Abstract: The present invention generally relates to pH-dependent mutant interleukin-2 polypeptides that exhibit reduced IL-2 receptor binding at neutral pH and retained IL-2 receptor binding at reduced pH. In addition, the invention relates to immunoconjugates comprising said pH-dependent mutant IL-2 polypeptides, polynucleotide molecules encoding the pH-dependent mutant IL-2 polypeptides or immunoconjugates, and vectors and host cells comprising such polynucleotide molecules. The invention further relates to methods for producing the pH-dependent mutant IL-2 polypeptides or immunoconjugates, pharmaceutical compositions comprising the same, and uses thereof.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Abstract: Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

Abstract: Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Abstract: Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Abstract: Systems, methods, and other embodiments associated with producing a proximity display of correlated load metrics associated with members of a cluster are described. One example method includes acquiring metrics data (e.g., load data) from nodes in a cluster. The example method may also include determining a cluster element state based on the metrics data and determining relationships between members of the set of related cluster elements. The method may also include identifying element metric representations for cluster elements based on cluster element states and identifying locations on a proximity display at which element metric representations are to be displayed. The locations may depend on relationships between cluster element states. The method may also include displaying element metric representations at the computed locations to produce a proximity display of correlated load metrics.

Abstract: Systems, methods, and other embodiments associated with producing a proximity display of correlated load metrics associated with members of a cluster are described. One example method includes acquiring metrics data (e.g., load data) from nodes in a cluster. The example method may also include determining a cluster element state based on the metrics data and determining relationships between members of the set of related cluster elements. The method may also include identifying element metric representations for cluster elements based on cluster element states and identifying locations on a proximity display at which element metric representations are to be displayed. The locations may depend on relationships between cluster element states. The method may also include displaying element metric representations at the computed locations to produce a proximity display of correlated load metrics.