Abstract: A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches.

Abstract: Systems and methods disclosed herein include a method implemented by a computing system, the method comprising: receiving tracking data associated with a tracked event and executing an automated video processing procedure including: (i) initiating one or more live video feeds of the tracked events; (ii) receiving a first live video feed of a first portion of the tracked event; (iii) selecting one or more segments of the received first live video feed for further processing; (iv) receiving a second live video feed of a second portion of the tracked event; (v) selecting one or more segments of the received second live video feed for further processing; (vi) modifying the selected one or more segments to improve performance of the automated video processing procedure; and (vii) compiling the modified one or more segments into a first video of the tracked event.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for receiving training data comprising data records with identified presence of modalities, training a multi-modal generative model based on the training data, and imputing missing modalities of input data records using the multi-modal generative model, wherein the multi-modal generative model comprises (i) a modality-agonistic latent variable encoder and (ii) one or more modality-specific latent variable encoders configured to receive output of the modality-agonistic latent variable encoder as input.

Abstract: A method for training a shape optimization neural network to produce an optimized point cloud defining desired shapes of materials with given properties is provided. The method comprises collecting a subject point cloud including points identified by their initial coordinates and material properties and jointly training a first neural network to iteratively modify a shape boundary by changing coordinates of a set of points in the subject point cloud to maximize an objective function and a second neural network to solve for physical fields by satisfying partial differential equations imposed by physics of the different materials of the subject point cloud having a shape produced by the changed coordinates output by the first neural network. The method also comprises outputting optimized coordinates of the set of points in the subject point cloud, produced by the trained first neural network.

Abstract: Systems and techniques that facilitate data diversity visualization and/or quantification for machine learning models are provided. In various embodiments, a processor can access a first dataset and a second dataset, where a machine learning (ML) model is trained on the first dataset. In various instances, the processor can obtain a first set of latent activations generated by the ML model based on the first dataset, and a second set of latent activations generated by the ML model based on the second dataset. In various aspects, the processor can generate a first set of compressed data points based on the first set of latent activations, and a second set of compressed data points based on the second set of latent activations, via dimensionality reduction. In various instances, a diversity component can compute a diversity score based on the first set of compressed data points and second set of compressed data points.

Abstract: A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process.

Abstract: This Continuation-in-Part extends the wave-interference-based collapse model first proposed in the Modified Schrödinger Equation (MSE) framework to five foundational quantum phenomena: tunneling, entanglement, measurement collapse, time asymmetry, and the resolution of Many-Worlds interpretations. The invention models collapse as a physical consequence of interference between the observer wave and the quantum system wavefunction, characterized by a curvature-based localization mechanism. This framework enables tunable collapse control, non-binary measurement outcomes, and outcome selection through engineered interference, providing a unified physical mechanism with broad technological applications.

Abstract: A method for a quantum computer is presented. The method comprises receiving a target matrix comprising only real eigenvalues and block encoding the target matrix. A polynomial approximation is precomputed for a function to be applied to the target matrix. Coefficients are selected for a generating function that match the precomputed polynomial approximation. A polynomial history state is generated, the polynomial history state comprising a superposition of polynomials onto the block encoded target matrix by at least mapping the generating function to a quantum algorithm.

Abstract: Methods and systems for predicting the values of implicit device parameters for quantum devices are described. An example computer-implemented method for predicting values of implicit device parameters for a quantum device having nanowires is described. The computer-implemented method includes training a machine learning model to create a mapping between observable device aspects of quantum devices and at least one implicit device parameter associated with the quantum devices. The computer-implemented method further includes obtaining measurements data relating to the observable device aspects from a quantum device under observation. The computer-implemented method further includes using the trained machine learning model, processing the measurements data obtained from the observed quantum device to infer values for the at least one implicit device parameter associated with the observed quantum device.

Abstract: A data stream of performance metrics characterizing a technology landscape may be received. From a plurality of performance prediction models and based on the performance metrics, a subset of performance prediction models may be selected. The subset of performance prediction models may be combined into a composite prediction model. The composite prediction model may be loaded into a model processor for scoring against the data stream of performance metrics to obtain a performance prediction for the technology landscape based thereon.

