The present disclosure describes systems and techniques directed to producing an all-in-focus image with a camera of a mobile device, in particular, cameras with shallow depth-of-field. User equipment includes a sensor for determining distance to an object in a camera's field-of-view. Based on a depth map of the field-of-view, a plurality of segments is inferred, each segment defining a unique focus area within the camera's field-of-view. An autofocus lens of the camera sweeps to a respective focal distance associated with each of the plurality of segments. The camera captures sample images at each focal distance swept by the autofocus lens. The user equipment produces an all-in-focus image by combining or merging portions of the captured sample images.
H04N 23/67 - Focus control based on electronic image sensor signals
H04N 23/80 - Camera processing pipelinesComponents thereof
H04N 23/959 - Computational photography systems, e.g. light-field imaging systems for extended depth of field imaging by adjusting depth of field during image capture, e.g. maximising or setting range based on scene characteristics
2.
Recursively-Cascading Diffusion Model for Image Interpolation
Despite recent progress, existing frame interpolation methods still struggle with extremely high resolution images and challenging cases such as repetitive textures, thin objects, and fast motion. To address these issues, provided is a cascaded diffusion frame interpolation approach that excels in these scenarios while achieving competitive performance on standard benchmarks.
G06T 3/4007 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
G06T 3/4076 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution using the original low-resolution images to iteratively correct the high-resolution images
G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
Systems, methods, and apparatus for self-evolving decoding at inference. In an aspect, operations include processing, by a Large Language Model (LLM) of N layers, an input by an inference operation of the LLM; obtaining, from the LLM, logits of an evolution layer of the LLM, the evolution layer being subsequent to a first layer of the LLM; for a plurality of layers that occur before the evolution layer, processing the logits of the layer with the logits of the evolution layer to generate an approximated gradient; based on the approximated gradient and the logits of the evolution layer, generating adjusted logits for the evolution layer; and processing the adjusted logits for the evolution layer to generate an output for the LLM.
A software defined vehicle (SDV) operating system may include components for executing software packages that declare unit types (e.g., interfaces) and define service units that each implement a unit type. For each unit type, there may be several service units that each provide a different implementation of that unit type. The SDV operating system may manage a service discovery module that registers service units for each unit type in a centralized registry. While executing a software package that declares a unit type, the service discovery module may fetch, from the centralized registry, an implementation of the unit type by a service unit defined by a different software package. While still executing the software package (i.e., at runtime), the SDV operating system may load a service unit defined by the software package with the fetched implementation. The SDV operating system may then execute the service unit based on the fetched implementation.
A method includes receiving an audio input that represents an utterance of a voice command that is preceded by a predefined hotword. The first computing device is configured to process voice commands that are preceded by the predefined hotword and is in proximity of a second computing device that is also configured to process voice commands that are preceded by the same, predefined hotword. The method also includes receiving a local area wireless signal from the second computing device. Based on receiving the local area wireless signal from the second computing device, the method also includes placing the first computing device into a sleep mode, bypassing further processing of the voice command, and bypassing outputting a visual indication that the first computing device is processing the voice command.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output temporal sequence of data elements conditioned on an input. In one aspect, a method comprises: obtaining the input, wherein the input comprises a noise input comprising a respective latent representation for each of a plurality of segments of the temporal sequence; updating, for each segment, the latent representation for the segment using a latent denoising neural network, the updating comprising, for each segment other than the first segment: obtaining a memory vector representing one or more hidden states generated by the latent denoising neural network when updating the latent representations for one or more preceding segments; updating the latent representation for the segment at each of a plurality of iterations; and generating the output temporal sequence of data elements by processing the latent representations for the plurality of segments.
Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.
Systems, methods, and apparatus for generating binaural audio waveform from mono waveform data. In an aspect, operations include generating, based on a mono waveform data and positional data, left signal data and right signal data, wherein the left signal data and the right signal data are initial estimates of perceived signals of the mono waveform based on the positional data; processing the left signal data and right signal data, based on the positional data, to generate amplitude scaled left signal data and amplitude scaled right signal data; and separately processing the amplitude scaled left signal data and the amplitude scaled right signal data by a denoising vocoder to generate left output signal data and right output signal data that together define a binaural audio waveform based on the mono waveform data.
G10L 19/008 - Multichannel audio signal coding or decoding using interchannel correlation to reduce redundancy, e.g. joint-stereo, intensity-coding or matrixing
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
A method for detecting malware by modifying executable code includes identifying executable code that includes branch instructions. The method includes determining whether any of the branch instructions of the executable code mask maliciousness of the executable code. The determining includes modifying first one or more of the branch instructions of the executable code, causing execution of the executable code with the modified first one or more branch instructions in a first testing environment, and evaluating a result of the execution of the executable code with the modified first one or more branch instructions. The result can indicate whether the executable code is malicious. The method includes, responsive to determining that the branch instructions of the executable code mask the maliciousness of the executable code, performing one or more preventative actions with respect to the executable code.
A method for participation, in a virtual meeting, of an absent invited virtual meeting user includes receiving input of a first user that has been invited to participate in the virtual meeting. The input of the first user indicates an inability to attend the virtual meeting and provides first data to be discussed during the virtual meeting. The method includes causing a virtual meeting UI to be presented during the virtual meeting between multiple participants. The UI includes a UI element associated with the first data provided by the first user that is not present during the virtual meeting. The method includes generating a summary of the virtual meeting. The summary covers presentation of at least a portion of the first data during the virtual meeting. The method includes causing the summary to be accessible by a client device of the first user.
A user equipment (UE) selects (1101) a cell of a non-terrestrial network (NTN) that supports Internet-of-Things (loT) devices, transmits (1104), to a core network (CN) via the cell, a registration request message indicating that the UE requires an emergency messaging service (EMS), and establishes (1112), with the cell, a protocol data unit (PDU) session for the EMS.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling the participation of generative models in a multi-agent system. One of the methods includes receiving an input for a task to be processed by a group of generative models to generate a final output for the task; and processing the input by the group of the generative models across a plurality of steps, including: for each intermediate step, obtaining context data at the intermediate step; generating based on the context data, control data for a target generative model in the group; providing the context data and the control data to the target generative model in the group; obtaining an output from the target generative model generated in response to the context data and the control data; and updating the context data for the task based on the output from the target generative model.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for upscaling segmentation masks for image processing. One of the methods includes generating, using a trained machine learning model, a semantic mask of a first image, wherein the semantic mask includes an image classification and a confidence value for pixels of the first image across N image classes; generating, using the image classification and confidence values for the pixels of the first image, a semantic mask subset that identifies a subset of classes for each of the pixels of the first image; generating an upscaled version of the semantic mask subset; generating a second semantic mask subset based on the upscaled version of the semantic mask subset; and processing an upscaled version of the first image using the second semantic mask subset to obtain a processed output image.
