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Enhanced in-plane polarization in two-dimensional GaInS 3 via strain engineering for self-powered photodetector

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Transactions of Materials Research

Transactions of Materials Research 2025年第1期1卷

作者： Zongdong Sun Jie Liu Na Zhang Wanfu Shen Chunguang Hu Liang Li Feng Yan Fangfang Xia Huiqiao Li Yuan Li Tianyou Zhai State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology (HUST) Wuhan 430074 China State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin 300072 China Key Laboratory of Materials Physics Anhui Key Laboratory of Nanomaterials and Nanotechnology Institute of Solid State Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei 230031 China Department of Applied Physics Research Institute of Intelligent Wearable Systems The Hong Kong Polytechnic University Kowloon 999077 Hong Kong China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057 China Optics Valley Laboratory Wuhan 430074 China

Self-powered two-dimensional (2D) polarization-sensitive photodetectors have propelled the advancement of the next-generation optoelectronics. However, currently such devices mainly depend on the stacking of multiple 2D heterojunctions to realize this function, which demands precise operational procedures and strict band alignment. Herein, we present the achievement of self-powered polarization photodetection in 2D GaInS 3 via strain engineering. This primarily depends on the intrinsic in-plane anisotropic structure and internal spontaneous polarization of 2D GaInS 3 . Remarkedly, the strained GaInS 3 devices exhibit superior optoelectronic performance with a high on/off ratio (>10 4 ), and large anisotropy ratio (∼5.4). Furthermore, the strained device can achieve self-powered high-resolution polarization imaging. This work offers a guideline valuable for developing high-performance 2D self-powered polarization photodetectors.

关键词： Two-dimensional materials Self-powered photodetector Polarization Strain engineering In-plane anisotropy

Coordinated Control of Path Tracking and Yaw Stability for Distributed Drive Electric Vehicle Based on AMPC and DYC

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arXiv 2023年

作者： Wu, Dongmei Guan, Yuying Xia, Xin Du, Changqing Yan, Fuwu Li, Yang Hua, Min Liu, Wei Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan430070 China Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Foshan528200 China Hubei Research Center for New Energy & Intelligent Connected Vehicle Wuhan University of Technology Wuhan430070 China Department of Civil and Environmental Engineering University of California Los AngelesCA90024 United States Technical Center of Dongfeng Commercial Vehicle Wuhan China School of Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Maintaining both path-tracking accuracy and yaw stability of distributed drive electric vehicles (DDEVs) under various driving conditions presents a significant challenge in the field of vehicle control. To address this limitation, a coordinated control strategy that integrates adaptive model predictive control (AMPC) path-tracking control and direct yaw moment control (DYC) is proposed for DDEVs. The proposed strategy, inspired by a hierarchical framework, is coordinated by the upper layer of path-tracking control and the lower layer of direct yaw moment control. Based on the linear time-varying model predictive control (LTV MPC) algorithm, the effects of prediction horizon and weight coefficients on the path-tracking accuracy and yaw stability of the vehicle are compared and analyzed first. According to the aforementioned analysis, an AMPC path-tracking controller with variable prediction horizon and weight coefficients is designed considering the vehicle speed's variation in the upper layer. The lower layer involves DYC based on the linear quadratic regulator (LQR) technique. Specifically, the intervention rule of DYC is determined by the threshold of the yaw rate error and the phase diagram of the sideslip angle. Extensive simulation experiments are conducted to evaluate the proposed coordinated control strategy under different driving conditions. The results show that, under variable speed and low adhesion conditions, the vehicle's yaw stability and path-tracking accuracy have been improved by 21.58% and 14.43%, respectively, compared to AMPC. Similarly, under high speed and low adhesion conditions, the vehicle's yaw stability and path-tracking accuracy have been improved by 44.30% and 14.25%, respectively, compared to the coordination of LTV MPC and DYC. The results indicate that the proposed adaptive path-tracking controller is effective across different speeds. Furthermore, the proposed coordinated control strategy successfully enhances the vehicle's stabili

