Abstract: An information processing system includes at least a first information processing device and a second information processing device. The second information processing device configured to transmit data relating to an atomic structure to the first information processing device. The first information processing device configured to receive the data relating to the atomic structure from the second information processing device, input the data relating to the atomic structure into a first model, acquire first information based on an output from an intermediate layer of the first model, and transmit the first information to the second information processing device. Additionally, the second information processing device configured to input the first information received from the first information processing device into a second model to acquire a predetermined value for the atomic structure.

Abstract: A data processing method includes: acquiring, by one or more processors, compressed data generated from data, wherein values of the compressed data are stored at first storage locations, values of the data are stored at second storage locations; acquiring, by the one or more processors, index data includes indices indicative of the first storage locations; acquiring, by the one or more processors, at least two packed indices from the index data, the at least two packed indices being generated from the index data; and inputting, by the one or more processors, the at least two packed indices into at least two selectors.

Abstract: There is proposed a device capable of performing processing regarding decision of optimum loading positions even if it does not know all packages to be loaded beforehand. One embodiment of the present invention includes: at least one memory; and at least one processing circuitry. The at least one processing circuitry is configured to execute: generating loading state information regarding an object loading state of a predetermined space where a plurality of objects will be loaded, under an assumption that a first object to be loaded is loaded at a loading position candidate in the predetermined space; and inputting the loading state information into a loading state evaluation model that outputs, when the loading state information is input therein, an evaluation value with respect to the object loading state of the predetermined space, and acquiring the evaluation value.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: A segment detecting device according to an embodiment includes at least one memory; and at least one processor. The at least one processor receives at least one of (i) an input signal including a first signal and a second signal or (ii) feature data representing one or a plurality of features of the input signal, estimates a level of the second signal by inputting the input signal or the feature data into a neural network, and determines a segment including the second signal in the input signal based on the level of the second signal.

Abstract: Apparatus, methods, and systems for cross-domain time series data conversion are disclosed. In an example embodiment, a first time series of a first type of data is received and stored. The first time series of the first type of data is encoded as a first distributed representation for the first type of data. The first distributed representation is converted to a second distributed representation for a second type of data which is different from the first type of data. The second distributed representation for the second type of data is decoded as a second time series of the second type of data.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: A compiler device, for generating an instruction sequence to be executed by an arithmetic processing device, includes at least one memory and at least one processor. The at least one processor is configured to receive a first instruction sequence for a first process and a second instruction sequence for a second process to be executed after the first process; generate third instructions, each third instruction being generated by merging a first instruction included in the first instruction sequence and a second instruction included in the second instruction sequence; and generate a third instruction sequence by concatenating the third instructions, instructions included in the first instruction sequence that are not merged into the third instructions, and instructions other than the second instruction among the plurality of instructions included in the second instruction sequence that are not merged into the one or more third instructions.

Abstract: An anomality prediction system, which predicts an anomality of a machine, includes: one or more memories; and one or more processors configured to: obtain a state variable including at least one of output data from at least one sensor that detects a state of at least one of the machine or a surrounding environment, internal data of control software controlling the machine, or computational data obtained based on at least one of the output data or the internal data; generate, by inputting the obtained state variable into a machine learning model, a degree of anomality of the machine based on output from the machine learning model; and notify information based on the generated degree of anomality, wherein the notified information includes at least one of the generated degree of anomality at one or more time points, or one or more levels of anomality based on the generated degree of anomality.

Abstract: A control device includes at least one memory, and at least one processor configured to detect a voice segment from sound data, the sound data being detected while a controlled object operates, and stop the controlled object based on following conditions: a speaking speed is a predetermined speed threshold or greater, the speaking speed being calculated based on a portion of the sound data in the voice segment; and a length of the voice segment is a predetermined length threshold or less.

Abstract: With respect to an inference method performed by at least one processor, the method includes inputting, by the at least one processor, into a learned model, second non-processed image data and second parameter data of a simulator, and inferring, by the at least one processor using the learned model, second processed image data. The learned model has been trained so that first processed image data, obtained as an output in response to first non-processed image data and first parameter data of the simulator for the first non-processed image data being input, approaches first simulator processed image data, obtained as a result of the simulator for the first non-processed image data by using the first parameter data.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: A system used for generating a three-dimensional representation from one or more two-dimensional images includes a plurality of imaging devices arranged to image a real object whose three-dimensional representation is to be generated; and a marker utilized in calculating a pose of the imaging device, the pose being utilized in generating the three-dimensional representation of the real object. At least one of the plurality of imaging devices is arranged to image the real object and the marker from below to obtain a two-dimensional image including the real object and the marker.

Abstract: An information processing system comprises at least one memory and at least one processor. The at least one processor is configured to receive a request of a user; determine whether or not to permit processing of the request based on a term of use of the user; and execute the processing using a neural network based on the request whose processing is determined as being permitted. The term of use includes a condition related to a structure of a processing target of the neural network.

Abstract: An information processing system includes a first information processing device and a second information processing device. The first information processing device is configured to receive the atomic information from the second information processing device, calculate a processing result corresponding to the atomic information by inputting the atomic information into a neural network, and transmit the processing result to the second information processing device. The second information processing device is configured to transmit atomic information to the first information processing device.

Abstract: A training device includes processor. The processor inputs a first atomic structure including a surface and an adsorbed molecule close to the surface into a model to obtain an energy outputted from the model in response to the input, and obtains a first error based on the outputted energy of the first atomic structure and a ground truth value of the energy of the first atomic structure, input a fourth atomic structure including a cluster and an adsorbed molecule close to the cluster into the model to obtain an energy outputted from the model in response to the input, and obtains a fourth error based on the outputted energy of the fourth atomic structure and a ground truth value of the energy of the fourth atomic structure, and update a parameter of the model by the first and the fourth error. The surface and the cluster include the same atom.

Abstract: An information processing device includes one or more memories and one or more processors. The one or more processors are configured to receive information on a plurality of graphs from one or more second information processing devices; select a plurality of graphs which are simultaneously processable using a graph neural network model among the plurality of graphs; input information on the plurality of graphs which are simultaneously processable into the graph neural network model and simultaneously process the information on the plurality of graphs which are simultaneously processable to acquire a processing result for each of the plurality of graphs which are simultaneously processable; and transmit the processing result to the second information processing device which has transmitted the corresponding information on the graph.

Abstract: An inferring device includes one or more memories and one or more processors. The one or more processors are configured to obtain three-dimensional structures of a plurality of molecules; and input the three-dimensional structures of the plurality of molecules into a neural network model and infer one or more physical properties of the plurality of molecules.

Abstract: A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Abstract: An information processing device includes one or more processors. The one or more processors are configured to optimize, for a specific elementary reaction in a reaction using a catalyst including a plurality of elementary reactions, an arrangement of a promoter element in the catalyst based on activation energy acquired using a trained model, and search for the promoter element based on the activation energy acquired using the trained model for each type of the promoter element.