Abstract: This disclosure provides systems, methods and apparatuses for a high priority route selection descriptor (RSD) selection timer. A protocol data unit (PDU) session may be established for an application associated with a user equipment (UE). If the PDU session is associated with a lower RSD priority than the highest RSD priority for the application, the UE may initiate an RSD selection timer. After expiration of the RSD selection timer, the UE may try to establish another PDU session for the application with the highest RSD priority for the application. If another PDU session with the highest RSD priority is accepted, the UE may release the initial PDU session with the lower RSD priority and establish a new PDU session with the highest RSD priority. Such implementations may increase data rates, quality of service, and reliability for the application, decrease latency of the application, or decrease power consumption of the UE.

Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

Abstract: The present disclosure provides a data acquisition and control system of pulsed power supply for multi-load, and relates to the technical field of pulsed power. The system of the present disclosure is configured to control 18 sets of pulsed power supplies to provide 18 coils with stable and reliable excitation current featuring multiple output time sequences and flexible adjustability, such that a near-earth space plasma environment ground-based simulation device can simulate the magnetic field topology of the earth magnetosphere and realize a background magnetic field required by physical experiments. The system mainly achieves the following functions: control over charge and discharge of pulsed power supplies, selection of power supplies put in use, “delay trigger” setting, voltage monitoring, status monitoring of key components and output current display; and it is also necessary to realize failure alarm and failure handling for the purpose of ensuring the safety of equipment and personnel.

Abstract: This application describes a battery equalization method and device, and a battery management system. The battery equalization method includes: obtaining a first closed circuit voltage of N cells in a duration of a pulse charge current and a second closed circuit voltage of the N cells in a duration of a pulse discharge current, where the N cells constitute a battery, and N is a positive integer; determining a relationship of SOC values between the N cells based on the first closed circuit voltage and the second closed circuit voltage; and performing charge equalization on target cells, where the target cells are determined from the N cells based on the relationship of SOC values.

Abstract: Aspects disclosed herein include a method for deoxidization of a metal material comprising one or more metal oxide materials, the method comprising: reducing a concentration of the one or more metal oxide materials in the metal material from an initial concentration to a final concentration, thereby forming a deoxidized metal material; wherein the step of reducing comprises: etching the metal material using a reactive gas to expose one or more metal oxide materials; and separating the exposed one or more metal oxide materials from the etched metal material; wherein the step of separating comprises: processing the etched metal material in a solvent and extracting the exposed one or more metal oxide materials.

Abstract: A cleaning system may include a vacuum cleaner and a docking station. The vacuum cleaner may include an air inlet, a suction motor having a suction motor inlet and a suction motor outlet, and a cleaner dust cup configured to transition between a collection position and an emptying position, the cleaner dust cup being upstream of the suction motor inlet when in the collection position and downstream of the suction motor outlet when in the emptying position. The docking station may include a base, a support extending from the base, a station dust cup removably coupled to the support, and a receptacle coupled to the support and configured to receive at least a portion of the vacuum cleaner, the cleaner dust cup being fluidly coupled to and upstream of the station dust cup when the vacuum cleaner is inserted in the receptacle.

Abstract: This document describes methods and compositions for converting organic wastes into medium chain carboxylic acids (MCCAs). This document also describes methods and compositions for obtaining high yields of caproate from a fermentation broth comprising MCCAs.

Abstract: An online system selects an item to present to a user of the online system. The online system accesses user interaction data for the user. The online system transmits the user interaction data to a model serving system and receives, from the model serving system, item embeddings for the items with which the user interacted. The model serving system may use an LLM to generate the item embeddings based on the user interaction data. The online system generates a user embedding array based on the item embeddings. The online system applies a transformer network to the user embedding array to generate a user embedding describing the user. To select an item to present to the user, the online system compares the generated user embedding to item embeddings for a set of candidate items. The online system selects a candidate item based on the interaction scores.

Abstract: A contact arrangement includes a plurality of contact groups. At least one of the contact groups includes a plurality of shared contacts, a plurality of dedicated contacts, and a plurality of ground contacts. The shared contacts in a first mode or a second mode transmit signals corresponding to the first mode or the second mode. The dedicated contacts transmit the signals corresponding to the first mode and do not transmit the signals corresponding to the second mode. The ground contacts surround the shared contacts and the dedicated contacts.

Abstract: A cleaning system may include a vacuum cleaner and a docking station. The vacuum cleaner may include an air inlet, a suction motor having a suction motor inlet and a suction motor outlet, and a cleaner dust cup configured to transition between a collection position and an emptying position, the cleaner dust cup being upstream of the suction motor inlet when in the collection position and downstream of the suction motor outlet when in the emptying position. The docking station may include a base, a support extending from the base, a station dust cup removably coupled to the support, and a receptacle coupled to the support and configured to receive at least a portion of the vacuum cleaner, the cleaner dust cup being fluidly coupled to and upstream of the station dust cup when the vacuum cleaner is inserted in the receptacle.

