Fujitsu Corporation

Fujitsu Semiconductor Limited

Makiko Konoshima

Yui Noma

Tomonori Kubota

Hiroyuki Masatsugu

Yuichiro Teshigahara

Akira Nakagawa

Eishi Morimatsu

Kiichi Matsuda

Hirotaka Tamura

Atsuko Tada

Morito Shiohara

Takashi Itoh

Ryoichi Funabashi

Shinichi Shirakawa

Yosuke Yamaguchi

Jun Ohkubo

A noise estimation method includes decomposing a first matrix in which values of elements are represented by binary values into a coefficient matrix and a basic matrix, and estimating an element including noise among elements of the first matrix based on a result of comparison between a second matrix obtained by combining the coefficient matrix with the basic matrix and the first matrix.

Noise estimation method, non-transitory computer-readable storage medium, and noise estimation apparatus

An information processing device includes: a memory configured to hold values of state variables included in an evaluation function presenting energy and a weight value for each set of the state variables; and a processor coupled to the memory and configured to: calculate an energy change value when each of the values of the state variables is set as a next change candidate based on the values of the state variables and the weight value; calculate a total energy change value by adding a penalty value according to an excess amount violating an inequality constraint, to each of the energy change values calculated for the state variables, the excess amount being calculated based on a coupling coefficient and a threshold value; and change any value of the state variables in the memory based on a set temperature value, a random number value, and the total energy change values.

Semiconductor integrated circuit

An optimization apparatus includes: a memory; and a processor coupled to the memory, the processor being configured to (a): execute transformation processing that includes transforming an input first cost function to a second cost function by using at least any one of 'Legendre transformation,' 'Lagrange function,' and &#x201c;Wolfe's duality theorem,&#x201d; (b): execute binary expansion processing that includes acquiring values in an Ising format by carrying out binary expansion for values relating to coefficients of the second cost function, (c): execute Ising machine processing that includes seeking values that represent a ground state regarding the values in the Ising format relating to the coefficients of the second cost function, and (d): execute reverse binary expansion processing that includes acquiring values that minimize the first cost function by carrying out reverse binary expansion for the sought values that represent the ground state.

Optimization apparatus, method implemented by optimization apparatus, and non-transitory computer-readable storage medium for storing program

A learning method includes: randomly selecting one or more feature vectors from feature vectors for learning to form a sample set, by a processor; selecting, from the feature vectors for learning, one of feature vectors appended with a label different from a label appended to a feature vector included in the sample set as a reference vector, the selecting being carried out based on a generalized average of distance from a feature vector included in the sample set, by the processor; and learning a hyperplane that divides a feature vector space, the learning being carried out using a pair of one of feature vectors appended with a label different from a label appended to the reference vector, among the feature vectors for learning, and the selected reference vector, by the processor.

Learning method, information processing device, and recording medium

A data placement device creates a similarity index for each of computational resources based on a similarity between each of the pieces of acquired data and each of the pieces of data stored in the computational resources. The data placement device allocates on the basis of the similarity index of each of the computational resources with respect to the pieces of the data, the pieces of the data to each of the computational resources by using a matching system in which the similarity index associated with each allocation becomes stable in a direction in which the similarity index is small. The placement device places the pieces of the acquired data into the computational resources on the basis of the allocation result.

Computer-readable recording medium, data placement method, and data placement device

A data compression apparatus includes a memory; and a processor configured to generate compressed matrix data, compare a threshold and an index value calculated about a specific value data string that is a data string obtained by coupling specific values specified from element values that are not zero values in each row of the compressed matrix data, specify a given constant as respective coefficients when the index value is larger than the threshold, calculate reciprocals of respective specific values as the respective coefficients when the index value is equal to or smaller than the threshold, and output matrix data after operation that is a result of rounding based on the number of places of significant figures of a decimal part in each element that corresponds about products of respective elements of the compressed matrix data and the respective coefficients calculated, regarding the respective elements of the compressed matrix data.

Data compression apparatus and data compression method and storage medium

A data arrangement apparatus executes a process request on data stored in a database, calculates similarity of a process in a combination of the process request and the data, divides the data in such a manner as to distribute the similarity and processes a process request accepted for the divided data.

