Abstract: A high-order correlation preserved incomplete multi-view subspace clustering method and system.

Abstract: A later-fusion multiple kernel clustering machine learning method and system based on proxy graph improvement is provided. The method includes: S1. acquiring a clustering task and a target data sample; S2. initializing a proxy graph improvement matrix; S3. running k-means clustering and graph improvement on each view corresponding to the acquisition of the clustering task and the target data sample, and constructing an objective function by combining kernel k-means clustering and graph improvement methods; S4. cyclically solving the objective function constructed in step S3 so as to obtain a graph matrix, which is fused with basic kernel information; and S5. performing spectral clustering on the obtained graph matrix, so as to obtain a final clustering result. By means of the method, an optimized basic division not only has information of a single kernel, but can also obtain global information by means of a proxy graph.

Abstract: A hyperspectral image band selection method based on latent feature fusion comprises: S11, inputting a hyperspectral image cube and segmenting the inputted hyperspectral image cube into several regions by superpixel segmentation; S12, learning low-dimensional latent features corresponding to the several regions from the several regions respectively to obtain a latent feature matrix of all the regions; S13, calculating an average Laplacian matrix and an average latent feature matrix of the hyperspectral image cube; S14, fusing the latent feature matrix, the average Laplacian matrix, and the average latent feature matrix of all the regions to obtain a low-dimensional self-representation matrix of the hyperspectral image cube; and S15, clustering the low-dimensional self-representation matrix by a k-means algorithm to obtain an optimal band subset of the hyperspectral image cube.

Abstract: A spectral clustering method and system based on unified anchor and subspace learning is provided. The spectral clustering method based on unified anchor and subspace learning includes: S1: acquiring a clustering task and a target data sample; S2: performing unified anchor learning on multi-view data corresponding to the acquired clustering task and the acquired target data sample, and adaptively constructing an objective function corresponding to an anchor graph according to a learned unified anchor; S3: optimizing the constructed objective function by using an alternating optimization method to obtain an optimized unified anchor graph; and S4: performing spectral clustering on the obtained optimized unified anchor graph to obtain a final clustering result.

Abstract: A consensus graph learning-based multi-view clustering method includes: S11, inputting an original data matrix to obtain a spectral embedding matrix; S12, calculating a similarity graph matrix and a Laplacian matrix based on the spectral embedding matrix; S13, applying spectral clustering to the calculated similarity graph matrix to obtain spectral embedding representations; S14, stacking inner products of the normalized spectral embedding representations into a third-order tensor and using low-rank tensor representation learning to obtain a consistent distance matrix; S15, integrating spectral embedding representation learning and low-rank tensor representation learning into a unified learning framework to obtain a objective function; S16, solving the obtained objective function through an alternative iterative optimization strategy; S17, constructing a consistent similarity graph based on the solved result; and S18, applying spectral clustering to the consistent similarity graph to obtain a clustering resul

Abstract: An unsupervised feature selection method based on latent space learning and manifold constraints includes: S11, inputting an original data matrix to obtain a feature selection model; S12, embedding latent space learning into the feature selection model to obtain a feature selection model with the latent space learning; S13, adding a graph Laplacian regularization term into the feature selection model with the latent space learning to obtain an objective function; S14, solving the objective function by adopting an alternative iterative optimization strategy; and S15, sequencing each feature in the original matrix, and selecting the first k features to obtain an optimal feature subset. Feature selection is performed in a learned potential latent space, and the space is robust to noise. The potential latent space is modeled by non-negative matrix decomposition of a similarity matrix, and the matrix decomposition can unambiguously reflect relationships between data instances.

Abstract: A multi-view clustering method and system based on matrix decomposition and multi-partition alignment are provided.

Abstract: A system for unsupervised deep representation learning based on image translation is provided. The system includes an image translation transformation module used for performing a random translation transformation on an image and generating an auxiliary label; an image mask module connected with the image translation transformation module and used for applying a mask to the image after translation transformation; a deep neural network connected with the image mask module and used for predicting an actual auxiliary label of the image after the mask is applied and learning the deep representation of the image; a regression loss function module connected with the deep neural network and used for updating parameters of the deep neural network based on a loss function; and a feature extraction module connected with the deep neural network and used for extracting the representation of the image.

Abstract: A late fusion multi-view clustering method and system based on local maximum alignment are provided. The late fusion multi-view clustering method based on local maximum alignment includes the following steps: S1: acquiring a clustering task and a target data sample; S2: initializing a permutation matrix of each view and a combination coefficient of each view, and performing average partition of kernel k-means clustering on an average kernel to obtain a neighbor matrix of each view; S3: calculating basic partition of each view, and establishing a late fusion multi-view clustering objective function based on maximum alignment; S4: acquiring basic partition having local information, and establishing a late fusion multi-view clustering objective function based on local maximum alignment; S5: solving the established late fusion multi-view clustering objective function based on local maximum alignment in a cyclic manner to obtain optimal partition; and S6: performing k-means clustering on the optimal partition.

