Abstract: The present disclosure provides use of Clostridium ghonii combined with a tumor angiogenesis inhibitor in preparing a pharmaceutical product for treating a tumor. The present disclosure further provides a drug for treating a tumor, where the drug includes active ingredients of Clostridium ghonii and a tumor angiogenesis inhibitor.

Abstract: The present disclosure discloses a method for detecting a natural frequency of a blade by a single blade tip timing sensor. In the method, two segments of displacement data vectors are subjected to dot product to obtain a product data vector after multiplication of corresponding serial numbers, the product data vector is subjected to low-frequency filtering, the filtered product vector is subjected to Hilbert transform to obtain an instantaneous phase, the instantaneous phase is subjected to discrete Fourier transform, an absolute value of a natural frequency difference between blades is extracted from amplitude frequency data, blades on a bladed disk are subjected to permutation and combination to obtain absolute values of the natural frequency differences between all the blades, a sum of the absolute values of the natural frequency differences between each blade and other blades is calculated.

Abstract: The technology of this application relates to a multi-task network model-based communication method, apparatus, and system. The method may include a first communication apparatus processing an input signal by using a first backbone network model, to obtain a fusion feature, where the fusion feature is obtained by fusing a plurality of first features which are obtained by performing feature extraction on the input signal. The method may further include performing compression and channel coding on the fusion feature to obtain first information, and sending the first information to a second communication apparatus.

Abstract: The present disclosure provides a method for separating a neural crest derived cell from peripheral blood. In the present disclosure, a mononuclear cell is separated from the peripheral blood and then directly cultured, thereby maximizing use of a neural crest stem cell with a differentiation potential to avoid loss of the neural crest stem cell. In the method of the present disclosure, a sample to be separated is derived from the peripheral blood. The method shows less trauma and low cost. Most importantly, compared to extracting the neural crest derived cell from tissues, the neural crest derived cell extracted from the peripheral blood can be used clinically as a type of biomarker.

Abstract: The present disclosure relates to use of a Clostridium ghonii spore combined with pembrolizumab in cancer treatment. It is found for the first time that the Clostridium ghonii spore combined with pembrolizumab can significantly improve a curative effect of colon cancer and reduce a dose of the pembrolizumab, and thus is efficient and low-toxic. Oncolysis by Clostridium ghonii can affect immunogenicity of a tumor microenvironment (TME) by various ways, converts an immunosuppressive state of the TME into an immune-activated state, adjusts the immunosuppressive TME, and breaks an immune tolerance. An optimal combination of the Clostridium ghonii spore and the pembrolizumab thoroughly removes about 20% of mouse tumor tissues. A benefit range of patients with tumors treated by a PD-1 antibody is expanded. The combination even has an obvious curative effect on patients failed the treatment by the PD-1 antibody.

Abstract: Systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. In particular, the present disclosure provides a Vector-quantized Image Modeling (VIM) approach that involves pretraining a machine learning model (e.g., Transformer model) to predict rasterized image tokens autoregressively. The discrete image tokens can be encoded from a learned Vision-Transformer-based VQGAN (example implementations of which can be referred to as ViT-VQGAN). The present disclosure proposes multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional image generation, conditioned image generation (e.g., class-conditioned image generation), and unsupervised representation learning.

Abstract: A displaying base plate and a manufacturing method thereof, and a displaying device. The displaying base plate includes a substrate, and a first electrode layer disposed on one side of the substrate, wherein the first electrode layer includes a first electrode pattern; a first planarization layer disposed on one side of the first electrode layer that is away from the substrate, wherein the first planarization layer is provided with a through hole, and the through hole penetrates the first planarization layer, to expose the first electrode pattern; and a second electrode layer, a second planarization layer and a third electrode layer that are disposed in stack on one side of the first planarization layer that is away from the substrate, wherein the second electrode layer is disposed closer to the substrate, the second electrode layer is connected to the first electrode pattern and the third electrode layer.

Abstract: Oral care based imaging computer-implemented systems and methods for analyzing attributes of a facial image portion of at least one person depicted in a digital image. An oral care digital imaging computer-implemented method for analyzing a facial image portion of a person depicted in a digital image may include analyzing the facial image portion for positive and negative attributes as defined by pixel data of the digital image and generating a probability value based on the positive and negative attributes in the facial image portion in the digital image.

Abstract: Example encoding/decoding methods, communication apparatus, and systems are provided. One example encoding method includes encoding first data by using an encoding model, to obtain N pieces of encoded data, where N is an integer greater than 2. De-correlation is performed on the N pieces of encoded data to obtain N pieces of de-correlated encoded data, where a part or all of the N pieces of de-correlated encoded data may be for determining second data, the second data is for restoring third data, and the third data is the first data, or an approximation degree between the third data and the first data is greater than a specified threshold The N pieces of de-correlated encoded data are sent.

