Hefei Boe Display Technology Co., Ltd.

Boe Technology Group Co., Ltd.

Beijing Xiaomi Mobile Software Co., Ltd.

Other

Emc Corporation

Beijing Boe Technology Development Co., Ltd.

Memverge, Inc.

Global Energy Interconnection Research Institute Co. Ltd

Wei Kang

Jianming Liu

Wenbo Dong

Yue Li

Jie Yu

Yue Zhao

Chaoyue Wang

Liyong Guo

Zengwei Yao

Povey Daniel

Long Lin

Fangjun Kuang

Mingshuang Luo

Xunwang Yu

Feng Wang

Chen Gong

Robert W Beauchamp

Kevin Xu

Sitaram D Pawar

Yingchao Zhou

Ning Xu

Kunwu Huang

Haiyan Wang

Peng Zhao

Huiming Wang

Peiyu Zhuang

Lu Niu

Jun Ge

Guoliang Zhao

Zhanfeng Deng

Weigang Zhong

Xingfu Zhang

Guangyao Qiao

Zhenjiang Shi

Qiuyu Shi

Xinyue He

Quandong Wang

Yuxiang Kong

Daniel Povey

Disclosed area scattering film, a display module and a display device. The scattering film includes: a scattering particle layer on a bearing layer, the scattering particle layer including a plurality of scattering particles and having a surface away from the bearing layer being a curved surface; 90% or more of the scattering particles in the scattering particle layer have a particle size larger than or equal to 5 μm and less than or equal to 30 μm.

Scattering film, display module and display device

The present disclosure provides a method and apparatus for training a neural network, and a method and apparatus for audio processing. The method includes: encoding training audio data input to an encoder network to obtain a first encoding result, and predicting a text label corresponding to the training audio data input to a prediction network to obtain a first prediction result; jointing the first encoding result with the first prediction result to obtain a first joint result; pruning the first encoding result and the first prediction result according to the first joint result to obtain a second encoding result and a second prediction result; performing a joint processing on the second encoding result and the second prediction result input to a joiner network to obtain a second joint result, and adjusting network parameters of the encoder network, the prediction network and the joiner network according to the second joint result.

Method and apparatus for training neural network, and method and apparatus for audio processing

A displaying module and a method for manufacturing the same, and a displaying, device, which relates to the technical field of displaying. The displaying module includes a liquid-crystal display panel, an adhering layer and an optical film that are arranged in layer configuration; the adhering layer is disposed between a shadow surface of the liquid-crystal display panel and the optical film, and is configured for adhesively bonding the liquid-crystal display panel and the optical film; and an orthographic projection of the adhering layer on the liquid-crystal display panel covers at least an active area of the liquid-crystal display panel, and an orthographic projection of the optical film on the liquid-crystal display panel covers at least the active area of the liquid-crystal display panel.

Displaying module and method for manufacturing the same, and displaying device

A method and apparatus for audio processing, an electronic device and a storage medium are provided. The method includes: obtaining an audio encoding result, wherein each element in the audio encoding result has a coordinate in an audio frame number dimension and a coordinate in a text label sequence dimension; in response to an output result of an iframe in a decoding path being a non-null character, respectively increasing the coordinate in the audio frame number dimension and the coordinate in the text label sequence dimension corresponding to an output position of the iframe by 1 to obtain an output position of a (i+1)frame in the decoding path; and determining an output result corresponding to the output position of the (i+1)frame according to the output result of the iframe in the decoding path and an element of the (i+1)frame in the audio encoding result.

Method and apparatus for audio processing, electronic device and storage medium

A method of training a speech recognition model is provided. The method includes that: speech data of each of a plurality of training samples is inputted into a teacher model and a to-be-trained speech recognition model separately. Additionally, an embedding outputted by the teacher model and encoded data outputted by the to-be-trained speech recognition model are obtained. Furthermore, quantized codebook data is obtained by performing a multi-codebook quantization on the embedding. A loss is calculated based on the encoded data, the quantized codebook data, and text data in the training sample. Moreover, a trained speech recognition model is obtained by stopping training the to-be-trained speech recognition model when the loss is less than or equal to a preset loss threshold and/or trained times is greater than preset trained times.

Method of training speech recognition model, electronic device and storage medium

A power electronic transformer structure includes: a support, a high-frequency transformer, a base, high-voltage side modules, and a low-voltage side module. The high-voltage side modules are respectively located at the front, top, and back of the support; the low-voltage side module is located at the bottom of the support; the high-frequency transformer is located at the middle of the support.

