Abstract: A computer-implemented method for providing guidance on the use of machine learning tools is provided. Aspects include receiving, from a user, an input data set and a type of problem to solve based on the input data set and identifying a set of machine learning pipelines from a database comprising a plurality of machine learning pipelines. Aspects also include recommending, to the user, a first machine learning pipeline of the set of machine learning pipelines from the set of machine learning to the user, wherein each of the plurality of machine learning pipelines includes a pipeline score and wherein the first machine learning pipeline has a highest pipeline score of the set of machine learning pipelines and providing, to the user, a rule set associated with the first machine learning pipeline, wherein the rule set includes one or more suggested settings associated with the first machine learning pipeline.

Abstract: A computer-implemented method for model selection is provided. The computer-implemented method includes building models for predicting characteristics of pipeline models, receiving user specifications for pipeline model performance, defining a metric for weighing the characteristics of the pipeline models, using the metric to iteratively reduce a number of the pipeline models capable of meeting the user specifications to a reduced number of the pipeline models, determining which one pipeline model of the reduced number of the pipeline models exhibits a best capability of meeting the user specifications and deploying the one pipeline model.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to determining time series model stability and robustness in refreshment. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a computation component that can employ weighted model evaluation to compute stability of time series pipelines over respective holdout datasets and a determination component that can select, based on the computed pipeline stabilities, a most stable time series pipeline.

Abstract: Systems, computer program products and/or computer-implemented methods described herein relate to a process to optimize performance of an operating neural network. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components, which can comprise an identification component that, employing an operating, multi-layered virtual computation module of looped neurons, identifies a first neuron of a first cluster of a first layer of the looped neurons as being an outlier neuron, an adjustment component that reassigns the outlier neuron from the first cluster to a second cluster of the first layer, and a scheduling component that, based on a dependency among layers of the multi-layered virtual computation module, including the first layer, adjusts a cross-layer functionality of the looped neurons for a workload currently being performed by the multi-layered virtual computation module.

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.

Abstract: A method for forecasting server demand includes collecting a historical number of scoring requests from a network using a serverless architecture. A scoring request capacity per server is determined using the historical number of scoring requests. A prediction model predicts a first future value of scoring requests for a first future time span. A current number of servers in a pool of servers handling the scoring requests. Using the prediction model, a determination of whether the current number of servers is capable of handling the first future value of scoring requests for the first future time span is made. Upon determining that the current number of servers is incapable of handling the first future value of scoring requests, one or more additional servers are warmed up. The warmed-up additional servers are added to the pool of servers prior to an arrival of the first future time span.

Abstract: A method for migrating virtual machine snapshots to application container platforms is disclosed. In one embodiment, such a method includes migrating, from a virtual machine platform to a volume on a container platform, a base disk file associated with a virtual machine. The base disk file has one or more delta disk files associated therewith, where each delta disk file records changes made to the virtual machine after a snapshot was taken. After migrating the base disk file, the method repeatedly performs the following for each delta disk file: takes a snapshot of the volume on the container platform; migrates the delta disk file from the virtual machine platform to the container platform; and writes the delta disk file to the volume. In certain embodiments, the delta disk files are written to the volume in an order in which they were created on the virtual machine.

Abstract: A method for migrating virtual machine snapshots to application container platforms is disclosed. In one embodiment, such a method includes migrating, from a virtual machine platform to a volume on a container platform, a base disk file associated with a virtual machine. The base disk file has one or more delta disk files associated therewith, where each delta disk file records changes made to the virtual machine after a snapshot was taken. After migrating the base disk file, the method repeatedly performs the following for each delta disk file: takes a snapshot of the volume on the container platform; migrates the delta disk file from the virtual machine platform to the container platform; and writes the delta disk file to the volume. In certain embodiments, the delta disk files are written to the volume in an order in which they were created on the virtual machine.

Abstract: The present invention relates to a TFT liquid crystal display, more specifically, to a cholesteric liquid crystal display employing both field-induced nematic vertical alignment texture and field-induced eddy alignment texture as video astable states and cholesteric planer texture and focal conic texture as power-free bistable states. Thus, the display provides not only video speed motion pictures with unlimited grayscale but also excellent static images.

Abstract: Provided are a Lactobacillus plantarum against Helicobacter pylori infection and the use thereof. The Lactobacillus plantarum against Helicobacter pylori infection is named Lactobacillus plantarum Lp05 and was deposited in the China General Microbiological Culture Collection Center on 9 Oct. 2021, with the deposit number CGMCC No. 23547. Further provided are a method for culturing the Lactobacillus plantarum against Helicobacter pylori infection and a product for inhibiting Helicobacter pylori infection. The Lactobacillus plantarum against Helicobacter pylori infection of the present application has good gastric acid resistance, inhibits the proliferation of Helicobacter pylori, and reduces the adhesion effect of Helicobacter pylori to gastric epithelial cells, thereby creating conditions for the eradication of Helicobacter pylori. The product for inhibiting Helicobacter pylori infection does not cause adverse reactions nor drug resistance of pathogenic bacteria.

Abstract: Deploying machine learning models is provided. A new machine learning model is received for a given problem that corresponds to a service running in a container. A cluster of machine learning models of a plurality of clusters of machine learning models corresponding to the given problem is selected. A cluster performance score is determined for the cluster based on combining a model performance score of each machine learning model in the cluster in accordance with a corresponding weight of each machine learning model. It is determined whether the cluster performance score of the cluster is greater than a minimum cluster performance score threshold. The new machine learning model is added to the cluster to increase predictive accuracy for the given problem while the service is running without interruption in response to determining that the cluster performance score of the cluster is greater than the minimum cluster performance score threshold.

Abstract: The present invention provides a Bifidobacterium animalis subsp. lactis strain BLa36 for relieving constipation, a method for culturing same, and use thereof. The Bifidobacterium animalis subsp. lactis strain BLa36 for relieving constipation is designated as Bifidobacterium animalis subsp. lactis BLa36, which has been deposited at the China General Microbiological Culture Collection Center on Dec. 2, 2021, with an accession number of CGMCC No. 24029, and the address for the deposition is Yard No. 1(3), West Beichen Road, Chaoyang District, Beijing, China. The Bifidobacterium animalis subsp. lactis has good resistance to stomach acid and can relieve constipation.

Abstract: A computer-implemented method, according to one embodiment, includes obtaining information about a plurality of remote storage systems. Each of the remote storage systems includes a cache. The method further includes generating a routing table based on the information, the routing table indicating potential write data migration paths from a cache of a local storage system to the caches of the remote storage systems. A write data migration plan is generated based on the routing table. The write data migration plan specifies which of the caches of the remote storage systems to migrate at least some write data originally intended to be stored on the cache of the local storage system to. In response to a determination that a first predetermined condition associated with the cache of the local storage system is met, the method includes causing the write data migration plan to be performed.

Abstract: A system and related method identify a weakness of a workflow in a complex system. The method collects runtime data about the complex system. The complex system comprises a plurality of subcomponents, and the method identifies an abnormal operation in the complex system. The method constructs a multi-dimensional cause-and-effect relation matrix among the plurality of subcomponents, and filters one or more related operations using the multi-dimensional cause-and-effect relation matrix.

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: 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: The present invention relates to a TFT liquid crystal display, more specifically, to a cholesteric liquid crystal display employing both field-induced nematic vertical alignment texture and field-induced eddy alignment texture as video astable states and cholesteric planer texture and focal conic texture as power-free bistable states. Thus, the display provides not only video speed motion pictures with unlimited grayscale but also excellent static images.

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.