Abstract: There is provided a vehicle including: a plurality of dimming glass units used for a plurality of windows; a sensor that acquires an entering situation of natural light at a predetermined position; and a controller. The controller performs: specifying a first dimming glass unit that is a control target out of the plurality of dimming glass units in accordance with the entering situation of natural light; and controlling a light shielding property of the first dimming glass unit. The controller controls the first dimming glass unit so as to be into a light shielding state when the entering situation of natural light satisfies a first condition indicating that an occupant in the vehicle feels glaringness, and releases the light shielding state of the first dimming glass unit when the entering situation of natural light satisfies a second condition indicating that the occupant in the vehicle does not feel glaringness.

Abstract: A temporary adhesive has excellent spin coating properties of a circuit side of a wafer and a support, and excellent heat resistance when the circuit side of the wafer or the support is attached to an adhesion layer or a rear surface of the wafer is processed, and is capable of easily separating the circuit side of the wafer from the support after polishing the rear surface of the wafer, and simply removing an adhesive attached to the wafer or the support after the separation. The adhesive contains a component (A) to be cured by a hydrosilylation reaction, and a component (B) containing an epoxy-modified polyorganosiloxane at a ratio in % by mass of the component (A) to the component (B) of 99.995:0.005 to 30:70. The component (B) is an epoxy-modified polyorganosiloxane having an epoxy value of 0.1 to 5.

Abstract: An ultrasound treatment tool includes: an ultrasound transducer configured to generate ultrasound vibration; a vibration transmission portion configured to transmit the ultrasound vibration; and a fastener that includes a first fastening portion screwed with the ultrasound transducer and a second fastening portion screwed with the vibration transmission portion, the fastener being configure to connect the ultrasound transducer and the vibration transmission portion. The fastener is configured with a material that has a higher strength than a proximal end portion of the vibration transmission portion, the proximal end portion being screwed with the second fastening portion.

Abstract: A fastener includes: a main body including a first fastening portion configured to be screwed together with a ultrasound transducer configured to generate ultrasound vibration, and a second fastening portion configured to be screwed together with a vibration transmission portion configured to transmit the ultrasound vibration, the main body being configure to connect the ultrasound transducer and the vibration transmission portion; and a rupture portion configured to be ruptured as a result of a specific torque being applied when the first fastening portion is screwed together with the ultrasound transducer or when the second fastening portion is screwed together with the vibration transmission portion.

Abstract: An optical device includes: a restriction member that restricts part of reflected light from a target object, the restriction member being formed from resin, the restriction member having an opening and a hole, the opening extending in a first direction crossing a propagation direction of the reflected light and formed at an upstream end in the propagation direction, the hole extending through the restriction member in the propagation direction; an optical member that transmits the reflected light that has passed through the opening; and a correction member that holds the optical member, and that comes into contact with part of an inner circumferential surface of the hole to correct a width of the opening.

Abstract: A technique for stably optimizing a variable of model so as to conform the learning data set is provided, even when there is a statistical deviation in a learning data set distributed and accumulated in a plurality of nodes, or communication between nodes is asynchronous and sparse.

Abstract: Provided is a technique for performing learning of a neural network including an encoder and a decoder such that a certain latent variable included in a latent variable vector is larger or the certain latent variable included in the latent variable vector is smaller as a magnitude of a certain property included in an input vector is larger. A neural network learning device performs learning of a neural network including an encoder that converts an input vector into a latent variable vector and a decoder that converts the latent variable vector into an output vector such that the input vector and the output vector are substantially equal to each other, and the learning is performed to cause the latent variable to have monotonicity with respect to the input vector.

Abstract: Provided is a technique for performing learning of a neural network including an encoder and a decoder such that a certain latent variable included in a latent variable vector is larger or the certain latent variable included in the latent variable vector is smaller as a magnitude of a certain property included in an input vector is larger. A neural network learning device performs learning of a neural network including an encoder that converts an input vector into a latent variable vector and a decoder that converts the latent variable vector into an output vector such that the input vector and the output vector are substantially identical to each other, and the learning is performed in such a manner that a condition that all weight parameters of the decoder are non-negative values or all weight parameters of the decoder are non-positive values is satisfied.

Abstract: A sound source separation filter information estimation device (10) estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

Abstract: An acoustic signal enhancement device includes: a spatiotemporal covariance matrix estimation unit 2 configured to estimate spatiotemporal covariance matrices Rf(j) and Pf(j); a reverberation suppression unit 3 configured to obtain a reverberation suppression filter Gf(j) of the sound source j using the estimated spatiotemporal covariance matrices Rf(j) and Pf(j) for each sound source j and to generate a reverberation suppression signal vector using the obtained reverberation suppression filter Gf(j) and the observation signal vector Xt,f; a sound source separation unit 4 configured to obtain an enhanced sound yt,f(j) of the sound source j and power of the sound source j using the generated reverberation suppression signal vector for each sound source j (where 1?j?J) corresponding to the target sound; and a control unit 5 configured to perform control such that processes of these units are repeatedly performed.

Abstract: A signal separation device for acquiring a source signal from a mixed signal observed by a plurality of sensors includes: a database that stores feature information of a clean signal; separation matrix calculation means for repeatedly performing processes of, based on a separated signal obtained by multiplication of a mixed signal converted into a time-frequency representation by a separation matrix and on the feature information stored in the database, calculating a parameter to be used for an objective function for optimizing the separation matrix, and calculating a separation matrix for minimizing the objective function using the parameter; and output means for outputting a separated signal calculated using the optimized separation matrix obtained by the separation matrix calculation means.

