Abstract: The present invention provides a point cloud attribute entropy encoding method and device, and a point cloud attribute entropy decoding method and device. The encoding method comprises: if an overall encoding processing condition of point cloud attribute residual coefficients is satisfied, performing overall encoding processing on the point cloud attribute residual coefficients, and then ending the encoding; and if not, performing local one-by-one encoding processing on the point cloud attribute residual coefficients until the encoding is ended. The entropy decoding method comprises: if an overall decoding processing condition of the point cloud attribute residual coefficients is satisfied, performing overall decoding processing on the point cloud attribute residual coefficients, and then ending the decoding; and if not, performing local one-by-one decoding processing on the point cloud attribute residual coefficients until the decoding is ended.

Abstract: The invention relates to the field of health food technologies, and particularly to a composition and an application thereof in preparing a product for protecting mitochondria. The invention provides a composition, the raw materials of which include Haematococcus pluvialis, Lycium ruthenicum, Cordyceps militaris and nicotinamide. The composition has the effects of improving an oxidative stress level under a mitochondrial dysfunction and delaying mitochondrion-related aging. Compared with a single constituent, constituents in the composition cooperate with each other to produce a synergistic effect, which significantly improves an effect of protecting a mitochondrial function.

Abstract: An example display assembly includes: a continuous display including: a first rigid segment attached to and substantially coplanar with an inner surface of a first assembly of the folding device; a second rigid segment attached to and substantially coplanar with an inner surface of a second assembly of the folding device; a primary flexible segment; a third rigid segment disposed between the second rigid segment and the primary flexible segment; and a fourth rigid segment disposed between the first rigid segment and the primary flexible segment; a primary supporting plate attached to the continuous display; a first supplemental supporting plate attached to the primary supporting plate adjacent to the third rigid segment; and a second supplemental supporting plate attached to the primary supporting plate adjacent to the fourth rigid segment.

Abstract: A Chinese herbal medicine composition for improving hearing impairment comprises at least two of avocado extract, ecklonia cava extract and perilla extract. The Chinese herbal medicine composition can effectively reduce a NO content secreted by cells, reduce an inflammatory reaction of cells, and have a good effect of improving LPS-induced hearing loss. Moreover, ingredients of the Chinese herbal medicine composition are medicine-and-food homologous ingredients, which have no obvious cytotoxicity and obvious side effects.

Abstract: A robot system for active and passive upper limb rehabilitation training based on a force feedback technology includes a robot body and an active and passive training host computer system. Active and passive rehabilitation training may be performed at degrees of freedom such as adduction/abduction and flexion/extension of left and right shoulder joints, and flexion/extension of left and right elbow joints according to a condition of a patient. In a passive rehabilitation training mode, the robot body drives the upper limb of the patient to move according to a track specified by the host computer, to gradually restore a basic motion function of the upper limb. In an active rehabilitation training mode, the patient holds the tail ends of the robot body with both hands to interact with a rehabilitation training scene, and can feel real and accurate force feedback.

Abstract: A method for controlling an acceleration rate of a vehicle includes monitoring a first current speed and a first acceleration rate of the vehicle based on the vehicle moving from a standstill. The method also includes setting an initial target acceleration rate to an adjusted target acceleration rate based on the first acceleration rate satisfying a first acceleration adjustment condition and the first current speed satisfying a second acceleration adjustment condition. The method further includes monitoring a second acceleration rate and a second current acceleration rate of the vehicle based on setting the initial target acceleration rate to the adjusted target acceleration rate. The method still further includes setting the adjusted target acceleration rate to the initial target acceleration rate based on the second acceleration rate satisfying a first target acceleration condition or the second current speed satisfying a second target acceleration condition.

Abstract: A computer implemented method, a data processing system and a computer program product to determine a likelihood of pneumothorax of a patient, the method including assessing a digital image of a chest x-ray of the patient, applying a standard detection pipeline to the digital image, applying a confounding factor detector to the digital image, and applying a high-resolution detection pipeline to the digital image.

Abstract: The present disclosure discloses a method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features, wherein the method comprising: 1) collecting eligible clinical images of patients and preprocessing the collected images; 2) extracting computer vision features from a segmented image of a hepatic tumor region; 3) extracting the manual radiomics features from the segmented image of the hepatic tumor region; 4) by combining the computer vision features and the radiomics features, screening by univariate filtering and then by LASSO regression; 5) using the features resulted from screening and clinical features together for modeling by a multivariable logistic regression model, and using the Akaike information criterion (AIC) to search backward and select clinical features suitable for the best model, so as to implement the prediction of hepatocellular carcinoma pathological grading.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: Embodiments of this application provide an antenna and a preparation method thereof, a millimeter-wave sensor, and a terminal. Gaps exist between a plurality of coupling stubs and a microstrip feeder.

Abstract: A wearable upper limb rehabilitation training robot with precise force control includes a wearable belt, a multi-degree-of-freedom robot arm, and a control box. The robot is worn on the waist of a person by using a belt, and driven by active actuators, to implement active and passive rehabilitation training in such degrees of freedom as adduction/abduction/anteflexion/extension of left and right shoulder joints and anteflexion/extension of left and right elbow joints. In addition, a force/torque sensor is mounted on a tip of the robot arm, to obtain a force between the tip of the robot arm and the human hand during rehabilitation training as a feedback signal, to adjust an operating state of the robot, thereby realizing the precise force control during the rehabilitation training.

Abstract: The present invention provides a point cloud intra prediction method and device based on weights optimization of neighbors. The invention relates to intra prediction for point cloud attribute compression, by optimizing the weights of the neighboring points on the basis of the density of the point cloud in three directions, i.e. x, y and z directions, and specifically, calculating the optimized weight of each neighboring point by optimizing corresponding coefficients of three coordinate components, i.e. x, y and z coordinate components of distances. The invention can improve the accuracy of intra prediction by means of enhancing the utilization of the overall geometric information of the point cloud, and then transformation, quantification and entropy are carried out on the prediction residuals, such that a better point cloud attribute compression performance is achieved.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.