Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may access a plurality of data synthetization machine learning models configured for generating synthetic data. Respective ones of the data synthetization machine learning models may correspond to respective ones of the groups of data sources. The computing device generates first synthetic data by inputting, to a first data synthetization machine learning model, first data received from a first data source group, and generates second synthetic data by inputting, to a second data synthetization machine learning model, second data received from a second data source group. The computing device determines an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data.

Abstract: A system that calibrates timing relationships between signals involved in performing write operations is described. This system includes a memory controller which is coupled to a set of memory chips, wherein each memory chip includes a phase detector configured to calibrate a phase relationship between a data-strobe signal and a clock signal received at the memory chip from the memory controller during a write operation. Furthermore, the memory controller is configured to perform one or more write-read-validate operations to calibrate a clock-cycle relationship between the data-strobe signal and the clock signal, wherein the write-read-validate operations involve varying a delay on the data-strobe signal relative to the clock signal by a multiple of a clock period.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may access a plurality of data synthetization machine learning models configured for generating synthetic data. Respective ones of the data synthetization machine learning models may correspond to respective ones of the groups of data sources. The computing device generates first synthetic data by inputting, to a first data synthetization machine learning model, first data received from a first data source group, and generates second synthetic data by inputting, to a second data synthetization machine learning model, second data received from a second data source group. The computing device determines an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data.

Abstract: A behavior detection module receives a training database and applies a transformation to the attributes that improves the uniformity of the values associated with each attribute. The transformed training database is used to construct a random forest classifier (RFC). The RFC includes a plurality of decision trees and generates a classification label estimate for a data entry with a plurality of attributes. The classification label estimate is determined based on classification estimates from the plurality of decision trees. Each parent node of a decision tree is associated with a condition of a transformed attribute that directs the data entry to a corresponding child node depending on whether the condition is satisfied or not. The data entry is directed through the tree to one out of a set of leaf nodes, and a classification label associated with the leaf node.

Abstract: A system that calibrates timing relationships between signals involved in performing write operations is described. This system includes a memory controller which is coupled to a set of memory chips, wherein each memory chip includes a phase detector configured to calibrate a phase relationship between a data-strobe signal and a clock signal received at the memory chip from the memory controller during a write operation. Furthermore, the memory controller is configured to perform one or more write-read-validate operations to calibrate a clock-cycle relationship between the data-strobe signal and the clock signal, wherein the write-read-validate operations involve varying a delay on the data-strobe signal relative to the clock signal by a multiple of a clock period.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may create a training data set from a first portion of the received data and may create a plurality of validation data sets from a second portion of the received data. For example, each validation data set may correspond to a respective one of the groups of data sources. The computing device may train, using the training data set, a plurality of machine learning models configured for synthesizing data. For instance, respective ones of the machine learning models may correspond to respective ones of the groups of data sources. Further, the computing device may validate the respective machine learning models using the respective validation data set corresponding to the respective group to which the respective machine learning model being validated corresponds.

Abstract: A method and system for generating automated front-end code for a website from design files is described. In one embodiment, a method for generating automated front-end code for a website includes obtaining at least one design file associated with a design of a website from a client device. Hypertext markup language (HTML) code and a cascading style sheet (CSS) file is automatically generated from the at least one design file from information obtained from a plurality of layers associated with the design file. The method includes extracting a plurality of extracted image files from the at least one design file. The method further includes providing front-end code for the website that includes the HTML code, the CSS file, and the plurality of extracted image files to the client device.

Abstract: A system for bidder support in combinatorial auctions includes a machine readable storage medium storing instructions and a processor to execute the instructions throughout a duration of an auction. The processor executes the instructions to receive bids for a multi-item multi-unit (MIMU) combinatorial auction. The processor executes the instructions to track the status of each sub-auction of the MIMU auction. The status for each sub-auction includes a value of the respective sub-auction and a last winning bid of the respective sub-auction. The processor executes the instructions to determine bidder support information including winning levels, deadness levels, winning bids, and live bids based on the status of each sub-auction.

Abstract: System and method for managing one or more experimental requests. For example, the method includes receiving multiple experimental requests, determining a schedule for executing the multiple experimental requests based upon attributes associated with each experimental request, and assigning the multiple experimental requests to remote laboratories for execution based upon the schedule and features of the remote laboratories.

Abstract: System and method for performing experiments. For example, the method includes receiving indications of experimental workflows, generating workflow configuration requirements for each experimental workflow, configuring each experimental workflow based upon parameters associated with the workflow configuration requirements, receiving experimental requests for the experimental workflows, determining a schedule for executing the experimental requests, assigning each experimental request one or more remote laboratories for execution based upon the schedule, generating a set of instructions for performing experiments related to each experimental request, determining a plurality of sequence schedules for completing the set of instructions, receiving an indication of a sequence schedule selected from the plurality of sequence schedules, and transmitting commands to execute the set of instructions according to the selected sequence schedule.

