Abstract: A jumping motion control method for a biped robot includes: before feet of the biped robot leaves a support surface, estimating a motion trajectory of the biped robot that leaves the support surface according to a period of time when the biped robot stays or flips in the air; calculating a first motion angle of each joint of legs of the biped robot according to the motion trajectory and inverse kinematics; determining a constraint condition according to a motion type to which an action to be performed by the biped robot corresponds; optimizing the first motion angles according to the constraint condition to obtain a second motion angle of each joint of legs of the biped robot; and controlling a jumping motion of the biped robot according to the second motion angles.

Abstract: Provided are an information receiving method, an information sending method, a communication node, and a storage medium. The information receiving method includes receiving (S110) first information sent by a second communication node; and determining (S120) a temporary ID of the second communication node according to a resource serial number of a transmission resource used for transmission of the first information. The temporary ID includes a bit sequence corresponding to the resource serial number. The resource serial number includes at least one of the following: a round serial number, a slot serial number, a group serial number, a spreading code serial number, or a subchannel serial number.

Abstract: Provided are a method and apparatus for transmitting downlink control information. The method includes transmitting the downlink control information (DCI) through a physical downlink control channel (PDCCH); and scheduling multiple transport blocks (TBs) through the DCI. The scheduled multiple TBs are indicated by new data indicator (NDI) information and hybrid automatic repeat request (HARQ) process information in the DCI.

Abstract: Provided are a configuration method and apparatus, a receiving method and apparatus, a device, and a storage medium. The configuration method includes: determining a modulation and coding scheme (MCS) set based on a higher-layer configuration parameter; and configuring an MCS of data based on the MCS set; where the higher-layer configuration parameter indicates whether data transmission supports a 16 quadrature amplitude modulation (16QAM) modulation scheme, and the MCS set includes one or more of: a first MCS set and a second MCS set.

Abstract: A robot control method, a robot, and a computer-readable storage medium are provided. The method includes: obtaining a linear motion model of a robot; determining a predicted state corresponding to each moment in a preset time period based on the linear motion model; determining an expected state corresponding to each moment in the preset time period; and determining a compensation value of a velocity of joint(s) at each moment from k-th moment to k+N?1-th moment based on the predicted state corresponding to each moment in the preset time period and the expected state corresponding to each moment in the preset time period, determining instruction parameter(s) at the k-th moment based on the compensation value of the velocity of the joint(s) at the k-th moment, and adjusting a position of each of the joint(s) of the robot according to the instruction parameter(s) at the k-th moment.

Abstract: Methods, apparatus and systems for determining a transmission resource for transmitting a wake-up signal in a wireless communication are described. In one embodiment, a method performed by a wireless communication node is disclosed. The method comprises: determining an initial transmission resource for wake-up signal (WUS) transmission; determining an updated transmission resource for WUS transmission based at least on the initial transmission resource and an offset; and transmitting, on the updated transmission resource, a WUS to at least one wireless communication device, wherein the offset is selected from a plurality of predetermined values based on a location of a paging occasion (PO) corresponding to the WUS.

Abstract: A data transmission method and device, and a resource determination method and device, the method including user equipment (UE) transmitting a signal on one or more first uplink channel resources, where the signal includes one of the following: a single subcarrier signal, a multi-subcarrier signal, or a random access signal.

Abstract: A garment can include a first fabric panel and a second fabric panel joined to the first fabric panel, the second fabric panel comprising reflective thread, the reflective thread defining a grid pattern including a plurality of first threads parallel to each other and a plurality of second threads parallel to each other and angled with respect to each of the plurality of first threads.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: A pneumatic tire includes: bead cores formed by winding bead wires in a ring shape and having a polygonal cross-section shape in a meridian cross-section; a carcass extended between bead portions on both sides in a width direction, the carcass being folded back to an outer side in the width direction of the bead portion, the carcass being formed by coating a carcass cord with a coating rubber; an inner organic fiber reinforced layer wound around the bead core and formed by coating an organic fiber cord with a coating rubber; and a rubber reinforcing layer between the inner organic fiber reinforced layer and the carcass to cover at least an apex on an innermost side of the bead core in the width direction. A shortest distance between the bead wire and the carcass cord is 3 mm or more.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: A textual user input is received and a plurality of different text-to-content models are run on the textual user input. A selection system attempts to identify a suggested content item, based upon the outputs of the text-to-content models. The selection system first attempts to generate a completed suggestion based on outputs from a single text-to-content model. It then attempts to mix the outputs of the text-to-content models to obtain a completed content suggestion.

Abstract: A textual user input is received and a plurality of different text-to-content models are run on the textual user input. A selection system attempts to identify a suggested content item, based upon the outputs of the text-to-content models. The selection system first attempts to generate a completed suggestion based on outputs from a single text-to-content model. It then attempts to mix the outputs of the text-to-content models to obtain a completed content suggestion.

Abstract: The present disclosure relates to processing operations configured for an image recognition pipeline that is used to tailor real-time management of image recognition processing for technical scenarios across a plurality of different applications/services. Image recognition processing is optimized at run-time to ensure that latency requirements are met so that image recognition processing results are returned in a timely manner that aids task execution in an application-specific instances. An image recognition pipeline may manage a plurality of image recognition models that comprise a combination of image analysis service (IAS) models and deep learning models.

Abstract: The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification.

Abstract: The present disclosure relates to processing operations configured for an image recognition pipeline that is used to tailor real-time management of image recognition processing for technical scenarios across a plurality of different applications/services. Image recognition processing is optimized at run-time to ensure that latency requirements are met so that image recognition processing results are returned in a timely manner that aids task execution in an application-specific instances. An image recognition pipeline may manage a plurality of image recognition models that comprise a combination of image analysis service (IAS) models and deep learning models.