Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: In various examples, a system can obtain a first time series data set, the first time series data set including a plurality of data elements. Each data element can include value data and corresponding time data. Based on the first time series data set, the system can generate a second data set and a third dataset. The second dataset can indicate one or more data elements with missing value data and the third dataset can include extremeness data. The extremeness data can indicate an extremeness score for each data element of the plurality of data elements. Additionally, based on the first time series data set, the second data set and a third dataset, the system can implement a set of operations that generate a substitute value data for each data element of the one or more data elements that is missing value data.

Abstract: This application relates to apparatus and methods for automatically predicting values for a future time period based on time series data of a previous time period. In some examples, a computing device employs multiple algorithms or predictions models to determine baseline predictions and bias predictions accounting for both normal and surge-induced events in the future time period. Accuracy of the algorithms and exogenous variables, such as holidays, events, temporal indicators, are leveraged to accurately predict future values. Baseline predictions using baseline algorithms are aggregated with bias predictions associated with surge events to determine final predictions for the future time period without compromising on the accuracy and efficiency of the predictions for both normal and surge-induced events.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: This invention relates to a technique to enhance the clinical efficiency of radiotherapy and readiosurgery using perturbative beaming and tissue specific radiobiology, comprising, Considering the normal steady beam dose rate, then increasing the temporal stochastic fluctuation of the output intensity of the radiation beam from a medical linear accelerator or cobalt gamma knife beam setup, developing a computer-based methodology by considering the tumor specific radiobiology and magnitude of the tailor made dose rate fluctuation using an embedded system device consisting of requisite hardware and software as described herein.

Abstract: This invention relates to a technique to enhance the clinical efficiency of radiotherapy and readiosurgery using perturbative beaming and tissue specific radiobiology, comprising, Considering the normal steady beam dose rate, then increasing the temporal stochastic fluctuation of the output intensity of the radiation beam from a medical linear accelerator or cobalt gamma knife beam setup, developing a computer-based methodology by considering the tumor specific radiobiology and magnitude of the tailor made dose rate fluctuation using an embedded system device consisting of requisite hardware and software as described herein.