Abstract: A system and a method for approximate nearest neighbor search are disclosed. A query storage circuit stores query information related to at least one query from a host in a query property table. A generator and allocator circuit is configured to generate graph information using a batch of at least one vertex corresponding to the at least one queries from the query property table and to allocate the at least one queries to at least one logic unit (LU) based on the graph information. A search circuit has the at least one LU and is configured to compute at least one distance, using the graph information, between the at least one vertex and at least one candidate neighbor of the at least one vertex to generate at least one distance result. The query property table is modified based on the at least one distance result.

Abstract: A device may include memory media configured as cache media; and one or more circuits configured to perform operations including receiving memory access information, performing a mixture model analysis based on the memory access information to produce one or more scores, and updating the memory media based on the one or more scores. The memory media may include determining that at least one of the one or more scores is above a threshold and loading a portion of memory corresponding to the at least one of the one or more scores to the memory media. Updating the memory media may include determining that at least one of the one or more scores is below a threshold and removing a portion of memory corresponding to the at least one of the one or more scores from the memory media.

Abstract: An object of the present invention is to provide an image recognition system, an image recognition server, and an image recognition method having a new high security framework that can achieve utilization of multi-device diversity. The image recognition system according to the present disclosure includes a computationally non-intensive encryption algorithm based on random unitary transformation and achieves a high level of security. In addition, the image recognition system achieves high recognition performance by using ensemble learning to integrate recognition results based on the dictionaries of different devices.

Abstract: Provided is a concealment signal generation unit that acquires learning constellation data and identification constellation data output from a signal processing circuit for optical communication, reduces and conceals the number of pieces of data from each of the pieces of constellation data through random projection, and generates a learning concealment signal and an identification concealment signal based on each piece of constellation data after the reduction and concealment of the number of pieces of data. Provided are a sparse dictionary learning unit that learns a concealment sparse dictionary using a sparse dictionary learning algorithm based on the learning concealment signal; and an identification unit that estimates a state of the optical communication using the concealment sparse dictionary based on the identification concealment signal.

Abstract: A data accumulation unit that acquires traffic data of a network obtained in time series and creates a plurality of data sets having different time intervals, a training unit that evaluates a correlation between the plurality of data sets by a plurality of latent functions and calculates a weight coefficient, and a prediction unit that calculates a predicted average using the latent functions calculated by the training unit and predicts network traffic of a future time scale are included.

Abstract: The present disclosure has been made in view of such a problem, and an object of the present disclosure is to make it possible to predict fluctuation of unsteady traffic with a small amount of calculation.

Abstract: The present disclosure has an object of proposing a method and a device for estimating the state of a transmission path or an optical transmitter capable of mechanically estimating a factor causing an error with a small amount of constellation data and a low computing amount.

Abstract: The present disclosure is to perform beamforming corresponding to the influence of a dynamic environment in which a user moves. The present disclosure relates to a beamforming prediction device that includes: a storage unit that stores a dictionary D obtained by learning fingerprints based on trajectories, and a fingerprint database based on trajectories; a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal; a fingerprint estimation unit that applies the trajectory of the mobile terminal to an input of the dictionary D, and calculates the sparse coefficient X corresponding to the trajectory of the mobile terminal; and a beamforming calculation unit that calculates beamforming of the mobile terminal, using the sparse coefficient X calculated by the fingerprint estimation unit and the fingerprint database.

Abstract: The present disclosure has been made in view of such a problem, and an object of the present disclosure is to make it possible to predict fluctuation of unsteady traffic with a small amount of calculation.

Abstract: The present disclosure has an object of proposing a method and a device for estimating the state of a transmission path or an optical transmitter capable of mechanically estimating a factor causing an error with a small amount of constellation data and a low computing amount.

Abstract: An object of the present invention is to provide an image recognition system, an image recognition server, and an image recognition method having a new high security framework that can achieve utilization of multi-device diversity. The image recognition system according to the present disclosure includes a computationally non-intensive encryption algorithm based on random unitary transformation and achieves a high level of security. In addition, the image recognition system achieves high recognition performance by using ensemble learning to integrate recognition results based on the dictionaries of 4 different devices.