Abstract: Provided are an adenosine triphosphatase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2 (ATP2A2) gene-based molecular marker for identifying a chicken feed efficiency trait, and an identification method and use thereof. A nucleotide sequence of the molecular marker is shown in SEQ ID NO: 1, and a base at locus 926 in the nucleotide sequence is T or G. In the present application, by identifying a genotype of the molecular marker in a chicken genome and then selecting the chicken feed efficiency trait according to the genotype, a breeding method for early selection of the poultry feed efficiency trait is established. The method is simple, rapid, and low-cost, does not require special instruments, and is suitable for the needs of marker-assisted selection experiments.

Abstract: The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Abstract: Disclosed are a semantic matching and retrieval method and apparatus. The semantic matching and retrieval method includes steps of obtaining both the vector representation of a query text and the vector representation of a document text; obtaining the final vector representation of the query text; obtaining the final vector representation of the document text; calculating, based on the final vector representation of the query text and the final vector representation of the document text, the similarity score between the query text and the document text; and selecting, based on the similarity scores between the query text and a plurality of document texts, a document text matching the query text from the plurality of document texts.

Abstract: Disclosed are a semantic matching and retrieval method and apparatus. The semantic matching and retrieval method includes steps of obtaining both the vector representation of a query text and the vector representation of a document text; obtaining the final vector representation of the query text; obtaining the final vector representation of the document text; calculating, based on the final vector representation of the query text and the final vector representation of the document text, the similarity score between the query text and the document text; and selecting, based on the similarity scores between the query text and a plurality of document texts, a document text matching the query text from the plurality of document texts.

Abstract: A method includes obtaining a request for one of multiple operational modes from an application installed on an extended reality (XR) device or an XR runtime/renderer of the XR device. The method also includes selecting a first mode of the operational modes, based at least partly on a real-time system performance of the XR device. The method also includes publishing the selected first mode to the XR runtime/renderer or the application. The method also includes performing a task related to at least one of image rendering or computer vision calculations for the application, using an algorithm associated with the selected first mode.

Abstract: In one embodiment, a method includes accessing a plurality of image frames captured by one or more cameras, classifying one or more first objects detected in one or more first image frames of the plurality of image frames as undesirable, applying a pixel filtering to the one or more first image frames to replace one or more first pixel sets associated with the one or more first objects with pixels from one or more second image frames of the plurality of image frames to generate a final image frame, providing the final image frame for display.

Abstract: A method includes rendering, on displays of an extended reality (XR) display device, a first sequence of image frames based on image data received from an external electronic device associated with the XR display device. The method further includes detecting an interruption to the image data received from the external electronic device, and accessing a plurality of feature points from a depth map corresponding to the first sequence of image frames. The plurality of feature points includes movement and position information of one or more objects within the first sequence of image frames. The method further includes performing a re-warping to at least partially re-render the one or more objects based at least in part on the plurality of feature points and spatiotemporal data, and rendering a second sequence of image frames corresponding to the partial re-rendering of the one or more objects.

Abstract: A system and method for adaptive volume-based scene reconstruction for XR platform application are provided. The system includes an image sensor and a processor to perform the method for display distortion calibration. The method includes determining a processor computation load. The method also includes, based on the determined computation load, adjusting one or more parameters for the 3D scene reconstruction to compensate for the determined computation load. The method further includes rendering a reconstructed 3D scene.

Abstract: A method includes obtaining a request for one of multiple operational modes from an application installed on an extended reality (XR) device or an XR runtime/renderer of the XR device. The method also includes selecting a first mode of the operational modes, based at least partly on a real-time system performance of the XR device. The method also includes publishing the selected first mode to the XR runtime/renderer or the application. The method also includes performing a task related to at least one of image rendering or computer vision calculations for the application, using an algorithm associated with the selected first mode.

Abstract: In one embodiment, a method includes accessing a plurality of image frames captured by one or more cameras, classifying one or more first objects detected in one or more first image frames of the plurality of image frames as undesirable, applying a pixel filtering to the one or more first image frames to replace one or more first pixel sets associated with the one or more first objects with pixels from one or more second image frames of the plurality of image frames to generate a final image frame, providing the final image frame for display.

Abstract: A computer-implemented method of providing an emotion-aware reactive interface in an electronic device includes receiving an image of a user as an input and identifying a multi-modal non-verbal cue in the image. The method further includes interpreting the multi-modal non-verbal cue to determine a categorization and outputting a reactive interface event determined based on the categorization.

