Abstract: Systems and methods are provided for performing random walk graph computing. One method may comprise generating a subset of walkers on a graph, maintaining the generated subset of walkers in a walker pool in a memory, loading a coarse-grained block of the graph from a non-volatile storage into a block buffer of the memory, generating pre-sampled edges for vertices in the coarse-grained block, storing the pre-sampled edges into a pre-sampled edge buffer allocated for the coarse-grained block and moving one or more walkers of the generated subset of walkers using the pre-sampled edges stored in the pre-sampled edge buffer. The generated subset of walkers may have an initial number determined based on a memory space allocated to the walker pool.

Abstract: A method and an apparatus for sequence labeling on an entity text, and a non-transitory computer-readable recording medium are provided. In the method, a start position of an entity text within a target text is determined. Then, a first matrix is generated based on the start position of the entity text. Elements in the first matrix indicates focusable weights of each word with respect to other words in the target text. Then, a named entity recognition model is generated using the first matrix. The named entity recognition model is obtained by training using first training data, the first training data includes word embeddings corresponding to respective texts in a training text set, and the texts are texts whose entity label has been labeled. Then, the target text is input to the named entity recognition model, and probability distribution of the entity label is output.

Abstract: A method and an apparatus for named entity recognition, and a non-transitory computer-readable recording medium are provided. In the method, text elements are traversed according to a text span to obtain candidate entity words. Then, a class to which the candidate entity word belongs is recognized. The recognizing of the class includes generating a prompt template corresponding to the candidate entity word, and concatenating the text to be recognized and the prompt template to obtain a concatenated text; generating vector representations of the text elements in the concatenated text; generating the vector representation of the candidate entity word according to the vector representations of the text elements of each candidate entity word in the concatenated text, and the vector representation of the text element of the mask word; and classifying the vector representation of the candidate entity word to obtain the class of the candidate entity word.

Abstract: Disclosed herein are methods and compositions for corresponding positions on an array with differently labeled affinity reagents immobilized at the positions. A first and a second affinity reagents are immobilized on at least some of the first and second positions, respectively. The first affinity reagent and the second affinity reagent are associated with a first luciferase polypeptide and a second luciferase polypeptide, respectively. The first luciferase polypeptide does not cross react with the second substrate and the second luciferase polypeptide does not cross react with the first substrate. The method further comprises contacting the array with the first substrate, which reacts with the first luciferase polypeptide and detecting the first luminescent signal and detecting the second luminescent signal at positions, thereby corresponding positions on an array with the first affinity reagent or the second affinity reagent immobilized at the positions.

Abstract: Disclosed are a table recognition method and apparatus. The table recognition method includes steps of obtaining an image vision feature and a character content feature of a table image; fusing the image vision feature and the character content feature of the table image to acquire a first fusion feature, and carrying out recognition based on the first fusion feature to acquire a table structure; and performing, based on the table structure, character recognition on the table image to acquire table character contents.

Abstract: Provided are a hooked probe, a method for ligating a nucleic acid and a method for constructing a sequencing library. The hooked probe comprises a target specific area and a hooked area ligated thereto; the target specific area comprises a sequence complementarily paired with at least part of the single chain of the nucleic acid fragment to be ligated; the hooked area comprises a sequence unpaired with the nucleic acid fragment; the end of the hooked area is a ligatable end; and the ligatable end can ligate the end of the single chain of the nucleic acid fragment.

Abstract: The present disclosure provides a control method of a UAV, a control device of a UAV, and a computer-readable storage medium, and relates to the technical field of UAVs. The control method of a UAV includes: determining a deviation between a vertical mapping point on the ground and a landing point of the UAV, the deviation comprising a deviation in a horizontal axis direction of a camera coordinate system and a deviation in a vertical axis direction of the camera coordinate system; and generating speed control amounts of the UAV in the horizontal axis direction and the vertical axis direction of the camera coordinate system by a controller, using the deviation in the horizontal axis direction and the deviation in the vertical axis direction.

Abstract: A biological sample image collection device (100), comprising support (30) and an optical imaging assembly (50), also comprises: a plurality of movable platforms (40), for placing biological samples (20) wherein the plurality of movable platforms (40) are arranged on the support (30) in an array; the plurality of movable platforms (40) can move relative to the support (30); and forces acting on the support (30) during the movement of the movable platforms can cancel each other out, so as to avoid vibrations affecting the support (30) and the biological samples (20) are canceled. The optical imaging assembly (50) collects images of the biological samples (20) on the movable platforms (40) when the plurality of movable platforms (40) move, relative to the center of the array, in the same direction and at the same speed. Further provided is a gene sequencer including the biological sample image collection device (100).

Abstract: A method and an apparatus for fusing position information, and a non-transitory computer-readable recording medium are provided. In the method, words of an input sentence are segmented to obtain a first sequence of words in the input sentence, and absolute position information of the words in the first sequence is generated. Then, subwords of the words in the first sequence are segmented to obtain a second sequence including subwords, and position information of the subwords in the second sequence are generated, based on the absolute position information of the words in the first sequence, to which the respective subwords belong. Then, the position information of the subwords in the second sequence are fused into a self-attention model to perform model training or model prediction.

