09 - Scientific and electric apparatus and instruments
14 - Precious metals and their alloys; jewelry; time-keeping instruments
Goods & Services
Pedometers; personal electronic devices used to track
fitness goals and statistics; wearable activity trackers;
wearable electronic devices, namely, smart-bracelets and
smart-wristbands comprised primarily of recorded software
for activity and fitness tracking; wearable activity tracker
straps; multifunctional electronic devices for measuring,
receiving, processing, transmitting, tracking, and uploading
to the Internet information including time, date, steps
taken, calories burned, distance travelled, active time,
speed, pace, hours slept, quality of sleep, silent wake
alarm, heart rate, blood oxygen levels, navigational
information, and routes, including by means of artificial
intelligence (AI); downloadable and recorded computer
software for receiving, processing, transmitting, tracking,
and displaying information relating to fitness, body fat,
body mass index, sleep activity, blood oxygen levels, and
heart rate, including by means of artificial intelligence
(AI); downloadable and recorded computer software for
providing personalized health, fitness, and recovery
recommendations based on wearable sensor data. Watches; wrist watches; sports watches; watch bands; watch
straps; watch straps made of metal, leather, plastic,
silicone, or rubber.
2.
QUESTION ANSWERING USING ENTITY REFERENCES IN UNSTRUCTURED DATA
Methods, systems, and computer-readable media are provided for collective reconciliation. In some implementations, a query is received, wherein the query is associated at least in part with a type of entity. Top-ranked search results are caused to be generated based at least in part on the query. An entity of the type of entity is identified in the top-ranked results and a response to the query is generated from the top-ranked results, the response including a reference to the entity that includes text distinct from the terms of the query.
A method includes receiving a response generated by an assistant LLM that is directed toward the user during an assistant turn in conversation between a user and the assistant LLM. Based on the response generated by the assistant LLM, the method also includes predicting, by the assistant LLM, one or more possible terms the user may speak in a follow-up query to the response generated by the assistant LLM. The method also includes biasing an automated speech recognition (ASR) model toward recognizing the one or more possible terms predicted by the assistant LLM, receiving audio data characterizing the follow-up query to the response generated by the assistant LLM during the user turn subsequent to the assistant turn in the voice-based conversation, and processing, using the ASR model biased toward recognizing the one or more possible terms, the audio data to generate a transcription of the follow-up query.
Techniques of maintaining user comfort while using augmented reality smartglasses include performing an online calibration of frame deformation to correct display position in the lens. Such a calibration involves modeling the frame portion between the world-facing camera and the eye-tracking camera as a hinge that rotates about an axis on and normal to the frame portion. That is, the frame portion consists of two line segments that are joined at an axis at an unknown rotation (angle) to be determined.
A user equipment (UE) can implement a method for managing request process identifiers. The method includes: transmitting, from the UE to a radio access network (RAN) node, an uplink transmission using one or more configured grant (CG) resources; receiving, at the UE, downlink control information including a request process identifier based on a number of CG uplink channel occasions in a CG period such that the request process identifier is determined using the number of CG uplink channel occasions in the CG period multiplied by an output of a floor function, the floor function calculated using a symbol index for a respective CG uplink channel occasion and a periodicity of the CG period; and retransmitting, from the UE, a transmission of the uplink transmission based on the request process identifier.
Systems, devices, apparatus, and methods, including computer programs encoded on storage media, are described herein for beam reporting based on UE grouping. A first UE (102a) receives (312, 612, 912), a beam quality report of a second UE (102b) and detects (314, 914) whether a first beam quality based on beam measurements at the first UE (102a) and a second beam quality at the second UE (102b), according to the beam quality report, satisfy a grouping criterion. Based on the detecting (314, 914), the first UE (102a) sends, to a network entity (104), an indication (310a/610a, 316/916) that the first UE (102a) and the second UE (102b) belong to a UE group that provides (320) a single beam report for UEs in the UE group to the network entity (104).
Wireless communication devices and methods, including computer programs encoded on storage media, implement power sharing modes for uplink transmissions (and managing power levels thereof) from multiple antenna panels of a user equipment, UE.A network entity configures the UE to transmit uplink communications from multiple antenna panels with respective transmission power levels determined according to a semi-static power sharing mode, a dynamic power sharing mode or a fixed power sharing mode. In some cases, the UE provides a power headroom, PH, report regarding the determined transmission power levels. Uplink communications are transmitted using power levels determined by the UE that splits the available power across the multiple panels to fit within the available power budget.
H04W 52/14 - Separate analysis of uplink or downlink
H04W 52/36 - Transmission power control [TPC] using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
An apparatus and method for adaptive memory refresh scheduling utilize a score-and-filter-based arbitration logic to select bank pairs for refresh operations. A score generation logic determines priority scores for memory banks based on current operational states, including bank page states and transaction histories. A multi-stage filtering process identifies eligible banks by evaluating timing constraints and bank-refresh window requirements. A dual-stage arbitration logic processes the priority scores of the eligible banks to identify an optimal bank index and bank group pair. The arbitration logic employs parallel arbiters and weighted summation logic to select a winning bank index for generating a dual-bank refresh command. By dynamically selecting refresh candidates based on real-time bank activity and state transitions, the system manages refresh scheduling to maintain high throughput and bank utilization across varying workload conditions. The disclosed technology is applicable to high-performance memory controllers, including those compliant with dual-bank refresh protocols.
This disclosure provides systems, methods, and apparatuses for quality of service (QoS) differentiation for Internet Protocol (IP) access in a wireless communication system. Datagrams with different quality of services can traverse the same Internet Protocol security (IPsec) tunnel over a non-third generation partnership project (non-3GPP) access network. A user equipment (UE) (101) or network node (such as non-3GPP Interworking Function (N3IWF) or trusted non-3GPP gateway function (TNGF)) (115) generates a first datagram to convey a first encrypted PDU. The first datagram includes a first outer IP header with a first differentiated services code point (DSCP) value matching a second DSCP value corresponding to the first encrypted PDU (419). The UE (101) or the N3IWF/TNGF (115) transmits the first datagram via the IPsec tunnel in the non-3GPP access network. The non-3GPP access network performs QoS differentiated handling of the first datagram based on the DSCP value of the outer IP header (421).
An optical communication system includes a pair of transceivers and an optical circuit switch with multi-core fibers at each port that provides a communication bridge between the pair of transceivers. The transceivers each transmit a forward transmission signal through a first single-core fiber and receives a reverse transmission signal through a second single-core fiber. A fan-in/fan-out device can be positioned between the switch and each transceiver along an optical path. The fan-in/fan-out device is configured to couple the light or transmission signal transmitted through the single-core fiber with a core of a multi-core fiber. The multi-core fiber is further coupled to the port.
A merge mode for video coding is described that has motion vector difference based subblock-based temporal motion vector prediction. A base motion vector is selected for a current block. The current block is partitioned into sub-blocks. The sub-blocks are then decoded. For each sub-block of at least some of the sub-blocks, a motion shift that includes a direction and a distance is identified, the motion shift is applied to the base motion vector to obtain a refined motion vector, and each sub-block is decoded using the refined motion vector.
