Abstract: Disclosed is a display device which includes pixel drivers, each of which includes a first transistor switched by a voltage of a node and electrically connected between a second electrode and a second power supply line, a second transistor electrically connected between the node and a data line and switched by a write scan signal, and a capacitor including a first capacitor electrode electrically connected to the node and a second capacitor electrode electrically connected between the second power supply line and the first transistor. First electrodes of light emitting elements overlap each of the first capacitor electrodes of the pixel drivers.

Abstract: Various embodiments include a software module deployment method comprising: acquiring a self-description file of at least one edge device with a management device, the self-description file at least describing a software running environment and hardware resource information of the edge device; deploying a software module mirror image on a suitable edge device and establishing a connection relationship between different software module instances deployed on the edge device; automatically allocating, with the management device, a port number to be used when communication is actually performed to each software module instance deployed on the edge device; and generating a configuration file according to the software module instance deployed on each edge device, the connection relationship between different software module instances on the edge device, and the port number.

Abstract: An encoding method, a decoding method and a decoder are provided. The method includes that: a bitstream is parsed to determine reconstructed feature data; and feature analysis is performed on the reconstructed feature data by using an intelligent task network to determine a target result.

Abstract: A method of video decoding includes that: a decoder inputs a feature bitstream of a current picture into a decoding network and obtains first feature information outputted by an i-th middle layer of the decoding network, wherein i is a positive integer; and the decoder inputs the first feature information into a j-th middle layer of a task analysis network and obtains a task analysis result outputted by the task analysis network, wherein j is a positive integer. Provided are also a method for video encoding and a video decoder.

Abstract: A method including receiving an input comprising natural language texts; segmenting the natural language texts into sections; summarizing the natural language texts; developing a first model based on the plurality of sections and the summary of the natural language texts; identifying one or more salient sentences within the natural language texts using the first model; determining a sentence quality score based on how informative a salient sentence is; determining a sentence similarity score based on a salient sentence's similarity to another salient sentence; developing a second model based on the sentence quality score and the sentence similarity score; combining the first model and the second model into a final model; selecting sentences based on the final model; and generating an extractive summarization using the selected sentences.

Abstract: A method including: receiving an input comprising natural language texts; selecting, via a knowledge selector, one of a plurality of knowledge categories from an external memory based on a context of the input; retrieving one or more helpful knowledge pieces from the selected knowledge category; augmenting the input using the one or more helpful knowledge pieces; feeding the augmented input into a text-to-text model; and generating an output answer based on the text-to-text model.

Abstract: There is included a method and apparatus comprising computer code for instance-wise adaptive knowledge injection in a pre-trained language model (PTLM) including determining a necessity of external knowledge in a plurality of queries of a first dataset based on a likelihood that a respective query is solved by internal knowledge of a target model. Then, the one or more queries determined to need external knowledge may be augmented with pieces of external knowledge. A combined dataset may be generated by combining the first dataset and the one or more augmented queries, and the combined dataset may be applied to the target model.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

Abstract: This application relates to a speech synthesis method and apparatus, a model training method and apparatus, and a computer device. The method includes: obtaining to-be-processed linguistic data; encoding the linguistic data, to obtain encoded linguistic data; obtaining an embedded vector for speech feature conversion, the embedded vector being generated according to a residual between synthesized reference speech data and reference speech data that correspond to the same reference linguistic data; and decoding the encoded linguistic data according to the embedded vector, to obtain target synthesized speech data on which the speech feature conversion is performed. The solution provided in this application can prevent quality of a synthesized speech from being affected by a semantic feature in a mel-frequency cepstrum.

Abstract: A method including: receiving an input comprising natural language texts at an encoder; adding a token to the input; obtaining a last-layer hidden state as a natural language text representation; feeding the natural language text representation into a single-layer classification head; predicting a salience allocation based on the single-layer classification head; developing a salience-aware cross-attention (SACA) decoder to determine salience in the natural language text representation; mapping a plurality of salience degrees to a plurality of trainable salience embeddings; estimating an amount of signal to accept from the plurality of trainable salience embeddings; incorporating the salience allocation and the signal in a cross-attention layer model; and generating a summarization based on the SACA decoder and the cross-attention layer model.

