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Ga/GaSb nanostructures:Solution-phase growth for highperformance infrared photodetection

Nano Research 2023年第2期16卷 3304-3311页

作者： Huanran Li Su You Yongqiang Yu Lin Ma Li Zhang Qing Yang Hefei National Research Center of Physical Sciences at the Microscale Department of ChemistryLaboratory of Nanomaterials for Energy Conversion(LNEC)University of Science and Technology of China(USTC)Hefei 230026China Micro Electromechanical System Research Center of Engineering and Technology of Anhui Province School of Electrical Science and Applied Physics Hefei University of Technology(HFUT)Hefei 230009China Nano Science and Technology Institute University of Science and Technology of China(USTC)Suzhou 215123China

Gallium antimonide(GaSb)-based nanostructures have been reported via various vapor-phase synthetic routes while there is not a report on the growth of GaSb nanostructures via a complete one-step solution-phase synthetic *** we report the design and synthesis of tadpole-like Ga/GaSb nanostructures by a one-step solution-phase synthetic route typically from the precursors of commercial triphenyl antimony(Sb(Ph)_(3))and trimethylaminogallium(Ga(NMe_(2))_(3))at 260°C in *** GaSb nanocrystals are grown based on a solution–liquid–solid(SLS)mechanism with zinc blende phase,and their size and shape can be controlled in the procedures via manipulating the reaction ***,the tadpole-like Ga/GaSb nanostructures can be applied for the fabrication of a GaSb/Si nanostructured heterojunction-like photodetector over silicon wafer,which demonstrates excellent photoresponse and detection performances from wavelength of 405 to 1,064 nm with high photoresponding ***,the photodetector exhibits a high responsivity of 18.9 A·W^(−1),a superior detectivity of 1.1×10^(13)Jones,and an ultrafast response speed of 44 *** present work provides a new strategy to group III–V antimonide-based semiconducting nanostructures that are capable for the fabrication of photodetector with broadband,high-detectivity,and high-speed photodetecting performances.

关键词： Ga/GaSb nanostructure metal-semiconductor heterojunction narrow bandgap semiconductor solution-liquid-solid(SLS)growth model GaSb/Si heterojunction photodetector hybrid nanostructured photodetector infrared photodetection

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GANs-Based Model Extraction for Black-Box Backdoor Attack 8th

GANs-Based Model Extraction for Black-Box Backdoor Attack

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8th International Conference on Cognitive systems and information Processing, ICCSIP 2023

作者： Fu, Xiurui Tao, Fazhan Si, Pengju Fu, Zhumu School of Information Engineering Henan University of Science and Technology Henan Luoyang China Longmen Laboratory Henan Luoyang China

ISBN: (纸本)9789819980178

Machine learning (ML) models, e.g., deep neural networks (DNNs), are susceptible to backdoor attack. Backdoor attack involves embedding concealed backdoors into deep neural networks during training, which are activated by attacker-specified triggers in malicious inputs, causing the model to produce erroneous outputs. In the context of backdoor attacks, neural network models may experience misjudgment, such as an instance where a traffic sign image denoting "stop" is mistakenly identified as a "proceed" sign by a traffic sign identification model under the influence of the attack. This misclassification poses a significant safety risk to the domain of autonomous driving. Traditional white-box attacks require to access the training samples. Different from white-box attacks, black-box attacks adopt the settings that the training set and model internals is inaccessible. In practice, the training dataset is usually not shared due to privacy or copyright concerns, therefore black-box attacks are more realistic than white-box ones. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. Our attack strategy consists in recovering a local model to substitute for the targeted DNN, using the local model to generate triggers, where inputs with the trigger will subsequently be misclassified by the targeted model. We incorporate Generative Adversarial Networks (GANs) to approximate the extracted model to the targeted model. All neural network models seems inherently possess "intrinsic" backdoors, even in the absence of attacks or poisoning. Grounded in this perspective, we generate triggers associated with the model’s "intrinsic" backdoors. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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One-bit splitting deceptive jamming against SAR

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Defence technology（防务技术） 2022年第10期18卷 1760-1777页

