Abstract: The present application proposes a method and system for quantitative derivation of an on-line evaluation layered model for evaluating concrete dam operation performance, and a storage medium, and relates to the technical field of dam operation safety monitoring and management. A logic architecture of quantitative derivation of the concrete dam operation performance mainly has two aspects, a longitudinal dimension and a transverse dimension. The longitudinal dimension is the principal line of assessing longitudinal evaluation of the on-line evaluation layered model, and an analysis idea of the concrete dam from local to global and from details to whole is objectively reflected from six levels of monitoring data, diagnosis methods, monitored items, evaluation indicators, key parts and an overall project.

Abstract: A computer-implemented method for facilitating encoding of video data. The method includes performing an operation to determine prediction residuals associated with a unit of the video data, and, processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data. The model parameters are arranged to facilitate encoding of at least the unit of the video data. The method can be applied to multiple ones of such unit of the video data.

Abstract: There is provided a computer-implemented method for learned video compression, which includes processing a current frame (xt) and previously decoded frame ({circumflex over (x)}t-1) of a video data using a motion estimation model to estimate a motion vector (vt) for every pixel, compressing the motion vector (vt) and reconstructing the motion vector (vt) to a reconstructed motion vector ({circumflex over (v)}t), applying an enhanced context mining (ECM) model to obtain enhanced context ({umlaut over (C)}E) from the reconstructed motion vector ({circumflex over (v)}t) and previously decoded frame feature (x?t-1), compressing the current frame (xt) with the assistance of the enhanced context ({umlaut over (C)}E) to obtain a reconstructed frame ({circumflex over (x)}?t), and providing the reconstructed frame ({circumflex over (x)}?t) to a post-enhancement backend network to obtain a high-resolution frame ({circumflex over (x)}t).

Abstract: According to one or more aspects, apparatus, systems, and methods may be provided for language agnostic full link gray deployment of microservices and middleware. According to an aspect a method may be provided for propagating and routing traffic between microservices of an application. The method includes receiving, at a plugin of a proxy corresponding to a service of the application, an outbound request from the service. The outbound request may indicate a trace identifier (ID) identifying a trace of the outbound request in the application. The method may further include assigning, by the plugin, a custom label to the outbound request. The custom label may be associated with the trace ID and indicate a version of the application towards which the outbound request is to be routed. The method may further include sending, by the plugin to a router of the proxy, the outbound request for routing.

Abstract: A video processing method includes performing a conversion between a video including a video unit and a coded representation of the video, where, after the video unit is encoded or decoded with an intra prediction mode, one or more frequence tables and/or one or more sorted intra prediction mode (IPM) tables are selectively updated according to a rule, where the one or more frequence tables include information about frequence of the intra prediction mode used for processing the video unit in the conversion, where the frequence indicates an occurrence of the intra prediction mode used for the conversion, and where the one or more sorted IPM tables indicate the intra prediction mode used in the processing.

Abstract: An AZB detection method for video coding, which includes the steps of detecting if a residual signal includes a spatial domain GAZB; detecting if the residual signal includes a frequency domain GAZB if no spatial domain GAZB is detected in the previous step; detecting if the residual signal includes a PAZB if no frequency domain GAZB is detected in the previous step; and determining that the residual signal is a non-AZB signal if no PAZB is detected in the previous step. The proposed method achieves promising time savings for test sequences of different resolutions, with negligible rate-distortion performance loss.

Abstract: A method for learning-based point cloud geometry compression includes: given a source point cloud, regressing an aligned mesh that is driven by a set of parameters from a deformable template mesh, quantizing the set of parameters into a parameter bitstream, generating an aligned point cloud from the quantized parameters by mesh manipulation and mesh-to-point-cloud conversion, extracting features from both the source point cloud and the aligned point cloud based on sparse tensors including coordinates and features, the coordinates being encoded into a coordinate bitstream, warping the features of the aligned point cloud onto the coordinates of the source point cloud, obtaining residual features through feature subtraction, processing the residual features using an entropy model into a residual feature bitstream, and obtaining a reconstructed point cloud by processing the parameter bitstream, the coordinate bitstream and the residual feature bitstream.

Abstract: Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ?) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ?) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them.

Abstract: The present disclosure provides a video encoding method, a decoding method, and an apparatus. The video encoding method includes: obtaining an original reference video frame and an original target video frame to be encoded; adjusting a resolution of the original target video frame to obtain an adjusted target video frame with a first preset resolution; and performing feature extraction on the adjusted target video frame to obtain a target feature through a feature extraction network corresponding to the first preset resolution; encoding the original reference video frame and the target features respectively to obtain a video bitstream, and performing video frame reconstruction based on the video bitstream to generate a reconstructed video frame with a same resolution as the original target video frame.

Abstract: The present invention provides a method for predicting whether an individual has early myocardial infarction. The method includes: providing a blood sample of the individual; testing a marker in the blood sample to obtain the quantity of the marker; comparing the obtained number with a preset threshold, and judging whether the individual will suffer from myocardial infarction by the comparison, wherein the marker is any one or more nucleotide sequences of miRNA, lncRNA or circRNA.

