Abstract: This disclosure describes systems, methods, and devices for estimating dimple etch recess depth in a gate-all-around transistor. A method may include receiving, by a device, first measurements of the gate-all-around transistor, the first measurements based on first optical data from a spacer etch stage of fabricating the gate-all-around transistor; inputting, by the at least one processor, using a feed forward network, the first measurements to a machine learning model trained to estimate dimple etch recess in the gate-all-around transistor; inputting, by the at least one processor, to the machine learning model, second optical data from a dimple etch stage of fabricating the gate-all-around transistor; and generating, by the at least one processor, using the machine learning model, based on the second optical data and the first measurements, second measurements comprising the first measurements and dimple etch recess estimates for the gate-all-around transistor.

Abstract: A system for determining position signals of electrodes using a retrained machine-learning model includes at least a catheter including a plurality of electrodes configured to collect a plurality of potential signals and a magnetic sensor configured to collect magnetic data, and at least a computing device including a memory. The processor receives a first training set, wherein the first training set includes patient-agnostic data; receives a second training set, wherein the second training set includes patient-specific data, trains a mapping machine-learning model using the first training set, retrains the mapping machine-learning model using the second training set, receives at least a first signal, wherein the first signal includes a potential signal of the plurality of potential signal and the magnetic data, and generates, using the retrained machine-learning model, as a function of the at least a first signal, a first position signal for an electrode of the plurality of electrodes.

Abstract: Data flows in a virtualized computing environment are efficiently updated by a hardware-based networking device configured to disaggregate processing of data packets of the data flows from hosts of the virtualized computing environment. A connection table is accessed that defines connection flows for data packets having a source from an endpoint in a virtual network of the virtualized computing environment or a destination to the endpoint in the virtual network of the virtualized computing environment. The hardware-based networking device re-simulates full packet processing paths for each of the flows in the connection table and updates the flows in the connection table to ensure that the flows in the connection table implement policies of the virtualized computing environment that were updated after corresponding flows in the connection table were added to the connection table.

Abstract: A method for determining regression of a database query associated with a first execution plan includes receiving a first performance measure for the database query and the first execution plan; determining a presence of at least one stored performance measure for the database query in a data repository; adding the first performance measure and identifiers for the database query and the first execution plan to a synchronization list; retrieving a second performance measure associated with a second execution plan from the data repository; determining a regression of the database query by comparing the first performance measure to the second performance measure; in response to a determination that the database query is regressed, adding the first performance measure, the second performance measure, and identifiers for the database query and the first execution plan to a regression report.

Abstract: A persistent memory of an ear-wearable device stores a plurality of neural network data objects each defining a respective neural network. The ear-wearable device includes a digital signal processor comprising a neural network processor. The digital signal processor is operable to: classify an ambient environment of a sound signal into one of a plurality of classifications; select one of the neural network data objects to enhance the sound signal based on the classification; and load neural network data from the selected neural network data objects into a memory. The neural network processor enhances the sound signal using the neural network data. The ear-wearable device includes an audio processing circuit that reproduces the enhanced sound signal via a receiver of the ear-wearable device.

Abstract: A detection device (100) includes a detection mat (102) having a plurality of detection coils (106), and at least one pair of groups of detection coils (106), the pair of groups of detection coils (106) includes first and second groups of detection coils (106). The first and second group of detection coils (106) comprises first and second first and second impedance values. The detection device (100) includes one or more drive sub-systems (112) and a comparison sub-system (112). The drive sub-systems (112) are operatively coupled to the detection mat (102) and configured to excite at least one pair of groups of detection coils (106). The comparison sub-system (114) is operatively coupled to the detection mat (102) and configured to receive a differential current signal from the pair of groups of detection coils (106), the comparison sub-system (114) is configured to generate a control signal based on the differential current signal.

