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arXiv 2023年

作者： Chen, Ziming Hoye, Robert L.Z. Yip, Hin-Lap Fiuza-Maneiro, Nadesh López-Fernández, Iago Otero-Martínez, Clara Polavarapu, Lakshminarayana Mondal, Navendu Mirabelli, Alessandro Anaya, Miguel Stranks, Samuel D. Liu, Hui Shi, Guangyi Xiao, Zhengguo Kim, Nakyung Kim, Yunna Shin, Byungha Shi, Jinquan Liu, Mengxia Zhang, Qianpeng Fan, Zhiyong Loy, James C. Zhao, Lianfeng Rand, Barry P. Arfin, Habibul Saikia, Sajid Nag, Angshuman Zou, Chen Lin, Lih Y. Xiang, Hengyang Zeng, Haibo Liu, Denghui Su, Shi-Jian Wang, Chenhui Zhong, Haizheng Xuan, Tong-Tong Xie, Rong-Jun Bao, Chunxiong Gao, Feng Gao, Xiang Qin, Chuanjiang Kim, Young-Hoon Beard, Matthew C. Department of Chemistry Centre for Processible Electronics Imperial College London LondonW12 0BZ United Kingdom Inorganic Chemistry Laboratory Department of Chemistry University of Oxford South Parks Road OxfordOX1 3QR United Kingdom Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Materials Science and Engineering City University of Hong Kong Hong Kong Hong Kong School of Energy and Environment City University of Hong Kong Hong Kong Hong Kong CINBIO Universidade de Vigo Materials Chemistry and Physics Group Department of Physical Chemistry Campus Universitario As Lagoas Marcosende Vigo36310 Spain Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Physics CAS Key Laboratory of Strongly Coupled Quantum Matter Physics University of Science and Technology of China Anhui Hefei230026 China Daejeon34141 Korea Republic of Department of Electrical Engineering Yale University New HavenCT06511 United States Energy Sciences Institute Yale University West HavenCT06516 United States Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Hong Kong Department of Physics Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Andlinger Center for Energy and the Environment Princeton University PrincetonNJ08544 United States Pune411008 India Zhejiang University Zhejiang Hangzhou310058 China University of Washington SeattleWA98195 United States MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics & Nanomaterials College of Materials Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Luminescent Materials and Devices Institute of Polymer Opto

In recent years, the field of metal-halide perovskite emitters has rapidly emerged as a new community in solid-state lighting. Their exceptional optoelectronic properties have contributed to the rapid rise in external... 详细信息

关键词： Perovskite

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

arXiv

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arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

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Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)

arXiv

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arXiv 2023年

作者： Waldmannstetter, Diana Ezhov, Ivan Wiestler, Benedikt Campi, Francesco Kukuljan, Ivan Ehrlich, Stefan Vinayahalingam, Shankeeth Baheti, Bhakti Chakrabarty, Satrajit Baid, Ujjwal Bakas, Spyridon Schwarting, Julian Metz, Marie Kirschke, Jan S. Rueckert, Daniel Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Neuherberg Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Healthcare Technologies SETLabs Research Munich Germany Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre Nijmegen Netherlands Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St.Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden

Accurate image registration is pivotal in bio-medical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize sub-resolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Erratum: “Induced out-of-plane piezoelectricity and giant Rashba spin splitting in Janus WSiZ3H (Z = N, P, As) monolayers toward next-generation electronic devices” [J. Appl. Phys. 135, 074301 (2024)]

引用

Journal of applied Physics 2024年第10期135卷

作者： Tuan V. Vu Bui D. Hoi A. I. Kartamyshev Nguyen N. Hieu Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 70000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 70000 Vietnam Department of Physics University of Education Hue University Hue 530000 Vietnam Institute of Research and Development Duy Tan University Da Nang 550000 Vietnam Faculty of Natural Sciences Duy Tan University Da Nang 550000 Vietnam

The authors regret that one of the affiliations (affiliation 2) was incorrectly shown in the original manuscript. The corrected list of affiliations is as shown

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm

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Physical Review Research 2022年第3期4卷 033140-033140页

作者： Jean-Loup Ville Alexis Morvan Akel Hashim Ravi K. Naik Marie Lu Bradley Mitchell John-Mark Kreikebaum Kevin P. O'Brien Joel J. Wallman Ian Hincks Joseph Emerson Ethan Smith Ed Younis Costin Iancu David I. Santiago Irfan Siddiqi Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Inst. for Quantum Computing and Dept. of Applied Mathematics University of Waterloo Waterloo Ontario Canada N2L 3G1 Quantum Benchmark Inc. Kitchener Ontario Canada N2H 5G5

Recent progress in noisy intermediate-scale quantum (NISQ) hardware shows that quantum devices may be able to tackle complex problems even without error correction. However, coherent errors due to the increased complexity of these devices is an outstanding issue. They can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like quantum imaginary time evolution are susceptible to these errors. This article presents the combination of both noise tailoring using randomized compiling and error mitigation with purification. We also show that cycle benchmarking gives an estimate of the reliability of the purification. We apply this method to the quantum imaginary time evolution of a transverse field Ising model and report an energy estimation error and a ground-state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum information processing Quantum simulation

