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Record High Temperatures in the Ocean in 2024

Advances in Atmospheric sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/cas data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/cas data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/cas, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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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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Integration of a novel attribute and classical topology metrics of hyper-networks for automatic diagnosis of Major depressive disorder 22

Integration of a novel attribute and classical topology metr...

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22nd IEEE International Conference on E-Health networking, Application and Services, HEALTHCOM 2020

作者： Li, Yongchao Chen, Nan Wang, Yin Yang, Lin Zheng, Weihao Yao, Zhijun Hu, Bin Gansu Province Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

ISBN: (纸本)9781728162676

Conventional hyper-network coefficients ignore the weighted hyper-edge information which could be vital in researching the specificity of brain disease. Functional hyper-networks for 64 healthy controls (HC) and 56 patients with major depressive disorder (MDD) were constructed using the least absolute shrinkage and selection operator (Lasso). Not only the classical topology metrics but also a novel hyper-edge weight (HEW) attribute were extracted as features to promote the functional-based auto-diagnosis accuracy of MDD. We compared the categorization performance of each hyper-network coefficient. A multi-feature ensemble model was applied to fuse different kinds of features. We obtained 82.15 % accuracy with the classical hyper-network clustering coefficient (HCC) and 84.08 % accuracy with the HEW attribute on the MDD dataset. The performance was further improved to 89.24% by combining all the properties of the hyper-networks. The multi-feature ensemble model combining different hyper-network coefficients provides new insights into the automatic diagnosis with diverse information of MDD. © 2021 IEEE.

关键词： Topology

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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Anxiety recognition based on multimodal social media data and cross-attention mechanism

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Neurocomputing 2025年 646卷

作者： Jianghong Zhu Zhenwen Zhang Zepeng Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou 730000 China School of Medical Technology Beijing Institute of Technology Beijing 100081 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai 200000 China

Anxiety disorder is a common mental illness that involves persistent and recurrent episodes of intense anxiety and sudden feelings of fear or terror, which seriously affect the patient’s study, work and life. Social media contains a large amount of multimodal data reflecting people’s inner activities and emotional states, providing a new way for recognizing anxiety disorders. However, existing methods tend to excessively focus on the text data, and neglect the role of other types of data such as images. To address this issue, this paper proposes a Multimodal Anxiety Recognition model (MAR-IDCA) based on Image Description and Cross-Attention mechanism. Firstly, for each image of the user’s post, caption generation model is applied to obtain the abstract semantic information of the image, and the possible character information in the image is obtained through optical character recognition technology, so as to generate the description of the image. Secondly, through an image encoder and two text encoders, the visual feature of the image, the textual features of the image description and the post text are extracted respectively. Then, a multi-head cross-attention mechanism is employed to fuse the visual feature, image description feature and post text feature. Finally, combined with other auxiliary information of user posts, the final multimodal data representation is obtained and classified. Experiments on two multimodal anxiety recognition datasets show that, compared with the existing models, MAR-IDCA has better anxiety recognition performance.

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Ranking enhanced dialogue generation

arXiv

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arXiv 2020年

作者： Hao, Changying Pang, Liang Lan, Yanyan Sun, Fei Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Compressed network in network Models for Traffic Classification

Compressed Network in Network Models for Traffic Classificat...

引用

IEEE Conference on Wireless Communications and networking

作者： Min Lu Bin Zhou Zhiyong Bu Kecheng Zhang Zhenhua Ling Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai China University of Chinese Academy of Sciences Beijing China Key Laboratory of Wireless Sensor Network and Communications SIMIT CAS Shanghai China University of Science and Technology of China Hefei China

Accurate traffic classification is critical for network QoS provisioning and cyberspace security. Recently, classifying different traffic using convolutional neural networks (CNN) has achieved high accuracy. However, these large CNN models have millions of parameters, which are not suitable for edge computing hardware deployment. In this work, we propose a compressed network in network (NIN) model for traffic identification. A stepwise pruning and knowledge distillation (KD) is designed for training the compressed model, which aims at reducing storage and computing resources. Our method is validated with the public ISCX VPN-nonVPN traffic dataset. Experimental results show that without degrading classification accuracy, our minimum model can save more than 50% of the number of parameters and 30% of the computation time comparing with the uncompressed NIN model. The test set average F₁ score of 0.9805 of the minimum model is higher than that of the state-of-the-art model, which is a CNN model.

