Abstract: A method of wireless communications by a user equipment (UE) includes communicating with a network based on a machine learning model for wireless communication. The method also includes monitoring a status of the machine learning model for wireless communication. The method further includes reporting the status of the machine learning model for wireless communication using a predetermined resource according to a predetermined format. The method also includes falling back to communicating with a fallback procedure, instead of the machine learning model for wireless communication, to maintain wireless communication with the network in response to the status of the machine learning model indicating a model failure.

Abstract: The present invention relates to the use of hypoxia-inducible factor (HIF) inhibitors in cancer immunotherapy. Specifically, the disclosure provides methods of treating a cancer in a subject in need of cancer immunotherapy, comprising administering a HIF-1a inhibitor to the subject and a second cancer immunotherapeutic agent to the subject, wherein the HIF-1a inhibitor comprising echinomycin, and wherein the second cancer immunotherapeutic agent comprising an anti-CTLA-4 antibody including Ipilimumab or Tremelimumab.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, devices use machine learning (ML) models to support wireless communications. For example, a user equipment (UE) may download ML model information from a network to determine an ML model. The network may additionally configure a status reporting procedure, a fallback procedure, or both for the ML model. In some examples, based on a configuration, the UE may transmit a status report to a base station according to a reporting periodicity, a UE-based trigger, a network-based trigger, or some combination thereof. Additionally or alternatively, the UE may determine to fallback from operating using the ML model to operating in a second mode based on a fallback trigger. In some examples, to restore operating using a downloaded ML model, the UE may download an updated ML model or receive iterative updates to a previously downloaded ML model.

Abstract: A configuration for reporting OOD samples for neural network optimization. The apparatus receives, from a base station, a configuration to report an OOD dataset for a machine learning model. The apparatus detects an occurrence of one or more OOD events. The apparatus reports the OOD dataset comprising the one or more OOD events based on the configuration to report OOD dataset. The apparatus receives, from the base station, an update to the machine learning model. The OOD dataset may comprise raw data related to the one or more OOD events, or may comprise extracted latent data corresponding to features of raw data related to the one or more OOD events.

Abstract: The invention discloses a Schleiferilactobacillus harbinensis (former name Lactobacillus harbinensis) M1 and use thereof. The preservation number of Schleiferilactobacillus harbinensis M1 is GDMCC No. 60305. The strain is sensitive to antibiotics such as ampicillin, tetracycline and chloramphenicol, and its safety meets the requirements of EFSA. Its supernatant has a significant inhibitory effect on Listeria monocytogenes, Streptococcus hemolyticus and Staphylococcus aureus. The present invention also protects the use of Schleiferilactobacillus harbinensis M1 in fermented soymilk; using the bacteria to ferment soymilk alone has the advantages of strong acid production ability, high amount of viable bacteria, few or even completely no beany flavor substances, and significant higher content of characteristic aroma components of fermented milk, which greatly improves the sensory flavor of fermented soymilk.

Abstract: The present disclosure is directed to a microbiostatic cleansing composition for use on skin and hard surfaces that reduces microbe resistance. The cleansing composition may be, for example, bacteriostatic or virostatic and includes a release agent that entangles the microbes without killing them allowing them to be removed from skin and hard surface in an inactive state.

Abstract: This application provides a method for constructing a network structure optimizer performed by an electronic device.

Abstract: In some embodiments, a method trains a first prediction network to predict similarity between images in videos. The training uses boundaries detected in the videos to train the prediction network to predict images in a same scene to have similar feature descriptors. The first prediction network generates feature descriptors that describe library images from videos in a video library offered to users of a video delivery service. A search image is received and the prediction network predicts one or more library images for one or more videos that are predicted to be similar to the received image. The one or more library images for the one or more videos are provided as a search result.

Abstract: A method receives ratings for videos from a first user that is using a video delivery service. A first model includes connection networks where each connection network corresponds to a rating. The method inputs each rating into a connection network in an order. Also, parameters for the ratings and ratings other than the rating received from the first user are modeled in a respective connection network. Values for the set of parameters are trained such that the plurality of connection networks predict conditional probabilities that the first user would provide the rating corresponding to the each connection network in the order. The conditional probabilities are based on the first user providing ratings that are previously located in the order. The parameters are then used to generate a list of videos to recommend to the first user using the first model.

Abstract: In some embodiments, a method trains a first prediction network to predict similarity between images in videos. The training uses boundaries detected in the videos to train the prediction network to predict images in a same scene to have similar feature descriptors. The first prediction network generates feature descriptors that describe library images from videos in a video library offered to users of a video delivery service. A search image is received and the prediction network predicts one or more library images for one or more videos that are predicted to be similar to the received image. The one or more library images for the one or more videos are provided as a search result.

Abstract: A method receives ratings for videos from a first user that is using a video delivery service. A first model includes connection networks where each connection network corresponds to a rating. The method inputs each rating into a connection network in an order. Also, parameters for the ratings and ratings other than the rating received from the first user are modeled in a respective connection network. Values for the set of parameters are trained such that the plurality of connection networks predict conditional probabilities that the first user would provide the rating corresponding to the each connection network in the order. The conditional probabilities are based on the first user providing ratings that are previously located in the order. The parameters are then used to generate a list of videos to recommend to the first user using the first model.

Abstract: Provided herein is an eyeglasses frame, wherein one optical frame which can fix a lens and has two fixed segments is provided, one connecting segment of an eyeglasses leg is provided on the optical frame adjacent to each fixed segment, respectively, one end of the eyeglasses leg extends from a frame side to another end of the eyeglasses legs far away from the frame, the eyeglasses leg is fixed on the connecting segment, can be inverted, extends by being basically parallel to the optical frame at a first end and extends backwards from the optical frame at a second frame. Also provided herein is and optical frame used in the eyeglasses frame and a mounting method for the eyeglasses frame and eyeglasses.