Abstract: A calibration circuit of a pulse-frequency modulation (PFM) converter includes a signal generator circuit and a calibration control circuit. The signal generator circuit generates and outputs an emulated slope signal to a comparator circuit under a calibration mode, wherein the emulated slope signal has an emulated slope following an initial voltage, and the emulated slope corresponds to a slope of a sensed signal indicative of electrical characteristic of an inductor of the PFM converter. The calibration control circuit refers to an output signal that is generated from a PFM control circuit in response to an output signal of the comparator circuit, to calibrate at least one circuit of the PFM converter.

Abstract: A method includes steps described below. In response to detecting that a voice-over entry corresponding to an online video is triggered, a voice-over recording page corresponding to the online video is provided for a voice-over actor-side client; the online video is played on the voice-over recording page, and speech information of a voice-over actor collected by the voice-over actor-side client in a process of playing a target video clip which does not include a speech feature in the online video is acquired; a corresponding voice-over video is generated according to the speech information and the online video; the voice-over video is sent to an uploader-side client corresponding to the online video for review by an uploader, and a review result fed back by the uploader-side client is received; and in response to determining that the review result satisfies a preset requirement, the voice-over video is published.

Abstract: A feedback control circuit of a pulse-frequency modulation (PFM) converter includes an on-time timer circuit and a detection circuit. The on-time timer circuit generates an on-time control signal for controlling an on-time duration of a switch circuit included in a power stage circuit of the PFM converter. The detection circuit controls the on-time timer circuit to adaptively adjust the on-time control signal according to a pulse interval between two successive inductor current pulses of the PEM converter.

Abstract: A method includes steps described below. In response to detecting that a voice-over entry corresponding to an online video is triggered, a voice-over recording page corresponding to the online video is provided for a voice-over actor-side client; the online video is played on the voice-over recording page, and speech information of a voice-over actor collected by the voice-over actor-side client in a process of playing a target video clip which does not include a speech feature in the online video is acquired; a corresponding voice-over video is generated according to the speech information and the online video; the voice-over video is sent to an uploader-side client corresponding to the online video for review by an uploader, and a review result fed back by the uploader-side client is received; and in response to determining that the review result satisfies a preset requirement, the voice-over video is published.

Abstract: A method accurately evaluates a corresponding operation capacity for an operating characteristic of a to-be-evaluated object in a to-be-evaluated time period in combination with already-operated historical data of the to-be-evaluated object, which specifically includes: for a capacity influence factor in an operating process of an airspace unit, constructing a capacity similarity characteristic model to form a capacity similarity characteristic index set; acquiring historical data of an evaluation object, on the basis of the capacity similarity characteristic index set, classifying historical data samples of different time periods by a clustering algorithm, and generating a capacity similarity time period sample set to which an evaluation time period of the current evaluation object belongs; and classifying historical capacity values of the capacity similarity time period sample set by a density clustering algorithm, and calculating a capacity reference value on the basis of a maximum class cluster.

Abstract: A computing system configured to execute instructions for a first graphical processing unit (GPU) on a second GPU is provided. The computing system may include the second GPU and a processor. The processor may be configured to receive second GPU state data that indicates one or more global properties of the second GPU. The processor may be further configured to receive one or more binary instructions for texture operation configured to be executed on the first GPU. Based on the second GPU state data, the processor may be further configured to apply a texture value patch to the one or more binary instructions. Applying the texture value patch may translate the one or more binary instructions into one or more translated binary instructions configured to be executed on the second GPU.

Abstract: The present application discloses a final joint of an immersed tunnel, a prefabrication method and an installation method, wherein the final joint includes two end surfaces connected with installed adjacent tube sections; the two end surfaces are both tilted surfaces, so that the longitudinal profile of the final joint along an installation direction is of an inverted trapezoid structure; and the final joint further may be of a structure with a tube section I and a tube section II which are connected with each other.

Abstract: A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.

Abstract: A computing system configured to execute instructions for a first graphical processing unit (GPU) on a second GPU is provided. The computing system may include the second GPU and a processor. The processor may be configured to receive second GPU state data that indicates one or more global properties of the second GPU. The processor may be further configured to receive one or more binary instructions for texture operation configured to be executed on the first GPU. Based on the second GPU state data, the processor may be further configured to apply a texture value patch to the one or more binary instructions. Applying the texture value patch may translate the one or more binary instructions into one or more translated binary instructions configured to be executed on the second GPU.

Abstract: The present application discloses a final joint of an immersed tunnel, a prefabrication method and an installation method, wherein the final joint includes two end surfaces connected with installed adjacent tube sections; the two end surfaces are both tilted surfaces, so that the longitudinal profile of the final joint along an installation direction is of an inverted trapezoid structure; and the final joint further may be of a structure with a tube section I and a tube section II which are connected with each other.