关键词： Training Computational modeling Conferences Cyberspace Quality of service Hardware Security

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Toripalimab plus chemotherapy as second-line treatment in previously EGFR-TKI treated patients with EGFR-mutant-advanced NSCLC:a multicenter phase-II trial

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Signal Transduction and Targeted Therapy 2021年第11期6卷 3295-3303页

作者： Tao Jiang Pingyang Wang Jie Zhang Yanqiu Zhao Jianying Zhou Yun Fan Yongqian Shu Xiaoqing Liu Helong Zhang Jianxing He Guanghui Gao Xiaoqian Mu Zhang Bao Yanjun Xu Renhua Guo Hong Wang Lin Deng Ningqiang Ma Yalei Zhang Hui Feng Sheng Yao Jiarui Wu Luonan Chen Caicun Zhou Shengxiang Ren Shanghai Pulmonary Hospital Tongji University School of MedicineShanghaiChina State Key Laboratory of Cell Biology Innovation Center for Cell Signaling NetworkCAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina Henan Cancer Hospital ZhengzhouChina The First Affiliated Hospital of Zhejiang University HangzhouChina Zhejiang Cancer Hospital HangzhouChina Jiangsu Province Hospital NanjingChina The Fifth Medical Center of PLA General Hospital BeijingChina Tangdu Hospital Fourth Military Medical UniversityXi’anChina The First Affiliated Hospital of Guangzhou Medical University GuangzhouChina Shanghai Junshi Biosciences Co.Ltd ShanghaiChina Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina

This multicenter phase-II trial aimed to investigate the efficacy,safety,and predictive biomarkers of toripalimab plus chemotherapy as second-line treatment in patients with EGFR-mutant-advanced *** who failed from first-line EGFR-TKIs and did not harbor T790M mutation were *** plus carboplatin and pemetrexed were administrated every three weeks for up to six cycles,followed by the maintenance of toripalimab and *** primary endpoint was objective-response rate(ORR).Integrated biomarker analysis of PD-L1 expression,tumor mutational burden(TMB),CD8+tumor-infiltrating lymphocyte(TIL)density,whole-exome,and transcriptome sequencing on tumor biopsies were also *** patients were enrolled with an overall ORR of 50.0%and disease-control rate(DCR)of 87.5%.The median progression free survival(PFS)and overall survival were 7.0 and 23.5 months,*** most common treatment-related adverse effects were leukopenia,neutropenia,anemia,ALT/AST elevation,and *** analysis showed that none of PD-L1 expression,TMB level,and CD8+TIL density could serve as a predictive *** analysis of whole-exome and transcriptome sequencing data revealed that patients with DSPP mutation had a decreased M2 macrophage infiltration and associated with longer PFS than those of wild *** plus chemotherapy showed a promising anti-tumor activity with acceptable safety profiles as the second-line setting in patients with EGFR-mutant *** mutation might serve as a potential biomarker for this combination.A phase-III trial to compare toripalimab versus placebo in combination with chemotherapy in this setting is ongoing(NCT03924050).

关键词： NSCLC chemotherapy treatment

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Quantum adiabatic doping for atomic Fermi-Hubbard quantum simulations

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Physical Review A 2021年第4期103卷 043320-043320页

作者： Jue Nan Jian Lin Yuchen Luo Bo Zhao Xiaopeng Li Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing and Department of Physics Fudan University Shanghai 200433 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shanghai Qi Zhi Institute Xuhui District Shanghai 200032 China

It is important to dope the antiferromagnetic state while preserving low entropy, in the quantum simulation of the Fermi-Hubbard model. In a recent work [Phys. Rev. Lett. 123, 233603 (2019)], we proposed a quantum adiabatic doping protocol using an incommensurate lattice potential, where its feasibility is demonstrated with a certain particle-doping fraction. Here, we carry out a systematic study of quantum adiabatic doping for a wide range of doping fractions including both particle doping and hole doping, with both commensurate and incommensurate cases considered. We find that there is still a localizationlike slowing-down problem even at commensurate fillings, and that it becomes less harmful in the hole-doped regime. With interactions, the adiabatic preparation is found to be more efficient because the interaction effect destabilizes localization. For both free and interacting cases, we find adiabatic doping has a better performance in the hole-doped regime than the particle-doped regime. We also investigate adiabatic doping starting from a half-filling Mott insulator, which is found to be more efficient for certain filling fractions.

关键词： Anderson localization Fermi gases Superconductivity Fermionic condensates Ultracold gases Electric moment Magnetic moment Density matrix renormalization group Extended Hubbard model Tensor network methods

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