Abstract: A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.

Abstract: Provided is a double-channel paper feeding mechanism and a sheet medium processing device. The double-channel paper feeding mechanism includes a fixed frame, a movable frame and a connection mechanism. The fixed frame is provided with a first channel plate and a second channel plate. A first portion of the first channel plate and a first portion of the second channel plate form a conveying channel. An opening for inserting the movable frame is formed between second portions of the two channel plates. The movable frame is provided with a third channel plate and a fourth channel plate. When the movable frame is mounted on the fixed frame, the second portion of the first channel plate and the third channel plate are arranged oppositely and form a first input channel in connection with the conveying channel, the second portion of the second channel plate and the fourth channel plate form a second input channel in connection with the conveying channel.

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: Provided are a differential protection method and system, applied to a multi-terminal T-connection transmission line. The method includes: selecting two slaves without a connection and connecting the two slaves to construct a slave group; determining a target slave and an auxiliary slave in the slave group, where a first communication path is connected along a channel one, a channel two and a channel three, and a second communication path is connected along the channel three, the channel two and the channel one; transmitting, by the target slave, two frames of messages, and acquiring a delay difference between a transceiving delay of the first communication path and a transceiving delay of the second communication path, where a first frame of messages is transmitted to the target slave via the first communication path, a second frame of messages is transmitted to the target slave via the second communication path.

Abstract: The present invention provides an ascorbate peroxidase mutant MaAPX1M36K and an application thereof, belonging to the technical field of biotechnology. An amino acid sequence of the mutant MaAPX1M36K of the present invention is shown in SEQ ID NO. 3. In the present invention, the mutant MaAPX1M36K reconstruction protein is obtained by the method of prokaryotic expression. It is found that the mutant MaAPX1M36K improves catalytic efficiency by nearly 5 times, providing a technical reference for further study and application of APX1.

Abstract: A condenser includes an air duct defining an air channel therein; an air supply device fixedly connected to the air duct and a condensation member having a refrigerant inlet and a refrigerant outlet, the condensation member being at least partly disposed within the air channel. The condensation member includes a plurality of first condensation pipe segments consecutively arranged in multiple layers in an axial direction of the air duct and communicated with each other, each of the first condensation pipe segments is spirally formed by a first condensation pipe into a respective torus, a spiral line of said each first condensation pipe segments is located in a surface of the respective torus, and the respective torus formed in a first layer of the multiple layers is stacked above the respective torus formed in a second layer of the multiple layers along the axial direction of the air duct.

Abstract: Compounds for use in treating or preventing human immunodeficiency virus (HIV) infection are disclosed. The compounds have the following formula (I): including stereoisomers and pharmaceutically acceptable salts thereof, wherein R1, R2, L, W1, W2, X, Y, and Z are as defined herein. Methods associated with the preparation and use of such compounds, as well as pharmaceutical compositions comprising such compounds, are also disclosed.

Abstract: A method for identifying a defect opening profile includes: acquiring a vertical component of a magnetic flux leakage signal of a defect; identifying right-angle features and corresponding right-angle position points of the defect from the vertical component; obtaining all possible right-angle types at each right-angle position point of the defect according to the corresponding right-angle feature of the vertical component; traversing all the possible right-angle types at each right-angle position point to determine respective optimal right-angle type at each right-angle position point; and drawing the defect opening profile according to the respective optimal right-angle type at each right-angle position point.

Abstract: Provided are a differential protection method and system, applied to a multi-terminal T-connection transmission line. The method includes: selecting two slaves without a connection and connecting the two slaves to construct a slave group; determining a target slave and an auxiliary slave in the slave group, where a first communication path is connected along a channel one, a channel two and a channel three, and a second communication path is connected along the channel three, the channel two and the channel one; transmitting, by the target slave, two frames of messages, and acquiring a delay difference between a transceiving delay of the first communication path and a transceiving delay of the second communication path, where a first frame of messages is transmitted to the target slave via the first communication path, a second frame of messages is transmitted to the target slave via the second communication path.