Abstract: A method for zero-shot event extraction, performed by a computer device. The method includes training a context encoder, a first definition encoder, and a second definition encoder with auto extracted context-definition alignment data; retrieving a plurality of verbal synsets from a lexical database; refining a representation model based on the context-definition alignment data and the plurality of verbal synsets; encoding a plurality of candidate event type definitions; encoding the refined representation model with the trained context encoder; and determining whether the encoded representation model belongs to one of the plurality of candidate event type definitions based on a cosine similarity between the encoded representation model, the trained context encoder, the first trained definition encoder, and the second trained definition encoder.

Abstract: Provided herein are RNA that: are collectively self-amplifying in an intracellular environment, comprise N1-methyl-pseudouridines and uridines, and have a mole percentage or mole proportion of the N1-methylpseudouridines to the total of the uridines and the Ni-methylpseudouridines or a mole ration of the N1-methylpseudouridines to the uridines.

Abstract: A method performed by at least one processor includes receiving a first input stream of a task and a second input stream of a solution. The method further includes selecting the first input stream or the second input stream. The method further includes providing the selected input stream to an image conversion model and a language model. The method further includes creating, based on the selected input stream, a model ensemble of the conversion model and the language model. The method further includes outputting a prediction based on the model ensemble. The method may further include generating an image corresponding to text, converting a textual task into a multimodal task, and solving the multimodal task.

Abstract: A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

Abstract: A mixed speech recognition method, a mixed speech recognition apparatus, and a computer-readable storage medium are provided. The mixed speech recognition method includes: monitoring an input of speech input and detecting an enrollment speech and a mixed speech; acquiring speech features of a target speaker based on the enrollment speech; and determining speech belonging to the target speaker in the mixed speech based on the speech features of the target speaker. The enrollment speech includes preset speech information, and the mixed speech is non-enrollment speech inputted after the enrollment speech.

Abstract: An electronic device includes a first part and a second part that can be folded or expanded relative to each other. The electronic device has a closed state and an open state When the electronic device is in the closed state, a frame of the first part and a frame of the second part partially or totally overlap. The first part includes a first feeding antenna. The second part includes a first parasitic antenna, and when the electronic device is in the closed state, the first parasitic antenna is not grounded and can be coupled to the first feeding antenna.

Abstract: Methods and apparatuses are provided for performing sequence to sequence (Seq2Seq) speech recognition training performed by at least one processor. The method includes acquiring a training set comprising a plurality of pairs of input data and target data corresponding to the input data, encoding the input data into a sequence of hidden states, performing a connectionist temporal classification (CTC) model training based on the sequence of hidden states, performing an attention model training based on the sequence of hidden states, and decoding the sequence of hidden states to generate target labels by independently performing the CTC model training and the attention model training.

Abstract: An unsupervised text to speech system utilizing a lexicon to map input text to the phoneme sequence, which is expanded to the frame-level forced alignment with a speaker-dependent duration model. An alignment mapping module that converts the forced alignment to the unsupervised alignment (UA). Afterword, a Conditional Disentangled Sequential Variational Auto-encoder (C-DSVAE), serving as the self-supervised TTS AM, takes the predicted UA and a target speaker embedding to generate the mel spectrogram, which is ultimately converted to waveform with a neural vocoder.

Abstract: An antenna apparatus used in an electronic device having a flexible display, the flexible display can be bent at a rotating shaft, the flexible display includes a primary screen and a secondary screen respectively configured on two sides of the rotating shaft. The antenna apparatus may include a first metal strip disposed on the primary screen frame close to one end of the rotating shaft, and a second metal strip disposed on the secondary screen frame close to the same end of the rotating shaft. The first metal strip may be implemented as a plurality of antennas through dual-feed design. When the flexible display is in a folded state, the second metal strip may be coupled to the first metal strip to generate radiation. In this case, the second metal strip may be used as a parasitic antenna of the first metal strip.

Abstract: The present disclosure relates to a composite logic gate circuit, including: a simple logic gate circuit including a first logic gate circuit and an inverter circuit, a first PMOS transistor, and a first NMOS transistor. The first logic gate circuit is configured to receive a first input signal and a second input signal, and to output a first output signal. The inverter circuit includes a second PMOS transistor and a second NMOS transistor. A source of the second PMOS transistor is coupled to a power input terminal, a drain is coupled to a drain of the second NMOS transistor, and a gate is configured to receive the first output signal. A source of the second NMOS transistor is coupled to a ground terminal, and a gate is configured to receive the first output signal. A source of the first PMOS transistor is coupled to the drain of the second PMOS transistor, a drain is coupled to a drain of the first NMOS transistor, and a gate is configured to receive a third input signal.