Abstract: A system that calibrates timing relationships between signals involved in performing write operations is described. This system includes a memory controller which is coupled to a set of memory chips, wherein each memory chip includes a phase detector configured to calibrate a phase relationship between a data-strobe signal and a clock signal received at the memory chip from the memory controller during a write operation. Furthermore, the memory controller is configured to perform one or more write-read-validate operations to calibrate a clock-cycle relationship between the data-strobe signal and the clock signal, wherein the write-read-validate operations involve varying a delay on the data-strobe signal relative to the clock signal by a multiple of a clock period.

Abstract: A packet forwarding network may include spine and leaf switches that forward network traffic between end hosts. The packet forwarding network may be implemented on multiple network racks in a rack-based system. A controller may control the underlying spine and leaf switches to form on-premise virtual private cloud (VPC) resources. In particular, the controller may form enterprise VPC (EVPC) tenants, each having a virtual router that performs routing between different segments within the corresponding EVPC tenant. The different segments may separately include web, application, and database servers, as end hosts. The controller may form a system VPC tenant having a virtual system router that performs routing between different EVPC tenants. A segment in an internal VPC tenant formed by the controller and/or an external VPC tenant formed by the controller may provide external network access for one or more of the EVPC tenants.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may create a training data set from a first portion of the received data and may create a plurality of validation data sets from a second portion of the received data. For example, each validation data set may correspond to a respective one of the groups of data sources. The computing device may train, using the training data set, a plurality of machine learning models configured for synthesizing data. For instance, respective ones of the machine learning models may correspond to respective ones of the groups of data sources. Further, the computing device may validate the respective machine learning models using the respective validation data set corresponding to the respective group to which the respective machine learning model being validated corresponds.

Abstract: A system that calibrates timing relationships between signals involved in performing write operations is described. This system includes a memory controller which is coupled to a set of memory chips, wherein each memory chip includes a phase detector configured to calibrate a phase relationship between a data-strobe signal and a clock signal received at the memory chip from the memory controller during a write operation. Furthermore, the memory controller is configured to perform one or more write-read-validate operations to calibrate a clock-cycle relationship between the data-strobe signal and the clock signal, wherein the write-read-validate operations involve varying a delay on the data-strobe signal relative to the clock signal by a multiple of a clock period.

Abstract: A data system includes a plurality of storage drives each comprising a multi-lane serial drive interface. The data system also includes a control system configured to receive, over a host link, a write operation for storage of data, process a storage address of the write operation against storage allocation information to apportion the data for storage among more than one target storage drive, and transfer corresponding portions of the data to the target storage drives.

Abstract: The present invention facilitates efficient and effective utilization of storage management features. In one embodiment, a memory device comprises a memory interface, an ECC generation component, and storage components. The memory interface is configured to receive an access request to an address at which data is stored. The memory interface can also forward responses to the request including the data and ECC information associated with the data. The ECC generation component is configured to automatically establish an address at which the ECC information is stored based upon the receipt of the access request to an address at which data is stored. In one exemplary implementation, the internal establishment of the address at which the ECC information is stored is automatic. The storage components are configured to store the information.

Abstract: A gimbal for connecting pipes having a core portion and at least one joint shield portion that surrounds the core portion. The core portion, which has a core portion internal surface and a core portion external surface, has at least two pipe engaging portions that are separated by at least one flexible portion, which has a flexible portion internal surface and a flexible portion external surface, and a plurality of joint cover portions. Each joint cover portion extends from the core portion external surface of the core portion to provide a protective cover for the flexible portion external surface of at least one flexible portion of the core portion and is pivotably connected to a least one joint shield portion. The shield portion is configured to provide a protective shield for at least one flexible portion of the core portion.

Abstract: In some embodiments, a meta-data inspection data store may contain hierarchical components and subcomponents of an industrial asset and define points of interest. An industrial asset inspection platform may access that information and generate an inspection plan, including an association of at least one sensor type with each of the points of interest. The platform may then store information about the inspection plan in an inspection plan data store and receive inspection data (e.g., from a manual inspection, from an inspection robot, from a fixed sensor, etc.). A smart tagging algorithm may be executed to associate at least one point of interest with an appropriate portion of the received inspection data based on information in the inspection plan data store.

Abstract: A packet forwarding network may include spine and leaf switches that forward network traffic between end hosts. The packet forwarding network may be implemented on multiple network racks in a rack-based system. A controller may control the underlying spine and leaf switches to form on-premise virtual private cloud (VPC) resources. In particular, the controller may form enterprise VPC (EVPC) tenants, each having a virtual router that performs routing between different segments within the corresponding EVPC tenant. The different segments may separately include web, application, and database servers, as end hosts. The controller may form a system VPC tenant having a virtual system router that performs routing between different EVPC tenants. A segment in an internal VPC tenant formed by the controller and/or an external VPC tenant formed by the controller may provide external network access for one or more of the EVPC tenants.

Abstract: A behavior detection module constructs a random forest classifier (RFC) that takes into account asymmetric misclassification costs between a set of classification labels. The classification label estimate is determined based on classification estimates from the plurality of decision trees. Each parent node of a decision tree is associated with a condition of an attribute that splits a parent node into two child nodes by maximizing an improvement function based on a training database. The improvement function is based on an asymmetric impurity function that biases the decision tree to decrease the error for a label with high misclassification cost over the other, at the cost of increasing the error of the other label with a lower misclassification cost.