H04N 19/139 - Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
H04N 19/119 - Adaptive subdivision aspects e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/70 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
12.
PROMPT-DRIVEN IMAGE EDITING USING MACHINE LEARNING
A media application receives an initial image and a textual request to change the initial image, the initial image including a subject with a face. The media application generates, from the initial image, a preserving mask that corresponds to the face of the subject. The media application provides the textual request, the initial image, and the preserving mask as input to a diffusion model. The diffusion model outputs a denoised initial image based on the initial image; performs text conditioning of the textual request and forward diffusion to generate a noisy translated image that satisfies the textual request; and outputs based on the noisy translated image, extracted features, and self-attention maps, a denoised translated image. The media application blends the denoised initial image, the preserving mask, and the denoised translated image to form an output image, wherein the preserving mask prevents modification to the face from the initial image.
Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A finger cuff assembly includes a finger cuff having a cavity defining an inner surface and an array of barometric pressure sensors arranged on the inner surface. The array of barometric pressure sensors is configured to map a pressure surface near an artery in the finger to provide a plurality of data channels for measuring the blood pressure. The finger cuff assembly also includes one or more optical sensors arranged on the inner surface adjacent to the array of barometric pressure sensors and an inflatable bladder arranged with the array of barometric pressure sensors, wherein inflating the bladder increases a pressure on the array of barometric pressure sensors against the finger. The finger cuff assembly also includes a control module having circuitry for receiving data from the plurality of data channels and estimating the blood pressure using the data. As the pressure on the array of barometric pressure sensors increases, pulses from the artery are detected on the plurality of data channels and used by the control module for estimating the blood pressure.
A61B 5/022 - Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skinOphthaldynamometers
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value using optical sensors, e.g. spectral photometrical oximeters
15.
QUERY RESPONSE INTERFACE WITH SERVER SIDE GENERATIVE MODEL(S)
Various implementations include processing, at a client device, an instance of audio data capturing a user voice query using an automatic speech recognition model to generate a sequence of instances of tokenizable query text. In many implementations, one or more instances of the sequence can be transmitted to a remote computing system prior to generating the entire sequence. In a variety of implementations, each instance in the sequence can be processed using a generative model which includes a streaming multi-head attention portion. Responsive output can be transmitted from the remote computing system to the client device, where the client device renders the responsive output to the user. In many implementations, the time between the user speaking the user query and the client device rendering the responsive output is reduced, thus decreasing latency in the system.
Provided are systems and methods that leverage machine learning to perform audio editing with improved precision and flexibility. Some example systems utilize a vector-based audio editing representation to condition and control a machine-learned audio editing model. This approach allows for detailed and precise control over audio edits by encoding the edits numerically and processing the encoding with a trained model. The system can handle a variety of edits, including audio generation, removal, transformation, time-shifting, and/or enhancement, thereby providing a comprehensive tool for audio manipulation.
A first node of a radio access network (RAN) transmits (304), to a second node, a request to operate as a secondary node (SN) and provide dual connectivity (DC) to a user equipment (UE), with the first node operating as a master node (MN), the request including an indication of continuous conditional secondary cell addition or change (CPAC); receives (306), from the second node and in response to the request, a first conditional SN (C-SN) configuration; and transmits (308), to the UE, the first conditional SN configuration and a second conditional SN configuration related to at least one cell not associated with the second node
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for SSB structures for fast UE beam tracking. A UE (102) receives (306), from a network entity (104), a first time-domain portion of an SSB associated with a first beam and a second time-domain portion of the SSB associated with a second beam. The first time-domain portion and the second time-domain portion occur within a same symbol. The UE (102) communicates (310) with the network entity (104) based on an intra-symbol beam sweeping procedure that includes a measurement (308) of the first time-domain portion of the SSB and the second time-domain portion of the SSB.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for composed image retrieval. In one aspect, a method performed by one or more computers is described. The method includes: receiving a query including: (i) an image depicting a scene, and (ii) a text prompt describing a context of the scene; processing the image, using a visual encoder, to generate a visual embedding of the image; processing the visual embedding of the image, using a mapping neural network, to generate one or more language tokens of the image; generating multiple language tokens of the text prompt; processing the language tokens of the image and text prompt, using a language encoder, to generate a language embedding of the query; and selecting, from a number candidate images, one or more of the candidate images using the language embedding of the query.
A method includes receiving a TTS end event indicating that audible output of first TTS audio from a user device is finished. Based on receiving the TTS end event, the method also includes instructing an ASR system to use a first level of ASR processing for performing speech recognition. As a user speaks a natural language query that solicits a second response from a LLM-powered assistant, the method includes performing, by the ASR system, using the first level of ASR processing, speech recognition on the audio data to generate a speech recognition result and processing, by the LLM-powered assistant, the speech recognition result to generate the second response. Based on receiving a TTS start event indicating that second TTS audio is about to be audibly output from the user device, the method includes instructing the ASR system to use a second level of ASR processing.
A reference line index indicating a first reference line from a plurality of available reference lines peripheral to a current block for intra-prediction is decoded from a compressed bitstream. A determination is made whether the reference line index indicates a reference line other than a default reference line with respect to the current block. In response to the reference line index indicating a reference line other than the default reference line, a determination is made as to whether to combine predictions from the first reference line and the default reference line. An intra predictor for the current block is generated based on the first reference line and the determination. The determination may be made based on a flag decoded from the compressed bitstream and that indicates whether to combine predictions from the first reference line and the default reference line.
H04N 19/105 - Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
H04N 19/11 - Selection of coding mode or of prediction mode among a plurality of spatial predictive coding modes
H04N 19/157 - Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/593 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
H04N 19/70 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
22.
DEVICE STATE AWARE DYNAMIC AUTOMATIC SPEECH RECOGNITION OPTIMIZATION
A method (400) includes receiving a TTS end event (50) indicating that audible output of first TTS audio (174) from a user device (110) is finished. Based on receiving the TTS end event, the method also includes instructing an ASR system (200) to use a first level of ASR processing for performing speech recognition. As a user speaks a query (102) that solicits a response (165) from a LLM-powered assistant (160), the method includes performing, by the ASR system, using the first level of ASR processing, speech recognition on the audio data to generate a speech recognition result and processing, by the LLM-powered assistant, the speech recognition result to generate the second response. Based on receiving a TTS start event indicating that second TTS audio is about to be audibly output from the user device, the method includes instructing the ASR system to use a second level of ASR processing.
Methods and devices outside a core network facilitate controlling user equipment's (UE) access using a specific radio access technology (RAT). A method (1100) performed by a UE receiving (1148) first broadcast system information including a RAT-use constraint related to a specific RAT. The method further includes assessing (1179) the RAT-use constraint for the UE and then selectively requesting (1156) network access using the specific RAT depending on the result of the assessing. A method (1200) performed by a base station (104) using a specific RAT includes receiving (1201), from a network core, a RAT-use constraint for UEs having a given UE-network relationship, and receiving (1202), from a UE, a network access request to connect to the network using the specific RAT. The method further includes rejecting (1236) the network access request upon assessing that the UE has the given UE-network relationship with the network.
