Abstract: A digital model generation platform populates an image repository with digital representations of physical clay-based models by generating one or more candidate images based on a combination of clay model features. The digital model generation platform integrates imaginative product displays where users engage with a program on a touch screen, allowing them to combine both pre-generated and user-requested categories to generate image models of suggested clay-based models. The displayed image, either preconfigured (i.e., in the populated image repository) or AI-generated in real time, guides users by indicating the required elements, such as colors, to recreate the model. Additionally, the mobile application introduces AI image upscaling, categorizing user-provided images (e.g., dinosaurs, superheroes) and generating more detailed upscaled images and 3D models based on the chosen category. The application outputs a 3D printer file, enabling users to create molds of upscaled 3D models.

Abstract: A guided circular saw system having a track and a ramp that is connected to a benchtop by a pair of hinge members. Hinge members are formed of a top section, a middle section and a bottom section and facilitate raising and lowering of the track in a quick and easy manner without tools. The track is raised to facilitate placing a workpiece under the track and then the track is lowered in place thereby engaging and holding the workpiece in place. A sled attached to a cutting device and the sled and cutting device slides along the track thereby cutting the workpiece. The track includes sacrificial grip strips that extend along the sides of the track and include an upper layer of material that is rigid and a lower layer of material that is compressible and has a high coefficient of friction and thereby holds the workpiece in place.

Abstract: A record processing and storage system is operable to generate data source assignment data by segregating a plurality of data sources into a plurality of data source sets mapped to a corresponding plurality of storage clusters of the record processing and storage system. For each of the plurality of storage clusters, a corresponding subset of a plurality of pages is generated to include only ones of a plurality of records received from data sources included in a corresponding one of the plurality of data source sets, and the corresponding subset of the plurality of pages via storage resources of the each of the plurality of storage clusters. For each of the plurality of storage clusters, deduplication of each page included in the corresponding subset of the plurality of pages is facilitated based on metadata comparisons with other pages included in the corresponding subset of the plurality of pages.

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 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 an accelerated tuning of hyperparameters of a model supported with prior learnings data include assessing subject models associated with a plurality of distinct sources of transfer tuning data, wherein the assessing includes implementing of: [1] a model relatedness assessment for each of a plurality of distinct pairwise subject models, and [2] a model coherence assessment for each of the plurality of distinct pairwise subject models; constructing a plurality of distinct prior mixture models based on the relatedness metric value and the coherence metric value for each of the plurality of distinct pairwise subject models, identifying sources of transfer tuning data based on identifying a distinct prior mixture model having a satisfactory model evidence fraction; and accelerating a tuning of hyperparameters of the target model based on transfer tuning data associated with the distinct prior mixture model having the satisfactory model evidence fraction.

Abstract: The invention is a system that prevents the opening of a door into a room or other area by an intruder. The system includes an aperture positioned in flooring, the aperture located adjacent to a first side edge and bottom edge of a closed door. The system also includes a block comprising an internal passageway with about the same dimensions as the aperture. The system further includes an elongated pin comprising a first end and a second end, wherein the pin is configured to pass through the block internal passageway and into the aperture. The block is held in position over the aperture when the pin passes through the block internal passageway and into the aperture. When the block is held in position, the closed door is prevented from opening.

Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel; running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.

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: A database system operates to: receive, via a communication network, a record stream from a data source that includes a plurality of row data, wherein each of the plurality of row data includes a set of records for storage as relational database rows of at least one database table and further indicates a batch number corresponding to the set of records; add each of the plurality of row data to a pending row data pool; and perform parallelized generation of a plurality of pages from the plurality of row data to each include multiple records of the plurality of row data via a plurality of processing core resources based on each processing core resource in the plurality of processing core resources including power, processing a corresponding subset of the plurality of row data, independently from and in parallel with processing of other subsets of the plurality of row data via other ones of the plurality of processing core resources.

Abstract: Systems and methods for tuning hyperparameters of a model include receiving a tuning request for tuning hyperparameters, the tuning request includes a first and a second objective function for the machine learning model. The first and second objective functions may output metric values that do not improve uniformly. Systems and methods additionally include defining a joint tuning function that is based on a combination of the first and second objective functions; executing a tuning operation; identifying a Pareto efficient frontier curve defined by a plurality of distinct hyperparameter values; applying metric thresholds to the Pareto efficient frontier curve; demarcating the Pareto efficient frontier curve into at least a first infeasible section and a second feasible section; searching the second feasible section of the Pareto efficient frontier curve for one or more proposed hyperparameter values; and identifying at least a first set of proposed hyperparameter values based on the search.

Abstract: Methods and systems for improved analytics are provided. A Software as a Service (SaaS) platform may be implemented as a distributed system using a public infrastructure, such as a public cloud, and an on-premises infrastructure, such as a private cloud. A control plane for the SaaS platform may reside in the public cloud, while a data plane for the SaaS platform may reside in the on-premises private cloud.

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: 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: 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: 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.