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: A system for training an artificial intelligence (AI) model for an AI chip to implement an AI task may include an AI training unit to train weights of an AI model in floating point, a convolution quantization unit for quantizing the trained weights to a number of quantization levels, and an activation quantization unit for updating the weights of the AI model so that output of the AI model based at least on the updated weights are within a range of activation layers of the AI chip. The updated weights may be stored in fixed point and uploadable to the AI chip. The various units may be configured to account for the hardware constraints in the AI chip to minimize performance degradation when the trained weights are uploaded to the AI chip and expedite training convergence. Forward propagation and backward propagation may be combined in training the AI model.

Abstract: A system for training an artificial intelligence (AI) model for an AI chip may include an AI training unit to train weights of an AI model in floating point, and one or more quantization units for updating the weights of the AI model while accounting for the hardware constraints in the AI chip. The system may also include customization unit for performing one or more linear transformations on the updated weights. The system may also perform output equalization for one or more convolution layers of the AI model to equalize the inputs and/or outputs of each layer of the AI model to within the range allowed in the physical AI chip. The system may further update the weights by performing shift-based quantization that mimics the characteristics of a hardware chip. The updated weights may be stored in fixed point and uploadable to an AI chip implementing an AI task.

Abstract: A cellular neural network architecture may include a processor and embedded cellular, neural network (CeNN) executable in an artificial intelligence (AI) integrated circuit and configured to perform certain AI functions. The CeNN may include multiple convolution layers, each having multiple binary weights. In some examples, a method may configure a given layer of the CeNN and one or more additional layers of the CeNN to retrieve the output of the given layer for debugging or training the CeNN. In configuring the one or more additional layers, the method may use an identity layer.

Abstract: A cellular neural network architecture may include a processor and an embedded cellular neural network (CeNN) executable in an artificial intelligence (AI) integrated circuit and configured to perform certain AI functions. The CeNN may include multiple convolution layers, such as first, second, and third layers, each layer having multiple binary weights. In some examples, a method may configure the multiple layers in the CeNN to produce a residual connection. In configuring the second and third layers, the method may use an identity matrix.

Abstract: In some examples, given an AI model in floating point, a system may use one or more artificial intelligence (AI) chips to train a global gain vector for use to convert the AI model in floating point to an AI model in fixed point for uploading to a physical AI chip. The system may determine initial gain vectors, and in each of multiple iterations, obtain the performance values of the AI chips based on the gain vectors and update the gam vectors for the next iteration. The gain vectors are updated based on a velocity of gain. The performance value may be based on feature maps of an AI model before and after the converting. The performance value may also be based on interference over a test dataset. Upon completion of the iterations, the system determines the global gain vector that resulted in the best performance value during the iterations.

Abstract: A sample characterization system is disclosed. In embodiments, the sample characterization system includes a controller communicatively coupled to an inspection sub-system, the controller including one or more processors configured to execute a set of program instructions stored in memory, the set of program instructions configured to cause the one or more processors to: acquire one or more target image frames of a sample; generate a target tensor with the one or more acquired target image frames; perform a first set of one or more decomposition processes on the target tensor to form generate one or more reference tensors including one or more reference image frames; identify one or more differences between the one or more target image frames and the one or more reference image frames; and determine one or more characteristics of the sample based on the one or more identified differences.

Abstract: In a method to functionalize high-density chromatography matrix functionalization method, the hydrophilic microsphere having a surface polyhydroxy structure or the hydrophilic microsphere coated with a polyhydroxy polymer is used as chromatography matrix, under anhydrous conditions with catalyst, catalyze surface hydroxyl group of the matrix to react with divinyl sulfone, and achieve functionalization of the hydroxyl group on the surface of the matrix. The chromatography matrix surface functionalization method of the present disclosure has few steps, simple process and high functional density, and the reagents and solvents used are conventional reagents and can be recycled and reused. No byproduct is generated during the reaction with high atom economy and low cost, and the density control can be achieved by adjusting the reaction time and catalyst type.