Abstract: Disclosed are methods, systems, and non-transitory computer readable memory for processing, analyzing, and visualizing complex digital object sets using large language models. For instance, a method may include receiving a set of digital objects; indexing the received set of digital objects to generate indexed data; generating a timeline prompt based on the indexed data; processing the timeline prompt to generate a timeline response; and outputting the timeline response as an interactive timeline of based on the set of digital objects.

Abstract: Disclosed are methods, systems, and non-transitory computer readable memory for processing, analyzing, and visualizing complex digital object sets using large language models. For instance, a method may include receiving a set of digital objects; indexing the received set of digital objects to generate indexed data; generating a timeline prompt based on the indexed data; processing the timeline prompt to generate a timeline response; and outputting the timeline response as an interactive timeline of based on the set of digital objects.

Abstract: The present disclosure relates to implementations of a photonic circuit, and particularly to a photonic circuit that includes one or more matrix circuits. For example, the present disclosure relates to photonic circuit implementations of unitary matrices, and of arbitrary real and/or complex matrices factorized using unitary matrices, that utilize special generalized Mach-Zehnder interferometers (SGMZIs) as building blocks of various matrix circuit architectures.

Abstract: An apparatus for artificial intelligence (AI) bathymetry is disclosed. The apparatus includes a sonic unit attached to a boat, the sonic unit configured to generate a plurality of metric data as a function of a plurality of ultrasonic pulses and a plurality of return pulses. An image processing module is configured to generate a bathymetric image as a function of the plurality of metric data, identify, as a function of the bathymetric image, an underwater landmark, and register the bathymetric image to a map location as a function of the underwater landmark. A communication module is configured to transmit the registered bathymetric image to at least a computing device. An autonomous navigation module is configured to determine a heading for the boat as a function of a path datum and command boat control to navigate the boat as a function of the heading.

Abstract: A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

Abstract: The present disclosure relates to implementations of a photonic circuit, and particularly to a photonic circuit that includes one or more matrix circuits. For example, the present disclosure relates to photonic circuit implementations of unitary matrices, and of arbitrary real and/or complex matrices factorized using unitary matrices, that utilize special generalized Mach-Zehnder interferometers (SGMZIs) as building blocks of various matrix circuit architectures.

Abstract: Systems and methods for executing queries on tensor datasets are disclosed. A system can identify a query for a multi-dimensional sample dataset. Each sample of the multi-dimensional sample dataset can include one or more tensors. Each tensor of the one or more tensors can be associated with a respective identifier that is common to each sample of the multi-dimensional sample dataset. The query specifying a first identifier of a first tensor of the multi-dimensional sample dataset and a first range of a first dimension of the first tensor, or one or more operations such as sampling, grouping, ungrouping, or transformation operations, to perform on the first tensor of the multi-dimensional sample dataset. The system can parse the query, and execute the query to generate query results. The system can provide the query results as output.

Abstract: A system may access an input image. The system may generate a plurality of segments based on one or more segmentation models and the input image, each segment from among the plurality of segments representing a corresponding salient object. The system may generate a depth map based on a depth estimation model. The system may layer the plurality of segments, based on the depth map and border regions between pairs of segments, to generate a plurality of ordered segments. The system may execute a vision-language model to generate a text annotation of the image based on the plurality of ordered segments.

Abstract: A method of generating summaries of content items using one or more large language models (LLMs) is disclosed. A first content item is identified. The first content item includes a set of sub-content items. A level of abstraction is determined for the content item. A prompt is automatically engineered for providing to the one or more LLMs. The prompt includes a reference to the first content item and the level of the abstraction for the first content item. A response to the prompt is received from the LLM. The response includes a second content item. The second content item includes a representation of the first content item that is generated by the LLM. The representation omits or simplifies one or more of the set of sub-content items based on the level of abstraction. The representation is used to control an output that is communicated to a target device.