关键词： Electric vehicles

PPT: Pre-trained Prompt Tuning for Few-shot Learning

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arXiv 2021年

作者： Gu, Yuxian Han, Xu Liu, Zhiyuan Huang, Minlie The CoAI Group Tsinghua University Beijing China The THUNLP Group Tsinghua University Beijing China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Beijing Academy of Artificial Intelligence BAAI Beijing China

Prompts for pre-trained language models (PLMs) have shown remarkable performance by bridging the gap between pre-training tasks and various downstream tasks. Among these methods, prompt tuning, which freezes PLMs and only tunes soft prompts, provides an efficient and effective solution for adapting largescale PLMs to downstream tasks. However, prompt tuning is yet to be fully explored. In our pilot experiments, we find that prompt tuning performs comparably with conventional full-model tuning when downstream data are sufficient, whereas it is much worse under fewshot learning settings, which may hinder the application of prompt tuning. We attribute this low performance to the manner of initializing soft prompts. Therefore, in this work, we propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. We name this Pretrained Prompt Tuning framework "PPT". To ensure the generalization of PPT, we formulate similar classification tasks into a unified task form and pre-train soft prompts for this unified task. Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Our approach is effective and efficient for using large-scale PLMs in practice. The code is publicly available at https://***/thu-coai/PPT. © 2021, CC BY.

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Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization

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arXiv 2025年

作者： Yu, Zeqin Ni, Jiangqun Zhang, Jian Deng, Haoyi Lin, Yuzhen School of Computer Science and Engineering Sun Yat-sen University China School of Cyber Science and Technology Sun Yat-sen University China Department of New Networks Peng Cheng Laboratory China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China

Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily lives. However, previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scenarios. In this paper, we propose a novel Reinforced Multi-teacher Knowledge Distillation (Re-MTKD) framework for the IFDL task, structured around an encoder-decoder ConvNeXt-UperNet along with Edge-Aware Module, named Cue-Net. First, three Cue-Net models are separately trained for the three main types of image forgeries, i.e., copy-move, splicing, and inpainting, which then serve as the multi-teacher models to train the target student model with Cue-Net through self-knowledge distillation. A Reinforced Dynamic Teacher Selection (Re-DTS) strategy is developed to dynamically assign weights to the involved teacher models, which facilitates specific knowledge transfer and enables the student model to effectively learn both the common and specific natures of diverse tampering traces. Extensive experiments demonstrate that, compared with other state-of-the-art methods, the proposed method achieves superior performance on several recently emerged datasets comprised of various kinds of image forgeries. © 2025, CC BY-NC-SA.

关键词： Image coding

Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

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SSRN

SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

No One Left Behind: Real-World Federated Class-Incremental Learning

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arXiv 2023年

作者： Dong, Jiahua Li, Hongliu Cong, Yang Sun, Gan Zhang, Yulun Van Gool, Luc The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China The Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China The Computer Vision Lab ETH Zürich Zürich8092 Switzerland

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known and fixed in advance. Moreover, some new local clients that collect novel categories unseen by other clients may be introduced to FL training irregularly. These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories. To tackle the above issues, we propose a novel Local-Global Anti-forgetting (LGA) model. It ensures no local clients are left behind as they learn new classes continually, by addressing local and global catastrophic forgetting. Specifically, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss. They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure consistent class-relations within different tasks. Moreover, a proxy server is designed to tackle global forgetting caused by Non-IID class imbalance between different clients. It augments perturbed prototype images of new categories collected from local clients via self-supervised prototype augmentation, thus improving robustness to choose the best old global model for local-side semantic distillation loss. Experiments on representative datasets verify superior performance of our model against comparison methods. The code is available at https://***/JiahuaDong/LGA. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

A Trimming-π Strategy for Constructing Functional Conductive Metal–Organic Frameworks Using Metalloporphyrazine Units

