Abstract: A method, apparatus, non-transitory computer readable medium, apparatus, and system for video generation include first obtaining a training set including a training video. Then, embodiments initialize a video generation model, sample a subnet architecture from an architecture search space, and a identify a subset of the weights of the video generation model based on the sampled subnet architecture. Subsequently, embodiments train, based on the training video, a subnet of the video generation model to generate synthetic video data. The subnet includes a subset of the weights of the video generation model.

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Abstract: Described herein is a premix including an alkyl polyglycoside, preferably C10-C16 alkyl polyglycoside, a hydrotrope and a solvent including water. Also described herein are a method of using the premix in preparing a liquid detergent formulation and a method for preparing a liquid detergent composition by using the premix.

Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.

Abstract: Described herein is a fabric care composition including at least one hydrophobically modified polyalkyleneimine as dye fixative polymer. Also described herein is a method for providing an improved color care effect during washing or treatment of colored fabric, the method including use of the hydrophobically modified polyalkyleneimine as dye fixative polymer.

Abstract: Disclosed are a model parameter training method and a terminal based on federation learning, and a medium. The method includes: determining a feature intersection of a first sample of the first terminal and a second sample of a second terminal, training the first sample based on the feature intersection to obtain a first mapping model, sending the first mapping model to the second terminal; receiving a second encryption mapping model sent by the second terminal, predicting a missing feature of the first sample of the first terminal according to the second encryption mapping model to obtain a first encryption supplementary sample; receiving a first encryption federation learning model parameter sent by a third terminal, training a federation learning model to be trained according to the first encryption federation learning model parameter, and calculating a first encryption loss value; and sending the first encryption loss value to the third terminal.

Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Abstract: Disclosed herein are carboxymethylated polylysines as dispersing agents and/or chelating agents. Additionally disclosed are detergent compositions and peroxy bleaching compositions including carboxymethylated polylysines. Further disclosed is a method for formulating a detergent composition or a peroxy bleaching composition with the carboxymethylated polylysines.

Abstract: Systems and methods for classifying products are disclosed. A first data sample having a first portion and a second portion is identified from a training dataset. A first mask is generated based on the first data sample, where the first mask is associated with the first portion of the first data sample. A second data sample is generated based on a noise input. The first mask is applied to the second data sample for outputting a third portion of the second data sample. The third portion of the second data sample is combined with the second portion of the first data sample for generating a first combined data sample. Confidence and classification of the first combined data sample are predicted. The first combined data sample is added to the training dataset in response to predicting the confidence and the classification.

Abstract: Described herein is a fabric care composition including at least one hydrophobically modified polyalkyleneimine as dye fixative polymer. Also described herein is a method for providing an improved color care effect during washing or treatment of colored fabric, the method including use of the hydrophobically modified polyalkyleneimine as dye fixative polymer.

Abstract: Systems and method for classifying manufacturing defects are disclosed. A first machine learning model is trained with a training dataset, and a data sample that satisfies a criterion is identified from the training dataset. A second machine learning model is trained to learn features of the data sample. When an input dataset that includes first and second product data is received, the second machine learning model is invoked for predicting confidence of the first and second product data based on the learned features of the data sample. In response to predicting the confidence of the first and second product data, the first product data is removed from the dataset, and the first machine learning model is invoked for generating a classification based the second product data.

Abstract: Methods, devices, systems and apparatus for determining emphysema thresholds for processing a pulmonary medical image are provided. In one aspect, a method includes: determining lung lobe regions in the pulmonary medical image, and, for each of the lung lobe regions, clustering CT values in the lung lobe region to divide the lung lobe region into a first sub region and a second sub region and acquiring a CT value corresponding to an intersection of a first CT value distribution function for the first sub region and a second CT value distribution function for the second sub region in the lung lobe region as an emphysema threshold for the lung lobe region.

Abstract: Systems and method for classifying manufacturing defects are disclosed. In one embodiment, a first data sample satisfying a first criterion is identified from a training dataset, and the first data sample is removed from the training dataset. A filtered training dataset including a second data sample is output. A first machine learning model is trained with the filtered training dataset. A second machine learning model is trained based on at least one of the first data sample or the second data sample. Product data associated with a manufactured product is received, and the second machine learning model is invoked for predicting confidence of the product data. In response to predicting the confidence of the product data, the first machine learning model is invoked for generating a classification based the product data.

Abstract: A method and a system are presented for controlling a performance of a fusion model. The method includes obtaining a first set and a second set of candidate models for a first and second neural networks, respectively. Each of the first and second set of candidate models is pre-trained with a first source and a second source, respectively. For each possible pairing of one candidate model from the first neural network and one candidate model from the second neural network, a model distance Dm is determined. A subset of possible pairings of one first candidate model and one second candidate model is selected based on the model distance Dm between them. Using the subset of possible parings, the first neural network and the second neural network are combined to generate two branches for a fusion model neural network.

Abstract: A system for manufacturing defect classification is presented. The system includes a first neural network receiving a first data as input and generating a first output, a second neural network receiving a second data as input and generating a second output, wherein first neural network and the second neural network are trained independently from each other, and a fusion neural network receiving the first output and the second output and generating a classification. The first data and the second data do not have to be aligned. Hence, the system and method of this disclosure allows various type of data that are collected during manufacturing to be used in defect classification.

Abstract: Disclosed are a model parameter training method and a terminal based on federation learning, and a medium. The method includes: determining a feature intersection of a first sample of the first terminal and a second sample of a second terminal, training the first sample based on the feature intersection to obtain a first mapping model, sending the first mapping model to the second terminal; receiving a second encryption mapping model sent by the second terminal, predicting a missing feature of the first sample of the first terminal according to the second encryption mapping model to obtain a first encryption supplementary sample; receiving a first encryption federation learning model parameter sent by a third terminal, training a federation learning model to be trained according to the first encryption federation learning model parameter, and calculating a first encryption loss value; and sending the first encryption loss value to the third terminal.

Abstract: Methods, devices, systems and apparatus for determining emphysema thresholds for processing a pulmonary medical image are provided. In one aspect, a method includes: determining lung lobe regions in the pulmonary medical image, and, for each of the lung lobe regions, clustering CT values in the lung lobe region to divide the lung lobe region into a first sub region and a second sub region and acquiring a CT value corresponding to an intersection of a first CT value distribution function for the first sub region and a second CT value distribution function for the second sub region in the lung lobe region as an emphysema threshold for the lung lobe region.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for image splicing are provided. In one aspect, a method includes obtaining at least two groups of to-be-spliced images and scanning information corresponding to the at least two groups of to-be-spliced images, determining an overlap area of the at least two groups of to-be-spliced images and a physical sequence of splicing according to the scanning information, registering the at least two groups of to-be-spliced images based on the overlap area to obtain a registration result, and splicing the at least two groups of registered to-be-spliced images according to the physical sequence of splicing and the registration result.