Abstract: Provided is a position control apparatus of an unmanned marine observation device, the position control apparatus including a main body part including a hollow portion into which the unmanned marine observation device is coupled, a movement part extending outward from the main body part and configured to move the unmanned marine observation device and the position control apparatus in the ocean, a GPS signal receiver configured to receive a GPS signal and produce position information on the basis of the GPS signal, and a controller configured to control an operation of the movement part on the basis of the GPS signal.

Abstract: Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Abstract: A flow splitting device for gas reverse circulation drilling, including: an upper joint for connecting with a double-wall drill pipe; an inner tube which is arranged in the upper joint and defines a first passageway in communication with an inner chamber of the double-wall drill pipe, a second passageway in communication with an annular space in the double-wall drill pipe formed between the inner tube and the upper joint; a lower joint, having an upper end fixedly connected with the upper joint and a lower end for connecting with a drill tool; and a flow guiding member provided between the upper joint and the lower joint. A flexible sealing mechanism is provided outside the upper joint. The flexible sealing mechanism extends radially outward relative to the upper joint and the lower joint to form a sealing contact with a wellbore wall.

Abstract: Disclosed in the embodiments of the present application are a network access method and apparatus for a terminal, an electronic device and a storage medium. Said method comprises: a terminal determining a beam sweeping interval and a sweeping frequency point, and receiving a base station signal sent or forwarded by a satellite; determining the downlink signal quality of the base station according to the base station signal; and if it is determined, according to the downlink signal quality, that the terminal can successfully find a satellite, executing a network access process of the terminal.

Abstract: The present application relates to a peptide having protective activity against cell damage caused by particulate matter, and to uses for the peptide.

Abstract: Machine learning model training is provided. A model training result of a machine learning model is predicted utilizing a classification model based on a plurality of different combinations of input data set properties, settings of the machine learning model, and machine learning model training environment properties. Model training duration of the machine learning model is predicted utilizing a regression model based on those combinations that had a predicted successful model training result. Capacity unit hours is determined for each respective combination having the predicted successful model training result based on a corresponding predicted model training duration of the machine learning model. A particular combination of input data set properties, settings of the machine learning model, and machine learning model training environment properties that has minimum capacity unit hours is selected. The machine learning model is trained using the particular combination.

Abstract: A voltage conversion apparatus, a control method, and a power supply device. The voltage conversion apparatus includes a control circuit. The control circuit is configured to: obtain a first sampling voltage and a second sampling voltage; detect a resonant period based on the second sampling voltage when it is determined, based on the first sampling voltage, that a voltage range of an output voltage of the voltage conversion apparatus changes and a voltage threshold is exceeded; and output a drive signal based on the detected resonant period. The drive signal is used to control turn-on and turn-off of a first switching transistor and a second switching transistor.

Abstract: A substrate processing apparatus includes: a conveyor belt configured to have an outer surface on which a bottom surface of a substrate is seated; and a polishing head unit configured to face an upper surface of the substrate, wherein the polishing head unit includes: a polishing head connected to a driver; a polishing pad configured to face the polishing head; a polishing pad fixing ring disposed between the polishing head and the polishing pad; and a temperature sensor configured to overlap the polishing pad fixing ring and to be spaced apart from the polishing pad fixing ring.

Abstract: Embodiments of this application disclose a network key processing system, including user equipment, a security anchor network element, and an access and mobility management network element, where the security anchor network element is configured to: obtain a first key parameter from a slice selection network element, where the first key parameter includes identifier information of N network slices; generate N slice-dedicated keys based on the first key parameter; and send the N slice-dedicated keys to the corresponding N network slices respectively; the access and mobility management network element is configured to: obtain the first key parameter, and send the first key parameter to the user equipment; and the user equipment is configured to: generate the N slice-dedicated keys for the N network slices based on the first key parameter, and access the N network slices based on the generated N slice-dedicated keys.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for estimating the results of a performance test on an updated software application. A method, the method comprising receiving an updated software application, wherein the size of the updated software application is a first size and generating a plurality of small probe, wherein the size of each of the small probe data is a second size, wherein the second size is less than the first size. Conducting a first performance test on the plurality of small probe data and calculating an estimated elapsed time for a performance test on the updated software application. Conducting the performance test on the updated software application and determining if the updated software is given a PASS or FAIL for the performance test, based in part on the elapsed time of the performance test on the updated software application.

