Abstract: Systems and methods are provided for implementing an ultra-scalable high-performance computing (“HPC”) network using dense wavelength-division multiplexing (“DWDM”). The HPC system includes an interconnection of GPU devices, multiplexer/demultiplexer (“mux/demux”) devices, amplifiers, wavelength selective switches (“WSSs”), and optical circuit switches (“OCSs”). Each OCS includes a plurality of micro-electromechanical systems (“MEMS”) mirrors and a plurality of input/output (“I/O”) ports each communicatively coupled to one WSS mux/demux device one WSS. Each WSS mux/demux device is either communicatively coupled to one of the I/O ports of an OCS or one of a plurality of GPU mux/demux devices via an amplifier. Each GPU mux/demux device is communicatively coupled to a number of GPU devices, each including another number GPUs and one or more optoelectronic devices.

Abstract: Systems and methods for representing two-dimensional representations as three-dimensional avatars are provided herein. In some examples, one or more input video streams are received. A first subject, within the one or more input video streams, is identified. Based on the one or more input video streams, a first view of the first subject is identified. Based on the one or more input video streams, a second view of the first subject is identified. The first subject is segmented into a plurality of planar object. The plurality of planar objects are transformed with respect to each other. The plurality of planar objects are based on the first and second views of the first subject. The plurality of planar objects are output in an output video stream. The plurality of planar objects provide perspective of the first subject to one or more viewers.

Abstract: A confidence estimation tool uses a calibrated confidence mapping model to estimate confidence for a model-generated candidate root cause. The tool uses a generative artificial intelligence (“AI”) model to determine, based on a description of a current event, a candidate root cause of the current event. The tool determines a description-based confidence score using the description of the current event and descriptions of a set of relevant historical events in a target domain. The tool also determines a cause-based confidence score using the candidate root cause of the current event and root causes of the set of relevant historical events. Finally, the tool determines a final confidence score using the description-based and cause-based confidence scores. Even if the generative AI model is configured for general-domain applications, by referencing relevant historical events, the tool can accurately estimate confidence for a model-generated candidate root cause within the target domain.

Abstract: Systems and methods for generating custom art fonts with consistent style include receiving user input that identifies a base font style for a custom font and includes descriptive text that defies one or more text effects to use for the custom font. Depth maps are selected for characters to be included in the custom font. The depth maps are preprocessed to add noise to the depth maps. A generative model generates custom font images conditioned with the text prompt and the depth maps. The custom font images are then used to render text on a display screen of a computing device.

Abstract: A data processing system for providing a service to extract information from a resource includes: a network interface for communicating over a computer network; a scraper tool to receive user instruction specifying a target resource and to extract content from the specified resource, wherein the user instruction further specifies a desired restructuring of the extracted content; and a prompt generator to structure the extracted content into a prompt for an Artificial Intelligence (AI) model, the prompt further directing the AI model to restructure the extracted content based on the user instruction. The prompt generator is to call the AI model with the generated prompt. The service is to receive restructured content from the AI model and provide the restructured content to a workstation submitting the user instruction, the restructured content presenting the content of the target resource in a form according to the user instruction.

Abstract: Systems and methods for processing a stream of input images are provided. An example method includes receiving a stream of input images and a pointing angle associated with the stream of input images, wherein each input image in the stream of input images comprises a plurality of pixels; interpolating an effective analytical projection, for each input image of the stream of input images, from a grid of predetermined analytical projections, based on the respective pointing angle and plurality of pixels of each of the input images of the stream of input images, wherein the grid of predetermined analytical projections comprises a plurality of spaces that each correspond to respective predetermined pointing angles; generating a modified stream of input images, by mapping pixels of the input stream of images to projected pixels of the modified stream of images, using the effective analytical projection; and displaying the modified stream of images.

Abstract: The technology relates to systems and methods for generating advanced feedback for a draft message. The operations may include receive text for a message being drafted in a messaging application; upon an analysis condition being satisfied, analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback score for the message; and based on the feedback score crossing a feedback threshold, trigger generation of advanced feedback for the message. The operations may also or alternatively include receive an initial sent message from a messaging application; analyze the message by applying at least one of a message-analysis model or heuristic to generate a feedback score for the message; based on the feedback score crossing a feedback threshold, transmit a feedback alert message for surfacing in the messaging application; and based on receiving an interaction, trigger generation of advanced feedback for the message.

Abstract: A computing system for monitoring language model compliance with a rubric of one or more output characteristics. The computing system includes processing circuitry configured to interface with a trained generative language model that receives input of a prompt including natural language text input and, in response, generates an output that includes natural language text output. The processing circuitry is further configured to monitor compliance of the generative language model with the rubric, by feeding the output of the generative language model to a rubric classifier configured to generate a predicted classification for an output characteristic in the rubric, and output the predicted classification.

Abstract: Systems and methods for determining predicted olfactory perception are provided. In particular, a method comprises receiving an input indicating an odorant, generating an odorant vector representing the odorant, generating an olfactory receptor vector, and determining one or more predicted olfactory percepts associated with the odorant based on the odorant vector and the olfactory receptor vector.

Abstract: The description relates to cameras, and camera calibration for enhancing user experiences. One example can receive a first image of a user at a first location relative to a camera. The first image can include the user's upper body but does not include the user from head to toe. The example can receive a second image of the user at a second location relative to a camera. The second image can include the user's upper body but does not include the user from head to toe. The example can estimate a distance of the second location from the first location relative to the camera and calibrate a height and tilt angle of the camera from the first image, the second image, and the estimated distance and without a full body image of the user.

