Abstract: A method for dynamic adaptive threading is provided. The method comprises receiving a query request for a recommended number of threads from an application. The method comprises determining the recommended number of threads according to a resource status of a system-on-a-chip (SoC) platform. The method comprises transmitting the recommended number of threads to the application.

Abstract: An optical element driving mechanism is provided, which includes a movable assembly, a fixed portion, and a driving assembly. The movable assembly is used for connecting an optical element. The movable assembly is movable relative to the fixed portion. The driving assembly is used for driving the movable assembly to move relative to the fixed portion.

Abstract: An optical element driving mechanism is provided, which includes a movable assembly, a fixed portion, a driving assembly, and an adhesive element. The movable assembly is used for connecting an optical element. The movable assembly is movable relative to the fixed portion. The fixed portion includes a case and a bottom. The driving assembly is used for driving the movable assembly to move relative to the fixed portion. The case is affixed on the bottom through the adhesive element.

Abstract: An optical element driving mechanism is provided, which includes a movable assembly, a fixed portion, a driving assembly, and a circuit assembly. The movable assembly is used for connecting an optical element. The movable assembly is movable relative to the fixed portion. The driving assembly is used for driving the movable assembly to move relative to the fixed portion. The circuit assembly connects the movable assembly and the fixed portion.

Abstract: The present invention disclose a medical image-based system for predicting lesion classification and a method thereof. The system comprises a feature data extracting module for providing a raw feature data based on a medical image, and a predicting module for outputting a predicted class and a risk index according to the raw feature data. The predicting module comprises a classification unit for generating the predicted class and a prediction score corresponding thereto according to the raw feature data, and a risk evaluation unit for generating the risk index according to the prediction score. The system provides medical personnels a reference score and a risk index to determine progression of a certain disease.

Abstract: An electronic device is configured to execute instructions: compiling a first AI model and second AI model(s) to a first compiled file and second compiled file(s), respectively, wherein the first compiled file comprises a first data set and a first command set, and the second compiled file(s) comprises second data set(s) and second command set(s); generating light version file(s) for the AI model(s), wherein the light version file(s) comprises the second command set(s) and data patch(es); storing the first compiled file and the light version file(s) to a storage device; loading the first compiled file from the storage device to a memory; loading the light version file(s) from the storage device to the memory; generating the second data set(s) according to the first data set and the data patch(es); and executing the second AI model(s) according to the generated second data set(s) and the second command set(s) in the memory.

Abstract: An optical lens system includes six lens elements from an object side to an image side, the six lens elements are, in order from the object side to the image side, a first lens element, a second lens element, a third lens element, a fourth lens element, a fifth lens element and a sixth lens element. Each of the six lens elements has an object-side surface towards the object side and an image-side surface towards the image side. The image-side surface of the second lens element is concave in a paraxial region thereof. The third lens element has positive refractive power. The image-side surface of the fourth lens element is concave in a paraxial region thereof. The image-side surface of the sixth lens element includes at least one inflection point.

Abstract: The present disclosure describes a semiconductor structure and a method for forming the same. The semiconductor structure can include a substrate, a first vertical structure and a second vertical structure formed over the substrate, and a conductive rail structure between the first and second vertical structures. A top surface of the conductive rail structure can be substantially coplanar with top surfaces of the first and the second vertical structures.

Abstract: The present disclosure provides an electronic device including a redistribution layer, a plurality of passive components, and an electronic component. The redistribution layer includes a first insulating layer, a second insulating layer, and a plurality of traces electrically connected to each other through a first opening of the first insulating layer and a second opening of the second insulating layer, wherein the first insulating layer has a first side away from the second insulating layer, and the second insulating layer has a second side away from the first insulating layer. The passive components are disposed on the first side. The electronic component is disposed on the second side. The plurality of passive components are electrically connected to the electronic component through the plurality of traces.

Abstract: According to one aspect, intersection scenario description may be implemented by receiving a video stream of a surrounding environment of an ego-vehicle, extracting tracklets and appearance features associated with dynamic objects from the surrounding environment, extracting motion features associated with dynamic objects from the surrounding environment based on the corresponding tracklets, passing the appearance features through an appearance neural network to generate an appearance model, passing the motion features through a motion neural network to generate a motion model, passing the appearance model and the motion model through a fusion network to generate a fusion output, passing the fusion output through a classifier to generate a classifier output, and passing the classifier output through a loss function to generate a multi-label classification output associated with the ego-vehicle, dynamic objects, and corresponding motion paths.