Abstract: A method and apparatus for generating a NLFM signal in real time, and a computer storage medium are disclosed, including: determining a signal parameter of a signal according to a system parameter, the signal parameter includes: a signal bandwidth, a signal pulse width and a PSLR; determining a power spectrum density function according to PSLR; calculating the power spectrum density function to obtain a group delay vector; calculating a frequency axial vector according to a system sampling rate; calculating a time axial vector according to the signal pulse width; performing linear interpolation calculation on the group delay vector to obtain an instantaneous frequency vector; integrating the instantaneous frequency vector to obtain a phase vector; determining a signal time domain discrete vector; and generating a digital signal according to the signal time domain discrete vector, and performing digital-to-analog conversion on the digital signal to obtain the NLFM signal.

Abstract: Provided are an ion thruster and a fabrication method thereof. The method for fabricating the ion thruster comprises: stacking and laminating a plurality of prefabricated ceramic chips (p) to form a front portion (51); stacking and laminating a plurality of prefabricated green ceramic chips (p) to form a rear portion (B); assembling the front portion (51) and the rear portion (B) and placing in a sintering mold, and allowing the front portion (51) to be closely fitted with a tapered portion (b1) of the rear portion (B); placing a main cathode (1) into a cathode hole (k1) on the front portion and filling the cathode hole (k1) with a ceramic slurry to fix the main cathode (1); and placing the sintering mold in a heating furnace for sintering. For the ion thruster, a modular processing method is adopted. A method of stacking a plurality of prefabricated green ceramic chips (p) together and laminating them is used when each module is manufactured.

Abstract: A method and an apparatus for sequence labeling on an entity text, and a non-transitory computer-readable recording medium are provided. In the method, a start position of an entity text within a target text is determined. Then, a first matrix is generated based on the start position of the entity text. Elements in the first matrix indicates focusable weights of each word with respect to other words in the target text. Then, a named entity recognition model is generated using the first matrix. The named entity recognition model is obtained by training using first training data, the first training data includes word embeddings corresponding to respective texts in a training text set, and the texts are texts whose entity label has been labeled. Then, the target text is input to the named entity recognition model, and probability distribution of the entity label is output.

Abstract: This invention provides the download system of APP, determination method of installation type and storage medium.

Abstract: Knowledge graph processing method and device are disclosed. The method includes steps of obtaining an entity set containing a first entity, a second entity, and relation information; acquiring text information and image information related to the first entity and the second entity; generating a first structural information vector of the first entity and a second structural information vector of the second entity, and creating a first text information vector of the first entity, a first image information vector of the first entity, a second text information vector of the second entity, and a second image information vector of the second entity; and building a joint loss function so as to attain a first target vector of the first entity, a second target vector of the second entity, and a target relation vector of the relation information between the first entity and the second entity.

Abstract: Provided are a method and an apparatus for range ambiguity suppression based on orthogonal nonlinear frequency modulation (NLFM) waveforms. The method includes that: according to transmitted orthogonal NLFM signals, a waveform sequence of the transmitted signals corresponding to an obtained echo signal is determined; a set of range matched filters is constructed according to the waveform sequence and the orthogonal NLFM signals; range compression is performed on the echo signal by using the set of range matched filters to obtain range-compressed data; and synthetic aperture radar (SAR) imaging is performed according to the range-compressed data, to obtain an imaging result, and the imaging result is outputted. A non-transitory computer-readable storage medium is also provided.

Abstract: This invention provides the download system of APP, determination method of installation type and storage medium.

Abstract: A method and an apparatus for fusing position information, and a non-transitory computer-readable recording medium are provided. In the method, words of an input sentence are segmented to obtain a first sequence of words in the input sentence, and absolute position information of the words in the first sequence is generated. Then, subwords of the words in the first sequence are segmented to obtain a second sequence including subwords, and position information of the subwords in the second sequence are generated, based on the absolute position information of the words in the first sequence, to which the respective subwords belong. Then, the position information of the subwords in the second sequence are fused into a self-attention model to perform model training or model prediction.

Abstract: Provided are a hooked probe, a method for ligating a nucleic acid and a method for constructing a sequencing library. The hooked probe comprises a target specific area and a hooked area ligated thereto; the target specific area comprises a sequence complementarily paired with at least part of the single chain of the nucleic acid fragment to be ligated; the hooked area comprises a sequence unpaired with the nucleic acid fragment; the end of the hooked area is a ligatable end; and the ligatable end can ligate the end of the single chain of the nucleic acid fragment.

Abstract: The present disclosure provides a control method of a UAV, a control device of a UAV, and a computer-readable storage medium, and relates to the technical field of UAVs. The control method of a UAV includes: determining a deviation between a vertical mapping point on the ground and a landing point of the UAV, the deviation comprising a deviation in a horizontal axis direction of a camera coordinate system and a deviation in a vertical axis direction of the camera coordinate system; and generating speed control amounts of the UAV in the horizontal axis direction and the vertical axis direction of the camera coordinate system by a controller, using the deviation in the horizontal axis direction and the deviation in the vertical axis direction.

Abstract: A method and an apparatus for generating sample data, and a non-transitory computer-readable recording medium are provided. In the method, at least two weak supervision recommendation models of a recommendation system are generated; a dependency relation between the at least two weak supervision recommendation models is learned by training a neural network model; and the sample data is re-labelled using the trained neural network model to obtain updated sample data.