Abstract: A self-stabilizing integrally-molded pillar filling structure for underground mines is provided, belonging to the field of supporting structures, which includes an airbag, and the airbag is provided with a feed filling port and a gas inflation port; a telescopic bearing piece, and the telescopic bearing piece is fixedly provided on an inner surface of the airbag and is capable of expanding and contracting with volume change of an inner cavity of the airbag; and steel hoops, and the steel hoops are fixedly provided on an outer surface of the airbag.

Abstract: A method includes receiving a training set of utterances for training a machine-learning model to identify one or more intents for one or more utterances, and augmenting the training set of utterances with out-of-domain (OOD) examples. The augmenting includes: generating a data set of OOD examples, filtering out OOD examples from the data set of OOD examples, determining a difficulty value for each OOD example remaining within the filtered data set of the OOD examples, and generating augmented batches of utterances including utterances from the training set of utterances and utterances from the filtered data set of the OOD based on the difficulty value for each OOD. Thereafter, the machine-learning model is trained using the augmented batches of utterances in accordance with a curriculum training protocol.

Abstract: Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: Disclosed herein are novel selectable markers and uses thereof. Specifically, provided herein are uses of nucleotide sequences encoding a glutamine synthetase (GS) derived from platypus, turtle, rat, opossum, wombat, or zebra finch as selectable markers for identifying genomic loci with high transcriptional activity or host cells having high productivity of a protein of interest, and/or allowing accelerated cell isolation expressing a protein of interest. Related methods of screening, methods of production, and expression systems are also included.

Abstract: Techniques are disclosed herein for improving the performance of an end-to-end (E2E) Automatic Speech Recognition (ASR) model in a target domain. A set of test examples are generated. The set of test examples comprise multiple subsets of test examples and each subset of test examples corresponds to a particular test category. A machine language model is then used to convert audio samples of the subset of test examples to text transcripts. A word error rate is determined for the subset of test examples. A test category is then selected based on the word error rates and a set of training examples is generated for training the ASR model in a particular target domain from a selected subset of test examples The training examples are used to fine-tune the model in the target domain. The trained model is then deployed in a cloud infrastructure of a cloud service provider.

Abstract: Techniques are disclosed herein for routing an utterance to action for a digital assistant with generative artificial intelligence. An input query comprising particular data can be received from a user. An action and a set of input argument slots within a schema associated with the action can be identified based on the input query. The input argument slots can be filled by determining whether one or more parameters are derivable from the particular data and filling the input argument slot with a version of the parameters that conforms to the schema. An execution plan that comprises the action that includes the set of filled input argument sots can be sent to an execution engine configured to execute the action for generating a response to the input query.

Abstract: Techniques are disclosed herein for contextual query rewriting. The techniques include inputting a first user utterance and a conversation history to a first language model. The first language model identifies an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity, modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance, and outputs the modified utterance. The computing system provides the modified utterance as input to a second language model. The second language model performs a natural language processing task based on the input modified utterance and outputs a result. The computing system outputs a response to the first user utterance based on the result.

Abstract: Techniques are disclosed herein for configuring agents for use by digital assistants that use generative artificial intelligence. An agent may be in the form of a container that is configured to have one or more actions that can be executed by a digital assistant. The agent may be configured by initially defining specification parameters for the agent based on natural language input from a user. Configuration information for the one or more assets can be imported into the agent. One or more actions may then be defined for the agent based on importing of the configuration information, the natural language input from the user, or both. A specification document can be generated for the agent and can comprise various description metadata, such as agent, asset, or action metadata, or combinations thereof. The specification document may be stored in a data store that is communicatively coupled to the digital assistant.

Abstract: Techniques are disclosed herein for implementing digital assistants using generative artificial intelligence. An input prompt comprising a natural language utterance and candidate agents and associated actions can be constructed. An execution plan can be generated using a first generative artificial model based on the input prompt. The execution plan can be executed to perform actions included in the execution plan using agents indicated by the execution plan. A response to the natural language utterance can be generated by a second generative artificial intelligence model using one or more outputs from executing the execution plan.

Abstract: Techniques are disclosed for fine-tuning a pre-trained machine learning model to be used by a digital assistant for supporting a user's interactions. In one aspect, a method includes accessing a set of training examples, generating a set of synthesized training examples using an iterative process including accessing a dialog script and corresponding prompt template and response template for a predefined scenario, generating one or more prompts based on the dialog script and corresponding prompt template, generating one or more responses associated with each of the one or more prompts based on the dialog script and the response template, and linking each of the responses with the associated prompts to generate one or more synthesized training examples in the set of synthesized training examples. The pre-trained machine learning model is then fine-tuned using the set of training examples and the set of synthesized training examples.

