Abstract: The present claimed invention shortens cleaning time of a container of a sample dispersing device and reduces variation of a cleaning state. The present claimed invention is a sample dispersing device 100 that disperses a powder sample (W) on an upper surface of an analytical member 10 and that comprises a container 2 that has a placing surface 2x on which the analytical member 10 is placed, an introducing mechanism 3 that introduces the powder sample (W) into inside of the container 2, and a covering member 4 that covers an inner surface of the container 2 and that can be attached to and removed from the container 2.

Abstract: Provided is a work vehicle that can be transported by being loaded onto a trailer without having the outriggers removed. The work vehicle (rough terrain crane) is transported by being loaded onto a low bed trailer including a low bed and a high portion (first high bed, second high bed) higher than the low bed in at least one of the front and the rear of the low bed, and includes: outrigger units (front outrigger unit, rear outrigger unit) that are mounted to the front and rear of a vehicle body; and a lift unit that lifts and lowers the outrigger units (front outrigger unit, rear outrigger unit with respect to the vehicle body.

Abstract: Provided are a hot dip galvanized steel sheet having a predetermined chemical composition, and a steel microstructure comprising, by vol %, ferrite: 0 to 50%, tempered martensite: 1% or more, retained austenite: 5% or more, fresh martensite: 0 to 15%, total of pearlite and cementite: 0 to 5%, and balance: bainite, when measuring by GDS, a maximum value of Al concentration of an Al rich layer present at an interface of the base steel sheet and the hot dip galvanized layer is 2.0 mass % or more and an Sis/Sib right under the interface is 0.90 or less, a number density of recessed parts with a depth of more than 2 ?m at the interface is 2.0/100 ?m or less per interface length, and a tensile strength is 980 MPa or more, and a method for producing the same.

Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

Abstract: A method, system, and computer program product are disclosed. The method includes receiving a target molecule and extracting substructures from the target molecule. The method also includes generating a distance matrix for a pair of the substructures based on corresponding atom indexes and determining distances between atoms of the pair based on the distance matrix. Further, the method includes calculating a distance between the pair of the substructures based on at least one of the distances and generating a molecular descriptor based on the distance.

Abstract: A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

Abstract: Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

Abstract: A computer implemented method of generating new chemical compounds is provided. The method includes preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes compressing each of the feature vectors into a relational vector, and mapping each of the relational vectors to a map having at least two dimensions. The method further includes presenting the map on a display device. The method further includes receiving a selection of a position on the map, wherein the position is converted to a new relational vector, and decompressing the new relational vector to a candidate feature vector. The method further includes generating a new chemical structure from the candidate feature vector.

Abstract: Provided is a reservoir computing system that is miniaturized and has a reduced learning cost. The reservoir computing system uses a reservoir that includes a first optical output section that outputs a first optical signal; a first optical waveguide that propagates the first optical signal output by the first optical output section; an optical receiving section that receives the first optical signal from the first optical waveguide; a storage section that stores received optical data corresponding to the first optical signal and output by the optical receiving section; and a feedback section that applies, to the first optical signal, feedback corresponding to the received optical data stored in the storage section.

Abstract: To realize a reservoir computing system with a small size and reduced learning cost, provided is a laser apparatus including a laser; a feedback waveguide that is operable to feed light output from the laser back to the laser; an optical splitter that is provided in a path of the feedback waveguide and is operable to output a portion of light propagated in the feedback waveguide to outside; and a first ring resonator that is operable to be optically connected to the feedback waveguide, as well as a reservoir computing system including this laser apparatus.

Abstract: Provided is a work vehicle that can be transported by being loaded onto a trailer without having the outriggers removed. The work vehicle (rough terrain crane) is transported by being loaded onto a low bed trailer including a low bed and a high portion (first high bed, second high bed) higher than the low bed in at least one of the front and the rear of the low bed, and includes: outrigger units (front outrigger unit, rear outrigger unit) that are mounted to the front and rear of a vehicle body; and a lift unit that lifts and lowers the outrigger units (front outrigger unit, rear outrigger unit with respect to the vehicle body.

Abstract: An approach to training a molecule generative model with interpretable a latent space to identify substructures for a generated molecule generative from the latent space generated from an input molecule with a target property may be provided. A molecule generative model may be trained with a dataset of molecular structures with associated properties and known substructures. The model may generate a latent space in which a substructure predictor model may further be trained to predict the number of substructures of a molecule with target properties from an input molecule with the target properties and identified substructures.

Abstract: To realize a reservoir computing system easily implemented as hardware, provided is a reservoir computing system including a reservoir operable to output an inherent output signal in response to an input signal. An input node is operable to supply the reservoir with an input signal corresponding to input data, and an output node is operable to output an output value corresponding to an output signal that is output by the reservoir in response to the input data. An adaptive filter is operable to output output data based on a result obtained by weighting a plurality of the output values output from the output node at a plurality of timings with a plurality of weights. Also provided are a learning method and a computer program product.

Abstract: To realize a reservoir computing system easily implemented as hardware, provided is a reservoir computing system including a reservoir operable to output an inherent output signal in response to an input signal. An input node is operable to supply the reservoir with an input signal corresponding to input data, and an output node is operable to output an output value corresponding to an output signal that is output by the reservoir in response to the input data. An adaptive filter is operable to output output data based on a result obtained by weighting a plurality of the output values output from the output node at a plurality of timings with a plurality of weights. Also provided are a learning method and a computer program product.

Abstract: A computer implemented method of generating new chemical compounds is provided. The method includes preparing a data-driven substructure feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes preparing a predefined component feature vector, creating a regression model to predict a target value for the chemical or physical property, and performing a search algorithm to identify substructure features that affect the target value for the chemical or physical property. The method further includes generating a candidate structure having the target value for the chemical or physical property, and synthesizing the candidate structure.

Abstract: Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

Abstract: A photonic neural component includes optical transmitters, optical receivers, inter-node waveguides formed on a board, transmitting waveguides configured to receive optical signals emitted from the optical transmitters and transmit the received optical signals to the inter-node waveguides, mirrors to partially reflect optical signals propagating on the inter-node waveguides, receiving waveguides configured to receive reflected optical signals produced by the mirrors and transmit the reflected optical signals to the optical receivers, and filters configured to apply weights to the reflected optical signals. The transmitting waveguides and receiving waveguides are formed on the board such that one of the transmitting waveguides and one of the receiving waveguides crosses one of the inter-node waveguides with a core of one of the crossing waveguides passing through a core or clad of the other.

Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

Abstract: A computer implemented method of generating new chemical compounds is provided. The method includes preparing a feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes compressing each of the feature vectors into a relational vector, and mapping each of the relational vectors to a map having at least two dimensions. The method further includes presenting the map on a display device. The method further includes receiving a selection of a position on the map, wherein the position is converted to a new relational vector, and decompressing the new relational vector to a candidate feature vector. The method further includes generating a new chemical structure from the candidate feature vector.