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arXiv 2023年

作者： Zhang, Ke Yang, Yan Yu, Jun Jiang, Hanliang Fan, Jianping Huang, Qingming Han, Weidong The Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Regional Medical Center for National Institute of Respiratory Diseases Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou310016 China AI Lab at Lenovo Research 100094 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Department of Medical Oncology at Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou China The College of Mathematical Medicine Zhejiang Normal University Jinhua China

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multimodal tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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RAR-U-NET: A residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels

arXiv

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arXiv 2020年

作者： Wang, Ziyang Zhang, Zhengdong Voiculescu, Irina Department of Computer Science University of Oxford United Kingdom State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

Segmentation algorithms for medical images are widely studied for various clinical and research purposes. In this paper, we propose a new and efficient method for medical image segmentation under noisy labels. The method operates under a deep learning paradigm, incorporating four novel contributions. Firstly, a residual interconnection is explored in different scale encoders to transfer gradient information efficiently. Secondly, four copy-and-crop connections are replaced by residual-block-based concatenation to alleviate the disparity between encoders and decoders. Thirdly, convolutional attention modules for feature refinement are studied on all scale decoders. Finally, an adaptive denoising learning strategy (ADL) is introduced into the training process to avoid too much influence from the noisy labels. Experimental results are illustrated on a publicly available benchmark database of spine CTs. Our proposed method achieves competitive performance against other state-of-the-art methods over a variety of different evaluation measures. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

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A survey on few-shot class-incremental learning

arXiv

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arXiv 2023年

作者： Tian, Songsong Li, Lusi Li, Weijun Ran, Hang Ning, Xin Tiwari, Prayag Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China Department of Computer Science Old Dominion University NorfolkVA23529 United States School of Information Technology Halmstad University Halmstad30118 Sweden

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Energy Consumption Minimization for Mobile Edge Generation

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Meng Zhong, Ruikang Mu, Xidong Liu, Yuanwei Peng, Mugen Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom BelfastBT3 9DT United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

The novel concept of mobile edge generation (MEG) is investigated, where the generative artificial intelligence (GAI) model is partitioned into sub-models to be distributed in the network edge, thus enabling latent feature exchange between the edge server and user equipments (UEs). A seed coding module is introduced to encode the intermediate latent features generated by the GAI sub-model at the edge server into flexibly-sized seed for transmission to UEs, instead of transmitting large-size raw data. A weighted energy consumption minimization problem is formulated by jointly optimizing the seed coding ratio (SCR), transmit power, and computing frequencies while guaranteeing the quality-of-generation requirements including total latency and peak signal-to-noise ratio (PSNR). To enhance the resilience of the MEG models against the channel noise, a joint fine-tuning scheme based on low-rank adaption is proposed to train the introduced rank-reduced bypass matrices and seed coding module. Based on the fine-tuned results, a PSNR model regarding SCR and communication signal-to-noise ratio is established to overcome the optimization difficulty due to the lack of the explicit PSNR model. A proximal policy optimization-based MEG energy consumption optimization (MEG-ECO) algorithm is proposed to solve the formulated problem, where the order of magnitude balancing on state and penalty shaping are exploited for more efficient learning. Numerical results reveal that 1) the fine-tuned MEG models have superior resilience against the channel noise;2) the proposed MEG-ECO algorithm can significantly reduce energy consumption by up to 87.4% compared to conventional centralized generation and up to 33.5% against MEG without seed coding module;and 3) the energy consumption decreases when more partial models are assigned to the edge server, whereas this impact diminishes as the latency threshold is relaxed. © 2025 IEEE.

