Abstract: Disclosed examples include after a first tuning of hyperparameters in a hyperparameter space, selecting first hyperparameter values for respective ones of the hyperparameters; generating a polygonal shaped failure region in the hyperparameter space based on the first hyperparameter values; setting the first hyperparameter values to failure before a second tuning of the hyperparameters; and selecting second hyperparameter values for the respective ones of the hyperparameters in a second tuning region after the second tuning of the hyperparameters in the second tuning region, the second tuning region separate from the polygonal shaped failure region.

Abstract: Disclosed examples including generating a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models; selecting the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model; and providing tuning data associated with the joint model to a tuning session of a target model.

Abstract: User profiles of remote desktops are managed in a crash-consistent manner. When a user logs into a remote desktop, metadata of the user profile is loaded from persistent storage while registry settings and files of the user profile are loaded asynchronously with respect to the login. During the remote desktop session, snapshots of the remote desktop image in persistent storage are generated periodically, and a change log that indicates changes to the user profile is created therefrom. The user profile stored in persistent storage is updated lazily using the change log at opportunistic times after snapshot generation. When the user logs out of the remote desktop, the user profile stored in the persistent storage is updated with any additional changes to the user profile by extracting the changes from the copy-on-write cache associated with the most recent snapshot.

Abstract: A record processing and storage system is operable to receive a set of records for storage. The set of records are included in a plurality of pages stored by a page storage system, and each page of the plurality of pages includes a plurality of records in the set of records. Key value-based record distribution data is generated for the set of records based on a plurality of cluster key values of the set of records. A cluster key domain spanned by the plurality of cluster key values is divided into a plurality of key space sub-intervals based on the key value-based record distribution data. The set of records are segregated into a plurality of row subsets corresponding to the plurality of key space sub-intervals. A plurality sets of segments are generated by processing the plurality of row subsets in parallel.

Abstract: This disclosure relates to integrated circuit devices that may include a network processor in a data processing die and an on-package memory in a base die. The data processing die may implement one or more network functionalities that may exchange data with low-latency memory, high capacity in the base die. The data processing die may be programmable fabric, which may be dynamically reconfigured during operation.

Abstract: A method for execution by a record processing and storage system includes assigning each of a plurality of key space sub-intervals of a cluster key domain to a corresponding one of a plurality of processing core resources, and generating a plurality of segments from the set of records via the plurality of processing core resources. Each processing core resource in the plurality of processing core resources generates a subset of the plurality of segments by identifying a proper subset of the set of records based on having cluster key values included in a corresponding one of the plurality of key space sub-intervals, and by generating the subset of the plurality of segments to include the proper subset of the set of records.

Abstract: A method includes generating a plurality of pages from a plurality of records received from a plurality of data sources. Deduplication of the plurality of pages is facilitated based on a plurality of page metadata of the plurality of pages based on, for the each page of the plurality of pages. A filtered set of potentially-intersecting pages is identified for each given page as a proper subset of the plurality of pages stored in the page storage system based on first comparison parameters, and an intersecting set of pages that include a row number intersection with the given page is identified as a proper subset of the filtered set of potentially-intersecting pages based on second comparison parameters. Records with records with row numbers included in row number intersections with other pages in the intersecting set of pages are removed from the each page.

Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose

Abstract: A record processing and storage system is operable to receive a plurality of row data that each indicate a batch number. Each of the plurality of row data is added to a pending row data pool. A plurality of pages is generated from the plurality of row data via a plurality of processing core resources. Each processing core resource in the plurality of processing core resources processing a corresponding subset of the plurality of row data by retrieving, in each time slice of a plurality of time slices, one row data from the pending row data pool with a most favorably ordered batch number of row data in the pending row data pool. Each processing core resource further processes the one row data in the in each time slice to participate in generation of at least one of the plurality of pages.

Abstract: A system and method for accelerated tuning of hyperparameters includes receiving a multi-task tuning work request for tuning hyperparameters of a model, wherein the multi-task tuning work request includes: a full tuning task for tuning hyperparameters, wherein the full tuning task includes a first set of tuning parameters governing a first tuning operation; a partial tuning task for tuning the hyperparameters of the model, wherein the partial tuning task includes a second distinct set of tuning parameters governing a second tuning operation; executing the first tuning operation and the second tuning operation; generating a first suggestion set and a second suggestion set of one or more proposed values for the hyperparameters based on the execution of the full tuning task and the partial tuning task; and setting the partial tuning task as a proxy for the full tuning task thereby accelerating a tuning of the hyperparameters of the model.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning func-tion based on a combination of the first objective function and the second objective function; executing a tuning opera-tion of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparam-eter-based points along a convex Pareto optimal curve.

