Abstract: An HVAC heat exchanger with an array of tubes including one or more dead tubes is provided. In one embodiment, the heat exchanger is a microchannel heat exchanger operable to exchange heat with air in an HVAC system via refrigerant passing through the microchannel heat exchanger. The microchannel heat exchanger includes an array of flat tubes arranged between a first manifold and a second manifold. The array of flat tubes includes multiple tubes coupled in fluid communication with the first manifold and the second manifold to convey refrigerant between the first manifold and the second manifold through microchannels of the multiple tubes. The array of flat tubes also includes one or more dead tubes that do not convey refrigerant between the first manifold and the second manifold. Additional systems, devices, and methods are also disclosed.

Abstract: A method, apparatus and system for providing process-level forensics for a plurality of application containers includes for each of the plurality of application containers; monitoring forensics information of the application container, encoding the monitored forensics information using an encoder of a predetermined encoder/decoder pair to determine a forensics model, decoding the forensics model to determine a reconstructed representation of the forensics information, comparing the reconstructed representation of the forensics information to the monitored forensics information to determine an error and comparing the error to a threshold to determine if an error above the threshold exists. If the error is below the threshold, the forensics model is communicated to a higher-level manager to be used for higher-level management. If the error is above the threshold, the monitored forensics information of the application container is also communicated to the higher-level manager.

Abstract: A method, apparatus and system for providing process-level forensics for a plurality of application containers includes for each of the plurality of application containers; monitoring forensics information of the application container, encoding the monitored forensics information using an encoder of a predetermined encoder/decoder pair to determine a forensics model, decoding the forensics model to determine a reconstructed representation of the forensics information, comparing the reconstructed representation of the forensics information to the monitored forensics information to determine an error and comparing the error to a threshold to determine if an error above the threshold exists. If the error is below the threshold, the forensics model is communicated to a higher-level manager to be used for higher-level management. If the error is above the threshold, the monitored forensics information of the application container is also communicated to the higher-level manager.

Abstract: A technology that facilitates transparency of claim processing for a third-party payer is disclosed. Exemplary implementations may: receive a claim submission from a service provider and/or one or more agents thereof to seek settlement for goods and/or services provided to a beneficiary of a third-party payer; receive beneficiary data from the third-party payer; combine the claim-submission data and the beneficiary data to form claim data; store the claim data in an immutable and appendable format; obtain a determination of a state of the claim that is based at least in part on the claim data; update the claim data by appending the determination of the state of the claim; and provide access to the stored claim data to the third-party payer and the service provider and/or the one or more agents thereof.

Abstract: In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.

Abstract: In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines impression discounting features for ordering a set of candidates that match parameters of a search from a recruiter, wherein the impression discounting features include a recruiter-candidate feature indicating interaction between the recruiter and a candidate and a candidate popularity feature indicating interaction between the candidate and a set of recruiters. Next, the system applies a machine learning model to the impression discounting features and features for the set of candidates to produce a first set of scores for personalizing a ranking of the set of candidates for the recruiter. The system then generates the ranking according to the first set of scores. Finally, the system outputs, to the recruiter, at least a portion of the ranking as search results of the search.