Abstract: A method of wireless communication includes transmitting, upon determining that a wireless device operating in a multiple transmission reception point wireless configuration with a network device satisfies a condition, one or more uplink control transmissions by the wireless device using one or more of precoding matrices, wherein the one or more precoding matrices are based on a configuration information received from a network device.

Abstract: A system includes a memory device having a plurality of data blocks and a processing device, the processing device to perform operations identifying an erase operation being performed on a first portion of a plurality of data blocks. The operations further include determining a first rate of performance of the erase operation being performed on the first portion of the plurality of data blocks, identifying a write operation being performed on a second portion of the plurality of data blocks, and determining a second rate of performance of the write operation being performed on the second portion of the plurality of data blocks. The operations further include determining whether the second rate of performance corresponds to the first rate of performance and responsive to the second rate of performance not corresponding to the first rate of performance, adjusting the second rate of performance.

Abstract: A computer-implemented method is provided of using a machine learning model for disentanglement of prosody in spoken natural language. The method includes encoding, by a computing device, the spoken natural language to produce content code. The method further includes resampling, by the computing device without text transcriptions, the content code to obscure the prosody by applying an unsupervised technique to the machine learning model to generate prosody-obscured content code. The method additionally includes decoding, by the computing device, the prosody-obscured content code to synthesize speech indirectly based upon the content code.

Abstract: The present disclosure provides a collision prediction system and method. The method may include obtaining a target model of a target device. The method may also include determining first posture information of the target model and second posture information of an operating apparatus, wherein the target device is set on an end of the operating apparatus. The method may further include generating a collision prediction between the target model and the operating apparatus based on the first posture information and the second posture information, the collision prediction indicating whether there is a space accommodating the target device during an operation of the operating apparatus.

Abstract: A display panel and a mobile terminal are disclosed. The display panel comprises a substrate, a display function layer disposed on the substrate, a first blocking structure surrounding the display function layer, the first blocking structure comprising a transition area, a first inorganic layer at least covering the first blocking structure and the display function layer, an organic layer at least covering the display function layer and the first blocking structure, and a second inorganic layer covering the organic layer; wherein a tightening structure is disposed between the organic layer and the second inorganic layer in the transition area.

Abstract: A polymer ultrafiltration membrane with a bicontinuous highly interconnected porous structure, a preparation method and applications thereof are provided. The ultrafiltration membrane has a bottom layer and a polymer layer. The polymer layer is divided into a sublayer and a surface layer. The surface layer is of a uniform small pore structure with a narrow pore size distribution. The sublayer is of a bicontinuous highly interconnected three-dimensional network porous structure. The bicontinuous highly interconnected porous structure of the bicontinuous highly interconnected porous ultrafiltration membrane is characterized as follows: in the thickness direction of the sublayer, the cross-sectional porosity on any XY cross-section perpendicular to the thickness direction is 40-90%, preferably 60-90%, and further preferably 70-90%; and the difference in the cross-sectional porosities between any two XY cross-sections does not exceed 10%, preferably not exceed 8%, and also preferably not exceed 5%.

Abstract: Provided are an environmental sound pass-through method and apparatus applied to VR, a device and a storage medium. The method includes the steps below. Environmental audio information of an ambient environment is acquired by a microphone on a VR device based on a see-through mode of the VR device. The environmental audio information is located to determine sound source position information according to the time difference of the environmental audio information reaching microphones on the VR device, and a filtering process is performed on the environmental audio information to determine filtered audio information. Directional pickup is performed on the filtered audio information to determine to-be-processed audio information according to the sound source position information. A signal amplitude and pass-through loudness of the to-be-processed audio information are adjusted to determine target audio information, and the target audio information is played by a speaker of the VR device.

Abstract: A method, computer system and computer program product is presented for providing a self-supervised speech representation. In one embodiment, audio input is received including speech utterances. A label sequence is generated from these speech utterances by a teacher label generator. A speech representation is generated of a partially masked version of the speech utterance using a speech representation network. The speech utterance is passed into two random transformations that alter only speaker information prior to the partial masking. A predictor will then predict the label sequence. In one embodiment performance-based assessment is made on a cross-entropy loss between the generated label sequence and a predicted label sequence.

Abstract: Embodiments of a system, device and method for configuring and indicating a codebook are disclosed. In some aspects, a wireless communication device may receive from a wireless communication node, a signaling, the signaling indicating to use at least two codebook-related factors to generate a first codebook for at least 4 antenna ports. The wireless communication device may generate the first codebook using the at least two codebook-related factors.

Abstract: A system includes a hardware processor, a system memory storing a software code, and a machine learning (ML) model trained using style loss to predict image noise. The hardware processor is configured to execute the software code to receive a clean image and at least one noise setting of a camera used to capture a version of the clean image that includes noise, and provide the clean image and the at least one noise setting as a noise generation input to the ML model. The hardware processor is further configured to execute the software code to generate, using the ML model and based on the noise generation input, a synthesized noise map for renoising the clean image.

