Abstract: A method may include applying a protein sequence computation model to generate, based on an input protein sequence, a plurality of proposed protein sequences. A set of possible amino acid residues for each position in at least a portion of an output protein sequence may be identified based on the plurality of proposed protein sequences. A first protein structure having the output protein sequence may be generated by applying a protein structure computation model to select, for each position in at least the portion of the output protein sequence, an amino acid residue from a corresponding set of possible amino acid residues. The protein structure computation model may further determine the conformation of the amino acid residues selected for inclusion in the output protein sequence. In some cases, the first protein structure may be grafted onto a second protein structure to form a third protein structure.

Abstract: A continuous meta-learning system operating at a server is described. In one aspect, a computer-implemented method includes accessing, at the server, a deployed machine learning model, metadata, and new prediction data for the deployed machine learning model, detecting a novel trend in the deployed machine learning model based on the new prediction data, generating label suggestions for the novel trend using metadata, querying a plurality of users to verify the label suggestions, detecting a consensus of the plurality of users, the consensus based on a rate of responses from the plurality of users that confirm the label suggestions, and in response to detecting the consensus, training a new machine learning model based on the new prediction data and the consensus of the plurality of users. The computer-implemented method also includes deploying the new machine learning model at the server.

Abstract: A system for private and secure data portal is described. A method includes receiving, from a first client device, a permission request and a data request for a first dataset that is stored at a second client device, providing the permission request and the data request to the second client device, the second client device configured to generate, in response to the permission request and the data request, a data usage approval document and an encrypted synthesized dataset corresponding to the data usage approval document, the encrypted synthesized dataset includes a synthetic second dataset representative of the first dataset, receiving, from the second client device, the data usage approval document and the encrypted synthesized dataset, performing, at a server, a computation on the encrypted synthesized dataset based on the data request, and providing the data usage approval document and results of the computation to the first client device.

Abstract: A post-deployment drift detection monitoring of a predictive model is described. The method includes accessing a predictive performance metric of a machine learning model that is deployed at a server, the machine learning model being trained with an initial set of training data containing historical data, the predictive performance metric being based on the initial set of training data and an additional set of training data, the additional set of training data containing training data collected since training the machine learning model, detecting a drift based on the predictive performance metric exceeding a drift detection threshold, generating a drift warning notification to a client device, the drift warning notification indicating that the predictive performance metric exceeds the drift detection threshold, receiving a user feedback from the client device, and adjusting one of the machine learning model or the drift detection threshold based on the user feedback.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: A cleaner head for a vacuum cleaner includes a laser diode for illuminating debris located upon a region of a work surface located in front of the cleaner head with green light.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

Abstract: Described herein are dressings, compositions, and formulations for wound healing. These dressings, compositions, and formulations contain at least one quaternary ammonium polymer, quaternary ammonium copolymer, quaternary ammonium interpenetrating polymer network, quaternary ammonium polyelectrolyte complex, quaternary ammonium blend, or quaternary ammonium composite with broad spectrum antimicrobial properties.

Abstract: A receiving network device of a network obtains a command built to enable a control program of a device coupled to the receiving network device to provide to the receiving network device status of a key manager. The receiving network device obtains the status from the command and uses the status in performing one or more actions.

Abstract: A receiving network device of a network obtains a command built to enable a control program of a device coupled to the receiving network device to provide to the receiving network device shared confidential data. The receiving network device obtains the shared confidential data from the command and uses the shared confidential data in performing one or more actions.

Abstract: A receiving network device obtains a command built to enable a control program of a device coupled to the receiving network device to obtain power consumption data of one or more selected units of a network. The command includes an indication of the one or more selected units for which the power consumption data is to be obtained. The receiving network device obtains the power consumption data of the one or more selected units indicated by the command and provides a response to the request for the power consumption data of the one or more selected units.

Abstract: A receiving network device obtains a command built to enable a control program of a device coupled to the receiving network device to obtain temperature data of one or more selected units of a network. The command includes an indication of the one or more selected units for which the temperature data is to be obtained. The receiving network device obtains the temperature data of the one or more selected units indicated by the command and provides a response to the request for the temperature data of the one or more selected units.

Abstract: A receiving network device obtains a command built to enable a control program to trigger activation of diagnostics on one or more ports of one or more network devices. The command includes an indication of the one or more ports on which diagnostics is to be activated. The receiving network device initiates triggering activation of diagnostics on the one or more ports indicated by the command, and provides diagnostic status resulting from the activation of diagnostics on the one or more ports.

Abstract: A receiving network device obtains a command built to enable a control program to initiate a swap of one or more components of a network. The command includes an indication of the one or more components for which the swap is to be initiated. The receiving network device initiates the swap of the one or more components indicated by the command, and provides status resulting from the initiation of the swap of the one or more components.

Abstract: A receiving network device obtains a command built to enable a control program of a device coupled to the receiving network device to obtain optical power data of one or more components of one or more network devices. The command includes an indication of the one or more components for which the optical power data is to be obtained. The receiving network device obtains the optical power data of the one or more components indicated by the command, and provides a response to the request for the optical power data of the one or more components.

Abstract: On-chip testing of a superconductive integrated circuit device includes receiving a superconductive circuit design having superconductive logic elements. Further, a first testability characteristic for first test circuitry at a first node within the superconductive circuit design is determined. The first testability characteristic corresponds to one or more of a test generation control level and a test observability control level. An updated superconductive circuit design from the superconductive circuit design is generated based on the first testability characteristic for the first test circuitry. The superconductive circuit design includes the first test circuitry at the first node.

Abstract: Described herein is a water-soluble unit dose with a particle having a ratio of Linear Alkylbenzene Sulfonate to Alkylethoxylated Sulfate of greater than 1.

Abstract: A process for manufacturing a water soluble product including the steps of: providing a water soluble fibrous first ply; providing a water soluble fibrous second ply formed on a surface other than the first ply, wherein the second ply is separate from the first ply; superposing the first ply and the second ply; and joining a first portion of the first ply to a second portion of the second ply to form the water soluble product.

Abstract: Systems and methods are provided for incremental 3D printing, wherein wireframes are generated and printed (scheduled for print) during the design process. In another aspect, systems and methods are provided for printing arbitrary meshes. A 3D printer system is described having, for example, five degree-of-freedom (5DOF). The 5DOF printer may be used to perform any of the methods described herein and combinations of the methods.