Abstract: A light deflector includes a movable device; a drive unit configured to drive the movable device; an input wiring board configured to apply electric power from the drive unit to the movable device; and an anisotropic conductive resin film through which the input wiring board is connected to an input part. The movable device includes a movable portion including a reflector, between a pair of drive beams; a pair of supporting parts supporting the pair of drive beams; and a mount unit secured to the pair of supporting parts. The pair of drive beams supports the movable portion to allow the movable portion to oscillate around an oscillation axis. Each of the pair of drive beams includes a beam member and an actuator. At least one of the pair of supporting parts includes the input part to which electric power is input from the input wiring board to the actuator.

Abstract: A charged material containing a metal raw material of at least one of ferrochromium containing metal Si or ferrosilicon and unreduced slag containing Cr oxide generated by oxidation refining is charged into an AC electric furnace including three electrodes, a mass ratio of a metal Si amount to a Cr oxide amount being from 0.30 to 0.40, and a C concentration being from 2.0% by mass to a saturation concentration.

Abstract: A distributed deep learning system that can achieve speeding-up by processing learning in parallel at a large number of learning nodes connected with a communication network and perform faster cooperative processing among the learning nodes connected through the communication network is provided. The distributed deep learning system includes: a plurality of computing interconnect devices 1 connected with each other through a ring communication network 3 through which communication is possible in one direction; and a plurality of learning nodes 2 connected with the respective computing interconnect devices 1 in a one-to-one relation, and each computing interconnect device 1 executes communication packet transmission-reception processing between the learning nodes 2 and All-reduce processing simultaneously in parallel.

Abstract: There is provided a hole machining method, using a tap tool which rotates about an axis of rotation and which includes a threaded part which has a blade for machining a threaded groove in a pilot hole, and a non-engaging part which forms a space between itself and the pilot hole in a cross-sectional view in which the axis of rotation and a center axis of the pilot hole are aligned, the method including the steps of machining a threaded groove by rotating the tap tool and advancing it toward a workpiece side in a first state in which the center axis and the axis of rotation are aligned, rotating the tap tool about the axis of rotation and moving the tap tool in an interior of the pilot hole toward the non-engaging part side in a direction orthogonal to the axis of rotation to transition the tap tool from the first state to a second state in which engagement between the threaded part and the threaded groove is released, and retracting the tap tool from the threaded hole.

Abstract: A resin component (A1) and a compound (B0) represented by General Formula (b0) in which X0 represents a bromine atom or an iodine atom, Rm represents a hydroxy group, nb1 represents an integer of 1 to 5, and nb2 represents an integer of 0 to 4, 1?nb1+nb2?5 is satisfied, Yb0 represents a divalent linking group or a single bond, Vb0 represents a single bond, an alkylene group, or a fluorinated alkylene group, R0 represents a hydrogen atom, a fluorinated alkyl group having 1 to 5 carbon atoms, or a fluorine atom, and Rb1 to Rb15 each independently represents a hydrogen atom or a substituent, and at least two of Rb1 to Rb5 represent a fluorine atom or at least one of Rb1 to Rb5 represents a perfluoroalkyl group

Abstract: A resist composition including a resin component which has a constitutional unit (a01a) containing a polycyclic lactone-containing cyclic group and a constitutional unit (a02) represented by General Formula (a0-2), and a compound (B0) represented by General Formula (b0), Ra05 to Ra08 each independently represents a hydrogen atom, X0 represents a bromine atom or an iodine atom, Rm represents a hydroxy group, 1?nb1+nb2?5 is satisfied, Yb0 represents a divalent linking group or a single bond, Vb0 represents a single bond, R0 represents a hydrogen atom, Mm+ represents an m-valent organic cation, m represents an integer of 1 or greater

Abstract: Each of learning nodes calculates gradients of a loss function from an output result obtained by inputting learning data to a learning target neural network, converts a calculation result into a packet, and transmits the packet to a computing interconnect device. The computing interconnect device receives the packet transmitted from each of the learning nodes, acquires a value of the gradients stored in the packet, calculates a sum of the gradients, converts a calculation result into a packet, and transmits the packet to each of the learning nodes. Each of the learning nodes receives the packet transmitted from the computing interconnect device and updates a constituent parameter of a neural network based on a value stored in the packet.

Abstract: A plant vitality adjuvant for suppressing bacterial wilt disease of plants is provided, the plant vitality adjuvant containing, as a main raw material, a fermented composition obtained by fermenting and aging a plurality of items belonging to fruits, citrus fruits, burdock and carrot belonging to edible roots, grains, sesames, seaweed, and saccharides.

Abstract: Individual distributed processing nodes packetize distributed data for each weight of a neural network of a learning object in an order of a number of the weight, transmit the distributed data to an aggregation processing node, acquire aggregation data transmitted from the node in order, and update the weight of the neural network. The node acquires the transmitted distributed data, packetizes the aggregation data for which the distributed data of all the distributed processing nodes is aggregated for each weight, and transmits the aggregation data to the individual nodes. The individual nodes monitor an unreceived data amount which is a difference between data amounts of the transmitted distributed data and the acquired aggregation data, and when the unreceived data amount becomes equal to or larger than a threshold Ma, stops transmission of the distributed data until the unreceived data amount becomes equal to or smaller than a threshold Mb (Mb<Ma).