Abstract: A record processing and storage system is operable to receive a plurality of labeled row data from a data source. Each labeled row data of the plurality of labeled row data includes at least one record and a corresponding row number of a plurality of row numbers. A plurality of pages are generated from records included in the labeled row data. The plurality of pages are stored via a page storage system. A plurality of page metadata corresponding to the plurality of pages is generated, where each of the plurality of page metadata is generated based on at least corresponding one row number of at least one labeled row data with records included in a corresponding one of the plurality of pages. Deduplication of duplicated records included the plurality of pages is facilitated based on the plurality of page metadata.

Abstract: A cup holder assembly includes a base. A side wall is coupled to the base and extends cup holder assembly-upward from the base to generally define a cavity for receiving a cup. A bracket is pivotably coupled to the base and is operable to pivot between a first position and a second position. A retention feature is coupled to the bracket and is configured to engage with the base if the bracket is in the second position to retain the bracket in the second position. An electronic device is operable between an unassembled position and an assembled position in which the electronic device is engaged with the bracket, wherein the electronic device is configured to move with the bracket as the bracket pivots between the first and second positions if the electronic device is in the assembled position.

Abstract: The present disclosure relates to a thin coated optical fiber that enables connector assembly without stripping the optical fiber. In particular, the thin coating comprises a hard coating, a dye concentrate, and an adhesion promoter. The formulation of the coating promotes adhesion to a glass cladding of the optical fiber and to a ferrule bore (into which the optical fiber is inserted) by not causing silane decomposition of the coating. Moreover, the coating is colored to enable, among other things, fiber identification within a connector. The thin coated optical fibers exhibit good mechanical and optical performance properties as discussed herein.

Abstract: A system for maintaining relative orientation of optical fibers in an array is provided. Fibers extend parallel and include a first fiber and an adjacent fiber. The system includes a fiber holder with alignment holes configured to receive stripped fiber sections. The alignment holes extend from the first surface to the second surface, and the alignment holes include a first alignment hole configured to receive a stripped fiber section of the first fiber and an adjacent alignment hole configured to receive a stripped fiber section of the adjacent fiber. The fiber holder has a wall separating the first alignment hole and the adjacent alignment hole that permits rotation of the stripped fiber section of the adjacent fiber in the adjacent alignment hole without causing rotation of the stripped fiber section of the first fiber in the first alignment hole.

Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose

Abstract: An adaptive cutting system is presented that facilitates cutting workpieces in new and different ways in a fun, easy, fast, accurate and safe manner. The system includes a benchtop having a grid of bench dog holes across its surface as well as a pair of table tracks embedded within its surface. An edge track extends around the benchtop and a pair of hinge members are connected to the edge tracks that also connect to a saw track that is movable between a raised and lowered position. The system also includes narrow rip stops, wide rip stops, bench dogs and a miter gauge all of which work in concert with the on-table features to facilitate on-table measurement and alignment of workpieces for performing cutting operations.

Abstract: A system and method for accelerated tuning of hyperparameters includes receiving a multi-task tuning work request for tuning hyperparameters of a model, wherein the multi-task tuning work request includes: a full tuning task for tuning hyperparameters, wherein the full tuning task includes a first set of tuning parameters governing a first tuning operation; a partial tuning task for tuning the hyperparameters of the model, wherein the partial tuning task includes a second distinct set of tuning parameters governing a second tuning operation; executing the first tuning operation and the second tuning operation; generating a first suggestion set and a second suggestion set of one or more proposed values for the hyperparameters based on the execution of the full tuning task and the partial tuning task; and setting the partial tuning task as a proxy for the full tuning task thereby accelerating a tuning of the hyperparameters of the model.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.

Abstract: Disclosed are covering and strapping systems and methods for covering granular product such as grain. An example system includes a cover having a top side and a bottom side, multiple attachment implements directly connected to the top side of the cover, and a plurality of securing straps, each of the securing straps being connected to a respective attachment implement and thereby indirectly connected to the top side of the cover without running through a tunnel. The securing straps can extend beyond the edge of the cover for securing to a structure.