A device or receive-circuit has a first receive-chain (Rx-chain) configured to receive and process Non-Terrestrial-Network-cellular (NTN-cellular) signals received from one or more NTN-cellular access nodes, and a second Rx-chain configured to receive and process Global Navigation Satellite System (GNSS) signals wirelessly transmitted from one or more GNSS satellites, with the first Rx-chain and the second Rx-chain being at least partially integrated with each other, including sharing at least an antenna structure, a low-noise amplifier (LNA), and an Rx signal path through the antenna structure and the LNA. Further, the device or receive circuit may include a Radio Frequency Front End (RFFE) of which the LNA is a component, and the RFFE may switch between or split apart the first and second Rx-chains for downstream processing, or the Rx-chains may be split apart after a downstream analog-to- digital converter (ADC) to help avoid signal degradation from the splitting.
H04B 1/00 - Details of transmission systems, not covered by a single one of groups Details of transmission systems not characterised by the medium used for transmission
G01S 19/36 - Constructional details or hardware or software details of the signal processing chain relating to the receiver frond end
H04B 1/403 - Circuits using the same oscillator for generating both the transmitter frequency and the receiver local oscillator frequency
Techniques and apparatuses are described for performing howling prevention. In example aspects, a hearable (102) includes an acoustic circuit (116). The hearable (102) employs howling prevention (124) to monitor for one or more conditions that can lead to the unintentional generation of howling (122) via the acoustic circuit (116). Upon detecting a condition, the hearable (102) appropriately configures the acoustic circuit (116) to prevent howling (122) from occurring. Using various sensing techniques, the hearable (102) can quickly detect the condition and proactively adjust a gain of the acoustic circuit (116) to maintain stability of the acoustic circuit (116) and avoid howling (122). With howling prevention (124), an overall user experience with hearables (102) is improved while supporting features such as active noise cancellation and/or a transparency mode. Furthermore, some hearables (102) can be configured to perform howling prevention (124) without the need for additional hardware.
Methods, systems, and apparatus, including computer-readable storage media for content group generation for a content delivery campaign. Content groups are generated from a resource identifier and a description. Digital content items are created for each content group, including digital content from the resource identifier and the description, as well as new digital content items. Candidate content groups are ranked according to request coverage gain and optionally one or more other ranking criteria. Request coverage gain is a measure of how much more request coverage is gained through keywords of one content group relative to the request coverage of one or more other content groups. By ranking according to request coverage gain, the selected candidate content groups are differentiated relative to one another, capturing potential content requests that would otherwise be missed by a campaign of content groups not selected based on request coverage gain.
An example cloud-based voice interconnect system includes data processing hardware of a cloud-based computing platform, a network, and a public telecom carrier system. The data processing hardware is in communication with memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including providing a private virtualized computing environment, and implementing a private cloud-based session border controller (SBC) in the virtualized computing environment. The public telecom carrier system is connected to the private cloud-based SBC via the network, and is configured to provide telecom services between the private cloud-based SBC and customers of the public telecom carrier system.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for multi-vector retrieval via fixed dimensional encodings. In one aspect, a method includes: obtaining a set of embedding vectors of a query in an embedding vector space; obtaining an encoded dataset including, for each data item in a set of data items, a respective encoded vector of the data item in a target vector space; encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space; performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of the data items in the encoded dataset; and identifying, from the k-nearest neighbors search, a top-k subset of the set of data items.
This document describes systems and techniques directed at semiconductor fault detection. In aspects, a semiconductor device includes a physical structure that facilitates detection and localization of defects. The physical structure includes at least one conductive interconnect that extends through two or more layers of a semiconductor device, enabling an electrical detection of faults. Such systems and techniques can help improve yield, accelerate failure analysis debugging, and improve reliability of semiconductor devices.
G01R 31/26 - Testing of individual semiconductor devices
H01L 23/485 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements consisting of lead-in layers inseparably applied to the semiconductor body consisting of layered constructions comprising conductive layers and insulating layers, e.g. planar contacts
H01L 23/528 - Layout of the interconnection structure
20.
HYBRID ANSWERS ON A HEAD-WEARABLE DISPLAY USING AN EDGE LARGE LANGUAGE MODEL AND EXTENDED LARGE LANGUAGE MODEL
To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD), the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.
To monitor and report road quality, a server device is configured to receive, from a plurality of vehicles, respective reports, each of the reports indicating a geographic road location of a vehicle and a road quality indication for the geographic location; update, using the reports, a table correlating geographic road locations and road quality indications; determine average road quality indicia for a geographic road location, based on the road quality indications in the table; and in response to a query from a communication device, provide the communication device with an information update based on at least the average road quality indicia.
G07C 5/08 - Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle, or waiting time
B60W 50/04 - Monitoring the functioning of the control system
G07C 5/00 - Registering or indicating the working of vehicles
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
An image generation method is performed by one or more data processing apparatus, and comprises: obtaining an image showing an object; generating one or more additional images related to the object; fine-tuning a machine-learned text-to-image model using one or more of the additional images; providing, to the machine-learned text-to-image model, a prompt to generate an output image showing the object, and obtaining, from the machine-learned text-to-image generation model, the output image.
Methods, systems, and apparatus for classification. In one aspect, a method includes receiving an input and a request to classify the input into one of a plurality of classes, processing the input using a multimodal model to generate (i) a description of the input and (ii) a class prediction, processing the description of the input and the class prediction using a text encoder embedding neural network to generate a (i) text description feature embedding and (ii) a prediction feature embedding, generating, from at least the description feature embedding and the prediction feature embedding, a query feature embedding representing the input, and classifying the input into one of the plurality of classes using the query embedding.
