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Structuring Nonlinear Wavefront Emitted from Monolayer Transition-Metal Dichalcogenides

研究（英文） 2020年第4期 392-401页

作者： Xuanmiao Hong Guangwei Hu Wenchao Zhao Kai Wang Shang Sun Rui Zhu Jing Wu Weiwei Liu Kian Ping Loh Andrew Thye Shen Wee Bing Wang Andrea Al Cheng-Wei Qiu Peixiang Lu Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Advanced Science Research Center City University of New York New York 10031 USA Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117542 Institute of Materials Research and Engineering Agency for Science Technology and Research 2 Fusionopolis Way Innovis#08-03Singapore 138634 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Department of Chemistry National University of Singapore 3 Science Drive 3 Singapore 17543 Department of Physics National University of Singapore 2 Science Drive 3 Singapore 117542 Centre for Advanced 2D Materials National University of Singapore Block S14 6 Science Drive 2 Singapore 117546 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Advanced Science Research Center City University of New York New York 10031 USA Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074 China Hubei Key Laboratory of Optical

The growing demand for tailored nonlinearity calls for a structure with unusual phase discontinuity that allows the realization of nonlinear optical chirality,holographic imaging,and nonlinear wavefront ***-metal dichalcogenide(TMDC)monolayers offer giant optical nonlinearity within a few-angstrom thickness,but limitations in optical absorption and domain size impose restriction on wavefront control of nonlinear emissions using classical light *** contrast,noble metal-based plasmonic nanosieves support giant field enhancements and precise nonlinear phase control,with hundred-nanometer pixel-level resolution;however,they suffer from intrinsically weak nonlinear ***,we report a multifunctional nonlinear interface by integrating TMDC monolayers with plasmonic nanosieves,yielding drastically different nonlinear functionalities that cannot be accessed by either *** a hybrid nonlinear interface allows second-harmonic(SH)orbital angular momentum(OAM)generation,beam steering,versatile polarization control,and holograms,with an effective SH nonlinearityX(2)of-25nm/*** designer platform synergizes the TMDC monolayer and plasmonic nanosieves to empower tunable geometric phases and large field enhancement,paving the way toward multifunctional and ultracompact nonlinear optical devices.

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Diamond Surface Functionalization via Visible Light-Driven C-H Activation for Nanoscale Quantum Sensing

arXiv

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arXiv 2023年

作者： Rodgers, Lila V.H. Nguyen, Suong T. Cox, James H. Zervas, Kalliope Yuan, Zhiyang Sangtawesin, Sorawis Stacey, Alastair Jaye, Cherno Weiland, Conan Pershin, Anton Gali, Adam Thomsen, Lars Meynell, Simon A. Hughes, Lillian B. Bleszynski Jayich, Ania C. Gui, Xin Cava, Robert J. Knowles, Robert R. de Leon, Nathalie P. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States Princeton University Department of Chemistry PrincetonNJ08540 United States School of Physics Center of Excellence in Advanced Functional Materials Suranaree University of Technology Nakhon Ratchasima30000 Thailand School of Physics University of Melbourne ParkvilleVIC3010 Australia School of Science RMIT University MelbourneVIC3000 Australia Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Wigner Research Centre for Physics POB 49 BudapestH-1525 Hungary Department of Atomic Physics Budapest University of Technology and Economics Műegyetem rakpart 3. BudapestH-1111 Hungary Australian Synchrotron Australian Nuclear Science and Technology Organisation 800 Blackburn Road ClaytonVIC3168 Australia Physics Department University of California Santa Barbara Santa BarbaraCA93106 United States Materials Department University of California Santa Barbara Santa BarbaraCA93106 United States

Nitrogen-vacancy (NV) centers in diamond are a promising platform for nanoscale nuclear magnetic resonance (NMR) sensing1. Despite significant progress towards using NV centers to detect and localize nuclear spins down to the single spin level2,3, NV-based spectroscopy of individual, intact, arbitrary target molecules remains elusive. NV molecular sensing requires that target molecules are immobilized within a few nanometers of NV centers with long spin coherence time. The inert nature of diamond4 typically requires harsh functionalization techniques such as thermal annealing2,5 or plasma processing6–8, limiting the scope of functional groups that can be attached to the surface. Solution-phase chemical methods can be more readily generalized to install diverse functional groups, but they have not been widely explored for single-crystal diamond surfaces. Moreover, realizing shallow NV centers with long spin coherence times requires highly ordered single-crystal surfaces, and solution-phase functionalization has not yet been shown to be compatible with such demanding conditions. In this work, we report a versatile strategy to directly functionalize C–H bonds on single-crystal diamond surfaces under ambient conditions using visible light. Hydrogen atom abstraction from surface C–H bonds generates carbon-centered radicals that can either intercept various radical acceptors or be oxidized to carbocations to form C–F, C–Cl, C–S, and C–N bonds at the surface. We verify covalent bond formation using lab and synchrotron-based surface analysis. This functionalization method is compatible with charge stable NV centers within 10 nm of the surface with spin coherence times comparable to the state of the art5. As a proof of principle, we use shallow ensembles of NV centers to detect nuclear spins from functional groups attached to the surface. Our approach to surface functionalization based on visible light-driven C–H bond activation opens the door to deploying NV centers as a br

