Abstract: The present invention relates generally to the field of microphone connectors, and more particularly to a dual connector including an analog connector and a digital port. The digital port may be positioned directly adjacent or within the analog connector. For example, a Type-C USB port may positioned directly above one or more pins of an XLR connector or between one or more pins of the XLR connector. Advantageously, the microphone may be configured to connect with a variety of host devices and may facilitate functioning as a USB-C microphone via the digital port for producing a 32-bit floating-point recording or audio stream.

Abstract: The present invention pertains to paper product devices for holding toilet paper, particularly toilet roll spindles and holders that simultaneously provide illumination in an area. Embodiments described for the present invention use a toilet paper spindle housing, one or more springs designed to hold the electrical device in place, a power source, often battery powered, one or more lights within the housing to illuminate an area around and including a toilet, an ambient light sensor to detect an ambient light level, a toilet paper slide preventer and motion sensor guard, and one or more passive infrared sensors to detect movement in an area. The embodiment may illuminate an area for a specific time duration once motion or occupancy is detected. In one embodiment, the toilet roll spindle housing attaches vertically to a freestanding base and may be comprised of a power source, a photocell sensor, and infrared sensors.

Abstract: An intravascular catheter for peri-vascular and/or peri-urethral tissue ablation includes multiple needles advanced through supported guide tubes which expand around a central axis to engage the interior surface of the wall of the renal artery or other vessel of a human body allowing the injection an ablative fluid for ablating tissue, and/or nerve fibers in the outer layer or deep to the outer layer of the vessel, or in prostatic tissue. The system may also include a means to limit and/or adjust the depth of penetration of the ablative fluid into and beyond the tissue of the vessel wall. The catheter may also include structures which provide radial and/or lateral support to the guide tubes so that the guide tubes expand uniformly and maintain their position against the interior surface of the vessel wall as the sharpened injection needles are advanced to penetrate into the vessel wall.

Abstract: The present invention provides recombinant viral segments comprising an artificial intron, DNA constructs encoding these viral segments, and recombinant viruses comprising these viral segments. Also provided are methods of making and using the recombinant viruses described herein.

Abstract: An anti-cant indicator assembly, components thereof, and associated methods. The anti-cant indicator assembly indicates the orientation of a weapon with respect to vertical. The anti-cant indicator assembly includes a mount configured to mount to the weapon, a level including a vial containing a bubble and a pivot connection connecting the level to the mount. The pivot connection is configured to permit movement of the level with respect to the mount about the pivot connection between an operational position in which the bubble can be referenced by the user for indicating orientation of the weapon with respect to vertical, and a stowed position different from the operational position. The pivot connection can include a retainer configured to releasably retain the level in at least one of the operational position or the stowed position. The level can be selectively movable with respect to the mount to calibrate the anti-cant indicator.

Abstract: A system, device and method for nail care is provided. The nail care system includes a shaping system, a polish removal system and/or a cuticle management system; a vision system; a nail polish application system; and a mobility system. The nail system may further include an accelerated drying system, a hand massage system, a nail identification/diagnosis/estimation of conditions system, an enclosure, a hand/foot rest system, a computer software system, a computer hardware system, a cartridge/pod system, and a multi-tool system. Related apparatuses, techniques and articles are also described.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input a signal received from a portable device to a machine learning program trained to output a location of the portable device relative to a vehicle, collect operating data of one or more vehicle components, predict an action of a vehicle user based on the predicted location, and, based on the predicted action of the vehicle user, actuate one or more vehicle components. The machine learning program is trained with a training dataset that is updatable to include the signal, the output predicted location, the collected operating data, the predicted action, and an identified action performed by the vehicle user.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a neural network included in a memory augmented neural network based on one or more images and corresponding ground truth in a training dataset by transforming the one or more images to generate a plurality of one-hundred or more variations of the one or more images including variations in the ground truth and process the variations of the one or more images and store feature points corresponding to each variation of the one or more images in memory associated with the memory augmented neural network. The instructions can include further instructions to process an image acquired by a vehicle sensor with the memory augmented neural network, including comparing a feature variance set for the image acquired by the vehicle sensor to the stored processing parameters for each variation of the one or more images, to obtain an output result.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input a signal received from a portable device to a machine learning program trained to output a location of the portable device relative to a vehicle, collect operating data of one or more vehicle components, predict an action of a vehicle user based on the predicted location. and, based on the predicted action of the vehicle user, actuate one or more vehicle components. The machine learning program is trained with a training dataset that is updatable to include the signal, the output predicted location, the collected operating data, the predicted action, and an identified action performed by the vehicle user.

