Abstract: An electronic device including an antenna structure is provided. A side frame between a first position and a second position of the electronic device is configured as a first radiator of the antenna structure. A side frame between the second position and a third position of the electronic device is configured as a second radiator of the antenna structure. A first slot is provided at the second position of the side frame. A second slot is formed between the second radiator and a ground. The first radiator extends in a first direction, and is spaced from the third radiator in a second direction. Projections of the third radiator and the first radiator in the second direction at least partially overlap. A first feed point is disposed on the third radiator or the first radiator, and the first feed point is configured to feed the antenna structure.

Abstract: An electronic device and an antenna switching method provide a first antenna, a second antenna, and a controller. When the electronic device is in different communication states, the controller may set some radiators as parasitic branches based on the respective different communication states.

Abstract: A method includes receiving an input audio waveform; inputting the input audio waveform into a variational autoencoder (VAE) to generate a latent representation of the input audio waveform; generating a modified latent representation of the input audio waveform by inputting the latent representation of the input audio waveform and a text prompt into a latent diffusion model; and generating a modified audio waveform by inputting the modified latent representation of the input audio waveform into a VAE decoder.

Abstract: An antenna apparatus includes a first branch, a frame branch, and a second branch. The frame branch is provided with a first gap, and the frame branch is divided into a first frame branch and a second frame branch by the first gap. The first branch, the second branch, the first frame branch and the second frame branch are conductive, not in contact with each other and are insulated from each other A first feeding circuit is electrically connected to the second frame branch, configured to transmit a first excitation signal to the second frame branch, and excite the second frame branch to radiate a first radio wave. A second feeding circuit is electrically connected to the second branch, configured to transmit a second excitation signal to the second branch, and excite the second branch to radiate a second radio wave.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive multiple audio signals obtained from ones of a plurality of microphones of a microphone array, implement an audio zooming based on the audio signals by selectively focusing and enhancing first ones of the audio signals and by attenuating other ones of the audio signals, and control an output of audio based on the audio zooming, and the audio zooming includes a consolidating of a plurality of directional features of the first ones of the audio signals within a field around the microphone array and a countering based on determining directional aspects of the other ones of the audio signals from outside of the field.

Abstract: A chip system includes: a memory die; and at least two non-memory dies, including a first non-memory die and a second non-memory die. The first non-memory die includes a first controller, the second non-memory die includes a second controller, and the first controller and the second controller are configured to control access to the memory die.

Abstract: A method includes inputting an input text sequence into a language model to generate a plurality of speech samples; applying one or more preference models to the plurality of speech samples to generate a set of preferred samples and a set of non-preferred samples; applying direct performance improvement optimization to preference data associated with the set of preferred samples and the set of non-preferred samples to generate optimized samples; and training the language model based on the optimized samples.

Abstract: A method includes inputting a mixture signal comprising speech, background noise, and reverberation into a denoising stage that generates a denoised mixture signal with reduced noise compared to the mixture signal; fusing the denoised mixture signal with the mixture signal to generate a fused signal; and inputting the fused signal into a dereverberation and restoration (DR) stage to generate an output signal with reduced reverberation compared to the mixture signal.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to obtain an audio signal from a channel of at least one microphone of a plurality of microphones, estimate a Kalman gain based on the audio signal, share the Kalman gain to a plurality of channels of other ones of the plurality of microphones, and output an AHS signal from the channel and the plurality of channels, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Abstract: The present disclosure is related to biomedicine, and specifically relates to a PCSK9-targeting RNAi agent and use thereof. The present disclosure significantly enhances the inhibitory effect of RNAi agents on PCSK9 by optimizing the modification method, thereby offering improved application potential for the prevention or treatment of PCSK9-related diseases.

