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: A system and a method are disclosed where a server determines parameters for establishing a remote online notarization session, the remote online notarization session including participants, the participants including at least a signer and a notary. The server establishes the session according to the parameters, and monitors, during the session, information from the signer client device and the notary client device. The server detects a failure from the information based on at least one of the signer client device and the notary client device having failed to comply with a parameter of the parameters. Responsive to detecting the failure, the server pauses the remote online notarization session and outputting a selectable cure option to at least one of the participants that, when selected initiates a workflow that, when concluded, cures the failure and enables the remote online notarization session to proceed.

Abstract: Devices and techniques are generally described for anomalous computer activity detection. In various examples, first computer activity data associated with a first account may be determined. A first linear detection event that corresponds to the first computer activity data may be determined. In some examples, a set of gradient-based data associated with the first linear detection event may be determined. The set of gradient-based data may represent comparative analysis of the first computer activity data with computer activity data of other accounts. In some examples, first data representing the first linear detection event and the set of gradient-based data may be generated. In various cases, network access for the first account may be disabled based on the first data.

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: A method for producing ethylene and/or other olefins by steam cracking includes charging one or more crackers with a paraffin-containing feed and withdrawing a crude gas from the one or more crackers. The crude gas is subjected at least in part to a treatment comprising a crude gas compression and a thermal separation using a C2 refrigerant and a C3 refrigerant. A crude gas compressor is used for the crude gas compression, wherein the ethylene refrigerant is compressed using a C2 refrigerant compressor. The propylene refrigerant is compressed using a C3 refrigerant compressor. The crude gas compressor comprises two serial compressor trains. The compressor trains, the C2 refrigerant compressor and the C3 refrigerant compressor are each operated at least in part using electrical drives, which have at least partially identical performance features, are provided as structurally identical variable-speed drives, and are each fed via frequency converters.

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.

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: 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: 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: An example method of correcting a defect in a seat comprises the steps of: (a) imaging a seat to obtain an image of the seat, a portion of the seat having a defect; (b) applying a localized boundary around the portion of the seat having the defect within the image of the seat; (c) translating the localized boundary into a seat specific map of a baseline model of the seat such that a portion of the baseline model of the seat that corresponds to the portion of the seat having the defect within the image of the seat is disposed within a translated localized boundary; (d) selecting a predetermined path for an automated device to correct the portion of the seat having the defect based on the portion of the baseline model of the seat; and (e) correcting the portion of the seat having the defect.

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: An example method of correcting a defect in a seat comprises the steps of: (a) imaging a seat to obtain an image of the seat, a portion of the seat having a defect; (b) applying a localized boundary around the portion of the seat having the defect within the image of the seat; (c) translating the localized boundary into a seat specific map of a baseline model of the seat such that a portion of the baseline model of the seat that corresponds to the portion of the seat having the defect within the image of the seat is disposed within a translated localized boundary; (d) selecting a predetermined path for an automated device to correct the portion of the seat having the defect based on the portion of the baseline model of the seat; and (e) correcting the portion of the seat having the defect.

Abstract: The invention relates to a dispenser 20 for the spray delivery of a preparation 18. The dispenser comprises: a reservoir 22 suitable for containing the preparation 18, a mechanical pump 24, a load chamber 26, a nozzle 28 having a delivery axis, and a delivery cone 30 having an axis. The mechanical pump is able, whenever operated: to take a predetermined amount of preparation from the reservoir, to supply said amount of preparation to the load chamber upstream of the nozzle, and to create a delivery pressure in the load chamber. The nozzle is able to deliver said amount of preparation in the form of an aerosol spray 180. The dispenser according to the invention is characterized in that the delivery cone comprises, in the proximity of the nozzle, at least one through-hole 320 suitable for putting the inside of the delivery cone into communication with the surrounding environment.

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: 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.