Abstract: An estimation device includes processing circuitry configured to generate a feature quantity by performing invertible transformation on a payload of a packet character by character with respect to each packet determined to be abnormal or normal by an abnormality detector, and give a determination result as to whether a packet is abnormal or normal to the generated feature quantity, learn a model that classifies whether the packet is abnormal or normal by machine learning using the feature quantity of the payload of the packet and the determination result as to whether the packet is abnormal or normal as teacher data, extract a number of dimensions of the feature quantity in which a contribution degree to classification is equal to or greater than a predetermined value in the learned model, and estimate a cause part of abnormality in the payload of the packet determined to be abnormal.

Abstract: An object of the present invention is to provide a band estimation device, a band estimation method, and a program capable of calculating a necessary band of a link of a communication system with high accuracy, and ensure the communication quality and improve the band utilization efficiency. In the present invention, a machine learning model is created by learning a set of an upper limit value (line information) of a communication band of each line and observed traffic data for a communication system in which a plurality of lines is accommodated in a link between communication devices, a probability density function of a traffic amount at each time is estimated on the basis of the generated machine learning model and the line information at each time, and an upper limit value of a confidence interval of the estimated probability density function is set as a necessary band.

Abstract: An acquisition unit acquires data for which a label is to be predicted. A learning unit learns a model representing a probability distribution of a label of the acquired data by using, as a filter, a correct answer label of the data so as to correctly predict a label for an adversarial example in which noise is added to the data.

Abstract: A SiC single crystal, including: a seed crystal; a first growth portion formed in a direction that is substantially orthogonal to a <0001> direction; a second growth portion formed in a direction that is substantially orthogonal to the <0001> direction and substantially orthogonal to the direction in which the first growth portion is formed; a third growth portion that is formed on a surface of the seed crystal opposite the first growth portion; and a fourth growth portion that is formed on a surface of the seed crystal opposite the second growth portion.

Abstract: An inference device executes a first conversion step of converting an output from an intermediate layer using a bounded nonlinear function in a final layer of a deep neural network having the intermediate layer and the final layer. Moreover, the inference device executes a second conversion step of converting a value obtained by conversion in the first conversion step using an activation function.

Abstract: A learning device includes processing circuitry configured to acquire data with a label to be predicted, and learn a model that represents probability distribution of the label of the acquired data using an eigenvector corresponding to a maximum eigenvalue in a Fisher information matrix for the data in the model.

Abstract: A detection device includes acquisition circuitry, conversion circuitry, and detection circuitry. The acquisition circuitry acquires data to be classified using a model. The conversion circuitry converts the data acquired using noise in a predetermined direction. The detection circuitry detects an adversarial example using a change in output between the data acquired and the data converted, at a time when the data acquired and the data converted are input to the model.

Abstract: A detecting device obtains a network log and a host log of a device. The detecting device converts the network log into a network feature value in a form which is inputtable to a multimodal generative model which generates output data on the basis of multiple latent variables represented by random variables. The detecting device converts the host log into a host feature value in a form which is inputtable to the generative model. The detecting device also inputs at least one of the network feature value and the host feature value to the generative model and calculates output data. The detecting device detects an abnormality about the device using an anomaly score calculated on the basis of the output data.

Abstract: An acquisition unit 15a acquires data in a task. The learning unit 15b learns a generation model representing a distribution of a probability of the data in the task so that a mutual information amount between a latent variable and an observed variable is minimized in the model.

Abstract: A learning apparatus includes: a first estimation unit—configured to learn normal communication data, estimate probability density of the normal communication data, and update a parameter of a model; a clustering unit configured to cluster the normal communication data according to the probability density estimated by the first estimation unit; a second estimation unit configured to perform learning for each of clusters resulting from the clustering by the clustering unit, and estimate the probability density of the normal communication data of the cluster and update a parameter of a model representing characteristics of the probability density of the normal communication data of the cluster by using, as an initial value of the parameter, a parameter having already been learned in the first estimation unit; and an integration unit configured to integrate the estimated probability density of the clusters.

Abstract: An information processing apparatus includes processing circuitry configured to calculate, with respect to datasets into which data is divided based on individual labels serving as candidates for an index when the data is divided, an amount of information for each of division methods that use respective labels, divide the data into a plurality of datasets based on the division method that provides highest amount of information, of amounts of information calculated, and create, with use of the datasets divided, a learned model for each of the datasets.

