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Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第7期22卷 e16962页

作者： Liu, Hejing Li, Qiudan Zhan, Yongcheng Zhang, Zhu Zeng, Daniel D. Leischow, Scott J. Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen Peoples R China Calif Polytech State Univ San Luis Obispo Orfalea Coll Business San Luis Obispo CA 93407 USA Arizona State Univ Coll Hlth Solut Phoenix AZ USA

Background: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. Objective: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. Methods: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency-inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were

关键词： JUUL e-cigarette Reddit cross-sectional analysis electronic nicotine delivery system underage JUUL use

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Conditional visibility aware view synthesis via parallel light fields

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NEUROCOMPUTING 2024年 588卷

作者： Shen, Yu Li, Yuke Liu, Yuhang Wang, Yutong Chen, Long Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Co Ltd Beijing 100083 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China Northwestern Polytech Univ Sch Software Xian 710072 Peoples R China

In the area of neural rendering -based novel view synthesis, illumination is important since shadows cast by objects under various light sources provide indications about their geometries and materials. However, due to high physical device complexity and simulation distortion, large-scale photorealistic multiple illumination multi -view datasets are difficult to obtain. In order to address this problem, a physical -virtual interactive parallel light fields based collection method is proposed in this paper. The physical part of parallel light fields is firstly used to capture 3D models and 2D images of objects under different lights. Then a Reakto-Sim adaptation module was proposed to enhance realism by estimating material characteristic. Instead of manually setting, the learned resulting material parameters are then utilized to initialize virtual engine blender for subsequent rendering and data collection. Besides, to better handle self -occlusion problem in the acquired parallel light fields dataset, a conditional visibility module is designed in modeling visibility of each sampling point along a sampling ray. Compared with the Neuray, by introducing Conditional Normalizing Flow, visibility are assumed as samples from some distribution due to the fact that visibilities along the ray should be monotonically decreasing and are within the range of [0 , 1] . The visibility are calculated in a data driven manner, which brings more flexibility. By pretraining the conditional visibility network in parallel light field dataset, experiments demonstrate that more photorealistic inputs improve Peak -Signal -Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) by 0.11% and 0.68% in validation dataset NeRF synthesis and LLFF. Besides, compared to Neuray, the proposed conditional visibility module is more flexible and get a PSNR improvement of 0.55 and 0.5 in NeRF synthesis and LLFF dataset, respectively.

关键词： Parallel theory Light fields Neural rendering View synthesis Conditional visibility Normalizing Flow

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Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images-Taking the Fujian Coastal Area (Mainly Sanduo) as an Example

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REMOTE SENSING 2021年第6期13卷 1083-1083页

作者： Liang, Chenbin Cheng, Bo Xiao, Baihua He, Chenlinqiu Liu, Xunan Jia, Ning Chen, Jinfen Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100190 Peoples R China Natl Marine Hazard Mitigat Serv Beijing 100194 Peoples R China

Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling;(2) the poor robustness of the model;(3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.

关键词： coastal aquaculture areas semantic segmentation semi- weakly-supervised learning GAN conditional adversarial learning

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Corrections to “A High-Speed Seam Extraction Method Based on the Novel Structured-Light Sensor for Arc Welding Robot: A Review”

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IEEE Sensors Journal 2019年第4期19卷 1590-1590页

作者： Lei Yang En Li Teng Long Junfeng Fan Zize Liang University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

there was an error in the title of [1] ; it is a regular paper, not a review paper. The right title of this article is “A High-Speed Seam Extraction Method Based on the Novel Structured-Light Sensor for Arc Welding ... 详细信息

关键词： Welding Robot sensing systems Fans complex systems

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CenterNet3D: An anchor free object detector for point cloud

arXiv

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arXiv 2020年

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Boosting Postsurgical Outcomes of Orthotopic Hepatocellular Carcinoma via an EpCAM-Targeting Theranostic Nanoparticle

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PARTICLE & PARTICLE systems CHARACTERIZATION 2019年第8期36卷 1900085-1900085页

作者： Hu, Min Wang, Kun Shang, Wenting Han, Ziyu Jiang, Hongmei Fang, Chihua Tian, Jie Southern Med Univ Zhujiang Hosp Dept Hepatobiliary Surg Guangzhou 510280 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Jinan Univ Affiliated Hosp 1 Dept Hepatobiliary Surg Guangzhou 510630 Guangdong Peoples R China Univ Chinese Acad Sci Inst Automat Beijing 100080 Peoples R China Res Ctr Digital Med Guangdong Prov Clin & Engn Technol Guangzhou 510280 Guangdong Peoples R China

