Abstract: The present invention relates to highly polarizable 3D organic perovskites of the general formula ABX3, prepared by introducing halogen functional groups in the A-site cation (in which the A and B sites are occupied by organic cations and the X site is a monovalent non-metallic counterion). The (DCl)(NH4)(BF4)3 crystal exhibits a strong linear electrooptic (EO) effect with an effective EO coefficient of 20 pmV?1, which is 10 times higher than that of metal halide perovskites. These 3D organic perovskites are solution processed and compatible with silicon, and illustrate the potential of rationally-designed all-organic perovskites for use in on-chip modulators, electro-optic devices, piezoelectric devices, or silicon photonics devices.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D annotation provided by an annotator and by propagating the 2D annotation through multiple images of a sequence of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets based on similarities between the first 3D image dataset and the other 3D image datasets. The automatic annotation of the first 3D image dataset and/or the other 3D image datasets may be conducted based on one or more machine-learning models trained for performing those tasks.

Abstract: Examples of performing selective Fine Timing Measurement (FTM) are described. For an FTM scan cycle, a first AP (i.e., an initiator) may determine scanning parameter values of a plurality of second APs (i.e., potential responders) based on previously performed FTM scans. The first AP may determine weights of the plurality of second APs based on the scanning parameter values and select a set of target APs based on the weights. The first AP may then scan the set of target APs for the FTM scan so that the rest of the plurality of second APs are relieved from participating in the FTM scan thereby reducing the performance impact on the rest of the plurality of second APs. After the FTM scan cycle is completed, the first AP may update the weights and select another set of target APs based on the updated weights to perform another FTM scan cycle.

Abstract: This application discloses a method for controlling a virtual race car based on artificial intelligence (AI). The method includes: controlling, using a client, a first virtual race car to move on a virtual map of a round of racing game, the first virtual race car being a pursuing race car and the first account being an account participating in the round of racing game; displaying, on the client, that an endurance value of a second virtual race car is reduced when a predefined location of the first virtual race car hits the second virtual race car on the virtual map, the second virtual race car being a fleeing race car in the round of racing game controlled by a second account logged into the round of racing game; and displaying, on the client, that the second virtual race car wins the round of racing game when the endurance value of the second virtual race car is greater than a preset threshold for a specified time.

Abstract: Disclosed is a method and a system for automatic positioning of a medical equipment with respect to a patient. The method includes obtaining sensor data related to the patient, from a plurality of sensors fixed relative to the medical equipment. The method further includes processing the sensor data to determine at least one pose characteristic of the patient and at least one shape characteristic of the patient. The method further includes determining at least one adjustment parameter for the medical equipment based on the at least one pose characteristic of the patient and the at least one shape characteristic of the patient. The method further includes adjusting the medical equipment based on the at least one adjustment parameter.

Abstract: Systems, methods, and instrumentalities are described herein for constructing a multi-view patient model (e.g., a 3D human mesh model) based on multiple single-view models of the patient. Each of the single-view models may be generated based on images captured by a sensing device and, dependent on the field of the view of the sensing device, may depict some keypoints of the patient's body with a higher accuracy and other keypoints of the patient's body with a lower accuracy. The multi-view patient model may be constructed using respective portions of the single-view models that correspond to accurately depicted keypoints. This way, a comprehensive and accurate depiction of the patient's body shape and pose may be obtained via the multi-view model even if some keypoints of the patient's body are blocked from a specific sensing device.

Abstract: Systems, methods and instrumentalities are described herein for automatically devising and executing a surgical plan associated with a patient in a medical environment, e.g., under the supervision of a medical professional. The surgical plan may be devised based on images of the medical environment captured by one or more sensing devices. A processing device may determine, based on all or a first subset of the images, a patient model that may indicate a location and a shape of an anatomical structure of the patient and determine, based on all or a second subset of the images, an environment model that may indicate a three-dimensional (3D) spatial layout of the medical environment. The surgical plan may be devised based on the patient model and the environment model, and may indicate at least a movement path of a medical device towards the anatomical structure of the patient.

Abstract: A three-dimensional (3D) model of a person may be obtained using a pre-trained neural network based on one or more images of the person. Such a model may be subject to estimation bias and/or other types of defects or errors. Described herein are systems, methods, and instrumentalities for refining the 3D model and/or the neural network used to generate the 3D model. The proposed techniques may extract information such as key body locations and/or a body shape from the images and refine the 3D model and/or the neural network using the extracted information. In examples, the 3D model and/or the neural network may be refined by minimizing a difference between the key body locations and/or body shape extracted from the images and corresponding key body locations and/or body shape determined from the 3D model. The refinement may be performed in an iterative and alternating manner.

Abstract: Systems, methods and instrumentalities are described herein for automating a medical environment. The automation may be realized using one or more sensing devices and at least one processing device. The sensing devices may be configured to capture images of the medical environment and provide the images to the processing device. The processing device may determine characteristics of the medical environment based on the images and automate one or more aspects of the operations in the medical environment. These characteristics may include, e.g., people and/or objects present in the images and respective locations of the people and/or objects in the medical environment. The operations that may be automated may include, e.g., maneuvering and/or positioning a medical device based on the location of a patient, determining and/or adjusting the parameters of a medical device, managing a workflow, providing instructions and/or alerts to a patient or a physician, etc.