Abstract: A technique for interacting with map-related information integrates the use of a machine-trained language model. Upon submission of a query, the technique uses the machine-trained language model to assess at least one intent associated with the query. The technique then invokes an intent-specific processing flow to provide an output result. Each processing flow invokes the use of at least one processing engine to perform an engine-specific task, such as geocoding, route finding, or image retrieval. A processing flow can also call on the machine-trained language model one or more additional times. In some cases, the technique includes a feedback mechanism for soliciting additional information from a user.

Abstract: Described herein are technologies related to tokenizing alphanumeric text through use of a tokenization algorithm that is at least partially implemented as a finite state machine. The tokenization algorithm is configured to output numeric identifiers that represent tokens or sub-tokens in the alphanumeric text.

Abstract: In a method to digitally simulate an evolving quantum state of a qubit register of a quantum computer, the quantum state is represented as a state vector of complex-valued amplitudes, where each amplitude is associated with an individual qubit of the qubit register. A directed acyclic graph defining a set of quantum gates of a quantum-computer program is then received. A linear order for the DAG is constructed by minimizing a partial cost function successively re-computed during construction of the linear order, the partial cost function approximating a cost of transforming the state vector according to a subset of the set of quantum gates applied in the linear order. The state vector is transformed according to the set of quantum gates applied in the linear order, and one or more of the complex-valued amplitudes of the transformed state vector are computationally output.

Abstract: A computer system that records a replayable execution trace based on recording cache coherency protocol (CCP) messages into a first trace, and on recording memory snapshot(s) into a second trace. Based on determining that tracing of execution of a first execution context is to be enabled, the computer system initiates logging, into the second trace, of one or more memory snapshots of a memory space of the first execution context, and enables a hardware tracing feature of a processor. Enabling the tracing feature causes the processor to log, into the first trace, CCP message(s) generated in response to one or more memory access into the memory space of the first execution context. After enabling the hardware tracing feature of the processor, the computer system also logs or otherwise handles a write into the memory space of the first execution context by a second execution context.

Abstract: A multimode optical waveguide network comprises a parent waveguide and a plurality of child waveguides. Each waveguide is a multimode optical waveguide having a first surface region, multiple second surface regions, and at least one guiding element attached to a surface of the waveguide or embedded within the waveguide, each second surface region of the parent waveguide optically coupled to the first surface region of a corresponding child waveguide. The guiding element(s) of the parent waveguide is arranged to guide a beam, from or to its first surface region, to or from any selected second surface region of its multiple second surface regions. The guiding element(s) of each of the waveguides is configurable for selecting the second surface region of that waveguide and/or responsive to at least one beam characteristic for selecting the second surface region of that waveguide via modulation of the at least one beam characteristic.

Abstract: A sensor device is described herein. The sensor device includes a multi-dimensional optical sensor and processing circuitry, wherein the multi-dimensional optical sensor generates images and the processing circuitry is configured to output data that is indicative of hemodynamics of a user based upon the images. The sensor device is non-invasive, and is able to be incorporated into wearable devices, thereby allowing for continuous output of the data that is indicative of the hemodynamics of the user.

Abstract: A method, computer program product, and computing system for receiving an input speech signal. A transcription of the input speech signal may be received. One or more sensitive content portions may be identified from the transcription of the input speech signal. The one or more sensitive content portions from the transcription of the input speech signal may be obscured, thus defining an obscured transcription of the input speech signal. An obscured speech signal may be generated based upon, at least in part, the input speech signal, the transcription of the input speech signal, and the obscured transcription of the input speech signal.

Abstract: A system and method and for retrieving assets from a personalized asset library includes receiving a search query for searching for assets in one or more asset libraries, the one or more asset libraries including a personalized asset library; encoding the search query into embedding representations via a trained query representation machine-learning (ML) model; comparing, via a matching unit, the query embedding representations to a plurality of asset representations, each of the plurality of asset representations being a representation of one of the plurality of candidate assets; identifying, based on the comparison, at least one of the plurality of the candidate assets as a search result for the search query; and providing the identified plurality of candidate assets for display as the search result. The plurality of asset representations for the one or more assets in the personalized content library are generated automatically without human labeling.

Abstract: Eye and hand tracking systems in head-mounted display (HMD) devices are arranged with lensless camera systems using optical masks as encoding elements that apply convolutions to optical images of body parts (e.g., eyes or hands) of HMD device users. The convolved body images are scrambled or coded representations that are captured by a sensor in the system, but are not human-recognizable. A machine learning system such as a neural network is configured to extract body features directly from the coded representation without performance of deconvolutions conventionally utilized to reconstruct the original body images in human-recognizable form. The extracted body features are utilized by the respective eye or hand tracking systems to output relevant tracking data for the user's eyes or hands which may be utilized by the HMD device to support various applications and user experiences. The lensless camera and machine learning system are jointly optimizable on an end-to-end basis.

Abstract: Systems and methods are provided for implementing confidence enhancement for responses by document-based large language models (“LLMs”) or other AI/ML systems. A first prompt is generated based on data items that are previously received or accessed. The first prompt is used by a first LLM or AI/ML system to extract requested information from the data items. One or more citations are generated and presented within a structured object together with a representation of the extracted information, in some cases, as output from a second LLM or AI/ML system. In some cases, the citations and/or the representation may be verified by a third LLM or AI/ML system, and reliability indicators may be generated for the citations and/or the representation based on determined accuracy of the citations and/or the representation. In this manner, the common issue of hallucinations may be mitigated.