Abstract: A decentralized node may generate a network of decentralized nodes individually configured to store, receive, and transmit data based on rules associated with the decentralized nodes. A decentralized node may associate a decentralized identity of an entity with a select decentralized node of the decentralized nodes. A decentralized node may present a user interface including one or more access controls at a edge device, the access controls configuring rules to be applied to third-party access and modification of decentralized identity data associated within the decentralized identity of the entity and stored at the selected decentralized node. A decentralized node may identify the decentralized identity data accessible to the select decentralized node based on the rules. A decentralized node may store the decentralized identity data within the decentralized identity associated with the entity at the select decentralized node.

Abstract: Methods and systems for calibrating simulated measurement signals generated by a parametric measurement model are described herein. Regression on real measurement signals is performed using a parametric model. The residual fitting error between the real measurement signals and simulated measurement signals generated by the parametric model characterizes the error of the parametric model at each set of estimated values of the one or more floating parameters. Simulated measurement signals are generated by the parametric model at specified values of the floating parameters. A residual fitting error associated with the simulated measurement signals generated at the specified values of the floating parameters is derived from the residual fitting errors calculated by the regression on the real measurement signals. The simulated measurement signals are calibrated by adding the residual fitting error to the uncalibrated, simulated measurement signals.

Abstract: A method comprising receiving, by a payment service system (PSS), a first biometric dataset produced by a wearable device of a user having a user account with the PSS associated with a respective wellness profile; training a machine learning model that uses as input user behavior and outputs a reward offer associated with behavior that improves a wellness score; determining, using the model, the reward offer for the user with a merchant; presenting the reward offer, wherein activation of the reward offer causes an association with the user account; receiving, from the mobile device, an indication that the user has activated the reward offer via a transaction with the merchant; obtaining a second biometric dataset at a time that is based on activation of the reward offer; and updating the user's wellness profile based on a comparison of the two biometric datasets.

Abstract: A decentralized node may generate a network of decentralized nodes individually configured to store, receive, and transmit data based on rules associated with the decentralized nodes. A decentralized node may associate a decentralized identity of an entity with a select decentralized node of the decentralized nodes. A decentralized node may present a user interface including one or more access controls at a edge device, the access controls configuring rules to be applied to third-party access and modification of decentralized identity data associated within the decentralized identity of the entity and stored at the selected decentralized node. A decentralized node may identify the decentralized identity data accessible to the select decentralized node based on the rules. A decentralized node may store the decentralized identity data within the decentralized identity associated with the entity at the select decentralized node.

Abstract: A decentralized node may generate a network of decentralized nodes individually configured to store, receive, and transmit data based on rules associated with the decentralized nodes. A decentralized node may associate a decentralized identity of an entity with a select decentralized node of the decentralized nodes. A decentralized node may present a user interface including one or more access controls at a edge device, the access controls configuring rules to be applied to third-party access and modification of decentralized identity data associated within the decentralized identity of the entity and stored at the selected decentralized node. A decentralized node may identify the decentralized identity data accessible to the select decentralized node based on the rules. A decentralized node may store the decentralized identity data within the decentralized identity associated with the entity at the select decentralized node.

Abstract: Blockchain supported resource transfer communication protocol techniques are described. In an implementation, a semantic message is received encoding parameters to be implemented as part of a resource transfer involving a digital wallet. A quote is generated specifying attributes as part of implementing communication connections to perform the resource transfer in compliance with the parameters specified by the semantic message. An acceptance message is received indicating acceptance of the quote, the acceptance message including credentials of the digital wallet. Generation is initiated of an application executable as part of a blockchain as complying with the parameters and the attributes. Data describing the application as executable as part of the blockchain is transmitted for distribution to the digital wallet. The resource transfer is initiated through execution of the application as part of the blockchain.

Abstract: Blockchain supported resource transfer communication protocol techniques are described. In an implementation, a semantic message is received encoding parameters to be implemented as part of a resource transfer involving a digital wallet. A quote is generated specifying attributes as part of implementing communication connections to perform the resource transfer in compliance with the parameters specified by the semantic message. An acceptance message is received indicating acceptance of the quote, the acceptance message including credentials of the digital wallet. Generation is initiated of an application executable as part of a blockchain as complying with the parameters and the attributes. Data describing the application as executable as part of the blockchain is transmitted for distribution to the digital wallet. The resource transfer is initiated through execution of the application as part of the blockchain.