关键词： Chemical activation

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution

arXiv

引用

arXiv 2023年

作者： Pennacchietti, Matteo Cunard, Brady Nahar, Shlok Zeeshan, Mohd Gangopadhyay, Sayan Poole, Philip J. Dalacu, Dan Fognini, Andreas Jöns, Klaus D. Zwiller, Val Jennewein, Thomas Lütkenhaus, Norbert Reimer, Michael E. Institute for Quantum Computing Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada National Research Council of Canada OttawaONK1A 0R6 Canada University of Ottawa OttawaONK1N 6N5 Canada Single Quantum B.V. Delft2628 CJ Netherlands Department of Physics Paderborn University Paderborn33098 Germany Department of Applied Physics Royal Institute of Technology Albanova University Centre Stockholm106 91 Sweden

An on-demand source of bright entangled photon pairs is desirable for quantum key distribution (QKD) and quantum repeaters. The leading candidate to generate entangled photon pairs is based on spontaneous parametric down-conversion (SPDC) in a non-linear crystal. However, there exists a fundamental trade-off between entanglement fidelity and efficiency in SPDC sources due to multiphoton emission at high brightness, which limits the pair extraction efficiency to 0.1% when operating at near-unity fidelity. Quantum dots in photonic nanostructures can in principle overcome this trade-off;however, the quantum dots that have achieved entanglement fidelities on par with SPDC sources (99%) have poor pair extraction efficiencies of 0.01%. Here, we demonstrate a 65-fold increase in the pair extraction efficiency compared to quantum dots with equivalent peak fidelity from an InAsP quantum dot in a photonic nanowire waveguide. We measure a raw peak concurrence and fidelity of 95.3% ± 0.5% and 97.5% ± 0.8%, respectively. Finally, we show that an oscillating two-photon Bell state generated by a semiconductor quantum dot can be utilized to establish a secure key for QKD, alleviating the need to remove the quantum dot energy splitting of the intermediate exciton states in the biexciton-exciton cascade. © 2023, CC BY.

关键词： Semiconductor quantum dots

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Correction to: A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility

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Neuroinformatics 2022年第1期20卷 37-38页

作者： Mathew Birdsall Abrams Jan G Bjaalie Samir Das Gary F Egan Satrajit S Ghosh Wojtek J Goscinski Jeffrey S Grethe Jeanette Hellgren Kotaleski Eric Tatt Wei Ho David N Kennedy Linda J Lanyon Trygve B Leergaard Helen S Mayberg Luciano Milanesi Roman Mouček J B Poline Prasun K Roy Stephen C Strother Tong Boon Tang Paul Tiesinga Thomas Wachtler Daniel K Wójcik Maryann E Martone INCF Secretariat Karolinska Institutet Stockholm Sweden. mathew@***. Institute of Basic Medical Sciences University of Oslo Oslo Norway. McGill Centre for Integrative Neuroscience McGill University Montreal QC Canada. Monash Biomedical Imaging Monash University Clayton VIC Australia. McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA USA. Department of Otolaryngology - Head and Neck Surgery Harvard Medical School Boston Boston MA USA. Monash eResearch Centre Monash University Melbourne VIC Australia. Department of Neuroscience School of Medicine University of California San Diego La Jolla CA USA. KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden. Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Perak Malaysia. Department of Psychiatry University of Massachusetts Medical School Worcester MA USA. Serendipitea.World Hasselby Sweden. Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine New York NY USA. Institute of Biomedical Technologies National Research Council (CNR) Milan Italy. Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic. Montreal Neurological Institute Faculty of Medicine and Health Sciences McGill University Montreal Canada. Computational Neuroscience & Neuroimaging Laboratory School of Bio-Medical Engineering Indian Institute of Technology (BHU) Varanasi UP India. Rotman Research Institute Baycrest Centre Department of Medical Biophysics University of Toronto Toronto ON Canada. Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Bandar Seri Iskandar Malaysia. Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands. Department of Biology II Ludwig-Maximilians-Univers