A system, such as a voice assistant device, is disclosed which includes a base that houses at least one speaker and supports a display screen. The base is configured to hold the display screen at an angle relative to a surface, creating a predefined space between the screen's lower edge and the surface. To optimize sound, multiple speakers can be oriented in different directions, with one speaker potentially facing a front grille while another is aimed in another direction behind the display. The system may further integrate a camera and a radar transceiver within the bezel of the display screen.
H04R 1/34 - Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by using a single transducer with sound reflecting, diffracting, directing or guiding means
25.
COMPOSING MACHINE LEARNING MODELS TO PERFORM NEW TASKS
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for composing machine learning models to perform new tasks.
Implementations relate to selecting a particular device, from an ecosystem of devices, to provide responses to a device-agnostic request of the user while a scenario is occurring. The user specifies a scenario and contextual features are identified from one or more devices of the ecosystem to generate scenario features indicative of the scenario occurring. The scenario features are stored with a correlation to a device that is specified by the user to handle responses while the scenario is occurring. When a subsequent device-agnostic request is received, current contextual features are identified and compared to the scenario features. Based on the comparison, the specified assistant device is selected to respond to the device-agnostic request.
A software-based extension of the instruction set of a processor includes instructions for the processor to prefetch virtual address translations and insert the prefetched translations into a translation lookaside buffer (TLB). A page walk may be performed to find a virtual address in a group of page tables and provide the address translation to the TLB. The TLB may be arranged in multiple levels and the instructions may specify a level for the prefetched entry to be inserted. The instruction may provide a hint to the processor for selecting candidate virtual address for prefetch based on a characteristic of an address such as a likelihood of reuse, a priority level of the data in the virtual address or other characteristic. A page walk can be performed asynchronously without affecting normal operations of a program. Instructions may specify between an instruction a data TLB for insertion of a new TLB entry.
G06F 12/1027 - Address translation using associative or pseudo-associative address translation means, e.g. translation look-aside buffer [TLB]
G06F 12/0811 - Multiuser, multiprocessor or multiprocessing cache systems with multilevel cache hierarchies
G06F 12/0862 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches with prefetch
28.
HYBRID ANSWERS ON A HEAD-WEARABLE DISPLAY USING AN EDGE LARGE LANGUAGE MODEL AND EXTENDED LARGE LANGUAGE MODEL
To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD) or for other reasons, the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a student language model neural network for deployment in a cascade with a teacher neural network. That is, by training a student neural network using techniques that incorporate the difficulty of accurately predicting the target token for each output position of a target output of a training example for each training example for both the student and the teacher language model neural networks, the described techniques result in a student teacher cascade with higher overall task performance per unit of computational cost.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated layout generation by an artificial intelligence system. Methods can include obtaining two or more discrete units of content. Based on the two or more discrete units of content a new layout is generated in a canvas. The layout generation can include: generating a bounding box as a presentation space for each given unit of content; generating positioning data specifying locations within the canvas at which each bounding box is located; assigning each bounding box to a corresponding user interface layer; and generating a compressed text representation of the new layout. The new layout can be rendered based on the text representation.
A method efficiently restructures account data indicative a first plurality of keywords each mapped to a respective query space, a first plurality of campaigns, and associations therebetween. The method includes consolidating the first plurality of campaigns into a smaller, second plurality of campaigns, based on a degree of overlap between respective query spaces to which keywords associated with different campaigns are mapped. The method also includes generating a second plurality of keywords consisting of a subset of the first plurality of keywords, which includes, for each campaign in the second plurality of campaigns, determining whether to remove associations to particular keywords based on an incremental value added by the query spaces that are mapped to those keywords. The method also includes storing restructured account data indicative of the second plurality of keywords, the second plurality of campaigns, and new associations therebetween.
A method of semantic-based image copying includes generating a text prompt. Generating the text prompt includes by applying a source image to a first generative artificial intelligence (Al) model to generate a descriptive caption for the source image. The method also includes generating a visual embedding based on the source image, and generating a new image using a second generative Al model and based on the text prompt and the visual embedding.
Implementations described herein relate to enabling natural language communications with an autonomous vehicle. In some implementations, processor(s) of a system can initiate and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle whereas, in additional or alternative implementations, the processor(s) can answer an incoming electronic communication and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle. In other additional or alternative implementations, the processor(s) can also conduct conversations with a local communication participant that is located proximate to the autonomous vehicle. Notably, the processor(s) can be implemented locally at the autonomous vehicle or remotely from the autonomous vehicle (e.g., at a remote server).
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
G10L 13/00 - Speech synthesisText to speech systems
H04W 4/40 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
A method for optimizing file storage includes receiving columnar data to store at a columnar data store with columns ordered with an initial ordering. The method includes determining, based on historical access patterns for the columnar data store, an updated ordering for the columns. The method includes storing the columnar data at a first location of the columnar data store using the updated ordering. The method includes determining that the stored columnar data is to be compacted and compressing at least a portion of the columnar data using each of a plurality of compression techniques. The method includes, based on compressing the at least a portion of the columnar data, selecting one of the plurality of compression techniques. The method includes storing the columnar data at a second location of the columnar data store using the selected one of the plurality of compression techniques.
41 - Education, entertainment, sporting and cultural services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing computer security training and educational testing services in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Organizing computer security competitions in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Providing training in the field of computer network attack, defense, response and investigation. Providing computer security consulting services in the field of computer network attack, defense, response and investigation; Computer programming services for developing a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Computer security threat detection and analysis for protecting data provided in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents.
(1) Downloadable computer software for use in processing and generating natural language queries; downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. (1) Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for use in processing and generating natural language queries; Downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; Downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; Downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; Downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; Downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.
(1) Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits (1) Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
(1) Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits (1) Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
45.
SECURITY LOG TYPE CLASSIFICATION WITH AN ARTIFICIAL INTELLIGENCE MODEL
A system and method for security log classification using an artificial intelligence (AI) model. The method includes obtaining a log comprising a sequence of characters, extracting, using a token vocabulary, a sequence of tokens from the sequence of characters, providing the sequence of tokens as input to a trained artificial intelligence (AI) model, obtaining one or more outputs from the trained AI model, and extracting, from the one or more outputs, (i) a label reflecting a type of log, and (ii) a level of confidence that the label applies to the log.