关键词： Deep reinforcement learning

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Electrically controllable laser frequency combs in graphene-fibre microresonators

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Light(science & Applications) 2020年第1期9卷 220-228页

作者： Chenye Qin Kunpeng Jia Qianyuan Li Teng Tan Xiaohan Wang Yanhong Guo Shu-Wei Huang Yuan Liu Shining Zhu Zhenda Xie Yunjiang Rao Baicheng Yao Key Laboratory of Optical Fibre Sensing and Communications(Education Ministry of China) University of Electronic Science and Technology of ChinaChengduChina National Laboratory of Solid State Microstructures and School of Electronic Science and EngineeringSchool of Physics and College of Engineering and Applied SciencesNanjing UniversityNanjingChina Department of Electrical Computerand Energy EngineeringUniversity of Colorado BoulderBoulderCO 80309USA Key Laboratory for Micro-Nano Optoelectronic Devices(Education Ministry of China) School of Physics and ElectronicsHunan UniversityChangshaChina Research Centre for Optical Fibre Sensing Zhejiang LaboratoryHangzhouChina

Laser frequency combs emitting ultrafast pulses of light,at equidistantly discrete frequencies,are cornerstones of modern information *** recent years,the generation of soliton combs in microcavities with ultrahighquality factors has established microcombs as out-oflaboratory ***,the material and geometry of a laser cavity,which determine comb formation,are difficult to electrically *** dynamic control can further enrich the diversity of comb outputs and help to actively stabilize them.

关键词： laser frequency fibre

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Water consumption and biodiversity:Responses to global emergency events

引用

science Bulletin 2024年第16期69卷 2632-2646页

作者： Dandan Zhao Junguo Liu Laixiang Sun Klaus Hubacek Stephan Pfister Kuishuang Feng Heran Zheng Xu Peng Daoping Wang Hong Yang Lei Shen Fei Lun Xu Zhao Bin Chen Marko Keskinen Shaohui Zhang Jialiang Cai Olli Varis Water&Development Research Group Department of Built EnvironmentAalto UniversityEspoo 15200Finland School of Water Conservancy North China University of Water Resources and Electric PowerZhengzhou 450046China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Geographical Sciences University of MarylandCollege Park 20742USA School of Finance and Management SOASUniversity of LondonLondon WC1H0XGUK Integrated Research on Energy Environment and SocietyEnergy and Sustainability Research Institute GroningenUniversity of GroningenGroningen 9747AGthe Netherlands ETH Zurich Institute of Environmental EngineeringZurich 8093Switzerland The Bartlett School of Sustainable Construction University College LondonLondon WC1E6BTUK School of Business Jiangnan UniversityWuxi 214122China Department of Computer Science and Technology University of CambridgeCambridge CB21TNUK w2e Environmental Consulting GmbH Duebendorf 8600Switzerland Key Laboratory for Resource Use and Environmental Remediation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Land Science and Technology China Agricultural UniversityBeijing 100083China Institute of Blue and Green Development Shandong UniversityWeihai 264209China Fudan Tyndall Center Department of Environmental Science and EngineeringFudan UniversityShanghai 200438China School of Economics and Management Beihang UniversityBeijing 100191China International Institute for Applied Systems Analysis Laxenburg A-2361Austria

Given that it was a once-in-a-century emergency event,the confinement measures related to the coronavirus disease 2019(COVID-19)pandemic caused diverse disruptions and changes in life and work *** changes significantly affected water consumption both during and after the pandemic,with direct and indirect consequences on ***,there has been a lack of holistic evaluation of these ***,we propose a novel framework to study the impacts of this unique global emergency event by embedding an environmentally extended supply-constrained global multi-regional input-output model(MRIO)into the drivers-pressure-state-impact-response(DPSIR)*** framework allowed us to develop scenarios related to COVID-19 confinement measures to quantify country-sector-specific changes in freshwater consumption and the associated changes in biodiversity for the period of *** results suggest progressively diminishing impacts due to the implementation of COVID-19 vaccines and the socio-economic system’s self-adjustment to the new *** 2020,the confinement measures were estimated to decrease global water consumption by about 5.7% on average across all scenarios when compared with the baseline level with no confinement ***,such a decrease is estimated to lead to a reduction of around 5% in the related pressure on *** the interdependencies and interactions across global supply chains,even those countries and sectors that were not directly affected by the COVID-19 shocks experienced significant impacts:Our results indicate that the supply chain propagations contributed to 79% of the total estimated decrease in water consumption and 84%of the reduction in biodiversity loss on *** study demonstrates that the MRIO-enhanced DSPIR framework can help quantify resource pressures and the resultant environmental impacts across supply chains when facing a global emergency ***,we recommend the development o