Abstract: There is provided a care-needing person assistance system that can efficiently collect stimulus information. The care-needing person assistance system includes a stimulus information storage means that stores stimulus information about a stimulus for at least one of five senses of a care-needing person, and a value exchange means that provides compensation for provision of stimulus information to a provider who provides new stimulus information to be additionally stored in the stimulus information storage means.

Abstract: Provided is a distributed deep learning system including a plurality of distributed processing nodes, in which each of the plurality of distributed processing nodes includes a header reading unit configured to read pieces of layer information of headers of a first data frame that has arrived at an own node and a second data frame that has arrived next, and in which the pieces of layer information are compared with each other, calculation processing is executed for a data frame including data that belongs to a layer closer to an input layer, and calculation processing for a data frame including data that belongs to a layer closer to an output layer is skipped.

Abstract: A light emitting unit is provided. The light emitting unit comprises a substrate, a light emitting element placed on a first surface of the substrate, a front panel covering the first surface, and a back panel placed on a second surface of the substrate, which is opposite to the first surface. An opening for extracting light emitted by the light emitting element is formed in the front panel, and the opening is formed to overlap a part of a light emitting surface of the light emitting element. The back panel has a thermal conductivity higher than that of the front panel, and includes a projecting portion that projects toward the first surface. The projecting portion is in contact with at least one of the first surface, the light emitting element, or the front panel.

Abstract: A distributed deep learning system according to an embodiment includes M distributed processing nodes that perform deep learning of a neural network distributed from each other, and N aggregation processing nodes that are connected to each of the M distributed processing nodes via a first communication line and a second communication line, and perform aggregation of distributed processing results obtained at the M distributed processing nodes via the first communication line. Accordingly, even in a case of a plurality of users sharing the distributed deep learning system at the same time, efficient and stable distributed deep learning processing can be realized.

Abstract: A distributed processing node transmits distributed data for M groups as intermediate consolidated data from M communication units to a distributed processing node. A distributed processing node generates, for each group, updated intermediate consolidated data from the received intermediate consolidated data and distributed data, and transmits the updated intermediate consolidated data from the M communication units to a distributed processing node. The distributed processing node transmits the received intermediate consolidated data to a distributed processing node as consolidated data. The distributed processing node transmits the received consolidated data to a distributed processing node. Each of the distributed processing nodes updates weights of a neural network, based on the consolidated data.

Abstract: A computing interconnect apparatus includes a reception unit configured to receive a packet transmitted from each of learning nodes and acquire a value of a gradient stored in the packet, an adder configured to calculate a sum of the gradient acquired by the reception unit in parallel separately for each of processing units in accordance with the number of the processing units to be carried out being determined by bit precision of the gradient and a desired processing speed, and a transmission unit configured to write calculation results of the sum of the gradient separate for each of the processing units being obtained by the adder into packetization and transmit the calculation results to each of the learning nodes.

Abstract: A vehicle transmission includes a shaft; speed-changing gears; switching mechanisms; and a shifting mechanism. The shifting mechanism is provided with a double-meshing preventing mechanism configured to switch between a one-way state in which the switching mechanisms are hindered from moving in a downshift direction and allowed to move in an upshift direction and a free state in which the switching mechanisms are allowed to move in both the downshift direction and the upshift direction.

Abstract: A light emitting unit is provided. The light emitting unit comprises a substrate, a light emitting element placed on a first surface of the substrate, a front panel covering the first surface, and a back panel placed on a second surface of the substrate, which is opposite to the first surface. An opening for extracting light emitted by the light emitting element is formed in the front panel, and the opening is formed to overlap a part of a light emitting surface of the light emitting element. The back panel has a thermal conductivity higher than that of the front panel, and includes a projecting portion that projects toward the first surface. The projecting portion is in contact with at least one of the first surface, the light emitting element, or the front panel.

Abstract: A first distributed processing node transmits distributed data to a second distributed processing node as intermediate consolidated data. A third distributed processing node generates intermediate consolidated data after update from received intermediate consolidated data and distributed data, and transmits the intermediate consolidated data to a fourth distributed processing node. The first distributed processing node transmits the received intermediate consolidated data to fifth distributed processing node as consolidated data. The third distributed processing node transmits the received consolidated data to a sixth distributed processing node.

Abstract: A first distributed processing node transmits distributed data as intermediate consolidated data from a first communication port to a second distributed processing node. A third distributed processing node generates updated intermediate consolidated data from the received intermediate consolidated data and distributed data, and transmits the data from the first communication port to a fourth distributed processing node. The first distributed processing node transmits intermediate consolidated data received via a second communication port as consolidated data to a fifth distributed processing node from the second communication port. The third distributed processing node transmits consolidated data received via the first communication port to a sixth distributed processing node from the second communication port. Each of the distributed processing nodes updates a weight of a neural network based on the consolidated data.

Abstract: A vehicle control apparatus includes an inverter controller. The inverter controller holds a plurality of control maps for an inverter. The inverter supplies electric power to a drive motor. The drive motor drives a drive wheel of the vehicle. The inverter controller selects any one of the plurality of control maps on a basis of a notification instruction that instructs to notify a driver of information. The inverter controller controls an operation of the inverter on a basis of the control map selected.