Abstract: Provided are novel selectable markers and uses thereof. Specifically, provided are uses of nucleotide sequences encoding a glutamine synthetase (GS) derived from Alligator, green anole, or spotted gar as selectable markers for identifying genomic loci with high transcriptional activity or host cells having high productivity of a protein of interest, and/or for accelerating the identification process. Related methods of screening, methods of production, and expression systems are also included.

Abstract: Optical calculation method and system for relay protection based on Faraday magneto-optical rotation effect are provided. A cascaded optical computing approach is employed, where three optical current transformers are connected in series on a single optical path. Final output optical signal represented as a cascaded multiplication of outputs of three optical current transformers, carries a Faraday rotation angle containing differential current information. Differential current required for protection is computed by modulation ratio which is calculated by use of a carrier and modulation wave signals of the output optical signal detecting by filtering circuit from the third optical current transformer.

Abstract: Computer-implemented peer-engagement through graphical definition of contact-generating events in a virtualized metaverse includes visually presenting to a user, on an interactive interface, a multi-dimensional scene of a virtual environment. The user provides input to graphically define one or more contact-generating events within the multi-dimensional scene, which events are detected as being triggered by one or more virtualized avatars in the multi-dimensional scene. According to the triggering of the events and other predefined contact-engaging criteria, a contact-initiating prompt may be transmitted to a peer user associated with the one or more virtualized avatars in the multi-dimensional scene.

Abstract: Optical calculation method and system for relay protection based on Faraday magneto-optical rotation effect are provided. A cascaded optical computing approach is employed, where three optical current transformers are connected in series on a single optical path. Final output optical signal represented as a cascaded multiplication of outputs of three optical current transformers, carries a Faraday rotation angle containing differential current information. Differential current required for protection is computed by modulation ratio which is calculated by use of a carrier and modulation wave signals of the output optical signal detecting by filtering circuit from the third optical current transformer.

Abstract: A double-cam fastener driving machine, including an energy storage unit; an impact unit for actuating the energy storage unit to store energy which can bear the energy released from the energy storage unit to drive the fastener into the workpiece; a rotating actuating unit; a pushing mechanism; a double-cam mechanism, comprising a first cam and a second cam which can be optionally engaged and disengaged, the first cam is connected to the rotating actuating unit and driven by the rotating actuating unit to rotate, the second cam is connected and linked with the pushing mechanism. The double-cam mechanism to implement engagement and disengagement of rotating actuating unit and pushing mechanism, and the impact unit is not disengaged from the pushing mechanism, so that the double-cam mechanism can be engaged correctly during quick release, normal nailing or nail jamming of impact unit.

Abstract: In some aspects, a computing device may receive, at a data processing system, a set of utterances for training or inferencing with a named entity recognizer to assign a label to each token piece from the set of utterances. The computing device may determine a length of each utterance in the set and when the length of the utterance exceeds a pre-determined threshold of token pieces: dividing the utterance into a plurality of overlapping chunks of token pieces; assigning a label together with a confidence score for each token piece in a chunk; determining a final label and an associated confidence score for each chunk of token pieces by merging two confidence scores; determining a final annotated label for the utterance based at least on the merging the two confidence scores; and storing the final annotated label in a memory.

Abstract: Techniques for using logit values for classifying utterances and messages input to chatbot systems in natural language processing. A method can include a chatbot system receiving an utterance generated by a user interacting with the chatbot system. The chatbot system can input the utterance into a machine-learning model including a set of binary classifiers. Each binary classifier of the set of binary classifiers can be associated with a modified logit function. The method can also include the machine-learning model using the modified logit function to generate a set of distance-based logit values for the utterance. The method can also include the machine-learning model applying an enhanced activation function to the set of distance-based logit values to generate a predicted output. The method can also include the chatbot system classifying, based on the predicted output, the utterance as being associated with the particular class.

Abstract: The present invention relates to an outdoor unit for a refrigeration cabinet and a refrigeration cabinet. The outdoor unit comprises: a first unit comprising a compressor; a second unit comprising a condenser; wherein, the outdoor unit is configured to be installed in a first mode or a second mode; where in the first mode, the first unit and the second unit are installed adjacent to each other and connected with each other through fasteners, and the first unit and the second unit are connected through a first fluid pipeline passing through an adjacent surface between the first unit and the second unit; where in the second mode, the first unit and the second unit are respectively installed at a first position and a second position spaced apart, and the first unit and the second unit are connected through a second fluid pipeline extending between the first and second positions.