International Business Machines Corporation

Honda Motor Co., Ltd.

Canon Kabushiki Kaisha

Nippon Telegraph and Telephone Corporation

Tokyo Shibaura Denki Kabushiki Kaisha

Tanaka Kikinzoku Kogyo K.k.

Mitsubishi Denki Kabushiki Kaisha

Mitsubishi Electric Corporation

Oki Electric Industry Co., Ltd.

Ricoh Company, Ltd.

Kyocera Corporation

Globalfoundries Inc.

Mitsubishi Electric Research Laboratories, Inc.

Japan Tobacco Inc.

Mitsubishi Hitachi Power Systems, Ltd.

Hiroshi Inoue

Koji Yamanaka

Hideo Nakamura

Hiroshi Horii

Shigeru Komatsu

Kazuaki Ishizaki

Seisho Yasukawa

Nobuyasu Ezawa

Shinya Sato

Kazuya Tamura

Koji Yamada

Moriyoshi Ohara

Takuro Masuda

Aiko Sugawara

Yoshihiro Kawada

Mitsuru Hosoya

Akira Nakamura

Jun Takeda

Yasushi Sato

Kenji Kono

Daisuke Sato

Kazunori Ogata

Tamiya Onodera

Takuya Nakaike

Bingnan Wang

Keisuke Miura

Shohei Yoshida

Yoshitaka Hirata

Tomotake Aono

Akinori Hayashi

Kazunori Takahashi

Hideki Hosoya

Michio Nakamura

Kazuhito Koyama

Makoto Kishi

Satoshi Dodo

Yoshiyuki Ono

Sumio Kamoi

Tomohiro Asai

Osamu Kawaguchi

Tatsuya Sekiguchi

Junji Hori

Shouhei Yoshida

Satoshi Sato

Hiroto Kitai

Masato Sakui

Toshifumi Sasao

Makio Tamura

Akiko Yoshida

Shusaku Yoshida

Toshiaki Tsuchiya

Hiroyuki Kitada

Takuo Fukushima

Takafumi Okuyama

Daisuke Nakahara

Kenichi Endo

Kazutomo Tawa

Shigeomi Kubota

Toshiki Aoki

Jan M Wroblewski

Masaki Omata

Masahiko Ogino

Masahiro Teraguchi

Takashi Kaneko

Takuma Tsubaki

Tadashi Kurihara

Hiroshi Hirabayashi

Lei Zhou

Kota Takahashi

Yoshiaki Sugimoto

Tomoaki Takewa

Takuma Sasai

Minoru Fukumori

Makoto Kanemaru

Takeo Saito

Hitoshi Takada

Hideyuki Tsuchikiri

Kazuyuki Ito

Mika Ishizuka

Satoru Minowa

Takaharu Nakamura

Yasumasa Hayashida

Takeo Saitou

Tetsuya Okuyama

Tsukasa Miyamoto

Yoji Abe

Hitoshi Masuda

Isamu Ichizuka

Keiichi Hayashizaki

Eri Ogawa

Naoyuki Tajima

Masao Hidaka

Yuya Sato

Satoru Kondo

Mai Fujii

Yutaka Kangoori

Fumitada Yoshino

Hiroki Nomura

Yusaku Suenaga

Tomoya Murota

A method improves an execution speed of a host multi-core simulator simulating a target multi-core processor that has a hierarchical architecture including multiple corelets per core that, in turn include multiple functional units. The host multi-core simulator is implemented using multiple OS threads. The method selects layers in the hierarchical architecture to simulate on one of the OS threads, based on a shortest estimated layer execution time determined by (1.0+t/c*s)/min(c, t), wherein c is a number of cores in the simulator, t is a number of OS threads, and s is a threading overhead coefficient. The method respectively executes, from among the selected layers, a parallel simulation of the units therein that frequently communicate with each other on one of the multiple OS threads based on a communication frequency threshold, by assigning and using a respective user-level thread for each of the units from among a plurality of user-level threads.

User-level threading for simulating multi-core processor

An abnormality diagnosis device includes: a first interface to obtain a value of a driving current for driving a motor; and a processor to access a database including calculation data to be used to calculate a degree of abnormality of the motor, wherein the processor extracts a feature quantity for calculating the degree of abnormality from a current waveform specified by a value of the driving current, and calculates the degree of abnormality of the motor based on the extracted feature quantity and the calculation data.

Abnormality diagnosis device and abnormality diagnosis method

A computer-implemented method, a computer program product, and a computer system for multi-sample dropout in deep neural network training. A computer creates multiple dropout samples in a minibatch, starting from a dropout layer and ending at a loss function layer in a deep neural network. At the dropout layer in the deep neural network, the computer applies multiple random masks for respective ones of the multiple dropout samples. At a fully connected layer in the deep neural network, the computer applies a shared parameter for all of the multiple dropout samples. After the loss function layer in the deep neural network, the computer calculates a final loss value, by averaging loss values of the respective ones of the multiple dropout samples.

Multi-sample dropout for faster deep neural network training

A highly reliable facility evaluation information (rating information) that is free of arbitrary ratings. A server system includes a facility rating information creation and updating unit that grants a rating with regard to a facility to be rated on the basis of parking information. The parking information is obtained by assessing whether the present position of a mobile body, which is being carried together with a vehicle, has been parked in the area of the facility to be rated whereat parking is possible. A facility rating provision unit that outputs the rating regarding to the facility to be rated, which has been granted by the facility rating information creation and updating unit.

Facility rating device and facility rating method

Methods and systems for performing machine learning include selecting first and second training data from one or more training sets in one or more databases. Mixed training data is formed by subtracting a value of each data element in the second training data from a value of a corresponding data element in the first training data. A machine learning process is trained using the mixed training to augment data used by the machine learning process.