Abstract: A method including deforming the reference sample frame through generator in an initial generative model to generate reconstructed sample frames; inputting each reconstructed sample frame and the corresponding to-be-encoded sample frame into a first discriminator in the initial generative model to obtain a first identification result; splicing the to-be-encoded sample frames in timestamp order to obtain a spliced to-be-encoded sample frame, and splicing the reconstructed sample frames to obtain a spliced reconstructed sample frame; inputting the spliced to-be-encoded sample frame and the spliced reconstructed sample frame into a second discriminator in the initial generative model to obtain a second identification result; obtaining an adversarial loss value based on the first identification result and the second identification result; and training the initial generative model based on the adversarial loss value.

Abstract: A framework that comprises a reinforcement-learning-based neural-network for compressing, and for transmitting the compressed genomes over a data network in repeated steps each of a plurality of species. The framework also takes data on inefficient transmission of compressed genome in the preceding step, and feeds this data forward to modify the selection of the compression parameter in the present step. The invention provides the possibility that the genome of any species may be compressed optimally and transmitted in optimal efficiency. That is, big genome sequence is neither over compressed, which takes a lot of processing time leading to delays, nor under compressed which will require more time to transmit.

Abstract: A method of decoding a bitstream to output one or more pictures for a video stream. The method includes receiving a bitstream comprising one or more types of facial representation parameters; and decoding, using coded information of the bitstream, one or more pictures. The decoding includes decoding the one or more types of facial representation parameters; converting the one or more types of facial representation parameters into one or more dense motion flows having a common format; and generating a facial picture based on the one or more dense motion flows and a key reference picture of the one or more pictures.

Abstract: Methods and apparatuses are provided for performing generative face video compression by using a face feature translator. An exemplary method includes receiving a bitstream associated with a first type of facial feature data representing a facial picture; and decoding, using coded information of the bitstream, one or more pictures, wherein the decoding includes: transforming the first type of facial feature data into a second type of facial feature data; and reconstructing the facial picture based on the second type of facial feature data.

Abstract: A system and a method for a computer implemented method of image enhancement includes the steps of receiving an input image, wherein the input image is a low light image, processing the input image, by a pre trained image enhancer, to generate an initial enhanced image, accessing, from an image memory, a response value corresponding to a sample specific property a normal image, generating an adjustment factor based on the response value from the image memory, generating a final enhanced image by applying the adjustment factor to the initial enhanced image.

Abstract: Disclosed is a method for preparing aluminum hydroxide nanowires by a template process. The method includes: (1) dispersing nanocellulose in water to obtain a solution having a concentration of 0.1%-10% by mass; adding an aluminum salt solution to the solution obtained in step (1) and mixing; adding an alkaline solution thereto while stirring, and subjecting a resulting mixture to reactive precipitation, and washing with water and drying, to obtain the aluminum hydroxide nanowires, wherein a mass of an aluminum salt in the aluminum salt solution is in a range of 1-100% of a mass of the nanocellulose, and an alkali in the alkaline solution has a same amount in moles as the aluminum salt in the aluminum salt solution.

Abstract: The present disclosure provides a central staged combustion chamber with self-excited sweeping oscillating fuel injection nozzles, including an outer housing with a cavity inside, a main stage coaxially disposed with the outer housing, and a pilot stage coaxially disposed with the outer housing. An annular fuel passage is used for connecting a plurality of self-excited sweeping oscillating fuel injection nozzles, and the self-excited sweeping oscillating fuel injection nozzles are suitable for injecting oscillating liquid fuel into a primary swirling passage. The fuel is output in a fan shape through each of the self-excited sweeping oscillating fuel injection nozzles and is dispersed by an incoming flow through the swirling passage, so that the atomization performance and spatial distribution uniformity of the fuel can be greatly improved.

Abstract: Two-dimensional face presentation attacks are one of most notorious and pervasive face spoofing types, causing security issues to facial authentication systems. To tackle these issues, a cost-effective face anti-spoofing (FAS) system based on acoustic modality, named as Echo-FAS, is devised, which employs a crafted acoustic signal to probe the presented face. First, a large-scale, high-diversity, acoustic-based FAS database, named as Echo-Spoof, is built. Based upon Echo-Spoof, we design a two-branch framework combining global and local frequency features of the presented face to distinguish live vs. spoofing faces. Echo-FAS has the following merits: (1) it only needs one speaker and one microphone; (2) it can capture three-dimensional geometrical information of the presented face and achieve a remarkable FAS performance; and (3) it can be handily allied with RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust.

Abstract: A computer-implemented method for facilitating encoding of video data. The method includes performing an operation to determine prediction residuals associated with a unit of the video data, and, processing, using a neural network arrangement, the prediction residuals associated with the unit of the video data to determine model parameters associated with a rate-distortion model for the unit of the video data. The model parameters are arranged to facilitate encoding of at least the unit of the video data. The method can be applied to multiple ones of such unit of the video data.

Abstract: A method for learning-based point cloud geometry compression includes: given a source point cloud, regressing an aligned mesh that is driven by a set of parameters from a deformable template mesh, quantizing the set of parameters into a parameter bitstream, generating an aligned point cloud from the quantized parameters by mesh manipulation and mesh-to-point-cloud conversion, extracting features from both the source point cloud and the aligned point cloud based on sparse tensors including coordinates and features, the coordinates being encoded into a coordinate bitstream, warping the features of the aligned point cloud onto the coordinates of the source point cloud, obtaining residual features through feature subtraction, processing the residual features using an entropy model into a residual feature bitstream, and obtaining a reconstructed point cloud by processing the parameter bitstream, the coordinate bitstream and the residual feature bitstream.