Abstract: This application provides a method and apparatus, a device, and a storage medium for data processing and LLM training. The method includes: extracting raw data representing world knowledge from an information domain comprising websites, the raw data comprising a set of data elements, and each data element in the set of data elements comprising at least one of: a text portion; an image portion; an audio portion; or a video portion; using the each data element in the set of data elements as input, instructing a first LLM to infer a seed persona corresponding to the each data element; and storing the seed persona into a persona set comprising at least one persona.

Abstract: Method, apparatus, and non-transitory storage medium for hybrid acoustic howling suppression based on a frequency filter model and a deep neural network are provided. The method may include receiving a speech signal, the speech signal including target speech, feedback, and noise, and inputting the speech signal into a trained hybrid neural-network based howling suppression model, wherein the trained hybrid neural-network based howling suppression model is trained using training speech signal and pre-processed acoustic feedback from a first frequency filter model. The method may also include generating an enhanced speech signal with suppressed howling as an output of the trained hybrid neural-network based howling suppression model, wherein the enhanced speech signal is used to update parameters of the first frequency filter model.

Abstract: A foldable electronic device includes an antenna, where the antenna uses a first side frame and a second side frame that are disposed in the foldable electronic device as radiators. A part of the first side frame is used as a feed stub, and a part of the second side frame is used as a parasitic stub. A gap is provided on the parasitic stub, so that a radiation aperture of the parasitic stub is increased.

Abstract: Electronic devices and antenna structures are described. An example electronic device includes an antenna structure, wherein the antenna structure includes: a first radiator and a second radiator, wherein the first radiator includes a first end and a second end, and the second radiator includes a first end and a second end. The first end of the first radiator and the first end of the second radiator face each other and are not in contact with each other, and a slot is formed between the first end of the first radiator and the first end of the second radiator. The first radiator and the second radiator are disposed in a bent manner, and a spatial region formed between the first radiator, the second radiator, and the slot is T-shaped; and the second end of the first radiator is grounded, and the second end of the second radiator is grounded.

Abstract: A method and apparatus comprising computer code configured to cause a processor or processors to receive a text comprising a plurality of sentences, by a machine learning model, extract a nonverbal message from one of the sentences and add an annotation to the text, the annotation indicating the nonverbal message, and output a version of the text including the annotation.

Abstract: A method includes receiving, by an autopilot system, a user query; determining, by a perception kernel of the autopilot system, one or more states associated with the user query; executing, by a reasoning kernel of the autopilot system, one or more actions associated with the one or more states; and completing, by the autopilot system, execution of a task associated with the user query.

Abstract: A method includes receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker.

Abstract: Methods and devices to efficiently normalize text by processing inputted text based on a text normalization model that includes processing the input text in a first stage including a statistical model as a first output, processing the first output in a second stage including a rule based model as a normalized text, and outputting the normalized text.

Abstract: A method, system, apparatus, and computer-readable medium for voice conversion using a conditional disentangled sequential variational auto-encoder (C-DSVAE) is provided. The method, performed by at least one processor, includes receiving input speech segments, encoding the input speech segments via a shared encoder to generate a speaker embedding and a content embedding, and encoding a posterior distribution of the speaker embedding via a speaker encoder and encoding a posterior distribution of the content embedding via a content encoder to obtain encoded results. The method further includes enabling a content bias, reshaping the content embedding using the content bias, and generating a reconstructed speech output based on the encoded results and the reshaped content embedding.

Abstract: The present disclosure describes various methods, systems, and storage medium for training a language model to obtain an iterative Nash policy optimized (INPO) language model. The method includes initializing a first language model by a reference language model; for N-th iteration with N starting from 1 to M, generating a plurality of responses using the N-th language model for each prompt in a plurality of prompts, and constructing a preference dataset using a preference oracle based on the plurality of responses for each prompt, wherein the preference dataset comprises a winning response and a losing response; training the N-th language model to obtain a (N+1)-th language model by minimizing a value of an INPO function for all preference dataset for the plurality of prompts, wherein the INPO function comprises an expectation term, a regularization term, and a modification term; and outputting the (M+1)-th language model.