A method and system to control a pre-ranging synchronization process between a first device and a second device, the pre-ranging synchronization being for synchronizing wireless communication between the first device and the second device. The method includes the first device selecting, from an adaptive-frequency-hopping (AFH) channel map that the first device is set to use for wireless data communication with the second device, at least one channel on which to engage in the pre-ranging synchronization process with the second device. Further, the method includes, based on the selecting of the at least one channel from the AFH channel map, the first device engaging in the pre-ranging synchronization process with the second device on the at least one channel. The first device may then use the synchronized wireless communication in a ranging process between the first device and the second device.
A method and system for detecting and responding to a change in rate of control signaling. A processor receives, respectively for each of a plurality of time periods, timestamped logs of control-signaling messages communicated among entities in a system, the messages in each time period having message types and being in a respective time sequence that defines multiple message-type subsequences each being a series of messages of respective message types. The processor establishes, based on the received logs and the message types, and respectively for each time period, a statistical distribution of the message-type subsequences defined by the sequence of messages communicated among the entities during the time period. Further, the processor detects, based on a comparison of the statistical distributions established for first and second time periods, a change in rate of occurrence of a given message-type subsequence. The processor then takes action in response to the detecting.
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
H04L 41/069 - Management of faults, events, alarms or notifications using logs of notificationsPost-processing of notifications
H04L 43/04 - Processing captured monitoring data, e.g. for logfile generation
H04L 43/067 - Generation of reports using time frame reporting
The present disclosure describes various aspects of implementing an efficient initial credit exchange for source-synchronous links. In some aspects, an initiator (302) and a target (304) communicate data over a source-synchronous link pair. The target (304) provides an initial credits (410) to the initiator (302) for an amount of data that can be received by the target (304) using existing credit exchange wires without the addition of initialization wires between the initiator (302) and target (304) in a system-on-chip. The initiator (302) counts (415) the initial credits received from the target (304) and transmits (425) one or more clock cycles of data to the target (304) the one or more clock cycles being equal to or less than a number of the received initial credits (410).
G06F 13/364 - Handling requests for interconnection or transfer for access to common bus or bus system with centralised access control using independent requests or grants, e.g. using separated request and grant lines
H04L 47/283 - Flow controlCongestion control in relation to timing considerations in response to processing delays, e.g. caused by jitter or round trip time [RTT]
A waveguide include at least one one-dimensional (1D) grating disposed at the waveguide. The 1D grating includes a periodic structure defined along a single axis, the periodic structure including alternating regions of differing refractive indices. The 1D grating also includes a plurality of high-refractive-index regions within each period of the periodic structure. In another configuration, the waveguide includes at least one two-dimensional (2D) grating disposed at the waveguide. The 2D grating includes a lattice structure defined along a first axis and a second axis perpendicular to the first axis, the lattice structure comprising alternating regions of differing refractive indices. The 2D grating also includes a plurality of high-refractive-index regions within each unit cell of the lattice structure.
Methods, systems, and apparatus for implementing a quantum circuit that moves a surface code patch of qubits. In one aspect, a method includes performing a first surface code cycle in a system of measure and data qubits. A first CNOT gate is applied to a measure qubit and a first data qubit, where the first data qubit is coupled to the measure qubit in a first direction and the first CNOT gate targets one of the measure qubits and the first data qubit. A second CNOT gate is applied to the measure qubit and the first data qubit, where the second CNOT gate targets another of the measure qubit and the first data qubit. Performing the first surface code cycle transfers information stored by the measure qubit and information stored by the first data qubit to other qubits to logically move the measure qubit and the first data qubit.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task using a neural network that includes a Mixture of Experts (MoE) subnetwork configured to process an input tensor to generate an output tensor. The MoE Subnetwork includes a set of multiple expert blocks, each configured to process a corresponding partition of the input tensor to generate a corresponding partition of the output tensor.
Techniques include storing semantic information in data structures that are updated at a rate less than a nominal frame rate. For example, a semantic model is applied to a first camera image frame in a sequence of frames to produce semantic data for each pixel in the frame. In addition, a first edge image is derived from the first camera image frame to define boundaries for the semantic labels. A first semantic image may be derived from the semantic data and the first edge image. The semantic data is then stored in the data structures. For a second camera image frame of the sequence, a second edge image is derived but the semantic data is exported from the data structures to the second edge image to produce a second semantic image without generating new semantic data.
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
A logic circuit (900) includes a power source (Vdd), a biasing circuit (910), a digital cell (904), a first p-channel metal oxide semiconductor (PMOS) (902) electrically connected between the power source and the digital cell, a second PMOS (906) electrically connected between the power source and a first input (908, INP) of the biasing circuit, and a third PMOS (912) electrically connected between the power source and a second input (914, INM) of the biasing circuit, wherein the digital cell is connected to a common mode voltage (908, virtual Vdd) of the biasing circuit, such that the common mode voltage of the biasing circuit is substantially equal to a voltage (virtual Vdd) received by the digital cell.
A radio access network (RAN) node can implement a method for managing lower layer triggered mobility protocol procedure(s). The method includes: transmitting, from the RAN node to a user equipment (UE) communicatively coupled to the RAN node via a serving cell, a lower layer triggered mobility (LTM) configuration to configure a non-serving cell for the UE; in a first instance, when the non-serving cell is synchronized with the serving cell, transmitting, from the RAN node to the UE, a first indication for the UE to perform a serving cell change to the non-serving cell and refrain from performing a random access procedure; and in a second instance, when the non-serving cell is not synchronized with the serving cell, transmitting, from the RAN node to the UE, a second indication for the UE to perform the serving cell change and perform the random access procedure.
Provided are systems and methods that enable deterministic inference for machine learning models with variable behavior. In particular, the present disclosure relates to a system in which a machine-learned model has a variable processing portion that is configured to variably apply one or more of a plurality of different processing operations when processing an input. According to an aspect of the present disclosure, one or more seed values can be used to deterministically control which of the plurality of different processing operations are applied by the machine-learned model when processing a given set of input data.
Methods, systems, and techniques are disclosed herein for acquiring and indicating timing advance values for random access procedures. The disclosed methods and techniques may be used in operations of L 1/L2 trigger mobility (LTM) or multi-transmission-reception-point (mTRP) with two timing advance (2TA). At a high level, this disclosure pertains to performing random access (RA) procedure in LTM, mTRP 2TA, or similar procedures involving two or more cells or TRPs. The cell switching may involve some procedures and/or issues regarding TA acquisition and TA indication. For examples, a user equipment (UE) may need to acquire (e.g., via performing an RA procedure) a TA value for a candidate cell or target cell prior to completion of a LTM procedure. The disclosure also teaches how to indicate TA value applicable for a candidate cell or a target cell.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, associated with calculating a CSI dwelling time. A UE (102) calculates (510) a channel state information (CSI) dwelling time based on a measurement of a channel state information reference signal (CSI-RS) on at least one CSI-RS resource. Based on the CSI dwelling time, the UE (102) sends (512), to a network entity, at least one report.
