Abstract: A port determining method and apparatus, a terminal, and a readable storage medium. The port determining method according to embodiments of this application includes: determining, by a terminal, a scheme used for uplink transmission; and determining a demodulation reference signal (DMRS) port for the uplink transmission according to the scheme.

Abstract: The present application discloses an information transmission method and apparatus, a terminal, and a readable storage medium. The information transmission method of embodiments of the present application includes: determining, by a terminal, a first PTRS port and a transmission layer or DMRS port corresponding to the first PTRS port according to a first TPMI field, where the first TPMI field is a TPMI field associated with a target object associated with uplink transmission of the terminal, and the first PTRS port is a PTRS port actually used by the terminal; and performing uplink transmission by using the first PTRS port and the transmission layer or DMRS port corresponding to the first PTRS port.

Abstract: A computer system obtains the image including one or more text areas, and generates a sequence of feature maps from the image based on a downsampling rate. Each feature map has a first dimension and a second dimension, and the feature maps include a first feature map and a second feature map. Each of the first and second dimensions of the first feature map has a respective size that is reduced to that of a respective dimension of the second feature map by the downsampling rate. The second feature map is upsampled by an upsampling rate using a local context-aware upsampling network. The upsampled second feature map is aggregated with the first feature map to generate an aggregated first feature map. The one or more text areas are identified in the image based on the aggregated first feature map.

Abstract: One example method includes receiving, by a machine learning (“ML”) model of a conference client application, audio signals received from a microphone of a client device, the client device connected to a virtual meeting via the conference client application, the virtual meeting hosted by a virtual conference provider; determining, by the ML model, a plurality of candidate reactions associated with the audio signals, the ML comprising a plurality of convolutional neural network (“CNN”) layers and at least one fully connected layer; selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider.

Abstract: This application is directed to audio purification. An audio purification method, a computer system and a non-transitory computer-readable medium are provided. The audio purification method includes: obtaining image data corresponding to a sequence of image frames that focus on lip movement of a person; obtaining audio data that is synchronous with the lip movement in the sequence of image frames; and modifying the audio data using the image data.

Abstract: The present invention relates to compounds for and a method of detecting beta-lactamase activity in a sample. The sample is contacted with a nanoparticulate tag. The nanoparticulate tag comprises a metal or a combination of metals, or it comprises a nanotube of a metal, boron nitride and/or carbon. The respective metal is capable of forming one of a covalent bond, a coordinative bond and a non-covalent interaction with a thio or a seleno group. The sample is contacted with a compound of one of general formulas (I)-(III) and (VII)-(IX). At least one beta-lactam moiety of the compound is cleaved by the beta-lactamase activity in the sample. As a result a cleavage moiety Z-A-Z, Z-A-Z—R15, Z-A-Z—R16, Z-A-Z—R17, Z-A-Z—R18 or Z-G-N(R8)R9 is released that is immobilized on the surface of the nanoparticulate tag by a covalent bond via a Z atom. The presence of beta-lactamase activity is determined based on the presence of the cleavage moiety immobilized onto the surface of the nanoparticulate tag.

Abstract: The present invention relates to compounds for and a method of detecting beta-lactamase activity in a sample. The sample is contacted with a nanoparticulate tag. The nanoparticulate tag comprises a metal or a combination of metals, or it comprises a nanotube of a metal, boron nitride and/or carbon. The respective metal is capable of forming one of a covalent bond, a coordinative bond and a non-covalent interaction with a thio or a seleno group. The sample is contacted with a compound of one of general formulas (I)-(III) and (VII)-(IX). At least one beta-lactam moiety of the compound is cleaved by the beta-lactamase activity in the sample. As a result a cleavage moiety Z-A-Z, Z-A-Z—R15, Z-A-Z—R16, Z-A-Z—R17, Z-A-Z—R18 or Z-G-N(R8)R9 is released that is immobilized on the surface of the nanoparticulate tag by a covalent bond via a Z atom. The presence of beta-lactamase activity is determined based on the presence of the cleavage moiety immobilized onto the surface of the nanoparticulate tag.

Abstract: The present invention relates to compounds for and a method of detecting beta-lactamase activity in a sample. The sample is contacted with a nanoparticulate tag. The nanoparticulate tag comprises a metal or a combination of metals, or it comprises a nanotube of a metal, boron nitride and/or carbon. The respective metal is capable of forming one of a covalent bond, a coordinative bond and a non-covalent interaction with a thio or a seleno group. The sample is contacted with a compound of one of general formulas (I)-(III) and (VII)-(IX). At least one beta-lactam moiety of the compound is cleaved by the beta-lactamase activity in the sample. As a result a cleavage moiety Z-A-Z, Z-A-Z—R15, Z-A-Z—R16, Z-A-Z—R17, Z-A-Z—R18 or Z-G-N(R8)R9 is released that is immobilised on the surface of the nanoparticulate tag by a covalent bond via a Z atom. The presence of beta-lactamase activity is determined based on the presence of the cleavage moiety immobilized onto the surface of the nanoparticulate tag.

Abstract: The present invention relates to compounds for and a method of detecting beta-lactamase activity in a sample. The sample is contacted with a nanoparticulate tag. The nanoparticulate tag comprises a metal or a combination of metals, or it comprises a nanotube of a metal, boron nitride and/or carbon. The respective metal is capable of forming one of a covalent bond, a coordinative bond and a non-covalent interaction with a thio or a seleno group. The sample is contacted with a compound of one of general formulas (I)-(III) and (VII)-(IX). At least one beta-lactam moiety of the compound is cleaved by the beta-lactamase activity in the sample. As a result a cleavage moiety Z-A-Z, Z-A-Z-R15, Z-A-Z-R16, Z-A-Z-R17, Z-A-Z-R18 or Z-G-N(R8)R9 is released that is immobilised on the surface of the nanoparticulate tag by a covalent bond via a Z atom. The presence of beta-lactamase activity is determined based on the presence of the cleavage moiety immobilized onto the surface of the nanoparticulate tag.