Abstract: A method of calibrating a radar sensor includes receiving radar returns from a plurality of objects based on a radar signal sent from the radar sensor, each of the radar returns having a magnitude, at least a subset of the objects are known static objects, identifying a location and orientation of the radar sensor when the signal was sent, identifying expected reflectance values for each of the plurality of known static objects, calculating a conversion function configured to adjust the magnitudes of each of the radar returns for the known static objects to an estimated reflectance value based on the expected reflectance values for each of the known static objects, and adjusting an output of the radar sensor based on the conversion function.

Abstract: A method and apparatus for implementing a method are disclosed. The method includes providing point cloud data to a machine learning algorithm, the point cloud data detected in the vicinity of an autonomous vehicle. The method further includes differentiating, via the machine learning algorithm, in the point cloud, data directly representing a location of a first object and data indirectly representing a location of a second object. The method includes transforming the data indirectly representing the location of the second object into data directly representing the location of the second object and generating corrected point cloud data based on the data directly representing the location of the first object and the data directly representing the location of the second object. The method includes outputting the corrected point cloud data to the autonomous vehicle.

Abstract: The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to generate heatmaps based upon radar tensors, wherein a neural network receives the radar tensors as input and constructs the heatmaps as output. Point clouds are generated based upon the heatmaps. A computing system detects objects in an environment of an autonomous vehicle (AV) based upon the point clouds, and the computing system further causes the AV to perform a driving maneuver based upon the detected objects.

Abstract: Technologies described herein relate to learning parameter values of a preprocessing algorithm that is executed in a radar system. The preprocessing algorithm is configured to receive raw radar data as input and is further configured to generate three-dimensional point clouds as output, where the preprocessing algorithm generates the three-dimensional point clouds based upon the raw radar data and the parameter values that are assigned to the preprocessing algorithm. To learn the parameter values, the preprocessing algorithm is converted to auto-differentiated form and is added as an input network layer to a deep neural network (DNN) that is configured to identify objects represented in three-dimensional point clouds. The parameter values are learned jointly with weight matrices of the DNN.

Abstract: A system includes a first sensor system of a first modality and a second sensor system of a second modality. The system further includes a computing system that is configured to detect and identify objects represented in sensor signals output by the first and second sensor systems. The computing system employs a hierarchical arrangement of transformers to fuse features of first sensor data output by the first sensor system and second sensor data output by the second sensor system.

Abstract: The technologies described herein relate to a radar system that is configured to generate point clouds based upon radar tensors generated by the radar system. More specifically, the radar system is configured to identify bins in radar tensors that correspond to objects in an environment of the radar system, and to use energy values in other bins to construct a point cloud. A computing system detects objects in an environment of the radar system based upon the point clouds.

Abstract: Nucleic acid constructs and compositions that allow insertion of a FIX coding sequence into a target genomic locus such as an endogenous ALB locus and/or expression of the FIX coding sequence are provided. The nucleic acid constructs and compositions can be used in methods of introducing a F9 nucleic acid into a cell, methods of integration of a F9 nucleic acid into a target genomic locus, methods of expression of FIX in a cell, and in methods of treating hemophilia B or FIX deficiency in a subject.

Abstract: Provided herein are compositions and methods for adapting adeno-associated virus (AAV) particles comprising capsids of non-primate animal AAV, remote AAV, or a combination thereof. AAV adapted accordingly may be a viable gene therapy platform for the treatment of a patient in need thereof, and may be particularly useful in patients excluded from current treatment modalities involving current therapeutic AAV particles due to their high titer of antibodies against the current therapeutic AAV particles.

Abstract: The present invention relates to compositions and methods comprising an engineered assembly activating protein (EAAP).

Abstract: An inkjet ink composition includes water and effective amounts of one or more of: derivatized 2-pyrrolidinones; glycerol polyoxyethyl ethers; diols; or combinations thereof. The inkjet ink composition also includes an effective amount of functionalized nanocrystals.

Abstract: An inkjet ink composition includes water and effective amounts of one or more of: derivatized 2-pyrrolidinones; glycerol polyoxyethyl ethers; diols; or combinations thereof. The inkjet ink composition also includes an effective amount of functionalized nanocrystals.

Abstract: Embodiments of organic-polymer-based memory elements that are stable to repeated READ access operations are disclosed. Organic-polymer-based memory elements can suffer cumulative degradation that occurs over repeated READ access operations due to the introduction of electrons into the organic-polymer layer. In general, entry of electrons into the organic-polymer layer generally lags initiation of a hole current within the organic-polymer layer following application of a voltage potential across the memory elements. Therefore, stable memory elements can be fabricated by introducing electron-blocking layers and/or limiting the duration of applied voltages during READ access operations.

Abstract: A microlens array for a light emitting device is disclosed. The light emitting device includes a plurality of OLEDs, each OLED having a minimum planar dimension. The array includes a plurality of microlenses, each of which has a minimum planar dimension and a maximum planar dimension. The minimum planar dimensions of the microlenses are larger than the maximum wavelength of visible light emitted from the OLEDs. The maximum planar dimensions of the microlenses are smaller than the smallest minimum planar dimension of any of the OLEDs.

Abstract: Embodiments of organic-polymer-based memory elements that are stable to repeated READ access operations are disclosed. Organic-polymer-based memory elements can suffer cumulative degradation that occurs over repeated READ access operations due to the introduction of electrons into the organic-polymer layer. In general, entry of electrons into the organic-polymer layer generally lags initiation of a hole current within the organic-polymer layer following application of a voltage potential across the memory elements. Therefore, stable memory elements can be fabricated by introducing electron-blocking layers and/or limiting the duration of applied voltages during READ access operations.

Abstract: A microlens array for a light emitting device is disclosed. The light emitting device includes a plurality of OLEDs, each OLED having a minimum planar dimension. The array includes a plurality of microlenses, each of which has a minimum planar dimension and a maximum planar dimension. The minimum planar dimensions of the microlenses are larger than the maximum wavelength of visible light emitted from the OLEDs. The maximum planar dimensions of the microlenses are smaller than the smallest minimum planar dimension of any of the OLEDs.