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06F 21/54 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by adding security routines or objects to programs
The present disclosure provides methods, systems, and devices for providing simplified versions of model responses. A computing system receives a user query. The computing system generates a first model input to a generative model based on the user query. The computing system receives a first model output from the generative model. The computing system transmits the first model output for display to a user in a user interface. The computing system receives a simplification request associated with the first model output. The computing system generates a second model input, the second model input including one or more instructions to provide a simplified explanation of the first model input. The computing system receives a second model output from the generative model, the second model output comprising a simplified version of the first model output. The computing system transmits the second model output for display to a user.
An example method involves initializing a real-time meeting communication session, receiving information indicative of a mobile computing device of a user being present at a physical location of a first computing device, causing the mobile computing device of the user to display a first user interface (UI) element, responsive to a user selection of the first UI element, causing control of the real-time meeting communication session to be granted to the first computing device from the mobile computing device of the user, identifying a second computing device of the user, causing the second computing device of the user to display a second UI element, and allowing the user to participate in the real-time meeting communication session via the second computing device upon a user selection of the second UI element.
A corpus of textual data is generated with a machine-learned text generation model. The corpus of textual data includes a plurality of sentences. Each sentence is descriptive of a type of audio. For each of a plurality of audio recordings, the audio recording is processed with a machine-learned audio classification model to obtain training data including the audio recording and one or more sentences of the plurality of sentences closest to the audio recording within a joint audio-text embedding space of the machine-learned audio classification model. The sentence(s) are processed with a machine-learned generation model to obtain an intermediate representation of the one or more sentences. The intermediate representation is processed with a machine-learned cascaded diffusion model to obtain audio data. The machine-learned cascaded diffusion model is trained based on a difference between the audio data and the audio recording.
Methods, systems, devices, and tangible non-transitory computer readable media for using incoming communications to generate suggestions for navigation. The disclosed technology can include accessing route data that includes information associated with navigation from a starting location to a destination. Based on the route data, one or more routes from the starting location to the destination can be determined. Message data including one or more messages to a user can be accessed. Based on the message data and one or more machine-learned models, at least one entity and objectives that are associated with the one or more messages can be determined. Based on the one or more routes, the at least one entity, and the objectives, suggestions associated with the one or more messages can be determined. Furthermore, output including indications associated with the suggestions directed to the user can be generated via a user interface.
Implementations relate to dialog management of a large language model (LLM) utilized in generating natural language (NL) output during an ongoing dialog. Processor(s) of a system can: receive NL based input as part of the ongoing dialog, generate NL based output utilizing the LLM, and cause the NL based output to be rendered. Further, the processor(s) can receive subsequent NL based input as part of the ongoing dialog. In some implementations, the processor(s) can determine whether to modify a corresponding dialog context in generating subsequent NL based output, and modify the corresponding dialog context accordingly. For example, the processor(s) can restrict the corresponding dialog context, or supplant the corresponding dialog context with a corresponding curated dialog context. In additional or alternative implementations, the processor(s) can modify a corresponding NL based output threshold utilized in generating the subsequent NL based response to ensure the resulting NL based output is desirable.
A method includes ingesting event data over a network for a plurality of events obtained by disparate computing resources. Each event is associated with a respective timestamp and one or more ingestion-attributes. The method includes identifying whether the corresponding event is associated with any custom indexing-attributes defined by a user. The method also includes indexing the corresponding event into a data store as structured data based on the respective timestamp, the one or more ingestion-attributes, and any identified custom indexing-attributes. The method includes evicting any of the events of the event data in the data store for a period of time that satisfies an eviction time period threshold. The method also includes retrieving the data from the data store that is associated with the time range, the ingestion-attributes, or the one custom indexing-attributes.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
A wearable computing device includes a housing, a band having a circuit, an energy storage device, a power interface configured to deliver electrical power from the energy storage device to the circuit, a switching device configured to selectively couple the energy storage device to the power interface, and a processor. The processor is configured to determine a state of the wearable computing device and initiate a detection window of a certain duration when the state corresponds to a predetermined state and motion of the wearable computing device is detected. More specifically, initiating the detection window includes controlling operation of the switching device to couple the energy storage device to the power interface to deliver the electrical power to the circuit only at intermittent intervals to reduce an amount of the electrical power delivered to the circuit during the certain duration, thereby minimizing rusting at the power interface.
Methods, systems, and apparatus, including computer-readable storage media for determining whether to add character recognition (CR) data to multimodal input and executing models with multimodal input augmented with the generated CR data, to improve the execution or accuracy of output generated by the models. CR data is information describing the presence or characteristics of text across input of different modalities, such as video, images, or audio. The system can include a multimodal model trained to receive the multimodal input and generate a corresponding output, in response to the input, and can be trained to determine whether to include the CR data in the multimodal input. The determination of whether to use multimodal input augmented with CR data can improve the accuracy of a model output, the computational efficiency in processing multimodal input, or both.
The present disclosure is generally directed to a cable manifold and a system for rack cabling. A cable manifold includes a base surface, a plurality of sections, and one or more openings. Each of the plurality of sections comprises a respective plurality of sides extending transverse from the base surface, wherein each section shares one or more sides with at least one adjacent section The one or more openings extend through the base surface. One or more sections of the plurality of sections comprises a respective opening of the one or more openings configured to house a cable connector connected to a server tray removably coupled to the cable manifold such that when the cable manifold is removed from the server tray each cable connector housed by the plurality of openings are simultaneously removed from the server tray.
Aspects of the disclosure provide a mobile robot for automated server handling. The robot includes a shelf holding a plurality of server trays and a telescoping server loading tray. The telescoping server loading tray extends externally from the robot and retracts internally inside the robot. The telescoping server loading tray also includes an end having one or more locking and unlocking latches configured to engage a server tray. Aspects of the disclosure provide for determining coordinates using a position verification tool, which can be provided to the mobile robot for identifying locations of racks with target server trays.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a data item output in response to a query for a particular task using neural network and training one or more of the neural networks to generate one or more data item embeddings. In one aspect, a method comprises applying a learned adapter to a query embedding to generate an adapted query embedding for a new query for the particular task and selecting, as a relevant target data item for the new query, one or more of the target data items using the adapted query embedding for the new query and a target embedding for the target data items. In another aspect, a method comprises training an adapter using adapted query embeddings, positive target embeddings, and negative target embeddings for a plurality of fine-tuning examples while keeping a pre-trained query encoder neural network fixed.
