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arXiv 2025年

作者： He, Yuwen Wang, Wei Wang, Wanyu Jiang, Kui Wu, Wanyu Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Harbin Institute of Technology China

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flares’ relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Glare

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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Focal plane imaging system based on CMOS terahertz detector

Focal plane imaging system based on CMOS terahertz detector

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2024 International Conference on intelligent Manufacturing and Mechatronics, IMM 2024

作者： Lin, Yue Zhang, Hui Qi, Feng Liu, Zhaoyang Wang, Guanwen Wang, Ningbo School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China The Key Laboratory of OptoElectronic Information Processing Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China

Terahertz technology has shown remarkable potential for application in the field of imaging since it offers several particular advantages such as great penetration capabilities, non-ionization and high instantaneous bandwidth. This paper presents a focal plane imging system based on CMOS terathertz detector for forward-looking detection. The focal plane imaging system has several advantages, including fast scanning, simple composition, low cost, and easy integration of a CMOS detector. It is composed of a terahertz radiation source, a Polytetrafluoroethylene (PTFE) lens and a broadband CMOS detector. The detector was fabricated in a 65 nm CMOS process. The operating frequency range of the detector covers 75 GHz to 1000 GHz. Measurement results have shown that the focal plane imaging system is capable of stably imaging at frequencies of 100 GHz and 220 GHz. To address the issue of missing information in measurement images, the morphological closing algorithm is employed to process the original image, successfully filling in the missing information and improving image quality. © 2024 institute of Physics Publishing. All rights reserved.

关键词： Focusing

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A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation

arXiv

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arXiv 2024年

作者： Ma, Zhengrui Fang, Qingkai Zhang, Shaolei Guo, Shoutao Feng, Yang Zhang, Min Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

Simultaneous translation models play a crucial role in facilitating communication. However, existing research primarily focuses on text-to-text or speech-to-text models, necessitating additional cascade components to achieve speech-to-speech translation. These pipeline methods suffer from error propagation and accumulate delays in each cascade component, resulting in reduced synchronization between the speaker and listener. To overcome these challenges, we propose a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2x1), which integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework. We develop a non-autoregressive decoder capable of concurrently generating multiple text or acoustic unit tokens upon receiving fixed-length speech chunks. The decoder can generate blank or repeated tokens and employ CTC decoding to dynamically adjust its latency. Experimental results show that NAST-S2x outperforms state-of-the-art models in both speech-to-text and speech-to-speech tasks. It achieves high-quality simultaneous interpretation within a delay of less than 3 seconds and provides a 28× decoding speedup in offline generation. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for High Performance Computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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Rich-text document styling restoration via reinforcement learning

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Frontiers of Computer Science 2021年第4期15卷 93-103页

作者： Hongwei LI Yingpeng HU Yixuan CAO Ganbin ZHOU Ping LUO Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Search Product Center WeChat Search Application DepartmentTencentBeijing 100080China

Richly formatted documents,such as financial disclosures,scientific articles,government regulations,widely exist on ***,since most of these documents are only for public reading,the styling information inside them is usually missing,making them improper or even burdensome to be displayed and edited in different formats and *** this study we formulate the task of document styling restoration as an optimization problem,which aims to identify the styling settings on the document elements,e.g.,lines,table cells,text,so that rendering with the output styling settings results in a document,where each element inside it holds the(closely)exact position with the one in the original *** that each styling setting is a decision,this problem can be transformed as a multi-step decision-making task over all the document elements,and then be solved by reinforcement ***,Monte-Carlo Tree Search(MCTS)is leveraged to explore the different styling settings,and the policy function is learnt under the supervision of the delayed *** a case study,we restore the styling information inside tables,where structural and functional data in the documents are usually *** shows that,our best reinforcement method successfully restores the stylings in 87.65%of the tables,with 25.75%absolute improvement over the *** also discuss the tradeoff between the inference time and restoration success rate,and argue that although the reinforcement methods cannot be used in real-time scenarios,it is suitable for the offline tasks with high-quality ***,this model has been applied in a PDF parser to support cross-format display.

关键词： styling restoration monte-carlo tree search reinforcement learning richly formatted documents tables

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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GridMM: Grid Memory Map for Vision-and-Language Navigation

GridMM: Grid Memory Map for Vision-and-Language Navigation

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International Conference on Computer Vision (ICCV)

作者： Zihan Wang Xiangyang Li Jiahao Yang Yeqi Liu Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. To represent the previously visited environment, most approaches for VLN implement memory using recurrent states, topological maps, or top-down semantic maps. In contrast to these approaches, we build the top-down egocentric and dynamically growing Grid Memory Map (i.e., GridMM) to structure the visited environment. From a global perspective, historical observations are projected into a unified grid map in a top-down view, which can better represent the spatial relations of the environment. From a local perspective, we further propose an instruction relevance aggregation method to capture fine-grained visual clues in each grid region. Extensive experiments are conducted on both the REVERIE, R2R, SOON datasets in the discrete environments, and the R2R-CE dataset in the continuous environments, showing the superiority of our proposed method. The source code is available at https://***/MrZihan/GridMM.

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongshuai Wang Xiaojun Cai Defeng Li Shiwei Sun Cheng Chen Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features.

关键词：

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Non-autoregressive Streaming Transformer for Simultaneous Translation

arXiv

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arXiv 2023年

作者： Ma, Zhengrui Zhang, Shaolei Guo, Shoutao Shao, Chenze Zhang, Min Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Future Science and Engineering Soochow University China

Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality. However, training these models to achieve high quality while maintaining low latency often leads to a tendency for aggressive anticipation. We argue that such issue stems from the autoregressive architecture upon which most existing SiMT models are built. To address those issues, we propose non-autoregressive streaming Transformer (NAST) which comprises a unidirectional encoder and a non-autoregressive decoder with intra-chunk parallelism. We enable NAST to generate the blank token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and train it to maximize the non-monotonic latent alignment with an alignment-based latency loss. Experiments on various SiMT benchmarks demonstrate that NAST outperforms previous strong autoregressive SiMT baselines. Source code is publicly available at https://***/ictnlp/NAST. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine translation

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