Abstract: A method, apparatus, device, and storage medium for data processing. The method includes: obtaining a business object topology map and displaying the business object topology map on a current interface; where the business object topology map includes a plurality of business objects; receiving data processing logic edited by a user for at least one business object in the business object topology map; parsing the data processing logic to obtain a logical topology map corresponding to the data processing logic; obtaining instance data corresponding to the at least one business object; and processing the instance data based on the logical topology map to obtain a processing result. The method improves efficiency and accuracy of business data processing.

Abstract: Provided are compounds of Formula (I) having activity as inhibitors of G12C mutant KRAS protein, pharmaceutical compositions comprising the compounds, and uses and methods of treating certain disorders, such as cancer, including but not limited to lung, pancreatic and colorectal cancers.

Abstract: Described herein are compounds, agents, compositions, and methods related to the treatment of a viral infection (e.g., Hepatitis C viral infection). In particular, the compounds, agents, compositions, and methods described herein inhibit viral entry into a target cell.

Abstract: The present disclosure relates to the field of Internet technology. A transaction data processing method, a transaction data processing apparatus, a transaction data processing system, an electronic device, and a computer readable storage medium are provided. The method comprises: creating multiple data centers (301); with respect to at least two data centers in the multiple data centers, creating corresponding second-level accounts for a target payment institution respectively, wherein the second-level accounts support resource requirements of any third-level account corresponding to the target payment institution in the data centers (302); determining an available data center among the at least two data centers (303); and processing transaction data from the target payment institution by using a second-level account created for the target payment institution with respect to the available data center (304).

Abstract: A transaction system, comprising: a first-grade account, wherein the first-grade account is a platform-grade master account which is configured to receive external imported funds and can distribute the external imported funds to a second-grade account; the second-grade account is configured to receive the funds distributed by the first-grade account, and can distribute the received funds distributed by the first-grade account to a third-grade account; the third-grade account is configured to undertake a user-oriented transaction service; wherein when the transaction service undertaken by the third-grade account is a withdrawal service, a withdrawal amount is deducted from the third-grade account; and when the withdrawal amount deducted from the third-grade account is less than a total withdrawal amount corresponding to a current withdrawal service, the second-grade account is requested to retrieve a first residual withdrawal amount which is an amount obtained by subtracting the withdrawal amount deducted from

Abstract: Herein are described the preparation of a series of synthetic rocaglates, amidino- and amino-rocaglates, which display inhibition of protein translation and tumor cell proliferation (in vitro and in vivo). The methods described herein allow the preparation of libraries of modified rocaglates. This chemical modification of the rocaglate scaffold changes the C8b-hydroxyl of the natural product series to a more optimal hydrogen bond donor/acceptor.

Abstract: A video retrieval system may include a feature extractor configured to extract first feature descriptors for multiple image frames in the query video. The system may also include a feature extractor to extract second feature descriptors for multiple image frames in a candidate video in a video database. The system may include a comparator to compare the first and second feature descriptors to determine a subset of image frames in the candidate video that are similar to the first video. The system may output die query output by displaying the subset of image frames in a slide show. The system may also output the query by displaying a video formed by at least the subset of image frames. The feature extractor may be implemented in a convolution neural network (CNN) in an artificial intelligence (AI) chip. The system may include key frame extractor to detect key frames in the video.

Abstract: An integrated circuit may include multiple cellular neural networks (CNN) processing engines coupled to at least one input/output data bus and a clock-skew circuit in a loop circuit. Each CNN processing engine includes multiple convolution layers, a first memory buffer to store imagery data and a second memory buffer to store filter coefficients. Each of the CNN processing engines is configured to perform convolution operations over an input image simultaneously in a first clock cycle to generate output to be fed to an immediate neighbor CNN processing engine for performing convolution operations in a next clock cycle. The second memory buffer may store a first subset of filter coefficients for a first convolution layer of the CNN processing engine and store a reference location to the first subset of filter coefficients for a second convolution layer, where the filter coefficients for the first and second convolution layers are duplicate.

Abstract: An integrated circuit may include multiple cellular neural networks (CNN) processing engines coupled in a loop circuit and configured to perform an AI task. Each CNN processing engine includes multiple convolution layers, a first memory buffer to store imagery data and a second memory buffer to store filter coefficients. The CNN processing engines are configured to perform convolution operations over an input image simultaneously in one or more iterations. In each iteration, various sub-images of the input image are loaded to the first memory buffer circularly. A portion of the filter coefficients corresponding to the sub-image are loaded to the second memory buffer in a cyclic order. Data may be arranged in the second memory buffer to facilitate loading of duplicate filter coefficients among at least two convolution layers without requiring duplicate memory space. Methods of training a CNN model having duplicate weights are also provided.

Abstract: Herein are described the preparation of a series of synthetic rocaglates, amidino- and amino-rocaglates, which display inhibition of protein translation and tumor cell proliferation (in vitro and in vivo). The methods described herein allow the preparation of libraries of modified rocaglates. This chemical modification of the rocaglate scaffold changes the C8b-hydroxyl of the natural product series to a more optimal hydrogen bond donor/acceptor.