Aspects of the disclosure are directed to establishing and utilizing multiple flows, e.g., data paths, within a single connection between two end points in a network. Packets being transmitted between the endpoints can be load-balanced among multiple flows using a set of flow labels. The flow label is determined using scheduling logic. The flow labels include a flow weight that encodes how the packet is mapped to a given flow. The flow weight may be used to determine a congestion window for each flow in the connection. As packets are communicated between the endpoints, congestion control data and acknowledgement coalescing entries are updated before an acknowledgement is sent. Each flow maintains a counter of the number of acknowledgments received. The number of acknowledgments received is used to implement congestion control.
A near-eye display includes a processor to generate a sensor input value based on sensor data received from one or more sensors associated with the near-eye display and generate a context value based on a contextual score indicating a user state associated with the near-eye display. The contextual score is based in part on previous user interactions with a user interface of the near-eye display. The processor is also configured to compute an input event value based on the sensor input value and the context value and determine whether to trigger a change in virtual content displayed by the near-eye display based on comparing the input event value to a threshold.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for caching compilation outputs using optimization profiles. One of the methods includes identifying a computer program; and at each of a plurality of execution stages: identifying an optimization profile that is to be used when compiling the computer program; generating, from the computer program and from the optimization profile, a cache key; determining whether the cache key has an entry in a compilation cache that stores compilation outputs generated by a just-in-time compiler; obtaining, based on whether the cache key is determined to have an entry in the compilation cache, a compilation output that either (i) was previously generated during a prior execution stage or (ii) is newly generated by the just-in-time compiler during the current execution stage; and providing the compilation output for execution of the computer program.
An eyewear display device expands a field of view by projecting display light at multiple ranges of input angles to a waveguide (600) employing multiple portions of an incoupler (514) or multiple incouplers (514, 414) corresponding to the different angular ranges of display light to guide light to an exit pupil expander (416) configured to receive the display light from the different angular ranges and an outcoupler (608) that are sized to fit within an eyeglasses lens. The display light that is input from the different angular ranges is guided by the incouplers to be overlapped at an exit pupil expander (or, in some embodiments, at multiple exit pupil expanders that are overlaid with each other) to reduce the total ray footprint at the exit pupil expander.
For a transceiver including a plurality of receivers and a transmitter, controlling simultaneous reception of a first reception signal on a first one of the receivers and a second reception signal on a second one of the receivers, and controlling a timing of transmission of a transmission signal by the transmitter according to both reception at the first one of the receivers and reception at the second one of the receivers.
H04B 1/525 - Hybrid arrangements, i.e. arrangements for transition from single-path two-direction transmission to single-direction transmission on each of two paths or vice versa with means for reducing leakage of transmitter signal into the receiver
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for object detection using neural networks. In one aspect, one of the methods includes obtaining an input image; processing the input image using an sequence transduction neural network to generate an output sequence that comprises respective token at each of a plurality of time steps, wherein each token is selected from a vocabulary of tokens that comprises (i) a first set of tokens that each represent a respective discrete number from a set of discretized numbers and (ii) a second set of tokens that each represent a respective object category from a set of object categories; and generating, from the tokens in the output sequence, an object detection output for the input image.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated digital component generation. In some aspects, a method includes obtaining digital content data for the digital component. The digital content data includes at least a base image of a subject of the digital component. A prompt that includes a description of the subject is obtained. The prompt is processed using a language model to generate one or more keywords related to the subject. A determination is made, based on the one or more keywords, one or more style features for the digital component. The digital component is generated by processing the digital content data based at least on the one or more determined style features. The generated digital component is distributed to one or more client devices.
Systems and methods for machine-learned generation of data insight summaries are provided. A computing system can obtain numerical time series data comprising a plurality of numerical values associated with a plurality of times. The computing system can identify, based on the numerical time series data, one or more first mathematical relationships in the numerical time series data. The computing system can generate, based at least in part on the mathematical relationships, a first input context comprising first natural language data indicative of the mathematical relationships. The computing system can provide the first input context to a first machine-learned sequence processing model. The first machine-learned sequence processing model can generate, based at least in part on the first input context, one or more outputs describing the one or more first mathematical relationships. The computing system can output the one or more outputs.
A method for generating conversion measurement diagnostics for different content applications based on feature data from multiple features includes requesting the feature data associated with a particular account at a configured interval, processing the received feature data to generate diagnostic signals according to predetermined logic for each of a plurality of available diagnostics for a plurality of applications, then storing the diagnostic signals with timestamps within a common data layer. When a diagnostic status request associated with the particular account is received via a first application, first diagnostic signals are retrieved from the common data layer, where each of the first diagnostic signals is associated with first diagnostics enabled for the first application. Then, a user interface for the first application is provided to display a separate interface element for each of the first diagnostics, each interface element indicating corresponding ones of the first diagnostic signals.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
66.
SYSTEM(S) AND METHOD(S) FOR PROVIDING A GENERATIVE CONTENT GRAPHICAL CARD AT CLIENT DEVICE(S)
Implementations described herein relate to providing a generative content graphical card at client device(s) that enable user(s) of the client device(s) to interact with various generative model(s) (GM(s)). Processor(s) of a system can: receive an invocation of a generative content graphical card; and in response to receiving the invocation: causing the generative content graphical card to be visually rendered such that it overlays content displayed at the client device; process, using a GM, GM input (including at least the displayed content) to generate GM output; determine, based on the GM output, a plurality of suggestions that are each associated with a corresponding action; and cause the plurality of suggestions to be visually rendered. Further, the processor(s) can, in response to receiving a user selection of a given suggestion: cause the corresponding action to be performed; and cause a result of performance of the corresponding action to be visually rendered.
Systems and methods include receiving a request for presentation of a benchmark line chart diagram associated with a device identifier. The system can access device identifier data including category data, application data, or traffic volume data. The system can determine a branch of related hierarchical groups for the device identifier based on the device identifier data. The system can access data including cohort groups including a minimum number of device identifiers such that aggregate metric data associated with the cohort does not reveal any information about any single device identifier. The system can select a benchmark group for the device identifier. The system can access data including aggregate metrics associated with the selected benchmark group. The system can transmit data including instructions cause one or more processors to provide for display a benchmark line chart diagram and benchmark metric data indicative of aggregate metrics associated with the benchmark group.