Non-transitory computer-readable recording medium, data arrangement method, and data arrangement apparatus

A classification method includes: setting a position of a projective point based on information on a domain of definition of a feature value space, using a processor; and projecting a vector present in the feature value space to a sphere present in a space of dimensions higher by at least one dimension than a dimension of the feature value space by using the set position of the projective point, using the processor.

Setting a projective point for projecting a vector to a higher dimensional sphere

A storage unit stores structure data that includes coordinates of vertices of a plurality of polygons representing a three-dimensional structure. A computation unit calculates coordinates of a certain point with reference to the structure data. This point is used, together with the vertices of the polygons, to produce a graph from the structure data according to spatial arrangement of the polygons and further to calculate characteristic quantities based on the produced graph. The computation unit stores the calculated coordinates of the point in a memory device, as a piece of information relating to the structure data.

Apparatus and method for managing structure data

A search device according to an embodiment maps a feature vector onto a hyper-sphere on the basis of parameters which include an intersection and a distance, with the intersection at which an m-dimensional feature space and a straight line passing through the hyper-sphere present in a space greater in dimension than m intersect and the distance being from the north pole of the hyper-sphere to the feature space. In this case, the search device searches for the parameters which allow the positions of feature vectors mapped onto the hyper-sphere to be concentrated on a predetermined hemisphere of the hyper-sphere.

Search method, search program, and search device

A learning method includes: counting any one of or some of the number of labels added to each of feature amount vectors included in a learning data set, the number of types of the label, the number of feature amount vectors added with the same label, and the number of data pairs used for learning of a hyperplane, by a processor; first selecting, according to a result of the counting, one or more generation methods from a plurality of previously stored generation methods that generate the data pairs from the learning data set, by the processor; generating, using the selected generation methods, the data pairs from the feature amount vectors included in the learning data set, by the processor; and first learning, using the generated data pairs, the hyperplane that divides a feature amount vector space, by the processor.

Learning method, information conversion device, and recording medium

An information conversion method includes: first moving positions of a plurality of particles on a unit sphere according to a value of a probability density function, defining a positional vector of a particle on the unit sphere in a multidimensional space, as a normal vector of a hyperplane configured to divide a feature vector space, defining a predetermined evaluation function configured to evaluate the hyperplane, as the probability density function configured to indicate a probability of existence of a particle on the unit sphere, by a processor; and converting the feature vector to a binary string, considering a positional vector of the moved particle as a normal vector of the hyperplane, by the processor.

Authentication method, authentication device, and recording medium

A method comprises extracting a picture type and size per picture frame from encoded first moving picture data, calculating a first characteristic quantity per picture frame and a first variation point indicating a variation point of the first characteristic quantity on the basis of the extracted picture type and size, extracting a picture type and size per picture frame from encoded second moving picture data, calculating a second characteristic quantity per picture frame and a second variation point indicating a variation point of the second characteristic quantity on the basis of the extracted picture type and size, and comparing the first characteristic quantity and variation point with the second characteristic quantity and variation point, and determining whether or not the first moving picture is similar to the second moving picture.

Moving picture data comparing method and apparatus

An information conversion device includes a memory and a processor coupled to the memory. The processor executes a process including generating a conversion rule for converting a feature quantity vector into a binary string that is longer than a predetermined bit length. The process includes converting each of the feature quantity vectors into a binary string by using the conversion rule generated at the generating. The process includes calculating importance levels of the respective bits in the binary strings based on the distance-based relations among the feature quantity vectors. The process includes correcting the conversion rule into one that converts each of the feature quantity vectors into a binary string having the predetermined bit length based on the calculated importance levels. The process includes changing the feature quantity vectors into a binary string having the predetermined bit length by using the conversion rule corrected at the correcting.

Information conversion device, computer-readable recording medium, and information conversion method

A moving image editing apparatus including: a storage unit configured to store a first moving image; a stack memory configured to, each time a reading request for reading range of a portion of the first moving image from the storage unit, or a writing request for replacement range of a portion of the first moving image with a second moving image is received from a terminal, store the request; and a sending processing unit configured to output, when the reading request is read from the stack memory, to the terminal, a moving image in which the overlapping range of the reading range with regard to the first moving image has been replaced with the second moving image when the second moving image with which a range overlapping a portion of the reading range specified by the reading request is to be replaced has been received.

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Kawasaki, Japan

Makiko Konoshima