Fujitsu Patente

An information processing apparatus selects a first value for each of a plurality of control parameters for controlling a plurality of pulse signals used by a quantum computer in executing a multi-input quantum gate, estimates a first error from the first values by using an error function for estimating an error between an ideal value and an actual value of the multi-input quantum gate from values of the plurality of control parameters, searches for a second value of each of the plurality of control parameters by using the error function such that a second error estimated from the second values is less than the first error, acquires a result obtained by executing the multi-input quantum gate by using the second values from the quantum computer, and determines a third value to be set to each of the plurality of control parameters by using the result.

According to an aspect of an embodiment, a method may include obtaining first real-time observational data corresponding to current conditions within an operational area of a vehicle, the first real-time observational data corresponding to the operational area of the vehicle. A first forecast of one or more weather conditions associated with the operational area of the vehicle may be generated using an artificial intelligence (AI) forecasting model and based on the first real-time observational data. The first forecast may be tailored for and according to the operational area of the vehicle. The method may include generating navigation routes for the vehicle in the operational area based on the first forecast, the vehicle navigating the operational area according to the navigation routes.

An information processing program for causing a computer to execute a process including: generating a plurality of first quantum circuits by a local compilation method, the first quantum circuits representing an action of a time evolution operator for a first time period and having a depth smaller than a depth of a first target quantum circuit representing the action of the time evolution operator for the first time period; and generating a second quantum circuit by the local compilation method, the second quantum circuit being smaller in depth than a second target quantum circuit obtained by combining two or more of the generated first quantum circuits and representing the action of the time evolution operator for a second time period longer than the first time period, the second quantum circuit representing the action of the time evolution operator for the second time period.

An information processing apparatus expands an imaginary time evolution equation for calculating a thermal-equilibrium expectation value into equations of a plurality of degrees, generates sets of the degrees, and generates, for each set, a quantum circuit indicating quantum computation of a physical quantity value obtained by partial imaginary time evolution. A quantum computer is caused to repeatedly execute quantum computation based on the quantum circuit for each set. When doing so, a state in a stationary distribution of measurement results obtained by quantum computation based on a second quantum circuit corresponding to a second set is used as an input state at the start of quantum computation based on a first quantum circuit corresponding to a first set. The thermal-equilibrium expectation value of the physical quantity at the finite temperature is then calculated based on the values of the physical quantity after convergence for each set.

A generation program of a neural network is used as a subnetwork to be added to an image generation AI, the generation program causes a computer to execute a process including: training each of a plurality of neural networks using a training dataset that includes a plurality of pieces of training data where image data corresponding to specific concepts different for each of the neural networks is associated with a specific token and part of a plurality of tokens different from the specific token; and fusing the neural networks after the training to generate a subnetwork that corresponds to a plurality of concepts.

In an embodiment, generative Al-based image generation using attribute-based slider control is provided. A prompt indicative of a description of a first image is received. The first image is generated based on the prompt, by a text-to-image model. A set of attributes associated with the description is determined, based on a first language model. The set of attributes corresponds to semantics associated with the first image. Based on a second language model, a set of questions associated with prompt is generated. Slider boundary values and initial slider value are generated, based on the set of questions and the first image. A set of sliders is generated based on the slider boundary values and the initial slider value. Each slider corresponds to an attribute. A user input associated with the set of sliders is received and a second image is generated. The second image is rendered.

A reinforcement learning program that causes a computer to execute a process including: executing reinforcement learning of a machine learning model (21a), using a first three-dimensional (3D) molecular structure of a first compound to be adapted to a first object as a state, assignment of a type of an additional atom, a binding position of the additional atom to the first 3D molecular structure, and a 3D position of the additional atom, to the first compound in the state as an action, and an index to evaluate a second 3D molecular structure obtained by binding the additional atom to the first 3D molecular structure in the action as a reward.

