Abstract: According to one embodiment, a proficiency determination apparatus includes a processing circuit. The processing circuit acquires first time-series data about biological information of a worker in a predetermined period. The processing circuit calculates second time-series data about a physiological index indicating a mental stress on the worker by analyzing the first time-series data. The processing circuit calculates an appearance condition of the physiological index by analyzing the second time-series data. The processing circuit determines a proficiency of the worker based on the appearance condition of the physiological index.

Abstract: According to one embodiment, a learning apparatus includes a processing circuit. The processing circuit acquires a first training condition and a first model trained in accordance with the first training condition, sets a second training condition used to reduce a model size of the first model, different from the first training condition, in accordance with the second training condition and based on the first model, trains a second model whose model size is smaller than that of the first model, and in accordance with a third training condition that is not the same as the second training condition and complies with the first training condition, trains a third model based on the second model.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to one embodiment, a defect classification apparatus includes a processor. The processor acquires a first design image which is an image based on design data created by design software and relates to a first inspection target, and acquires a first real image which is captured by imaging the first inspection target produced based on the design data. The processor converts the first design image to a reference image represented by using a second real image captured by imaging a second inspection target without a defect. The processor calculates a reliability of the reference image. The processor detects a defect in the first inspection target by comparing the reference image and the first real image and classifies the defect, based on the reliability.

Abstract: According to one embodiment, a learning apparatus includes a processor. The processor determines, based on a data resolution of subject data obtained at a subject device, a plurality of data resolutions that differ from one another within a range covering the data resolution of the subject data, the data resolutions each indicating a corresponding amount of information per unit. The processor trains a scalable network with training samples corresponding to each of the plurality of data resolutions, the scalable network being a neural network adapted to change a data resolution of input data.

Abstract: A providing apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: store, in the memory, a first machine learning model capable of changing an amount of calculation of a model of a neural network; acquire device information; set, based on the device information, extraction conditions representing conditions for extracting second machine learning models from the first machine learning model; extract the second machine learning models from the first machine learning model based on the extraction conditions; and provide the second machine learning models to a device specified by the device information.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to an embodiment, an encoding device includes a deriving unit and an encoding unit. The deriving unit is configured to derive a first reference value based on fixed point precision representing roughness of a weighting factor that is used for multiplying a reference image. The encoding unit is configured to encode a first difference value that is a difference value between the weighting factor and the first reference value and the fixed point precision. The weighting factor is included in a first range of predetermined bit precision having the first reference value at approximate center. The first difference value is in the predetermined range.

Abstract: A defect inspection device includes at least one memory storing instructions and at least one processor.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: An encoding device includes a deriving unit and an encoding unit. The deriving unit is configured to derive a first reference value based on fixed point precision representing roughness of a weighting factor that is used for multiplying a reference image. The encoding unit is configured to encode a first difference value that is a difference value between the weighting factor and the first reference value and the fixed point precision. The weighting factor is included in a first range of predetermined bit precision having the first reference value at approximate center. The first difference value is in the predetermined range.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to an embodiment, an encoding device includes an encoder. The encoder is configured to generate a video stream. The video stream includes at least a first image group including a plurality of images encoded at a first bit rate and a second image group including a plurality of images. The first image group includes an intra-coded image. The plurality of images of the second image group are associated with the intra-coded image serving as a reference image to decode an inter-coded image, and are encoded at a second bit rate different from the first bit rate and having reproduction timing in synchronization with the plurality of images of the first image group.

Abstract: A medical image processing device of an embodiment includes a comparator and a position determination region determiner. The comparator compares a first image obtained by photographing a patient with a comparative image which is an image used in previous radiation treatment and in which a valid region used in position alignment in the radiation treatment is designated. The position determination region determiner determines a position determination region similar to the valid region included within the first image on the basis of a comparison result of the comparator.

Abstract: A video encoding apparatus is a video encoding apparatus for subjecting a video image to motion compensated prediction coding, comprising an acquisition module to acquire available blocks of blocks having motion vectors from encoded blocks adjacent to a to-be-encoded block and number of the available blocks, an acquisition/selection module to select one selection block from the encoded available blocks, a selection information encoder to encode selection information specifying the selection block using a code table corresponding to the number of available blocks, and an image encoder to subject the to-be-encoded block to motion compensated prediction coding using a motion vector of the selection block.

Abstract: According to one embodiment, a load estimation apparatus includes processing circuitry. The processing circuitry acquires sensor data from a measurement target, calculates one or more posture loads relating to one or more body parts of the measurement target based on the sensor data, and displays display data including a posture load graph in which the one or more posture loads are illustrated in time series.

Abstract: According to one embodiment, a learning apparatus includes a processing circuit. The processing circuit acquires first sequence data representing transition of inference performance according to a training progress of a first model trained in accordance with a first training parameter value concerning a specific training condition. The processing circuit performs iterative learning of a second model in accordance with a second training parameter value concerning the specific training condition and changes the second training parameter value based on the inference performance of the second model and the first sequence data in a training process of the second model.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: A method of dividing an input image signal into pixel blocks, and performing inter-prediction on the divided pixel blocks. This method includes selecting predicted motion information from a motion information buffer storing motion information in an encoded region, and predicting motion information of an encoding target block by using the predicted motion information. The method further includes acquiring representative motion information from a plurality of items of motion information in an encoded region in accordance with first information indicating a method of selecting the predicted motion information, thereby obtaining only the representative motion information.