Abstract: An apparatus includes a base having a platform configured for placement of one or more items on a surface of the platform, a frame coupled to the base, and a plurality of camera units coupled to the frame. Each camera unit is configured to capture images of at least a portion of the platform and the plurality of camera units includes a plurality of side-view camera units arranged above the platform along a horizontal plane, wherein each camera unit of the side-view camera units comprises two vertically stacked cameras and is configured to capture perspective images of at least a portion of the platform. An overhead-view camera unit is positioned over the platform, wherein the overhead-view camera unit comprises two cameras and is configured to capture top view images of the platform.

Abstract: Polycyclic aromatic hydrocarbons represented by the following general formula (I) wherein X is one of nitrogen, phosphorus, arsenic, antimony, bismuth, sulphur, selenium, tellurium; R independently represents an aromatic group and/or an aliphatic group; Q is one of a cyclic aliphatic hydrocarbon, a cyclic aromatic hydrocarbon, a polycyclic hydrocarbon, a polycyclic aromatic hydrocarbon, and/or a fused polycyclic aromatic hydrocarbon; wherein the substituents independently comprise one or more of a hydrogen atom, a deuterium atom, a fluorine atom, a chlorine atom, a bromine atom, a carbon atom, an oxygen atom (e.g. an alkylated oxygen atom), a nitrogen atom (e.g. an alkylated nitrogen atom), a cyano group, a nitro group, an alkyl group and/or anaryl group; p is an integer of 1 to 2; q is an integer of 1 to 4; Y1 and Y2 independently represent one or more of a hydrogen atom, a deuterium atom, a fluorine atom, a chlorine atom, a bromine atom, a carbon atom, an oxygen atom (e.g.

Abstract: Techniques are described herein for testing and characterizing user experiences with digital assets. Embodiments include a digital experience testing system for running usability tests with respect to user interface designs. The runtime testing environment may use a set of one or more compiled prototypes to test various facets of a user experience including what respondents do when interacting with a digital asset and how the experience engages the respondents' emotions. The digital experience testing system may analyze, enrich, synthesize, and/or otherwise process the results of one or more usability tests, based on the tracked user interactions, to provide guidance on user interface design optimizations.

Abstract: Operations of a prompt management system are disclosed. The operations may include: receiving a prompt for performing a set of tasks, assigning an agent group that includes a plurality of agents to perform a set of roles associated with a dataset in support of the set of tasks, causing the plurality of agents to perform the set of roles using a first machine-learning model, receiving a set of role results from the plurality of agents responsive to performing the set of roles, performing the set of tasks using at least a second machine-learning model, and providing a task result for display on a user interface device. The set of tasks may include executing an operation on the set of role results using the second machine-learning model, and generating a task result that includes a product of the operation executed on the set of role results.

Abstract: Operations of a prompt management system are disclosed. The operations may include: receiving a prompt for performing a set of tasks, assigning an agent group that includes a plurality of agents to perform a set of roles associated with a dataset in support of the set of tasks, causing the plurality of agents to perform the set of roles using a first machine-learning model, receiving a set of role results from the plurality of agents responsive to performing the set of roles, performing the set of tasks using at least a second machine-learning model, and providing a task result for display on a user interface device. The set of tasks may include executing an operation on the set of role results using the second machine-learning model, and generating a task result that includes a product of the operation executed on the set of role results.

Abstract: A method may include obtaining a normal set point of a solar panel and a wind velocity measurement corresponding to wind that affects the solar panel. The method may include determining an allowable range of tilt angles according to a first lookup table that describes a relationship between the wind velocity measurement and the allowable range of tilt angles. The method may include identifying whether the normal set point of the solar panel is outside of the allowable range of tilt angles, and responsive to identifying that the normal set point of the solar panel is outside of the allowable range of tilt angles, determining a temporary stow set point. The method may include rotating the solar panel to the temporary stow set point.

Abstract: Techniques and embodiments described herein include a scalable system for integrating panel-based research with user experience (UX) testing tools, such as online survey applications. The techniques provide for the parallelization of a single request across multiple panel providers. The parallelization across multiple panel providers may occur transparently to users, including the designers of UX testing tools and methodologies, without requiring any complex modifications of the underlying source code. The parallelization may further significantly reduce request processing speeds within the system. Additionally or alternatively, the techniques provide for runtime adjustments of configurations to adjust rates at which respondents with varying attributes are fielded. As a result, qualified UX test respondents may be fielded much more quickly, increasing system scalability by allowing the system to process more requests within a given timeframe.

Abstract: Techniques are described herein for using artificial intelligence to select, curate, normalize, enrich, and synthesize the results of user experience (UX) tests. In some embodiments, a system identifies a set of unstructured textual elements associated with one or more UX tests. The system may configure agents using generative language model services, including a reviewing agent that reviews and edit outputs of a machine learning classification model applied to the unstructured textual elements and/or a curating agent that selects unstructured textual elements to represent themes within the user experience test classified using the machine learning classification model. The outputs may be used to enhance the scalability, function, and efficiency of applications directed at improving product designs.