A display program causes a computer (1) to execute a process including based on a first dataset, generating (S13) a second dataset that is virtual and has a causal relationship between variables included in the first dataset, in accordance with an estimation result indicating the causal relationship, calculating (S14) reliability of each inter-variable relationship between a first variable serving as a cause in the causal relationship and a second variable serving as an effect in the causal relationship, based on difference between the first dataset and the second dataset and displaying (S16) the calculated reliability of each inter-variable relationship.

An information processing program for causing a computer to execute a process, the process including: classifying a plurality of items in to a plurality of groups; searching for a causal order so as to increase a likelihood of an estimation result of a regression model, the causal order indicating a presence of a causal relationship in a direction from one group to another group between at least some of the plurality of groups, the regression model estimating a value of a downstream item based on a value of an upstream item in a causal relationship; and outputting a directed acyclic graph generated based on the discovered causal order and representing a presence of a causal relationship in a direction from one item to another item, between at least some of the plurality of items.

An information processing program causes a computer to execute a process, the process including generating a causal graph coupling a plurality of nodes by directed edges based on a plurality of data serving as a basis in generating the causal graph, the causal graph being generated so as to optimize a value of an objective function that includes a first term indicating that the more destination connections and connection sources shared between different nodes in the causal graph, a higher is an evaluation thereof; and updating the generated causal graph so as to aggregate, among the plurality of nodes, two or more nodes sharing a common connection destination and a common connection source, the two or more nodes being aggregated into a single node.

The present disclosure relates to a method, a device, and a storage medium for multiple object tracking. According to an embodiment of the present disclosure, the method comprises: determining a plurality of object whole-body detection boxes in a current input image by performing object whole-body detection, and, determining a plurality of object head detection boxes in the current input image by performing object head detection; determining whole-body identifiers of the plurality of object whole-body detection boxes by performing whole-body trajectory association; determining head identifiers of the plurality of object head detection boxes by performing head trajectory association; determining a plurality of object whole-body prediction boxes corresponding to the plurality of object head detection boxes based on positions and sizes of the plurality of object head detection boxes; determining object whole-body association boxes of the plurality of object head detection boxes in the plurality of object whole-body detection boxes based on areas occupied by the plurality of object whole-body prediction boxes corresponding to the plurality of object head detection boxes in the input image; and updating the object whole-body trajectory set based on whole-body identifiers of the object whole-body association boxes.

An information processing device having a controller configured to: when iterations of updating parameters defining a variational quantum circuit are executed according to a variational quantum eigensolver method, determine for each first parameter, a number of level values settable as a value thereof based on a first degree of contribution of the first parameter to an amount of change in energy corresponding to a quantum state represented by the variational quantum circuit; calculate for each first parameter, a second degree of contribution thereof to the variational quantum eigensolver method, based on the energy in each of two or more patterns representing combinations of level values to be set as the value of the first parameter when setting one of the level values of the number determined for the first parameter; and set, as parameters to be updated, each second parameter that among the first parameters has a relatively high second degree of contribution.

An active space determination program causes a computer to execute a process including: acquiring occupation number data including data indicating time-series change of an electron occupation number in each of a plurality of molecular orbitals; computing a solution of a mathematical optimization model formulated with a constraint and an objective function, using the occupation number data as input, the constraint being that a sum of electron occupation numbers in the molecular orbitals is constant, the objective function giving a high evaluation to selection of a combination of molecular orbitals in which the time-series change of the electron occupation number has a negative correlation, relative to a combination of molecular orbitals in which the time-series change of the electron occupation number has a positive correlation, when selecting a subset to be used in quantum chemical calculation from among the molecular orbitals; and outputting a result of the computation.

A detection program causes a computer to execute a process includes acquiring an input image from a camera acquiring a first detection result by inputting the input image to a first detection model that performs object detection acquiring, a plurality of detection results by inputting the input image to a plurality of detection models trained based on different training data sets with a parameter of the first detection model used as an initial value, and generating a detection result in which the first detection result and the detection results are combined.