Abstract: An embodiment of the present disclosure provides an instruction sending method and apparatus and relates to the technical field of computers. The method includes: switching an extended page table (EPT) from a first EPT to a second EPT, in response to an instruction sending request for sending a target instruction to a second processor; obtaining a target code according to the second EPT, the target code being a code related to sending the target instruction; executing the target code, to make the first processor sends the target instruction to the second processor. The embodiment of the present disclosure is used to avoid an inter-processor instruction from being utilized by an attacker, and meanwhile, avoid performance of a virtual machine from being greatly influenced when an instruction is sent.

Abstract: Presented are systems, methods, apparatuses, or computer-readable media for configuring reference signaling. A wireless communication device may determine a set of resources. A number of resources in the set may be equal to or more than one and the set satisfies a character. Each of the resources may be associated with a respective value of information.

Abstract: A light-emitting substrate includes: a substrate; and at least one coupling portion and at least one auxiliary cathode pattern disposed on the substrate. Each auxiliary cathode region is provided with a coupling portion and an auxiliary cathode pattern coupled to the coupling portion. The auxiliary cathode pattern includes at least one disconnection portion, and each disconnection portion includes first, second and third conductive pattern layers. An orthographic projection of an edge of the second conductive pattern layer on the substrate is located within orthographic projections of edges of the third and first conductive pattern layers on the substrate. A total perimeter of at least one orthographic projection, on the substrate, of at least one edge of at least one third conductive pattern layer in the at least one disconnection portion is greater than a perimeter of an orthographic projection of an edge of the auxiliary cathode region on the substrate.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.

Abstract: Disclosed is a method and device for automatic detection of vessel draft depth, which processes the image of a vessel's hull and extracts local area image blocks with vessel's water gauge scale separately to improve the pertinence of data processing and reduce the complexity of data processing; and based on a multi-task learning network model, performing data processing on local area image blocks to extract scale characters and waterline position, reducing the computational complexity of the model; finally, based on the relative positions of the scale and waterline, determining the vessel's draft depth, thus achieving automatic acquisition of the vessel's draft depth, this method greatly improves the efficiency of reading the vessel's draft depth.

Abstract: The present invention relates to a method for carbon dioxide capture and concentration by partitioned multistage circulation based on mass transfer-reaction regulation. In the present invention, multiple means such as multistage circulating absorption, intelligent multi-factor regulation, pre-washing and cooling, inter-stage cooling, post-stage washing, slurry cleaning, cooling water waste heat utilization, small-particle-size and high-density spraying, external strengthening field such as a thermal field/ultrasonic field/electric field, and catalysis by composite catalyst are adopted, so that the target for low cost, low energy consumption, stability and high efficiency is realized. The secondary pollutants are effectively inhibited while carbon dioxide is efficiently captured; meanwhile, high-efficiency capture, low-energy desorption, and high-purity concentration of carbon dioxide are implemented.

Abstract: Dynamic snapshot scheduling techniques are provided using storage system metrics. One method comprises obtaining a schedule for generating snapshots of a portion of a storage system; automatically adjusting snapshot generation parameters in the schedule based on: (i) a current storage pool usage metric, (ii) an input/output metric of at least one storage resource in the portion of the storage system, (iii) a measure of snapshots in a destroying state, and/or (iv) a measure of a number of created snapshots; and initiating a generation of a snapshot of the storage system portion in accordance with the adjusted schedule. A snapshot generation frequency may be increased in response to an increase of: the current storage pool usage metric, the number of snapshots in the destroying state, and/or the number of created snapshots. A snapshot generation frequency may be decreased in response to an increase of the I/O metric of the at least one storage resource.

Abstract: Disclosed are methods apparatuses and computer readable media. In one aspect, a method includes receiving a rank indication or a first index associated with the rank indication, wherein the rank indication is used to determine G rank values, wherein G is a positive integer, and receiving a matrix indication or a second index associated with the matrix indication, wherein the matrix indication is used to determine a basic matrix. The method further includes determining a precoding matrix for a transmission according to at least one of the G rank values or the basic matrix.

Abstract: Techniques are described for performing physical uplink shared channel (PUSCH) transmission. An example wireless communication method includes performing, by a communication device, an uplink transmission, where the uplink transmission is performed using a precoder, where the precoder is based on one or more sounding reference signal (SRS) resources, one or more ranks, and/or one or more precoding matrix indicators, and where one precoding matrix indicator of the one or more precoding matrix indicators indicates a precoding matrix for a rank.

Abstract: A method includes: creating a training data set based on user input, the training data set including time series data of a price of an asset and stochastic event data of events related to the asset; creating an event intensity model that models an event intensity parameter of one of the events related to the asset, wherein the event intensity model comprises a proximal graphical event model (PGEM), and the creating the event intensity model includes learning parameters of the PGEM using machine learning and the training data set; creating a probabilistic time series model that predicts a probability distribution of a return of the asset, wherein the creating the probabilistic time series model includes learning parameters of the probabilistic time series model using machine learning and the training data set; and predicting a future return of the asset for a future time period using the probabilistic time series model.