Abstract: In one embodiment, a method includes obtaining, by a payment service system, an amount of security assets via a securities exchange system. The method includes determining amounts of security assets owned by the payment service system and users of the payment service system. The method includes receiving, via an application of the payment service system executing on a device of a user of the payment service system, a request from the user to sell an amount of a security asset owned by the user. The method includes facilitating execution of a sale of the amount of security assets. The method includes, prior to the sale request settling, depositing, by the payment service system, a currency account of the user with an amount of funds associated with a determined value of the security asset requested to be sold by the user.

Abstract: A metrology system may receive a model for measuring one or more selected attributes of a target including features distributed in a selected pattern based on regression of spectroscopic scatterometry data from a scatterometry tool for a range of wavelengths. The metrology system may further generate a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include undesired diffraction orders. The metrology system may further direct the spectroscopic scatterometry tool to generate scatterometry data of one or more measurement targets including fabricated features distributed in the selected pattern. The metrology system may further measure the selected attributes for the one or more measurement targets based on regression of the scatterometry data of the one or more measurement targets to the model weighted by the weighting function.

Abstract: In one embodiment, a method includes receiving, by a payment service system (PSS), a request for a cash advance drawn from a line of credit approved for a user. The line of credit is associated with an account maintained by the PSS and includes default repayment terms. The method includes in response to receiving the request, identifying, by the PSS, context characteristics of the request. The method includes determining, by the PSS using a machine learning model applied to the identified characteristics and historical context information stored at the payment service system, that the requested cash advance qualifies for repayment terms that are different form the default repayment terms. The method includes based on the determination, generating a set of modified repayment terms to be associated with the cash advance. The method includes transmitting, by the PSS, an indication of the modified repayment terms to a user device for authorization.

Abstract: In some examples, a payment service system (PSS) receives transaction data for a transaction of a user of the PSS, including an identifier associated with the user and an amount of the transaction. The PSS may use the identifier to identify a user account of the user, and may determine that the amount of the transaction will cause the balance of the account to satisfy a threshold. Based at least on determining that the user account satisfies a condition and that the transaction will cause the balance to satisfy the threshold, the PSS may process the transaction using funds from the balance and from an additional source of funds provided by another user. Based on the user account receiving a deposit of funds, the PSS may withhold a portion of the deposit as repayment for at least a portion of the funds received from the additional source.

Abstract: In one embodiment, a method includes receiving, by a payment service system (PSS) from a first device associated with a first user, a request to borrow an amount from a second user. The first user is associated with a first user account and the second user is associated with a second user account, both of which are maintained by the PSS. The method includes in response to receiving the request, identifying, by the PSS, a first transaction history associated with the first user account and a second transaction history associated with the second user account in a data store of the PSS. The method includes determining, by the PSS, repayment terms to be associated with the request to borrow the amount based on the first transaction history and the second transaction history.

Abstract: In one embodiment, a method includes receiving, by a payment service system (PSS) from a first device associated with a first user, a request to borrow an amount from a second user. The first user is associated with a first user account and the second user is associated with a second user account, both of which are maintained by the PSS. The method includes in response to receiving the request, identifying, by the PSS, a first transaction history associated with the first user account and a second transaction history associated with the second user account in a data store of the PSS. The method includes determining, by the PSS, repayment terms to be associated with the request to borrow the amount based on the first transaction history and the second transaction history.

Abstract: In one embodiment, a method includes receiving, by a payment service system (PSS), a request for a cash advance drawn from a line of credit approved for a user. The line of credit is associated with an account maintained by the PSS and includes default repayment terms. The method includes in response to receiving the request, identifying, by the PSS, context characteristics of the request. The method includes determining, by the PSS using a machine learning model applied to the identified characteristics and historical context information stored at the payment service system, that the requested cash advance qualifies for repayment terms that are different form the default repayment terms. The method includes based on the determination, generating a set of modified repayment terms to be associated with the cash advance. The method includes transmitting, by the PSS, an indication of the modified repayment terms to a user device for authorization.

Abstract: A metrology system may receive a model for measuring one or more selected attributes of a target including features distributed in a selected pattern based on regression of spectroscopic scatterometry data from a scatterometry tool for a range of wavelengths. The metrology system may further generate a weighting function for the model to de-emphasize portions of the spectroscopic scatterometry data associated with wavelengths at which light captured by the scatterometry tool when measuring the target is predicted to include undesired diffraction orders. The metrology system may further direct the spectroscopic scatterometry tool to generate scatterometry data of one or more measurement targets including fabricated features distributed in the selected pattern. The metrology system may further measure the selected attributes for the one or more measurement targets based on regression of the scatterometry data of the one or more measurement targets to the model weighted by the weighting function.