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Imine-Based Polymeric Mixed Ionic–Electronic Conductors Featuring Degradability and Biocompatibility for Transient Bioinspired Electronics

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Angewandte Chemie 2025年第10期137卷

作者： Dr. Junxin Chen Dr. Shengyu Cong Riping Liu Dr. Jiayao Duan Chaoyue Chen Dr. Dongsheng Yu Xiuyuan Zhu Chong Ran Prof. Du Cheng Prof. Zhengke Li Prof. Iain McCulloch Prof. Wan Yue Guangzhou Key Laboratory of Flexible Electronic Materials and Wearable Devices PCFM Lab of Ministry of Education School of Materials Science and Engineering State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou 510275 China PCFM Lab of Ministry of Education School of Materials Science and Engineering Sun Yat-sen University Guangzhou 510275 China. Andlinger Center for Energy and the Environment and Department of Electrical and Computer Engineering Princeton University Princeton NJ 08544 USA.

Degradable features are highly desirable to advance next-generation organic mixed ionic–electronic conductors (OMIECs) for transient bioinspired artificial intelligence devices. It is highly challenging that OMIECs exhibit excellent mixed ionic-electronic behavior and show degradability simultaneously. Specially, in OMIECs, doping is often a tradeoff between structural disorder and charge carrier mobilities. Here, we describe a regiochemistry-driven backbone curvature approach to prepare OMIECs, enabling doped state ordered within efficient ionic-electronic conduction in organic electrochemical transistors (OECTs) and presenting degradable characteristics. Significantly, i -3gTIT shows an outstanding mobility (1.99 cm 2 V −1 s −1 ) and μ C* (302 F V −1 cm −1 s −1 ), and presents higher disorder-tolerance upon doping and faster degradation behavior than its regioisomer, o -3gTIT . Especially, the resulting OECT-based inverter shows a high voltage gain of 31.6 V V −1 at a low driving voltage of 0.6 V. Moreover, we demonstrate an application of transient OECT, i. e., biodegradable solid-state electrolyte of OECT-based artificial synapses. Remarkably, the regiochemistry-driven film crystallinity modulation enables the conversion from volatile to non-volatile operation in such synapses. The transient synapse based on i -3gTIT achieves over 90 % recognition accuracy for small digit handwritten images, showing potential in security neuromorphic computing. Our work is the first presentation enabling excellent mixed conduction of OMIECs with degradable features for transient bioinspired electronics.

关键词： mixed ionic-electronic conductor degradability biocompatibility electrochemical transistor artificial synapse

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Sub-Graph Contrast for Scalable Self-Supervised Graph Representation Learning

Sub-Graph Contrast for Scalable Self-Supervised Graph Repres...

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IEEE International Conference on Data Mining (ICDM)

作者： Yizhu Jiao Yun Xiong Jiawei Zhang Yao Zhang Tianqi Zhang Yangyong Zhu Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Shanghai Institute for Advanced Communication and Data Science Fudan University China Department of Computer Science IFM Lab Florida State University FL USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. Thus, it remains a great challenge to capture rich information in large-scale graph data. Besides, these methods mainly focus on supervised learning and highly depend on node label information, which is expensive to obtain in the real world. As to unsupervised network embedding approaches, they overemphasize node proximity instead, whose learned representations can hardly be used in downstream application tasks directly. In recent years, emerging self-supervised learning provides a potential solution to address the aforementioned problems. However, existing self-supervised works also operate on the complete graph data and are biased to fit either global or very local (1-hop neighborhood) graph structures in defining the mutual information based loss terms. In this paper, a novel self-supervised representation learning method via Sub-graph Contrast, namely Subg-Con, is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information. Instead of learning on the complete input graph data, with a novel data augmentation strategy, Subg-Con learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead. Compared with existing graph representation learning approaches, Subg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization. Extensive experiments verify both the effectiveness and the efficiency of our work compared with both classic and state-of-the-art graph representation learning approaches on multiple realworld large-scale benchmark datasets from different domains.

关键词： Correlation Scalability Supervised learning Benchmark testing Task analysis Optimization Mutual information

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Real-time suturing simulation for virtual reality medical training

Real-time suturing simulation for virtual reality medical tr...