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Corrigendum to “Recent advancements in carbon-based materials for resistive switching applications” [Carbon, 228 (2024) (199320) 1–17 119320]

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Carbon 2024年 230卷

作者： Snehal L. Patil Omkar Y. Pawar Tukaram D. Dongale Sehui Chang Sooman Lim Young Min Song School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea Artificial Intelligence (AI) Graduate School GIST Gwangju 61005 Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea Department of Flexible and Printable Electronics LANL-JBNU Engineering Institute-Korea Jeonbuk National University Jeonju 54896 Republic of Korea Computational Electronics and Nanoscience Research Laboratory School of Nanoscience and Biotechnology Shivaji University Kolhapur Maharashtra 416004 India

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JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

arXiv

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arXiv 2023年

作者： Choudhary, Kamal Wines, Daniel Garrity, Kevin F. Williams, Maureen Tavazza, Francesca Li, Kangming Hattrick-Simpers, Jason Gupta, Vishu Romero, Aldo H. Krogel, Jaron T. Saritas, Kayahan Fuhr, Addis Ganesh, Panchapakesan Kent, Paul R.C. Yan, Keqiang Lin, Yuchao Ji, Shuiwang Blaiszik, Ben Reiser, Patrick Friederich, Pascal Agrawal, Ankit Tiwary, Pratyush Beyerle, Eric Minch, Peter Rhone, Trevor David Takeuchi, Ichiro Wexler, Robert B. Mannodi-Kanakkithodi, Arun Ertekin, Elif Mishra, Avanish Mathew, Nithin Wood, Mitchell Rohskopf, Andrew Dale Wang, Shih-Han Achenie, Luke E.K. Xin, Hongliang Biacchi, Adam J. Material Measurement Laboratory National Institute of Standards and Technology MD20899 United States Department of Materials Science and Engineering University of Toronto 27 King’s College Cir TorontoON Canada Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Ludwig Institute for Cancer Research Princeton University PrincetonNJ08544 United States Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Nanophase Materials Science Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Globus University of Chicago IL60637 United States Data Science Learning Division National Lab ArgonneIL60439 United States Institute of Nanotechnology Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Department of Chemistry Institute of Materials Science and Engineering Washington University in St. Louis St. LouisMO63130 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Department of Mechan

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://***/jarvis_leaderboard/ © 2023, CC BY.

关键词： Electronic structure

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Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography

arXiv

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arXiv 2021年

作者： Guo, Zhen Song, Jung Ki Barbastathis, George Glinsky, Michael E. Vaughan, Courtenay T. Larson, Kurt W. Alpert, Bradley K. Levine, Zachary H. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Centre Singapore138002 Singapore Sandia National Laboratory AlbuquerqueNM87123 United States Applied and Computational Mathematics Division National Institute of Standards and Technology BoulderCO80305 United States Quantum Measurement Division National Institute of Standards and Technology GaithersburgMD20899 United States

Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks to determine and apply a prior distribution in the reconstruction process. Our neural networks learn the prior directly from synthetic training samples. The neural nets thus obtain a prior distribution that is specific to the class of objects we are interested in reconstructing. In particular, we used deep generative models with 3D convolutional layers and 3D attention layers which are trained on 3D synthetic integrated circuit (IC) data from a model dubbed CircuitFaker. We demonstrate that, when the projection angles and photon budgets are limited, the priors from our deep generative models can dramatically improve the IC reconstruction quality on synthetic data compared with maximum likelihood estimation. Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning. We expect that if the process were reproduced with experimental data, the advantage of the machine learning would persist. The advantages of machine learning in limited angle X-ray tomography may further enable applications in low-photon nanoscale imaging. © 2021, CC BY.

关键词： Imaging systems

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All-optical noise spectroscopy of a solid-state spin

arXiv

引用

arXiv 2021年

作者： Farfurnik, Demitry Singh, Harjot Luo, Zhouchen Bracker, Allan S. Carter, Samuel G. Pettit, Robert M. Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Naval Research Laboratory 4555 Overlook Avenue SW WashingtonDC20375 United States Laboratory for Physical Sciences University of Maryland College ParkMD20740 United States Intelligence Community Postrdoctoral Research Fellowship Program University of Maryland College ParkMD20742 United States

Noise spectroscopy elucidates the fundamental noise sources in spin systems, thereby serving as an essential tool toward developing spin qubits with long coherence times for quantum information processing, communication, and sensing. But existing techniques for noise spectroscopy that rely on microwave fields become infeasible when the microwave power is too weak to generate Rabi rotations of the spin. Here, we demonstrate an alternative all-optical approach to performing noise spectroscopy. Our approach utilizes coherent Raman rotations of the spin state with controlled timing and phase to implement Carr-Purcell-Meiboom-Gill pulse sequences. Analyzing the spin dynamics under these sequences enables us to extract the noise spectrum of a dense ensemble of nuclear spins interacting with a single spin in a quantum dot, which has thus far only been modeled theoretically. By providing spectral bandwidths of over 100 MHz, our approach enables the studies of spin dynamics and decoherence for a broad range of solid-state spin qubits. Copyright © 2021, The Authors. All rights reserved.

关键词： Rotation

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