Abstract: Various embodiments of mounting structures for solar photovoltaic (PV) modules and methods for constructing such mounting structures are described. A mounting structure is usable to secure PV modules in portrait orientation or landscape orientation. PV modules are secured to PV module support rails, which may be secured to purlins of a mounting structure using clamps. In some embodiments, self-adhesive grounding patches are used to establish electrical grounding paths in various embodiments of mounting structure.

Abstract: Disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. A system for performing the disclosed techniques includes receiving (RX) antennas to receive RX signals, each RX signal received over a respective resource element of a resource grid. Individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. The RX signals include a combination of a plurality of transmitted (TX) streams. The system further includes a processing device to process the RX signals using one or more neural network models to determine TX data symbols transmitted via the plurality of TX streams.

Abstract: Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of neural units using connections, where each neural unit is connected to every other neural unit either directly or indirectly to create fully connected layers within the ANN. However, by representing an artificial neural network utilizing paths from an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to fully connected layers within the ANN. More specifically, the ANN may be trained sparse from scratch in order to avoid a more expensive procedure of training the ANN and compressing it afterwards.

Abstract: Disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. A system for performing the disclosed techniques includes receiving (RX) antennas to receive RX signals, each RX signal received over a respective resource element of a resource grid. Individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. The RX signals include a combination of a plurality of transmitted (TX) streams. The system further includes a processing device to process the RX signals using one or more neural network models to determine TX data symbols transmitted via the plurality of TX streams.

Abstract: Artificial neural networks (ANNs) are computing systems inspired by the human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of artificial neurons using connections, where each artificial neuron is connected to every other artificial neuron either directly or indirectly to create fully connected layers within the ANN. By substituting ternary matrices for one or more fully connected layers within the ANN, a complexity and resource usage of the ANN may be reduced, while improving the performance of the ANN.

Abstract: Various embodiments of mounting structures for solar photovoltaic (PV) modules and methods for constructing such mounting structures are described. A mounting structure is usable to secure PV modules in portrait orientation or landscape orientation. PV modules are secured to PV module support rails, which may be secured to purlins of a mounting structure using clamps. In some embodiments, self-adhesive grounding patches are used to establish electrical grounding paths in various embodiments of mounting structure.

Abstract: Artificial neural networks (ANNs) are computing systems inspired by the human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of artificial neurons using connections, where each artificial neuron is connected to every other artificial neuron either directly or indirectly to create fully connected layers within the ANN. By substituting ternary matrices for one or more fully connected layers within the ANN, a complexity and resource usage of the ANN may be reduced, while improving the performance of the ANN.

Abstract: Hub body (16) for a composite wheel (10), in particular a composite gear wheel (12), comprising an axis of rotation (14), a first front side of the hub (20), a second front side of the hub (22), an outer lateral surface (24) arranged along the axis of rotation (14) between the first front side of the hub (20) and the second front side of the hub (22), wherein the outer lateral surface (24) has an engagement gearing (28) with at least one engagement tooth (30) as well as a convex outer lateral section (64), as well as a composite wheel (10), in particular a composite gear wheel (12), with such a hub body (16), as well as a steering unit for a motor vehicle with such a composite wheel.

Abstract: Neural network-based structures for action user equipment device detection, estimation of time-of-arrival, and estimation of carrier frequency offset utilized with the narrowband physical random-access channel of wireless communication systems. The structure includes a neural network to generate predictions of active user equipment devices, and a twin neural network to generate time-of-arrival predictions for signals from the user equipment devices and carrier frequency offset predictions for signals from the user equipment devices.

Abstract: The present invention relates to anti-tumor lymphocytes, and a variant cyclic GMP-AMP synthase (cGAS) carrying an amino acid substitution of one or both arginines at amino acid positions 255 and 236 and/or an amino acid substitution of one or both lysines at amino acid positions 254 and 258; or a nucleic acid molecule encoding said variant cGAS for use in the treatment of a tumor in a subject.

Abstract: A system for storing a sterile medicinal solution in a multiple dose container and for delivering one dose at a time of the medicinal solution to a disposable syringe, where the multiple dose container is an airless container, which consists of a stiff outer container and a contracting inner bag, into which no air enters for pressure balancing when delivering medicinal solution and fastened to the multiple dose container is an adapter with a one-way valve and a female Luer container. A disposable syringe is provided with a matching, male Luer connector, with which it may be docked with the adapter to receive a dose of the medicinal solution by pulling up the disposable syringe.

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: An apparatus configured to assess health of electronic components in vehicles. This may be achieved by simply driving a vehicle into or through a physical device or over a physical device and determining the health of the vehicle. The apparatus may be comprised of a physical enclosure with antennas and a RF emission or other electromagnetic emission signature analyzer. The antennas capture unintended electronic emissions from the vehicle which provide the electromagnetic signatures of the electronic component to the signature analyzer. The evolution of the unintended electromagnetic emissions signature as the electronics ages is measured and evaluated, providing a measure of the health of the electronics within the vehicle. The apparatus may have an ultra-sensitive RF collection portion with a sensitivity preferably of at least ?150 dBm. The RF collection portion may measure bandwidths of at least 100 MHz and may have a frequency resolution of 1 Hz or better.

Abstract: An apparatus configured to assess health of electronic components in vehicles. This may be achieved by simply driving a vehicle into or through a physical device or over a physical device and determining the health of the vehicle. The apparatus may be comprised of a physical enclosure with antennas and a RF emission or other electromagnetic emission signature analyzer. The antennas capture unintended electronic emissions from the vehicle which provide the electromagnetic signatures of the electronic component to the signature analyzer. The evolution of the unintended electromagnetic emissions signature as the electronics ages is measured and evaluated, providing a measure of the health of the electronics within the vehicle. The apparatus may have an ultra-sensitive RF collection portion with a sensitivity preferably of at least ?150 dBm. The RF collection portion may measure bandwidths of at least 100 MHz and may have a frequency resolution of 1 Hz or better.

Abstract: Artificial neural networks (ANNs) are computing systems that imitate a human brain by learning to perform tasks by considering examples. By representing an artificial neural network utilizing individual paths each connecting an input of the ANN to an output of the ANN, a complexity of the ANN may be reduced, and the ANN may be trained and implemented in a much faster manner when compared to an implementation using fully connected ANN graphs.

Abstract: Various embodiments and implementations of graph-neural-network (GNN)-based decoding applications are disclosed. The GNN-based decoding schemes are broadly applicable to different coding schemes, and capable of operating on both binary and non-binary codewords, in different implementations. Advantageously, the inventive GNN-based decoding is scalable, even with arbitrary block lengths, and not subject to typical limits with respect to dimensionality. Decoding performance of the inventive GNN-based techniques demonstrably matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while exhibiting improvements with respect to number of iterations required and scalability of the GNN-based approach. These inventive concepts are implemented, according to various embodiments, as methods, systems, and computer program products.

Abstract: One embodiment of a method for generating representations of scenes includes assigning each image included in a set of images of a scene to one or more clusters of images based on a camera pose associated with the image, and performing one or more operations to generate, for each cluster included in the one or more clusters, a corresponding three-dimensional (3D) representation of the scene based on one or more images assigned to the cluster.

Abstract: Disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. A system for performing the disclosed techniques includes receiving (RX) antennas to receive RX signals, each RX signal received over a respective resource element of a resource grid. Individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. The RX signals include a combination of a plurality of transmitted (TX) streams. The system further includes a processing device to process the RX signals using one or more neural network models to determine TX data symbols transmitted via the plurality of TX streams.