H04B 7/06 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04W 8/22 - Processing or transfer of terminal data, e.g. status or physical capabilities
36.
VIEWPOINT INVARIANT VISUAL SERVOING OF ROBOT END EFFECTOR USING RECURRENT NEURAL NETWORK
Training and/or using a recurrent neural network model for visual servoing of an end effector of a robot. In visual servoing, the model can be utilized to generate, at each of a plurality of time steps, an action prediction that represents a prediction of how the end effector should be moved to cause the end effector to move toward a target object. The model can be viewpoint invariant in that it can be utilized across a variety of robots having vision components at a variety of viewpoints and/or can be utilized for a single robot even when a viewpoint, of a vision component of the robot, is drastically altered. Moreover, the model can be trained based on a large quantity of simulated data that is based on simulator(s) performing simulated episode(s) in view of the model. One or more portions of the model can be further trained based on a relatively smaller quantity of real training data.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for using PT-RS (202) in uplink multi-beam transmission schemes. A UE (102) receives (408a, 808a), from a network entity (104), a configuration for a multi-beam PUSCH transmission. The UE (102) transmits (412, 812), to the network entity (104), the multi-beam PUSCH transmission and a PT-RS based on a number of PT-RS ports and a DMRS port associated with the number of PT-RS ports.
H04B 7/06 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
A first node (MN 104A) of a radio access network (RAN) transmits (340), to a second node (S-SN 106B) of the RAN, a request for a reference conditional secondary node (C-SN) configuration related to a continuous primary secondary cell (PSCell) addition or change (CPAC) procedure; receives (342), from the second node and in response to the request, the reference C-SN configuration; and transmits (308), to the UE, a C-SN configuration based on the reference C-SN configuration, for performing a plurality of conditional candidate cell changes based on the C-SN configuration.
Methods and devices in a wireless network embody techniques used for switching between sensing tasks, modes or configurations or between communication and sensing. A method (300) performed by a sensing device in a wireless communication network includes receiving (340), from a network entity (110), a first configuration and a second configuration. The first and second configurations are associated with at least one of a sensing mode, a communication mode, or both sensing and communication. The method includes performing (342) a first task according to the first configuration. The method continues with detecting (344) a trigger event, and selectively starting (348) a second task performed according to the second configuration.
A user equipment (UE) determines a user consent preference with respect to data collection. The UE communicates, with a core network (CN) and in a non-access stratum (NAS) message, an indication of the user consent preference.
Methods and devices in a wireless network enable transmitting (304), from a network entity (NE) to a user equipment (UE), a control signaling for configuring an initial UE-beam operation. The initial UE-beam operation is related to using a single-beam operation or a multiple-beam operation between the NE and the UE. The NE and UE communicate (312) based on the initial UE-beam operation.
H04B 7/06 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
H04L 5/00 - Arrangements affording multiple use of the transmission path
42.
OPTIMIZED CONTROL OF ELEMENTS OF SEMICONDUCTOR DEVICES UTILIZING MULTIMODAL MACHINE-LEARNED MODELS
Aspects of optimized control of elements of semiconductor devices utilizing multimodal machine-learned models are disclosed. An optimizer unit may optimize the operation of elements of a semiconductor device utilizing a multimodal machine-learned model. The optimizer unit may determine optimal operations of element(s) of the semiconductor device based on a maximum performance state of the element(s), a minimum performance state of the element(s), and a performance residency state of the element(s). The maximum performance state may be determined based on a thermal control policy and thermal aspects of the element(s) as well as thermal aspects of an enclosure of the semiconductor device. The minimum performance state may be determined based on a latency policy and latency of the element(s). The maximum performance state may ensure thermal control of the element(s) and the minimum performance state may ensure a maximum latency of the element(s).
Methods and systems, including computer-readable media, are described for a multiplication scheduler for quantized inference (MSQI) computations. The multiplication scheduler is implemented as a MSQI hardware unit on a compute tile/unit of a special-purpose hardware integrated circuit, such as an inference accelerator or tensor processing unit (TPU). The multiplication scheduler is designed to optimize multiplication operations in hardware ML inference accelerators by minimizing or eliminating redundant computations (e.g., multiplications) on prevalent quantized formats.
This disclosure provides methods and apparatuses for multiplexing communication data resources and sensing resources. A network entity (120) transmits one or more sensing configurations 172 to a UE (130) that configure the UE with multiple sensing modes, including at least a first sensing mode and a second sensing mode. The UE performs at least first sensing operations for the first sensing mode using one or more first resources, and second sensing operations for the second sensing mode using one or more second resources. The network entity further communicates with the UE using one or more third resources multiplexed (174) with first resources and second resources.
Systems and methods for optimizing content layout using behavior metrics are provided. Behavioral data is collected for a content item presented on a client device. The behavioral data indicates various locations within a display area of the content item at which a user action (e.g., clicking) occurs. The behavioral data is used to assign weights to various components of a layout scheme for the content item. A user action that occurs within an area of the content item associated with a particular component of the layout scheme contributes to the weight of the corresponding component. The weights associated with each component of the layout scheme are used to optimize the layout for the content item. Components with greater assigned weights are highlighted or emphasized. The display sizes of components in the optimized layout scheme may correspond to the weights associated with the components.