关键词： Global emergency events Water-biodiversity causal effect COVID-19 Biodiversity MRIO-enhanced DPSIR framework Supply-chain network High-resolution water consumption dataset Supply-constrained multi-regional input-output(mixed MRIO)model

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UNION: An unreferenced metric for evaluating open-ended story generation

arXiv

引用

arXiv 2020年

作者： Guan, Jian Huang, Minlie Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for open-ended text generation including story or dialog generation because of the notorious one-to-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references. To alleviate this issue, we propose UNION, a learnable UNreferenced metrIc for evaluating Open-eNded story generation, which measures the quality of a generated story without any reference. Built on top of BERT, UNION is trained to distinguish human-written stories from negative samples and recover the perturbation in negative stories. We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence. Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-the-art metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Keiki: Towards realistic danmaku generation via sequential GANs

arXiv

引用

arXiv 2021年

作者： Wang, Ziqi Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of Digital Games University of Malta Msida2080 Malta

Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games. Search-based methods, however, can hardly model patterns of danmakus—the bullet hell shooting entity—explicitly and the resulting levels often look non-realistic. In this paper, we present a novel bullet hell game platform named Keiki, which allows the representation of danmakus as a parametric sequence which, in turn, can model the sequential behaviours of danmakus. We employ three types of generative adversarial networks (GANs) and test Keiki across three metrics designed to quantify the quality of the generated danmakus. The time-series GAN and periodic spatial GAN show different yet competitive performance in terms of the evaluation metrics adopted, their deviation from human-designed danmakus, and the diversity of generated danmakus. The preliminary experimental studies presented here showcase that potential of time-series GANs for sequential content generation in games. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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EmgAuth: An EMG-based Smartphone Unlocking System Using Siamese Network

EmgAuth: An EMG-based Smartphone Unlocking System Using Siam...

引用

IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Boyu Fan Xuefeng Liu Xiang Su Pan Hui Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science University of Helsinki Finland

ISBN: (数字)9781728146577

ISBN: (纸本)9781728146584

Screen lock is a critical security feature for smart-phones to prevent unauthorized access. Although various screen unlocking technologies including fingerprint and facial recognition have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth leverages the Myo armband to collect the EMG data of smartphone users and enables users to unlock their smartphones when picking up and watching their smartphones. In particular, when training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband. We conduct experiments including 40 participants and the evaluation results show that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different sizes and at different locations, and is applicable for users with different postures. EmgAuth bears great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.

关键词： Electromyography authentication Siamese Net-work unlocking smartphone Smartphone unlocking Electromyogram Consumers Authentication Facial Recognition siamese unauthorized access Fingerprints

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CoTK: An open-source toolkit for fast development and fair evaluation of text generation

arXiv

引用

arXiv 2020年

作者： Huang, Fei Wan, Dazhen Shao, Zhihong Ke, Pei Guan, Jian Niu, Yilin Zhu, Xiaoyan Huang, Minlie 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 Beijing100084 China

In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions. We present CoTK, an open-source toolkit aiming to support fast development and fair evaluation of text generation. In model development, CoTK helps handle the cumbersome issues, such as data processing, metric implementation, and reproduction. It standardizes the development steps and reduces human errors which may lead to inconsistent experimental settings. In model evaluation, CoTK provides implementation for many commonly used metrics and benchmark models across different experimental settings. As a unique feature, CoTK can signify when and which metric cannot be fairly compared. We demonstrate that it is convenient to use CoTK for model development and evaluation, particularly across different experimental settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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