Abstract: The disclosure relates to methods and compositions for treating Type 1 diabetes, such as compositions comprising an effector domain linked to a protein that binds to MAdCAM.

Abstract: The present application provides for IL-2 muteins, compositions comprising the same, and methods of using the same.

Abstract: The present application provides for IL-2 muteins, compositions comprising the same, and methods of using the same.

Abstract: A laundry treating appliance for treating laundry items according to an automatic cycle of operation can include a cabinet defining an interior and having an access opening providing access to the interior. A tub can be located within the interior and can at least partially define a liquid chamber. A drum is rotatably mounted within the liquid chamber and at least partially defines a treating chamber. A clothes mover is located within the treating chamber and rotatable about a vertical axis. The clothes mover can include a base and a barrel.

Abstract: Anomalies in a target object can be detected and diagnosed using improved Mahalanobis-Taguchi system (MTS) techniques. For example, an anomaly detection and diagnosis (ADD) system can receive a set of measurements associated with attributes of a target object. A Mahalanobis distance (MD) can be determined using a generalized inverse matrix. An abnormal condition can be detected when the MD is greater than a predetermined threshold value. The ADD system can determine an importance score for each measurement of a corresponding attribute. The attribute whose measurement has the highest importance score can be determined to be responsible for the abnormal condition.

Abstract: Anomalies in a target object can be detected and diagnosed using improved Mahalanobis-Taguchi system (MTS) techniques. For example, an anomaly detection and diagnosis (ADD) system can receive a set of measurements associated with attributes of a target object. A Mahalanobis distance (MD) can be determined using a generalized inverse matrix. An abnormal condition can be detected when the MD is greater than a predetermined threshold value. The ADD system can determine an importance score for each measurement of a corresponding attribute. The attribute whose measurement has the highest importance score can be determined to be responsible for the abnormal condition.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for accounting for extensions or reductions of the period for which a resource (e.g., computer processor time, scientific apparatus, storage units, devices, etc.) is allocated. According to exemplary embodiments, allocation-based aggregated effects of extension and relinquishment are modeled. The modeled effects are used to offset allocation forecasts based on historical data. As a result, the dimensionality of the problem of incorporating in-house data is greatly reduced as compared to other techniques, and allocation forecasts can be made more accurately and efficiently.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for correcting censored or constrained historical data with various possible types of computing devices, including cloud-based devices, personal computing devices, and edge-based devices. The corrected data may be used in forecasting, for example to forecast demand for a limited resource. In some embodiments, the data is modeled at a higher level of granularity than an individual record. The aggregated demand may then be pro-rated over a group of categories or users where a given category of users that might be small or nonexistent over a certain time frame may be better accommodated. Moreover, it may be easier or more efficient to make assumptions and employ computing resources at the aggregate level.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for accounting for extensions or reductions of the period for which a resource (e.g., computer processor time, scientific apparatus, storage units, devices, etc.) is allocated. According to exemplary embodiments, allocation-based aggregated effects of extension and relinquishment are modeled. The modeled effects are used to offset allocation forecasts based on historical data. As a result, the dimensionality of the problem of incorporating in-house data is greatly reduced as compared to other techniques, and allocation forecasts can be made more accurately and efficiently.

Abstract: Exemplary embodiments are generally directed to methods, mediums, and systems for correcting censored or constrained historical data with various possible types of computing devices, including cloud-based devices, personal computing devices, and edge-based devices. The corrected data may be used in forecasting, for example to forecast demand for a limited resource. In some embodiments, the data is modeled at a higher level of granularity than an individual record. The aggregated demand may then be pro-rated over a group of categories or users where a given category of users that might be small or nonexistent over a certain time frame may be better accommodated. Moreover, it may be easier or more efficient to make assumptions and employ computing resources at the aggregate level.

Abstract: Systems and methods for handling input search engine data. A data store is searched based upon one or more data items contained within the input search engine data. The result of searching the data store is used to determine which data items in the input search engine data might be a search term. Search terms are extracted based upon the result of searching the data store.

Abstract: Systems and methods for handling input search engine data. A data store is searched based upon one or more data items contained within the input search engine data. The result of searching the data store is used to determine which data items in the input search engine data might be a search term. Search terms are extracted based upon the result of searching the data store.

Abstract: Systems and methods for handling input search engine data. A data store is searched based upon one or more data items contained within the input search engine data. The result of searching the data store is used to determine which data items in the input search engine data might be a search term. Search terms are extracted based upon the result of searching the data store.