Abstract: A bird feeder includes a container and a lid. The container includes a body and a neck. The neck includes an opening and the outer surface of the neck includes a first thread. The lid is configured to engage the neck. The lid includes a top wall and a side wall bounding a peripheral edge of the top wall. An inner surface of the side wall includes a second thread that is configured to engage the first thread of the neck so as to close the lid onto the container. The top wall includes an opening and a perch is disposed on the lid. An attachment member is formed on an outer surface of the lid.

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: 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 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 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: 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: A headwear device is configured for use with eyeglasses. The headwear device includes a visor, a crown, and two temple members extending rearwardly from the visor. The headwear device further includes two clips extending from the two temple members. Each clip includes a cylindrical portion defining an axial recess. The axial recess is designed and dimensioned to receive a temple of the eyeglasses.

Abstract: A method for execution by a record processing and storage system includes receiving a plurality of records and corresponding row numbers. Pages are generated from the received records. Page metadata is generated for each page that includes row number span data based on row numbers of the records included in each page. Pairs of pages are identified in the plurality of pages based on having row number span data in their page metadata that include a row number span overlap. For each pair of pages, row number span data is updated for a first page in the pair by removing the row number span overlap with a second page in the pair. Reads of pages are performed based on their row number span data. Only records of each first page of each pair of pages having row numbers that are within the updated row number span data are read.

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: 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 system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.

Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

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 bird feeder includes a container and a lid. The container includes a body and a neck. The neck includes an opening and the outer surface of the neck includes a first thread. The lid is configured to engage the neck. The lid includes a top wall and a side wall bounding a peripheral edge of the top wall. An inner surface of the side wall includes a second thread that is configured to engage the first thread of the neck so as to close the lid onto the container. The top wall includes an opening and a perch is disposed on the lid. An attachment member is formed on an outer surface of the lid.

Abstract: In an example embodiment, an attitude, orbit, and de-orbit control system (AODCS) for a satellite is provided. In an example embodiment, the AODCS system comprises one or more selectively retractable booms. The one or more selectively retractable booms are collectively configured to provide a selectively adjustable drag during de-orbiting of a satellite over a predefined de-orbiting time.

Abstract: Systems and methods include receiving a tuning work request for tuning hyperparameters of a third-party model or system; performing, by a machine learning-based tuning service, a first tuning of the hyperparameters in a first tuning region; identifying tuned hyperparameter values for each of the hyperparameters based on results of the first tuning; setting a failure region based on the tuned hyperparameter values of the first tuning; performing, by the machine learning-based tuning service, a second tuning of the hyperparameters in a second tuning region that excludes the failure region; identifying additional tuned hyperparameter values for each of the hyperparameters based on results of the second tuning; and returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party model or system with one of the tuned hyperparameter values and the additional hyperparameter values.

Abstract: A method for execution by a record processing and storage system includes receiving a plurality of records and corresponding row numbers. Pages are generated from the received records. Page metadata is generated for each page that includes row number span data based on row numbers of the records included in each page. Pairs of pages are identified in the plurality of pages based on having row number span data in their page metadata that include a row number span overlap. For each pair of pages, row number span data is updated for a first page in the pair by removing the row number span overlap with a second page in the pair. Reads of pages are performed based on their row number span data. Only records of each first page of each pair of pages having row numbers that are within the updated row number span data are read.

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: The present disclosure relates to a thin coated optical fiber that enables connector assembly without stripping the optical fiber.

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