A Correction to this paper has been published: https://***/10.1007/s12021-021-09522-x

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Lessons Learned from Assessing Trustworthy AI in Practice

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Digital Society 2023年第3期2卷 1-25页

作者： Vetter, Dennis Amann, Julia Bruneault, Frédérick Coffee, Megan Düdder, Boris Gallucci, Alessio Gilbert, Thomas Krendl Hagendorff, Thilo van Halem, Irmhild Hickman, Eleanore Hildt, Elisabeth Holm, Sune Kararigas, Georgios Kringen, Pedro Madai, Vince I. Wiinblad Mathez, Emilie Tithi, Jesmin Jahan Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Computational Vision and Artificial Intelligence Lab Goethe University Frankfurt Frankfurt Am Main Germany Z-Inspection® Initiative Venice Italy Health Ethics and Policy Lab ETH Zurich Zurich Switzerland Strategy and Innovation Careum Foundation Zurich Switzerland Philosophie Departement Collège André-Laurendeau Montréal Canada École Des Médias Université du Québec À Montréal Montréal Canada Department of Medicine Division of Infectious Diseases and Immunology New York University Grossman School of Medicine New York City USA Department of Computer Science University of Copenhagen Copenhagen Denmark Digital Life Initiative Cornell Tech New York City USA Cluster of Excellence “Machine Learning: New Perspectives for Science” University of Tuebingen Tuebingen Germany School of Law University of Bristol Bristol UK Center for the Study of Ethics in the Professions Illinois Institute of Technology Chicago USA Department of Business Management and Analytics Arcada University of Applied Sciences Helsinki Finland Department of Food & Resource Economics University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Reykjavik Iceland QUEST Centre for Responsible Research Berlin Institute of Health Charité Universitätsmedizin Berlin Berlin Germany Faculty of Computing Engineering and the Built Environment School of Computing and Digital Technology Birmingham City University Birmingham UK Parallel Computing Labs Intel Santa Clara USA School of Economics Innovation and Technology Kristiania University College Oslo Norway Data Science Graduate School Seoul National University Seoul South Korea

Building artificial intelligence (AI) systems that adhere to ethical standards is a complex problem. Even though a multitude of guidelines for the design and development of such trustworthy AI systems exist, these guidelines focus on high-level and abstract requirements for AI systems, and it is often very difficult to assess if a specific system fulfills these requirements. The Z-Inspection® process provides a holistic and dynamic framework to evaluate the trustworthiness of specific AI systems at different stages of the AI lifecycle, including intended use, design, and development. It focuses, in particular, on the discussion and identification of ethical issues and tensions through the analysis of socio-technical scenarios and a requirement-based framework for ethical and trustworthy AI. This article is a methodological reflection on the Z-Inspection® process. We illustrate how high-level guidelines for ethical and trustworthy AI can be applied in practice and provide insights for both AI researchers and AI practitioners. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of real-world AI systems, as well as key recommendations and practical suggestions on how to ensure a rigorous trustworthiness assessment throughout the lifecycle of an AI system. The results presented in this article are based on our assessments of AI systems in the healthcare sector and environmental monitoring, where we used the framework for trustworthy AI proposed in the Ethics Guidelines for Trustworthy AI by the European Commission’s High-Level Expert Group on AI. However, the assessment process and the lessons learned can be adapted to other domains and include additional frameworks.

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Optical Thouless pumping transport and nonlinear switching in a topological low-dimensional discrete nematic liquid crystal array

arXiv

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arXiv 2022年

作者： Jung, Pawel S. Parto, Midya Pyrialakos, Georgios G. Nasari, Hadiseh Rutkowska, Katarzyna Trippenbach, Marek Khajavikhan, Mercedeh Krolikowski, Wieslaw Christodoulides, Demetrios N. CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Faculty of Physics Warsaw University of Technology Warsaw Poland Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Faculty of Physics University of Warsaw Warsaw Poland Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences University of Southern California Los AngelesCA90089 United States Laser Physics Centre Research School of Physics and Engineering Australian National University CanberraACT0200 Australia Science Program Texas A&M University at Qatar Doha Qatar

We theoretically investigate a Thouless pumping scheme in the 1D topological Su-Schrieffer-Heeger (SSH) model for single and multiple band-gaps systems when implemented in a discrete nematic liquid crystal arrangement. For an electrically controlled SSH waveguide array, we numerically demonstrate edge-to-edge light transport at low power levels. On the other hand, at higher powers, the transport is frustrated by light-induced nonlinear defect states, giving rise to robust all-optical switching. © 2022, CC BY.