A processing unit selects a first constraint and performs a search process repeating first and second processes. The first process determines, based on a first local field, whether to allow a change in a first state variable value. If allowed, the second process updates the first local field, based on a first weight coefficient for the first state variable, updates a second local field for the first constraint, based on a second weight coefficient for the first state variable and the first constraint, and updates the first local field, based on the second local field before and after updating. The processing unit updates a second local field for a second constraint unused, based on changes in state variable values caused by the search process and a second weight coefficient for the second constraint, and corrects the first local field, based on the second local field before and after updating.

A quantum computing apparatus (10) includes a plurality of substrates (20, 30, 40) that are stacked and include a substrate (20) on which a qubit device (11) is provided, and through-vias (35A, 35B, 45A, 45B) provided in at least a substrate (40) disposed in an uppermost layer and a substrate (30) disposed in a lowermost layer, among the plurality of substrates (20, 30, 40), and electrically coupled to the substrate (20) disposed in an adjacent layer.

A processing unit determines a first solution to a surrogate problem represented by a second evaluation function obtained by adding a product of an auxiliary variable and a weight coefficient corresponding to a constraint to a first evaluation function of a mixed-integer programming problem including integer and continuous variables, using the branch and bound method with linear relaxation of the integer variable. The processing unit determines a second solution to the mixed-integer programming problem by local search using an initial solution where the integer variable in the first solution is set to an integer value while fixing the continuous variable value, determines a third solution to the mixed-integer programming problem while fixing the integer variable value included in the second solution, decreases the weight coefficient corresponding to the constraint satisfied by the third solution, and then repeats determining the first, second, and third solutions and decreasing the weight coefficient.

An information processing apparatus includes a processor configured to generate a set of a plurality of routes for a plurality of moving bodies that depart from a specific node and return to the specific node via a plurality of nodes, the plurality of routes representing an order in which the plurality of moving bodies visit the nodes generate, by using the generated set of routes, a route selection problem with constraints on a shipment volume of a node serving as a shipment source of packages solve the generated route selection problem and output a result of solving the route selection problem.

A first wiring made of a superconductor and a first electrode made of a superconductor connected to the first wiring are formed on a substrate. A resist mask that covers at least the first wiring is formed. A surface of the first electrode using the resist mask is etched. An insulator film on the surface of the first electrode using the resist mask is formed. A second electrode made of a superconductor on the surface of the insulator film using the resist mask is formed.

A computer-implemented method comprising: generating, based on a plurality of candidate output tokens generated by a large language model, LLM, a plurality of candidate token embeddings, respectively and generating, based on at least one string describing prohibited content, at least one content embedding; comparing the candidate token embeddings with the at least one content embedding to determine for each candidate output token a similarity score indicating similarity between the candidate output token and the prohibited/unwanted content; for each candidate output token, generating, based on the similarity score and based on a selection probability assigned by the LLM to the candidate output token, an output score; and selecting, based on the output scores, a candidate output token for output by the LLM.

A computer implemented method for detecting computer generated images comprising: loading an input image, inputting the input image and a representation describing the input image into a denoising model for denoising the input image using the representation, generating a denoised image embedding from the denoised image, generating an input image embedding from the input image, comparing a difference between the input image embedding and the denoised image embedding with a similarity decision threshold to determine whether the input image is a real image or a computer generated image.

A computer-implemented method for traffic simulation, the method comprising: executing a preliminary agent-based simulation model to generate a preliminary simulation prediction of traffic in a geographic area; accepting input of traffic data for the geographic area; performing data assimilation on the preliminary simulation prediction and the traffic data to generate assimilated traffic data.

An information processing program that causes a computer to perform a process including: classifying a plurality of items to be determined into one or more categories based on a plurality of appearance images of the plurality of items, the plurality of appearance images being stored in a storage area (41a) in association with corresponding prices of the plurality of items; calculating, for each of the one or more categories into which the items are classified, a statistical value of prices of a plurality of items in the category, based on the plurality of prices stored in the storage area (41a); changing a pattern for the classifying and performing the classifying and the calculating so that one or more of the statistical values satisfy a given condition; and outputting category information (41b) indicating a category of each of the plurality of items when the given condition is satisfied.