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Angewandte Chemie 2025年

作者： Junqiang Jiao Mingyu Yang Xiangxin Ye Yi Zhang Prof. Yi Jiang Prof. Zheng Meng State Key Laboratory of Precision and Intelligent Chemistry Department of Chemistry University of Science and Technology of China Hefei Anhui 230026 P.R. China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Material Science and Engineering Donghua University Shanghai 201620 P. R. China

Developing functional metal–organic frameworks (MOFs) with high electrical conductivity is crucial for their applications as advanced electronic materials. In this work, we for the first time construct a new family of functional and highly conductive MOFs using metalloporphyrazine (MPz) ligands based on a trimming-π concept via cutting the benzene ring from molecular metallopthalocynine (MPc). The deprotonation-after-coordination synthetic method affords crystalline MPz−Cu−NH MOFs with square lattices. Four-point probe conductivity measurements reveal the high room temperature electrical conductivity of MPz-based MOFs ranging from 3.5×10 −2 to 1.3×10 −1 S cm −1 , two orders of magnitude higher than the MPc-based MOF counterparts. Temperature-dependent conductivity measurements and electronic band structure analysis demonstrate ultra-small activation energies with potential metallic conducting behavior for the MPz−Cu−NH MOFs. Encapsulation of the aromatic guest molecules with different electron-donating and -withdrawing features allows the conductivity modulation of the CuPz−Cu−NH in a wide range spanning two orders of magnitude. These conductive MPz−Cu−NH MOFs with built-in MPz functional units exhibit MPz identity-dependent sensing performance, and realize highly sensitive detection of NH 3 and NO 2 using a low driving voltage of 0.1 V.

关键词： Conductive metal–organic frameworks Metalloporphyrazines Electronic properties Chemiresistive sensing

Boron-Fluoride Dual-atom Synergistic Regulated Interface Coating Enables Stable Zn-Metal Anodes

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Angewandte Chemie 2025年

作者： Chenglong Liu Tian Liu Ruiqi Liu Yuying Liu Jin Ma Qianqian Ji Na Li Chao Wang Qichong Zhang Wensheng Yan National Synchrotron Radiation Laboratory University of Science and Technology of China Hefei Anhui 230029 China Key Laboratory of Precision and Intelligent Chemistry Department of Chemistry University of Science and Technology of China Hefei Anhui 230026 China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Suzhou Jiangsu 215123 China

Aqueous zinc-based batteries provide promising opportunities for next-generation rechargeable batteries. Nevertheless, Zn anode encounters severe challenges, such as Zn dendrite formation, surface corrosion, and hydrogen evolution reaction (HER). Here, we report a strategy to spontaneously construct a boron−fluoride dual-atom regulated SEI (ZnBOF), which involves the formation of a B-compound coating through an etching process followed by an in situ F substitution during the initial electrochemical cycling. The ZnBOF/Zn anode benefits preferential deposition of Zn 2+ along the (002) plane without Zn dendrite, and the side reactions including by-product and HER are dramatically suppressed. A combination of characterization methods, such as X-ray absorption spectroscopy, shows that the B-containing passivation layer facilitates the transport of Zn 2+ and mitigates water-related side reactions, and the F atoms serve as zincophilic sites that enhance the transfer kinetics of Zn 2+ . As expected, the well-designed ZnBOF/Zn anode exhibits ultra-stable Zn plating/stripping for 5000 h at 2 mA cm −2 . The assembled ZnBOF/Zn||MnO 2 batteries show impressive cycling stability, remaining 96.2% of the initial capacity (234.3 mAh g −1 ) after 1700 cycles at 1.0 A g −1 . Therefore, this work reveals a dual-atom synergistic regulated strategy to fabricate a robust SEI for Zn anode, which contributes to the development of aqueous zinc-based batteries.

关键词： Boron-fluoride dual-atom regulated Enhanced reversible cycling performance Ultra-stable solid-electrolyte Interphase X-ray absorption spectroscopy Zn-metal Anode

Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

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Epilepsy Classification for Mining Deeper Relationships betw...

2020 International Conference on computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography