Abstract: An antibody-drug conjugate represented by formula (I): (where Ab is an antibody, X is a group represented by formula (X-1), formula (X-2) or formula (X-3): (where at the left represents the binding site with NH and at the right represents the binding side with D), D is a group represented by formula (D-1) or formula (D-2): (where represents the binding site with X), and n is in the range of about 1 to about 8).

Abstract: The present invention provides a Plexin-binding regulating agent containing a cyclic peptide having an Arg-Trp-Thr structure or a Leu-Ser-Trp structure or a pharmaceutically acceptable salt of the cyclic peptide.

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.

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 distributed deep learning system includes a plurality of computers connected to each other over a communication network, wherein each iteratively performs forward propagation calculation and backpropagation calculation based on learning data, and sends a calculation result of the backpropagation calculation to the communication network, and an Allreduce processing apparatus connected to the computers over the communication network, that processes the calculation results received from the plurality of computers, and returns the calculation results to transmission sources, wherein the computers each include a forward propagation calculator, a backpropagation calculator, a transfer processor that stores the calculation result of the backpropagation calculation in a transfer buffer each time the backpropagation calculator calculates the calculation result of the backpropagation calculation for each of layers, and a communicator that sequentially transmits the calculation results of the backpropagation calculati

Abstract: A distributed processing system includes a plurality of lower-order aggregation networks and a higher-order aggregation network. The lower-order aggregation networks include a plurality of distributed processing nodes disposed in a ring form. The distributed processing nodes generate distributed data for each weight of a neural network of an own node. The lower-order aggregation networks aggregate, for each lower-order aggregation network, the distributed data generated by the distributed processing nodes. The higher-order aggregation network generates aggregated data where the aggregation results of the lower-order aggregation networks are further aggregated, and distributes to the lower-order aggregation networks. The lower-order aggregation networks distribute the aggregated data distributed thereto to the distributed processing nodes belonging to the same lower-order aggregation network. The distributed processing nodes update weights of the neural network based on the distributed aggregated data.

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 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.