G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
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
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 9/451 - Execution arrangements for user interfaces
G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
A system and method provides for the transfer of the output of content from a user device to an external device for output by the external device. External devices may be detected in a physical space, and identified based on previous connection with the user device, based on a shared network or shared system of connected devices including the user device, based on image information captured by the user device and previously stored anchoring information that identifies the external devices, and the like. An external device may be selected for potential output of the content based on previously stored configuration information associated with the external device including, for example, output capabilities associated with the external device. The identified external device may output the transferred content in response to a user verification input, verifying that the content is to be output by the external device.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
H04W 4/029 - Location-based management or tracking services
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software for editing, uploading, capturing,
showing, playing, streaming, transmitting, viewing,
previewing, displaying, tagging, distributing, publishing,
reproducing, linking, commenting on, finding, generating,
processing, sharing, storing, and revising videos,
electronic media, multimedia content, movies, pictures,
images, text, photos, user-generated content, audio content,
and information, and also for adding special effects to
videos, electronic media, multimedia content, movies,
pictures, images, text, photos, user-generated content, and
audio content; downloadable software using AI for the
production, display, streaming, enhancing, and editing of
video, images, music, sound, audio, speech and text;
downloadable software for facilitating and processing
multi-modal natural language, speech, text, image, video,
and sound input and queries; downloadable software for use
in the fields of artificial intelligence, machine learning,
natural language generation, statistical learning,
mathematical learning, supervised learning, and unsupervised
learning; downloadable computer software for social
networking and interacting with online communities;
downloadable software for building, managing, updating,
developing, training, evaluating, and monitoring generative
user experiences powered by machine learning, deep learning,
and artificial intelligence; downloadable software for
developing AI applications and features to enhance user
experience and ecosystem. Providing online non-downloadable software for editing,
uploading, capturing, showing, playing, streaming,
transmitting, viewing, previewing, displaying, tagging,
distributing, publishing, reproducing, linking, commenting
on, finding, generating, processing, sharing, storing, and
revising videos, electronic media, multimedia content,
movies, pictures, images, text, photos, user-generated
content, audio content, and information, and also for adding
special effects to videos, electronic media, multimedia
content, movies, pictures, images, text, photos,
user-generated content, and audio content; software as a
service (SAAS) services featuring software for editing,
uploading, capturing, showing, playing, streaming,
transmitting, viewing, previewing, displaying, tagging,
distributing, publishing, reproducing, linking, commenting
on, finding, generating, processing, sharing, storing, and
revising videos, electronic media, multimedia content,
movies, pictures, images, text, photos, user-generated
content, audio content, and information, and also for adding
special effects to videos, electronic media, multimedia
content, movies, pictures, images, text, photos,
user-generated content, and audio content; providing online
non-downloadable software using AI for the production,
display, streaming, enhancing, and editing of video, images,
music, sound, audio, speech and text; providing online
non-downloadable software for facilitating and processing
multi-modal natural language, speech, text, image, video,
music, audio, and sound input and queries; 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
information from searchable indexes and databases of
information, including text, music, sound, audio, images,
videos, software algorithms, mathematical equations,
electronic documents, and databases, by means of
non-downloadable chatbot software (term considered too vague
by the International Bureau pursuant to Rule 13 (2) (b) of
the Regulations); software as a service (saas) services for
building, managing, updating, developing, training,
evaluating, and monitoring generative user experiences
powered by machine learning, deep learning, and artificial
intelligence; providing online non-downloadable computer
software for social networking and interacting with online
communities; providing online non-downloadable software for
developing AI applications and features to enhance user
experience and ecosystem; research, design, and development
services in the field of artificial intelligence and machine
learning; research, design, and development of AI software
features to enhance user experience.
53.
Integrated Circuit Design with Logic Cells Associated with Dependent Operating Elements Arranged in a Widening Structure
This document describes systems and techniques for designing an integrated circuit with logic cells associated with dependent operating elements arranged in a widening structure. For example, a method includes associating logic cells into a plurality of groups associated with one of a plurality of dependent operating elements that each depend on a base operating element. The plurality of dependent operating elements are arranged in a widening structure at which the base operating element is at a proximal end of the widening structure and one or more levels of dependent operating elements are hierarchically arranged from the base operating element at the proximal end to a distal level of one or more operating elements at a distal end. Each of the plurality of groups of logic cells are clustered around each of the plurality of dependent operating elements with which the logic cells are associated.
A superconducting structure is provided. In one example, the superconducting layer includes a substrate, a patterned seed layer on the substrate and a superconducting layer on the patterned seed layer and the substrate. The superconducting layer includes a first portion with a first resistivity directly on the substrate and a second portion with a second resistivity directly on the patterned seed layer.
A method for text asset selection includes determining preferred text-content pairs based at least in part on predicted performance outcomes for a plurality of text-content pairs. The method also includes obtaining a plurality of content items from a digital content collection; obtaining a plurality of text assets; generating a plurality of sample text-content pairs; determining performance outcomes for the plurality of sample text-content pairs; generating a text asset performance model based on the performance outcomes for the plurality of sample text-content pairs; applying the text asset performance model to each target text-content pair of a plurality of target text content pairs to generate predicted performance outcomes for the plurality target text-content pairs; determining one or more preferred text-content pairs based at least in part on the predicted performance outcomes for the plurality of target text-content pairs; and providing the one or more preferred text-content pairs to one or more devices.
Techniques and apparatuses are described for handling a one-to-multiple handshake with asynchronous completion. In example aspects, a system-on-chip is implemented with at least one handler, at least one second entity, and at least one subsystem having multiple first entities and at least one scheduler. To facilitate communications between the multiple first entities of the subsystem and the second entity, the handler performs a request-and-acknowledgement handshake with the scheduler. Additionally, the handler performs multiple request-and-acknowledgement handshakes with the second entity. The handler completes the request-and-acknowledgement handshake with the scheduler based on the completion of the multiple request-and-acknowledgement handshakes with the second entity. This enables the handler to address a situation in which the multiple request-and acknowledgement handshakes with the second entity complete in an asynchronous manner.
Techniques for an artificial intelligence (AI) function in spreadsheet documents is provided herein. A user is provided with access to a spreadsheet document via a user interface (UI). A user query pertaining to an operation associated with an AI model is received based on a user interaction with one or more cells of the spreadsheet document. The UI is updated to include the received user query in the cell(s) and one or more UI elements that enable the user to initiate the operation associated with the AI model. Responsive to a detection of a user interaction with the UI element(s), a prompt associated with the user query is provided as an input to the AI model. One or more outputs of the AI model are obtained, the output(s) including response data pertaining to the user query. The UI is updated to include the response data in the cell(s).
Implementations set forth herein relate to an application that can receive gestures at portions of generative content so that generative content can be intelligently selected and/or supplemental generative content can be rendered without requiring another detailed prompt from the user. Each portion of generative content can be identified based on semantic relationship between terms that would comprise the portion. These portions of the generative content can be predetermined or determined in response to receiving input from the user (e.g., a gesture). A particular portion of generative content can be assigned one or more GUI features that can indicate portion(s) of the generative content is available for receiving one or more gestures. When the user directs a gesture at the particular portion of the generative content, one or more models can be utilized to select portion(s) of the generative content and/or to provide supplemental generative content for the user.
G06F 3/04842 - Selection of displayed objects or displayed text elements
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
G06F 3/0488 - 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
A multi-layer printed circuit board (PCB) and a method of manufacturing thereof are provided. The PCB includes a core layer, a first stack of alternative layers including metal layers and dielectric layers stacked alternatively over a top surface of the core layer, a second stack of alternative layers including metal layers and dielectric layers stacked alternatively over a bottom surface of the core layer, and an embedded via structure through the core layer and connecting a first metal layer of the first stack of alternative layers to a second metal layer of the second stack of alternative layers. The embedded via structure includes a first signal path layer through the core layer, a first ground layer through the core layer and enclosing the first signal path layer, and a first dielectric layer through the core layer and between the first signal path layer and the first ground layer.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a translation lookaside buffer to use stream id masking. A system includes a translation lookaside buffer unit configured to map an input translation request to a physical address, in which each input translation request includes a virtual address and stream id. The system further includes stream id masking logic that is configured to mask the stream id of related input translation requests, generating a masked translation request. Each masked translation request includes a virtual address and masked stream id, in which the translation lookaside buffer services the input translation request using the masked translation request.
Methods, systems, and apparatus, for aligning clock edges of a plurality of on-chip clock controllers. One of the methods includes receiving, by an on-chip edge alignment controller, a scan enable signal. A counter is incremented using a fastest clock among a plurality of on-chip clock controllers. When the counter reaches a value that is based on a least-common multiple between the fastest clock and a slowest clock, a trigger output value is generated on a trigger output port.
