Abstract: Detecting fine-grained similarity in image includes determining a core area of a search image by generating an image salient map from a plurality of layers of the search image and determining a connected area based on the image salient map. Feature descriptors are generated from the core area of the search image. A plurality of capsule vectors are generated from different ones of a plurality of keypoints of the feature descriptors. Capsule vectors of the search image are compared with capsule vectors of each image of the dataset to generate a top-K matrix. Similarity scores for the top-K matrix are calculated. One or more image of the dataset having fine-grained similarity with the search image are selected based a bundled similarity score for each image of the dataset. The bundled similarity score is a summation of the similarity scores of the image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a hardware datapath for a hardware accelerator computer chip.

Abstract: Analysis of edge closures of metal surface particles based on a graph structure.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a radar device may process a transmitted radar signal and a received radar signal to obtain at least one energy level measurement. The radar device may determine that the at least one energy level measurement satisfies a threshold value. The radar device may perform a listen before talk procedure based at least in part on determining that the at least one energy level measurement satisfies the threshold value. Numerous other aspects are provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive information that indicates whether to perform a first random access channel (RACH) procedure for a non-terrestrial network or a second RACH procedure for a terrestrial network, wherein the first RACH procedure is configured to support a larger number of UEs contemporaneously performing a RACH procedure than the second RACH procedure. Responsive to the information indicating that the UE is to perform the first RACH procedure, the UE may perform the first RACH procedure. Numerous other aspects are provided.

Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for radar target tracking based on return signal strength. A wireless device, such as a radar device, may measure a received signal strength of a detected signal. The received signal strength may be associated with at least one bin of a radar image. The wireless device may compare the received signal strength to a filtered signal strength. The filtered signal strength may be associated with a threshold difference in signal strength from the filtered signal strength. The wireless device may track the at least one bin of the radar image in response to the received signal strength being less than or equal to the threshold difference in signal strength from the filtered signal strength.

Abstract: Methods, systems, and devices for wireless communications are described. A network may be configured to provide a beam measurement configuration to a UE that indicates durations for monitoring transmission beams, and tuning a component of the UE between the monitoring. The durations may include a first portion that a UE may use for a tuning operation and a second portion that the UE may use to monitor for or measure a respective reference signal. In various examples, the indicated durations may include a third portion that the UE may use for another tuning operation, or the durations may overlap with one another during an overlap duration that the UE may use for another tuning operation. During the indicated durations, the network may refrain from transmitting other downlink data or control information for the UE, and the UE may refrain from monitoring for such other downlink data or control information.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive system information associated with a set of neighboring cells included in a non-terrestrial network (NTN). The UE may be connected to or camped in a current cell included in the NTN. The current cell may be associated with a current platform. The UE may monitor a neighboring cell, of the set of neighboring cells, based at least in part on the system information. Numerous other aspects are provided.

Abstract: A computer-implemented method for determining a classification and/or regression result based on a provided input signal. The method includes: providing a first part, configured to denoise the provided input signal based on the input signal and a randomly drawn first value; randomly drawing a plurality of first values; determining, by the first part, a plurality of denoised signals, wherein denoised signals are each determined based on the provided input signal and a first value from the plurality of first values; determining, by a model, a plurality of predicted values based on the denoised values, wherein each predicted value characterizes a classification of a denoised signal or a regression results based on a denoised signal; providing an aggregated signal characterizing an aggregation of the predicted values, wherein the aggregated signal characterizes the classification and/or regression result determined by the method.

Abstract: Computer-implemented method for training a machine learning system to denoise a provided input signal. The method includes: providing a first input signal and a first value to a first part of the machine learning system, wherein the first input signal characterizes a noisy signal; determining, by the first part, a first output signal for the first input signal and the first value; determining, by a second part of the machine learning system, a second value based on the first output signal, wherein the second value characterizes a probability of the first output signal to characterize a noisy signal; determining, by the second part, a third value based on a supplied second input signal, wherein the second input signal characterizes a non-noisy signal and wherein the third value characterizes a probability of the second input signal to characterize a non-noisy signal; training the machine learning system.

Abstract: Detecting fine-grained similarity in image includes determining a core area of a search image by generating an image salient map from a plurality of layers of the search image and determining a connected area based on the image salient map. Feature descriptors are generated from the core area of the search image. A plurality of capsule vectors are generated from different ones of a plurality of keypoints of the feature descriptors. Capsule vectors of the search image are compared with capsule vectors of each image of the dataset to generate a top-K matrix. Similarity scores for the top-K matrix are calculated. One or more image of the dataset having fine-grained similarity with the search image are selected based a bundled similarity score for each image of the dataset. The bundled similarity score is a summation of the similarity scores of the image.

