Abstract: An anatomy-aware contouring editing method includes receiving an image, wherein the image represents an anatomically recognizable structure; identifying a first image segment representing part of the anatomically recognizable structure; annotating the first image segment to generate a label of the part; drawing a contour along a boundary of the part; receiving a first input from a user device indicative of a region of contour failure, wherein the region of contour failure includes a portion of a contour that requires editing; editing the contour for generating an edited contour based on the first input and anatomical information; and updating another contour of another part of the anatomically recognizable structure based on the edited contour, wherein the another part is anatomically related to the part.

Abstract: A computer-implemented method, including receiving, by a processor set, a query including a query string for a system catalog; identifying, by the processor set, a default index structure of the system catalog; executing, by the processor set, the query based on the default index structure of each index in the system catalog; ranking, by the processor set, a performance of each execution of the query by each index; mapping, by the processor set, a query pattern to a corresponding index of the system catalog; selecting, by the processor set, the index to perform the query using a machine learning (ML) model trained with a knowledge base that includes the ranking and the mapping; executing, by the processor set, the query on the selected index; and in response to executing the query on the selected index, returning, by the processor set, a result of the query.

Abstract: A droplet driving method includes displaying a droplet path editing interface in response to a droplet path editing request, where the droplet path editing interface at least includes a path planning window; receiving a path selecting operation in the path planning window and displaying, in the path planning window, the droplet moving path information corresponding to the path selecting operation; controlling a driving signal for the microfluidic chip according to the droplet moving path information to drive a droplet to move along a moving path corresponding to the moving path information when the microfluidic chip receives the driving signal.

Abstract: A method for intelligently distributing messages across multiple devices and associated chat applications is disclosed. In one embodiment, such a method includes registering multiple devices and associated chat applications to receive intelligent distribution of messages. The method monitors, using an agent, a message copied from a first device of the multiple devices to a clipboard. The method automatically analyzes content of the message to determine which other devices and associated chat applications of the plurality are eligible to receive the message. The message is then distributed to other devices and associated chat applications that are deemed eligible to receive the message. In certain embodiments, the message originates from a first chat application of a first type and the message is distributed to a second chat application of a second type that is different from the first type. A corresponding system and computer program product are also disclosed.

Abstract: Computer-implemented methods for performing shutdown of a computing system using custom shutdown intervals are provided. Aspects include receiving, by the computing system, a command to shutdown the computing system and issuing a first command to shutdown a first subsystem of the computing system. Aspects also include determining, after a first interval since issuing the first command, that the first subsystem has not shutdown and issuing a second command to shutdown the first subsystem of the computing system, wherein the first interval is obtained from a trained machine learning model.

Abstract: A method for evaluating a degassing effect of a cross-linked polyethylene cable, includes: acquiring a cable sample to be detected; heating the cable sample to be detected; under a darkroom condition, acquiring at least one light spot image of said transparent cable sample under irradiation of a monochromatic laser light source; extracting light intensity data of the at least one light spot image in a preset light spot image range; normalizing the light intensity data, and obtaining the maximum value and an average value of normalized light intensity coefficients of said transparent cable sample in a preset thickness range of the insulating layer; calculating a degassing uniformity coefficient according to the maximum value and the average value; and obtaining an evaluation result of the degassing effect of the cross-linked polyethylene cable according to the maximum value, the average value, and the degassing uniformity coefficient.

Abstract: Described herein are systems, methods, and instrumentalities associated with automatic determination of hemodynamic characteristics. An apparatus as described may implement a first artificial neural network (ANN) and a second ANN. The first ANN may model a mapping from a set of 3D points associated with one or more blood vessels to a set of hemodynamic characteristics of the one or more blood vessels, while the second ANN may generate, based on a geometric relationship of the set of points in a 3D space, parameters for controlling the mapping. The apparatus may obtain a 3D anatomical model representing at least one blood vessel of a patient based on one or more medical images of the patient, and determine, based on the first ANN and the second ANN, a hemodynamic characteristic of the at least one blood vessel of the patient at a target location of the 3D anatomical model.

Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.

Abstract: It relates to a cosmetic composition for caring for the skin, comprising: (i) at least one N-substituted aminosulfonic acid compound; (ii) at least one hydroxy acid; and (iii) at least one penetration enhancer It also relates to a non-therapeutic method for caring for the skin, comprising applying the composition to the skin for a period of time and then rinsing the skin.

Abstract: A text processing method, a model training method, and an apparatus related to the field of artificial intelligence is provided. The method includes: obtaining target knowledge data; processing the target knowledge data to obtain a target knowledge vector; processing to-be-processed text to obtain a target text vector; fusing the target text vector and the target knowledge vector based on a target fusion model, to obtain a fused target text vector and a fused target knowledge vector; and processing the fused target text vector and/or the fused target knowledge vector based on a target processing model, to obtain a processing result corresponding to a target task. The foregoing technical solution can improve accuracy of a result of processing a target task by the target processing model.

Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.

Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.

Abstract: Disclosed is a selective field-assisted machining system. The system includes a micron-level high-speed identification module, an in-situ laser assisted module, an ultrasonic vibration module, an energy field loading high-speed control module, and a diamond tool. The micron-level high-speed identification module is used to quickly identify the type of a material substrate of a workpiece to be processed, process the identification information into a corresponding control signal, and send same to the energy field loading high-speed control module to implement selective processing of the workpiece to be processed, i.e. to process brittle particles using in-situ laser assisted machining and to process a soft metal substrate using ultrasonic vibration processing. In the present invention, ultra-precision cutting of brittle particles and a soft metal substrate can be completed at the same time in a single processing process.

Abstract: Shutdown intervals between nodes of a dependency tree are determined. The dependency tree represents a system to be shut down and the nodes represent system components of the system. The determining the shutdown intervals includes calculating, for a pair of nodes of the dependency tree, a shutdown interval. The calculating is based on a relationship between the pair of nodes. The calculating is repeated for a plurality of pairs of nodes to obtain a plurality of shutdown intervals. The plurality of shutdown intervals is used in a shutdown of the system components. At least one system component represented by at least one node on a higher node level of the dependency tree is to be shut down prior to at least one other system component represented by at least one other node on a lower node level of the dependency tree.

Abstract: A method, computer program product, and computer system for dynamically allocating shared memory among multiple execution threads via use of a dynamically changing memory threshold. A trained machine learning model (MLM) is executed to calculate the memory threshold (MTH) using values of one or more input parameters as input to the MLM. After executing the MLM, a request is received from a requesting execution thread for a requested amount (MR) of the shared memory. In response to the request, the shared memory is distributed among one or more current execution threads currently using the shared memory and the requesting execution thread. The redistributing is performed as a function MTH, MR, MU, and MC, wherein MU is a total amount of the shared memory currently being used by the one or more current execution threads, and wherein MC is a memory capacity of the shared memory.

Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.

Abstract: A text processing model training method, and a text processing method and apparatus in the natural language processing field in the artificial intelligence field are disclosed. The training method includes: obtaining training text; separately inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model; the sample data includes a sample semantic feature and a sample label; the prediction data includes a prediction semantic feature and a prediction label; and the teacher model is a pre-trained language model used for text classification; and training a model parameter of the student model based on the sample data and the prediction data, to obtain a target student model. The method enables the student model to effectively perform knowledge transfer, thereby improving accuracy of a text processing result of the student model.

Abstract: A system includes a comparator having a first input, a second input, and an output. The system also includes a first voltage divider having an input and an output, wherein the input of the first voltage divider is coupled to a first power rail, and the output of the first voltage divider is coupled to the first input of the comparator. The system also includes a second voltage divider having an input and an output, wherein the input of the second voltage divider is coupled to a second power rail, and the output of the second voltage divider is coupled to the second input of the comparator. The system further includes a power multiplexer coupled to the first power rail, the second power rail, and a first circuit, and a control circuit coupled to the output of the comparator and the power multiplexer.