Abstract: According to one embodiment, a method, computer system, and computer program product for adjusting an audible area of an avatar's voice is provided. The present invention may include receiving, at a microphone, a source audio; creating a received audio; calculating, by a generative model, a voice propagation distance of a user based on the source audio, the received audio, and a templated text sentence describing a category of a mixed reality environment experienced by the user; drawing a virtual circle within the mixed reality environment centered on a user avatar representing the user and with a radius equal to the voice propagation distance; and transmitting the source audio to one or more participants within the mixed-reality environment represented by one or more participant avatars located within the virtual circle.

Abstract: In an approach to improve the privacy of chat groups within a virtual world, embodiments of the present invention expand a chat area of a chat group to form an experience annulus according to predetermined distance that a voice volume of private-chat-group member can propagate. Responsive to identifying an external user is interested in the chat group, embodiments generate and set a current topic representing a conversation in the chat group as an externally hearable topic that is perceivable by the external user and generate a faux multi-person conversation associated with the externally hearable topic that corresponds to a real conversation made by members in the chat group. Further, embodiments assign the faux utterances to chat group members based on a corresponding speaker index and utilize one or more corresponding avatars of the chat group members to present the faux utterances to the external user.

Abstract: A method for training a generator for images from a semantic map that assigns to each pixel of the image a semantic meaning of an object to which this pixel belongs. The images generated by the generator and the at least one real training image that belong to the same semantic training map are supplied to a discriminator. The discriminator ascertains a semantic segmentation of the image assigned to it, the segmentation assigning a semantic meaning to each pixel of this image. From the semantic segmentation ascertained by the discriminator, it is evaluated whether the image supplied to the discriminator is a generated image or a real training image.

Abstract: Several designs of a gas distribution device for a substrate processing system are provided. The gas distribution device includes a dual plenum showerhead. Additionally, designs for a light blocking structure used with the showerheads are also provided.

Abstract: Systems and methods for predictive coordination of ego radar and interferer radar parameters are disclosed. In some aspects, a processor associated with a first radar system transmitting a first radar waveform to detect a target may identify a presence of a second radar system transmitting a second radar waveform in a vicinity of the first radar system. The processor may further predict an upcoming interference by the second radar waveform of the detection of the target by the first radar system. In addition, the processor may, in response to the predicting the upcoming interference, determine an adjustment to a first parameter of the first radar waveform and/or to a second parameter of the second radar waveform.

Abstract: Several designs of a gas distribution device for a substrate processing system are provided. The gas distribution device includes a dual plenum showerhead. Additionally, designs for a light blocking structure used with the showerheads are also provided.

Abstract: A method for predicting the performance of a given classifier with respect to one or more given samples of input data. The method includes: providing further classifiers that, together with the given classifier f*, form a set F of classifiers f; computing, for the x, using each classifier f from the set F, classification scores fk(x) with respect to all available classes k=1, . . . , K covered by the classifiers; determining, for pairs (f,f?) of classifiers f and f? from the set F, divergences of the classification scores fk(x) and f?k(x) for all k=1, . . . , K as pairwise divergences dis(f,f?,x) of the classifiers f and f? with respect to the one or more samples x; and determining the performance P(f*,x) of the classifier f* with respect to the one or more samples x based at least in part on pairwise divergences dis(f*,f?,x) between the classifier f* and other classifiers f?.

Abstract: A device and a computer-implemented method for machine learning. The method includes: providing a latent variable of a diffusion model representing the synthetic digital image, wherein providing the latent variable comprises sampling the latent variable from random noise, or providing a digital reference image, and adding noise with the diffusion model to the digital reference image to determine the latent variable, or mapping the digital reference image with an encoder to an input of the diffusion model, and adding noise with the diffusion model to the input to determine the latent variable, mapping the latent variable with the diffusion model depending on parameters of the diffusion model to features of the diffusion model, mapping the features with the diffusion model depending on parameters of the diffusion model to an output of the diffusion model.

Abstract: Embodiments of the present disclosure provide a drift optimization system for a vehicle-mounted machine based on a global positioning system (GPS). The system comprises a vehicular sensor, a camera unit, a GPS receiver, a memory, and a processor of a current vehicle. The processor is configured to set a datum point; determine a classification result of a GPS position by judging each GPS position in a GPS position sequence acquired from a GPS receiver during a preset time period; send a first command in response to the GPS position being a secure GPS position; and reset the datum point in response to the GPS position being an abnormal GPS position and first N1-1 GPS positions prior to the GPS position in the GPS position sequence being abnormal GPS positions.

Abstract: A computer-implemented method for determining a first element and a second element. The first element characterizes a classification or a regression result of a sensor signal, and the second element characterizes a confidence interval of likely classifications or regression results. The first element and second element are determined by an early-exit neural network.