The present disclosure provides a diffractive element between a light engine and a waveguide and a corresponding incoupler architecture for eyewear displays. The eyewear display includes a light engine to project light associated with displaying an image; a waveguide including a plurality of incouplers; and a diffractive element between the light engine and the waveguide to receive the light projected by the light engine, split the light into a plurality of diffraction orders of light, and direct one diffraction order of light of the plurality of diffraction orders of light to one incoupler of the plurality of incouplers.
A method includes obtaining an image and, based thereon, generating a bilateral grid representing transform coefficients configured to map pixel values of the input image to filter guide values. Each respective transform coefficient may be based on a corresponding class of an image feature represented by a corresponding pixel of the image. The corresponding class may be one of multiple image feature classes represented by the image. The method may also include generating, based on the bilateral grid, a transform coefficient corresponding to a selected pixel of the image, and applying the transform coefficient to a pixel value of the selected pixel to generate a filter guide value. The method may further include generating an output image by applying an operator to the pixel value based on the filter guide value. A parameter of the operator may differ across the multiple classes as a function of the filter guide value.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic protocols to generate network measurements in privacy preserving ways are described. In one aspect, a method includes sending, by a network measurement system and to a client device, data indicating a size for a partial identifier for an application of the client device. The network measurement system receives, from the client device, (i) a partial masked identifier generated by masking a complete identifier for the client device or a user of the client device and removing a portion of a resulting complete masked identifier based on the size and (ii) a first encrypted identifier generated by encrypting the complete masked identifier using an encryption key of the client device.
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
To perform inter-frequency measurements, a UE in an NTN cell receives a reference location of the NTN cell and one or more pairs of (i) a TN frequency, and (ii) a bitmap indication identifying one or more of a plurality of segments of the NTN cell with which the TN frequency is associated, the plurality of segments being determined by dividing the NTN cell in N sectors around the reference location, when the bitmap indication includes N bits. The UE searches for a TN cell on the TN frequency only if a current location of the UE is within the one or more of the plurality of segments with which the TN frequency is associated according to the bitmap indication.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task. In particular, the machine learning task is performed by augmenting a trained neural network with residual memorization.
Multiple wavefronts and multiple wavefront groups are configured for a tile of a current frame. Each wavefront group of the multiple wavefront groups comprises a set of consecutive coding unit rows. Each wavefront of the multiple wavefronts comprises coding unit rows selected at intervals across the tile such that an nth row of each of the multiple wavefront groups belongs to an nth wavefront. For each wavefront of at least some of the multiple wavefronts, a probability model is initialized for a first row of the wavefront. The probability model is updated during coding of subsequent rows of the wavefront using finishing probability values from a previous row of the wavefront.
H04N 19/196 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters
H04N 19/14 - Coding unit complexity, e.g. amount of activity or edge presence estimation
H04N 19/174 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a slice, e.g. a line of blocks or a group of blocks
H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.
Provided are methods, systems, devices, and tangible non-transitory computer readable media for providing data including vehicle map service data. The disclosed technology can perform operations including accessing vehicle map service data including information associated with a geographic area and sensor observations of a vehicle. The vehicle map service data is based on a vehicle map service protocol specifying layers associated with portions of the vehicle map service data to which each client system is subscribed. Further, the layers to which the client systems are subscribed can be determined and access to the plurality of layers to which each of the vehicle client systems is subscribed can be provided to each of the client systems. Access to the layers can include authorization to send or receive one or more portions of the vehicle map service data associated with a corresponding layer.
H04W 4/44 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
G01C 21/00 - NavigationNavigational instruments not provided for in groups
G01C 21/36 - Input/output arrangements for on-board computers
A computer-implemented method for transcribing an utterance includes receiving, at a computing system, speech data that characterizes an utterance of a user. A first set of candidate transcriptions of the utterance can be generated using a static class-based language model that includes a plurality of classes that are each populated with class-based terms selected independently of the utterance or the user. The computing system can then determine whether the first set of candidate transcriptions includes class-based terms. Based on whether the first set of candidate transcriptions includes class-based terms, the computing system can determine whether to generate a dynamic class-based language model that includes at least one class that is populated with class-based terms selected based on a context associated with at least one of the utterance and the user.
Provided are improved machine learning-based text editing models. Specifically, example implementations include a flexible semi-auto-regressive text-editing approach for generation, designed to derive the maximum benefit from non-auto-regressive text-editing and autoregressive decoding. In contrast to conventional sequence-to-sequence (seq2seq) models, the proposed approach is fast at inference time, while being capable of modeling flexible input-output transformations.
The technology is generally directed to a coherent optical system that performs phase folding for one-dimensional coherent optical signals using a first optical coupler configured to output a first signal portion and a second signal portion, a second optical coupler to receive the first signal portion through a first branch and the second signal portion through a parallel second branch, and a phase-folding time delay unit including at least a first time delay component positioned on the first branch and a second time delay component positioned on the second branch. An output of the second optical coupler is a phase-folded version of the one-dimensional modulated optical signal that is capable of being demodulated by a one-dimensional vector receiver.
A method includes receiving a natural language query specifying an action for an assistant interface to perform and selecting one or more business large language models (LLMs) for the assistant interface to interact with to fulfill performance of the action. For each business LLM, method also includes accessing an adapter module to structure the natural language query into a respective prompt specifically formulated for the corresponding business LLM, issuing, for input to the corresponding business LLM, the respective prompt, and receiving corresponding response content from the corresponding business LLM that conveys details regarding performance of a corresponding portion of the action. The method also includes presenting, for output from the user device, presentation content based on the corresponding response content received from each corresponding business LLM.
This document describes techniques and apparatuses for automatic white-balance for a camera system. The techniques and apparatuses utilize a precursor image to detect one or more detected faces and determine a tone. The camera system retrieves tonal data based on a group of images determined to contain a same face as the detected face. Based on this tonal data, a difference in white balance is determined based on the difference in tone of the detected face within the precursor image and the associated tonal data. Camera settings are adjusted based on the difference in white balance to enable capture of an image having an improved tone.
H04N 23/88 - Camera processing pipelinesComponents thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
H04N 23/12 - Cameras or camera modules comprising electronic image sensorsControl thereof for generating image signals from different wavelengths with one sensor only
H04N 23/57 - Mechanical or electrical details of cameras or camera modules specially adapted for being embedded in other devices
H04N 23/611 - Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
H04N 23/63 - Control of cameras or camera modules by using electronic viewfinders
75.