Abstract: A computer-implemented method for training an encoder. The encoder is configured for determining a latent representation of an image. Training the encoder includes: determining a latent representation and a noise image by providing a training image to the encoder, wherein the encoder is configured for determining a latent representation and a noise image for a provided image; masking out parts of the noise image, thereby determining a masked noise image; determining a predicted image by providing the latent representation and the masked noise image to a generator of a generative adversarial network; training the encoder by adapting parameters of the encoder based on a loss value, wherein the loss value characterizes a difference between the predicted image and the training image.

Abstract: A computer-implemented method of processing digital image data. The method includes: determining, by an encoder configured to map a first digital image to an extended latent space associated with a generator of a generative adversarial network, GAN, system, a noise prediction associated with the first digital image, determining, by the generator of the GAN system, at least one further digital image based on the noise prediction associated with the first digital image and a plurality of latent variables associated with the extended latent space.

Abstract: A device and a computer implemented method of anomaly detection, including processing a digital representation of a signal or image with a wavelet decomposition to generate a first, second, and third plurality of decomposed representations, processing a first decomposed representation of the third plurality of decomposed representations with a first generative model to determine a first likelihood, processing a second decomposed representation of the third plurality of decomposed representations with a second generative model to determine a second likelihood, processing a second decomposed representation of the second plurality of decomposed representations with a third generative model to determine a third likelihood, processing a second decomposed representation of the first plurality of decomposed representations with a fourth generative model to determine a fourth likelihood, detecting an anomaly when at least one of the first likelihood, the second likelihood, the third likelihood and the fourth likeliho

Abstract: A substrate processing system includes an upper chamber and a gas delivery system to supply a gas mixture to the upper chamber. An RF generator generates plasma in the upper chamber. A lower chamber includes a substrate support. A dual ion filter is arranged between the upper chamber and the lower chamber. The dual ion filter includes an upper filter including a first plurality of through holes configured to filter ions. A lower filter includes a second plurality of through holes configured to control plasma uniformity.

Abstract: Technology for inspection for detecting a defect of a printed matter using machine logic informed by machine learning. Some embodiments of the present invention may include one, or more, of the following features: (i) generates defect datasets; (ii) generates defect libraries; (iii) uses the generated defect libraries for deep learning training; and (iv) uses machine learning to detect defects using computer code (for example, a *.jpg format file) corresponding to an image of a piece of printed matter instead of using a visual image (that is, an image of the type that is created when a person takes a picture using a traditional film camera).

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to detecting a closed ring in a three-dimensional (3D) point cloud via cycle basis. A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components can comprise a filtering component that can filter a first undirected graph of a three-dimensional (3D) point cloud, by eliminating one or more edges of the first undirected graph that are longer than an adaptive threshold, wherein filtering the first undirected graph can produce a second undirected graph; and a detection component that can detect a minimum cycle basis of the second undirected graph to determine a cycle path that can traverse an irregular annular shape that is represented by the 3D point cloud.

Abstract: A data transmission method, apparatus, and device, and a computer storage medium are provided. The method includes: obtaining an application packet of a target application; transmitting the application packet to a first routing device by using a network data channel, and transmitting, by using the first routing device, the application packet to a proxy server; meanwhile, transmitting the same application packet to a second routing device by using a short-range wireless data channel different from the network data channel, and transmitting, by using the second routing device, the application packet to the proxy server. The proxy server performs, a deduplication mechanism on the application packet and transmit the application packet to an application server corresponding to the target application.

Abstract: A system is configured for reconfiguration of a Synchronization Signal Block (SSB) pattern. The system is configured for obtaining data including a configuration for a Synchronization Signal Block (SSB) transmission carrying a physical broadcast channel (PBCH), the configuration specifying, for an SSB of the configuration, resource elements (REs) allocated for transmitting a primary synchronization signal (PSS) to a user equipment (UE) and REs allocated for transmitting a secondary synchronization signal (SSS) to the UE. The system is configured for selecting a set of REs that are unused in the configuration for the SSB transmission, specifying a filling sequence for extending a synchronization signal or an SSB to the set of REs that are unused in the configuration of the SSB transmission, generating data including an enhanced configuration for the SSB transmission that includes the extended synchronization signal or the extended SSBs, and transmitting the SSB transmission using the enhanced configuration.

Abstract: A system is configured for reconfiguration of a Synchronization Signal Block (SSB) pattern. The system is configured for obtaining data including a configuration for a Synchronization Signal Block (SSB) transmission carrying a physical broadcast channel (PBCH), the configuration specifying, for an SSB of the configuration, resource elements (REs) allocated for transmitting a primary synchronization signal (PSS) to a user equipment (UE) and REs allocated for transmitting a secondary synchronization signal (SSS) to the UE. The system is configured for selecting a set of REs that are unused in the configuration for the SSB transmission, specifying a filling sequence for extending a synchronization signal or an SSB to the set of REs that are unused in the configuration of the SSB transmission, generating data including an enhanced configuration for the SSB transmission that includes the extended synchronization signal or the extended SSBs, and transmitting the SSB transmission using the enhanced configuration.