Abstract: A flexible, twistable waveguide device is provided comprising a first flange for connecting the waveguide device to a first RF component and a second flange for connecting the waveguide to a second RF component, a waveguide body formed as a single piece, wherein the waveguide body transmits the RF waves through an interior cavity of the waveguide body, and wherein the waveguide body comprises a first linear section, a curved section, and a second linear section, the first linear section extending from the first flange to a first end of the curved section and the second linear section extending from the second flange to a second end of the curved section, wherein the waveguide body is elastically deformable, in up to six degrees of freedom, from an undeformed configuration to a deformed configuration, the deformed configuration being deformed in at least one of the six degrees of freedom.

Abstract: A self-supporting space environment protection device and method of manufacturing the device are provided. The device includes a self-supporting polymer-based body manufactured for installation on a component of a space-based system and composed of a space radiation resistant polymer-based material. The device further includes a low-absorptivity finish applied to an external surface of the self-supporting polymer-based body, the low-absorptivity finish having a solar absorptivity (?) value lower than the space radiation resistant polymer-based material, and a low-emissivity finish applied to the internal surface of the self-supporting polymer-based body, the low-emissivity finish having an infrared emissivity (?) value lower than the space radiation resistant polymer-based material.

Abstract: A wall cap includes a plate and an intake pipe extending from one side of the plate. One or more plate walls extend from the other side of the plate. The wall cap further includes an extension member that engages with the plate walls. The engagement of the extension member with the plate walls may be through the engagement of tabs (on the engagement member) with protrusions (on the plate walls). The extension member includes markings at pre-set intervals. The markings assist in allowing for the extension member to be cut to accommodate different thicknesses of the wall. The wall cap further includes a hood assembly for attachment to the extension member.

Abstract: A vent assembly for venting air from ducting of an air conditioning unit through a wall includes an outer portion and an inner portion. The outer portion is attached to an exterior surface of the wall and includes a frame, a hood, and a back portion. The frame engages with the exterior surface of the wall. The hood extends from the frame and includes an upper surface and a pair of opposed side walls. The upper surface extends from the frame. The side walls extend from the frame and are connected to the upper surface. The back portion is attached to the hood and includes a back wall and a damper. The back wall has a back opening. The damper extends across and covers the back opening. The damper is connected to the back wall to allow for hinged movement of the damper with respect to the back wall. The inner portion is attached to the outer portion and extends through the wall. The inner portion includes a pipe connected to the back portion and is aligned with the back opening.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

Abstract: Implementations relate to providing, in response to a query, machine learning model output that is based on output from a trained machine learning model. The machine learning model output can include a predicted answer to the query, that is predicted based on the trained machine learning model. The machine learning model output can additionally or alternatively include an interactive interface for the trained machine learning model. Some implementations relate to generating a trained machine learning model “on the fly” based on a search query. Some implementations additionally or alternatively relate to storing, in a search index, an association of a machine learning model with a plurality of content items from resource(s) on which the machine learning model was trained.

Abstract: Methods of reshaping ferrules used in optical fiber cables assemblies are disclosed. The reshaping methods reduce a core-to-ferrule concentricity error (E), which improves coupling efficiency and optical transmission. The methods include measuring a true center of the ferrule, wherein the true center is based on an outer surface of the ferrule; and reshaping at least a portion of the ferrule to change the true center of the ferrule, wherein the reshaping includes enlarging a portion of the ferrule. A variety of reshaping techniques are also disclosed.

Abstract: A method for rejecting biased data includes receiving a bias training data set based on a probability distribution of bias-sensitive variables of a target population and segmenting the bias training data set into clusters based on at least one respective bias-sensitive variable of the target population, each cluster including a bias cluster weight. The method also includes receiving a training data set for a machine learning model and segmenting the training data set into training clusters. Each training cluster is associated with at least one corresponding bias-sensitive variable of the target population and includes a corresponding training data set weight. The method also includes adjusting each training data set weight to match a respective bias cluster weight to form an adjusted training data set and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: Disclosed are processes, systems, and reaction systems for the oligomerization of ethylene to form an oligomer product in a reaction zone using a catalyst system having i) a chromium component comprising an N2-phosphinyl amidine chromium compound complex, an N2-phosphinyl formamidine chromium compound complex, an N2-phosphinyl guanidine chromium compound complex, or any combination thereof, and ii) an aluminoxane. Ethylene can be contacted with an organic reaction medium to form an ethylene feedstock mixture prior to contact with the catalyst system. The ethylene feedstock mixture can be contacted with the catalyst system inside or outside of the reaction zone.

Abstract: A method for rejecting biased data includes receiving a bias training data set based on a probability distribution of bias-sensitive variables of a target population and segmenting the bias training data set into clusters based on at least one respective bias-sensitive variable of the target population, each cluster including a bias cluster weight. The method also includes receiving a training data set for a machine learning model and segmenting the training data set into training clusters. Each training cluster is associated with at least one corresponding bias-sensitive variable of the target population and includes a corresponding training data set weight. The method also includes adjusting each training data set weight to match a respective bias cluster weight to form an adjusted training data set and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: A method for rejecting biased data using a machine learning model includes receiving a cluster training data set including a known unbiased population of data and training a clustering model to segment the received cluster training data set into clusters based on data characteristics of the known unbiased population of data. Each cluster of the cluster training data set includes a cluster weight. The method also includes receiving a training data set for a machine learning model and generating training data set weights corresponding to the training data set for the machine learning model based on the clustering model. The method also includes adjusting each training data set weight of the training data set weights to match a respective cluster weight and providing the adjusted training data set to the machine learning model as an unbiased training data set.

Abstract: Disclosed are processes, systems, and reaction systems for the oligomerization of ethylene to form an oligomer product in a reaction zone using a catalyst system having i) a chromium component comprising an N2-phosphinyl amidine chromium compound complex, an N2-phosphinyl formamidine chromium compound complex, an N2-phosphinyl guanidine chromium compound complex, or any combination thereof, and ii) an aluminoxane. Ethylene can be contacted with an organic reaction medium to form an ethylene feedstock mixture prior to contact with the catalyst system. The ethylene feedstock mixture can be contacted with the catalyst system inside or outside of the reaction zone.

Abstract: An imaging system is provided that includes at least one detector configured to acquire imaging information, a processing unit, and a display unit. The processing unit is operably coupled to the at least one detector, and is configured to reconstruct an image using the imaging information. The image is organized into voxels having non-uniform dimensions. The processing unit is configured to perform a penalized likelihood (PL) image reconstruction using the imaging information. The PL image reconstruction includes a penalty function. Performing the penalty function includes interpolating a voxel size in at least one dimension from an original size to an interpolated size before determining a penalty function, determining the penalty function using the interpolated size to provide an initial penalty, interpolating the initial penalty to the original size to provide a modified penalty, and applying the modified penalty in the PL image reconstruction.

Abstract: Methods of reshaping ferrules used in optical fiber cables assemblies are disclosed. The reshaping methods reduce a core-to-ferrule concentricity error (E), which improves coupling efficiency and optical transmission. The methods include measuring a true center of the ferrule, wherein the true center is based on an outer surface of the ferrule; and reshaping at least a portion of the ferrule to change the true center of the ferrule, wherein the reshaping includes enlarging a portion of the ferrule. A variety of reshaping techniques are also disclosed.