Abstract: A data analysis computer system is provided that receives a timeseries dataset and generates implied data from the dataset. The dataset is further vectorized to reduce the dimensionality of the data. Users provide input to identify windows of data that either positively or negatively correlate to instances of a given type of occurrence within the data. The user defined windows are converted to fixed sized windows and a machine learning algorithm constructs a model from the data. The model is used to predict instances of the given type of occurrence in newly received data. Validation of the predications may be performed.

Abstract: Methods and systems of using reinforcement learning to optimizing promotions. A promotion can be offered to a user using a reinforcement learning model with a sensitivity parameter, the reinforcement module estimating a time period during which the user will respond to the first information. The user's reaction to the promotion can be observed. The reinforcement learning model can be adapted based on the user's reaction.

Abstract: Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.

Abstract: Techniques for generating opinion-based content responsive to a user input are described. The system may receive a user input, and determine dialog context data corresponding to a dialog between a user and the system, and including the user input. The system may determine generation of content responsive to the user input requires opinion-based knowledge, and may extract entities from the dialog context data, and determine natural language data of a knowledge base that includes entities similar to the extracted entities. The system may processes the natural language data and the dialog context data to determine a subset of the natural language data that is responsive to the user input. The system may generate output data responsive to the user input using the responsive natural language data and the dialog context.

Abstract: Systems and processes for application integration with a digital assistant are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, receiving a natural-language user input; identifying, with the one or more processors, an intent object of a set of intent objects and a parameter associated with the intent, where the intent object and the parameter are derived from the natural-language user input. The method further includes identifying a software application associated with the intent object of the set of intent objects; and providing the intent object and the parameter to the software application.

Abstract: Provided are methods and systems to verify user identity for voice enabled devices. A voice input can instruct a voice enabled device to perform a plurality of functions/services that require user verification. Primary user verification can be performed by associating voice characteristics of the voice input to a profile associated with a user/user device. A signal (e.g., a BLE beacon) can be sent to the user device that causes the user device to perform secondary user verification. The secondary user verification can be based on a biometric input, passcode verification, authenticated message reply, for example. Based on the secondary user verification, an operational command associated with the voice input can be executed.

Abstract: An apparatus comprising means for: obtaining at least one audio signal; obtaining at least one parameter respectively for each of at least two frequency bands associated with the at least one audio signal; and selecting a frequency band of the at least two frequency bands based on comparing at least one further respective parameter for each of the at least two frequency bands wherein the at least one further respective parameter is determined from each of the at least two frequency bands; generating an output comprising a selection of the at least one parameter associated with the selected frequency band of the at least two frequency bands, such that the selection of the at least one parameter associated with the selected frequency band is configured to reduce a bitrate or size of the output.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, and/or devices that are configured to separate multi-source audio signal samples into discrete source separated channel audio samples using trained multi-modal audio source channelization models. Multi-source audio signal samples are difficult to separate because they can include multiple target audio sources (e.g., individual speakers) that are often inter-mixed and overlaid with other audio sources such as noise, music, reverberations, and other audio artifacts. The multi-modal audio source channelization models discussed herein are trained to generate source separated channel audio samples based on audio signal samples and on video signal samples.

Abstract: In one example, a method performed by a processing system including at least one processor includes establishing a communication group including at least three users of an extended reality environment as members, tracking locations and directional positions of the members of the communication group within the extended reality environment and within physical environments of the members, determining that a second user of the at least three users is an intended recipient of a first utterance made by a first user of the at least three users, and presenting the first utterance to the second user, where a directionality associated with a presentation of the first utterance is based on a location and a directional position of the first user relative to the second user.

Abstract: Disclosed are storage devices and methods relating to reading staggered bits recording patterns in which the data bits on adjacent tracks recorded on magnetic media are staggered in such a way that a single reader detects a combined signal from data bits on multiple tracks at selected signal sampling locations. A signal shaping algorithm is used to deconvolve the combine signals, thus isolating each of the tracks from one another so that single track bit sequences can be decoded. In this way, staggered bits patterns can use intertrack interference effects for advantage, allowing readers to read multiple bit tracks simultaneously and without ambiguity.