DYNAMIC CARRIER SELECTION IN WIRELESS COMMUNICATION SYSTEMS
Systems, methods, and apparatuses for wireless communication include techniques for dynamic selection of carriers. A network entity (120) can configure a user equipment (130) with carriers and carrier selection criteria. The UE selects carriers for uplink and downlink communication based on the carrier selection criteria or based on an indication of a carrier received from a network entity. The UE can select carriers for performing a random access procedure using multiple carriers. The UE can select between a first carrier and a second carrier to transmit or receive transport blocks or portions of transport blocks on multiple carriers. The UE can monitor the performance of a carrier and report a performance failure or a carrier to the network entity.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for CSI accuracy enhancement in wireless communication systems. A UE (102) receives (304) a configuration for a CSI report including a CMR and a target BLER, the CSI report being based on at least one of: a first factor corresponding to an association of a SINR and a BLER, a second factor corresponding to a number of repetitions of a PDSCH, or a third factor corresponding to whether the CSI report is for a PDCCH, a PDSCH, or both the PDCCH and the PDSCH. The UE (102) receives (308), from the network entity (104), a CSI-RS on the CMR. The UE (102) transmits (310), to the network entity (104), the CSI report including CSI calculated based on the CSI-RS and the at least one of: the first factor, the second factor, or the third factor.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a data collection configuration scheme in a wireless communication system. A UE receives (306), from a network entity (104), a data collection configuration (450) for performing data collection at the UE (102). The data collection configuration including at least one of: data collection identity information (452), data collection object information (454), or data collection type information (456). The UE (102) transmits (316), to the network entity (104), a report based on the data collection configuration.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reporting functionality combination information. A user equipment, UE (102) transmits (405), to a network entity (104), functionality combination information indicating at least two functionalities for simultaneous performance by the UE (102). The UE (102) communicates (415), with the network entity (104), based on the functionality combination information.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for cryptographically implemented data restrictions. One of the methods includes receiving a data access request prompted by user interaction on an application executing on the client device; transmitting an attestation nonce request to a secure processor of the client device; generating a cryptographic key pair and an attestation nonce; transmitting data comprising (i) the public key from the cryptographic key pair and (ii) the generated attestation nonce; validating an attestation packet received from the server in response to the transmission of the data; in response to validating the attestation packet, transmitting data restrictions specified by the user of the client device; generating a signed policy including the data restrictions; and transmitting the signed policy to the server specifying the data restrictions for the server to abide by.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
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
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating an output of a deployed machine learning system. In one aspect a method comprises maintaining a set of one or more signatures that each represent a respective set of inputs to a base machine learning model that have been determined to degrade a performance of the base machine learning model, receiving a new input to the base machine learning model, for each of the set of one or more signatures, processing the new input and the signature using an auxiliary machine learning model to generate a respective auxiliary output that indicates whether the new input belongs to the respective set of inputs represented by the signature, and modulating an output of the base machine learning model for the new input based on the respective auxiliary outputs for the set of one or more signatures.
A method efficiently tests campaign configuration modifications with reduced risk of performance degradation. The method includes obtaining content sponsor data for content sponsor(s) associated with respective campaign(s), generating a prompt based at least in part on the content sponsor data, and generating candidate modifications at least in part by applying the prompt to a generative artificial intelligence (Al) model. The method also includes generating test configuration(s) that differ from the configuration(s) of the respective campaign(s) in accordance with a first candidate modification, and applying the test configuration(s) to a virtual test environment that is isolated from a production environment. The method also includes determining performance metric(s) associated with applying the test configuration(s) to the virtual test environment.
A docking system may include a dock that stores a dock identifier and an electronic device that docks with the dock. The electronic device can receive the dock identifier, obtain wireless network information, and create a hash value based on the dock identifier and additional information, such as wireless network information. The hash value may be analyzed using a stored hash to identify a match. In response to the match, the system may permit access to a restricted feature while the electronic device remains docked.
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
Provided are systems and methods that enable computerized systems to optimize predictions of joint label probabilities of lists of items returned in response to a query. The proposed approaches take into account the effects of relational interactions among items included in the same list and can improve predictions of a deployed model for either predicting engagement rates, ranking, and/or distillation. Unlike methods that combine label engagement loss with a ranking loss, the proposed methods directly model the joint probability of the vector of labels in a list of examples included in a training dataset (e.g., by modeling conditional probabilities) instead of modeling the marginal and conditional projections of the joint probability and empirically balancing between them to infer some approximation of the joint probability. The approach gives models the ability to refine their label predictions based on the contexts of co-recommended items.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing network inputs using recurrent interface networks.
Generally, the present disclosure is directed to methods and systems for automatically generating data that encodes natural language conversations between at least two parties. The conversational data may be automatically generated by one or more language generative models. As such, the automatically generated conversational data may be referred to as synthetic conversational data. The synthetic conversational data may simulate the speech patterns (e.g., prompts, responses to prompts, and combinations thereof) of one or more hypothetical or real humans (e.g., users) participating a conversation. In various applications, the synthetic conversational data is employed to train, pre-train, fine-tune, and/or evaluate the performance of at least one of the generative language models employed to generate the synthetic conversational data and/or other generative language models. Such other generative language models may be employed in various interactive recommendation systems, chat-bots, or any other application that interacts with one
A computing device may receive an indication of a handwriting input that includes a sequence of handwritten characters, where the sequence of handwritten characters includes a sequence of word abbreviations. The computing device may determine, using a language model, a sequence of candidate words that correspond to the sequence of word abbreviations. The computing device may output, for display at a display device, a user interface that presents tire sequence of candidate words.
G06F 3/023 - Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
G06F 3/04883 - 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 for inputting data by handwriting, e.g. gesture or text
G06F 40/274 - Converting codes to wordsGuess-ahead of partial word inputs
87.
Learning to Rank via Univariate Ordinal Regression
A relaxed solution of ordinal regression, which constrains the solution to a grid of thresholds learned over training example data is disclosed. A single shift parameter is predicted for each example, shifting that grid to produce a distribution of label predictions. Ranking losses for both pairwise on pairs of examples, and listwise on lists of several examples are disclosed. The use of distillation for both pointwise and ranking losses are described. Use of relaxed ordinal regression with a pre-set grid of thresholds are described, and. an additional temperature parameter is introduced to the different losses and settings as a. method that allows for more flexibility on the solution's modality, which can help in providing unimodality when possible or necessary, but allowing for multimodality when necessary.
Techniques and apparatuses are described for performing on-head detection and/or hearable-adjustment detection using active acoustic sensing. A hearable, such as an earbud, is capable of performing audioplethysmography. Audioplethysmography is an active acoustic method capable of sensing subtle physiologically-related changes observable at a user's outer and middle ear. The hearable forms at least a partial seal in or around the user's outer ear, which enables formation of an acoustic circuit involving the seal, the hearable, an ear canal, and an ear drum. By transmitting and receiving acoustic signals, the hearable can recognize changes in the acoustic circuit to perform aspects of on-head detection and/or hearable-adjustment detection. The size, cost, and power usage of the hearable can help make these features accessible to a larger group of people and improve the user experience with hearables.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for multiplexing UCI (508) for multiple codewords on one or more beams. A UE (102) multiplexes, on PUSCH resources (202), UCI (508) for a plurality of codewords to generate a multiplexed UCI. The plurality of codewords is associated with the one or more beams. The UE (102) transmits (316, 716), to a network entity (104), the multiplexed UCI (508) on the PUSCH resources (202).