Hepatectomy is one of the main treatments for hepatocellular carcinoma (HCC). However, because microscopic tumor residues are often present after surgery, the recurrence rate of HCC remains extremely high. A multimodality imaging-guided multifunctional nanoparticle, indocyanine-green-gadolinium-copper sulfide@bovine-serum-albumin-epithelial-cell-adhesion molecule (EpCAM), is developed for HCC treatment based on a novel theranostic strategy. After intravenous injection of these nanoparticles into HCC-bearing mice, remarkably selective accumulation and highly efficient retention of the nanoparticles in tumor sites are observed. This is due to the EpCAM's specific targeting ability, which also results in enhanced HCC contrast in a tri-modal visualization, which unites magnetic resonance, photoacoustic, and fluorescence imaging. Moreover, nanoparticle uptake into the HCC allows photothermal therapy (PTT) as an interoperative adjuvant strategy for further eliminating possible microscopic residues and boosting HCC surgery outcomes. This theranostic strategy not only helps with precise diagnosis of HCC but enables intraoperatively imaging guidance for accurate tumor resection. Moreover, postoperation longitudinal observation demonstrates that intraoperative imaging-guided resection alongside a PTT-integrated treatment strategy can result in a significant improvement of overall survival rate. These multifunctional EpCAM-targeting nanoparticles may respresent a novel theranostic strategy to improve postsurgical HCC treatment.

关键词： fluorescence-guided surgery hepatocellular carcinoma intraoperative photothermal therapy triple-modality imaging

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Multiple Actor-Critic Structures for Continuous-Time Optimal control Using Input-Output Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2015年第4期26卷 851-865页

作者： Song, Ruizhuo Lewis, Frank Wei, Qinglai Zhang, Hua-Guang Jiang, Zhong-Ping Levine, Dan Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington UTA Res Inst Ft Worth TX USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China NYU Polytech Sch Engn Dept Elect & Comp Engn Brooklyn NY 11201 USA Univ Texas Arlington Dept Psychol Arlington TX 76019 USA

In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.

关键词： Actor-critic approximate dynamic programming (ADP) category optimal control shunting inhibitory artificial neural network (SIANN)

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Learning-based Six-axis Force/Torque Estimation Using GelStereo Fingertip Visuotactile Sensing

Learning-based Six-axis Force/Torque Estimation Using GelSte...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhang, Chaofan Cui, Shaowei Cai, Yinghao Hu, Jingyi Wang, Rui Wang, Shuo Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Visuotactile sensors have recently attracted much attention in robot communities due to the benefit of high spatial resolution sensing. However, force/torque estimation by visuotactile sensors remains a challenging problem. In this paper, we propose a learning-based six-axis force/torque estimation network using GelStereo visuotactile sensor, which can provide two-dimensional (2D) and three-dimensional (3D) displacements of markers embedded in the sensor surface. The convolutional neural networks are employed to extract multi-modal tactile deformation features;and a novel contact positional encoding method is proposed to eliminate the influence of translation invariance in convolutional operators. The well-trained model achieves the best RMSE of 0.290 N in force and 0.0084 Nm in torque. Furthermore, the proposed force/torque estimation network is integrated with a force-feedback policy for adaptive grasping tasks. The experimental results demonstrate the effectiveness of the proposed method and its potential application in robotic grasping and manipulation tasks.

关键词： Torque Three-dimensional displays Two dimensional displays Estimation Grasping Robot sensing systems Feature extraction

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A Point-Based Neural Network for Real-Scenario Deformation Prediction in Additive Manufacturing 18

A Point-Based Neural Network for Real-Scenario Deformation P...

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IEEE 18th International Conference on Automation Science and Engineering (IEEE CASE)

作者： Zhao, Meihua Xiong, Gang Wang, Weixing Fang, Qihang Shen, Zhen Wan, Li Zhu, Fenghua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Peoples R China Ten Dimens Guangdong Technol Co Ltd Foshan 528225 Peoples R China

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

In additive manufacturing (AM), accurate prediction for the deformation of printed objects contributes to compensation in advance, which is crucial to improving the accuracy of products. Many factors affect the deformation, such as the shape of the object, the properties of the material, and parameters in the printing process. Existing methods suffer from difficulties in modeling and generalizing between different shapes. In this paper, we formulate the error prediction in AM as a point-wise deviation prediction task and propose a point-based deep neural network to learn the complex deformation patterns by local and global contextual feature extraction. Furthermore, a data processing flow is proposed for automatically handling the real-scenario data. As an application case, we collect a dataset of dental crowns fabricated by the digital light processing 3D printing and validate the proposed method on the dataset. The results show that our network has a promising ability to predict nonlinear deformation. The proposed method can also be applied to other AM techniques.

关键词： Solid modeling Three-dimensional displays Shape Neural networks Error compensation Self-supervised learning Three-dimensional printing

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Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第11期45卷 2612-2624页

作者： Qiao, Hong Xi, Xuanyang Li, Yinlin Wu, Wei Li, Fengfu Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Grad Sch Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Hefei 230027 Peoples R China Chinese Acad Sci Inst Appl Math Acad Math & Syst Sci Beijing 100190 Peoples R China

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position-and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational meth

关键词： Active attention adjustment association biologically inspired visual model memory object recognition

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