cost functions for each corresponding variable based upon the respective predetermined relationship and a second message for each of the variables for each corresponding cost function based upon the respective predetermined relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes repeating steps (b), (c), and (d) until a consensus is formed between the first and second messages until the optimization solution is determined based upon the consensus.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables in which each of the cost functions has a predetermined relationship with select ones of the variables. The variables comprise a sub-solution of a spline indicative of a curved path along an estimated trajectory. The method includes generating a first message for each of the cost functions for each corresponding variable based upon the relationship and a second message for each of the variables for each corresponding cost function based upon the relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes forming a consensus between the first and second messages until the optimization solution is determined.

Abstract: A method of selection that maximizes an expected reward in a contextual multi-armed bandit setting gathers rewards from randomly selected items in a database of items, where the items correspond to arms in a contextual multi-armed bandit setting. Initially, an item is selected at random and is transmitted to a user device which generates a reward. The items and resulting rewards are recorded. Subsequently, a context is generated by the user device which causes a learning and selection engine to calculate an estimate for each arm in the specific context, the estimate calculated using the recorded items and resulting rewards. Using the estimate, an item from the database is selected and transferred to the user device. The selected item is chosen to maximize a probability of a reward from the user device.

Abstract: A method for generating a recommendation item for members of a group includes registering a plurality of users as members of the group, identifying a subgroup of members of the group of users wherein the subgroup requests a recommendation item from a recommendation engine, calculating, using a multi-armed bandit algorithm, a recommendation item for the subgroup of members. The recommendation item is provided to the subgroup members for their evaluation. After evaluating the recommendation item, the individual users rate the recommendation item which updates the recommendation engine with preferences representing the members of the subgroup.

Abstract: A method and device determines an optimization solution for an optimization problem. The method includes receiving the optimization problem having cost functions and variables in which each of the cost functions has a predetermined relationship with select ones of the variables. The method includes generating a first message for each of the cost functions for each corresponding variable based upon the respective predetermined relationship and a second message for each of the variables for each corresponding cost function based upon the respective predetermined relationship. The method includes generating a disagreement variable for each corresponding pair of variables and cost functions measuring a disagreement value between the first and second beliefs. The method includes repeating steps (b), (c), and (d) until a consensus is formed between the first and second messages until the optimization solution is determined based upon the consensus.

Abstract: Disclosed are methods and apparatus for identifying users of content. The methods include identifying contextual information of a group of users, gathering user access data of the users on the basis of the contextual information of the group of users, analyzing temporal information of the user access data, and identifying particular users in the group of users on the basis of the analyzed temporal information and the contextual information.

Abstract: Systems and methods of automated document composition using clusters are disclosed. In an example, a method comprises determining a plurality of composition scores ?A(A, B), the composition scores each computing separately on a plurality of worker nodes in the cluster. The method also includes determining coefficients (?i(A) at a master node in the cluster based on the composition scores (?i) from each of the worker nodes. The method also includes outputting an optimal document (D*) using the coefficients (?i).

Abstract: Systems and methods of parallel automated document composition are disclosed. In an example, a method comprises determining composition scores ?i(A,B) for a document, the composition scores computing in parallel. The method also comprises determining coefficients (?i) in parallel for each of the i pages in the document. The method also comprises composing a document based on the composition scores (?i) and the coefficients (?i).

Abstract: A control valve apparatus couples pressurized fluid to first and second actuators using first and second double valves. The first double valve includes first and second crossover passages and first and second pilots. The second double valve includes third and fourth crossover passages and third and fourth pilots. A first interconnection couples the first crossover passage with the third pilot fluid inlet. A second interconnection couples the second crossover passage with the fourth pilot fluid inlet. A third interconnection couples the third crossover passage with the first pilot fluid inlet. A fourth interconnection couples the fourth crossover passage with the second pilot fluid inlet. Thus, operation of each pilot depends upon receiving pressurized fluid from the other double valve which is only present when the other double valve is not in a fault condition.

Abstract: A double valve is constructed from first and second unitary valve assemblies each having a respective inlet port, a respective outlet port, a respective exhaust port, a respective first cross-mirror port, and a respective second cross-mirror port. A first plate is coupled to the first and second unitary valve assemblies and includes respective passages to provide a common inlet port coupled to the respective inlet ports of the first and second unitary valve assemblies, a common outlet port coupled to the respective outlet ports of the first and second unitary valve assemblies, and a common exhaust port coupled to the respective exhaust ports of the first and second unitary valve assemblies.