Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Abstract: A virtual keyboard setting method includes: determining an operation region and a trigger region, the operation region being a region operated in a screen for using an input method, and the trigger region being a region covered by an external entity when touching the screen; determining a display region of a virtual keyboard according to the operation region; determining an area of a virtual key in the virtual keyboard according to the trigger region; and setting the virtual keyboard according to the display region of the virtual keyboard and the area of the virtual key.

Abstract: Systems and methods involving nanomaterial-based electrodes, such as supercapacitor and battery electrodes that can be flexible, are described.

Abstract: Provided by the present disclosure are a transmitting electronic device and receiving electronic device and methods for wireless communication, wherein the transmitting electronic device for wireless communication comprises a processing circuit.

Abstract: The present disclosure relates to a synthesis method of ?-nicotinamide mononucleotide (?-NMN) and an intermediate thereof. In the present disclosure, phospholipid metabolism enzymes phospholipase D (PLD) and phospholipase C (PLC) widely present in the biosphere are used as catalysts to prepare ?-NMN through two-step enzymolysis or one-pot synthesis; and an intermediate, namely phosphatidyl nicotinamide riboside (PNR), is obtained during the two-step enzymolysis. The present disclosure has simple reaction steps, low production cost, and environmental friendliness, and is suitable for large-scale industrial production.

Abstract: The present disclosure relates to systems and methods for a machine-learning platform for the safe serialization of a machine-learning application. Individual library components (e.g., a pipeline, a microservice routine, a software module, and an infrastructure model) can be encrypted using one or more keys. The keys can be stored in a location different from the storage location of the machine-learning application. Prior to incorporation of the library component into a machine-learning model, one or more keys can be retrieved from the remote storage location to authenticate that the one or more encrypted library components are authentic. The process can reject any of the one or more component, when the encrypted library component fails authentication. If a component is rejected, the system can roll back to a previous, authenticated version of the library component. The authenticated library components can be compiled into machine-learning software.

Abstract: An intra-user equipment (UE) multiplexing method is executed in a UE. The UE obtains respective timeline conditions for multiplexing uplink transmission with high priority and uplink transmission with low priority when detecting collision in time between the uplink transmissions with different priorities. The UE performs multiplexing of the uplink transmissions with different priorities when the respective timeline conditions are satisfied.

Abstract: A sidelink prioritization method executable in a user equipment (UE) is disclosed. Upon receiving an initial priority value of a sidelink service, the UE determines a traffic type of the side link channel and generates a refined priority value of the sidelink service from the initial priority value according to the traffic type of the sidelink service.

Abstract: A HARQ feedback method is provided and includes: configuring, by a base station, a first timing parameter for a user equipment (UE), wherein the first timing parameter is configured to indicate a time interval between a first time unit for the UE receiving a downlink transmission and a second time unit for the UE transmitting a HARQ feedback signal of the downlink transmission to the base station; determining, by the base station, that a collision occurs in an attempt to transmit the HARQ feedback signal by the UE; and transmitting, by the base station, downlink control information (DCI), which carries a second timing parameter, to the UE, wherein the second timing parameter is different from the first timing parameter and is configured to adjust the second time unit to avoid the collision. The present disclosure further provides SPS methods, related paging apparatuses and non-transitory storage mediums.

Abstract: The present disclosure relates to systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.

Abstract: A method for handling high-priority uplink (UL) transmissions is provided. The method includes: obtaining an earliest one of the high-priority UL transmissions which is satisfied with a timeline condition; combining the earliest one of the high-priority UL transmissions with at least one of the high-priority UL transmissions which is satisfied with the timeline condition and later than the earliest one of the high-priority UL transmissions to form a multiplexing channel; and dropping one of the high-priority UL transmissions which is not satisfied with the timeline condition and the multiplexed high-priority UL transmission, wherein the multiplexed high-priority UL transmission is obtained by combining the earliest one of the high-priority UL transmissions with at least one of the high-priority UL transmissions later than the earliest one of the high-priority UL transmissions. A user equipment device is also provided.

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: A method, an apparatus, a computer storage medium and a computer device for geological characteristic constraint-based reservoir parameter prediction are provided. The method includes: selecting dominant seismic attributes according to the relevance between different types of seismic attributes of a target stratum and reservoir parameters (S100); on the basis of the dominant seismic attributes, classifying seismic waveforms of the target stratum by a preset waveform classification network model and according to waveform features, so as to obtain a waveform classification result (S200); taking the waveform classification result as a constraint, and constructing different deep neural network models corresponding to different geological characteristics (S300); fusing different trained deep neural network models into a set of spatially varying neural network prediction model (S500); and predicting the reservoir parameters of the target stratum.

Abstract: The present disclosure relates to an electronic device, a method, and a storage medium for a wireless communication system. Various embodiments regarding resource allocation and management for sidelink communication are described. In an embodiment, an electronic device for a first terminal device includes a processing circuit configured to: search for time-frequency resources based on at least two allocation modes to obtain a candidate resource set; select a time-frequency resource from the candidate resource set; and notify a second terminal device of the selected time-frequency resource to be used for sidelink communication with the second terminal device. The processing circuit is further configured to send resource selection information to a base station, where the resource selection information indicates whether a specified resource is selected.

Abstract: The present disclosure relates to an electronic device, a method, and a storage medium for a wireless communication system. Various embodiments of mobility management for multiple transmission and reception points (multi-TRP) are described. In an embodiment, an electronic device for a first TRP includes a processing circuit, where the processing circuit is configured to: in response to that the first TRP and the second TRP are a master TRP and a secondary TRP respectively serving a terminal device, provide measurement configuration information to the terminal device, receive a measurement report from the terminal device, and update TRPs serving the particular terminal device based at least on measurement quality of the first TRP and the second TRP.

Abstract: The present disclosure relates to method and device for enhancing feedback channel in wireless communication. There is provided a receiving-side electronic device for a receiving side of a communication system, which includes a processing circuit configured to receive initial transmission information from a first electronic device among more than one transmitting-side electronic devices for a transmitting side of the communication system; in case that data information in the initial transmission information cannot be successfully decoded, acquire auxiliary information; and feed back the auxiliary information to the first electronic device, so that the first electronic device can perform the information retransmission by using the resources determined based on the auxiliary information.

Abstract: Provided are an electronic device and method for wireless communication, and a computer-readable storage medium. The electronic device for wireless communication comprises a processing circuit, which is configured to at least select, from a candidate resource set formed of predetermined candidate time-frequency resource blocks, a time-frequency resource block for the initial transmission and/or retransmission of a data block.

Abstract: The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.

Abstract: The embodiments of the present disclosure relate to a fixing apparatus of a wireless charger. According to an embodiment, the fixing apparatus of the wireless charger comprises: a first support; a second support opposite to the first support; a third support disposed between the first support and the second support and used to fix a mobile device to the wireless charger; and an elastic component connected to the first support and the second support respectively, wherein the elastic component is configured to enable the fixing apparatus to move between an initial position and a fixed position. The fixing apparatus of the wireless charger provided in the embodiments of the present disclosure have many advantages such as convenient usage, simple production processes, and low production costs, etc.

Abstract: The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.