Data augmentation for image classification tasks

Provided is an abnormality diagnosis device including: a physical quantity measurement processor configured to acquire, as time-series data, a physical quantity of an electric motor or a load; a feature parameter calculation processor configured to calculate feature parameters; a correlation function creation processor configured to create a correlation function for two or more feature parameters, and calculate a correlation coefficient corresponding to a measurement result of the physical quantity that changes depending on an abnormality occurrence state; a database configured to store in advance reference data in which a remaining time until a failure is caused and the correlation coefficient are associated with each other; and a diagnosis processor configured to extract from the reference data the remaining time corresponding to the correlation coefficient, and identify the remaining time until the electric motor or the load causes a failure or a part that has caused a failure.

A system control device that transmits a manifest file describing information necessary for acquiring video data to a viewing client in a system that performs distribution of the video data via a network includes: generation means for generating the manifest file on the basis of at least one of identification information indicating a viewer attribute, a time zone, and a state of the system according to a prescribed control policy; and transmission means for transmitting the manifest file generated by the generation means to the viewing client.

System control apparatus, system control method and program

Sorting an array consisting of large number of elements. The present invention provides an apparatus for executing a multiway merging process which generates one output sequence from N input sequences on an array consisting of a large number of elements. The apparatus includes: an execution unit configured to execute the multiway merging process on N input sequences without rearranging the elements based on a plurality of input sequences; and a generation unit configured to rearrange the elements constituting the input sequences according to an output sequence that has been generated by the multiway merging process in the execution unit so as to generate a sorted array of elements.

Sorting an array consisting of a large number of elements

Provided is a navigation server or the like capable of promoting enhancement of convenience in traffic of moving bodies with a comprehensive consideration of various factors of each link. There is found a server route R configured by link groups that connect a starting point pand a destination point pof a navigation clientof interest, the server route R having a minimum total value &#x3a3;Cof second moving costs Cin the link groups. The second costs Cresult from first moving costs Ccorrected based on various factors, such as combinations of 'static factors' and 'dynamic factors'.

Navigation server and navigation system

A mixing device of a first signal and a second signal on a time-frequency plane, includes a control signal generation unit to generate a control signal indicating whether to perform prioritized mixing that includes amplification of the first signal and attenuation of the second signal; and a gain derivation unit to derive a first gain for amplifying the first signal and a second gain for attenuating the second signal based on the control signal. The control signal takes at least a first value and a second value different from the first value, wherein the first value is not continuous beyond a predetermined bandwidth on a frequency axis. The mixing device applies the prioritized mixing to the first and second signals when the control signal indicates the first value, and applies simple addition to the first and second signals when the control signal indicates the second value.

Mixing device, mixing method, and non-transitory computer-readable recording medium

A method configures a cache to implement a LRU management technique. The cache has N entries divided into B buckets. Each bucket has a number of entries equal to P entries*M vectors, wherein N=B*P*M. Any P entry within any M vector is ordered using an in-vector LRU ordering process. Any entry within any bucket is ordered in LRU within the vectors and buckets. The LRU management technique moves a found entry to a first position within a same M vector, responsive to a lookup for a specified key, and permutes the found entry and a last entry in a previous M vector, responsive to the found entry already being in the first position within a vector and the same one of the M vectors not being a first vector in the bucket in the moving step.

Cache replacement with no additional memory space

A quality information collecting system acquiring video data distributed in response to a viewing request and collecting quality-related information regarding video viewing from a viewing client playing back a video includes a quality information control functional unit configured to determine whether or not to request the viewing client having made a viewing request to report quality-related information, based on a network communication situation in which the video data is distributed, and a quality information collecting and analysis functional unit configured to receive, in accordance with a determination of the quality information control functional unit, the quality-related information transmitted from the viewing client.

Quality information collection system, quality information collection method and program

A method includes generating, using a processor, a graph including a plurality of nodes and a plurality of paths between the nodes, the graph representing a system comprising an arrangement of elements, and a data structure in which, for each node pair of the plurality of nodes, a count of the number of paths of length S between the node pair is stored in association with the length S. The method includes modifying the graph, using the processor, to obtain a modification of the graph related to the count of the number of paths of length S, and estimating, based on the data structure and the modification, an objective function that is quantitatively representative of the connections between the nodes and being indicative of performance of the system.

Efficient graph optimization

A computer-implemented method and systems are provided for performing machine learning for an image classification task. The method includes overlaying, by a processor, a second image on a first image to form a mixed image, by averaging an intensity of each of a plurality of co-located pixel pairs in the first and the second image. The method also includes training, by the processor, a machine learning process configured for the image classification task using the mixed image to augment data used by the machine learning process for the image classification task.

A computer executes a first determination procedure determining a first link which at least partially overlaps a disaster area representing a range reached by an influence of a predetermined disaster, among links constituting a network, a second determination procedure determining a second link which at least partially overlaps a road area representing a range of a road width of a predetermined road used to recover from the disaster, among the links constituting the network, a route search procedure searching for a plurality of routes from a start point node to an end point node constituting the network, a grouping procedure grouping the plurality of routes searched for by the route search procedure into groups depending on a first parameter value that is based on the first link included in the routes, a computing procedure computing, for each of the plurality of routes searched for by the route search procedure, a second parameter value that is based on the first link and the second link included in the routes, and a selecting procedure selecting a predetermined number of routes among one or more routes grouped into the same group on the basis of the second parameter value computed by the computing procedure for each of the groups grouped by the grouping procedure.

Route calculation method, route calculation apparatus and program

Growing community of inventors

Tokyo, Japan

Hiroshi Inoue