关键词： Nematic liquid crystals

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Application of robust hybrid tree-based machine learning methods in accurate prediction of underground rock saturation exponent

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Measurement 2025年 255卷

作者： Ayat Hussein Adhab Ankur Bhogayata Anupam Yadav Y R Meena Rishiv Kalia B PRIYADHARSHINI Manish Kumar Bisht Morug Salih Mahdi Aseel Salah Mansoor Usama Kadem Radi Nasr Saadoun Abd Mehrdad Mottaghi Department of Pharmacy Al-Zahrawi University College Karbala Iraq Marwadi University Research Center Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Rajkot 360003 Gujarat India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Civil Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India School of Management Graphic Era Hill University Haldwani India Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India College of MLT Ahl Al Bayt University Iraq Gilgamesh Ahliya University Baghdad Iraq Collage of Pharmacy National University of Science and Technology Dhi Qar 64001 Iraq Medical Technical College Al-Farahidi University Iraq Faculty of Chemistry Kabul University Kabul Afghanistan

The accurate estimation of the saturation exponent is critical for the reliable evaluation of hydrocarbon reserves in petroleum-bearing formations. Traditional laboratory-based measurements for this parameter are both costly and time-consuming. To address this issue, this study introduces a robust data-driven framework employing hybrid tree-based machine learning algorithms, namely Random Forest, Gradient Boosting Machine, Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Extra Trees, and Decision Tree, to predict the rock saturation exponent using five input features: porosity, permeability, true resistivity, resistivity index, and water saturation. A total of 943 real field data points from Iraqi oil and gas fields were used. Model hyperparameters were optimized using Tree-structured Parzen Estimator (TPE) combined with 5-fold cross-validation to improve generalization and minimize overfitting. The results demonstrated that Gradient Boosting Machine and Random Forest achieved the highest predictive precision with R 2 values of 0.89 and 0.83 on the test set, and AARE% of 8.25% and 8.31%, respectively. Conversely, the Decision Tree algorithm yielded the lowest performance. Notably, this study presents a novel application of a comparative and systematically optimized ensemble modeling strategy for saturation exponent prediction, filling a research gap in the robust, interpretable, and accurate modeling of this complex petrophysical parameter. This research advances previous efforts by integrating outlier detection, sensitivity analysis, and optimization-based hyperparameter tuning, resulting in a practical and efficient workflow applicable to diverse geological datasets.

关键词： Oil bearing formations

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Perovskite Solar Fibers:Current Status,Issues and Challenges

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Advanced Fiber Materials 2019年第2期1卷 101-125页

作者： Andrew Balilonda Qian Li Mike Tebyetekerwa Rogers Tusiime Hui Zhang Rajan Jose Fatemeh Zabihi Shengyuan Yang Seeram Ramakrishna Meifang Zhu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials International Joint Laboratory for Advanced Fiber and Low-dimension MaterialsCollege of Materials Science and EngineeringDonghua UniversityShanghai 201620People’s Republic of China Research School of Electrical Energy and Materials EngineeringCollege of Engineering and Computer ScienceThe Australian National UniversityCanberraACT 2601Australia Nanostructured Renewable Energy Materials Laboratory Faculty of Industrial Sciences and TechnologyUniversiti Malaysia Pahang26300 PekanMalaysia Centre for Nanofibers and Nanotechnology National University of SingaporeSingapore 117581Singapore

Perovskite-based solar cells with high power conversion efficiencies(PCEs)are currently being demonstrated in solid-state device *** elevated performances can possibly be attained with different non-standard geometries,for example,the fiber-shaped perovskite solar cells,in the light of careful design and ***-shaped solar cells are promising in smart textiles energy harvesting towards next-generation electronic applications and *** can be made with facile process and at low ***,fiber-shaped perovskite solar devices have been reported,particularly with the focus on the proof-of-concept in such non-traditional *** this line,there are so many technical aspects which need to be addressed,if these photovoltaic(PV)cells are to be industrialized and produced ***,a well-organized and comprehensive discussion about the reported devices in this arena is *** challenges that need to be addressed,the possible solutions and the probable applications of these PV cells are also *** still,the perovskite fiber-shaped PV cells with other fiber PV devices reported in literature in terms of their scope,characteristic designs,performances,and other technical considerations have been summarised.

关键词： Fiber-shaped solar cells Perovskites Photovoltaics Smart textiles Fiber materials

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