A method of managing system health is provided. The method includes calculating a reconstruction missing value with respect to the second domain data, determining a degree of degradation of the system on the basis of the reconstruction missing value, predicting a second remaining useful life (RUL) prediction value {tilde over (y)} of the system on the basis of the second domain data, based on a result of the determination of the degree of degradation, optimizing a degradation compensation function on the basis of a distribution of a first RUL prediction value y of the system predicted based on the first domain data in a pre-learning process of the diagnosis model, and predicting a final RUL prediction value {tilde over (y)}′ obtained by compensating for the second RUL prediction value {tilde over (y)}, by using the optimized degradation compensation function.

A method of managing system health is provided. The method includes calculating a reconstruction missing value with respect to the second domain data, determining a degree of degradation of the system on the basis of the reconstruction missing value, predicting a second remaining useful life (RUL) prediction value {tilde over (y)} of the system on the basis of the second domain data, based on a result of the determination of the degree of degradation, optimizing a degradation compensation function on the basis of a distribution of a first RUL prediction value y of the system predicted based on the first domain data in a pre-learning process of the diagnosis model, and predicting a final RUL prediction value {tilde over (y)}′ obtained by compensating for the second RUL prediction value {tilde over (y)}, by using the optimized degradation compensation function.

Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.

A method for detecting fraudulent interactions may include receiving interaction data, including a first plurality of interactions with (first) fraud labels and a second plurality of interactions (without fraud labels). Second fraud label data for each of the second plurality of interactions may be generated with a first neural network (e.g., classifying whether each interaction is fraudulent or not). Generated interaction data and generated fraud label data may be generated with a second neural network. Discrimination data for each of the second plurality of interactions and generated interactions may be generated with a third neural network (e.g., classifying whether the respective interaction is real or not). Error data may be determined based on the discrimination data (e.g., whether the respective interaction is correctly classified). At least one of the neural networks may be trained based on the error data. A system and computer program product are also disclosed.

Provided is a near-infrared spectroscopy-based method for chemical pattern recognition of the authenticity of the traditional Chinese medicine Gleditsiae Spina. The method uses the combination of a near-infrared spectroscopy acquisition method, a 1st derivative pretreatment method and a successive projection algorithm, a Kennard-Stone algorithm and a marching algorithm to perform chemical pattern recognition on the authenticity of the Gleditsiae Spina. The results of the pattern recognition method are accurate and reliable, and Gleditsiae Spina and counterfeits thereof can be accurately distinguished. The present application is the first to establish a method for the chemical pattern recognition of the quality of Gleditsiae Spina based on near-infrared spectroscopy, and can accurately distinguish between Gleditsiae Spina and counterfeits thereof, and provides scientific basis for the quality evaluation of Gleditsiae Spina.

Provided is a near-infrared spectroscopy-based method for chemical pattern recognition of the authenticity of the traditional Chinese medicine Gleditsiae Spina. The method uses the combination of a near-infrared spectroscopy acquisition method, a 1st derivative pretreatment method and a successive projection algorithm, a Kennard-Stone algorithm and a marching algorithm to perform chemical pattern recognition on the authenticity of the Gleditsiae Spina. The results of the pattern recognition method are accurate and reliable, and Gleditsiae Spina and counterfeits thereof can be accurately distinguished. The present application is the first to establish a method for the chemical pattern recognition of the quality of Gleditsiae Spina based on near-infrared spectroscopy, and can accurately distinguish between Gleditsiae Spina and counterfeits thereof, and provides scientific basis for the quality evaluation of Gleditsiae Spina.

A method for training a generative adversarial network, in particular a Wasserstein generative adversarial network. The generative adversarial network includes a generator and a discriminator, the generator and the discriminator being artificial neuronal networks. The method includes training the discriminator. In the step of training the discriminator, a parameter of the discriminator is adapted as a function of a loss function, the loss function including a term that represents the violation of the Lipschitz condition as a function of a first input datum and a second input datum and as a function of a first output of the discriminator when processing the first input datum and a second output of the discriminator when processing the second input datum, the second input datum being created starting from the first input datum by applying the method of the virtual adversarial training.

A method, system and computer-readable storage medium for performing a counterfactual generation operation. The counterfactual generation operation includes: receiving a subject data point; classifying the data point via a trained classifier, the classifying providing a classified data point; identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and, providing the counterfactual to a destination.

A method, system and computer-readable storage medium for performing a counterfactual generation operation. The counterfactual generation operation includes: receiving a subject data point; classifying the data point via a trained classifier, the classifying providing a classified data point; identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and, providing the counterfactual to a destination.

Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.