Waveguides for displays constructed from a combination of flat and curved surfaces using plural incouplers include additional incouplers incorporated into a waveguide spaced from one another at precise angles, e.g., that match or correspond to grating angles associated with the waveguides, allowing the injection of light in multiple locations to the same grating structure while still maintaining k-space closure and thus preventing unintended refraction or distortion. Regions immediately surrounding incouplers and outcouplers in the waveguide include flat surfaces, while regions between the incouplers and outcouplers-include one or more curved surfaces.
A device includes a plurality of cores having a plurality of configurable self-repair pipelines, wherein each core of the plurality of cores comprises a plurality of pipeline flops for routing self-repair data to the plurality of cores in parallel, wherein a series of connected pipeline flops forms one of the plurality of configurable self-repair pipelines.
Methods, systems, and apparatus for reducing latency of configuration of image sensor and image signal processor. A computing system can include a machine learning (ML) processing engine that can process denoising diffusion ML models for execution on statically compiled ML accelerators. The system can determine that a partitioned graph representation, which includes connected subgraphs that each represent at least one layer of the neural network of the ML model, forms a directed acyclic graph. The system can insert cache nodes in the graph representation, where each cache node corresponds to a respective subgraph and is configured to cache output of the respective subgraph. The system can generate an execution dataflow graph including a plurality of iterations of the partitioned graph representation, where, at one or more iterations during model inference operations, the execution dataflow graph uses inputs from cache nodes and excludes execution of subgraphs corresponding to the cache nodes.
Video coding using tiling may include encoding a current frame by identifying a tile-width for encoding a current tile of the current frame, the tile-width indicating a cardinality of horizontally adjacent blocks in the current tile, identifying a tile-height for encoding the current tile of the current frame, the tile-height indicating a cardinality of vertically adjacent block in the current tile, and generating an encoded tile by encoding the current tile, such that a row of the current tile includes tile-width horizontally adjacent blocks from the plurality of blocks, and a column of the current tile includes tile-height vertically adjacent blocks from the plurality of blocks. Encoding the current frame may include outputting the encoded tile, wherein outputting the encoded tile includes including an encoded-tile size in an output bitstream, the encoded-tile size indicating a cardinality of bytes for including the encoded tile in the output bitstream.
H04N 19/152 - Data rate or code amount at the encoder output by measuring the fullness of the transmission buffer
H04N 19/119 - Adaptive subdivision aspects e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
H04N 19/174 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a slice, e.g. a line of blocks or a group of blocks
H04N 19/436 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation using parallelised computational arrangements
A method includes receiving training data including a corpus of multilingual unspoken textual utterances, a corpus of multilingual un-transcribed non-synthetic speech utterances, and a corpus of multilingual transcribed non-synthetic speech utterances. For each un-transcribed non-synthetic speech utterance, the method includes generating a target quantized vector token and a target token index, generating contrastive context vectors from corresponding masked audio features, and deriving a contrastive loss term. The method also includes generating an alignment output, generating a first probability distribution over possible speech recognition hypotheses for the alignment output, and determining an alignment output loss term. The method also includes generating a second probability distribution over possible speech recognition hypotheses and determining a non-synthetic speech loss term. The method also includes pre-training an audio encoder based on the contrastive loss term, the alignment output loss term, and the non-synthetic speech loss term.
One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.
H04N 19/59 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
H04N 19/117 - Filters, e.g. for pre-processing or post-processing
H04N 19/176 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
H04N 19/42 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
Techniques are described herein for providing smart suggestions for image zoom regions. A method includes: receiving a search query; performing a search using the search query to identify search results that include image search results including a plurality of images that are responsive to the search query; for a given image of the plurality of images included in the image search results, determining at least one zoom region in the given image; and providing the search results including the image search results, including providing the given image and an indication of the at least one zoom region in the given image.
Methods, systems, and apparatus, including computer-readable media, are described for a hardware circuit configured to implement a neural network. The circuit includes multiple super tiles. Each super tile includes a unified memory for storing inputs to a neural network layer and weights for the layer. Each super tile includes multiple compute tiles. Each compute tile executes a compute thread that is used to perform the computations to generate an output for the neural network layer. Each super tile includes arbitration logic coupled to the unified memory and each compute tile. The arbitration logic is configured to: pass inputs stored in the unified memory to the compute tiles; pass weights stored in the unified memory to the compute tiles; and pass, to the unified memory, the output generated for the layer based on computations performed at the compute tiles using the inputs and the weights for the layer.
Implementations are directed to providing a voice wrapper to an existing third-party text-based chatbot to enable the existing third-party text-based chatbot to engage in corresponding voice-based conversations. The voice wrapper can include a plurality of components. For instance, the voice wrapper can include a plurality of input components for utilization in responding to a spoken utterance, and in lieu of the existing third-party text-based chatbot, and/or to modify input to be provided to the existing third-party text-based chatbot in responding to the spoken utterance. Also, for instance, the voice wrapper can include a plurality of output components for utilization in responding to the spoken utterance, to reduce perceived latency of the existing third-party text-based chatbot, and/or to modify output generated by the existing third-party text-based chatbot in responding to the spoken utterance.
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 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
99.
METHOD FOR BEAM-SPECIFIC COMMON CHANNEL CONFIGURATION IN WIRELESS COMMUNICATION SYSTEMS
This disclosure provides systems, devices, apparatus, and methods, including computer readable media, for beam-specific common channel configuration. A UE (102) receives (1502) from a network entity (104) control signaling indicating a plurality of beam-specific configurations for a first set of parameters for a channel and a beam-common configuration for a second set of parameters for the channel. The plurality of beam-specific configurations are associated with a plurality of SSBs. The beam-common configuration is associated with all of the plurality of S SBs. The UE (102) receives (1508) from the network entity (104) one or more SSBs of the plurality of SSB. The UE (102) communicates (1510) with the network entity (104) in a transmission occasion associated with an SSB of the one or more SSBs. The transmission occasion is communicated on the channel based on the first set of parameters corresponding to the associated SSB and the second set of parameters.
H04W 74/0833 - Random access procedures, e.g. with 4-step access
H04B 7/08 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
H04B 7/06 - Diversity systemsMulti-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
100.
WEARABLE COMPUTING DEVICE WITH LOCALIZED IMPEDANCE MEASUREMENT DEVICES
The present disclosure is directed to a wearable computing device that includes a housing defining an upper surface and a lower surface for placement adjacent to a wrist of a user. The wearable computing device includes a plurality of biometric measurement devices arranged on the lower surface of the housing. The plurality of biometric measurement devices is configured for measuring, at least, a localized impedance at the wrist of the user. The wearable computing device also includes a processor configured to execute instructions stored in a memory device. The instructions includes receiving continuous passive measurements from the plurality of biometric measurement devices relating to the localized impedance at the wrist of the user and detecting impedance changes in a tissue composition of the user using the passive measurements from the plurality of biometric measurement devices relating to the localized impedance at the wrist of the user longitudinally.
