Abstract: An innovative equipment system comprises a tap water reservoir system, a disinfectant reactor system, a pump system, a disinfection and safety door spraying system, a sewage recycling system and a sewage treatment system, wherein the tap water reservoir system is mainly configured to store tap water in advance according to actual cargo volumes and disinfection requirements, and generally in tons; and the disinfectant reactor system comprises boron-doped diamond electrode modules, a reactor indicating lamp module, a visible reactor, a smart control screen, at least one water tank and a structure protection module. The Innovative disinfection and sterilization equipment system can easily kill bacteria and viruses such as Escherichia coli, Staphylococcus aureus, Salmonella typhimurium, and new coronavirus in a short time and enhance disinfection efficiency.

Abstract: A method for automatically creating an equipment inventory from a plurality of images of a site. The method includes receiving, by a processor, the plurality of images of the site, labeling, by the processor, equipment present in each image of the plurality of images of the site, creating, by the processor, an overlap curve for neighboring images of the plurality of images of the site, determining, by the processor, a useful image of each side of the site based on the overlap curve, the useful image including a subset of the plurality of images, creating, by the processor, an overlay of equipment in the useful image of each side of the site, counting, by the processor, equipment present in the overlay of each side of the site, and generating, by the processor, the equipment inventory by adding a number of equipment counted in the overlay of each side of the site.

Abstract: Systems, methods, and software of processing Deep Neural Networks (DNN) comprising a plurality of nodes with connections between the nodes. In one embodiment, a system initializes the DNN by assigning initial values to weights associated with the connections, and identifies an initial subset of the connections randomly selected from the DNN. The initial subset after training with a training dataset has a training accuracy below an accuracy threshold. The system performs a growth process by adding additional connections from the DNN to the initial subset to generate a qualified subset of the connections. The qualified subset of the connections after training with the training dataset has a training accuracy that reaches the accuracy threshold.

Abstract: An image feature extraction method for person re-identification includes performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction; performing the aligned local descriptor extraction by processing an original image by affine transformation and performing a summation pooling operation on image block features of same regions to obtain an aligned local descriptor; reserving spatial information between inner blocks of the image for the aligned local descriptor; and performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature. The method can resolve the problem of feature misalignment caused by posture changes of pedestrian, etc., and eliminate the effect of unrelated backgrounds on re-recognition, thus improving the precision and robustness of person re-identification.

Abstract: A computer system may use machine learning to determine functional zones within a geographical region. The computer system may determine position data and functional data for locations within a geographical region. The computer system may use machine learning to determine functional zones with a two-stage clustering approach. During the first stage, the computer system may determine geoclusters by clustering locations based on the position data. During the second stage, the computer system may determine functional zones by clustering the geoclusters based on the functional data.

Abstract: Disclosed is a deep discriminative network for person re-identification in an image or a video. Concatenation are carried out on different input images on a color channel by constructing a deep discriminative network, and an obtained splicing result is defined as an original difference space of different images. The original difference space is sent into a convolutional network. The network outputs the similarity between two input images by learning difference information in the original difference space, thereby realizing person re-identification. The features of an individual image are not learnt, and concatenation are carried out on input images on a color channel at the beginning, and difference information is learnt on an original space of the images by using a designed network. By introducing an Inception module and embedding the same into a model, the learning ability of a network can be improved, and a better differentiation effect can be achieved.

Abstract: An image feature extraction method for person re-identification includes performing person re-identification by means of aligned local descriptor extraction and graded global feature extraction; performing the aligned local descriptor extraction by processing an original image by affine transformation and performing a summation pooling operation on image block features of same regions to obtain an aligned local descriptor; reserving spatial information between inner blocks of the image for the aligned local descriptor; and performing the graded global feature extraction by grading a positioned pedestrian region block and solving a corresponding feature mean value to obtain a global feature. The method can resolve the problem of feature misalignment caused by posture changes of pedestrian, etc., and eliminate the effect of unrelated backgrounds on re-recognition, thus improving the precision and robustness of person re-identification.

Abstract: Disclosed is a deep discriminative network for person re-identification in an image or a video. Concatenation are carried out on different input images on a color channel by constructing a deep discriminative network, and an obtained splicing result is defined as an original difference space of different images. The original difference space is sent into a convolutional network. The network outputs the similarity between two input images by learning difference information in the original difference space, thereby realizing person re-identification. The features of an individual image are not learnt, and concatenation are carried out on input images on a color channel at the beginning, and difference information is learnt on an original space of the images by using a designed network. By introducing an Inception module and embedding the same into a model, the learning ability of a network can be improved, and a better differentiation effect can be achieved.

