Abstract: A system is presented. The system includes an acquisition subsystem configured to obtain images corresponding to a target domain. Moreover, the system includes a processing subsystem in operative association with the acquisition subsystem and including a memory augmented domain adaptation platform configured to compute one or more features of an input image corresponding to a target domain, identify a set of support images based on the features of the input image, where the set of support images corresponds to the target domain, augment an input to a machine-learnt model with a set of features, a set of masks, or both corresponding to the set of support images to adapt the machine-learnt model to the target domain, and generate an output based at least on the set of features, the set of masks, or both. Additionally, the system includes an interface unit configured to present the output for analysis.

Abstract: Antibodies that bind to programmed cell death protein 1 (PD-1), compositions comprising such antibodies, and methods of making and using such antibodies are disclosed.

Abstract: A method may include training one or more machine learning models to predict a decline in employee performance. The machine learning models may be trained in a federated manner to avoid the exchange of personal data. The trained machine learning models may be applied to data associated with an employee that corresponds to one or more leading indicators of employee burnout. In response to the trained machine learning models predicting a decline in the performance of the employee, the root causes of the predicted decline in the performance of the employee may be identified by applying an explainability algorithm such as Shapley Additive Explanations (SHAP). A report including a corrective action for the predicted decline in employee performance may be generated based on the root causes. Related systems and computer program products are also provided.

Abstract: Illustrative embodiments of determining characteristics of reflective surfaces and transparent materials are disclosed. In at least one illustrative embodiment, a method of determining characteristics of transparent materials includes using a camera to capture light from a target structure with light regions and dark regions. Before the light is captured by the camera, it passes through a transparent specimen, reflects off of a reflective surface, and passes through the transparent specimen a second time. Deformations in the transparent specimen caused by, e.g., static or dynamic stresses deflect the light passing through it. The amount of the deflection can be used to determine the stresses in the specimen.

Abstract: Apparatuses and methods enable connecting tunnels channeling data flow from a user terminal and to a mobile network through a virtual switch in a network device which is configured to provide a service by processing data in the data flow. A method performed by a device having one or more processors includes establishing a first tunnel between the device and a node of the mobile network, and a second tunnel between the device and another network device of the mobile network, the first tunnel and the second tunnel operating according to Internet protocols. The method further includes connecting the first tunnel to the second tunnel using a virtual switch running on the device, and connecting a virtual machine running on the device to the virtual switch, the virtual machine being configured to provide a service by processing data in the data flow.

Abstract: Data migration based on data change rate analysis is provided. A minimum data replication duration is calculated for each respective data migration event of a set of data migration events in an existing data migration wave plan by adjusting a planned data replication duration until a data change rate per day of servers is equal to an estimated server data replication rate per day when the existing data migration wave plan is not feasible. A new data replication start date, a new data replication end date, and a new data migration cutover date is determined for each respective data migration event based on the minimum data replication duration for each respective data migration event. A new data migration wave plan is generated based on the new data replication start date, the new data replication end date, and the new data migration cutover date for each respective data migration event.

Abstract: A method may include collecting data from a variety of data sources associated with a user. The data sources may include personal data sources, corporate data sources, and public data source. The data collected from the variety of data sources may be enriched through categorization and aggregation. For example, browser history may be categorized based on types of website and aggregated to reflect the quantity of interactions with each category of website. A multi-dimensional digital profile may be generated based on the enriched data. For instance, the digital profile may include a social, emotional, spiritual, environmental, occupational, intellectual, and physical dimension. One or more recommendation corresponding to one or more of a burnout prediction, wellness recommendation, learning plan, skill gap, and personality type may be generated based on the digital profile. Related systems and computer program products are also provided.

Abstract: A code generator platform may receive source metadata and a target data model. The code generator platform may determine a parameter, of the target data model, that is associated with the attribute. The code generator platform may map, based on the attribute and the source metadata, the data to the parameter of the target data model. The code generator platform may generate, based on mapping the data to the parameter, data transformation code associated with the data and the target data model, wherein the data transformation code, when executed, generates target data that corresponds to the data according to the target data model. The code generator platform may perform an action associated with the data transformation code to permit the data transformation code to be executed in order to update a target database with the target data.

Abstract: Techniques describes herein include using software tools and feature vector comparisons to analyze and recommend images, text content, and other relevant media content from a content repository. A digital content recommendation tool may communicate with a number of back-end services and content repositories to analyze text and/or visual input, extract keywords or topics from the input, classify and tag the input content, and store the classified/tagged content in one or more content repositories. Input text and/or input images may be converted into vectors within a multi-dimensional vector space, and compared to a plurality of feature vectors within a vector space to identify relevant content items within a content repository. Such comparisons may include exhaustive deep searches and/or efficient tag-based filtered searches. Relevant content items (e.g., images, audio and/or video clips, links to related articles, etc.

