Abstract: A method, system, device, and medium for recharging a battery module are disclosed. The method comprises: identifying initial rechargeable batteries within the battery module; selecting from the initial rechargeable batteries, a reference rechargeable battery that satisfies preset charging and discharging conditions, and designating the remaining batteries of the initial rechargeable batteries as target rechargeable batteries; and determining a target capacity for each of the target rechargeable batteries based on the historical charging and discharging parameters of the reference rechargeable battery and each of the target rechargeable batteries; establishing a recharge termination condition for each of the target rechargeable batteries based on the target capacity; and recharging each of the target rechargeable batteries in accordance with its respective recharge termination condition. Considering the unique conditions of each battery, settings for recharge are individualized.

Abstract: A method and a user equipment for reporting a remaining delay budget information are provided. The method includes: receiving a radio resource control configuration, wherein the radio resource control configuration indicates the user equipment to report the remaining delay budget information of a logical channel or a logical channel group through a status report message; determining whether a triggering condition is met, wherein the triggering condition is associated with a threshold of remaining delay budget; and in response to the triggering condition being met, transmitting the status report message including the remaining delay budget information, wherein the remaining delay budget information indicates at least one remaining delay budget and at least one associated buffer size.

Abstract: Methods and systems for multi-application video conferencing are described herein. A computing device may establish a communications session with a user computing device, then establish a group video call session with a group video call application. The computing device may provide, to the user computing device, video output associated with the group video call session. The computing device may receive, from the user computing device, input (e.g., video, audio, commands). The computing device may convert, based on an identity of the group video call application, the input into a format compatible with the group video call application. The computing device may then provide, to the group video call application, the converted input.

Abstract: A process for treating aged oil by electron beam irradiation is disclosed, comprising the following steps: step 1, homogenizing the aged oil and then feeding the aged oil into a distributor; step 2, forming a liquid layer having a certain thickness on the distributor; step 3, installing and debugging an electron beam irradiation device on the distributor; step 4, installing and debugging a phased-array microwave emitter on the distributor after installing and debugging the electron beam irradiation device; step 5, turning on the electron beam irradiation device and the phased-array microwave emitter; and step 6, dividing the treated aged oil and then allowing the aged oil after dividing to enter an oil pool and a water pool for standing. Oil flows out from an upper layer of the oil pool, a lower layer of the oil pool flows back to an aged oil storage pool.

Abstract: A method of measuring cross-correlation or similarity between input features and filters of neural networks using an RRAM-crossbar architecture to carry out addition/subtraction-based neural networks for in-memory computing. The correlation calculations use L1 norm operations of AdderNet. The RCM structure of the RRAM-cross bar has storage and computing collocated, such that processing is done in the analog domain with low power, low latency and small area. In addition, the impact due to the nonidealities of RRAM device can be alleviated by the implicit ratio-based feature of the structure.

Abstract: The present disclosure provides compounds and pharmaceutically acceptable salts thereof, and methods of using the same. The compounds and methods have a range of utilities as therapeutics, diagnostics, and research tools. In particular, the subject compositions and methods are useful for reducing signaling output of oncogenic proteins.

Abstract: The present disclosure provides compounds and pharmaceutically acceptable salts thereof, and methods of using the same. The compounds and methods have a range of utilities as therapeutics, diagnostics, and research tools. In particular, the subject compositions and methods are useful for reducing signaling output of oncogenic proteins.

Abstract: A method and a device for forecasting an electric load, an electronic device, and a computer-readable storage medium are provided. The method includes: acquiring historical load data prior to a forecast date; generating a load sequence based on the historical load data; performing variational mode decomposition on the load sequence, to obtain multiple intrinsic mode components and a residual that are corresponding to the load sequence; and inputting the multiple intrinsic mode components and the residual into respective forecasting models, and determining a load value on the forecast date based on forecasting results of all the forecasting models. With the method and the device, the electronic device, and the computer-readable storage medium, the load sequence is decomposed based on the variational mode decomposition, so that more dimensional feature information in the load sequence can be mined. Moreover, the residual is also used as valid data.