Abstract: Devices, systems, and methods for a combination guidewire and tome shaft comprising three wires. The upper wire is manipulated to actively steer the shaft, while the lower wire creates a bowed region for cutting tissue. Insulating sheaths may cover the end of the shaft other than the bowed region of the lower wire. A voltage source allows electrification of the lower wire during cutting.

Abstract: Implementations relate to determining a rendering type for an application that is executing automatically. Based on user interactions with an application that is associated with specified input from the user while the user is interacting with the application, a confidence metric is generated for each specified input and a rendering type is determined based on the confidence metrics. Subsequently, when the user requests that a sequence of actions be performed, the application will be displayed according to the rendering type.

Abstract: A method and system for optimizing resource utilization in cloud-based data processing platforms is disclosed. A set of usage metrics of the data cluster is received for each cluster of a plurality of data clusters using a web crawler. A justification based on a predefined threshold is determined corresponding to each of the set of usage metrics of the data cluster through one or more application programming interfaces (APIs). One or more recommendations are generated based on the justification for optimal usage of the data cluster through the one or more APIs. The one or more recommendations are executed through at least one of an associated configuration file or the one or more APIs.

Abstract: An example method is performed at a near-field charging pad with a processor, a power-transferring element, a signature-signal-receiving circuit, and the processor of the near-field charging pad is in communication with a data source that includes predefined signature signals that each identify one of (i) a wireless power receiver, (ii) an object other than a wireless power receiver, and (iii) a combination of a wireless power receiver and an object other than a wireless power receiver. The method includes: after sending a plurality of test radio frequency (RF) power transmission signals, detecting, using the signature-signal-receiving circuit, respective amounts of reflected power; generating, based on variations in the respective amounts of reflected power, a signature signal; and determining whether (i) an authorized wireless power receiver is present on the near-field charging pad and/or (ii) an object other than a wireless power receiver is present on the near-field charging pad.

Abstract: A method for restoration of a captured image includes: blurring the captured image based on removing high spatial frequency content and maintaining colour characteristics of the captured image; generating a global colour attention map corresponding to the blurred captured image, where the global colour attention map indicates spatial representation of colour composition among regions within the blurred captured image; splitting the captured image into a plurality of patches; extracting one or more task features from the plurality of patches, where the one or more task features indicate characteristics related to a corresponding task; and generating stitched features corresponding to the plurality of patches, respectively, based on correlating the one or more task features and the global colour attention map, where the stitched features indicate an integrated representation of the captured image based on a global context and the corresponding task.

Abstract: A computer-implemented method for vision transforming includes, for each of one or more channels of each of a set of tiles of an image: splitting the channel into at least a first channel portion and a second channel portion; processing the first channel portion using depthwise convolution; processing the second channel portion with multi-head self-attention; and combining the processed first channel portion and the processed second channel portion; and identifying an object in the image at least partially based on the combined processed first channel portion and processed second channel portion for each of the one or more channels of each of the set of tiles of the image.

Abstract: A computing system is provided including processing circuitry configured to implement a ranker module configured to jointly compute relevance scores of a set of candidate keywords to a query, and to generate a ranked list of candidate keywords based on the relevance scores. The ranker module includes a cross attention encoder configured to jointly compute the relevance scores by obtaining contextual embeddings for query tokens in the query and union of keyword tokens in the set of keywords, selectively pooling the contextual embeddings for the query tokens and the union of keyword tokens, inputting the selectively pooled contextual embeddings into a classifier that has been trained to predict a relevance score of the plurality of keywords to the query based on the contextual embeddings to output a relevance score for each query-keyword pair for the keywords in the set of candidate keywords, and outputting the ranked list of candidate keywords.

Abstract: A fluid delivery system having a fluid delivery device configured to deliver a fluid in a spray form. The fluid may be a first fluid and a second fluid mixed together in a manner causing the first fluid to be delivered in a spray form. The first fluid may be a viscous fluid, such as a hemostatic agent, and the second fluid may be a pressurized fluid. The second fluid may be directed at the first fluid to cause the first fluid to be delivered in a spray form. The fluid delivery device may be adjustable to adjust the spray form. The lumens through the fluid delivery device and through the fluid delivery system are formed by channel walls and are uninterrupted by additional/separate components (e.g., valves, etc.) therein affecting fluid flow through the lumens.