Abstract: System, method, and software for recommending documents to users. A recommendation system identifies recent documents and historical information indicating consumption by users of past documents. The system generates a historical reading matrix based on the historical information, and generates a vector matrix for the recent documents and the past documents. The system generates an estimated reading matrix based on the historical reading matrix and the vector matrix, with the estimated reading matrix having first entries that represent actual reading scores for the past documents. The system calculates estimated reading scores for the recent documents based on the vector matrix, and populates second entries of the estimated reading matrix corresponding to the recent documents with the estimated reading scores. The system performs a factorization on the estimated reading matrix to generate a refined reading matrix, and generates recommendations of the recent documents based on the refined reading matrix.

Abstract: A computer system may use machine learning to determine functional zones within a geographical region. The computer system may determine position data and functional data for locations within a geographical region. The computer system may use machine learning to determine functional zones with a two-stage clustering approach. During the first stage, the computer system may determine geoclusters by clustering locations based on the position data. During the second stage, the computer system may determine functional zones by clustering the geoclusters based on the functional data.

Abstract: Techniques are provided for anonymizing data in a data stream.

Abstract: A capability for sparsifying a representation of pairwise cost information is presented herein. The capability for sparsifying a representation of pairwise cost information may be used to sparsify a representation of pairwise cost information for a set of nodes. The sparsification of a representation of pairwise cost information for a set of nodes may provide thereby a sparsified representation of the pairwise cost information for the set of nodes. The sparsification of the representation of pairwise cost information for the set of nodes may be based on clustering of the nodes of the set of nodes into clusters. The sparsification of the representation of pairwise cost information for the set of nodes may be based on calculation of intra-cluster costs and inter-cluster costs, where the intra-cluster costs and inter-cluster costs are calculated based on the pairwise cost information of the representation of the pairwise cost information for the set of nodes.

Abstract: A capability for providing policy enforcement in a topology abstraction system is presented. The capability for providing policy enforcement in a topology abstraction system may support use of topology abstraction policies to control abstraction of topology information of a topology (e.g., a network topology of a communication network or any other suitable type of topology). The capability for providing policy enforcement in a topology abstraction system providing an abstract representation of a topology may support use of topology abstraction policies to control selection (or acceptance) of topology elements for inclusion within the abstract representation of a topology and filtering (or rejection) of topology elements from being included within the abstract representation of a topology.

Abstract: Techniques are provided for anonymizing data in a data stream.

Abstract: A capability for sparsifying a representation of pairwise cost information is presented herein. The capability for sparsifying a representation of pairwise cost information may be used to sparsify a representation of pairwise cost information for a set of nodes. The sparsification of a representation of pairwise cost information for a set of nodes may provide thereby a sparsified representation of the pairwise cost information for the set of nodes. The sparsification of the representation of pairwise cost information for the set of nodes may be based on clustering of the nodes of the set of nodes into clusters. The sparsification of the representation of pairwise cost information for the set of nodes may be based on calculation of intra-cluster costs and inter-cluster costs, where the intra-cluster costs and inter-cluster costs are calculated based on the pairwise cost information of the representation of the pairwise cost information for the set of nodes.

Abstract: A capability for providing policy enforcement in a topology abstraction system is presented. The capability for providing policy enforcement in a topology abstraction system may support use of topology abstraction policies to control abstraction of topology information of a topology (e.g., a network topology of a communication network or any other suitable type of topology). The capability for providing policy enforcement in a topology abstraction system providing an abstract representation of a topology may support use of topology abstraction policies to control selection (or acceptance) of topology elements for inclusion within the abstract representation of a topology and filtering (or rejection) of topology elements from being included within the abstract representation of a topology.

Abstract: A network device of a communication network is configured to implement coordinated scheduling and processor rate control. In one aspect, packets are received in the network device and scheduled for processing from one or more queues of that device. An operating rate of a processor of the network device is controlled based at least in part on at least one of an arrival rate of the packets and a number of the packets stored in the one or more queues. As one example of processor rate control based on packet arrival rate, the operating rate of the processor may be controlled by accumulating a designated number of the received packets, determining an average arrival rate of the accumulated packets, and setting the operating rate of the processor based on the average arrival rate.

Abstract: A network device of a communication network is configured to implement a switch fabric that includes collector-based cell reordering functionality. In one embodiment, the switch fabric comprises a plurality of distributors, a plurality of routing elements each having inputs coupled to respective outputs of multiple ones of the distributors, and a plurality of collectors each having inputs coupled to respective outputs of multiple ones of the routing elements. At least a given one of the collectors comprises a sorting circuit configured to reorder streams of cells received from respective ones of the routing elements into a single reordered stream of cells utilizing a merge sort.