Abstract: The present application discloses a fireproof coating material, and belongs to the technical field of fireproof coatings. The fireproof coating material includes 40-80 parts of an aqueous curing agent, 20-50 parts of water and 80-200 parts of a mother liquor of the fireproof coating material, wherein the mother liquor of the fireproof coating material consists of 50-80 parts of a flame retardant, 15-25 parts of a dispersant and 190-220 parts of water. The flame retardant used can not only effectively improve the problems of poor compatibility and mixing uniformity of a flame retardant with an aqueous latex in the existing fireproof coating material, but also can utilize O and H contained in a flame retardant system to form a carbonized layer during dehydration to isolate oxygen, preventing combustion, and enhancing the fire retardant performance.

Abstract: A lossless data compressor prevents normalization overruns on-the-fly as symbol occurrence counts are rounded to generate symbol frequencies, allowing an encoding table generator to generate encoding table entries without waiting for the symbol frequency table to finish filling. Rounding errors are accumulated as symbols are normalized and compensated for by reducing a symbol frequency when the symbol frequency is at least 2 and the accumulated errors have exceeded a threshold. The symbol frequency is also reduced when the number of remaining states in the encoding table is insufficient for a number of remaining unprocessed symbols and states for a current encoding table entry. Since error compensation occurs as symbols are being normalized, encoding table generation is not forced to wait for all symbols in the block to be processed, reducing latency. Three pipeline stages can operate on three input blocks: symbol counting, normalization/error compensation/encoding table generation, and data encoding.

Abstract: Embodiments of the present application disclose a three-dimensional model generating method and apparatus, and an electronic device. The three-dimensional model generating method includes: performing feature extraction on an aerial three-dimensional model and a ground three-dimensional model of a to-be-modeled object respectively to obtain at least three sets of feature vectors; performing registration on the aerial three-dimensional model and the ground three-dimensional model based on the feature vectors, and determining a first transition area of a fused three-dimensional model; reconstructing a triangular patch of the first transition area to obtain a second transition area; and performing texture filling on the second transition area, and determining a target three-dimensional model of the to-be-modeled object.

Abstract: A data-compression analyzer can rapidly make a binary decision to compress or not compress an input data block or can use a slower neural network to predict the block's compression ratio with a regression model. A Concentration Value (CV) that is the sum of the squares of the frequencies and a Number of Zero (NZ) symbols are calculated from an un-sorted symbol frequency table. A rapid decision to compress is signaled when their product CV*NZ exceeds a horizontal threshold THH. During training, CV*NZ is plotted as a function of compression ratio C % for many training data blocks. Different test values of THH are applied to the plot to determine true and false positive rates that are plotted as a Receiver Operating Characteristic (ROC) curve. The point on the ROC curve having the largest Youden index is selected as the optimum THH for use in future binary decisions.

Abstract: A lossless data compressor prevents normalization overruns on-the-fly as symbol occurrence counts are rounded to generate symbol frequencies, allowing an encoding table generator to generate encoding table entries without waiting for the symbol frequency table to finish filling. Rounding errors are accumulated as symbols are normalized and compensated for by reducing a symbol frequency when the symbol frequency is at least 2 and the accumulated errors have exceeded a threshold. The symbol frequency is also reduced when the number of remaining states in the encoding table is insufficient for a number of remaining unprocessed symbols and states for a current encoding table entry. Since error compensation occurs as symbols are being normalized, encoding table generation is not forced to wait for all symbols in the block to be processed, reducing latency. Three pipeline stages can operate on three input blocks: symbol counting, normalization/error compensation/encoding table generation, and data encoding.

Abstract: A data-compression analyzer can rapidly make a binary decision to compress or not compress an input data block or can use a slower neural network to predict the block's compression ratio with a regression model. A Concentration Value (CV) that is the sum of the squares of the frequencies and a Number of Zero (NZ) symbols are calculated from an un-sorted symbol frequency table. A rapid decision to compress is signaled when their product CV*NZ exceeds a horizontal threshold THH. During training, CV*NZ is plotted as a function of compression ratio C % for many training data blocks. Different test values of THH are applied to the plot to determine true and false positive rates that are plotted as a Receiver Operating Characteristic (ROC) curve. The point on the ROC curve having the largest Youden index is selected as the optimum THH for use in future binary decisions.

Abstract: A train-linking lossless data compressor examines a block of data and uses a same coder to generate a same code when all data values in the input block are identical. When the input data is not all the same value, then a Gaussian coder, a Laplace coder, and a delta coder are activated in parallel. The three compressed code lengths are compared and the smallest code length is output as the compressed code when it is smaller than a copy code length. The copy code is a tag followed by copying all the data in the input block. When the smallest of the three compressed code lengths is larger than the copy code length, the file is not compressible, and the copy code is output. No frequency table is required so latency is low. The delta coder subtracts data values from an average value of the last data block.

Abstract: The present disclosure describes methods, devices, and non-transitory computer readable storage medium for recognizing a human action using a graph convolutional network (GCN). The method includes obtaining, by a device, a plurality of joint poses. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes normalizing, by the device, the plurality of joint poses to obtained a plurality of normalized joint poses; extracting, by the device, a plurality of rough features using a modified spatial-temporal GCN (ST-GCN) from the plurality of normalized joint poses; reducing, by the device, a feature dimension of the plurality of rough features to obtain a plurality of dimension-shrunk features; refining, by the device, the plurality of dimension-shrunk features based on a self-attention model to obtain a plurality of refined features; and recognizing, by the device, a human action based on the plurality of refined features.

