Abstract: In a compatibility evaluation device, the acquisition means acquires outputs of a first predictor and a second predictor for evaluation data. The index determination means determines a generalized backward compatibility index defined by a combination of a plurality of relational expressions representing relationship between the output of the first predictor and the output of the second predictor. The calculation means calculates a score indicating compatibility of the first predictor and the second predictor, using the output of the first predictor, the output of the second predictor, and the generalized backward compatibility index.

Abstract: An image forming apparatus includes a process unit, a heating unit, and a controller unit. The process unit is configured to transfer an image onto a sheet. The heating unit is configured to fix the image transferred at the process unit onto the sheet, and to transfer a transfer layer onto an image formed on a sheet overlaid on a multilayer film. The controller unit is capable of operating in a first printing mode in which processes of transferring an image at the process unit, fixing the image at the heating unit, and transferring the transfer layer at the heating unit are executed, and a second printing mode in which processes of transferring an image at the process unit and fixing the image at the heating unit are not executed, and a process of transferring the transfer layer at the heating unit is executed.

Abstract: Provided are an analysis device, an analysis method, and a program capable of easily identifying a factor of a prediction error in prediction using a prediction model on the basis of various viewpoints. An analysis device (1) includes: a metric evaluation unit (2) that calculates and evaluates a plurality of types of metrics with respect to a prediction model, data of explanatory variables used in the prediction model, or data of target variables used in the prediction model; and a factor identification unit (3) that identifies a factor of an error in prediction by the prediction model according to a combination of evaluation results of the plurality of types of metrics.

Abstract: A learning device calculates an estimation target item reference value according to a fixed value of each estimation target object. The learning device acquires learning data that includes the fixed value of each estimation target object, a variable item value, and an estimation target item value according to the fixed value and the variable item value. The learning device trains, using the learning data and an evaluation function, a model that outputs an estimated value of the estimation target item value in response to input of the fixed value of each estimation target object and the variable item value.

Abstract: A first trench extending in a Y direction is formed in each of a semiconductor substrate located in a cell region and the semiconductor substrate located in an outer peripheral region. A second trench is formed in the semiconductor substrate in the outer peripheral region so as to surround the cell region in a plan view. A p-type body region is formed in the semiconductor substrate in each region. A plurality of p-type floating regions is formed in the semiconductor substrate in the outer peripheral region. A field plate electrode is formed at a lower portion of each of the first trench and the second trench. A gate electrode is formed at an upper portion of the first trench located in the cell region. A floating gate electrode is formed at an upper portion of each of the first trench located in the outer peripheral region and the second trench.

Abstract: A communication system is a communication system that includes first and second information processing devices. The first information processing device performs control such that a signal (which is a signal having backward compatibility) serving as an index by which the second information processing device receiving a frame stops the reception of the frame is transmitted to the second information processing device. The second information processing device performs control such that the reception of the frame is stopped based on the signal (which is a signal having backward compatibility) serving as an index by which reception of the frame is stopped when the frame transmitted from the first information processing device is received.

Abstract: A mirror support mechanism includes three first supporting members and three second supporting members. Each first supporting member includes a mirror supporting portion that is in contact with and support a corresponding one of three supported surfaces provided on the supported portion with rotational symmetry of 120 degrees around an optical axis, and two first beam portions connected to both sides of the mirror supporting portion. Each second supporting member includes a supporting portion to which ends of two first beam portions adjacent to each other are connected, the ends being not connected to the mirror supporting portion, and two second beam portions connected to both sides of the supporting portion, an end of the second beam portion not connected to the supporting portion being supported by a structure member provided on the rear side of the reflecting mirror.

Abstract: In the strange feeling prediction device, the prediction result acquisition means inputs diagnosis data to a target prediction model which is a trained prediction model serving as a target, and acquires a prediction result by the target prediction model. The label acquisition means acquires a strange feeling label indicating a strange feeling of an expert with respect to the prediction result. The strange feeling prediction model training means trains a strange feeling prediction model using the prediction result and the strange feeling label. The strange feeling prediction means outputs a strange feeling index indicating the strange feeling with respect to the prediction result outputted by the target prediction model, using the trained strange feeling prediction model.

Abstract: Predictively robust models are trained by embedding a distribution of each temporal data set among a plurality of temporal data sets into a feature vector, predicting a future feature vector of a distribution of a future data set, based on the feature vector of each temporal data set among a plurality of temporal data sets, creating the future data set from the future feature vector, perturbing the future data set to produce a plurality of perturbed future data sets, and training a learning function using the future data set and each perturbed future data set to produce a model.