Abstract: This disclosure provides recombinant DNA constructs and transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency, and enhanced drought tolerance or water use efficiency. Transgenic plants may include field crops as well as plant propagules and progeny of such transgenic plants. Methods of making and using such transgenic plants are also provided. This disclosure also provides methods of producing seed from such transgenic plants, growing such seed, and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: A multi-modal adaptive fusion deep clustering model based on an auto-encoder includes an encoder structure, a multi-modal adaptive fusion layer, a decoder structure and a deep embedding clustering layer. The encoder is configured to enable a dataset to be respectively subjected to three types of nonlinear mappings of the auto-encoder, a convolutional auto-encoder and a convolutional variational auto-encoder to obtain potential features, respectively. The multi-modal adaptive feature fusion layer is configured to fuse the potential features into a common subspace in an adaptive spatial feature fusion mode to obtain a fused feature. The decoder is configured to decode the fused feature by using a structure symmetrical to the encoder to obtain a decoded reconstructed dataset. The deep embedding clustering layer is configured to cluster the fused feature Z and obtain a final accuracy ACC by comparing a clustering result with a true label.

Abstract: This disclosure provides recombinant DNA constructs and transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency, and enhanced drought tolerance or water use efficiency. Transgenic plants may include field crops as well as plant propagules and progeny of such transgenic plants. Methods of making and using such transgenic plants are also provided. This disclosure also provides methods of producing seed from such transgenic plants, growing such seed, and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: This disclosure provides recombinant DNA constructs and transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency, and enhanced drought tolerance or water use efficiency. Transgenic plants may include field crops as well as plant propagules, plant parts and progeny of such transgenic plants. Methods of making and using such transgenic plants are also provided. This disclosure also provides methods of producing seed from such transgenic plants, growing such seed, and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: Embodiments of the present disclosure relate to a flame detector. The flame detector comprises a light guide system including a first end and a second end opposite to the first end, a light path being formed between the first end and the second end and extending along an optical axis; a first hole disposed at the first end, extending along the optical axis and forming a part of the light path, the first hole being configured to receive light emitted by a flame to be detected; and a second hole disposed at the second end, extending along the optical axis and forming a part of the light path, sizes of the first and second holes and a length of the light path being configured such that a detection angle of the light guide system is between 0.5 degrees and 3 degrees.

Abstract: This disclosure provides recombinant DNA constructs and transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency, and enhanced drought tolerance or water use efficiency. Transgenic plants may include field crops as well as plant propagules and progeny of such transgenic plants. Methods of making and using such transgenic plants are also provided. This disclosure also provides methods of producing seed from such transgenic plants, growing such seed, and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: This disclosure provides transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency and enhanced drought tolerance; propagules, progeny and field crops of such transgenic plants; and methods of making and using such transgenic plants. This disclosure also provides methods of producing hybrid seed from such transgenic plants, growing such seed and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: Provided is an afucosylated monoclonal antibody. The monoclonal antibody is a human IgG antibody and has an afucosylated sugar chain structure at a heavy-chain sugar chain binding site. Also provided are application of the monoclonal antibody in the preparation of antineoplastic drugs, and a composition containing the monoclonal antibody. Compared with a fucosylated monoclonal antibody, the provided afucosylated monoclonal antibody has higher biological activity in vitro and in vivo and can be used for developing more effective therapeutic monoclonal antibody drugs.

Abstract: This disclosure provides recombinant DNA constructs and transgenic plants having enhanced traits such as increased yield, increased nitrogen use efficiency, and enhanced drought tolerance or water use efficiency. Transgenic plants may include field crops as well as plant propagules, plant parts and progeny of such transgenic plants. Methods of making and using such transgenic plants are also provided. This disclosure also provides methods of producing seed from such transgenic plants, growing such seed, and selecting progeny plants with enhanced traits. Also disclosed are transgenic plants with altered phenotypes which are useful for screening and selecting transgenic events for the desired enhanced trait.

Abstract: Provided is an afucosylated monoclonal antibody. The monoclonal antibody is a human IgG antibody and has an afucosylated sugar chain structure at a heavy-chain sugar chain binding site. Also provided are application of the monoclonal antibody in the preparation of antineoplastic drugs, and a composition containing the monoclonal antibody. Compared with a fucosylated monoclonal antibody, the provided afucosylated monoclonal antibody has higher biological activity in vitro and in vivo and can be used for developing more effective therapeutic monoclonal antibody drugs.

Abstract: Embodiments of the present disclosure relate to a flame detector. The flame detector comprises a light guide system including a first end and a second end opposite to the first end, a light path being formed between the first end and the second end and extending along an optical axis; a first hole disposed at the first end, extending along the optical axis and forming a part of the light path, the first hole being configured to receive light emitted by a flame to be detected; and a second hole disposed at the second end, extending along the optical axis and forming a part of the light path, sizes of the first and second holes and a length of the light path being configured such that a detection angle of the light guide system is between 0.5 degrees and 3 degrees.