Abstract: Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Embodiments apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier “votes” for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Abstract: Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described, Embodiments apply a trained random forest classifier to low resolution patches generated from the low-resolution input image to classify the low resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier “votes” for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Abstract: A method for a vehicle management system with a perspective view camera generating perspective images of vehicles in a traffic direction, including performing an anti-perspective transform on the perspective images; separating each anti-perspective image into sub-images along one of a radial direction and a tangential direction relative to the traffic direction; determining a scale factor for each sub-image based upon measuring a scale of each vehicle at plural positions in one image using a frame difference method performing a scale transform for each sub-image using the corresponding scale factors for the sub-image; combining each of the scale transformed sub-images for each vehicle into corresponding enhanced anti-perspective images; performing vehicle detection for each vehicle based a combination of the enhanced anti-perspective images in which tracking is enhanced with an optimized detection box size range determined by the enhanced anti-perspective images; and performing vehicle tracking for each vehic

Abstract: A method for a vehicle management system with a perspective view camera generating perspective images of vehicles in a traffic direction, including performing an anti-perspective transform on the perspective images; separating each anti-perspective image into sub-images along one of a radial direction and a tangential direction relative to the traffic direction; determining a scale factor for each sub-image based upon measuring a scale of each vehicle at plural positions in one image using a frame difference method performing a scale transform for each sub-image using the corresponding scale factors for the sub-image; combining each of the scale transformed sub-images for each vehicle into corresponding enhanced anti-perspective images; performing vehicle detection for each vehicle based a combination of the enhanced anti-perspective images in which tracking is enhanced with an optimized detection box size range determined by the enhanced anti-perspective images; and performing vehicle tracking for each vehic

Abstract: According to one embodiment, a speech translation apparatus includes a speech recognition unit, a machine translation unit, an extracting unit, and a receiving unit. The extracting unit extracts words used for a meeting from a word set, based on information related to the meeting, and sends the extracted words to the speech recognition unit and the machine translation unit. The receiving unit receives the speech in a first language in the meeting. The speech recognition unit recognizes the speech in the first language as a text in the first language. The machine translation unit translates the text in the first language into a text in a second language.

Abstract: Provided are an information processing method and apparatus being applicable to a first electronic device, comprising establishing a communication link between the first and a second electronic device; displaying a first communication interface; judging whether a predetermined condition is satisfied; and displaying a second communication interface when it is judged that the predetermined condition is satisfied; wherein the second communication interface comprises a first area and a second area, the first and second areas are at least partially overlapped, and the first area comprises a first subarea and/or a second subarea, the second subarea is used for displaying first information acquired by the first electronic device, the first subarea is used for displaying second information acquired by the second electronic device, the second area is used for displaying shared information, and at least a part of the shared information is simultaneously displayed on the first and second electronic device.

Abstract: A defect detection system for an extreme ultraviolet lithography mask comprises an extreme ultraviolet light source (1), extreme ultraviolet light transmission parts (2, 3), an extreme ultraviolet lithography mask (4), a photon sieve (6) and a collection (7) and analysis (8) system. Point light source beams emitted by the extreme ultraviolet light source (1) are focused on the extreme ultraviolet lithography mask (4) through the extreme ultraviolet light transmission parts (2, 3); the extreme ultraviolet lithography mask (4) emits scattered light and illuminates the photon sieve (6); and the photon sieve (6) forms a dark field image and transmits the same to the collection (7) and analysis (8) system. The defect detection system for the extreme ultraviolet photolithographic mask uses the photon sieve to replace a Schwarzchild objective, thereby realizing lower cost, relatively small size and high resolution.

Abstract: A method of manufacturing a sub-wavelength extreme ultraviolet metal transmission grating is disclosed. In one aspect, the method comprises forming a silicon nitride self-supporting film window on a back surface of a silicon-based substrate having both surfaces polished, then spin-coating a silicon nitride film on a front surface of the substrate with an electron beam resist HSQ. Then, performing electron beam direct writing exposure on the HSQ, developing and fixing to form a plurality of grating line patterns and a ring pattern surrounding the grating line patterns. Then depositing a chrome material on the front surface of the substrate through magnetron sputtering. Then, removing the chrome material inside the ring pattern. Then, growing a gold material on the front surface of the substrate through atomic layer deposition.

Abstract: A defect detection system for an extreme ultraviolet lithography mask comprises an extreme ultraviolet light source (1), extreme ultraviolet light transmission parts (2, 3), an extreme ultraviolet lithography mask (4), a photon sieve (6) and a collection (7) and analysis (8) system. Point light source beams emitted by the extreme ultraviolet light source (1) are focused on the extreme ultraviolet lithography mask (4) through the extreme ultraviolet light transmission parts (2, 3); the extreme ultraviolet lithography mask (4) emits scattered light and illuminates the photon sieve (6); and the photon sieve (6) forms a dark field image and transmits the same to the collection (7) and analysis (8) system. The defect detection system for the extreme ultraviolet photolithographic mask uses the photon sieve to replace a Schwarzchild objective, thereby realizing lower cost, relatively small size and high resolution.