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arXiv 2024年

作者： Wang, Ping Chen, Chong Liao, Hao Vorobyov, Vadim V. Wrachtrup, Jörg Liu, Ren-Bao College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Centre for Quantum Coherence The Chinese University of Hong Kong New Territories Shatin Hong Kong The Hong Kong Institute of Quantum Information Science and Technology The Chinese University of Hong Kong New Territories Shatin Hong Kong National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China 3rd Institute of Physics Research Center SCoPE and IQST University of Stuttgart Stuttgart70569 Germany New Cornerstone Science Laboratory The Chinese University of Hong Kong New Territories Shatin Hong Kong

Test of macroscopic realism (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the non-invasive measurability loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of raw data recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing quantum coherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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Gender bias, social bias, and representation in Bollywood and Hollywood (vol 3, 100409, 2022)

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PATTERNS 2022年第4期3卷 100409页

作者： Khadilkar, Kunal KhudaBukhsh, Ashiqur R. Mitchell, Tom M. School of Computer Science Language Technologies Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA Golisano College of Computing and Information Sciences Software Engineering Department Rochester Institute of Technology 20 Lomb Memorial Drive New York NY 14623 USA School of Computer Science Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213 USA

We use a suite of cutting-edge natural language processing methods to quantify and characterize societal and gender biases in popular movie content. Our data set consists of English subtitles of popular movies from Bollywood–the Mumbai film industry—spanning 7 decades (700 movies). In addition, we include movies from Hollywood and movies nominated for the Academy Awards for contrastive purposes. Our findings indicate that while the overall portrayal of women has improved over time in popular movie dialogues from both Bollywood and Hollywood, modern films still exhibit considerable gender bias and are yet to achieve equal representation among genders. We also observe a strong bias favoring fair skin color in Bollywood content that occurred consistently across all time periods we considered. While our geographic representation analysis indicates improved inclusion over time for several Indian states, it also reveals a long-standing under-representation of many northeastern Indian states.

关键词： Bollywood gender bias Hollywood social bias

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Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets

arXiv

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arXiv 2022年

作者： Huang, Yongwei Fu, Hao Vorobyov, Sergiy A. Luo, Zhi-Quan School of Information Engineering Guangdong University of Technology University Town Guangdong Guangzhou510006 China Huawei Technologies Software Avenue Yuhuatai District Jiangsu Nanjing China The Department of Signal Processing and Acoustics Aalto University Espoo02150 Finland School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Guangdong518172 China

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer. © 2022, CC BY-NC-SA.

关键词： Linear matrix inequalities

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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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When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence system

arXiv

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arXiv 2023年

作者： Zhang, Hao Xu, Jin-Jian Cui, Hong-Wei Li, Lin Yang, Yaowen Tang, Chao-Sheng Boers, Niklas College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Ministry of Industry and Information Technology Nanjing211106 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Earth Sciences and Engineering Nanjing University 163 Xianlin Road Nanjing210023 China Huawei Technologies Co. Ltd Shanghai200040 China School of Civil and Environmental Engineering Nanyang Technological University Blk N1 50 7 Nanyang Ave Singapore639798 Singapore Earth System Modelling School of Engineering and Design Technical University of Munich Munich Germany Potsdam Institute for Climate Impact Research Telegrafenberg A31 Potsdam14473 Germany

Artificial intelligence (AI) has significantly advanced Earth sciences, yet its full potential in to comprehensively modeling Earth’s complex dynamics remains unrealized. Geoscience foundation models (GFMs) emerge as a paradigm-shifting solution, integrating extensive cross-disciplinary data to enhance the simulation and understanding of Earth system dynamics. These data-centric AI models extract insights from petabytes of structured and unstructured data, effectively addressing the complexities of Earth systems that traditional models struggle to capture. The unique strengths of GFMs include flexible task specification, diverse input-output capabilities, and multimodal knowledge representation, enabling analyses that surpass those of individual data sources or traditional AI methods. This review not only highlights the key advantages of GFMs, but also presents essential techniques for their construction, with a focus on transformers, pre-training, and adaptation strategies. Subsequently, we examine recent advancements in GFMs, including large language models, vision models, vision-language models, and foundation-model-based agents, particularly emphasizing the potential applications in remote sensing. Additionally, the review concludes with a comprehensive analysis of the challenges and future trends in GFMs, addressing five critical aspects: data integration, model complexity, uncertainty quantification, interdisciplinary collaboration, and concerns related to privacy, trust, and security. This review offers a comprehensive overview of emerging geoscientific research paradigms, emphasizing the untapped opportunities at the intersection of advanced AI techniques and geoscience. It examines major methodologies, showcases advances in large-scale models, and discusses the challenges and prospects that will shape the future landscape of GFMs. The paper highlights a dynamic field rich with possibilities, poised to unlock new insights into Earth’s complexities and furthe

关键词： Visual languages

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Experimental Study on Droplet Behavior and Mechanisms During Reflooding in a Single-Rod Channel

SSRN

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SSRN 2022年

作者： Chen, Mingjing Liu, Hanzhou Chen, Deqi Zhong, Lei Liu, Haidong Yu, Hongxing Yu, Ruan Feng, Peng Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing University Ministry of Education Chongqing 400044 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Key Laboratory of Aerospace Thermophysics Ministry of Industry and Information Technology School of Energy Science and Engineering Harbin Institute of Technology Harbin150001 China