Abstract: An ear-wearable device includes a digital signal processor that receives an audio signal from sound source of the ear-wearable device and reproduces the audio signal at a receiver that is placed within an ear of a user. The digital signal processor includes a deep neural network (DNN) logic circuit. The DNN logic circuit is operable to perform sound enhancement on the audio signal using a DNN stored in memory. An audio feature detector of the ear-wearable device is operable to detect an audio change via the digital signal processor that triggers a change of state of the DNN, the change of state affecting resource consumption by the DNN. The audio feature detector applies the change of state to the DNN. The DNN performs the sound enhancement in the changed state.

Abstract: An elongated tissue traction device adhered to target tissue and configured to lift the target tissue away from surrounding tissue. The tissue traction device may lift the target tissue as a surgical procedure is performed, such as resecting or cutting of the target tissue. The tissue traction device may be a bistable spring. The tissue traction device is in a first stable configuration when initially adhered to the target tissue. The tissue traction device may be shifted (e.g., actuated) into the second configuration, such as when the tissue is resected or cut around the target tissue to which the tissue traction device is adhered. In the second stable configuration, the tissue traction device curls and lifts the target tissue from the surrounding tissue.

Abstract: A method for mitigating unwanted audio noise in internet of things (IoT) based communication environment is provided. The method includes identifying and pairing one or more IoT devices with a voice assistant device, and then dividing the one or more paired IoT devices into a plurality of clusters. The method further includes detecting a user's location with respect to a location of the voice assistant device and then determining a cluster among the plurality of clusters corresponding to the user's location based on the detected user's location and thereafter using a recurrent neural networks (RNN) model, predicting an optimal sound output of the voice assistance device that is audible at the detected user's location. The method furthermore includes correcting the predicted optimal sound output of the voice assistance device using a sound parameter value associated with the determined cluster and a phase shift of the predicted optimal sound output.

Abstract: Systems and methods include, connecting to a first service edge node in a cloud-based system and obtaining one or more addresses each for one or more service edge nodes in the cloud-based system, wherein the one or more service edge nodes include public service edge nodes and private service edge nodes; connecting to a second service edge node of the one or more service edge nodes using the corresponding address; providing a request for an application to the second service edge node; and responsive to policy and accessibility determined via the cloud-based system, receiving access to the application via a connector adjacent to the application.

Abstract: Examples provide a method and system for quantitatively measuring air turbulence. A turbulence measuring application on each user device in a plurality of user devices associated with a plurality of users at a plurality of different locations within an aircraft generates vibration data and positional data associated with turbulence detected at each user's location. The data is analyzed to eliminate noise due to user movements and other non-turbulence related events. An aircraft server aggregates the vibration data and the positional data generated by the user devices scattered throughout the aircraft. The aggregated data is analyzed to eliminate noise due to non-turbulence related events to create quantitative air turbulence data. The quantitative air turbulence data generated by one or more aircraft is used with weather and other related data to generate more accurate and precise turbulence predictions for aircraft.

Abstract: An apparatus is provided to access a weight vector of a layer in a sequence of layers in the DNN. The weight vector includes a first sequence of weights having different values. A bitmap is generated based on the weight vector. The bitmap includes a second sequence of bitmap elements. Each bitmap element corresponds to a different weight and has a value determined based at least on the value of the corresponding weight. The index of each bitmap element in the second sequence matches the index of the corresponding weight in the first sequence. A new bitmap is generated by rearranging the bitmap elements in the second sequence based on the values of the bitmap elements. The weight vector is rearranged based on the new bitmap. The rearranged weight vector is divided into subsets, each of which is assigned to a different PE for a MAC operation.