Abstract: A thermal-runaway warning method, system, and terminal for a power station are provided. The method comprises: obtaining collected data of a battery module comprised in the power station, wherein the collected data comprises collected temperature values of the battery module collected by temperature measuring devices; normalizing the collected data to obtain a standard dataset; training a CNNLSTM-based temperature predicting model based on the standard dataset to obtain a trained CNNLSTM-based temperature predicting model; and obtaining a predicted temperature value of the battery module within an output time window based on the trained CNNLSTM-based temperature predicting model, and determining whether there is a risk of thermal runaway in the power station based on the predicted temperature value.

Abstract: There is provided a display substrate, including: a base; light-emitting units on a side of the base; a flat light-shielding functional layer, including a black matrix and a first planarization layer, on a side of the light-emitting units away from the base, light outgoing openings being provided in the black matrix and being in one-to-one correspondence with the light-emitting units, and the first planarization layer at least filling the light outgoing openings; and a color filter layer, including color filter patterns in one-to-one correspondence with the light outgoing openings, on a side of the flat light-shielding functional layer away from the base, an orthographic projection of each color filter pattern on the base covering an orthographic projection of the light outgoing opening corresponding to the color filter pattern on the base. A method for manufacturing a display substrate, a display panel and a display apparatus are further provided.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: A system and method for processing a 3D point cloud to generate a segmented point cloud in real time are disclosed, the method includes: receiving a sparse 3D point cloud captured by a detection and ranging sensor mounted to a vehicle, the 3D point cloud comprising a plurality of data points, each data point in the 3D point cloud having a set of coordinates in a coordinate system of the detection and ranging sensor; generating, from the 3D point cloud, a range map comprising a plurality of elements, each of the plurality of data points of the 3D point cloud occupying a respective element of the plurality of elements; labelling the data point in each respective element of the range map as one of a pole-like data point or a vertical-plane-like data point; and generating the segmented point cloud including one or more of the labeled data points.

Abstract: Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

Abstract: System and method for semantic segmentation of point clouds. The method may include: generating, via a first neural network, a birds-eye-view (BEV) image of the environment from the aggregated point cloud; generating, via a second neural network, a labelled BEV image from the BEV image, wherein each pixel in the labelled BEV image is associated with a class label from a set of class labels; generating a BEV feature map; and generating, via a third neural network, the road surface segmentation map in the form of a refined labelled BEV image based on the labelled BEV image by smoothing the labelled BEV image using the BEV feature map, wherein each pixel in the refined labelled BEV image is associated with a class label from the set of class labels.

Abstract: Methods and systems for allowing for techniques of automatically targeting focus and enlarging images upon detection that a receiver of shared video content is using a smaller screen. The presenter may indicate a focus point on the displayed video being shared to indicate where the targeted focusing and enlarging should be centered. A focus area and an enlargement factor may be calculated based on a difference in screen size between a presenter device and a destination device. The focus area and enlargement factor may be calculated such that elements of the shared video content are substantially the same size to a viewer of the destination device as they appear to the presenter on their presenter device. Different destination devices may have different focus areas and enlargement factors.

Abstract: Methods and systems for performing semantic scene completion of sparse 3D data are described. A frame of sparse 3D data is preprocessed into a sparse 3D tensor and a sparse 2D tensor. A partially completed 3D tensor is generated from the sparse 3D tensor using a 3D prediction network, and a semantically completed 2D tensor is generated from the sparse 2D tensor using a 2D prediction network. The partially completed 3D tensor is completed to obtain a semantically completed 3D tensor by assigning a given class label, which has been assigned to a given pixel in the semantically completed 2D tensor, to a voxel at a corresponding x-y coordinate in the partially completed 3D tensor.

Abstract: A computer-implemented method, a computing system, and a non-transitory machine-readable medium for semantic segmentation of a point cloud frame are provided. Point cloud frames including a target point cloud frame are received. For each sequence of a sliding set of sequences of point cloud frames, the sequence including the target point cloud frame, each point cloud frame in the sequence of point cloud frames is semantically segmented to apply semantic labels to points. A most prevalent semantic label is determined for each point in the target point cloud frame across the sliding set of sequences of point cloud frames.