Abstract: The present approach relates to a system capable of life-long learning in a deep learning context. The system includes a deep learning network configured to process an input dataset and perform one or more tasks from among a first set of tasks. As an example, the deep learning network may be part of an imaging system, such as a medical imaging system, or may be used in industrial applications. The system further includes a learning unit communicatively coupled to the deep learning network 102 and configured to modify the deep learning network so as to enable it to perform one or more tasks in a second task list without losing the ability to perform the tasks from the first list.

Abstract: An estimation system includes a first acquisition unit, a second acquisition unit, and an estimation unit. The first acquisition unit acquires model information. The model information is information about a human model rendered in a virtual space. The human model is generated based on model data of a human. The second acquisition unit acquires environmental information. The environmental information is information about an environment corresponding to the virtual space and potentially having a particular effect on the human model. The estimation unit estimates a condition of the human model based on the model information and the environmental information.

Abstract: In accordance with an embodiment, described herein is a system and method for generation of starter websites. A content management system (CMS), such as for example, an Oracle Content and Experience Cloud (CEC) environment, can be provided. The content management system can include a site generator within the CMS. The site generator can receive a selection of content managed by the CMS, wherein the content includes data associated with the content. The site generator can determine, based on the data associated with the content, a layout of the content on a document. The site generator can generate machine readable code that, when interpreted or executed by a machine, causes the machine to produce a document including the content wherein the content is displayed in the determined layout.

Abstract: Described herein are systems and methods for providing custom component compilation within a cloud-based content hub environment. In accordance with an embodiment, embodiments can allow developers to work with a variety of technologies and optimize a page or site for runtime performance, including in some instances the use of a custom (component) compiler, for use during compilation of a particular component of a website.

Abstract: A code generator platform may receive source metadata and a target data model. The code generator platform may determine a parameter, of the target data model, that is associated with the attribute. The code generator platform may map, based on the attribute and the source metadata, the data to the parameter of the target data model. The code generator platform may generate, based on mapping the data to the parameter, data transformation code associated with the data and the target data model, wherein the data transformation code, when executed, generates target data that corresponds to the data according to the target data model. The code generator platform may perform an action associated with the data transformation code to permit the data transformation code to be executed in order to update a target database with the target data.

Abstract: Selection and serving of content items may include receiving data indicative of a status of an energy source of a device with a request for a content item. A first received content item may be associated with a first energy consumption level and a second received content item may be associated with a second energy consumption level. The accessed content items are responsive to the request for a content item. The first energy consumption level may be higher than the second energy consumption level. The first content item or the second content item may be selected based, at least in part, on the received data indicative of the status of the energy source of the device, and data to display the selected content item may be provided to the device.

Abstract: A polyetherimide of improved color and processes for preparing the polyetherimide are disclosed.

Abstract: In accordance with an embodiment, described herein is a system and method for management and delivery of shoppable content data. A modeled content architecture can be provided within a content management system (CMS), such as for example, an Oracle Content and Experience Cloud (CEC) environment. The modeled content architecture can include document-type objects that represent a type of document, and field objects that represent a type of field that can be added to a document. The document-type objects and field objects can include attributes that describe the objects. An admin API operates as a contract between the CMS and a commerce provider system, for example an online retailer, and facilitates communication and data exchange between the CMS and the commerce provider.

Abstract: Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the data set to be individually and manually labeled by human labelers.

Abstract: Described herein are systems and methods automatic selection for dynamic site compilation in a cloud-based content hub environment. In accordance with an embodiment, embodiments provide an artificial intelligence/machine learning (AI/ML) engine that monitors and collects both content and consumption analytics associated with content items on a webpage. Based upon an analysis of such metrics, content can be automatically tagged such that the content item can be statically compiled with the website (optimized for viewing speed and user experience), or dynamically fetched/loaded on a load or refresh of the website.

Abstract: A method for preparing a poly(phenylene ether) includes feeding air to a continuous flow reactor that contains a reaction mixture including a phenol, a transition metal catalyst, and an organic solvent; and oxidatively polymerizing the reaction mixture at a specified temperature and pressure to form a poly(phenylene ether). The reaction mixture has a residence time in the continuous flow reactor of less than or equal to 30 minutes. Poly(phenylene ether)s prepared by the method and articles including the poly(phenylene ether)s are also described.