Abstract: Systems and methods to analyze and identify adaptations to document based on an audience of the document to improve a document quality are disclosed. Exemplary implementations may: analyze individual documents to determine its active lexicon from lexicons stored in electronic storage that the individual documents are comprised of; store the documents in correlation with the determined active lexicons in the electronic storage; generate, based on the lexicons and the correlations, audience scores for individual ones of the documents; store the audience scores in the electronic storage; receive a request to obtain a document; and effectuate presentation of the document and the audiences scores corresponding to the document.

Abstract: A method for verifying a user agent includes transmitting, by a core node, to a user agent, a predefined set of user requests. For each user request in the predefined set of user requests, the core node receives an embedding generated based on the user request, thereby receiving a plurality of embeddings based on the predefined set of user requests. The core node determines whether the plurality of generated embeddings satisfy a similarity criterion. The core node transmits, to the user agent, a task to generate a query plan and receives a query plan generated by the user agent for processing a user request identified in the task. The core node determines whether the query plan satisfies a query plan criterion, thereby producing query plan adequacy output. The core node determines whether to approve the user agent based on the similarity output and the query plan adequacy output.

Abstract: A method for identifying and clustering worker agents for processing requests includes receiving, by a core node, from a user agent, a user request. The core node updates, for each of the plurality of worker agents, an availability status, thereby producing a plurality of availability statuses. The core node computes, for each of the plurality of worker agents, a value of a drift metric. The core node clusters the plurality of worker agents to produce a plurality of clusters of worker agents, wherein each of the plurality of clusters contains worker agents that have similar semantic capabilities. Based at least on the user request, the plurality of availability statuses, and the plurality of clusters, the core node identifies a subset of the plurality of worker agents that are both available to process the user request and that are suitable for processing the user request.

Abstract: The present disclosure provides for novel systems and methods of audio transmission that alleviate shortcomings in the art, and provide novel mechanisms for robust and scalable audio transmission. In some embodiments, a method of audio transmission may transmit auxiliary data via at least one of a forward link and a reverse link.

Abstract: A method for determining scene risk for an autonomous vehicle includes receiving scene data, predicting risk scores using a trained risk prediction model, determining an appropriate autonomous vehicle behavior based on these risk scores, and controlling the vehicle accordingly. In some implementations, the method includes training the risk prediction model by generating initial risk scores from a labeled training dataset, learning an intermediate model from these scores, and training the risk prediction model using multimodal training data and secondary risk scores.

Abstract: Systems and methods for establishing a patient's current or future clinical or lab values are provided. A neural network is trained on a dataset of medical images, such as ultrasound images, that are tagged with information concerning the lab values of people who were imaged to produce the medical images. The trained neural network can then be provided with medical images of a patient, and the neural network can then make a determination as to the patient's current or future clinical or lab values.

Abstract: Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and/or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and/or gestures of the user to provide input or control of the system.

Abstract: The present specification provides a method for training a machine learning model into a task-specific model. The method comprises steps of prompting a Large Language Model (LLM) to generate definitions and manifestations and a test data set for information elements related to a task. The method comprises inputting the list of information elements, the definitions and the manifestations in the machine learning model as structured learning parameters and label by the machine learning model an initial data set to generate a labelled data set. The method performs an adversarial augmentation loop on the labelled data set, the adversarial augmentation loop: identifying improper embeddings in the labelled data set, re-labelling the improper labels in the labelled data set and generating a task-specific data set. The method evaluates performance of the machine learning model trained with the task-specific data set by instructing the machine learning model to classify the test data set.

Abstract: Systems and methods for implementing a threat model that classifies contextual events as threats.

Abstract: A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an illumination assembly. The illumination assembly is configured to illuminate a region that: (i) extends between a rear edge of the frontal shell and an extent of the frontal edge of the rear shell, (ii) is positioned adjacent to the extent of the rear edge of the frontal shell, and (iii) is positioned adjacent to the extent of the frontal edge of the rear shell.

Abstract: A method for the automated annotation of radiology findings includes: receiving a set of inputs, determining a set of outputs based on the set of inputs, assigning labels to the set of inputs, and annotating the set of inputs based on the labels. Additionally, the method can include any or all of: presenting annotated inputs to a user, comparing multiple sets of inputs, transmitting a set of outputs to a radiologist report, or any other suitable processes.