Abstract: The disclosed technology teaches recovering a first virtual machine or an instance with an Internet Protocol address, a first root volume and one or more data volumes that are corrupted. The first virtual machine is hosted by a first cloud server that hosts plurality of virtual machines. The disclosed technology includes instructing the first cloud server to launch a recovery virtual machine. The recovery virtual machine launches one or more new data volumes based upon captured file system images in one or more snapshots taken of corrupted data volumes of the first virtual machine prior to becoming corrupted. The recovery virtual machine detaches the corrupted data volumes and attaches the new data volumes launched to the first virtual machine. The Internet Protocol address of the first virtual machine remains unchanged.

Abstract: The described method may include receiving user input indicating a configuration identifying a large language model (LLM) and a subset of documents indicated in the configuration as being available to a tenant. The method may include generating one or more vectorizations of content of the subset of documents. The method may include receiving a request to generate a generative response. The method may include generating the generative artificial intelligence (AI) prompt using the content to ground the generative AI prompt. The subset of documents may be identified based on a comparison between a vectorization of the request and the one or more vectorizations and based at least in part on a determination that a user associated with the tenant is permitted to access the subset of documents. The method may include presenting a response to the generative AI prompt, the response generated by the LLM using the generative AI prompt.

Abstract: A cloud platform may include a model interface that receives from a client and at an interface for accessing a large language model, a prompt for a response from the large language model, and the client is associated with a set of configuration parameters via a cloud platform that supports the interface. The cloud platform may modify, in accordance with the set of configuration parameters, the prompt that results in a modified prompt and transmit, to the large language model, the modified prompt. The cloud platform may receive the response generated by the large language model and provide the response to a model that determines one or more probabilities that the response contains content from one or more content categories. The cloud platform may transmit the response or the one or more probabilities to the client.

Abstract: The present disclosure relates to a ULBP6 binding protein that inhibits the interaction between ULBP6 and NKG2D, and methods of treating cancer with said ULBP6 binding protein.

Abstract: A method and system for processing a purchase based on image recognition in a video stream being presented by a computing system. A method includes receiving a first user-input defining a first user-request to pause presentation of the video stream, and, responsive to the first user-input, pausing by the computing system the presentation of the video stream at a video frame. Further, the method includes detecting based on computer-vision analysis of the video frame, at least one object depicted by the video frame. Additionally, the method includes correlating the detected object with at least one purchasable item and presenting a prompt for purchase of the at least one purchasable item. Also, the method includes receiving a second user-input requesting to purchase a given one of the at least one purchasable item and processing, responsive to receiving the second user-input, a purchase of the given purchasable item for the user.

Abstract: This invention pertains to reliable, lightweight and efficient hybrid electric powertrain and components thereof to power hybrid electric aircraft from one or more sources of electrical energy. In particular, a novel architecture is proposed for power panels that receive, condition and distribute high levels of electrical power to and from the propulsion electric motors and the one or several sources of electrical energy. Systems and methods for architecting the power panels using efficient, reusable and modular power electronics building blocks (PEBBs) is described, along with additional systems and methods for the optimal control of the power panels and components thereof. In addition, novel systems and methods are described for the efficient and lightweight thermal management for hybrid electric powertrain components, ranging from distribution manifolds for the powertrain or power panels, to micro fluid channels for cooling electronics with extreme heat flux.

Abstract: The disclosure provides polypeptides and pharmaceutical compositions thereof. Such polypeptides can be useful, for example, in treating pain in a subject. In some embodiments, the polypeptides are useful for treating neuropathic and/or inflammatory pain.

Abstract: Systems and methods for Generic Data Parsing (GDP) applications may automatically parse unstructured and/or semi-structured data sources (e.g., extensible Markup Language (XML), JavaScript Object Notation (JSON), and/or Common Business-Oriented Language (COBOL)) and generate a normalized and/or structured (e.g., relational) data schema, and load the parsed data into the structured data schema, which may then be readily queried utilizing known end-user tools.

Abstract: Building machine learning models using a standardized library of machine learning model tools. An Application Programming Interface (API) serves as an interface between one or more computer applications that receive user commands for building machine learning models and the standardized library of tools. The API provides an interface between data scientists tasked with designing machine learning models conceptually and a standardized set of software engineering tools. The API enables incorporation of the relevant standardized software engineering tools to build the machine learning models that have been designed conceptually by the data scientists.

Abstract: An intelligent Radio Access Network (RAN) optimization framework. For an E2 Node, optimization services are initiated for handling by a Near-Real Time RAN Intelligent Controller (RIC) (Near-RT RIC). The E2 Node compares the performance to a first predetermined threshold. In response to the performance being below the first predetermined threshold, the E2 Node takes over optimization of the E2 Node. Otherwise, the Near-RT RIC continues optimization. The E2 Node compares the performance of the E2 Node to a second predetermined threshold. In response to the performance being below the second predetermined threshold, the Near RT RIC resumes handling the optimization. Otherwise, the E2 Node continues to handle the optimization of the performance of the E2 Node. For an Open-RAN Radio Unit (O-RU) Node, the O-RU Node performs local optimization of time-critical functions and a Non-RT RIC is able to perform optimization of non-time critical functions.

