Abstract: Disclosed by the present invention is an ultra-wideband white noise source based on a slicing super-continuum spectrum. The entropy source used is a super-continuum spectrum photon entropy source having a coverage range of several hundreds of nm, white noise can thus be generated in a wide frequency range, thereby effectively avoiding the bandwidth bottleneck of an electronic device. By separately adjusting the filter centers of two optical filters, the center frequency for generating the white noise can be adjusted so as to get adapted to different working situations. High bandwidth white noise can be generated by simply filtering the super-continuum spectrum and performing photoelectric conversion, and in comparison with the previous solutions, the solution of the present invention is simpler and can be easily implemented.

Abstract: A data encoding method includes determining a value of a syntax element of point cloud data of a point cloud. The point cloud data includes attribute values of the point cloud and the syntax element indicates a search range of the attribute values during prediction encoding. The method further includes performing prediction processing on the attribute values according to the search range to obtain residuals of the attribute values, and encoding the residuals and a difference between the value of the syntax element and a constant value to generate code stream data. The constant value is a positive number.

Abstract: A processing method includes encoding or decoding an N-th layer of a multi-tree using a breadth-first mode. The multi-tree is used for position division on a point cloud. The method further includes, in response to a number or a distribution of all point cloud points in a target node of the N-th layer meeting a preset condition, encoding or decoding the point cloud points in the target node using a depth-first mode to obtain a code stream of the target node. The code stream of the target node includes an identifier and indexes of nodes where the point cloud points of the target node are located at various layers of the multi-tree. The identifier indicates to switch from the breadth-first mode to the depth-first mode to encode or decode sub-nodes of the target node. N is an integer greater than or equal to 1.

Abstract: An encoding method includes encoding first M layers of a multi-tree using a breadth-first mode, and switching to a depth-first mode to encode at least one node in the M-th layer of the multi-tree. The multi-tree is obtained by dividing a plurality of three-dimensional data points using a multi-tree division method. M is an integer larger than or equal to 2. Sub-nodes of each of the at least one node are encoded using the breadth-first mode, and the sub-nodes of one of the at least one node include all sub-nodes obtained by performing at least one multi-tree division on the one of the at least one node until a leaf sub-node is obtained.

Abstract: A physically unclonable function (PUF) device includes a hybrid Boolean network module of a ring of N number of Boolean nodes connected end to end and a sampling module, wherein the hybrid Boolean network module comprises N number of xor logic gates and corresponding N number of multiplexers, wherein a function change module is disposed between an output end of a first xor logic gate of the N number of xor logic gates and an input end of a first multiplexer of the N number of multiplexers, wherein each Boolean node is provided with four input ends and three output ends, the four input ends respectively connected to an output end of each of two juxtaposing Boolean nodes, an initial excitation signal and a control delay signal, the three output ends respectively output to an input of each of two juxtaposing Boolean nodes, and the sampling module.

Abstract: A point cloud attribute encoding method includes performing binarization on an attribute value in point cloud data to obtain a binary attribute value, and performing arithmetic encoding on bits in the binary attribute value using at least one probability model. A bit depth of the binary attribute value is larger than or equal to 1.

Abstract: The present disclosure provides a high-speed random number generation method and device, comprising an entropy source module and an entropy sampling module. The entropy source module is an autonomous Boolean network formed by digital logic gates, the network is formed by an XNOR gate and (N?1) XOR gates, wherein the value of N is equal to 3n (n is a positive integer), and the entropy source can generate chaotic signals having wide and flat frequency spectrum. The entropy sampling module of the present disclosure is formed by D flip flops used for sampling and quantizing the chaotic signals to generate random number sequences. The random number sequences generated by the present disclosure can pass test standards (NIST and Diehard statistic tests) of random number industry and have excellent random statistic characteristics.

Abstract: A three-dimensional data point encoding method includes determining maximum values of range values of an initial block of target three-dimensional data points in a radial distance direction, a zenith angle direction, and an azimuth angle direction according to position coordinates of the target three-dimensional data points in a spherical coordinate system, performing partitioning processes on the initial block, and encoding the target three-dimensional data points according to partitioning results of the initial block. Performing the partitioning processes on the initial block includes performing at least one octree partitioning process on the initial block to obtain a plurality of first-type sub-blocks and performing at least one of a quadtree partitioning process or a binary tree partitioning process on at least one of the plurality of first-type sub-blocks.

Abstract: A three-dimensional data point encoding method, decoding method, and encoding device. The encoding method includes: determining maximum values of side lengths of a cuboid of three-dimensional data points to be encoded in three directions according to position coordinates of the three-dimensional data points to be encoded; performing at least one octree partition process on the cuboid, to obtain a plurality of first-type sub-blocks; performing at least one quadtree partition process or binary tree partition process on at least one first-type sub-block of the plurality of first-type sub-blocks; and encoding the three-dimensional data points to be encoded according to partition results of the cuboid.