Abstract: A speech conversion system is described that includes a hierarchical encoder and a decoder. The system may comprise a processor and memory storing instructions executable by the processor. The instructions may comprise to: using a second recurrent neural network (RNN) (GRU1) and a first set of encoder vectors derived from a spectrogram as input to the second RNN, determine a second concatenated sequence; determine a second set of encoder vectors by doubling a stack height and halving a length of the second concatenated sequence; using the second set of encoder vectors, determine a third set of encoder vectors; and decode the third set of encoder vectors using an attention block.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a neural network included in a memory augmented neural network based on one or more images and corresponding ground truth in a training dataset by transforming the one or more images to generate a plurality of one-hundred or more variations of the one or more images including variations in the ground truth and process the variations of the one or more images and store feature points corresponding to each variation of the one or more images in memory associated with the memory augmented neural network. The instructions can include further instructions to process an image acquired by a vehicle sensor with the memory augmented neural network, including comparing a feature variance set for the image acquired by the vehicle sensor to the stored processing parameters for each variation of the one or more images, to obtain an output result.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive an image including a physical landmark, output a plurality of synthetic images, wherein each synthetic image is generated by simulating at least one ambient feature in the received image, generate respective feature vectors for each of the plurality of synthetic images, and actuate one or more vehicle components upon identifying the physical landmark in a second received image based on a similarity measure between the feature vectors of the synthetic images and a feature vector of the second received image, the similarity measure being one of a probability distribution difference or a statistical distance.

Abstract: A computing system can determine a vehicle command based on a received spoken language command and determined confidence levels. The computing system can operate a vehicle based on the vehicle command. The computing system can further determine the spoken language command by processing audio spectrum data corresponding to spoken natural language with an automatic speech recognition (ASR) system.

Abstract: Systems, methods, and devices for speech transformation and generating synthetic speech using deep generative models are disclosed. A method of the disclosure includes receiving input audio data comprising a plurality of iterations of a speech utterance from a plurality of speakers. The method includes generating an input spectrogram based on the input audio data and transmitting the input spectrogram to a neural network configured to generate an output spectrogram. The method includes receiving the output spectrogram from the neural network and, based on the output spectrogram, generating synthetic audio data comprising the speech utterance.

Abstract: A computing system can translate a spoken natural language command into an intermediate constructed language command with a first deep neural network and determine a vehicle command and an intermediate constructed language response with a second deep neural network based on receiving vehicle information. The computing system can translate the intermediate constructed language response into a spoken natural language response with a third deep neural network and operate a vehicle based on the vehicle command.

Abstract: A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.

Abstract: A speech conversion system is described that includes a hierarchical encoder and a decoder. The system may comprise a processor and memory storing instructions executable by the processor. The instructions may comprise to: using a second recurrent neural network (RNN) (GRU1) and a first set of encoder vectors derived from a spectrogram as input to the second RNN, determine a second concatenated sequence; determine a second set of encoder vectors by doubling a stack height and halving a length of the second concatenated sequence; using the second set of encoder vectors, determine a third set of encoder vectors; and decode the third set of encoder vectors using an attention block.

Abstract: A computing system can translate a spoken natural language command into an intermediate constructed language command with a first deep neural network and determine a vehicle command and an intermediate constructed language response with a second deep neural network based on receiving vehicle information. The computing system can translate the intermediate constructed language response into a spoken natural language response with a third deep neural network and operate a vehicle based on the vehicle command.

Abstract: A computing system can determine a vehicle command based on a received spoken language command and determined confidence levels. The computing system can operate a vehicle based on the vehicle command. The computing system can further determine the spoken language command by processing audio spectrum data corresponding to spoken natural language with an automatic speech recognition (ASR) system.

Abstract: A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.