The present document describes techniques associated with context-aware on‑device intelligence. These techniques provide on-device large language model (LLM) intelligence based on a context of a user device. The context is determined by collecting textual, audio, and video data from the user device and transforming the data into embedding representations. The embedding representations are used to enhance the capabilities of the LLM, enabling the LLM to generate even more relevant and contextually aware responses including, for example mapping user journeys through different apps, predicting a user's next action, and providing search predictions tailored to the user's current context.
Systems and methods for quantum computation are provided. In one example, performing quantum computation using a masked circuit can include obtaining, by a classical computing system, data indicative of a quantum computation circuit. One or more quantum gates within the circuit can be masked to generate a masked quantum computation circuit. Data indicative of the masked quantum computation circuit can be sent to a quantum computing system. The classical computing system can receive masked results associated with one or more quantum computations based on the masked quantum computation circuit. The classical computing system can determine, based on the masked results, an output of the quantum computation circuit.
70.
GRAPHICAL USER INTERFACE FOR GENERATIVE MODELS WITH DYNAMIC PROMPT ADJUSTMENT
Processor(s) of a system can: receive user input; process, using a generative model (GM), a GM input based upon the user input to generate a first GM output that includes a first set of items associated with a corresponding prompt for subsequent processing by the GM; cause the first set of items to be visually rendered using a first set of GUI elements; in response to receiving a user selection of a GUI element corresponding to an item of the first set of items, process, using the GM, the prompt associated with the selected item to generate second GM output that includes a second set of items; cause the second set of items to be visually rendered using a second set of GUI elements; and determine updated prompt(s) associated with the first set of items based upon a user interaction with the second set of GUI elements.
Methods and wireless communication devices according to various embodiments are configured to enable an efficient targeted use of DualSteer devices with two 3GPP accesses. A method (1500) performed by a network entity (130) includes receiving (1550), from a DualSteer device, a registration request message including a first subscriber permanent identifier of a first 3GPP access and an indication of capability for dual steer. The method further includes, upon retrieving (1591Y) a correlation identifier associated with the first SUPI and a second SUPI of a second 3GPP access, transmitting (1569), to the DualSteer device, a registration accept message including a network indication of support for dua steer. The method then includes transmitting (1574), to the DualSteer device, a routing and steering policy rule for establishing a packet data unit session, the rule indicating a target 3GPP access.
Implementations relate to graphical user interfaces (GUIs) for interacting with generative model(s). Processor(s) of a system can: receive user input associated with a user of a client device; process, using a generative model (GM), a GM input including the user input and a general schema prompt to generate a GM output; determine, based on the GM output, GUI elements and a specific schema prompt specific to the user input and based on the general schema prompt; cause the GUI elements to be rendered; store a specific schema that has been determined based on the GM output; receive additional user input; process, using the GM, an additional GM input including the additional user input and specific schema prompt to generate an additional GM output; determine, based on the additional GM output, updated GUI elements and an updated specific schema prompt; and cause the updated GUI elements to be rendered.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
Methods and devices in a wireless network implement policies and rules for steering, switching, and splitting traffic over two 3GPP accesses. A wireless communication method (1300) performed by a DualSteer device, DSD, (102) with two 3GPP accesses includes receiving (1356), from a network entity (130), a dual steer traffic management rule when establishing a multi-access packet data unit session or a DualSteer packet data unit session. The method further includes handling (1360) traffic over the two 3GPP accesses by applying the DS traffic management rule based on an indication that one of the two 3GPP accesses is a primary 3GPP access or is a secondary 3GPP access among paired 3GPP accesses associated with a correlation identifier.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for user managed user identity mechanisms. A UE (102) transmits (714), to a core network (120), a first message indicating a user identity independent from a cellular communication network. The UE receives (782), from the core network (120) based on the user identity, a second message indicating an action performed with respect to a UIP that indicates a UE subscription in the cellular communication network.
The present document describes techniques associated with a sketch-to-image pipeline with automatic prompting. These techniques include a method to generate images in a two-stage process using an image-to-text model and a text-to-image model. The input image, which can be a drawn sketch, is processed in order to automatically generate a description of the content. The automatically generated description is combined with a style template to form a prompt. The prompt is used as input to the text-to-image model. The input image is also used as input to the text-to-image model to increase the likeness to the input image. The method simplifies image generation by removing the need for the user of the pipeline to type a prompt (while giving them the option to do so if they wish) and while giving them control over how closely to the input sketch they wish to adhere.
Some implementations are directed to editing a source image based on a user request to edit the source image. The source image and the user request to edit the source image can be processed, using an image-editing system, to generate one or more image editing instructions. The one or more image editing instructions can indicate an image mask that edit (or preserves) one or more portions of the source image and/or can indicate a target object to be present in the edited image to replace a source object in the source image. Based on the one or more image editing instructions and source image, an edited image that shares the one or more portions with the source image and that differs from the source image by replacing the source object in the source image with the target object can be generated.
Some implementations to a multi-pane graphical user interface (GUI) where, during a dialog session between a user and a generative model system, the generative model system generates first pane responses that are rendered in a first pane of the GUI and generates a second pane response that is rendered in a second pane of the GUI and that is dynamically updated over the dialog session. Further, first pane user inputs, that are directed to the first pane, can cause an additional first pane response to be generated and rendered at the first pane and/or can cause an update to the second pane response. Likewise, second pane user inputs, that are directed to the second pane, can cause a corresponding update to the second pane response and can cause an additional first pane response to be generated and rendered at the first pane.
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
G06F 9/451 - Execution arrangements for user interfaces
Implementations are provided for facilitating multi-turn dialogs with a generative model-based agent (GMAgent) that allow for multi-step analysis of external data source(s), including refinement of that analysis. In various implementations, data indicative of a first query and external data source(s) may be assembled into a first prompt. The first prompt may be processed using generative model(s) to generate first output data that includes first source code that is executable to perform an analytic task on data from the external data source(s). The first source code may be executed to perform the analytic task using the external data source(s) and generate analytic output. The analytic output may be assembled into a second prompt with a command to determine whether the analytic output satisfies the first query. The second prompt may be processed using generative model(s) to generate second output data that indicates whether the analytic output satisfies the first query.
Techniques are directed to generating a 3D image of an object in a scene from a 2D image of the object in the scene that involves generating a reprojected image having a mask defined by a representation of the object. The mask may include a set of pixels and, in some implementations, the set of pixels coincides with an edge of the representation of the object. The inpainting is performed using a model that is trained to fill in gaps within such masks and as such the inpainting does not require the 2D image be separated into background and foreground layers.