Abstract: The present disclosure provides a three-dimensional (3D) data point set processing method, the 3D data point set being divided by a multi-tree method. The 3D) data point set processing method includes encoding or decoding a Nth layer of the multi-tree in by using a breadth-first approach; and encoding or decoding a first node by using a depth-first approach when all 3D data points in the first node of the Nth layer fall into a same node of a Mth layer under the first node, wherein N and M are integers greater than or equal to one.

Abstract: The present disclosure provides a high-speed random number generation method and device, comprising an entropy source module and an entropy sampling module. The entropy source module is an autonomous Boolean network formed by digital logic gates, the network is formed by an XNOR gate and (N?1) XOR gates, wherein the value of N is equal to 3n (n is a positive integer), and the entropy source can generate chaotic signals having wide and flat frequency spectrum. The entropy sampling module of the present disclosure is formed by D flip flops used for sampling and quantizing the chaotic signals to generate random number sequences. The random number sequences generated by the present disclosure can pass test standards (NIST and Diehard statistic tests) of random number industry and have excellent random statistic characteristics.

Abstract: The present disclosure provides a three-dimensional (3D) data point set encoding method. The method includes performing position coordinate encoding on position coordinates of one or more 3D data points in the 3D data point set to obtain a first binary bitstream; performing binary encoding on attributes of the one or more 3D data points based on a position coordinates sequence after encoding the position coordinates of the one or more 3D data points to obtain a second binary bitstream; and performing entropy encoding on the first binary bitstream and the second binary bitstream respectively.

Abstract: The visualization of progress of a distributed computational job at multiple points of execution. After a computational job is compiled into multiple vertices, and then those multiple vertices are scheduled on multiple processing nodes in a distributed environment, a processing gathering module gathers processing information regarding processing of multiple vertices of a computational job, and at multiple instances in time in the execution of the computational job. A user interface module graphically presents a representation of an execution structure representing multiple nodes of the computational job, and dependencies between the multiple nodes, where the nodes may be a single vertex or a group of vertices (such as a stage).

Abstract: The visualization of progress of a distributed computational job at multiple points of execution. After a computational job is compiled into multiple vertices, and then those multiple vertices are scheduled on multiple processing nodes in a distributed environment, a processing gathering module gathers processing information regarding processing of multiple vertices of a computational job, and at multiple instances in time in the execution of the computational job. A user interface module graphically presents a representation of an execution structure representing multiple nodes of the computational job, and dependencies between the multiple nodes, where the nodes may be a single vertex or a group of vertices (such as a stage).

Abstract: The present invention is directed to methods for increasing sensitivities of immunoassays. The invention utilizes an acid elution condition that preferentially elute specifically bound immune complexes over non-specifically bound complexes from a solid phase, and designs immunoassay protocols that improve the ratio of specific binding to non-specific binding and thereby improving assay sensitivity. The protocol determines the signal of the labeled immunocomplexes eluted from a solid phase.

Abstract: The present invention is directed to methods for increasing sensitivities of immunoassays. The invention utilizes an acid elution condition that preferentially elute specifically bound immune complexes over non-specifically bound complexes from a solid phase, and designs immunoassay protocols that improve the ratio of specific binding to non-specific binding and thereby improving assay sensitivity. The protocol determines the signal of the labeled immunocomplexes eluted from a solid phase.

Abstract: The present disclosure relates to a new use of anlotinib, and relates in particular to anlotinib inhibiting osteosarcoma and chondrosarcoma growth and metastasis. It has been discovered for the first time that anlotinib can inhibit osteosarcoma and chondrosarcoma growth and metastasis. Anlotinib can significantly inhibit the growth of the osteosarcoma cell lines 143B, U2OS, MG63 and SJSA, induce cycle arrest in said cell lines, and can also inhibit osteosarcoma cell line migration and invasion. It has also been discovered for the first time that anlotinib can enhance the killing effect of the chemotherapeutic drug cisplatin on osteosarcoma cells.

Abstract: Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server trains a word embedding model using one-hot vectors of word pairs from a filtered first corpus of unstructured computer text and a filtered second corpus of unstructured computer text, using an artificial intelligence neural network. The server trains a long short-term memory model using vector matrices that correspond to sentences in the filtered second corpus of unstructured computer text, and labels. The server receives a message, generates a matrix for each sentence in the message by applying the trained word embedding model, generates one or more labels, and a probability for each label, for each sentence in the message by applying the trained long short-term memory model, and routes the message to a second client computing device based upon an assigned label.

Abstract: A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.

Abstract: Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server captures historical story data from an Agile development tracking system. For each completed story, the server generates a vector based upon story-specific features and assigns a label to the vector based upon a cycle time associated with the story. The server trains a classification model using a neural network on the vectors and labels. The server captures new story data from the Agile development tracking system. For each new story, the server generates a vector based upon story-specific features and executes the trained model on the vector to generate a cycle time prediction for the new story. Based upon the cycle time prediction, the server identifies deficiencies in the new story and generates an alert message.