Abstract: An optical device includes: a radiation portion that sends light toward a predetermined position; a lens body including plural lenses that are arranged in an array direction and on which reflection light reflected at the predetermined position is incident; a first support body having a light limiting portion and supporting the radiation portion, the light limiting portion extending in the array direction on the incident side of reflection light relative to the lens body, the light limiting portion limiting incidence of at least a portion of light having an influence on an image based on reflection light; and a second support body supporting the lens body and being movable, relative to the first support body, in an optical axis direction of reflection light.

Abstract: The signal separation device includes: cross product calculation means receiving an input of an observed signal that is a mixture of a plurality of target signals, and calculating a cross product of the observed signal; model calculation means updating a parameter of a model for estimating the cross product with a predetermined algorithm using an inverse matrix of a matrix that represents an estimate of the cross product; inverse matrix calculation means calculating the inverse matrix of a matrix by a SIMD command when the parameter is updated; and separation means calculating the target signals using a matrix representing an estimate of the cross product, the updated parameter, and the observed signal.

Abstract: The present disclosure relates to an apparatus and methods of estimating a traffic volume of moving objects. In particular, the present disclosure estimates the traffic volume based on amounts of traffic volume of the moving objects observed at observation points based on a routing matrix and a visitor matrix. The routing matrix indicates whether moving objects that pass through specific waypoints are to be observed at an observation point. The visitor matrix indicates whether a moving object departing or arriving at the observation point. The present disclosure enables estimating a traffic volume of moving objects on various routes based on observed data with errors in data and varying lengths in observation periods.

Abstract: Provided is a sensor network capable of acquiring, from a data center, learning data composed of a pair of sensor data (input) and a classification label (output) necessary for adding/changing a classification label for updating a classifier while reducing the volume of communication between a sensor and the center. One aspect of the present invention relates to a sensor network, including one or more sensor nodes and a data center. The sensor node includes: an encoding unit for encoding sensor data by an encoder part of an autoencoder; and a transmission unit for transmitting the encoded data. The data center includes: a reception unit for receiving data encoded from sensor data by the encoder part of the autoencoder; a decoding unit for decoding the encoded data by a decoder part of the autoencoder; and a storage unit for storing the decoded data therein.

Abstract: Provided is an acoustic signal enhancement device, including a time-space covariance matrix estimation unit 2 configured to estimate a time-space covariance matrix Rf(n),Pf(n) corresponding to a sound source n, using a power ?t,f(n) of the sound source n and an observation signal vector Xt,f composed of an observation signal xm,t,f from a microphone m; a reverberation suppression unit 3 configured to obtain a reverberation removal filter Gf(n) of the sound source n using the time-space covariance matrix Rf(n),Pf(n), and to generate a reverberation suppression signal vector Zt,f(n) corresponding to the observation signal xm,t,f for an emphasized sound of the sound source n using the reverberation removal filter Gf(n) and the observation signal vector Xt,f; and a sound source separation unit 4 configured to obtain an emphatic sound yt,f(n) of the sound source n and the power ?t,f(n) of the sound source n using the reverberation suppression signal vector Zt,f(n).

Abstract: A learning apparatus according to an embodiment is a learning apparatus that learns a neural network including a linear transformation layer achieved by a weight matrix with a complex number as an element, the learning apparatus including: a formulating unit that formulates a differential equation of a loss function with respect to each of conjugate variables corresponding to input variables of the linear transformation layer and a differential equation of the loss function with respect to each of parameters of the neural network; and a learning unit that learns the parameters of the neural network by backpropagation using the differential equations formulated by the formulating unit.

Abstract: An analyzing device includes separating means that takes matrix data representing a non-negative matrix Y as input, and separate the matrix Y into a plurality of block matrices Yi (i=1, . . . , I) configured of one column or two or more columns of the matrix Y, and updating means that executes repeated updating processing of a matrix Hi and a matrix Ui so that a product of the matrix Hi and the matrix Ui becomes close to the block matrices Yi, in parallel, with regard to at least two or more of the block matrices Yi. The updating means executes the repeated updating processing in parallel, on the basis of a graph structure given in advance in accordance with the matrix Y, so that the distance between predetermined matrices Hi and Hj (j?i, j ? {1, . . . , I}) becomes closer.

Abstract: A sound source separation filter information estimation device (10) estimates a covariance matrix having information on a correlation between sound source spectra and information on a correlation between channels as information on sound source separation filter information for separating an individual sound source signal from a mixed acoustic signal.

Abstract: Assuming that a total number of N selection subjects n (n=1, . . . , N) each select any one of a total number of M selection targets m (m=1, . . . , M), parameter estimation means: inputs acceptable limits ?n,m of each selection subject n and calculates patience ?n and a preference vector ?n, the acceptable limits ?n,m indicating limits of congestion degrees of respective selection subjects m that are acceptable to the selection subject n, the patience ?n indicating the largest value of the acceptable limits ?n,m of the selection subject n with respect to the selection targets m, the preference vector ?n indicating preference of the selection subject n when selecting the selection targets m; and estimates parameters of a model for obtaining acceptable limits ?i,m of each of a total number of I(>N) selection subjects i (i=1, . . . , I) by using the calculated patience ?n and the calculated preference vector ?n.