Power electronic transformer structure

Disclosed are a display module and a transparent display apparatus. The display module includes: a transparent light guide plate including a grid dot face and a light emergent face, and a light incident face connecting the grid dot face and the light emergent face; a side-type light source, located on the side of the transparent light guide plate where the light incident face is located; a transparent adhesive layer including a first face and a second face, with the first face being disposed in contact with the grid dot face of the transparent light guide plate, the refractive index of the transparent adhesive layer is different from the refractive index of the transparent light guide plate, and the transparent adhesive layer contains particles that scatter light; and a transparent reflecting layer, disposed in contact with the second face of the transparent adhesive layer.

Display module and transparent display apparatus

The embodiments of the present disclosure provide a liquid crystal display device. The liquid crystal display device includes: a display panel, including a display region and a non-display region surrounding the display region; a back plate, arranged opposite to the display panel, where the back plate comprises a bottom wall and a side wall extending from the bottom wall to one side of the display panel; a backlight source, arranged between the display panel and the back plate, where the backlight source comprises a light source arranged on the bottom wall of the back plate and a diffusion plate arranged on a light emitting side of the light source; and a middle frame, where the middle frame comprises a first support part, and a second support part connected with one side, deviating from the display panel, of the first support part.

Liquid crystal display device

Disclosed herein is an apparatus and method for a shuffle manager for a distributed memory object system. In one embodiment, a method includes forming a system cluster comprising a plurality of nodes, wherein each node includes a memory, a processor and a network interface to send and receive messages and data, wherein the network interface operates on remote direct memory access; creating a plurality of sharable memory spaces having partitioned data, wherein each space is a distributed memory object having a compute node, wherein the sharable memory spaces are at least one of persistent memory or DRAM cache; and storing data in an in-memory data structure when there is available memory in a compute node; and if there is an out of memory condition, serializing at least some of the in-memory data and spilling it to a distributed memory object system to persist shuffled data outside the compute node.

Shuffle manager in a distributed memory object architecture

Disclosed herein is a persistent memory (PMEM)-based distributed memory object system, referred to as the PMEM DMO system, that provides affordable means of integrating low-latency PMEM spaces with other devices, including servers that do not directly support PMEM. One embodiment comprises providing a cluster of servers with PMEM storage (PMEM servers) and connecting the PMEM servers to a plurality of applications servers using a low-latency network, such as a remote direct memory access; background processes on each of the application servers are tasked to perform input/output operations for the application servers to locally materialize objects from and synchronize/persist objects to the remote PMEM spaces on the PMEM servers. Data materialized from the PMEM servers is stored to the local cache of the application server for use. Also disclosed are data eviction policies for clearing the local cache of the application servers to make space for new data read.

Cost-effective deployments of a PMEM-based DMO system

Disclosed herein is an apparatus and method for a distributed memory object system. In one embodiment, a method includes forming a system cluster comprising a plurality of nodes, wherein each node includes a memory, a processor and a network interface to send and receive messages and data; creating a plurality of sharable memory spaces having partitioned data, wherein each space is a distributed memory object having a compute node, wherein the sharable memory spaces are at least one of persistent memory or DRAM cache; at a client, establishing an inter process communication between the client and a distributed memory object service; receiving a meta chunk including attributes about a file and a chunk map from a distributed memory object service, wherein the meta chunk includes chunk information including identity and location of a data chunk; and the client mapping the data chunk into virtual memory address space and accessing it directly.

Distributed memory object apparatus and method enabling memory-speed data access for memory and storage semantics

An online tree quota check tool computes quota usage of files in a directory tree while the structure of the directory tree keeps changing due to concurrent write access by other applications and clients. The quota check tool computes the quota usage without interfering with the ongoing file access operations that change the directory content and quota usage. A quota check database keeps track of which files have been checked and which have not, so that each file is checked and its quota usage is accumulated once and only once while directories are moved and directory scans are restarted as a result of the changing directory content. File system operations are modified to synchronize with the online tree quota check. The quota check tool can be single threaded or multi-threaded. A multi-threaded tool dispatches idle directory iteration threads to scan subdirectories in the directory tree.

Calculating quota usage without impacting file system services

Growing community of inventors

Beijing, China

Wei Kang