Techniques are directed to generating a 3D image of an object in a scene from a 2D image of the object in the scene that involves generating a reprojected image having a mask defined by a representation of the object. The mask may include a set of pixels and, in some implementations, the set of pixels coincides with an edge of the representation of the object. The inpainting is performed using a model that is trained to fill in gaps within such masks and as such the inpainting does not require the 2D image be separated into background and foreground layers.
Methods, systems, and apparatus, including computer-readable storage media for resource isolation between connections with shared hardware resources. A network device, such as a network interface card, is configured to determine dynamic resource limits for each connection, and backpressure each connection individually to avoid a global pause when the shared hardware resources are oversubscribed by the current connections. As a result, slower connections may be paused for exceeding resource limits, protecting faster connections from slowing down because resources are shared between both types of connections. Dynamic resource limits can be generated and updated not only per connection, but also based on subsets of the shared hardware resources assigned to different sources of data, as well assigned to different types of transactions communicated over a connection. A hardware-assisted transport layer can be configured to apply dynamic resource limits individually to different connections. from a variety of different upper-layer protocols (ULPs).
H04L 47/76 - Admission controlResource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for refining input prompts to generative neural networks. One of the methods includes receiving an input prompt to a generative neural network; generating, from the input prompt, a language model input; processing the language model input using a language model neural network to generate an output that (i) identifies one or more initial text segments from the text sequence and (ii) includes, for each of the identified initial text segments, one or more initial candidate refinements for the text segment; identifying, using the output, (i) one or more final text segments from the text sequence and (ii) for each of the final text segments, one or more final candidate refinements for the final text segment; and providing, for presentation in user interface, data identifying the one or more final candidate refinements for the final text segments.
Methods and systems for real-time identification of media trends at a content sharing platform are provided. Embeddings representing features of a media item identified during a current time window are generated. Based on these embeddings, the system determines whether the similarity between the features of the media item and those of one or more additional media items identified during the same time window meets predefined similarity criteria. If the similarity criteria are satisfied, the media item and the additional media items are determined to correspond to an emerging media trend on the platform. An indication of this emerging media trend is then provided to a user of the platform via a client device during the current time window.
Methods and systems for implementing secure aggregation with one-shot clients are described herein. A server receives, from each client, (i) an encrypted client input represented by a client input encrypted by a Key-Additive Homomorphic Encryption (KAHE) scheme using a client key, and (ii) an encrypted client key represented by the client key encrypted by an Additive Homomorphic Encryption (AHE) scheme using a public key received by the client from a decryptor. The server adds the encrypted client input to a combination (e.g., a running sum) of encrypted client inputs received from at least some of the clients. The server further adds the encrypted client key to a combination (e.g., a running sum) of encrypted client keys received from the clients which supplied their client inputs to the server. The server then transmits, to the decryptor, the running sum of encrypted client keys. In response, the server receives, from the decryptor, a decrypted key produced by decrypting, using a secret key corresponding to the public key, the running sum of encrypted client keys. The server then decrypts, using the decrypted key, the running sum of encrypted client inputs.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
H04L 9/14 - Arrangements for secret or secure communicationsNetwork security protocols using a plurality of keys or algorithms
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
85.
REAL-TIME, HIGH-RESOLUTION AND GENERAL NEURAL VIEW SYNTHESIS
A method including generating a plurality of feature maps based on a plurality of images triggered to capture at a same time, the plurality of images having a plurality of view perspectives, generating a layered depth map based on the plurality of feature maps, and generating an image based on the layered depth map and the plurality of images, the image having a view perspective not included in the plurality of view perspectives.
A method includes receiving user queries directed toward a cloud-based assistant service. For each received user query directed toward the cloud-based assistant service, the method also includes extracting one or more attributes from the user query and logging the user query into one or more of a plurality of category buckets based on the one or more attributes extracted from the user query. The method also includes determining when at least one of the plurality of category buckets includes a threshold number of the user queries logged into the at least one category bucket, and when the at least one of the plurality of category buckets includes the threshold number of the user queries, generating a distilled model of the cloud-based assistant service. The distilled model of the cloud-based assistant service is configured to execute on one or more target client devices.
A computer-implemented input-method editor process includes receiving a request from a user for an application-independent input method editor having written and spoken input capabilities, identifying that the user is about to provide spoken input to the application-independent input method editor, and receiving a spoken input from the user. The spoken input corresponds to input to an application and is converted to text that represents the spoken input. The text is provided as input to the application.
G06F 3/04886 - Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
Aspects of the subject technology relate to a method for using a voice command for multiple computing devices. First voice input data is received from a first computing device associated with a user account, where the first voice input data comprises a first voice command captured at the first computing device. Second voice input data is received from a second computing device associated with the user account where the second voice input data comprises a second voice command captured at the second computing device. An intended voice command is determined based on the obtained first and second voice input data. Based on the intended voice command, a first target computing device is determined. First instructions associated with the intended voice command are provided to the first target computing device for execution.
A method includes receiving a sequence of acoustic frames and generating, by an audio encoder, at each of a plurality of output steps, an acoustic higher-order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. For each acoustic frame in the sequence of acoustic frames paired with a corresponding video frame, the method includes generating, by an audiovisual encoder, an audiovisual higher-order feature representation for the corresponding acoustic higher-order feature frame and the corresponding video frame; and generating, by a joint network, at an output step, a probability distribution over possible speech recognition hypotheses based on the audiovisual higher-order feature representation. The method, for each corresponding acoustic frame in the sequence of acoustic frames not paired with a corresponding video frame, includes generating, by the joint network, at an output step, a probability distribution over possible speech recognition hypotheses based on the acoustic higher-order feature representation.
G10L 15/16 - Speech classification or search using artificial neural networks
G10L 15/197 - Probabilistic grammars, e.g. word n-grams
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 15/25 - Speech recognition using non-acoustical features using position of the lips, movement of the lips or face analysis
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
G10L 25/57 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for processing of video signals
The present disclosure is generally directed to a unified fiber management storage system that incorporates a removable splice cabinet into the back of an optical distribution frame (ODF) cabinet. The incorporation of the splice cabinet into the ODF cabinet results in a single system that can be installed and used within a data center, rather than multiple independent systems. Splice units may be positioned within the splice cabinets. Splice cassettes are positioned within the splice units. The splice cassettes are positioned vertically.