Abstract: Embodiments are directed to a method for predicting a location of user equipment (UE) (500) in a wireless communication network. The method includes receiving, by a Near-Real time RAN Intelligent Controller (Near RT RIC) (100), collated UE measurements associated with the UE (500), from an access node (1000) over an E2 interface. The collated UE measurements is determined based on a plurality of UE measurements associated with the UE (500) sent by at least three transmission receipt points (TRPs) (600a-c) to the access node (1000). The method also includes inputting, by the Near RT RIC (100), the collated UE measurements to a location prediction model (184) located at the Near RT RIC (100); and predicting, by the Near RT RIC (100), a location of the UE (500) by the location prediction model (184) based on the collated UE measurements.

Abstract: Embodiments relate to systems and methods for on-demand margin borrowing and lending with automated de-leveraging. The method for on-demand margin borrowing and lending with automated de-leveraging executing instructions stored on memory by a hardware processor, can involve: enabling margin on a subaccount for cross-collateralization of assets; setting leverage threshold values for the subaccount; processing a loan request to buy or sell an asset from a borrower interface; processing a loan offer to generate returns from idle assets from a lender interface; determining rate for assets; executing a margin transaction between the borrower interface and a lender interface, the margin transaction being smart contract code for a blockchain infrastructure; and computing different types of margin data, and generating output for transmission, storage, or display at an interface.

Abstract: Provided herein are system, apparatus, article of manufacture, method and/or computer program product, and/or combinations and sub-combinations thereof, for utilizing a personalized content system to enhance an effectiveness of content being shown to the user via personalized content associated with the content. This method allows the platform to present the most relevant personalized in-channel content to the users, in a user interface, which then correspondingly helps drive user interest. This method scans content to identify objects of interest located within the content, generates a query based on the identified objects of interest and a user profile, searches content sources for related content, associates the related content with an occurrence of a corresponding identified object of interest within the content and communicates, to the user device, the content and the related content for instantiation through a user interface of the user device.

Abstract: Methods, systems, and computer storage media for providing predicted task-actions for tasks from a plurality of task applications associated with a task management system. In operation, a task associated with a task application is accessed at a task-action prediction engine. An intent of the task is determined. The intent of the task is determined based on task features and an intent prediction machine learning model that is trained to predict intents associated with natural language text. Based on the intent of the task, a predicted task-action is selected based on a task-action computation model that is associated with predefined task-actions and predefined intent-task-categories. The predicted task-action can be communicated to a task management client that causes display of the predicted task-action in combination with a predicted task-action interface element associated with executing the predicted task-action.

Abstract: A system may include a virtual machine, of a cloud computing environment, that executes a target application workload to be intercepted. A software defined anomaly detection engine (that is separate from the target application workload and that is also executing in the virtual machine) may intercept the target application workload. A computer processor of the software defined anomaly detection engine may intercept network traffic that is external to the virtual machine and associated with the target application workload. The software defined anomaly detection engine may then automatically execute an anomaly detection algorithm in substantially real time on the intercepted network traffic to generate an intercept result. An anomaly detection alert signal may be transmitted based on a comparison of the intercept result and an anomaly threshold value.

Abstract: Processing within a computing environment is facilitated using a corpus processing system to assess and enhance quality of a corpus of unstructured documents for a specified task. The processing includes referencing, by a corpus processing engine, the corpus of unstructured documents to obtain unstructured document data, and applying, by a corpus quality metrics engine, a set of quality metrics to the document data to obtain a set of quality metric scores. Further, the process includes automatically selecting, by a quality metric selection engine, a subset of task-relevant quality metrics using the quality metric scores and the specified task, and automatically transforming, at least in part, multiple documents of the corpus to remediate one or more identified issues with the documents. The automatically transforming results in remediated documents tuned for the specified task, which are provided for the specified task to be performed.

Abstract: Systems, methods, and apparatuses related to a network for behavior modeling are described. A network can include surveillance devices that are each configured to collect pedestrian behavior data corresponding to a geographic region and output local pedestrian behavior model updates to a municipal communication node for the geographic region. The municipal communication node is configured to receive the local pedestrian behavior model updates and output the local pedestrian behavior model updates to a global pedestrian behavior model to aggregate the local pedestrian behavior model updates. A processing device is coupled to the municipal communication node and is configured to output the global pedestrian behavior model to the municipal communication node.

Abstract: Aspects of the present application relate to an optical phase shifter including a first waveguide defined in a first semiconductor layer, the first waveguide comprising a single-mode portion, a multi-mode portion, and a tapered portion coupling the single-mode portion to the multi-mode portion. A second waveguide is defined in a second semiconductor layer, the second waveguide having a tapered portion and a tip, wherein the tapered portion of the second waveguide overlaps with the tapered portion of the first waveguide. For tuning the phase change, a first electrically resistive path, defined at least partially in the first semiconductor layer, is included. The first electrically resistive path intersects the multi-mode portion of the first waveguide.

Abstract: Removing metal from metal-carbon material includes contacting the metal-carbon material with hydrogen chloride, thereby yielding a metal chloride in the gas phase and a solid product comprising carbon. The metal-carbon material and the solid product may both contain elemental carbon. A concentration of metal in the solid product is typically less than 1 wt %.

Abstract: A system for Multi-Party Computation (MPC) includes a general-purpose programming language, an MPC processor, an MPC Instruction Set Architecture (ISA), and a compiler. The general-purpose programming language is used for writing an MPC application. The MPC processor executes the MPC application. The MPC ISA corresponds to the MPC processor. The compiler generates an intermediate representation for the MPC application and generates machine code for the intermediate representation by mapping and assembling MPC ISA instructions.