作者： Bo Zhao Lei Huang Bing Li Shi-qi Liu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Information EngineeringShenzhen UniversityShenzhen518060China China Aerospace Science and Technology Corporation Beijing100048China

One-bit quantization is a promising technique due to its performance retention and complexity reduction in a deceptive jammer against synthetic aperture radar(SAR).In this paper,the 1-bit quantization technology is utilized to agilely generate split false targets in the SAR imagery once the radar signal is intercepted,which reduces the complexity of the jammer significantly with guaranteed focusing quality.A single-frequency threshold is used to decompose harmonics incurred by the 1-bit quantization,and its parameters are adjusted through different pulse repetition intervals to provide steerable *** this way,the SAR signal is split into coupled false scatterers during the 1-bit *** further deploying amplitude,time-delay,and Doppler frequency modulations on the 1-bit intercepted signal,the split false targets are *** proposed approach is compared with different deceptive jamming methods to show its validity in effectiveness and cost,and numerical experiments are also presented for verification.

关键词： Synthetic aperture radar(SAR) 1-Bit quantization Multi-false-target Single-frequency threshold

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Unassisted photoelectrochemical CO_(2)reduction by employingⅢ-Ⅴphotoelectrode with 15%solar-to-fuel efficiency

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Carbon Energy 2025年第3期7卷 1-10页

作者： Karthik Peramaiah Purushothaman Varadhan Vinoth Ramalingam Bilawal Khan Pradip Kumar Das Hao Huang Hui-Chun Fu Xiulin Yang Vincent Tung Kuo-Wei Huang Jr-Hau He Chemistry Program Center for Renewable Energy and Storage Technologies(CREST)and Division of Physical Science and EngineeringKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Institute of Sustainability for Chemicals Energyand Environment(ISCE2)Agency for ScienceTechnologyand Research(A*STAR)Singapore School of Computing Engineering and TechnologyRobert Gordon UniversityAberdeenUK Department of Materials Science and Engineering City University of Hong KongKowloonSARHong KongChina Guangxi Key Laboratory of Low Carbon Energy Materials School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilinChina Department of Chemical System and Engineering University of TokyoTokyoJapan

Solar-driven carbon dioxide reduction reaction(CO_(2)RR)provides an oppor tunity to produce value-added chemical feedstocks and ***,achieving efficient and stable photoelectrochemical(PEC)CO_(2)RR into selec tive products is challenging owing to the difficulties associated with the optical and the electrical configuration of PEC devices and electrocatalyst ***,we construct an efficient,concentrated sunlight-driven CO_(2)RR setup consisting of InGaP/GaAs/Ge triple-junction cell as a photoanode and oxide-derived Au(Ox-Au)as a cathode to perform the unassisted PEC CO_(2)*** one-sun illumination,a maximum operating current density of 11.5 mA cm^(-2) with an impressive Faradaic efficiency(FE)of~98%is achieved for carbon monoxide(CO)production,leading to a solar-to-fuel conversion efficiency of~15%.Under concentrated intensity of 10 sun,the photoanode records a maximum current density of~124 mAcm^(-2) and maintains~60%of FE for CO *** results demonstrate crucial advancements in usingⅢ-Ⅴbased photoanodes for concentrated PEC CO_(2)RR.

关键词： 3J photoanode CO_(2)reduction high STF unassisted PEC

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Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing

Representation Alignment and Adversarial Networks for Cross-...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Ying Liu, Jianjian Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Mao, Cunli Yunnan Provincial Key Laboratory of Artificial Intelligence Faculty of Information Engineering and Automation Kunming University of Science and Technology China

ISBN: (纸本)9798891761681

With the strong representational capabilities of pre-trained language models, dependency parsing in resource-rich languages has seen significant ***, the parsing accuracy drops sharply when the model is transferred to low-resource language due to distribution *** alleviate this issue, we propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless *** proposed model consists of two components, i.e., an alignment network in the input layer for selecting useful language-specific features and an adversarial network in the encoder layer for augmenting the language-invariant contextualized *** on the benchmark datasets show that our proposed model outperforms RoBERTa-enhanced strong baseline models by 1.37 LAS and 1.34 *** analysis shows that both alignment and adversarial networks are equally important in alleviating the distribution shifts problem and can complement each *** addition, the comparative experiments demonstrate that both the alignment and adversarial networks can substantially facilitate extracting and utilizing relevant target language features, thereby increasing the adaptation capability of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Emotional Synchronization for Audio-Driven Talking-Head Generation 24

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Zhao Luo, Yan Zuo, Zhichao Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331506681

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable. © 2024 IEEE.