Methods, systems, and apparatus, including computer-readable storage media for testing features of a database management system (DBMS). A DBMS testing framework generates new random test cases for testing database features on the system. The framework receives a query grammar specifying the structure of queries to generate and generates the queries randomly. The framework executes the queries with database features randomly enabled or disabled and generates performance data from the results of executing those queries. The framework identifies points of failure in the performance data, corresponding to instances in which queries executed with certain combinations of database features result in incorrect output, or degraded performance relative to executing the queries without the database features enabled. The testing framework divides the database preparation, query generation, and query execution parts of a test pipeline into separate components, which can be modified separately or left to proceed in a default operating mode.
A door for supplying air to a data center enclosure includes a frame mounted to the door and a plurality of louvers positioned in the frame such that the plurality of louvers extends at least partially across an opening defined through the door. Each louver of the plurality of louvers includes a sound absorbing core at least partially surrounded by a sound reflecting cover. Each louver can be modulated by a control system to direct air into the data center while also dampening noise levels inside the data center. The control system is electronically connected to a display to provide real time information pertaining to environmental conditions within the data center.
Systems and methods for converting two-dimensional (2D) static images to three-dimensional (3D) animated images are provided. Such a method includes: receiving, by a server device, one or more 2D static images, each 2D static image of the one or more 2D static images depicting a respective environment; generating a 3D mesh based on a 2D static image of the one or more 2D static images; determining a visual perspective trajectory along the 3D mesh, the visual perspective trajectory indicative of simulated movement within a 3D animated image at least partially along an axis associated with depth in the respective environment depicted by the 2D static image; and generating the 3D animated image based on the 3D mesh and the visual perspective trajectory such that the 3D animated image replicates the simulated movement.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a video output using an autoregressive token generation neural network model In one aspect, a system comprises obtaining a model input, processing the model input to generate an input sequence of embeddings that represents the model input, autoregressively generating a plurality of output sequences of tokens, wherein each output sequence of tokens corresponds to a respective output modality of tokens from a set of a plurality of modalities that includes a video modality and one or more other modalities, and generating a model output that includes a video output of the video modality by decoding the sequence of tokens.
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G06T 3/4046 - Scaling of whole images or parts thereof, e.g. expanding or contracting using neural networks
G06T 3/4053 - Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocodersCoding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
G10L 19/038 - Vector quantisation, e.g. TwinVQ audio
G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
95.
Machine-Learning Systems and Methods for Conversational Recommendations
Aspects of the disclosed technology include computer-implemented systems and methods for conversational recommendation systems, such as conversational chatbots that are configured to process user queries and generate responses. A recommendation system includes a conversational user interface configured to receive a user query and provide a recommendation response and a machine-learned sequence processing model that has been trained on training data including a plurality of triplets. Each triplet includes an example query, an example model reasoning plan associated with the example query, and an example response associated with the example query and the example model reasoning plan. The sequence processing model can be trained to provide conversational-based recommendations using a multi-stage recommendation process that includes a planning stage, a conversation stage, and a retrieval stage.
Methods, systems, and apparatus for microwave pulse distortion compensation using reflection parameters from error amplification pulse sequences. In one aspect, a method includes generating a pre-distorted control signal that implements a single qubit rotation operation and applying the pre-distorted control signal to a qubit to perform the rotation operation on the qubit. The pre-distorted control signal comprises an inverted transfer function, where the inverted transfer function comprises values of parameters obtained through fitting measured qubit parasitic rotation angles per gate to a reflection model that models pulse distortion in the quantum computing device; and the qubit parasitic rotation angles per gate are measured using a first pulse sequence that amplifies out-of-phase pulse distortion in the quantum computing device and a second pulse sequence that amplifies in-phase pulse distortion in the quantum computing device.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for recurrence in a transformer architecture. In one aspect, a method includes receiving input embeddings representing a sequence of words as input; generating as output attention vectors for each of the words, the attention vectors for each word indicating an importance of the word in the sequence relative to other words in the sequence; generating first and second linear transformations X1 and X2 of the attention vectors; determining, in a recurrent neural network, a hidden state corresponding to each attention vector using only element wise operations on the first linear transformation of the attention vectors during a recurrent step; and generating a set of output vectors using a multiplicative gating function in combination with the second linear transformation.
Provided are techniques for the calibration of distillation learning from a teacher model to a student model. Specifically, the present disclosure proposes systems and methods that provide convergence with both high quality and speed. That is, example proposed systems both enable the distillation loss to be minimized at the probability mean value in the probability domain of the teacher's predictions distributions while also providing a loss that is nicely (e.g., symmetrically and/or strongly) convex around an optimum in the logit and/or probability domains (e.g., including far from the minimum) to encourage fast convergence of gradient based methods (e.g., irrespective of distance from the minimum).
Methods, systems, and apparatus, including computer-readable storage media for content group generation for a content delivery campaign. Content groups are generated from a resource identifier and a description. Digital content items are created for each content group, including digital content from the resource identifier and the description, as well as new digital content items. Candidate content groups are ranked according to request coverage gain and optionally one or more other ranking criteria. Request coverage gain is a measure of how much more request coverage is gained through keywords of one content group relative to the request coverage of one or more other content groups. By ranking according to request coverage gain, the selected candidate content groups are differentiated relative to one another, capturing potential content requests that would otherwise be missed by a campaign of content groups not selected based on request coverage gain.
Methods and systems, including computer-readable media, are described for generating clock signals using digital circuitry. An example computer-implemented method can be performed using a security block of an integrated circuit. The method includes obtaining a first clock signal having a first frequency, determining a security risk level of the integrated circuit, and generating one or more parameters based on at least the security risk level. The method further includes generating a second clock signal having a second frequency based on the first clock signal and the one or more parameters and applying the second clock signal to drive the security block of the integrated circuit.
G06F 21/74 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information operating in dual or compartmented mode, i.e. at least one secure mode
G06F 21/75 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information by inhibiting the analysis of circuitry or operation, e.g. to counteract reverse engineering
G06F 21/72 - Protecting specific internal or peripheral components, in which the protection of a component leads to protection of the entire computer to assure secure computing or processing of information in cryptographic circuits
