Abstract: A electronic device includes: a body, a light-emitting component, a photosensitive component, and a processor, wherein the light-emitting component, the photosensitive component, and the processor are all disposed in the body, and the photosensitive component is connected to the processor; the light-emitting component is configured to emit probe light; the photosensitive component is configured to acquire optical information about reflected light of the probe light in response to receiving the reflected light; and the processor is configured to determine a movement trajectory of the light-emitting component based on the optical information.

Abstract: Disclosed in the present invention is an ultra-compact CAM array based on a single MTJ and an operating method thereof. The CAM array comprises an M*N CAM core for storing contents, additional reference rows for storing “0” and “1” and reference columns for storing “0” and “1”, a row decoder, a column decoder, transmission gates ENs, write drivers WDs, search current sources Isearchs and two-stage detection amplifiers. The present invention utilizes 1T-1MTJ cells to construct the CAM array, and combines the advantages of the MTJ and CMOS. While ensuring search energy efficiency, a unique structure of the MTJ is utilized to implement a less area overhead and a lower search delay compared with a traditional CMOS-based CAM, and non-volatility is achieved.

Abstract: Embodiments of this application disclose a method for displaying a virtual character in a plurality of real-world images captured by a camera is performed at an electronic device. The method includes: capturing an initial real-world image using the camera; simulating a display of the virtual character in the initial real-world image; capturing a subsequent real-world image using the camera after a movement of the camera; determining position and pose updates of the camera associated with the movement of the camera from tracking one or more feature points in the initial real-world image and the subsequent real-world image; and adjusting the display of the virtual character in the subsequent real-world image in accordance with the position and pose updates of the camera associated with the movement of the camera.

Abstract: A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

Abstract: A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

Abstract: Embodiments of this application disclose a method for displaying a virtual character in a plurality of real-world images captured by a camera is performed at an electronic device. The method includes: capturing an initial real-world image using the camera; simulating a display of the virtual character in the initial real-world image; capturing a subsequent real-world image using the camera after a movement of the camera; determining position and pose updates of the camera associated with the movement of the camera from tracking one or more feature points in the initial real-world image and the subsequent real-world image; and adjusting the display of the virtual character in the subsequent real-world image in accordance with the position and pose updates of the camera associated with the movement of the camera.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive, the ground-truth protein-ligand complex crystalline-structure similarity, and an associated bioactivity value. The system and method further produce 3-D visualizations of previously unknown protein-ligand pairs that show directly the importance assigned to protein-ligand interactions, the positive/negative-ness of the saliencies, and magnitude. Furthermore, the system and method make enhancements in the art by accurately predicting protein-ligand pair bioactivity from decoupled models, removing the need for docking simulations, as well as restricting attention of the machine learning between protein and ligand atoms only.

Abstract: Embodiments of this application disclose a position and pose determining method performed at an electronic device. The method includes: acquiring, by tracking a first feature point extracted from a marked image, position and pose parameters of a first image captured by a camera relative to the marked image; extracting a second feature point from the first image in a case that the first image fails to meet a feature point tracking condition; and acquiring, by tracking the first feature point and the second feature point, position and pose parameters of a second image captured by the camera relative to the marked image, and determining a position and a pose of the camera according to the position and pose parameters, the second image being an image captured by the camera after the first image.

Abstract: A system and method for computationally tractable prediction of protein-ligand interactions and their bioactivity. According to an embodiment, the system and method comprise two machine learning processing streams and concatenating their outputs. One of the machine learning streams is trained using information about ligands and their bioactivity interactions with proteins. The other machine learning stream is trained using information about proteins and their bioactivity interactions with ligands. After the machine learning algorithms for each stream have been trained, they can be used to predict the bioactivity of a given protein-ligand pair by inputting a specified ligand into the ligand processing stream and a specified protein into the protein processing stream. The machine learning algorithms of each stream predict possible protein-ligand bioactivity interactions based on the training data.

Abstract: Embodiments of this application disclose a position and attitude determining method. The method includes acquiring, by tracking a feature point of a first marked image, position and attitude parameters of an image captured by a camera; using a previous image of a first image as a second marked image in response to the previous image of the first image meeting a feature point tracking condition and the first image failing to meet the feature point tracking condition; acquiring, position and attitude parameters of the image captured by the camera relative to the second marked image; acquiring position and attitude parameters according to the position and attitude parameters of the image relative to the second marked image and position and attitude parameters of each marked image relative to a previous marked image; and determining a position and an attitude of the camera according to the position and attitude parameters.

Abstract: Embodiments of this application disclose a position and attitude determining method. The method includes acquiring, by tracking a feature point of a first marked image, position and attitude parameters of an image captured by a camera; using a previous image of a first image as a second marked image in response to the previous image of the first image meeting a feature point tracking condition and the first image failing to meet the feature point tracking condition; acquiring, position and attitude parameters of the image captured by the camera relative to the second marked image; acquiring position and attitude parameters according to the position and attitude parameters of the image relative to the second marked image and position and attitude parameters of each marked image relative to a previous marked image; and determining a position and an attitude of the camera according to the position and attitude parameters.

Abstract: Embodiments of this application disclose a position and pose determining method performed at an electronic device. The method includes: acquiring, by tracking a first feature point extracted from a marked image, position and pose parameters of a first image captured by a camera relative to the marked image; extracting a second feature point from the first image in a case that the first image fails to meet a feature point tracking condition; and acquiring, by tracking the first feature point and the second feature point, position and pose parameters of a second image captured by the camera relative to the marked image, and determining a position and a pose of the camera according to the position and pose parameters, the second image being an image captured by the camera after the first image.