Abstract: A controlled extraction of an electronic connector from an electronic circuit board includes a press mechanism of a device positioned in spaced relation over a base fixture of the device. The base fixture receiving an electronic circuit board which includes a connector. Shafts of a removal tool of the press mechanism can be inserted into a series of openings in the connector, respectively, in response to applying a downward force to the press mechanism. A capturing element on each of the shafts can be actuated where the capturing element couples to a wall defining the opening of each of the openings. The connector can be removed from the electronic circuit board by applying an upward force to the press mechanism which removes the connector with the capturing element of the shafts coupled to the wall of the opening, and releasing the press mechanism from the base fixture.

Abstract: Certain aspects of the present disclosure provide techniques for ranking users based on similarity. One example method generally includes receiving a request to view a list of users similar to a user based on personal information and receiving user data generated based on attribute data and financial data associated with the user comprising value data of the user. The method further includes obtaining clusters comprising value data of a plurality of users based on mapped data of the plurality of users and determining, by comparing the value data of the user data with the value data of the clusters, a cluster of vectors similar to the user data. The method further includes generating, based on a list of user identifiers using a distance function, a ranked list of similar users and transmitting the ranked list of similar users to a user device for display.

Abstract: The disclosure provides TNF-like ligand 1a (TL1a)-binding proteins comprising an antigen binding domain of an antibody which binds specifically to TL1a and inhibits interaction of TL1a and Death Receptor 3 (DR3) and which does not inhibit the interaction of TL1a and Decoy Receptor 3 (DcR3). The disclosure also provides uses of the TL1a-binding proteins.

Abstract: A method of fabricating a multilayer circuit board is provided which includes forming a layer of a the multilayer circuit board with an internal clearance region having a modified voltage-to-ground clearance of conductive material adjacent to an aperture of the multilayer circuit board. The modified voltage-to-ground clearance of conductive material is based on a configuration of a connector pin to be press-fit connected within the aperture of the multilayer circuit board, and the internal clearance region is enlarged in a direction of greatest normal force outward from the aperture with insertion of the connector pin into the aperture.

Abstract: Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Abstract: Disclosed is a method of routing a flow of packets from a source node to a destination node over multiple pathways only when the destination node has determined and advised the source node that a packet reordering resource is available for use with multipath operation. Also disclosed is a method of detecting packet loss without incurring timeout delays when routing packets in a flow of packets over multiple pathways from a source node to a destination node. Further disclosed is a method of dynamically avoiding slower paths when routing packets between a source node and a destination node along multiple pathways. Also disclosed is a method of avoiding overflow of a destination node reorder window when routing packets between a source node and a destination node along multiple pathways.

Abstract: Certain aspects of the present disclosure provide techniques for ranking users based on similarity. One example method generally includes receiving a request to view a list of users similar to a user based on personal information and receiving user data generated based on attribute data and financial data associated with the user comprising value data of the user. The method further includes obtaining clusters comprising value data of a plurality of users based on mapped data of the plurality of users and determining, by comparing the value data of the user data with the value data of the clusters, a cluster of vectors similar to the user data. The method further includes generating, based on a list of user identifiers using a distance function, a ranked list of similar users and transmitting the ranked list of similar users to a user device for display.

Abstract: Certain aspects of the present disclosure provide techniques for ranking users based on similarity. One example method generally includes receiving a request to view a list of users similar to a user based on personal information and receiving user data generated based on attribute data and financial data associated with the user comprising value data of the user. The method further includes obtaining clusters comprising value data of a plurality of users based on mapped data of the plurality of users and determining, by comparing the value data of the user data with the value data of the clusters, a cluster of vectors similar to the user data. The method further includes generating, based on a list of user identifiers using a distance function, a ranked list of similar users and transmitting the ranked list of similar users to a user device for display.

Abstract: A method for measuring financial health involves aggregating financial data from different data sources into a single financial data warehouse; extracting multiple customer financial characteristics from the financial data warehouse to build multiple financial profiles for a plurality of known customers; training a predictive machine learning model using the multiple financial profiles of multiple known customers to obtain a trained model; generating a grade mapping table using the trained model and a pre-defined business rule; applying the trained model to the financial data of a user to obtain a financial health score set; applying the grade mapping table to the financial data of the user to obtain a financial health grade set; and presenting the financial health grade set and the financial health score set to the user via a multimedia user interface.

Abstract: A tennis-ball collector which includes a chassis, a drive train, an energy source, wheels, and a motor. The tennis-ball collector is useful for collecting scattered tennis balls after a game or practice without the need to bend and pick them up by hand. The device moves balls using a vacuum system or a drive belt.

Abstract: Systems and methods for generating recommended offers are disclosed. An example method may be performed by one or more processors of a recommendation system and include correlating attributes of users with attributes of offers based on historical data associated with the users and offers, training a machine learning model to predict a user's interest in an offer based on the correlating, obtaining current user data, obtaining current offer data, providing the current user data and the current offer data to the trained machine learning model, generating, using the trained machine learning model, a predicted level of interest that the current user has in each respective current offer of the number of current offers, identifying, among the number of current offers, at least one current offer having a predicted level of interest for the current user greater than a value, and generating one or more recommended offers for the current user.

Abstract: User data from users/consumers is transformed into machine learning training data including historical offer attribute model training data, historical offer performance model training data, and user attribute model training data associated with two or more users/consumers, and, in some cases, millions, tens of millions, or hundreds of millions or more, users/consumers. The machine learning training data is then used to train one or more offer/attribute matching models in an offline training environment. A given current user's data and current offer data are then provided as input data to the offer/attribute matching models in an online runtime/execution environment to identify current offers predicted to have a threshold level of user interest. Recommendation data representing these offers is then provided to the user and the current user's actions with respect to the recommended offers is monitored and used as online training data.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: A method for welding at least one hanger to a fork. A friction welding process may be used to create a weld between the hanger and the fork, after which the heat-affected zone (HAZ) may be allowed to cool. Preferably the cooling occurs until martensite is formed, after which a post-tempering current is applied to the HAZ.

Abstract: Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Abstract: Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Abstract: A tennis-ball collector which includes a chassis, a drive train, an energy source, wheels, and a motor. The tennis-ball collector is useful for collecting scattered tennis balls after a game or practice without the need to bend and pick them up by hand. The device moves balls using a vacuum system or a drive belt.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: Big data analysis methods and machine learning based models are used to provide offer recommendations to consumers that are probabilistically determined to be relevant to a given consumer. Machine learning based matching of user attributes and offer attributes is first performed to identify potentially relevant offers for a given consumer. A de-duplication process is then used to identify and eliminate any offers represented in the offer data that the consumer has already seen, has historically shown no interest in, has already accepted, that are directed to product or service types the user/consumer already owns, for which the user does not qualify, or that are otherwise deemed to be irrelevant to the consumer.

Abstract: A system and method for use with a data management service provides aggregated statistics derived from a large amount of user data extracted from one or more transaction management systems. The aggregated statistics are based on client queries from client systems. The queries request statistical information about a queried user grouping. An input interpreter module uses machine learning to modify the queried user grouping into a plurality of improved user groupings. A statistics calculator module performs a set of calculations on the user data based on the improved user groupings, and returns the results to an output preparer module. The output preparer module uses machine learning to determine which aggregated statistic to return to the client system.