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作者： Yu, Peng Pan, Junjun Qin, Hong Hao, Aimin Wang, Haipeng State Key Laboratory of Virtual Reality Technology & Systems Beihang University Beijing China Peng Cheng Lab Shenzhen China Faculty of Media and Communication Bournemouth University Dorset United Kingdom Department of Computer Science State University of New York at Stony Brook New YorkNY United States Beijing Aerospace General Hospital Beijing China

At present, virtual reality (VR) -based medical simulators provide an efficient and cost-effective alternative without exposing risk to the traditional training approaches. As an essential and indispensable task in fundamental surgical skills training, the research of suturing simulation still remains insufficient in the field of virtual surgery. In this paper, we present a real-time suturing simulation framework which can handle the complex interactions between surgical instruments and soft tissue. The simulation consists of two stages: external interaction and internal coupling. External interaction involves the interplay between needle/suture and the soft tissue, which are both deformed by position-based dynamics (PBD) with different constraints. At the internal coupling stage, once the force exceeds a threshold, the needle tip will puncture and penetrate into the soft tissue and generate a path. To guarantee the needle/suture accurately following the path inside the soft tissue, we propose a novel coupling method by matching and generating the constraints among needle, suture, and penetration path. We have applied this suturing simulation into a VR laparoscopic surgery simulator with haptic force. Our experimental results demonstrate that our approach can achieve real-time performance with a high degree of visual realism and haptic fidelity. © 2020 John Wiley & Sons, Ltd.

关键词： Needles

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Scattering-free plasmonic Brewster effect via metasurfaces

arXiv

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arXiv 2024年

作者： Zhang, Xinyan Cui, Xingshuo Cai, Tong Cai, Weiqi Low, Tony Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Air and Missile Defense College Air Force Engineering University Xi’an710051 China AVIC Research Institute for Special Structures of Aeronautical Composites Jinan250023 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China

The Brewster effect, dating back to the pioneering work of Sir David Brewster in 1815, offers a crucial route to achieve 100% energy conversion between the incident and transmitted propagating waves at an optical interface and is of fundamental importance to many practical applications, such as polarization filtering, beam steering, and optical broadband angular selectivity. However, whether the Brewster effect of surface waves can be implemented without the involvement of negative-permittivity or negative-permeability materials remains elusive. This is due to the formidable challenge to fully suppress both the parasitic scattering into propagating waves and the reflection into surface waves under the incidence of surface waves. Here, we reveal a feasible route to achieve scattering-free plasmonic Brewster effect via isotropic metasurfaces, along with the usage of positive-permittivity and positive-permeability metamaterials with both anisotropic and magnetic responses. In essence, the anisotropic response of metamaterials is judiciously designed to fully suppress the parasitic scattering into propagating waves, while the magnetic response of metamaterials facilitates the full suppression of the reflection into surface waves supported by metasurfaces. Moreover, we find that this plasmonic Brewster effect via metasurfaces can be further engineered to occur for arbitrary incident angles, giving rise to the exotic phenomenon of all-angle scattering-free plasmonic Brewster effect. © 2024, CC BY.

关键词： Surface waves

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Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

arXiv

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arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

arXiv

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

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Dynamical phases of short-term memory mechanisms in RNNs

arXiv

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arXiv 2025年

作者： Kurtkaya, Bariscan Dinc, Fatih Yuksekgonul, Mert Blanco-Pozo, Marta Cirakman, Ege Schnitzer, Mark Yemez, Yucel Tanaka, Hidenori Yuan, Peng Miolane, Nina CNC Program Stanford University Stanford United States KUIS AI Center Department of Computer Engineering Koc University Turkey Geometric Intelligence Lab UC Santa Barbara Santa Barbara United States Kavli Institute for Theoretical Physics UC Santa Barbara Santa Barbara United States Stanford University Stanford United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University Stanford United States Howard Hughes Medical Institute Stanford University Stanford United States Phi Lab NTT Research Sunnyvale United States Center for Brain Science Harvard University Cambridge United States State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University China Research Institute of Intelligent Complex System Fudan University China

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. A key focus in neuroscience has been the study of sequential activity patterns, where neurons fire one after another within large networks, to explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we first introduce two unique mechanisms that can subserve short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Then, through analytical models, we identify fundamental properties that govern the selection of these mechanisms, i.e., we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these observations by training and evaluating more than 35,000 recurrent neural networks (RNNs) that we will publicly release upon publication. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience. © 2025, CC BY.

