Abstract: A system for intelligent selection of inputs for inference and quantification of uncertainty for active learning in a model includes computing an information gain by combining one or more metrics of interest and uncertainties in the inputs using information compression based on a first set of data as one or more pairs of values of inputs and outputs. One or more of the inputs are selected, with a potential information gain higher than other inputs, to query the model and record values of one or more of the outputs. The model computes a measurement of performance outputs based upon the first set of data. The model is updated based upon estimated metrics of interest performance and quantification of the associated uncertainty, and output for a change to a process based upon the estimated metrics of interest performance and quantification of the associated uncertainty.

Abstract: Disclosed are monoclonal antibodies, and antigen binding portions thereof, which neutralize human Lipocalin 2 (LCN-2). Related nucleic acids, recombinant expression vectors, host cells, populations of cells, and pharmaceutical compositions are also disclosed. Related methods of reducing or preventing ferroptotic death of retinal pigmented epithelium (RPE) cells, methods of reducing or preventing one or both of lipid peroxidation and inflammasome activation in RPE cells, methods of reducing or preventing a decrease in one or both of scotopic a- and b-wave responses in a retina, methods of increasing one or both of scotopic a- and b-wave responses in a retina, methods of increasing one or both of autophagy and glutathione peroxidase activity in RPE cells, and methods of treating or preventing age-related macular degeneration (AMD) are also disclosed.

Abstract: Methods and systems for calibrating a model are provided herein. In some embodiments, the methods include receiving, via a control circuit, test data for an operational parameter of a real-world system, such as an engine, from operational tests. The control circuit also receives model data for the operational parameter from simulations performed via a model of the engine. The control circuit then compresses the test data and the model data to generate compressed test data and compressed model data and fusing the compressed test data with the compressed model data to generate fused data. The control circuit performs parallel Bayesian inference simulations using the fused data to identify at least one value for a tuning parameter of the model. The control circuit may identify and select tuning parameters to match the model data with test data, the model data may be one or more model outputs (i.e., output parameters).

Abstract: A method for estimating a crack propagation rate includes receiving a first dataset for a new material design, the new material design including crack growth rate data, receiving second and third datasets for a plurality of different legacy systems associated with an existing material design, determining a legacy model for each of the plurality of different legacy systems based on the respective second and third datasets for each of the plurality of different legacy systems and the first dataset for the new material design, the legacy model based on a validity of each legacy system, the validity determined using a legacy model likelihood validity and a predictive uncertainty model validity, calculating a model weight to associate with each of the determined legacy models, and determining a first multi-source model for new data for the new material design based on the model weight.

Abstract: A method and system to receive a dataset for a new design; receive datasets for a plurality of different legacy systems; determine a legacy model for each of the plurality of different legacy systems based on the respective dataset for each of the plurality of different legacy systems and the dataset for the new design; calculate a model weight to associate with each of the determined legacy models; determine, based on a combination of the determined legacy models for the plurality of different legacy systems and the weight associated therewith, a multi-source model for new data for the new design; and save a record of the determined multi-source model.

Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.

Abstract: A method of inhibiting neutrophil activation in retinal pigment epithelial (RPE) cells comprising administering an inhibitor of interferon (IFN) ? to a subject in need thereof, as well as a method of restoring lysosomal function of RPE cells comprising administering a polypeptide comprising ?A1-crystallin or a nucleic acid encoding the polypeptide to a subject in need thereof, are provided. A method of rejuvenating Vacuolar-type H+-ATPase (V-ATPase) activity in RPE cells by modulating assembly and disassembly of the V-ATPase in a subject in need thereof also is provided. Additionally, a method of treating diabetic retinopathy in a subject comprising administering a polypeptide comprising ?A1-crystallin or a nucleic acid encoding the polypeptide to the subject is provided.

Abstract: A method of restoring lysosomal function of retinal pigment epithelial (RPE) cells and a method of preventing and/or treating age-related macular degeneration (AMD), Stargardt's macular retinal degeneration, neurodegenerative disease, or diabetic retinopathy in a subject are provided. The methods comprise administering (i) a nucleic acid encoding a polypeptide comprising a constitutively active form of transcription factor EB (TFEB) or (ii) the polypeptide to a subject in need thereof. Associated polypeptides, nucleic acids, vectors, and compositions thereof also are provided.

Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.

Abstract: In implementations of weighted color palette generation, one or more computing devices implement a generation system which receives input data including an input color palette. A first machine learning model receives the input color palette and generates an unweighted color palette based on the input color palette. A second machine learning model receives the generated unweighted color palette and generates a weighted color palette based on the generated unweighted color palette. The generation system renders the weighted color palette in a user interface.

Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.

Abstract: In implementations of weighted color palette generation, one or more computing devices implement a generation system which receives input data including an input color palette. A first machine learning model receives the input color palette and generates an unweighted color palette based on the input color palette. A second machine learning model receives the generated unweighted color palette and generates a weighted color palette based on the generated unweighted color palette. The generation system renders the weighted color palette in a user interface.

Abstract: Certain embodiments involve flow-based color transfers from a source graphic to target graphic. For instance, a palette flow is computed that maps colors of a target color palette to colors of the source color palette (e.g., by minimizing an earth-mover distance with respect to the source and target color palettes). In some embodiments, such color palettes are extracted from vector graphics using path and shape data. To modify the target graphic, the target color from the target graphic is mapped, via the palette flow, to a modified target color using color information of the source color palette. A modification to the target graphic is performed (e.g., responsive to a preview function or recoloring command) by recoloring an object in the target color with the modified target color.

Abstract: A method and system to receive a dataset for a new design; receive datasets for a plurality of different legacy systems; determine a legacy model for each of the plurality of different legacy systems based on the respective dataset for each of the plurality of different legacy systems and the dataset for the new design; calculate a model weight to associate with each of the determined legacy models; determine, based on a combination of the determined legacy models for the plurality of different legacy systems and the weight associated therewith, a multi-source model for new data for the new design; and save a record of the determined multi-source model.

Abstract: The present invention is a location based data analytics system and method that functions to track individuals and/or equipment within a bounded location that may be an indoor or outdoor location, or a combination of indoor and outdoor locations, within a geographic site that is a bounded location. The system captures the real-time movements of the individuals/equipment and generates location and time statistics based upon real-time movements. The system is also operable to generate analytics based upon the information collected relating to the movements of the individuals/equipment as well as insights. The location and movement information can be mapped in real-time or over time.

Abstract: The present invention is a location based data analytics system and method that functions to track individuals and/or equipment within a bounded location that may be an indoor or outdoor location, or a combination of indoor and outdoor locations, within a geographic site that is a bounded location. The system captures the real-time movements of the individuals/equipment and generates location and time statistics based upon real-time movements. The system is also operable to generate analytics based upon the information collected relating to the movements of the individuals/equipment as well as insights. The location and movement information can be mapped in real-time or over time.