Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes extracting a plurality of candidates for an entity in a knowledge graph based on a word in text, collecting related images related to the extracted candidates, generating image clusters of the collected related images for the respective candidates, calculating degrees of similarity between the generated image clusters, and determining, as the entity, a candidate of which the image cluster indicates a higher degree of similarity among the candidates.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.

Abstract: A computer-implemented learning method includes inputting a plurality of pieces of input data and labels representing the plurality of pieces of input data into an encoder configured to output context variables associated with each of the plurality of pieces of input data, inputting the plurality of pieces of input data and the context variables output by the encoder into a decoder configured to output decision labels associated with the plurality of pieces of input data respectively, and learning parameters of the encoder and the decoder so that each of the decision labels matches with a corresponding label of the labels representing the plurality of the plurality of pieces of input data.

Abstract: A disclosed method includes: converting, for each sample point, a set of performance item values for a sample point into coordinate values of a mesh element containing the set among plural mesh elements obtained by dividing a space mapped by the performance items; generating a binary decision graph representing a group of the coordinate values of the sample points; calculating the number of sample points including second sample points that dominates a first sample point and the first sample point, by counting the number of paths in the binary decision graph from a root node to a leaf node representing “1” through at least one of certain nodes corresponding to coordinate values that are equal to or less than coordinate values of the first sample point; and calculating a yield of the first sample point by dividing the calculated number by the number of the plural sample points.

Abstract: Among the combinations of failure candidates which combinations are generated by a generator, one combination that minimizes a cost derived in a cost calculator is selected. For the selected combination of failure candidates, a function, which specifically provides a correlation between one or more failure factor and an error (or error rate) of each failure element is output. A correct failure factor is estimated on the basis of the function.

Abstract: A processing unit includes: a cache memory including a plurality of memory elements; an error detection circuit configured to detect an error when a first timing for reading data from the cache memory is behind a threshold; a latch circuit configured to set a second timing for latching the data based on an output from the error detection circuit and to latch the data at the second timing; and a processing unit core to process the data latched by the latch circuit.

Abstract: A processing unit includes: a cache memory including a plurality of memory elements; an error detection circuit configured to detect an error when a first timing for reading data from the cache memory is behind a threshold; a latch circuit configured to set a second timing for latching the data based on an output from the error detection circuit and to latch the data at the second timing; and a processing unit core to process the data latched by the latch circuit.

Abstract: A leak current calculation apparatus includes an acquiring section for acquiring partial circuit information, and a grouping section for forming a plurality of groups each comprising a part of the partial circuits connected with each other and for generating group information. The apparatus includes a leak difference value calculating section for calculating a leak difference value, which is a difference between a provisional maximum value acquired by adding up the maximum values of the leak current values of all the partial circuits and a sum of maximum values of the leak current values contained in the group information of the groups, and a maximum leak current calculating section for calculating the maximum leak current value of the integrated circuit by adjusting the provisional maximum value with the leak difference value.

Abstract: A disclosed method includes: converting, for each sample point, a set of performance item values for a sample point into coordinate values of a mesh element containing the set among plural mesh elements obtained by dividing a space mapped by the performance items; generating a binary decision graph representing a group of the coordinate values of the sample points; calculating the number of sample points including second sample points that dominates a first sample point and the first sample point, by counting the number of paths in the binary decision graph from a root node to a leaf node representing “1” through at least one of certain nodes corresponding to coordinate values that are equal to or less than coordinate values of the first sample point; and calculating a yield of the first sample point by dividing the calculated number by the number of the plural sample points.

Abstract: Among the combinations of failure candidates which combinations are generated by a generator, one combination that minimizes a cost derived in a cost calculator is selected. For the selected combination of failure candidates, a function, which specifically provides a correlation between one or more failure factor and an error (or error rate) of each failure element is output. A correct failure factor is estimated on the basis of the function.

Abstract: In order to confirm a propagation range of a signal whose signal value is fixed by a control signal to restrain switchings is within a predetermined range, it is judged by results of the logic simulation whether or not a switching restraining mode is enabled. If it is enabled, a switching probability restraint information list including the detected time and an ID of the net whose signal value is fixed is set to the net whose signal value is fixed, and then is propagated to the next net according to the results of the logic simulation. If the circuit changes are appropriated conducted, the results of the logic simulation do not satisfy the propagation condition of the switching probability restraint information list. Accordingly, the switching probability restraint information list is not propagated over the predetermined range, and no problem is detected.

Abstract: An information processing system includes a management apparatus having a transmission preventing part preventing transmission of a predetermined usage allowing signal allowing usage of a predetermined device, in response to receiving a predetermined usage preventing instruction for the predetermined device.

Abstract: The circuit volume of a system under design is reduced by a circuit conversion involving consolidation (sharing) of common parts in the system by a representative part. The design data of the system post-conversion is used to verify operation of the system. However, the verification results for the system post-conversion express signals (e.g., signal X) of plural modules (e.g., modules a to c) as one signal waveform thereby making debugging difficult when a bug is found. Given this situation, from the verification results of the system post-conversion, signal-generation-use data is generated for generating the signal waveforms (here, respective signal waveforms for the modules a to c) of the system before conversion. After verification is complete, a signal waveform for each of the modules a to c is generated using the verification results for the system under design and the signal-generation-use data.

Abstract: Design data of a cell group is copied to obtain design data of an antecedent cell group and of a subsequent cell group. Design data of a combinational circuit is copied to generate the combinational circuits in plural corresponding to a given number of cycles n (n=2, 3, 4 . . . , where n=3 in FIG. 4). The design data of the combinational circuits are connected in series between the design data of the antecedent cell group and the design data of the subsequent cell group. As a result, design data of an input constraint circuit representing an input constraint(s) of the circuit can be generated.

Abstract: A single module includes a shared combinational circuit, a multiplexed sequential circuit, and a common I/F and is substituted for a multiplexed module formed of plural modules of an identical category and type and including plural CPUs. Specifically, the shared combinational circuit is substituted for n combinational circuits, the multiplexed sequential circuit is substituted for n sequential circuits, and the common I/F is substituted for n input pins and n output pins.

Abstract: A method includes generating Atool(j, i) representing an effect of a tool j for a chip i by using outputs from M kinds of tools that carries out a processing for design support for layout data of N kinds of chips; calculating a difference ?(i) between an actual parameter value of the chip i, which is stored in an actual value storage storing, for each chip, the actual parameter value, and an estimated parameter value of the chip i, which is an estimated value of the parameter value calculated from data for the chip i and stored in an estimated value storage storing, for each chip, the parameter estimate value, and calculating, for each chips i, an influence degree Xtool(j) of each tool j, which satisfies ?(i)=?Atool(j, i)*Xtool(j); and identifying a tool j whose influence degree Xtool(j) is largest.