Abstract: A communication apparatus for communicating a push message with another communication apparatus, comprises an obtaining unit configured to obtain time information to synchronize a period in which processing related to the push message is possible with the other communication apparatus; and a communication unit configured to execute, during a period of processing in which the processing related to the push message is possible and which is specified based on the time information obtained by the obtaining unit, push message communication with the other communication apparatus.

Abstract: A data classification system calculates, for each known class, which is a class indicated in training data used for performing learning of class classification, a known class likelihood indicating a likelihood of target data belonging to the known class among all known classes. The data classification system selects, as candidates for a class to which the target data belongs, classes in which at least one of the known classes is excluded based on the known class likelihood among all classes in the class classification. The data classification system calculates, at least for each of the classes included in the candidates, an all-class likelihood indicating a likelihood of the target data belonging to the class among all the classes. The data classification system estimates the class to which the target data belongs as any one of the classes among the candidates, based on the all-class likelihood.

Abstract: The invention relates to a method for transmitting from a server to a client, encoded media data corresponding to a projection of a wide view of a scene, the method comprising: Encapsulating the encoded data into tracks, Wherein information is provided for signaling that the client needs to parse other encoded data to display at least the encoded data encapsulated in a given track.

Abstract: A surgical support system includes a processor and a storage device. The processor performs processing of displaying on a monitor a surgery schedule screen indicating a surgery schedule. The storage device stores surgery content information, operating room information, and surgery time information in association with a surgery on the surgery schedule. The processor receives real-time surgery information regarding real-time surgery state from an operating room, and performs processing of displaying on the monitor the surgery schedule screen in which the real-time surgery information is associated with the surgery on the surgery schedule.

Abstract: A surgical support system includes a processor and a storage device. The processor performs processing of displaying on a monitor a surgery schedule screen indicating a surgery schedule. The storage device stores surgery time information, surgery content information, and surgery resource information that are used for creation of the surgery schedule. The processor performs processing of displaying on the monitor a surgery schedule screen that displays the surgery time information of surgery on the surgery schedule, the surgery content information of the surgery, and the surgery resource information regarding the surgery resource allocated to the surgery.

Abstract: A surgical support system includes a processor that establishes communication connection with a terminal. The processor performs processing of displaying a display screen on a terminal to present the display screen to a member other than a surgeon among operation team members via the terminal.

Abstract: A surgical support system includes a processor configured to establish communication connection with a first terminal and a second terminal. The processor performs processing of displaying a first check list on the first terminal to present the first check list to a first member. When accepting input to the first check list from the first member via the first terminal, the processor performs processing of displaying on the first terminal the first check list the check status of which is changed in accordance with the accepted input. The processor does not accept change of check status of the first check list from the second member.

Abstract: Forward compatible models are obtained by operations including training a learning function with a current training data set to produce a first model, the current training data set including a plurality of samples, generating a plurality of prospective models, each prospective model based on a variation of one of the current training data set or the first model, adjusting a plurality of sample weights based on output of one or more prospective models among the plurality of prospective models in response to input of the current training data set, and retraining the learning function with the current training data set and the plurality of sample weights to produce a second model.

Abstract: Distributionally robust models are obtained by operations including training, according to a loss function, a first learning function with a training data set to produce a first model, the training data set including a plurality of samples. The operations may further include training a second learning function with the training data set to produce a second model, the second model having a higher accuracy than the first model. The operations may further include assigning an adversarial weight to each sample among the plurality of samples set based on a difference in loss between the first model and the second model. The operations may further include retraining, according to the loss function, the first learning function with the training data set to produce a distrtibutionally robust model, wherein during retraining the loss function further modifies loss associated with each sample among the plurality of samples based on the assigned adversarial weight.

Abstract: An apparatus generates a plurality of albums including a first album and a second album, and includes a page generation unit to generate common pages to be arranged in both albums and individual pages including a first individual page to be arranged in the first album and not in the second album, and a second individual page to be arranged in the second and not in the first. The first album is album data that a first object is set as a main object. An image including the first object is arranged in the first individual page. The second album is album data that a second object is set as a main object. An image including the second object is arranged in the second individual page. A determination unit determines an arrangement order of the common pages and the individual pages.

Abstract: The learning unit 81 learns an attribute viewpoint model in which attributes of a target are explanatory variables for each target person so as to minimize a difference between a prediction result by a predictor that predicts an evaluation result of each target person based on a feature vector of the target person and a prediction result by a prediction model that predicts an evaluation result learned for each target person, using the attributes of the target as explanatory variables. The attribute generation unit 82 generates an attribute so that an evaluation result obtained according to the attribute applied to the learned prediction model satisfies the specified objective.