Abstract: An electronic device, a method of determining a memory access efficiency for a memory, and a storage medium are provided, which relate to a field of computer technology, and in particular to fields of chip, memory and processor technologies. The electronic device includes: a memory configured to store executable instructions and data to be processed; and a processor configured to execute the executable instructions so as to at least: read a data block to be tested from the memory; determine a memory access description information according to a size information of the data block to be tested; and determine, according to the memory access description information and a channel description information, a memory access efficiency of the processor in reading the data block to be tested, where the channel description information describes a plurality of channels for the processor to read the data to be processed from the memory.

Abstract: Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.

Abstract: A power supply circuit of a terminal device includes a voltage boost circuit, a battery chip, and a controller. The battery chip is configured to provide an output voltage of a battery for a load. An input end of the voltage boost circuit is connected to a positive electrode of the battery, and an output end of the voltage boost circuit is connected to the load. The output voltage of the battery is boosted when the temperature is relatively low or the output voltage of the battery is relatively low. In addition, when a relatively high pulse current occurs on the terminal and the output voltage of the voltage boost circuit is stepped down, both the battery chip and the voltage boost circuit is controlled to supply power to the load.

Abstract: The application discloses a terminal and a Type C interface anti-corrosion method. In the terminal, a processor is separately connected to a motion sensor and an interface chip. The interface chip is separately connected to the processor and a CC pin in a first Type C interface. The motion sensor is configured to monitor a motion status of the terminal, and the processor is configured to control, according to the motion status of the terminal, the interface chip to configure the CC pin of the first Type C interface to be in a low-level mode when it is determined that the motion status of the terminal changes from a moving state to a static state. When the motion status of the terminal changes from the moving state to the static state, the change in the motion status of the terminal is that the terminal is disconnected from an external device.

Abstract: A display substrate, a manufacturing method thereof, and a display device are provided. A pixel region is provided with a light emission function layer on a base substrate of the display substrate, and a separation region is provided with at least one first barrier structure. The first barrier structure includes a stopper pattern and a first separation component. A side surface of the first separation component has a recess, and a portion of the light emission function layer extending to the separation region is disconnected on the side of the first separation component. The separation region is provided with an inorganic layer structure on the base substrate. The inorganic layer structure includes multiple stacked inorganic film layers, the stopper pattern is located between two adjacent inorganic film layers and the first separation component is located on a side of the inorganic layer structure away from the base substrate.

Abstract: An interaction method, an interaction apparatus, and an electronic device are provided. The method includes: determining, in response to detecting a predefined size transformation operation, target transformation information of a target video based on whether a current size of the target video is a preset anchor point size, where the target video is a video played in a video play area; and transforming the target video based on the target transformation information, and playing the transformed target video.

Abstract: A display panel, a display module and a manufacture method thereof, and a communication device are provided. The display panel includes a display substrate and a multiplexing circuit arranged on a first surface of the display substrate. The display panel further includes a wave-absorbing material layer arranged on a second surface of the display substrate. The wave-absorbing material layer is configured to absorb electromagnetic wave interference signals. The second surface is a surface opposite to the first surface.

Abstract: Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models.

Abstract: Systems and methods for detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates. A repository of CT scans may be used for training of deep neural networks and additional data may be drawn from localized patient information from institutions and hospitals which grant permission.

Abstract: A millimeter-wave real-time imaging based safety inspection system and safety inspection method. The safety inspection system includes a conveying device (10), a millimeter wave transceiver module (11), an antenna array (17, 18), a switch array (16a, 16b), a switch control unit (15a, 15b), a quadrature demodulation and data acquisition module (12), and an image display unit (13). By using an Inverse Synthetic Aperture Radar (ISAR) imaging principle, the millimeter-wave real-time imaging based safety inspection system performs real-time imaging on an object to be inspected when the object moves, so that not only the imaging speed is improved, but also the field of view is enlarged. A safety inspector can determine whether an inspected person carries dangerous goods by observing a three-dimensional diagram of the inspected person, thereby eliminating the inconvenience caused by back-and-forth movement of a safety inspection device used by the safety inspector around the inspected person.

Abstract: The present disclosure provides a method for processing a task, a processor, a device and a readable storage medium, and the method comprises: for a predetermined type of computing task, allocating a plurality of instruction blocks in the computing task to a general-purpose processing core and a dedicated acceleration core; transferring, by a control unit in the dedicated acceleration core, an instruction completion indication of a predetermined co-processing unit coupled thereto to at least one processing unit of the general-purpose processing core through a signal path, the signal path being configured to couple the at least one general-purpose processing unit to the control unit; and if it is determined that the instruction completion indication is received, acquiring, by the general-purpose processing core, data from a first on-chip cache in the dedicated acceleration core through a data path for completing the computing task.

Abstract: A method and apparatus for executing an instruction are provided. In the method, an instruction queue is first generated, and an instruction from the instruction queue in preset order is acquired. Then, a sending step including: determining a type of the acquired instruction; determining, in response to determining that the acquired instruction is an arithmetic instruction, an executing component for executing the arithmetic instruction from an executing component set; and sending the arithmetic instruction to the determined executing component is executed. Last, in response to determining that the acquired instruction is a blocking instruction, a next instruction is acquired after receiving a signal for instructing an instruction associated with the blocking instruction being completely executed.

Abstract: A neural network data processing apparatus includes: an instruction parsing module, configured to split a DMA task into multiple subtasks and acquire configuration information of a data sub-block corresponding to each subtask, where the subtasks are in a one-to-one correspondence with data sub-blocks of transported neural network data; a data reading module, configured to read a first data sub-block according to the configuration information, where the first data sub-block is a data sub-block among data sub-blocks corresponding to multiple subtasks; a data processing module, configured to compress the first data sub-block; a data write-out module, configured to output compressed data resulting from the compression of the first data sub-block.

Abstract: Described herein are systems and methods that allow for dense depth map estimation given input images. In one or more embodiments, a neural network model was developed that significantly differs from prior approaches. Embodiments of the deep neural network model comprises more computationally efficient structures and fewer layers but still produces good quality results. Also, in one or more embodiments, the deep neural network model may be specially configured and trained to operate using a hardware accelerator component or components that can speed computation and produce good results, even if lower precision bit representations are used during computation at the hardware accelerator component.

Abstract: Systems and methods include a method for training machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). Training sample data is generated. Training sample cases are executed using the training sample data. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

Abstract: Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.

Abstract: Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models.

Abstract: The invention provides a compound of formula (I) or a salt thereof, wherein R1, R2, R3 and R4 have any of the values described in the specification, as well as compositions comprising a compound of formula (I). The compounds are useful as immunostimulatory agents.

Abstract: A neural network data processing apparatus includes: an instruction parsing module, configured to split a DMA task into multiple subtasks and acquire configuration information of a data sub-block corresponding to each subtask, where the subtasks are in a one-to-one correspondence with data sub-blocks of transported neural network data; a data reading module, configured to read a first data sub-block according to the configuration information, where the first data sub-block is a data sub-block among data sub-blocks corresponding to multiple subtasks; a data processing module, configured to compress the first data sub-block; a data write-out module, configured to output compressed data resulting from the compression of the first data sub-block.