关键词： Synchronization

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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Dual-Path Spectrogram Refinement Network for Robust Speaker Verification

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Journal of Shanghai Jiaotong University (science) 2025年 1-9页

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The accuracy and reliability of automatic speaker verification (ASV) face significant challenges in noisy environments. In recent years, joint training of speech enhancement front-end and ASV back-end has been widely applied to improve the robustness of ASV systems. Traditional joint training directly uses the enhanced speech features as the input of the back-end. However, the diversity of noise types and noise intensities will excessively suppress the enhanced features, resulting in speech distortion or residual noise. To alleviate this problem, we propose a dual-path spectrogram refinement network that enables the enhanced features to learn supplementary information from the separated noise features. In addition, we incorporate coordinate attention into the overall joint architecture to capture more comprehensive frequency and temporal information of the speaker from different spatial positions. We conduct extensive experiments on the VoxCeleb1 test set, the out-of-domain noise test set, and the VOiCES corpus. The experimental results demonstrate that our proposed method significantly improves the accuracy and robustness of speaker verification systems in both clean and noisy environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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Detection of abnormal Electricity Consumption Behavior of Users Based on K-means Clustering Algorithm Fusion and Improved SVM 7

Detection of abnormal Electricity Consumption Behavior of Us...

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7th International Conference on Intelligent information Processing, ICIIP 2022

作者： Huang, Haibin Xie, Xiaolan Liu, Yarong School of Information Science and Engineering Guilin University of Technology China Guangxi Key Laboratory of Embedded Technology and Intelligent System China

ISBN: (纸本)9781450396714

As the energy internet continues to advance, the degree of informatization of the power system has been continuously improved. However, finding out users' abnormal electricity consumption behavior is particularly important when there is a large amount of data about electricity consumption. As a solution to the above problems, this paper proposes a model for detecting abnormal consumption patterns of power users using k-means clustering algorithm and improved SVM. As a result of the experimental results, the model is able to detect abnormal consumption patterns of electricity by users very well. © 2022 ACM.

关键词： Informatization

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HCMHS: High-Order SNP Interactions Detection Based on Hierarchical Clustering and Multi-Task Harmony Search Algorithm

HCMHS: High-Order SNP Interactions Detection Based on Hierar...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tan, Kaiwen Tian, Jin Zhang, Yongbing Shu, Zhenqiu Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9798350386226

The interaction between SNPs plays a key role in revealing the genetic mechanisms of complex diseases. However, as the order of SNP interactions increases, the number of SNP combinations increases exponentially, presenting a serious combinatorial explosion problem. Although swarm intelligence algorithms can alleviate the combinatorial explosion problem by optimizing search paths and have been widely used in SNP interaction detection, the performance of swarm intelligence algorithms is greatly affected by initialization and search direction. To mitigate these issues, we propose a high-order SNP interaction detection algorithm based on hierarchical clustering and multitask harmony search (abbreviated as HCMHS). In harmony memory initialization, considering that similar SNPs are more likely to form pathogenic combinations, hierarchical clustering is first used to cluster SNPs into different clusters, and then based on these clusters, harmony memory is initialized. In harmony search, considering the correlation between different orders of SNP interactions, the search directions for SNPs of various orders are optimized through a multi-task framework. To verify the performance of HCMHS, we carried out experiments on 58 simulated datasets and one real dataset, and HCMHS achieved the best results compared to nine advanced algorithms. The code of HCMHS is available at https://***/huoluan17-tian/HCMHS. © 2024 IEEE.

关键词： Hierarchical clustering

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