Abstract: A method, system, and computer program product for self-learning reference mechanisms for model selection in AutoAI. The method identifies a set of data summary statistics within a data set. A data pattern group is identified within the set of data summary statistics. The data pattern group is determined to be mature. A model selection acceleration mechanism (MSAM) model is generated based on the data pattern group. The method predicts a set of top-k models for the data set based on the MSAM model.

Abstract: Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Abstract: Described are techniques for machine learning library management. The techniques include generating a table including a plurality of machine learning libraries and their current versions that are used in a deployed machine learning platform (MLP) instance, a first available version upgrade for a first machine learning library of the plurality of machine learning libraries, a security indication associated with the first available version upgrade relative to a current version implemented by the first machine learning library, and a compatibility indication between the first available version upgrade and the current version of the first machine learning library. The techniques further include generating a recommendation related to upgrading the first machine learning library based on the security indication and the compatibility indication.

Abstract: The present disclosure provides systems and methods for redelivering packages to customers, comprising a memory storing instructions and a processor configured to execute the instructions to receive, from a first user device, a notification of a delivery failure of a package, modify a database to assign the notification of the delivery failure and a cause for the delivery failure to the package identifier, modify the database to assign the package identifier to a group, among a plurality of groups, based on a delivery address associated with the package identifier, generate a map of the plurality of groups, and send the map for display on the user device. The map may comprise an activatable icon, and a location of the activatable icon on the map may be associated with the delivery address associated with the package identifier.

Abstract: A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.

Abstract: Methods and systems for automatic package tracking and prioritized reordering include receiving, through a network interface, information relating to an order, a first package identifier associated with a first package, and event data associated with a plurality of package identifiers that include the first package identifier, the order comprising a first group of items, where the first package is configured to hold the first group of items and is to be delivered to a first recipient within a first predetermined period of time through one or more existing routes; parsing the event data based on the first package identifier; determining whether the first package exists based on the information; and transmitting a signal to a computer system to deliver the first package or to reorder the first package based on the determinations.

Abstract: Techniques for optimizing machine learning models are provided. A first model in a first format is received. A second model is generated by applying one or more optimization techniques to the first model; the second model is an optimized version of the first model. The second model is converted into a common intermediate format. The second model is converted into binary data representing the second model. The binary data representing the second model is outputted.

Abstract: A flow splitting device for gas reverse circulation drilling, including: an upper joint for connecting with a double-wall drill pipe; an inner tube which is arranged in the upper joint and defines a first passageway in communication with an inner chamber of the double-wall drill pipe, a second passageway in communication with an annular space in the double-wall drill pipe formed between the inner tube and the upper joint; a lower joint, having an upper end fixedly connected with the upper joint and a lower end for connecting with a drill tool; and a flow guiding member provided between the upper joint and the lower joint. A flexible sealing mechanism is provided outside the upper joint. The flexible sealing mechanism extends radially outward relative to the upper joint and the lower joint to form a sealing contact with a wellbore wall.

Abstract: One or more computer processors calculate a cache prediction for a received inference request within an inference cache structured as a self-learning tree, wherein the inference request comprises a set of input values. The one or more computer processors responsive to the retrieved cache prediction exceeding a cache prediction threshold, transmit the cache prediction. The one or more computer processors parallel compute a model prediction for the received inference request utilizing a trained model. The one or more computer processors responsive to the retrieved model prediction exceeding a model prediction threshold, convert the trained model into a tree structure. The one or more computer processors update the inference cache with the converted train model. The one or more computer processors transmit the model prediction.

Abstract: Systems and computer-implemented methods select a subset of methods to generate data schemas for input data from a list of methods for generating data schemas, based on output of a regression model; generate a candidate schema for each method in the subset of methods to generate data schemas; and generate a master data schema for the input data by merging the candidate schema for each method in the subset of methods to generate data schemas, utilizing predetermined rules.