Abstract: A novel functional panel that is highly convenient, useful, or reliable is provided. The functional panel includes a first driver circuit, a second driver circuit, and a pixel set, the first driver circuit has a function of supplying a first selection signal and a second selection signal, a second driver circuit has a function of supplying an image signal and a control signal, and the control signal includes a first level and a second level. The pixel set includes a first pixel, and the first pixel includes a first element and a first pixel circuit. The first pixel circuit has functions of obtaining the image signal on the basis of the first selection signal, obtaining the control signal on the basis of the second selection signal, and holding a first state to a third state.

Abstract: A display apparatus having a photoelectric conversion function with high sensitivity is provided. The light extraction efficiency of the display apparatus is increased. The display apparatus includes a light-emitting device, a light-emitting and light-receiving device, a first lens, and a second lens. The light-emitting device has a function of emitting light of a first color. The light-emitting and light-receiving device has a function of emitting light of a second color and a function of receiving light of the first color and converting it into an electric signal. The light emitted by the light-emitting device is emitted to the outside of the display apparatus through the first lens. Light enters the light-emitting and light-receiving device from the outside of the display apparatus through the second lens.

Abstract: Viral vector-producing cells with improved ability to produce viral vector, a production method thereof, a kit containing the same, a method for selecting viral vector-producing cells, and the like are provided. A viral vector-producing cell in which the expression of a protein derived from the viral vector-producing cell is regulated, specifically, the expression of the aforementioned protein is regulated as compared with that in the viral vector-producing cells before regulation of the expression or HEK293 cells (Accession number ATCC CRL1573) shows an improved ability to produce viral vectors.

Abstract: According to an embodiment, a collation device includes a hardware processor configured to: generate, based at least in part on input data, an input vector comprising input data features indicating features of the input data, the input data features comprising D number of features, D being an integer equal to or larger than two; and generate first specification information that specifies d selected features among the input data features of the input vector, based at least in part on a plurality of reference vectors and the input vector, the plurality of reference vectors each comprising reference features in the same form as the input vector, the reference features comprising the D number of features, d being an integer equal to or larger than one and smaller than D.

Abstract: According to an embodiment, a collation device includes a hardware processor configured to: generate, based at least in part on input data, an input vector comprising input data features indicating features of the input data, the input data features comprising D number of features, D being an integer equal to or larger than two; and generate first specification information that specifies d selected features among the input data features of the input vector, based at least in part on a plurality of reference vectors and the input vector, the plurality of reference vectors each comprising reference features in the same form as the input vector, the reference features comprising the D number of features, d being an integer equal to or larger than one and smaller than D.

Abstract: According to an embodiment, a computing device includes a processing circuitry. The processing circuitry receives an input of tensor data. The processing circuitry sets a window in the tensor data. The processing circuitry compares, for each pair of coordinates in the tensor data within the window, a pixel value at the pair of coordinates with one or more thresholds, and selects a weight value corresponding to a comparison result. The processing circuitry adds the weight values selected for the respective pairs of coordinates to obtain a cumulative value. The processing circuitry derives a value based at least in part on the cumulative value.

Abstract: An information processing device according to one embodiment includes a first receiver, a second receiver, a first converter, a second converter, and a calculator. The first receiver receives input of first data belonging to a first modality. The second receiver receives input of second data belonging to a second modality that is different from the first modality. The first converter converts the first data into a first representation representing a point or a first area in a D-dimensional vector space (D is a natural number). The second converter converts the second data into a second representation representing a second area in the D-dimensional vector space. The calculator calculates similarity between the first data and the second data by using the first representation and the second representation.

Abstract: According to an embodiment, a device includes a memory and processing circuitry. When time taken by a user to carry out first behavior is equal to or more than a first threshold, or when number of times the first behavior is repeated is equal to or more than a second threshold, the processing circuitry is configured to output first guidance to lead the user to first expected behavior that is behavior the user is expected to carry out subsequent to the first behavior. When the time taken to carry out the first behavior is less than the first threshold, or when the number of times the first behavior is repeated is less than the second threshold, the processing circuitry is configured to omit the first guidance to lead the user to the first expected behavior or outputs second guidance that is simpler than the first guidance.

Abstract: According to an embodiment, an apparatus includes a calculator, an pixel evaluator, an accumulator, and an area evaluator. The calculator is configured to calculate a feature of an image for each pixel in image data. The pixel evaluator is configured to produce a score that evaluates the feature for each pixel. The accumulator is configured to calculate, for each pixel, a cumulative score obtained by accumulating all scores in an area including a minor angle formed by a half line in a first direction from the each pixel position and another half line in a second direction from the each pixel position. The area evaluator is configured to calculate an evaluation value that is a total of the scores in a quadrilateral area enclosed by two lines of the first direction and two lines of the second direction based on the cumulative scores at pixel positions at vertexes of the quadrilateral area.

Abstract: According to an embodiment, a computing apparatus includes a memory, and a processor. The memory stores N first vectors in a d-dimensional binary vector space consisting of binary values. The processor acquires a second vector in the d-dimensional binary vector space. The processor extracts M first vectors having a distance from the second vector satisfying a first condition out of the N first vectors, and calculate a distribution of distances of the M first vectors from the second vector. The processor acquires a first kernel function per a first distance between the M first vectors and the second vector in a first range. The processor generates a second kernel function based on the distribution and the first kernel functions. The processor calculates an occurrence probability of the second vector in the N first vectors based on the second kernel function.