In an embodiment, workflow for timeseries forecasting may be performed based on automated machine learning. Sensor data for measurement parameter is received from plurality of sensors installed in built environment and the received sensor data is stored in table of relational database. Cut-off record associated with previous training checkpoint is determined of the forecasting model for the measurement parameter. Records including new records are determined for which respective timestamps occur after the measurement timestamp of cut-off record. Size of the determined records are compared with threshold size and training dataset is prepared. The forecasting model is trained on the training dataset based on the comparison.

A storing unit stores area information indicating positions of a plurality of image areas set in a captured image of a front region of a self-checkout terminal including a scanner. A processing unit recognizes a product from the captured image. The processing unit detects a fraudulent action related to a scanning operation for causing the scanner to scan product information attached to the product, based on a movement path of the recognized product in the plurality of image areas, and a residence time of the product in a first image area that is closest to the scanner among the plurality of image areas.

An answer generation program causes a computer to execute a process including acquiring a question input to a first agent that generates information based on input information, the question being related to a video for monitoring a specific task, identifying a specific agent that has a function of either video recognition for the specific task or domain knowledge of the specific task, from among a plurality of second agents capable of cooperating with the first agent, based on the acquired question, and causing the first agent to output, as an answer to the question, an answer result based on generation information that is generated by the identified specific agent in accordance with an instruction from the first agent.

A processing unit determines, from a plurality of solutions to a combinatorial optimization problem stored in a storage unit, a plurality of higher-ranked solutions based on evaluation function values of the plurality of solutions, determines, for each of the plurality of higher-ranked solutions, a selection probability such that higher-ranked solutions having evaluation function values closer to that of the highest-ranked solution among the plurality of higher-ranked solutions are more likely to be selected, selects a first solution from the plurality of higher-ranked solutions according to the selection probabilities, selects a second solution different from the first solution, from the plurality of solutions, generates a third solution with a path relinking method using the selected first solution and second solution, and performs a solution search on the combinatorial optimization problem using the third solution as an initial solution.

A qubit device (100) includes a first qubit substrate (10a) having a first qubit (30a) formed on a first surface (12a), a second qubit substrate (10b) having a second qubit (30b) formed on a second surface (12b), a connection substrate (40) disposed to face the first surface and the second surface, and a spacer portion (50) provided to be located at least between the connection substrate and the first qubit substrate, wherein the first surface and the second surface are located in a same plane, and the spacer portion is provided to be in contact with a third surface (15a) that is provided in a peripheral portion of the first qubit substrate and is lower than the first surface.

According to an aspect of at least one embodiment, a method to improve a user interface may include obtaining transcript data include one or more words from a transcription of speech in the audio data. The transcript data may be generated by automated speech recognition technology from the audio data. The transcript data and criteria may be provided to a large language model configured to analyze the transcript data based on the criteria to select a word from the transcript data. A definition of the selected word may also be generated by the large language model. The selected word and the definition of the selected word may be obtained. The audio data may be broadcasted by a device and the selected word and the definition of the selected word may be presented on a display of the device with the broadcasting of the audio data.

A method of training a neural network as a natural language processing, NLP, model, comprises: inputting annotated training data to first architecture portions of the neural network, the first architecture portions being executed respectively in a plurality of distributed client computing devices in communication with a server computing device, the training data being derived from text data private to the client computing device in which the first architecture portion is executed, the server computing device having no access to any of the private text data; deriving from the training data, using the first architecture portions, weight matrices of numeric weights which are decoupled from the private text data; concatenating the weight matrices, in a second architecture portion of the neural network executed in the server computing device, to obtain a single concatenated weight matrix; and training, on the second architecture portion, the NLP model using the concatenated weight matrix.

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …