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: A computer-implemented method includes obtaining a training data set for multiple monitors for various services, which includes service properties and monitor metadata. The metadata for a given monitor defines resources utilized by a corresponding service. The method determines N feature vectors and a target resource class for each service based on the training data set. A machine learning model is trained in multiple training iterations using the training data set. In a given training iteration, N feature vectors of a selected service are provided to the machine learning model, which predicts a resource class of the selected service. A difference between the predicted resource class and the target resource class for the selected service is determined, based on which one or more parameters of the machine learning model can be updated. The trained machine learning model can be used to recommend a new monitor for a new service.

Abstract: A computer-implemented technique is described herein for defining and applying constraints that regulate a supervisee's interaction with applications. In one implementation, the technique provides a user interface presentation to a supervisor that lists a set of applications that run on plural application execution platforms. The user interface presentation also allows the supervisor to set platform-agnostic constraint information for any identified application. The platform-agnostic constraint information, once set for an application, constrains interaction by a supervisee with all versions of that same application. That is, the constraint information is said to be agnostic with respect to platform in the sense that it applies to a variety of application execution platforms that run the application. In one example, the platform-agnostic constraint information specifies a permitted amount of an activity that the supervisee is permitted to perform across all versions of an application.

Abstract: A server computing device is provided, including a processor configured to receive a homomorphically encrypted input embedding vector from a client computing device. At a transformer network, the processor may generate a plurality of homomorphically encrypted intermediate vectors at least in part by performing inferencing on the homomorphically encrypted input embedding vector. The processor may transmit the plurality of homomorphically encrypted intermediate output vectors to the client computing device. The processor may receive a plurality of homomorphically encrypted intermediate input vectors from the client computing device subsequently to transmitting the homomorphically encrypted intermediate output vectors to the client computing device. At the transformer network, the processor may generate a homomorphically encrypted output vector at least in part by performing additional inferencing on the homomorphically encrypted intermediate input vectors.

Abstract: Large language models (LLMs) and visual-language models (VLMs) are able to provide robust results based on specified formatting and organization. Although LLMs and VLMs are designed to receive natural language input, users often lack the skill, knowledge, or patience to utilize LLMs and VLMs to their full potential. By leveraging screen understanding, AI prompts (or “pills”) may automatically be generated for artificial-intelligence (AI) assistance and query resolution in a VLM/LLM environment. Using an image encoder, a current screenshot is processed into an image embedding and compared to text embeddings representing screenshot activities. By identifying the text embedding having the closest similarity to the image embedding, a screen activity being performed by the user may be determined. Suggested AI prompts (or “pills”) may then be generated in real-time to assist the user in performing the screen activity.

Abstract: Implementations of the subject matter described herein provide a solution for rate control based on reinforcement learning. In this solution, an encoding state of a video encoder is determined, the encoding state being associated with encoding of a first video unit by the video encoder. An encoding parameter associated with rate control in the video encoder is determining by a reinforcement learning model and based on the encoding state of the video encoder. A second video unit different from the first video unit is encoded based on the encoding parameter. In this way, it is possible to achieve a better quality of experience (QOE) for real time communication with computation overhead being reduced.

Abstract: A computing device assembly is provided, including a rack, and a plurality of compute units that are horizontally oriented and mounted within the rack in one of two vertical stacks. The computing device assembly further includes a plurality of switches that are vertically oriented and mounted along a front side of the rack laterally between the two vertical stacks of compute units. The computing device assembly further includes a plurality of horizontal cable backplanes mounted in a vertical stack along a rear side of the rack. The computing device assembly further includes a plurality of vertical cable shuffles mounted between the two vertical stacks of compute units and between the vertically oriented switches and the vertical stack of horizontal cable backplanes.

Abstract: A fine-grain selectable partially privileged container virtual computing environment provides a vehicle by which processes that are directed to modifying specific aspects of a host computing environment can be delivered to, and executed upon, the host computing environment while simultaneously maintaining the advantageous and desirable protections and isolations between the remaining aspects of the host computing environment and the partially privileged container computing environment. Such partial privilege is provided based upon directly or indirectly delineated actions that are allowed to be undertaken on the host computing environment by processes executing within the partially privileged container virtual computing environment and actions which are not allowed.

Abstract: A signal conditioning connector assembly is provided, including an enclosure, and a plurality of signal conditioner layers mounted within the enclosure. Each signal conditioner layer includes a substrate, signal conditioner circuitry mounted to the substrate, first electrodes forming a first connector on a first side of the signal conditioner circuitry, second electrodes forming a second connector on a second side of the signal conditioner circuitry, a heat spreader in thermal communication with a side of the signal conditioner circuitry opposite the substrate, and a liquid cooling pipe positioned adjacent and in thermal communication with the heat spreader. The liquid cooling pipe is configured to draw heat away from the heat spreader for thermal management. The signal conditioning connector assembly can be positioned adjacent an interface between the vertical cable shuffle and the horizontal cable backplane within the rack of the computing device assembly of the first and second aspects.

Abstract: A method of and system for method for ensuring data compliance in a computer environment includes retrieving data rules from a rule repository, the rule repository being a repository that stores one or more rules that are associated with storage or transfer of data by one or more devices in the computing environment, retrieving metadata about data flow in the computing environment from a policy governor, retrieving information about a data classification of data used by one or more services provided by the computing environment, and retrieving data about a network topography of the computing environment. The retrieved data is then used to generate a configuration file for configuring a Field Programmable Gate Array (FPGA) based on at least one of the retrieved data.

Abstract: Technologies for skill taxonomy management are described. Embodiments include extracting an input text from an online system and applying an unsupervised generative text machine learning model to the input text. The text generator generates a set of sentences based on a job title included in the input text. One or more skills are extracted from the set of sentences. The extracted one or more skills correspond to one or more skills in a skill taxonomy. A frequency distribution is generated over the extracted one or more skills. The one or more skills are ranked based on the frequency distribution. Based on the ranking, a subset of the extracted one or more skills is generated. The subset of the extracted one or more skills is provided to a downstream operation, process, or service of the online system.

Abstract: In a system including a processor and memory, the memory includes instructions that, when executed by the processor, cause the processor to control the system to perform receiving a video stream capturing objects; identifying, based on the received video stream, object areas corresponding to the objects, respectively; tracking the object areas in the received video stream; generating, based on the tracking of the object areas, visual data sets at a plurality of times, wherein each visual data set is generated at a different time and includes visual data representing each object area; determining a priority of each visual data in each visual data set; selecting, based on the determined priority of each visual data, a group of the visual data to be transmitted to a remote system; and transmitting, to the remote system, the selected group of the visual data.

Abstract: A computing system including a quantum computing device that includes Majorana islands at which Majorana zero modes (MZMs) are instantiated. The computing system further includes a controller configured to control the quantum computing device to perform a joint parity measurement at two or more MZMs. The controller is further configured to control the quantum computing device to perform quasiparticle poisoning (QPP) detection at the one or more Majorana islands to thereby generate error data. The error data includes one or more QPP indications associated with the one or more Majorana islands. The controller is further configured to receive the error data from the quantum computing device. The controller is further configured to update an accumulated error state of the one or more Majorana islands based at least in part on the error data, and to perform an update operation based at least in part on the accumulated error state.

Abstract: A vapor chamber includes a main body, a first vertical structure, and an enhanced boiling surface. The main body has a first surface and defines a first portion of an interior volume. The first vertical structure protrudes transverse to the main body and defines a second portion of the interior volume. The enhanced boiling surface is on at least a portion of the first vertical structure.

Abstract: Provided are methods, systems, and computer storage media for determining a command (e.g., intent) of an image based on image data features. A task associated with the determined command is generated based on a portion of the image data features. Task entities corresponding to the task are determined. The task and the corresponding task entities are generated and configured for use in a computer productivity application. Accordingly, present embodiments provide for generating command-specific tasks and task entities that may be integratable for use in a computer productivity application.

Abstract: Systems are provided for generating, modifying and using SBOMs for facilitating risk assessment and threat mitigation for corresponding programs, and particularly for large programming builds. The creation and modification of the SBOMs includes processes for omitting declarations referenced in chunk SBOMs of program chunks incorporated into a final programming build associated with a build SBOM, but which are not actually utilized by the final programming build, as well as processes for adding new declarations for code segments that are not declared in the related chunk SBOMs, even though the code segments are utilized by the final programming build. Systems are also configured to use SBOMs in combination with configuration restriction records to assess and resolve threat events in a manner that can prevent unnecessary remedial actions for threat events that appear to be relevant to one or more files or dependencies incorporated into a program.

Abstract: Logging cache line lifetime hints when recording an execution trace. A microprocessor detects occurrence of a first cache event that initiates a lifetime of a cache line within a memory cache, and initiates logging first trace information indicating a beginning of the lifetime of the cache line within the memory cache. Subsequently, the microprocessor detects occurrence of a second cache event that ends the lifetime of the cache line within the memory cache. Based on detecting the second cache event, the microprocessor initiates logging second trace information indicating an ending of the lifetime of the cache line within the memory cache.

Abstract: A quantum computing device is provided, including a logical qubit encoding surface including a plurality of plaquettes. Each plaquette of the plurality of plaquettes may include a plurality of measurement-based qubits. The plurality of measurement-based qubits may include four data qubits and a first ancilla qubit. The first ancilla qubit may be electrically connected to the four data qubits and a second ancilla qubit included in the logical qubit encoding surface.

Abstract: A computing system including a processor configured to receive packet preamble binary data and packet header binary data associated with a satellite. The processor may generate a simulated signal that encodes the packet preamble binary data and the packet header binary data. The processor may receive a satellite downlink signal. Within each of a plurality of sample intervals of the satellite downlink signal, the processor may compute a respective correlation between the satellite downlink signal and at least a portion of the simulated signal. The processor may select an identified sample interval of the plurality of sample intervals based at least in part on the plurality of correlations. The processor may decode binary satellite signal data based at least in part on the identified sample of the satellite downlink signal. The processor may output the binary satellite signal data.

Abstract: Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.