Abstract: Low-latency gesture detection is described, for example, to compute a gesture class from a live stream of image frames of a user making a gesture, for example, as part of a natural user interface controlling a game system or other system. In examples, machine learning components are trained to learn gesture primitives and at test time, are able to detect gestures using the learned primitives, in a fast, accurate manner. For example, a gesture primitive is a latent (unobserved) variable features of a subset of frames from a sequence of frames depicting a gesture. For example, the subset of frames has many fewer frames than a sequence of frames depicting a complete gesture. In various examples gesture primitives are learnt from instance level features computed by aggregating frame level features to capture temporal structure. In examples frame level features comprise body position and body part articulation state features.

Abstract: Resource processing during run time can be performed asynchronously from emulation of an application by a central processing unit. For example, an emulator can include a main processing thread that performs emulation processes. In response to encountering a shader, or other resource, to be processed, the emulator can invoke a separate asynchronous thread to perform such processing. Processed resources, such as translated shaders and generated textures, can be stored in a cache. In response to a command that uses a resource, such as a draw command that invokes a shader or other resource, the emulator can use the processed resource in the cache. If the processed resource is not in the cache, the emulator can skip processing the command that uses the resource. If processed resources can be obtained from other sources and loaded in the cache, processing of resources by the emulator can be eliminated.

Abstract: A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.

Abstract: Resource processing during run time can be performed asynchronously from emulation of an application by a central processing unit. For example, an emulator can include a main processing thread that performs emulation processes. In response to encountering a shader, or other resource, to be processed, the emulator can invoke a separate asynchronous thread to perform such processing. Processed resources, such as translated shaders and generated textures, can be stored in a cache. In response to a command that uses a resource, such as a draw command that invokes a shader or other resource, the emulator can use the processed resource in the cache. If the processed resource is not in the cache, the emulator can skip processing the command that uses the resource. If processed resources can be obtained from other sources and loaded in the cache, processing of resources by the emulator can be eliminated.

Abstract: Low-latency gesture detection is described, for example, to compute a gesture class from a live stream of image frames of a user making a gesture, for example, as part of a natural user interface controlling a game system or other system. In examples, machine learning components are trained to learn gesture primitives and at test time, are able to detect gestures using the learned primitives, in a fast, accurate manner. For example, a gesture primitive is a latent (unobserved) variable describing features of a subset of frames from a sequence of frames depicting a gesture. For example, the subset of frames has many fewer frames than a sequence of frames depicting a complete gesture. In various examples gesture primitives are learnt from instance level features computed by aggregating frame level features to capture temporal structure. In examples frame level features comprise body position and body part articulation state features.

Abstract: Embodiments are disclosed that relate to the correction of an estimated pose determined from depth image data. One disclosed embodiment provides, on a computing system, a method of obtaining a representation of a pose of articulated object from image data capturing the articulated object. The method comprises receiving the depth image data, obtaining an initial estimated skeleton of the articulated object from the depth image data, applying a random forest subspace regression function to the initial estimated skeleton, and determining the representation of the pose based upon a result of applying the random forest subspace regression to the initial estimated skeleton.

Abstract: Embodiments are disclosed that relate to the correction of an estimated pose determined from depth image data. One disclosed embodiment provides, on a computing system, a method of obtaining a representation of a pose of articulated object from image data capturing the articulated object. The method comprises receiving the depth image data, obtaining an initial estimated skeleton of the articulated object from the depth image data, applying a random forest subspace regression function to the initial estimated skeleton, and determining the representation of the pose based upon a result of applying the random forest subspace regression to the initial estimated skeleton.

Abstract: A method for obtaining gestural input from a user of a computer system. In this method, an image of the user is acquired, and a runtime representation of a geometric model of the user is computed based on the image. The runtime representation is compared against stored data, which includes a plurality of stored metrics each corresponding to a measurement made on an actor performing a gesture. With each stored metric is associated a stored representation of a geometric model of the actor performing the associated gesture. The method returns gestural input based on the stored metric associated with a stored representation that matches the runtime representation.

Abstract: Emulating legacy hardware using IEEE 754 compliant hardware is disclosed herein. In some aspects, the emulation includes locating an instruction that includes NaN (not a number) as at least one of an operand or a resultant. The emulation adjusts the resultant of the instruction, via additional code, to produce a final resultant of non-compliant (legacy) hardware. Legacy software, which was written in anticipation of processing by legacy hardware, may then be processed using compliant hardware.

Abstract: Emulating legacy hardware using IEEE 754 compliant hardware is disclosed herein. In some aspects, the emulation includes locating an instruction that includes NaN (not a number) as at least one of an operand or a resultant. The emulation adjusts the resultant of the instruction, via additional code, to produce a final resultant of non-compliant (legacy) hardware. Legacy software, which was written in anticipation of processing by legacy hardware, may then be processed using compliant hardware.