Abstract: An inference apparatus according to an embodiment includes at least one memory and at least one processor. The at least one processor calculates a Hamiltonian as an initial value regarding a substance based on a neural network algorithm, and calculates a non-equilibrium Green's function regarding the substance based on the Hamiltonian as the initial value.

Abstract: An information processing device includes one or more memories and one or more processors. The one or more processors are configured to: set a first structure of a plurality of atoms; and repeatedly calculate a structural change of the plurality of atoms from the first structure to a second structure of the plurality of atoms under a condition to be satisfied by at least the second structure with regard to an atomic structure to focus on, the condition including an inequality, to search for a trajectory of a structure of the plurality of atoms from the first structure to the second structure.

Abstract: An information processing device includes one or more memories; and one or more processors. The one or more processors are configured to search for a reaction path by using one or more trained models that, when receiving an input of a three-dimensional arrangement of two or more atoms forming a molecule, output a physical quantity regarding the molecule.

Abstract: An information processing device includes one or more memories and one or more processors. The one or more processors are configured to set a first state of a structure of a plurality of atoms to search for a reaction path from the first state up to a second state of the structure of the plurality of atoms in a chemical reaction in which the plurality of atoms are involved and search for the reaction path from the first state up to the second state by calculating, from the first state, a change in the structure of the plurality of atoms such that, in the second state, an index of mutual displacement of two or more specific atoms out of the plurality of atoms falls within a defined range.

Abstract: An estimation apparatus according to an embodiment includes at least one memory and at least one processor. At least one processor described above inputs a feature amount of each of a plurality of atoms to a neural network to update the feature amount, and generates a parameter corresponding to each of the plurality of atoms based on the updated feature amount. At least one processor described above determines each of a plurality of charges corresponding to each of the plurality of atoms by using the parameter.

Abstract: An information processing device includes one or more memories and one or more processors. The one or more processors are configured to input information regarding an atom of a substance to a first model; and obtain information regarding the substance from the first model. The first model is a model which includes: layers from an input layer up to a predetermined layer of a second model to which information regarding atoms is input and which outputs at least one of a value of an energy or a value of a force; and another layer, and which is trained to output the information regarding the substance.

Abstract: An image generation apparatus includes at least one memory, and at least one processor configured to acquire first latent information of a first image and second latent information of a second image, generate fusion latent information by using the first latent information and the second latent information, and generate a fusion image by inputting the fusion latent information into a trained generative model.

Abstract: A robot control device includes at least one memory, and at least one processor, wherein the at least one processor is configured to obtain environmental information in a real environment, obtain information related to an action to be performed by a robot in the real environment based on the environmental information and a first policy, obtain information related to a control value that causes the robot to perform the action based on the information related to the action and a second policy, and control the robot based on the information related to the control value. The first policy is learned by using a virtual robot in a simulation environment.

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: 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: 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 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.