Abstract: A semiconductor chip includes a first intellectual property block. There are a second intellectual property block and a third intellectual property block around the first intellectual property block. There is a multiple metal layer stack over the first intellectual property block, the second intellectual property block, and the third intellectual property block. An interconnect structure is situated in the upper portion of the multiple metal layer stack. The interconnect structure is configured for connecting the first intellectual property block and the second intellectual property block. In addition, at least a part of the interconnect structure extends across and over the third intellectual property block.

Abstract: A battery protection charging method and a charging system thereof are provided. The battery protection charging method includes obtaining an operation parameter of a battery. When a first protection condition is satisfied, a charging voltage of the battery is reduced to a first voltage. When a second protection condition is satisfied, the charging voltage of the battery is reduced to a second voltage. The second voltage is lower than the first voltage. The first (second) protection condition includes the cycle-life count is higher than a first (second) cycle-life threshold, the high voltage cumulative time is higher than a first (second) high voltage time threshold, and the high voltage-temperature cumulative time is higher than a first (second) high voltage-temperature time threshold.

Abstract: An apparatus is provided. The apparatus includes a laser generation device configured to emit a laser signal to a semiconductor fabrication component. The apparatus includes a reflection detection device configured to receive a reflection signal comprising light, of the laser signal, reflected by a surface of the semiconductor fabrication component. The reflection detection device includes an optical filter. The optical filter is configured to block light, of the reflection signal, that has a wavelength outside a defined range of wavelengths. The optical filter is configured to provide filtered light, from the reflection signal, that has a wavelength within the defined range of wavelengths. The reflection detection device includes a light sensor configured to generate an electrical signal based upon the filtered light. The apparatus includes a computer configured to determine, based upon the electrical signal, measures of electrostatic field strength at the surface of the semiconductor fabrication component.

Abstract: Methods of cutting gate structures, and structures formed, are described. In an embodiment, a structure includes first and second gate structures over an active area, and a gate cut-fill structure. The first and second gate structures extend parallel. The active area includes a source/drain region disposed laterally between the first and second gate structures. The gate cut-fill structure has first and second primary portions and an intermediate portion. The first and second primary portions abut the first and second gate structures, respectively. The intermediate portion extends laterally between the first and second primary portions. First and second widths of the first and second primary portions along longitudinal midlines of the first and second gate structures, respectively, are each greater than a third width of the intermediate portion midway between the first and second gate structures and parallel to the longitudinal midline of the first gate structure.

Abstract: A method includes fabricating a semiconductor device, wherein the method includes depositing a coating layer on a first region and a second region under a loading condition such that a height of the coating layer in the first region is greater than a height of the coating layer in the second region. The method also includes applying processing gas to the coating layer to remove an upper portion of the coating layer such that a height of the coating layer in the first region is a same as a height of the coating layer in the second region.

Abstract: Example aspects of the present disclosure are directed to systems and methods which feature a machine-learned video super-resolution (VSR) model which has been trained using a bi-directional training approach. In particular, the present disclosure provides a compression-informed (e.g., compression-aware) super-resolution model that can perform well on real-world videos with different levels of compression. Specifically, example models described herein can include three modules to robustly restore the missing information caused by video compression. First, a bi-directional recurrent module can be used to reduce the accumulated warping error from the random locations of the intra-frame from compressed video frames. Second, a detail-aware flow estimation module can be added to enable recovery of high resolution (HR) flow from compressed low resolution (LR) frames. Finally, a Laplacian enhancement module can add high-frequency information to the warped HR frames washed out by video encoding.

Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.

Abstract: A method includes filtering a point cloud transformation of a 3D object to generate a 3D lattice and processing the 3D lattice through a series of bilateral convolution networks (BCL), each BCL in the series having a lower lattice feature scale than a preceding BCL in the series. The output of each BCL in the series is concatenated to generate an intermediate 3D lattice. Further filtering of the intermediate 3D lattice generates a first prediction of features of the 3D object.

Abstract: A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a source domain and a target domain that are associated with a surrounding environment of a vehicle. The system and method also include analyzing the video data associated with the source domain and the target domain and determining a key frame of the source domain and a key frame of the target domain. The system and method additionally include completing an action localization model to model a temporal context of actions occurring within the key frame of the source domain and the key frame of the target domain and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses to complete spatio-temporal action localization of individuals and actions.

Abstract: Methods, and systems, including computer programs encoded on computer storage media for neural network architecture search.

Abstract: A method, computer readable medium, and system are disclosed for identifying residual video data. This data describes data that is lost during a compression of original video data. For example, the original video data may be compressed and then decompressed, and this result may be compared to the original video data to determine the residual video data. This residual video data is transformed into a smaller format by means of encoding, binarizing, and compressing, and is sent to a destination. At the destination, the residual video data is transformed back into its original format and is used during the decompression of the compressed original video data to improve a quality of the decompressed original video data.

Abstract: Methods, and systems, including computer programs encoded on computer storage media for neural network architecture search.

Abstract: An abnormal packet detection apparatus and method are provided. The abnormal packet detection apparatus stores a whitelist corresponding to a protocol port, wherein the whitelist includes at least one legal packet record. Each legal packet record includes a legal packet length, a legal source address, and a legal variation position set, and corresponds to a reference packet. The abnormal packet detection apparatus determines that a current packet length and a current source address of a to-be-analyzed packet are respectively the same as the legal packet length and the legal source address of a reference packet record among the at least one legal packet record, determines a current variation position of the to-be-analyzed packet by comparing the to-be-analyzed packet with the reference packet corresponding to the reference packet record, and generates a detection result by comparing the current variation position with the legal variation position set of the reference packet record.

Abstract: A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a surrounding environment of a vehicle. The system and method also include completing an action localization model to model a temporal context of actions occurring within the surrounding environment of the vehicle based on the video data and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses from the action localization model and the action adaption model to complete spatio-temporal action localization of individuals and actions that occur within the surrounding environment of the vehicle.

Abstract: A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a source domain and a target domain that are associated with a surrounding environment of a vehicle. The system and method also include analyzing the video data associated with the source domain and the target domain and determining a key frame of the source domain and a key frame of the target domain. The system and method additionally include completing an action localization model to model a temporal context of actions occurring within the key frame of the source domain and the key frame of the target domain and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses to complete spatio-temporal action localization of individuals and actions.

Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.

Abstract: An abnormal packet detection apparatus and method are provided. The abnormal packet detection apparatus stores a whitelist corresponding to a protocol port, wherein the whitelist includes at least one legal packet record. Each legal packet record includes a legal packet length, a legal source address, and a legal variation position set, and corresponds to a reference packet. The abnormal packet detection apparatus determines that a current packet length and a current source address of a to-be-analyzed packet are respectively the same as the legal packet length and the legal source address of a reference packet record among the at least one legal packet record, determines a current variation position of the to-be-analyzed packet by comparing the to-be-analyzed packet with the reference packet corresponding to the reference packet record, and generates a detection result by comparing the current variation position with the legal variation position set of the reference packet record.

Abstract: Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh.

Abstract: Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties. An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

Abstract: A method, computer readable medium, and system are disclosed for identifying residual video data. This data describes data that is lost during a compression of original video data. For example, the original video data may be compressed and then decompressed, and this result may be compared to the original video data to determine the residual video data. This residual video data is transformed into a smaller format by means of encoding, binarizing, and compressing, and is sent to a destination. At the destination, the residual video data is transformed back into its original format and is used during the decompression of the compressed original video data to improve a quality of the decompressed original video data.