Abstract: A method implemented by a client device includes accessing a plurality of image frames captured by one or more cameras of the client device and generating a working image frame based at least in part on one or more of the plurality of image frames. The method further includes classifying one or more first objects detected in the working image frame based at least in part on a determined desirability of the one or more first objects. The one or more first objects are determined to be undesirable. The method further includes applying a pixel filtering process to the working image frame to replace one or more first pixel sets associated with the first objects with pixels from one or more image frames of the plurality of image frames to generate a final image frame, and displaying the final image frame on a display of the client device.

Abstract: A method includes rendering, on displays of an extended reality (XR) display device, a first sequence of image frames based on image data received from an external electronic device associated with the XR display device. The method further includes detecting an interruption to the image data received from the external electronic device, and accessing a plurality of feature points from a depth map corresponding to the first sequence of image frames. The plurality of feature points includes movement and position information of one or more objects within the first sequence of image frames. The method further includes performing a re-warping to at least partially re-render the one or more objects based at least in part on the plurality of feature points and spatiotemporal data, and rendering a second sequence of image frames corresponding to the partial re-rendering of the one or more objects.

Abstract: A method includes obtaining, from a memory of an electronic device connected to a head mounted display (HMD), a first reference frame, wherein the first reference frame comprises a first set of pixels associated with a first time. The method includes, rendering, at the electronic device, a source image as a new frame, wherein the new frame includes a second set of pixels associated with a display to be provided by the HMD at a second time, and generating, by the electronic device, a differential frame, wherein the differential frame is based on a difference operation between pixels of the new frame with pixels of the first reference frame to identify pixels unique to the new frame. Still further, the method includes sending the differential frame to the HMD, and storing the new frame in the memory of the electronic device as a second reference frame.

Abstract: A method includes rendering, on displays of an extended reality (XR) display device, a first sequence of image frames based on image data received from an external electronic device associated with the XR display device. The method further includes detecting an interruption to the image data received from the external electronic device, and accessing a plurality of feature points from a depth map corresponding to the first sequence of image frames. The plurality of feature points includes movement and position information of one or more objects within the first sequence of image frames. The method further includes performing a re-warping to at least partially re-render the one or more objects based at least in part on the plurality of feature points and spatiotemporal data, and rendering a second sequence of image frames corresponding to the partial re-rendering of the one or more objects.

Abstract: A method includes rendering, on displays of an extended reality (XR) display device, a first sequence of image frames based on image data received from an external electronic device associated with the XR display device. The method further includes detecting an interruption to the image data received from the external electronic device, and accessing a plurality of feature points from a depth map corresponding to the first sequence of image frames. The plurality of feature points includes movement and position information of one or more objects within the first sequence of image frames. The method further includes performing a re-warping to at least partially re-render the one or more objects based at least in part on the plurality of feature points and spatiotemporal data, and rendering a second sequence of image frames corresponding to the partial re-rendering of the one or more objects.

Abstract: A device may include a camera configured to capture a live image data comprising an image of an object, and a processor coupled to the camera. The processor may be configured to recognize content of a selected portion of the live image data based on a contextual information relevant to the object using computer vision technology, and generate a visual information based on the recognized content. The device may also include an interface circuitry coupled to the processor. The interface circuitry may be configured to present the live image data, and overlay the live image data with the visual information.

Abstract: A method implemented by a client device includes accessing a plurality of image frames captured by one or more cameras of the client device and generating a working image frame based at least in part on one or more of the plurality of image frames. The method further includes classifying one or more first objects detected in the working image frame based at least in part on a determined desirability of the one or more first objects. The one or more first objects are determined to be undesirable. The method further includes applying a pixel filtering process to the working image frame to replace one or more first pixel sets associated with the first objects with pixels from one or more image frames of the plurality of image frames to generate a final image frame, and displaying the final image frame on a display of the client device.

Abstract: A method includes retrieving, by a device, contextual information based on at least one of an image, the device, user context, or a combination thereof. At least one model is identified from multiple models based on the contextual information and at least one object recognized in an image based on at least one model. At least one icon is displayed at the device. The at least one icon being associated with at least one of an application, a service, or a combination thereof providing additional information.

Abstract: One embodiment provides a method comprising classifying one or more objects present in an input comprising visual data by executing a first set of models associated with a domain on the input. Each model corresponds to an object category. Each model is trained to generate a visual classifier result relating to a corresponding object category in the input with an associated confidence value indicative of accuracy of the visual classifier result. The method further comprises aggregating a first set of visual classifier results based on confidence value associated with each visual classifier result of each model of the first set of models. At least one other model is selectable for execution on the input based on the aggregated first set of visual classifier results for additional classification of the objects. One or more visual classifier results are returned to an application running on an electronic device for display.