Abstract: This application provides a control method, a first electronic device, and a storage medium. The control method is applied to the first electronic device and includes determining whether a first prerequisite condition is satisfied and controlling a vibration component of a first electronic device to vibrate in a first vibration mode; and determining whether a second prerequisite condition is satisfied and controlling the vibration component to vibrate in a second vibration mode, wherein the first vibration mode and the second vibration mode are different, the first vibration mode is based on predefined parameters, and the second vibration mode is at least based on contact parameters between the first electronic device and a second electronic device.

Abstract: Certain aspects of the present disclosure provide techniques for supporting multiple types of random access occasions. A method that may be performed by a user equipment (UE) includes receiving, from a base station (BS), an indication of at least two types of random access occasions (ROs) including a first RO type and a second RO type for communicating in a first coverage area of the BS as part of a random access process for the UE to establish a connection with the BS in the first coverage area. The method further includes transmitting, to the BS, a preamble on an RO of one of the first RO type or the second RO type, wherein a preamble format of the preamble is based on whether the RO is of the first RO type or the second RO type.

Abstract: A computer-implemented method for training an image segmentation system. The method includes: obtaining a training image and a training label map, wherein the training label map indicates pixels of the training image as belonging to an anomaly class; providing the training image as input to the image segmentation system, wherein the image segmentation system determines values characterizing logits for different classes for pixels of the training image; determining a loss value based on a loss function, wherein the loss function comprises a first term that characterizes a logarithm of a sigmoid of logits determined for pixels in the training image, wherein the logarithm of the sigmoid is determined for pixels, which are labeled as belonging to the anomaly class in the training label map; training the image segmentation system (60) by minimizing the loss value.

Abstract: A circuit for conduction testing of a power supply of a plasma globe lamp includes a rectifying circuit and a switching power supply, the circuit for conduction testing of the power supply of the plasma globe lamp is connected with an alternating current output end of an external power supply, a first end of the rectifying circuit is connected with the alternating current output end of the external power supply, a second end of the rectifying circuit is connected with the switching power supply, and the rectifying circuit is configured to convert an alternating current output by the alternating current output end into a direct current and to output the direct current to the switching power supply, and the rectifying circuit includes at least two diodes and a substrate, and all the at least two diodes are integrated in the substrate.

Abstract: A computer-implemented method for generating a training image and a training label map. The method includes: obtaining a first image and a corresponding first label map and obtaining a second image and a corresponding second label map; determining a third image by providing the second image as input to a machine learning system which is configured for determining images in the style of the first image based on provided images; determining the training image by replacing pixels from the first image with pixels from the third image, wherein the pixels from the third image are determined based on a class from the second label map; determining the training label map by replacing class labels from the first label map with class labels indicating an anomaly class, wherein the class labels in the first label map are replaced for which corresponding pixels in the first image are replaced.

Abstract: Device and computer implemented method for processing a digital image for anomaly detection. The method includes providing a model that is trained with a data set that includes digital images to output probabilities that are assigned to classes in a set of classes for classifying or semantically segmenting the digital image depending on the digital image; determining the probabilities for the digital image with the model; determining a size of a sub-set of the set of classes depending on a sum of the probabilities that are assigned to the classes in the sub set and depending on a first threshold; and detecting an anomaly depending on the size; wherein the sum of the probabilities is smaller than the first threshold, determining the size of the sub-set comprises determining the first threshold depending on a weighted sum of scores, a score is weighted in the weighted sum.

Abstract: Disclosed are techniques for positioning. A receiver measures a time of arrival (ToA) of a positioning reference signal (PRS) transmission of a first PRS sequence transmitted by a first transmitter, measures a ToA of a PRS transmission of a second PRS sequence transmitted by a second transmitter, and determines an observed time difference of arrival (OTDOA) as a difference between the ToA of the PRS transmission of the first PRS sequence and the ToA of the PRS transmission of the second PRS sequence, wherein the OTDOA is less than half a maximum differential delay expected between the PRS transmission of the first PRS sequence and the PRS transmission of the second PRS sequence, and wherein a repetition duration of the first PRS sequence and the second PRS sequence is greater than 10 milliseconds (ms) and at least twice the maximum differential delay.

Abstract: Several designs of a gas distribution device for a substrate processing system are provided. The gas distribution device includes a dual plenum showerhead. Additionally, designs for a light blocking structure used with the showerheads are also provided.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may receive a reflection of a first radar waveform transmitted by the first UE. The UE may receive a second radar waveform from a second UE, wherein the second radar waveform is a direct transmission from the second UE or a reflection of the direct transmission. The UE may perform target detection based at least in part on the reflection of the first radar waveform and on the second radar waveform. Numerous other aspects are described.