Abstract: A negative electrode slurry for a lithium ion secondary battery, according to an aspect of the present disclosure, comprises a negative electrode active material, a thickener, a preservative component, and a solvent, the thickener comprising carboxymethyl-cellulose or a salt thereof, the solvent comprising water, and the preservative component comprising a cyclic hydroxamic acid ethanolamine salt.

Abstract: A negative electrode slurry for a lithium ion secondary battery, according to the present disclosure, is characterized by comprising a negative electrode active material, a thickener, a preservative component, and a solvent, the thickener comprising carboxymethyl-cellulose or a salt thereof, the solvent comprising water, and the preservative component comprising a compound having a tropone skeleton.

Abstract: A learning method, a learning model, a classifier, a generator, and a processing system are provided, which consider human vision in learning using a machine learning model for an image. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes both or either one of image data in which a component that is difficult to judge visually is left out and image data in which a noise component that is difficult to judge visually is added at a predetermined ratio.

Abstract: To provide a learning apparatus and an inferring apparatus that can prevent problems such that a computation amount increases, efficiency of a learning process decreases, and an inferring result does not have high accuracy. A learning apparatus and an inferring apparatus include a predictable area determining unit that determines whether target data has a predictable area in which an inferring result of an inferring process can be easily predicted. In the learning apparatus and the inferring apparatus, predetermined data processing is performed on a data area that is determined to be a predictable area, data required to infer an area that is not a predictable area is output to a machine learning model, and data processing is performed in an average time shorter than that of the inferring process.

Abstract: In a learning that uses a machine learning model for an image, a learning method, a learning model, a classifier, and a generator in which human vision is taken into consideration are provided. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes training data subjected to a process of leaving out a component that is difficult to visually judge to reduce an information amount or generated data at a predetermined ratio.

Abstract: A learning apparatus that can realize efficient machine learning is provided. A learning apparatus that learns a set value in a machine learning model based on predetermined image data for learning includes an inverting unit that inverts data of at least a part of respective channels in the image data for learning, an input unit that inputs the inverted data to the machine learning model, an output unit that can compare data obtained by inverting data output from the machine learning model with training data, and/or data output from the machine learning model with data obtained by inverting training data, and a learning process executing unit that learns the set value according to a result of the comparison.

Abstract: To provide a processing device, a processing method, a computer program, and a processing system that improve efficiency of an arithmetic processing by using a convolutional neural network (CNN). The processing device inputs data to a convolutional neural network including a convolutional layer and acquires an output from the convolutional neural network. The processing device includes a first converter that performs non-linear space conversion on data to be input to the convolutional neural network, and/or a second converter that performs non-linear space conversion on data output from the convolutional neural network.

Abstract: To provide a computer program, an image processing method, and an image processing apparatus that avoid deterioration due to free deformation with respect to a digital image. The computer program causes a computer to execute a process of receiving editing order including scaling, rotation, shifting, or distortion with respect to a digital image to be processed, identifying a target resolution of the digital image to be processed, generating a temporary reference image with a resolution predetermined times the target resolution based on the digital image, performing rotation, shifting, or distortion in the received editing with respect to the temporary reference image, and outputting the digital image with the target resolution by performing sampling at a rate corresponding to the target resolution with respect to the edited temporary reference image.

Abstract: To provide a learning apparatus and an inferring apparatus that can prevent problems such that a computation amount increases, efficiency of a learning process decreases, and an inferring result does not have high accuracy. A learning apparatus and an inferring apparatus include a predictable area determining unit that determines whether target data has a predictable area in which an inferring result of an inferring process can be easily predicted. In the learning apparatus and the inferring apparatus, predetermined data processing is performed on a data area that is determined to be a predictable area, data required to infer an area that is not a predictable area is output to a machine learning model, and data processing is performed in an average time shorter than that of the inferring process.

Abstract: To provide a processing device, a processing method, a computer program, and a processing system that improve efficiency of an arithmetic processing by using a convolutional neural network (CNN). The processing device inputs data to a convolutional neural network including a convolutional layer and acquires an output from the convolutional neural network. The processing device includes a first converter that performs non-linear space conversion on data to be input to the convolutional neural network, and/or a second converter that performs non-linear space conversion on data output from the convolutional neural network.

Abstract: A learning apparatus that learns a set value in a machine learning model based on predetermined image data for learning includes an inverting unit that inverts data of at least a part of respective channels in the image data for learning, an input unit that inputs the inverted data to the machine learning model, an output unit that can compare data obtained by inverting data output from the machine learning model with training data, and/or data output from the machine learning model with data obtained by inverting training data, and a learning process executing unit that learns the set value according to a result of the comparison.

Abstract: A learning method, a learning model, a classifier, a generator, and a processing system are provided, which consider human vision in learning using a machine learning model for an image. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes both or either one of image data in which a component that is difficult to judge visually is left out and image data in which a noise component that is difficult to judge visually is added at a predetermined ratio.

Abstract: In a learning that uses a machine learning model for an image, a learning method, a learning model, a classifier, and a generator in which human vision is taken into consideration are provided. The learning method learns a machine learning model that inputs or outputs image data with data for learning that includes training data subjected to a process of leaving out a component that is difficult to visually judge to reduce an information amount or generated data at a predetermined ratio.

Abstract: To provide a computer program, an image processing method, and an image processing apparatus that avoid deterioration due to free deformation with respect to a digital image. The computer program causes a computer to execute a process of receiving editing order including scaling, rotation, shifting, or distortion with respect to a digital image to be processed, identifying a target resolution of the digital image to be processed, generating a temporary reference image with a resolution predetermined times the target resolution based on the digital image, performing rotation, shifting, or distortion in the received editing with respect to the temporary reference image, and outputting the digital image with the target resolution by performing sampling at a rate corresponding to the target resolution with respect to the edited temporary reference image.