Abstract: According to an embodiment, a feature point detection device includes a generator to generate a K-class classifier and perform, for T times, an operation in which a first displacement vector is obtained that approximates D number of initial feature points of each training sample classified on a class-by-class basis to true feature points; a calculator to calculate, from the first displacement vectors, second displacement label vectors each unique to one second displacement vector, and a second displacement coordinate vector common to the second displacement vectors; a classifier to apply the K-class classifiers to the input image and obtain a second displacement label vector associated with a class identifier output from each K-class classifier; an adder to add up the second displacement label vectors; and a detector to detect D number of true feature points based on the initial feature points, the added label vector, and the second displacement coordinate vector.

Abstract: According to an embodiment, a computing device includes a processing circuitry. The processing circuitry receives an input of tensor data. The processing circuitry sets a window in the tensor data. The processing circuitry compares, for each pair of coordinates in the tensor data within the window, a pixel value at the pair of coordinates with one or more thresholds, and selects a weight value corresponding to a comparison result. The processing circuitry adds the weight values selected for the respective pairs of coordinates to obtain a cumulative value. The processing circuitry derives a value based at least in part on the cumulative value.

Abstract: According to an embodiment, an apparatus includes a calculator, an pixel evaluator, an accumulator, and an area evaluator. The calculator is configured to calculate a feature of an image for each pixel in image data. The pixel evaluator is configured to produce a score that evaluates the feature for each pixel. The accumulator is configured to calculate, for each pixel, a cumulative score obtained by accumulating all scores in an area including a minor angle formed by a half line in a first direction from the each pixel position and another half line in a second direction from the each pixel position. The area evaluator is configured to calculate an evaluation value that is a total of the scores in a quadrilateral area enclosed by two lines of the first direction and two lines of the second direction based on the cumulative scores at pixel positions at vertexes of the quadrilateral area.

Abstract: A recognition device includes a storage unit, an acquiring unit, a first calculator, a second calculator, a determining unit, and an output unit. The storage unit stores multiple training patterns each belonging to any one of multiple categories. The acquiring unit acquires a recognition target pattern to be recognized. The first calculator calculates, for each of the categories, a distance histogram representing distribution of the number of training patterns belonging to the category with respect to distances between the recognition target pattern and the training patterns belonging to the category. The second calculator analyzes the distance histogram of each of the categories to calculate confidence of the category. The determining unit determines a category of the recognition target pattern from the multiple categories by using the confidences. The output unit outputs the category of the recognition target pattern.

Abstract: According to an embodiment, a computing apparatus includes a memory, and a processor. The memory stores N first vectors in a d-dimensional binary vector space consisting of binary values. The processor acquires a second vector in the d-dimensional binary vector space. The processor extracts M first vectors having a distance from the second vector satisfying a first condition out of the N first vectors, and calculate a distribution of distances of the M first vectors from the second vector. The processor acquires a first kernel function per a first distance between the M first vectors and the second vector in a first range. The processor generates a second kernel function based on the distribution and the first kernel functions. The processor calculates an occurrence probability of the second vector in the N first vectors based on the second kernel function.

Abstract: According to one embodiment, a crack data collection method includes acquiring an image obtained by photographing an inspection object region for a crack in a structure, detecting a crack pixel group included in the inspection object region from the image, successively setting turning points from a starting point to an end point on a contour of the crack pixel group, and analyzing and collecting positions of the starting point, the turning points, and the end point and a vector of each of the points, as crack data.

Abstract: According to one embodiment, a crack data collection apparatus includes an acquisition unit, a detector, a calculator and a storage unit. The acquisition unit acquires an image obtained by photographing an inspection object region for a crack in a structure. The detector detects a crack pixel group included in the inspection object region from the image. The calculator successively sets turning points from a starting point to an end point on a contour of the crack pixel group, and calculates positions of the starting point, the turning points, and the end point and a vector of each of the points as crack data, The storage unit stores the crack data.

Abstract: According to an embodiment, a feature point detection device includes a generator to generate a K-class classifier and perform, for T times, an operation in which a first displacement vector is obtained that approximates D number of initial feature points of each training sample classified on a class-by-class basis to true feature points; a calculator to calculate, from the first displacement vectors, second displacement label vectors each unique to one second displacement vector, and a second displacement coordinate vector common to the second displacement vectors; a classifier to apply the K-class classifiers to the input image and obtain a second displacement label vector associated with a class identifier output from each K-class classifier; an adder to add up the second displacement label vectors; and a detector to detect D number of true feature points based on the initial feature points, the added label vector, and the second displacement coordinate vector.

Abstract: According to an embodiment, an image processing device includes a generator and a processor. The generator is configured to generate, from a plurality of unit images in which points on an object are imaged by an imaging unit at different positions according to distances between the imaging unit and the positions of the points on the object, a refocused image focused at a predetermined distance. The processor is configured to perform blurring processing on each pixel of the refocused image according to an intensity corresponding to a focusing degree of the pixel of the refocused image.

Abstract: According to an embodiment, a recognition device includes a memory to store therein learning patterns each belonging to one of categories; an obtaining unit to obtain a recognition target pattern; a first calculating unit to calculate, for each category, a distance histogram representing distribution of the number of learning patterns belonging to the categories with respect to distances between the recognition target pattern and the learning patterns belonging to the categories; a second calculating unit to analyze the distance histogram of each category, and calculate a feature value of the recognition target pattern; a third calculating unit to make use of the feature value and one or more classifiers, and calculate degrees of reliability of the recognition target categories; and a determining unit to make use of the degrees of reliability and, from among the one or more recognition target categories, determine a category of the recognition target pattern.