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 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: 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: 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: 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: 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: The present invention includes a system, apparatus and method for generating a three-dimensional image and/or printing a three dimensional structures, the system comprising: a medium comprising a photocurable monomer and an optical molecular switch molecule that has a non-fluorescent state and a fluorescent state, wherein the monomer forms a polymer upon exposure to the emitted light. Also taught are novel fluorescent photoswitch molecules.

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 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: 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: 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: 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: 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 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 electronic device holder is provided comprising a housing defining a space and having an open end portion. The housing includes a first portion slidably coupled to a second portion. A flexible member is positioned around the housing. A retention feature is disposed on the second portion. The retention feature extends into the space.

Abstract: A broadcasting system for an autonomous vehicle is disclosed and includes an antenna, a plurality of sensors, one or more processors, and a memory coupled to the processors. The antenna is configured to send and receive wireless communication, and the antenna receives publically available wireless signals. The sensors are configured to generate signals indicating a real-time velocity and a real-time direction of travel of the autonomous vehicle. The processors are in communication with the antenna and the plurality of sensors. The memory stores data comprising program code that, when executed by the one or more processors, causes the system to receive as input the publically available wireless signals and the signals indicating the real-time velocity and the real-time direction of travel of the autonomous vehicle. The system is further caused to determine a real-time velocity and real-time direction of travel of autonomous vehicle.

Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving at a remote tuning service a multi-criteria tuning work request for tuning hyperparameters of the model of a subscriber, 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 first conditionally constrained joint function for the model based on subjecting the first objective function to the second objective function; a second conditionally constrained joint function for the model based on subjecting the second objective function to the first objective function of the model; executing a tuning operation of the hyperparameters for the model; and identifying proposed hyperparameter values based on one or more hyperparameter-based points along a non-convex Pareto optimal curve.

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 tuning hyperparameters of a model includes: receiving at a remote tuning service a multi-criteria tuning work request for tuning hyperparameters of the model of a subscriber, 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 first conditionally constrained joint function for the model based on subjecting the first objective function to the second objective function; a second conditionally constrained joint function for the model based on subjecting the second objective function to the first objective function of the model; executing a tuning operation of the hyperparameters for the model; and identifying proposed hyperparameter values based on one or more hyperparameter-based points along a non-convex Pareto optimal curve.