Abstract: A semiconductor device includes a semiconductor fin. The semiconductor device includes a first silicon oxide layer contacting the semiconductor fin at a first interface and including nitrogen at a first concentration. The semiconductor device includes a second silicon oxide layer contacting the first silicon oxide layer at a second interface and including nitrogen at a second concentration that is greater than the first concentration.

Abstract: A method and apparatus for minimizing silicon germanium facets in planar metal oxide semiconductor structures is disclosed. For example, a device fabricated according to the method may include a semiconductor substrate, a plurality of gate stacks formed on the substrate, a plurality of source/drain regions formed from silicon germanium, and a shallow trench isolation region positioned between two source/drain regions of the plurality of source/drain regions. Each source/drain region of the plurality of source/drain regions is positioned adjacent to at least one gate stack of the plurality of gate stacks. Moreover, the shallow trench isolation region forms a trench in the substrate without intersecting the two source/drain regions.

Abstract: A display apparatus and an image processing method thereof are provided. The display apparatus includes a display panel and an image processing device. The image processing device receives a low-resolution image from a host. The image processing device tracks a user's gaze to define a region of interest (ROI). The image processing device performs a video super-resolution (VSR) reconstruction on an original ROI image corresponding to the ROI in the low-resolution image to generate a high-definition ROI image. The image processing device pastes the high-definition ROI image back to the ROI in the low-resolution image to generate a processed image. The image processing device controls the display panel to display the processed image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: Systems and methods for bird's eye view (BEV) segmentation are provided. In one embodiment, a method includes receiving an input image from an image sensor on an agent. The input image is a perspective space image defined relative to the position and viewing direction of the agent. The method includes extracting features from the input image. The method includes estimating a depth map that includes depth values for pixels of the plurality of pixels of the input image. The method includes generating a 3D point map including points corresponding to the pixels of the input image. The method includes generating a voxel grid by voxelizing the 3D point map into a plurality voxels. The method includes generating a feature map by extracting feature vectors for pixels based on the points included in the voxels of the plurality of voxels and generating a BEV segmentation based on the feature map.

Abstract: A system and method for completing risk object identification that include receiving image data associated with a monocular image of a surrounding environment of an ego vehicle and analyzing the image data and completing semantic waypoint labeling of at least one region of the surrounding environment of the ego vehicle. The system and method also include completing counterfactual scenario augmentation with respect to the at least one region. The system and method further include determining at least one driver intention and at least one driver response associated with the at least one region.

Abstract: A method and apparatus for minimizing silicon germanium facets in planar metal oxide semiconductor structures is disclosed. For example, a device fabricated according to the method may include a semiconductor substrate, a plurality of gate stacks formed on the substrate, a plurality of source/drain regions formed from silicon germanium, and a shallow trench isolation region positioned between two source/drain regions of the plurality of source/drain regions. Each source/drain region of the plurality of source/drain regions is positioned adjacent to at least one gate stack of the plurality of gate stacks. Moreover, the shallow trench isolation region forms a trench in the substrate without intersecting the two source/drain regions.

Abstract: A method for forming a semiconductor structure includes following operations. First fins are formed in a first region of a substrate, and second fins are formed in a second region of the substrate. Widths of the first fins are greater than widths of the second fins. An isolation structure is formed over the substrate. A first ion implantation is performed on the first fins. A portion of the isolation structure is removed to expose a portion of each first fin and a portion of each second fin. The widths of the first fins are equal to or less than the widths of the second fins after the removing of the portion of the isolation structure. A 3D capacitor is formed in the first region, and a FinFET device is formed in the second region. The 3D capacitor includes the first fins, and the FinFET device includes the second fins.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: A display control method applicable to an all-in-one (AIO) computer is provided. The AIO computer includes a first monitor and a second monitor. The display control method includes: receiving a control instruction from the first monitor; projecting a display content on the second monitor according to the control instruction; and selectively enabling a touch control function of the second monitor according to the control instruction.