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 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: 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 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: Methods, systems, and apparatuses related to source address memory management are described. For example, a controller can be coupled to a memory device to select a source block, a destination block, and a metadata block. The controller can store metadata indicative of an address of the source block in the metadata block. The controller can perform a memory management operation to transfer data from the source block to the destination block.

Abstract: The present disclosure provides a compound, a complex, a preparation method thereof, and a use thereof. The compound is represented by the following structural formula, in which R1 to R10 are the same or different and are each independently selected from hydrogen, a hydrocarbon group having a carbon number of C1 to C16, a substituted hydrocarbon group, an alkoxy group, an alkylthio group, an alkylamino group, a haloalkylthio group, a halogen-substituted alkoxy group, a halogen-substituted alkylamino group, an aryloxy group, an arylthio group, arylamino group, a diphenylphosphino group, a halogen group, a nitro group, or a nitrile group. The complex of one embodiment of the present disclosure has a high catalytic effect, and can be used to prepare a highly branched, controllable, low molecular weight polymer with a high activity.

Abstract: A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function Fij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score Li,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores Lj to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.

Abstract: The present invention relates to a biaxially oriented polyolefin multilayer heat shrinkable film, which is a multilayer heat shrinkable film with at least three laminated layers, and has internal and external surface layers of a resin composition comprising 70-80 wt % of an ethylene-norbornene copolymer having a glass-transition temperature (Tg) of 138° C. and a norbornene content of 76 wt %, and 20-30 wt % of an ethylene-propylene random copolymer having a melting point (Tm) of 140° C.; and a core layer comprising 54 wt % of an ethylene-propylene random copolymer having Tm of 140° C., 8 wt % of an ethylene-butylene random copolymer having Tm of 66° C., 20 wt % of an ethylene-norbornene copolymer having Tg of 78° C. and norbornene content of 65 wt %, and 18 wt % of a hydrogenated petroleum resin having softening point (Ts) of 140° C.

Abstract: In order to reduce computation time and cost involved with determining one or more optimal parameters for a pre-cooling strategy, for a modeled system, a two-step genetic algorithms is used to optimize energy consumption of the modeled system with respect to cost of the energy consumption. A first step of the two-step genetic algorithms determines a population of potential solutions that are used to initialize a second step of the two-step genetic algorithm. The second step of the two-step genetic algorithm determines the one or more optimal parameters for the pre-cooling strategy from the population output by the first genetic algorithm.

Abstract: A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function Fij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score Li,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores Lj to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.

Abstract: The present invention relates to a biaxially oriented polyolefin multilayer heat shrinkable film, which is a multilayer heat shrinkable film with at least three laminated layers, and has internal and external surface layers of a resin composition comprising 70-80 wt % of an ethylene-norbornene copolymer having a glass-transition temperature (Tg) of 138° C. and a norbornene content of 76 wt %, and 20-30 wt % of an ethylene-propylene random copolymer having a melting point (Tm) of 140° C.; and a core layer comprising 54 wt % of an ethylene-propylene random copolymer having Tm of 140° C., 8 wt % of an ethylene-butylene random copolymer having Tm of 66° C., 20 wt % of an ethylene-norbornene copolymer having Tg of 78° C. and norbornene content of 65 wt %, and 18 wt % of a hydrogenated petroleum resin having softening point (Ts) of 140° C.

Abstract: In order to reduce computation time and cost involved with determining one or more optimal parameters for a pre-cooling strategy, for a modeled system, a two-step genetic algorithms is used to optimize energy consumption of the modeled system with respect to cost of the energy consumption. A first step of the two-step genetic algorithms determines a population of potential solutions that are used to initialize a second step of the two-step genetic algorithm. The second step of the two-step genetic algorithm determines the one or more optimal parameters for the pre-cooling strategy from the population output by the first genetic algorithm.

Abstract: A battery system including at least one battery cell having an interior and having at least one exterior surface is provided with at least one cooling plate that is positioned in engagement with the exterior surface for absorbing heat from the interior of the battery cell. A cooling tube is thermally coupled to the cooling plate for absorbing heat from the cooling plate.