One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include receiving a read request directed to the polysaccharide storage media, mapping the polysaccharide structure to create a map in response to the read request, traversing the map of the polysaccharide structure to determine an X-base number, and obtaining the data by converting the X-base number to a binary form.

A system described herein may provide a technique for the identification of solutions, such as routing solutions based on a starting point and a destination, based on Key Performance Indicators (“KPIs”) associated with models that are included in, or are associated with, candidate solutions. A solution request may specify a set of KPI preferences, based on which certain KPIs may be more heavily weighted than others in the identification of a suitable solution. Multiple solutions may be identified, with measures of comparative differences or similarities identified. A requestor may be able to make an informed decision between multiple candidate solutions based on the identification of the comparative differences.

A GPU-based human body microwave echo simulation method includes: transmitting emulation input parameters from the memory of a CPU host into the display memory of a GPU device; configuring, at the CPU host, parallel computing network parameters to be run at the GPU device; initiating a kernel function for human body microwave echo simulation preset in the CPU host; computing the kernel function in parallel, in a plurality of processing kernels of the GPU device, in a multi-threaded manner, according to the parallel computing network parameters, to obtain simulation echoes of human body microwaves; transmitting the obtained simulation echoes of human body microwaves from the GPU device back to the CPU host. The method makes full use of the characteristic that a GPU can perform parallel computing to accelerate the echo simulation process, greatly improving the real-time performance of echo simulation of a human body microwave scanning and imaging system.

This control device has: a user information acquisition unit which acquires first user posture information that indicates the posture of a first user operating a robot; a pre-change robot information acquisition unit which, on the basis of the first user posture information, acquires pre-change posture information, which indicates the posture of the robot before the posture of the robot is changed; and a determination unit which determines, as the posture of the robot, a target posture, which is different from the posture of the first user, on the basis of the pre-change posture information and the first user posture information that is acquired by the user information acquisition unit at the time when the robot took the pre-change posture indicated by the pre-change posture information.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

Embodiments of the disclosure provide a method and device for robot interactions. In one embodiment, a method comprises: collecting to-be-processed data reflecting an interaction output behavior; determining robot interaction output information corresponding to the to-be-processed data; controlling a robot to execute the robot interaction output information to imitate the interaction output behavior; collecting, in response to an imitation termination instruction triggered when the imitation succeeds, interaction trigger information corresponding to the robot interaction output information; and storing the interaction trigger information in relation to the robot interaction output information to generate an interaction rule.

The disclosed computer-implemented method may include generating a three-dimensional (3D) feature map for a digital image using a fully convolutional network (FCN). The 3D feature map may be configured to identify features of the digital image and identify an image region for each identified feature. The method may also include generating a region composition graph that includes the identified features and image regions. The region composition graph may be configured to model mutual dependencies between features of the 3D feature map. The method may further include performing a graph convolution on the region composition graph to determine a feature aesthetic value for each node according to the weightings in the node's weighted connecting segments, and calculating a weighted average for each node's feature aesthetic value to provide a combined level of aesthetic appeal for the digital image. Various other methods, systems, and computer-readable media are also disclosed.

A computer-implemented method for generating and deploying a reinforced learning model to train a chatbot. The method includes selecting a plurality of conversations, wherein each conversation includes an agent and a user. The method includes identifying, in each of the conversations, a set of turns and on or more topics. The method further includes associating one or more topics to each turn of the set of turns. The method includes, generating a conversation flow for each conversation, wherein the conversation flow identifies a sequence of the topics. The method includes applying an outcome score to each conversation. The method includes creating a reinforced learning (RL) model, wherein the RL model includes a Markov is based on the conversation flow of each conversation and the outcome score of each conversation. The method includes deploying the RL model, wherein the deploying includes sending the RL model to a chatbot.