Discrete droplets have received extensive attention in reflooding processes under Loss Of Coolant Accident (LOCA) conditions due to their large vapor-liquid interface area, droplet collision, droplet evaporation, and other behaviors that have a significant impact on the heat transfer of Dispersed Flow Film Boiling (DFFB). Unfortunately, the accurate prediction of the characteristics of droplet velocity and distribution under DFFB cannot be achieved at present, due to the lack of sufficient visual data support. For this reason, a visualization experiment of high-temperature surface reflooding in a single-rod channel was carried out. In this paper, the effect of the thermal parameter, such as input heat flux on the transient droplet behavior, which include the droplet size and velocity, has been analaysed in detail. Furthermore, to provide physical support for building droplet distribution models containing more physical mechanisms in the future, several droplet formation and breakup mechanisms, which include the Helmholtz instability, capillary breakage and rewetting area sputter, has been discussed in this paper. © 2022, The Authors. All rights reserved.

关键词： Drop breakup

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AIM 2024 Challenge on Video Saliency Prediction: Methods and Results

arXiv

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arXiv 2024年

作者： Zhan, Gen Yang, Li Tang, Yunlong Liao, Yiting Lin, Jiongzhi Huang, Baitao Moradi, Morteza Moradi, Mohammad Rundo, Francesco Spampinato, Concetto Borji, Ali Palazzo, Simone Zhu, Yuxin Sun, Yinan Duan, Huiyu Cao, Yuqin Jia, Ziheng Hu, Qiang Min, Xiongkuo Zhai, Guangtao Fang, Hao Cong, Runmin Lu, Xiankai Zhou, Xiaofei Zhang, Wei Zhao, Chunyu Mu, Wentao Deng, Tao Tavakoli, Hamed R. Moskalenko, Andrey Bryncev, Alexey Vatolin, Dmitry Timofte, Radu ByteDance China ByteDance United States University of Rochester United States College of Electronic and Information Engineering Shenzhen University China Shenzhen Key Laboratory of Digital Creative Technology China Guangdong Key Laboratory of Intelligent Information Processing China University of Catania Italy STMicrolectronics ADG Central R&D Italy Quintic AI San Francisco United States Shanghai Jiao Tong University China School of Control Science and Engineering Shandong University China School of Software Shandong University China School of Automation Hangzhou Dianzi University China School of Information Science and Technology Southwest Jiaotong University China Nokia Technologies Finland Lomonosov Moscow State University Russia AIRI Moscow Russia MSU Institute for Artificial Intelligence Russia University of Würzburg Germany

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://***/challenges/***. © 2024, CC BY.

关键词： Video analysis

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Dual-channel quantum meta-hologram for display

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Advanced Photonics Nexus 2024年第1期3卷 86-92页

作者： Yubin Fan Hong Liang Yuhan Wang Shufan Chen Fangxing Lai Mu Ku Chen Shumin Xiao Jensen Li Din Ping Tsai City University of Hong Kong Department of Electrical EngineeringHong KongChina City University of Hong Kong Centre for BiosystemsNeuroscienceand NanotechnologyHong KongChina City University of Hong Kong The State Key Laboratory of Terahertz and Millimeter WavesHong KongChina The Hong Kong University of Science and Technology Department of PhysicsHong KongChina The Hong Kong University of Science and Technology IAS Center for Quantum TechnologiesHong KongChina Harbin Institute of Technology(Shenzhen) Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemGuangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsShenzhenChina Pengcheng Laboratory ShenzhenChina

Quantum technologies rely on creating and manipulating entangled sources,which are essential for quantum information,communication,and *** integrating quantum technologies and all-dielectric metasurfaces,the performance of miniature display devices can be enhanced to a higher *** display technology,such as virtual reality display,has achieved original commercial success,and was initially applied to immersive games and interactive *** the consumer market has quickly adopted this technology,several areas remain for improvement,including concerns around bulkiness,dual-channel display,and noise ***,we experimentally realize a quantum meta-hologram concept demonstration of a miniature *** fabricate an ultracompact meta-hologram based on 1μm thick titanium dioxide(TiO_(2)).The meta-hologram can be remotely switched with heralding technique and is robust against noise with the quantum entangled *** platform can alter the miniature display channel by manipulating heralding photons’polarization,removing speckles and multiple reflective light noise,improving imaging contrast,and potentially decreasing device *** contrast increases from 0.36 dB under speckle noise influences to 6.8 dB in quantum correlation *** approach has the potential to miniaturize quantum displays and quantum communication devices.

关键词： quantum meta-hologram display metasurface dual channel

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Effective and Efficient Graph Learning for Multi-view Clustering

arXiv

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arXiv 2021年

作者： Gao, Quanxue Xia, Wei Gao, Xinbo Zhang, Xiangdong Li, Qin Tao, Dacheng The State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen518172 China The School of Electronic Engineering Xidian University Xi'An710071 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix, and fail to explore the cluster structure of large-scale data. Moreover, they require a post-processing to get the final clustering, resulting in suboptimal performance. Furthermore, rank of the learned view-consensus graph cannot approximate the target rank. In this paper, drawing the inspiration from the bipartite graph, we propose an effective and efficient graph learning model for multi-view clustering. Specifically, our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm, which well characterizes both the spatial structure and complementary information embedded in graphs of different views. We learn view-consensus graph with adaptively weighted strategy and connectivity constraint such that the connected components indicates clusters directly. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size. Extensive experimental results indicate that our method is superior to state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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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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