Abstract: Provided are a verification code processing method and apparatus, and a device and a storage medium. The method comprises: upon receiving a trigger operation for verification code display, firstly acquiring a verification code image and verification code input prompt information; and then displaying the verification code image and the verification code input prompt information for verification code input. In the embodiments of the present disclosure, since a verification code image comprises one or more verification codes, at least one verification code is divided into a plurality of areas, and at least two areas are different in color, the overall structure of the verification code is destroyed, thereby improving the OCR resistance performance of the verification code, and achieving the purpose of effectively intercepting malicious behavior.

Abstract: This application provides a coding method and a communication apparatus, which may be applied to wireless data transmission. The method includes: A sending apparatus sends indication information to a receiving apparatus, where the indication information indicates first information and second information. The first information indicates a coding scheme of to-be-encoded data, and the coding scheme includes a coding matrix for channel coding and a channel coding mode. The second information indicates a distribution feature of the to-be-encoded data. The sending apparatus performs channel coding on the to-be-encoded data based on the coding scheme, to obtain encoded data. The receiving apparatus receives the indication information sent by the sending apparatus, and the receiving apparatus performs channel decoding on received data based on the indication information, to obtain decoded data.

Abstract: Embodiments of this application provide a radio channel data processing method, a communication apparatus, and a related device. In the method, a first communication device may send an uplink resource request message to a second communication device. The uplink resource request message includes source distribution information and a first modulation and coding scheme determined based on the source distribution information. The first communication device receives an uplink resource allocation message from the second communication device. The uplink resource allocation message includes a modulation and coding scheme allocated by the second communication device to the first communication device. The first communication device performs modulation and coding on an information bit based on the second modulation and coding scheme. This helps improve coding performance of the first communication device.

Abstract: This application discloses image encoding and decoding methods and apparatuses. The image encoding method includes performing, by a source device, compression encoding on an image to obtain base layer information. The method further includes obtaining enhancement layer information based on the base layer information and the image. The method further includes obtaining control layer information. The method further includes performing encoding and modulation on the control layer information, the base layer information, and the enhancement layer information separately to obtain a plurality of symbol sets. The method further includes mapping the plurality of symbol sets to a resource for sending. Embodiments of this application may ensure robustness in a transmission process and improve overall compression efficiency and performance.

Abstract: Oral care based imaging computer-implemented systems and methods for determining perceived attractiveness of a facial image portion of at least one person depicted in a digital image. The method has the following steps: a) obtaining a digital image comprising at least one oral feature of at least one person, wherein the digital image includes a facial image portion of the at least one person, the facial image portion having both positive and negative attributes as defined by pixel data of the digital image; b) analyzing the facial image portion; c) generating an Attractiveness Score indicative of a perceived attractiveness of the facial image portion based on the analyzed facial image portion in the obtained digital image; d) further generating an image description that identifies at least one area in said facial image portion based on the Attractiveness Score; and e) presenting the image description to a use.

Abstract: In a data processing method, a transmit end may send, to a receive end, coding information of N layers of first bit sequences and second information that does not include some or all bits in the N layers of first bit sequences, and the receive end may process the coding information and the second information, to obtain the N layers of first bit sequences. Because the transmit end sends the coding information to the receive end, information sent by the transmit end may not include some or all bits in the N layers of first bit sequences.

Abstract: A method for training a deep learning model includes: acquiring (n+1)th first label information output by a first model, the first model having been subject to n rounds of training, and acquiring (n+1)th second label information output by a second model, the second model having been subject to n rounds of training, the n being an integer greater than 1; generating an (n+1)th training set of the second model based on training data and the (n+1)th first label information, and generating an (n+1)th training set of the first model based on the training data and the (n+1)th second label information; performing an (n+1)th round of training on the second model by inputting the (n+1)th training set of the second model to the second model, and performing the (n+1)th round of training on the first model by inputting the (n+1)th training set of the first model to the first model.

Abstract: A method for training a deep learning model includes: obtaining (n+1)th annotation information output by a model to be trained, wherein the model to be trained has undergone n rounds of training, where n is an integer greater than or equal to 1; generating an (n+1)th training sample based on training data and the (n+)th annotation information; and performing an (n+1)th round of training on the model to be trained using the (n+1)th training sample.

Abstract: Embodiments of the present disclosure disclose image processing methods, electronic devices, and a storage medium. According to one example of the method, an electronic device may: process a first image to obtain prediction results of a plurality of pixels in the first image, the prediction results including semantic prediction results and center relative position prediction results, wherein the semantic prediction results indicate that the pixels are located in an instance region or a background region, and the center relative position prediction results indicate relative positions between the pixels and an instance center; and determine an instance segmentation result of the first image based on the semantic prediction result and the center relative position prediction result of each of the plurality of pixels.

Abstract: Embodiments of the present disclosure disclose an image processing method, an electronic device, and a storage medium. The method includes: converting an original image into a target image conforming to a target parameter; obtaining a target numerical index according to the target image; and performing, according to the target numerical index, timing prediction processing on the target image to obtain a timing state prediction result. Left ventricular function quantization can be implemented, the image processing efficiency is improved, and the prediction accuracy of a cardiac function index is improved.