关键词： Long short-term memory

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VIS4SL: A visual analytic approach for interpreting and diagnosing shortcut learning

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Knowledge-Based Systems 2025年 320卷

作者： Meng, Xiyu Tang, Tan Zhou, Yuhua Yan, Zihan Deng, Dazhen Wang, Yongheng Wu, Yuhan Wu, Yingcai College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Hangzhou310027 China The State Key Lab of CAD&CG Zhejiang University Hangzhou310012 China Laboratory of Art and Archaeology Image Zhejiang University Hangzhou310007 China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou311100 China

Shortcut learning, a phenomenon where deep neural networks inadvertently learn irrelevant features, has been extensively discussed due to its impact on model generalization and unexpected failures. Interpreting and diagnosing shortcut learning is challenging due to its diverse manifestations and multiple influencing factors. To assist data scientists in these tasks, we introduce VIS4SL, an interactive visual analytics approach that harnesses both human intelligence and computational power. VIS4SL integrates a perturbation-based method with comprehensive visualizations to facilitate an understandable analysis of learned features. We also present a set of comparative visualizations that allow for the evaluation of model explanations against robust proxies, particularly human explanations, to quantify the degree of shortcut learning and assess model components. Two case studies, involving natural image classification and visualization classification, demonstrate the efficacy of VIS4SL in practical applications. Our findings reveal that the model uses the orientation of bars to differentiate between bar charts and Pareto charts. Furthermore, we explore how interactive visualizations enhance data scientists’ understanding of shortcut learning, enabling the development of more precise deep learning models. © 2025 Elsevier B.V.

关键词： Video analysis

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Ten years of spasers and plasmonic nanolasers

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Light(science & Applications) 2020年第1期9卷 1133-1153页

作者： Shaimaa I.Azzam Alexander V.Kildishev Ren-Min Ma Cun-Zheng Ning Rupert Oulton Vladimir M.Shalaev Mark I.Stockman Jia-Lu Xu Xiang Zhang School of Electrical&Computer Engineering and Birck Nanotechnology Center Purdue UniversityWest LafayetteIN 47907USA Purdue Quantum Science and Engineering Institute Purdue UniversityWest LafayetteIN 47907USA State Key Lab for Mesoscopic Physics and School of Physics Peking UniversityBeijingChina Frontiers Science Center for Nano-optoelectronics&Collaborative Innovation Center of Quantum Matter BeijingChina Department of Electronic Engineering and International Center for Nano-Optoelectronics Tsinghua University100084 BeijingChina School of Electrical Computerand Energy EngineeringArizona State UniversityTempeAZ 85287USA The Blackett Laboratory Imperial College LondonSouth KensingtonLondon SW72AZUK Center for Nano-Optics(CeNO)and Department of Physics and Astronomy Georgia State UniversityAtlantaGA 30303USA Nanoscale Science and Engineering Center University of CaliforniaBerkeleyBerkeleyCA 94720USA Faculties of Sciences and Engineering University of Hong KongHong KongChina

Ten years ago,three teams experimentally demonstrated the first spasers,or plasmonic nanolasers,after the spaser concept was first proposed theoretically in *** overview of the significant progress achieved over the last 10 years is presented here,together with the original context of and motivations for this *** a general introduction,we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and *** is followed by an overview of crucial technological progress,including lasing threshold reduction,dynamic modulation,room-temperature operation,electrical injection,the control and improvement of spasers,the array operation of spasers,and selected applications of single-particle *** prospects are presented in relation to several directions of development,including further miniaturization,the relationship with Bose-Einstein condensation,novel spaser-based interconnects,and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.

关键词： lasers overcome operation

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Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data

arXiv

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arXiv 2021年

作者： Ye, Jiexia Zhao, Juanjuan Zheng, Furong Xu, Chengzhong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China State Key Lab of IOTSC Department of Computer Science University of Macau China

Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. Due to the delayed effect in latest complete OD flow collection, complex spatiotemporal correlations of OD flows in high dimension, it is more challengeable than other traffic prediction tasks of time series. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the realtime mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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