Abstract: Disclosed is an apparatus for charging a battery comprising a first charging device configured to communicate with at least one second charging device, the first charging device and the at least one second charging device configured to charge the battery, and comprising a first controller configured to control the first charging device, wherein the first controller determines the number of the at least one second charging devices by communicating with a second controller.

Abstract: A method for surgery planning is provided. The method may include: generating a surgery video displaying a process of a virtual surgery performed by a virtual surgical robot on a virtual patient based on a surgery plan relating to a robotic surgery to be performed on a patient by a surgical robot; transmitting the surgery video to a display component of an XR assembly used to render the surgery video for display to a user; recording one or more actions that the user performed on the virtual surgery based on user input received via an input component of the XR assembly; modifying the surgery plan based on the one or more recorded actions; and causing the surgical robot to implement the robotic surgery on the patient according to the modified surgery plan.

Abstract: A system and method for visualizing anatomical structure of a patient during a surgery are provided. An anatomical image and a first optical image of a patient may be obtained. The anatomical image may be captured by performing a medical scan on the patient before a surgery of the patient, and the first optical image may be captured during the surgery. A target image may be generated by combining the anatomical image and the first optical image. The target image may be rendered based on a viewpoint of a target operator of the surgery. A display device may be directed to display the rendered target image.

Abstract: The 3D pose of a person may be estimated by triangulating 2D representations of body keypoints (e.g., joint locations) of the person. The triangulation may leverage various metrics such as confidence scores associated with the 2D representations of a keypoint and/or temporal consistency between multiple 3D representations of the keypoint. The 2D representations may be arranged into groups, a candidate 3D representation may be determined for each group, taking into account of the confidence score of each 2D representation in the group, and the candidate 3D representation that has the smallest error may be used to represent the keypoint. Other 3D representation(s) of the keypoint determined from images taken at different times may be used to refine the 3D representation of the keypoint.

Abstract: The present disclosure provides systems and methods for medical assistant. The systems may obtain position information of a target inside a subject during an operation. The systems may also determine depth information of the target with respect to an operational region of the subject based on the position information. The systems may further direct an optical projection device to project an optical signal representing the depth information on a surface of the subject.

Abstract: The present disclosure provides a method for surgical automation. The method may include: obtaining video data generated in a surgical process using a camera; identifying a surgical stage in the surgical process based on the video data; and triggering an activation of a surgical equipment used for a surgical operation and/or providing a guidance of the surgical operation based on the surgical stage.

Abstract: A two-dimensional (2D) or three-dimensional (3D) representation of a patient may be provided (e.g., as part of a user interface) to enable interactive viewing of the patient's medical records. A user may select one or more areas of the patient representation. In response to the selection, at least one anatomical structure of the patient that corresponds to the selected areas may be identified based on the user selection. Medical records associated with the at least one anatomical structure of the patient may be determined based on one or more machine-learning models trained for detecting textual or graphical information associated with the at least one anatomical structure in the one or more medical records. The one or more medical records may then be presented, e.g., together with the 2D or 3D representation of the patient.

Abstract: A method and a system for managing healthcare records of a user are provided. The method includes storing an electronic medical record related to the user in form of a non-fungible token (NFT) written to a blockchain, associating a smart contract to the NFT in the blockchain, authorizing a request to access the electronic medical record related to the user based on the defined ownership of the electronic medical record stored in the blockchain, identifying one or more NFTs from the blockchain comprising one or more electronic medical records related to the user based on processing of the identifier information in associated one or more smart contracts therewith, in response to the request, and sending the one or more electronic medical records corresponding to the identified one or more NFTs to a requestor associated with the request.

Abstract: Automated patient positioning and modelling includes a hardware processor to obtain image data from an imaging sensor, classify the image data, using a first machine learning model, as a patient pose based on one or more pre-defined protocols for patient positioning, provide a confidence score based on the classification of the image data and if the confidence score is less than a pre-determined value, re-classify the image data using a second machine learning model; or if the confidence score is greater than a pre-determined value, identify the image data as corresponding to a patient pose based on one or more pre-defined protocols for patient positioning during a scan procedure.

Abstract: The present disclosure provides a nano-structural protein degradation tool, use, and a preparation method thereof, wherein the nano-structural protein degradation tool comprises: one of or a combination of several of a first degradation tool, a second degradation tool, and a third degradation tool, wherein the first degradation tool is formed by linking POI recognition groups to linkers; the second degradation tool is formed by linking the POI recognition groups to nanoparticles; and the third degradation tool is formed by linking the POI recognition groups to the nanoparticles through the linkers. The present disclosure further provides a lipid-based protein degradation tool, use, and a preparation method thereof, wherein the lipid-based protein degradation tool comprises: POI recognition groups, and lipid hybrid substances linked to the POI recognition groups.