Abstract: A system for detecting key frames in a video may include a feature extractor configured to extract feature descriptors for each of the multiple image frames in the video. The feature extractor may be an embedded cellular neural network of an artificial intelligence (AI) chip. The system may also include a key frame extractor configured to determine one or more key frames in the multiple image frames based on the corresponding feature descriptors of the image frames. The key frame extractor may determine the key frames based on distance values between a first set of feature descriptors corresponding to a first subset of image frames and a second set of feature descriptors corresponding to a second subset of image frames. The system may output an alert based on determining the key frames and/or display the key frames. The system may also compress the video by removing the non-key frames.

Abstract: A system for training an artificial intelligence (AI) model for an AI chip may include a forward network and a backward propagation network. The AI model may be a convolution neural network (CNN). The forward network may infer the output of the AI chip based on the training data. The backward network may use the output of the AI chip and the ground truth data to train the weights of the AI model. In some examples, the system may train the AI model using a gradient descent method. The system may quantize the weights and update the weights during the training. In some examples, the system may perform a uniform quantization over the weights. The system may also determine the distribution of the weights. If the weight distribution is not symmetric, the system may group the weights and quantize the weights based on the grouping.

Abstract: An artificial intelligence (AI) semiconductor having an embedded convolution neural network (CNN) may include a first convolution layer and a second convolution layer, in which the weights of the first layer and the weights of the second layer are quantized in different bit-widths, thus at different compression ratios. In a VGG neural network, the weights of a first group of convolution layers may have a different compression ratio than the weights of a second group of convolution layers. The weights of the CNN may be obtained in a training system including convolution quantization and/or activation quantization. Depending on the compression ratio, the weights of a convolution layer may be trained with or without re-training. An AI task, such as image retrieval, may be implemented in the AI semiconductor having the CNN described above.

Abstract: This disclosure relates to testing of integrated artificial intelligence (AI) circuit with embedded memory to improve effective chip yield and to mapping addressable memory segments of the embedded memory to multilayer AI networks at the network level, layer level, parameter level, and bit level based on bit error rate (BER) of the addressable memory segments. The disclosed methods and systems allows for deployment of one or more multilayer AI networks in an AI circuit with sufficient model accuracy even when the embedded memory has an overall BER higher than a preferred overall threshold.

Abstract: This disclosure relates to testing of integrated artificial intelligence (AI) circuit with embedded memory to improve effective chip yield and to mapping addressable memory segments of the embedded memory to multilayer AI networks at the network level, layer level, parameter level, and bit level based on bit error rate (BER) of the addressable memory segments. The disclosed methods and systems allows for deployment of one or more multilayer AI networks in an AI circuit with sufficient model accuracy even when the embedded memory has an overall BER higher than a preferred overall threshold.

Abstract: This disclosure relates to artificial intelligence (AI) circuits with embedded memory for storing trained AI model parameters. The embedded memory cell structure, device profile, and/or fabrication process are designed to generate binary data access asymmetry and error rate asymmetry between writing binary zeros and binary ones that are adapted to and compatible with a binary data asymmetry of the trained model parameters and/or a bit-inversion tolerance asymmetry of the AI model between binary zeros and ones. The disclosed method and system improves predictive accuracy and memory error tolerance without significantly reducing an overall memory error rate and without relying on memory cell redundancy and error correction codes.

Abstract: A windscreen wiper control system controls a drive mechanism for at least one windscreen wiper arm to effect a reciprocating movement of the at least one windscreen wiper arm within a wiping range between a first position and a second position. The control system includes a detector that detects, upon activation of the control system, an uncertain position of the windscreen wiper arm within the wiping range but different from the first position. Upon detection of such an uncertain position, the control system returns the windscreen wiper arm to the first position at a predetermined reduced speed, the reduced speed being applied at least in a sub-range of the wiping range in the vicinity of the first position.

Abstract: Described herein are compounds, agents, compositions, and methods related to the treatment of a viral infection (e.g., Hepatitis C viral infection). In particular, the compounds, agents, compositions, and methods described herein inhibit viral entry into a target cell.

Abstract: Computers for enabling an entity such as a robot to build other robots that are deployed into service and have a system for tracking performance through a tracking tree of essentially any width or depth and to receive tracking data for the entire width and depth of the tracking tree. In some embodiments, building the multi-line tracking plan includes transitioning an entity's existing tracking plan that is limited in width, depth or width and depth to a new tracking plan that is unlimited in width and depth. In some embodiments, computers can cause an entity to re-enter the tracking tree, within one or more downlines, thereby providing new tracking positions for the same entity within a single tracking tree. Embodiments may include computer systems that auto-balance volume that is generated from the downlines, such that the volume is placed where it would generate the greatest amount of tracking data for a sponsoring entity.

Abstract: A system for encoding data in an artificial intelligence (AI) integrated circuit solution may include a processor configured to receive image/voice data and generate a sequence of two-dimensional (2D) arrays each array being shifted from a preceding 2D array in the sequence by a time difference. The system may load the sequence of arrays into an AI integrated circuit, feed each of the 2D arrays in the sequence into a respective channel in an embedded cellular neural network architecture in the AI integrated circuit. The system may generate an image/voice recognition result from the embedded cellular neural network architecture and output the image/voice recognition result. The sequence of 2D arrays in the image recognition may include a sequence of output images. The sequence of 2D arrays in the voice recognition may include 2D frequency-time arrays. Sample data may be encoded in a similar manner for training the cellular neural network.