Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: A computer-implemented method comprises receiving by a servers of a development platform, an upload of a trained ML model and configuration data from a tenant user, for use by a mobile enterprise application that is made available to one or more tenants of the development platform, the mobile enterprise application capable of invoking the trained ML model, wherein the configuration data defines an input data format and an output data format of the trained ML model, The server generates API based on the configuration data and downloading the API to the mobile enterprise application, wherein the generating the API is performed without using an API provided by the tenant user. The generated API is further configured to pass input in the input data format to the trained ML model and configured to receive output in the output data format from the trained ML model for further processing or display.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: A computer-implemented method for enabling a mobile enterprise application of a database system to use a machine learning (ML) service comprises modifying, by a provider of the database system, a mobile enterprise application made available to one or more tenants of the database system to include a native code layer capable of downloading and invoking a trained ML model. One or more servers of the database system receives an upload of the trained ML model and configuration data from a tenant user, wherein the configuration data defines a format of input data and a format of output data of the trained ML model. The one or more servers download the trained ML model to the mobile enterprise application running on a mobile device in response to a request by the native code layer of the mobile enterprise application.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: A shaped abrasive particle including a body having a first major surface, a second major surface, and a side surface joined to the first major surface and the second major surface, and the body has at least one partial cut extending from the side surface into the interior of the body.

Abstract: Various shaped abrasive particles are disclosed. Each shaped abrasive particle includes a body having at least one major surface and a side surface extending from the major surface.

Abstract: A computer-implemented method for enabling a mobile enterprise application of a database system to use a machine learning (ML) service comprises modifying, by a provider of the database system, a mobile enterprise application made available to one or more tenants of the database system to include a native code layer capable of downloading and invoking a trained ML model. One or more servers of the database system receives an upload of the trained ML model and configuration data from a tenant user, wherein the configuration data defines a format of input data and a format of output data of the trained ML model. The one or more servers download the trained ML model to the mobile enterprise application running on a mobile device in response to a request by the native code layer of the mobile enterprise application.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a training dataset containing a first set of records associated with a first set of identifier (ID) values and an evaluation dataset containing a second set of records associated with a second set of ID values. Next, the system selects a random subset of ID values from the second set of ID values. The system then generates a sampled evaluation dataset comprising a first subset of records associated with the random subset of ID values in the second set of records. The system also generates a sampled training dataset comprising a second subset of records associated with the random subset of ID values in the first set of records. Finally, the system outputs the sampled training dataset and the sampled evaluation dataset for use in training and evaluating a machine learning model.

Abstract: A shaped abrasive particle including a body having a first major surface, a second major surface, and a side surface joined to the first major surface and the second major surface, and the body has at least one partial cut extending from the side surface into the interior of the body.

Abstract: Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble and convert data tensors as needed to generate output records based on input records and inferencing. The transformer processes each input record as follows. Input tensors of the input record are converted into converted tensors. Each converted tensor represents a respective feature of many features that are capable of being processed by the underlying trainable models. The trainable models are applied to respective subsets of converted tensors to generate an inference for the input record. The inference is converted into a prediction tensor. The prediction tensor and input tensors are stored as output tensors of a respective output record for the input record.

Abstract: The invention provides processes of purifying a peptide including a GCC agonist sequence selected from the group consisting of SEQ ID NOs: 1-251 described herein. The processes include a solvent exchange step before a freeze-drying (lyophilization) step.

Abstract: A shaped abrasive particle including a body having a first major surface, a second major surface, and a side surface joined to the first major surface and the second major surface, and the body has at least one partial cut extending from the side surface into the interior of the body.

Abstract: Various shaped abrasive particles are disclosed. Each shaped abrasive particle includes a body having at least one major surface and a side surface extending from the major surface.

Abstract: Various shaped abrasive particles are disclosed. Each shaped abrasive particle includes a body having at least one major surface and a side surface extending from the major surface.

Abstract: In an example embodiment, knowledge discovery using deep learning is combined with the scalability and personalization capabilities of generalized additive mixed effect (GAME) modeling. Specifically, features learned in a last fully connected layer of a deep learning model may be used to augment features used in a fixed or random effects training portion of a GAME model.

Abstract: Techniques for predicting sequential data using generative adversarial networks are disclosed herein. In some embodiments, a method comprises: receiving a request associated with a user of an online service; in response to the receiving of the request, retrieving a first plurality of sequential data points of the user from a profile of the user stored on a database of the online service, the first plurality of sequential data points comprising at least one attribute for each one of a plurality of sequential career points of the user; generating at least one predicted data point for the user based on the first plurality of sequential data points using a generative model, the generated at least one predicted data point comprising at least one attribute for a predicted career point for the user; and performing a function of the online service using the generated at least one predicted data point.