Abstract: A method may include obtaining a normal set point of a solar panel and a wind velocity measurement corresponding to wind that affects the solar panel. The method may include determining an allowable range of tilt angles according to a first lookup table that describes a relationship between the wind velocity measurement and the allowable range of tilt angles. The method may include identifying whether the normal set point of the solar panel is outside of the allowable range of tilt angles, and responsive to identifying that the normal set point of the solar panel is outside of the allowable range of tilt angles, determining a temporary stow set point. The method may include rotating the solar panel to the temporary stow set point.

Abstract: Techniques and embodiments are described herein for detecting and mitigating fraudulent activity within user experience (UX) test applications. In some embodiments, a system applies a set of rules and/or machine learning (ML) models to each respondent of an online survey or UX test. Different ML models may be trained to learn domain-specific patterns indicative of fraudulent activity. The system may then select the ML models based on attributes of the UX test and/or respondent. The selected rules and/or ML models may generate a probabilistic score representing a likelihood that the respondent is currently engaging in or will engage in fraudulent activity with respect to a UX test. If the score exceeds a threshold, then the system may take action to mitigate the fraudulent activity, such as triggering the removal of the user from an accepted respondent pool, halting further engagement between the respondent and the UX test, and generating alerts.

Abstract: The present invention further relates to a waterborne dispersion comprising (A) polymer P; (B) amphiphilic block copolymer obtained with a controlled radical polymerization process and comprising at least blocks [A] and [B], whereby block [A] comprises ethylenically unsaturated monomer(s) bearing water-soluble and/or water-dispersible functional groups (monomer(s) (i)), and block [B] comprises ethylenically unsaturated monomer(s) different from monomer(s) (i) (monomer(s) (ii)); and (C) crosslinker, characterized in that the polymer P is crosslinkable and comprises ethylenically unsaturated monomer(s) bearing crosslinkable functional groups different from monomer(s) (i) and monomer(s) (ii) (monomer(s) (iii)) in an amount of from 1 to 10 wt. %, based on the total weight of monomers used to prepare the polymer P, the amount of lock copolymer is higher than 1 wt. % and lower than 30 wt.

Abstract: Techniques are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience tests. In some embodiments, a system identifies a qualitative element within a result set for a user experience test. The system then selects a machine learning model to apply based on one or more attributes associated with the user experience test and generates a predicted visibility, quality, and/or relevance for the qualitative element. Based on the prediction, the system generates a user interface that curates a set of results of the user experience test.

Abstract: Systems, devices, and methods for determining a predicted impact point of a selected weapon and associated round based on stored ballistic information, provided elevation data, provided azimuth data, and provided position data.

Abstract: A reference depth image of a platform is captured using a three-dimensional (3D) sensor. A plurality of secondary depth images are captured, wherein for each secondary depth image, a depth difference parameter is determined by comparing the secondary depth image and the reference depth image. In response to determining that the depth difference parameter has stayed constant at a value higher than zero for a duration of a first time interval, it is determined that an item has been placed on the platform.

Abstract: In response to detecting a first triggering event corresponding to placement of a first item on a platform, a plurality of first images are captured of the first item and a plurality of cropped first images are generated based on the first images. A first item identifier associated with the first item is identified based on the cropped first images. In response to detecting a second triggering event corresponding to placement of a second item on the platform, a plurality of second images of the first item are captured and a plurality of cropped second images are generated from the second images. In response to determining that the cropped first images match with the cropped second images, the first item identifier is assigned to the first item depicted in the second images.

Abstract: A device for object tracking receives an image from a camera, where the image shows a set of points on a calibration board placed on a platform. The device determines a pixel location array that comprises pixel locations associated with the points in the image. The device determines, by applying a first homography to the pixel location array, a calculated location array identifying calculated physical location coordinates of the set of points in the global plane. The device determines that the difference between a reference location array and the calculated location array is more than a threshold value. In response, the device determines that the camera and/or the platform has moved from a respective initial location when the first homography was determined. The device determines a second homography by multiplying an inverse of the pixel location array by the reference location array and calibrates the camera using the second homography.

Abstract: Systems, devices, and methods for determining a predicted impact point of a selected weapon and associated round based on stored ballistic information, provided elevation data, provided azimuth data, and provided position data.

Abstract: Techniques are described herein for selecting, curating, normalizing, enriching, and synthesizing the results of user experience tests. In some embodiments, a system identifies a qualitative element within a result set for a user experience test. The system then selects a machine learning model to apply based on one or more attributes associated with the user experience test and generates a predicted visibility, quality, and/or relevance for the qualitative element. Based on the prediction, the system generates a user interface that curates a set of results of the user experience test.

Abstract: Techniques are described herein for providing adaptable testing and benchmarking of user experiences with respect to one or more products. In some embodiments, the techniques include systems and methods for predicting performance of facets of a user experience under new testing and benchmarking methodologies. The systems and methods may generate a prediction for the results of a UX test even though the methodology and mechanics to quantify the user experience may vary significantly from previous methodologies. The techniques allow for methodologies to evolve over time without losing historical context or the ability to meaningfully compare historical test results with tests run using updated testing and benchmark models. Further, the techniques allow for benchmarks to be computed in real-time or near real-time as methodologies change without requiring tests to be run according to the new methodologies.

Abstract: Systems, devices, and methods for determining a predicted impact point of a selected weapon and associated round based on stored ballistic information, provided elevation data, provided azimuth data, and provided position data.