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A self-employed taxpayer experimental study on trust,power,and tax compliance in eleven countries

Financial Innovation 2022年第1期8卷 2588-2610页

作者： Larissa M.Batrancea Anca Nichita Ruggero De Agostini Fabricio Batista Narcizo Denis Forte Samuel de Paiva Neves Mamede Ana Maria Roux-Cesar Bozhidar Nedev LeošVitek József Pántya Aidin Salamzadeh Eleanya K.Nduka Janusz Kudła Mateusz Kopyt Luis Pacheco Isabel Maldonado Nsubili Isaga Serkan Benk Tamer Budak Department of Business Babeş-Bolyai University7 Horea Street400174 Cluj-NapocaRomania Faculty of Economics “1 Decembrie 1918”University of Alba Iulia15-17 Unirii Street510009 Alba IuliaRomania Department of Psychol-ogy University of YorkHeslingtonYork YO105DDUK BPP Education Group Department of PsychologySchool of BusinessBPP University137 Stamford StreetLondon SE19NNUK Department of Computer Science IT University of CopenhagenRued Langgards Vej 72300 CopenhagenDenmark Office of CTO GN Audio As(Jabra)Lautrupbjerg 72750 BallerupDenmark Business Management Graduate Program Strategic Finance DepartmentMackenzie Pres-byterian University163 Rua Maria AntôniaSão Paulo 01302-907Brazil Center for Applied Social Sciences Mackenzie Presbyterian University930 Rua da ConsolaçãoSão Paulo 01302-907Brazil Department of Finance and Accounting Faculty of Economics and Business AdministrationSofia University“St.Kliment Ohridski”125 Tsarigradsko Shose Blvd.Block 31113 SofiaBulgaria Department of Public Finance Faculty of Finance and AccountingPrague University of Economics and BusinessWinston Churchill Sq.13067 Prague 3Czech Republic Department of Social Psychology Institute of PsychologyELTE Eötvös Loránd UniversityEgyetem tér 1-3Budapest 1053Hungary Faculty of Manage-ment University of TehranJalal-Ale-Ahmad StreetNasr BridgeTehran ***Iran Department of Economics University of NigeriaNsukkaEnugu StateNigeria Faculty of Economic Sciences University of Warsaw44/50 Długa Street00-241 WarsawPoland Department of Economics and Management/REMIT Portucalense UniversityRua Dr.António Bernardino de Almeida541-6194200-072 PortoPortugal School of Business Mzumbe UniversityMzumbeP.O.Box 6MorogoroTanzania Department of Public Finance Faculty of Economics and Administrative StudiesInonu University44280 BattalgaziMalatyaTurkey Department of Public Finance Faculty of EconomicsAdministrative and Social SciencesAlanya Alaaddin Keykubat University07400 AlanyaAntalyaTurkey

The slippery slope framework explains tax compliance along two main dimensions,trust in authorities and power of authorities,which influence taxpayers’compliance *** frequentist and Bayesian analyses,we investigated the framework’s assumptions on a sample of 2786 self-employed taxpayers from eleven post-communist and non-post-communist countries doing business in five economic *** using scenarios that experimentally manipulated trust and power,our results confirmed the framework’s assumptions regarding the attitudes of the self-employed taxpayers;trust and power fostered intended tax compliance and diminished tax evasion,trust boosted voluntary tax compliance,whereas power increased enforced tax ***,self-employed taxpayers from post-communist countries reported higher intended tax compliance and lower tax evasion than those from non-post-communist *** results offer tax authorities insights into how trust and power may contribute to obtaining and maintaining high tax compliance levels amid global economic challenges,downturns,and increasing tax compliance costs.

关键词： Self-employed taxpayers Slippery slope framework Voluntary tax compliance Enforced tax compliance Tax evasion

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Global priority areas for ecosystem restoration (vol 586, pg 724, 2022)

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NATURE 2022年第7926期609卷 E7-E7页