Abstract: A computer system finetunes a machine-learned language model to generate a personalized response to a user request. The system may generate a user representation for each of a plurality of users by applying a transformer model to a sequence of tokens representing a sequence of activities of the user. The system may train an evaluation model coupled to receive a user representation and a response to a user request and generate an estimated evaluation score indicating a level of personalization of the response to the user. The system may finetune a first machine-learned language model to generate a second machine-learned language model. The finetuned machine-learned language model is configured to provide personalized responses for customer services at an online concierge system.

Abstract: The present disclosure concerns nanofibers and methods of forming these nanofibers thereof. The method of forming nanofiber comprises providing 2D materials with charge bearing moieties on its planar surfaces and at its ends, reacting the charge bearing moieties on the planar surfaces with proton donors, proton acceptors, at least partially hydrophobic counterions or a second 2D materials with oppositely charge bearing moieties on its planar surfaces and at its ends in order to curl the 2D material, simultaneously reacting the charge bearing moieties at the ends with proton donors, proton acceptors, at least partially hydrophobic counterions or the second 2D materials with oppositely charge bearing moieties on its planar surfaces and at its ends, and crosslinking the neutralised charge bearing moieties at the ends in order for the 2D materials to interact with each other to form the nanofiber.

Abstract: An online system generates a knowledge graph database representing relationships between entities in the online system. The online system generates the knowledge graph database by at least obtaining descriptions for an item. The online system generates one or more prompts to a machine-learned language model, where a prompt includes a request to extract a set of attributes for the item from the description of the item. The online system receives a response generated from executing the machine-learned language model on the prompts. The online system parses the response to extract the set of attributes for the item. For each extracted attribute, the online system generates connections between an item node representing the item and a set of attribute nodes for the extracted set of attributes in the database.

Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process crowd-sourced information provided by users. The crowd-sourced information may include comments from users represented as unstructured text. The system further receives queries from users and answers the queries based on the crowd-sourced information collected by the system. The system generates a prompt for input to a machine-learned language model based on the query. The system provides the prompt to the machine-learned language model for execution and receives a response from the machine-learned language model. The response comprises the insight on the topic and evidence for the insight. The evidence identifies one or more comments used to obtain the insight.

Abstract: A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process high-level natural language queries received from users. The system receives a natural language query from a user of a client device. The system determines contextual information associated with the query. Based on this information, the system generates a prompt for the machine learning based language model. The system receives a response from the machine learning based language model. The system uses the response to generate a search query for a database. The system obtains results returned by the database in response to the search query and provides them to the user. The system allows users to specify high level natural language queries to obtain relevant search results, thereby improving the overall user experience.

Abstract: An online system trains a specific-purpose LLM. The online system obtains training examples and divides training examples across batches. The online system generates a specific response by applying parameters of the specific-purpose LLM to a batch of training examples. The online system generates a general response by applying parameters of a general-purpose LLM to the batch of training examples. The online system computes a human readability score representing the difference between the specific response and the general response. The online system computes an objective compliance score by applying an evaluation model to the specific response, the evaluation model trained to score the first response based on a specific objective. The online system updates the parameters of the specific-purpose LLM based on the human readability score and the objective compliance score.

Abstract: An online system performs an inference task in conjunction with the model serving system to infer one or more purposes of the order of a user that includes a list of ordered items. The model serving system may host a machine-learned language model, and in one instance, the machine-learned language model is a large language model. The online system generates recommendations to the user based on the inferred purpose of the order. The online system may generate one or more recommendations that are equivalent orders having the same or similar purpose as the existing order.

Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

Abstract: The present invention provides compounds of any one of Formulae (A), (I-11), (II), and (V) (e.g., compounds of Formula (A-1)-(A-18)), and methods for treating Waldenström's macroglobulinemia (WM) and other B cell neoplasm in a subject using the compounds. The methods comprise administering to a subject in need thereof an effective amount of the compounds. Also provided are methods to treat B cell neoplasms using the compounds in combination with inhibitors of Bruton's tyrosine kinase (BTK), interleukin-1 receptor-associated kinase 1 (IRAK1), interleukin-1 receptor-associated kinase 4 (IRAK4), bone marrow on X chromosome kinase (BMX), phosphoinositide 3-kinase (PI3K), transforming growth factor b-activated kinase-1 (TAK1), and/or a Src family kinase.