Abstract: An evaluation device receives input of latent variables of a variational autoencoder, clusters the input latent variables, and assigns, for each cluster, a label indicating the cluster to latent variables belonging to the cluster. After that, the evaluation device performs learning of a classifier so as to accurately classify the latent variables based on the assigned label, performs an adversarial attack resistance evaluation for the classifier after learning, and outputs a result of the resistance evaluation. Thus, the evaluation device can perform an adversarial attack resistance evaluation even for a variational autoencoder that uses unlabeled data as input data.

Abstract: An acquisition unit (15a) acquires data output by sensors. A learning unit (15b) substitutes a prior distribution of an encoder in a generative model including the encoder and a decoder and representing a probability distribution of the data with a marginalized posterior distribution that marginalizes the encoder, approximates a Kullback-Leibler information quantity using a density ratio between a standard Gaussian distribution and the marginalized posterior distribution, and learns the generative model using data. A detection unit (15c) estimates a probability distribution of the data using the learned generative model and detects an event in that an estimated occurrence probability of the data newly acquired is lower than a prescribed threshold as abnormality.

Abstract: A detecting device obtains a network log and a host log of a device. The detecting device converts the network log into a network feature value in a form which is inputtable to a multimodal generative model which generates output data on the basis of multiple latent variables represented by random variables. The detecting device converts the host log into a host feature value in a form which is inputtable to the generative model. The detecting device also inputs at least one of the network feature value and the host feature value to the generative model and calculates output data. The detecting device detects an abnormality about the device using an anomaly score calculated on the basis of the output data.

Abstract: A SiC single crystal, including: a seed crystal; a first growth portion formed in a direction that is substantially orthogonal to a <0001> direction; a second growth portion formed in a direction that is substantially orthogonal to the <0001> direction and substantially orthogonal to the direction in which the first growth portion is formed; a third growth portion that is formed on a surface of the seed crystal opposite the first growth portion; and a fourth growth portion that is formed on a surface of the seed crystal opposite the second growth portion.

Abstract: This method for producing a SiC single crystal includes a first growth step of growing a crystal from a seed crystal in a direction that is substantially orthogonal to the <0001> direction, a second growth step of growing the crystal in a direction that is substantially orthogonal to the <0001> direction and substantially orthogonal to the direction of crystal growth in the first growth step, a third growth step of growing the crystal along the direction of crystal growth in the first growth step but in the opposite orientation to the orientation of crystal growth in the first growth step, and a fourth growth step of growing the crystal along the direction of crystal growth in the second growth step but in the opposite orientation to the orientation of crystal growth in the second growth step.

Abstract: A sheet feeding apparatus includes a casing that comprises a sheet feeding port, a first sheet feed tray in which a first medium is loaded and that is arranged along a sheet feeding port, and a cover that rotates from a position opposite the first sheet feed tray of the casing to a first sheet feed tray side and covers the sheet feeding port. The cover includes a second sheet feed tray that causes a second medium differing in size from the first medium to be capable of being loaded in the sheet feeding port when the cover covers the sheet feeding port.

Abstract: At least one lithography apparatus that suppresses a decrease in the accuracy of stage control is provided. A lithography apparatus includes a moving unit configured to move with an original or a substrate mounted thereon, a plurality of measurement units configured to obtain information about a position of the moving unit, measurement areas of the respective measurement units overlapping each other, and a control unit configured to switch the measurement units used to obtain the information about the position of the moving unit, based on a switching position lying in an overlapping measurement area, wherein, in a case where a plurality of processes is performed on one of a plurality of processing targets on the original or on the substrate, the control unit makes the switching position changeable and controls the measurement units so that the same one of the measurement units is used in performing the plurality of processes.

Abstract: This method for producing a SiC single crystal includes a first growth step of growing a crystal from a seed crystal in a direction that is substantially orthogonal to the <0001> direction, a second growth step of growing the crystal in a direction that is substantially orthogonal to the <0001> direction and substantially orthogonal to the direction of crystal growth in the first growth step, a third growth step of growing the crystal along the direction of crystal growth in the first growth step but in the opposite orientation to the orientation of crystal growth in the first growth step, and a fourth growth step of growing the crystal along the direction of crystal growth in the second growth step but in the opposite orientation to the orientation of crystal growth in the second growth step.

Abstract: A stage apparatus includes: a coarse moving stage; a fine moving stage located at an interval from the coarse moving stage in a first direction; an electromagnetic actuator including a first core located on one of the coarse moving stage and the fine moving stage and a second core located on the other of the coarse moving stage and the fine moving stage and configured to accelerate or decelerate the fine moving stage in a second direction perpendicular to the first direction; and a controller configured to correct a command value of a thrust of the electromagnetic actuator in the second direction in accordance with a relative position of the fine moving stage with respect to the coarse moving stage in the first direction.