作者： Strassburg, Bernardo B. N. Iribarrem, Alvaro Beyer, Hawthorne L. Cordeiro, Carlos Leandro Crouzeilles, Renato Jakovac, Catarina C. Braga Junqueira, Andre Lacerda, Eduardo Latawiec, Agnieszka E. Balmford, Andrew Brooks, Thomas M. Butchart, Stuart H. M. Chazdon, Robin L. Erb, Karl-Heinz Brancalion, Pedro Buchanan, Graeme Cooper, David Diaz, Sandra Donald, Paul F. Kapos, Valerie Leclere, David Miles, Lera Obersteiner, Michael Plutzar, Christoph de M. Scaramuzza, Carlos Alberto Scarano, Fabio R. Visconti, Piero Rio Conservation and Sustainability Science Centre Department of Geography and the Environment Pontifical Catholic University Rio de Janeiro Brazil International Institute for Sustainability Rio de Janeiro Brazil Programa de Pós Graduacão em Ecologia Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Botanical Garden Research Institute of Rio de Janeiro Rio de Janeiro Brazil Forest Ecology and Management Group Wageningen University Wageningen The Netherlands Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Barcelona Spain Department of Geography Fluminense Federal University Niterói Brazil Department of Production Engineering Logistics and Applied Computer Science Faculty of Production and Power Engineering University of Agriculture in Kraków Kraków Poland School of Environmental Sciences University of East Anglia Norwich UK Department of Ecology and Evolutionary Biology University of Connecticut Storrs CT USA World Resources Institute Global Restoration Initiative Washington DC USA Tropical Forests and People Research Centre University of the Sunshine Coast Sunshine Coast Queensland Australia School of Biological Sciences University of Queensland St Lucia Queensland Australia Department of Zoology University of Cambridge Cambridge UK BirdLife International Cambridge UK RSPB Centre for Conservation Science Royal Society for the Protection of Birds Edinburgh UK International Union for Conservation of Nature (IUCN) Gland Switzerland World Agroforestry Center (ICRAF) University of the Philippines Los Baños The Philippines Institute for Marine and Antarctic Studies University of Tasmania Hobart Tasmania Australia Institute of Social Ecology University of Natural Resources and Life Sciences Vienna Vienna Austria Division of Conservation Biology Vegetation Ecology and Landscape Ecology University of Vienna Vienna Austria Department of Forest Sciences ‘Luiz de Queiroz’ College of Agriculture University of São Pau

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Low-rank solution methods for stochastic eigenvalue problems

arXiv

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arXiv 2018年

作者： Elman, Howard C. Su, Tengfei Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Applied Mathematics & Statistics Scientific Computation Program University of Maryland College ParkMD20742 United States

We study efficient solution methods for stochastic eigenvalue problems arising from discretization of self-adjoint partial differential equations with random data. With the stochastic Galerkin approach, the solutions are represented as generalized polynomial chaos expansions. A low-rank variant of the inverse subspace iteration algorithm is presented for computing one or several minimal eigenvalues and corresponding eigenvectors of parameter-dependent matrices. In the algorithm, the iterates are approximated by low-rank matrices, which leads to significant cost savings. The algorithm is tested on two benchmark problems, a stochastic diffusion problem with some poorly separated eigenvalues, and an operator derived from a discrete stochastic Stokes problem whose minimal eigenvalue is related to the inf-sup stability constant. Numerical experiments show that the low-rank algorithm produces accurate solutions compared to the Monte Carlo method, and it uses much less computational time than the original algorithm without low-rank approximation. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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All-Electrical Determination of Crystal Orientation in Anisotropic Two-Dimensional Materials

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Physical Review Letters 2018年第8期120卷 086801-086801页

作者： Lintao Peng Spencer A. Wells Christopher R. Ryder Mark C. Hersam Matthew Grayson Applied Physics Graduate Program Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA

The crystal orientation of an exfoliated black phosphorous flake is determined by purely electrical means. A sequence of three resistance measurements on an arbitrarily shaped flake with five contacts determines the three independent components of the anisotropic in-plane resistivity tensor, thereby revealing the crystal axes. The resistivity anisotropy ratio decreases linearly with increasing temperature T and carrier density reaching a maximum ratio of 3.0 at low temperatures and densities, while mobility indicates impurity scattering at low T and acoustic phonon scattering at high T.

关键词： Crystal orientation Electrical conductivity Semiconductors Transport phenomena Multilayer thin films Phosphorene Transition metal dichalcogenides Resistivity measurements

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A biometric based medical information system for the identification of comorbidity in Covid-19 patients

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AIP Conference Proceedings 2021年第1期2387卷

作者： V. Kumaresan K. Punitha S. Ragupathy B. Priestly Shan T. Gunasekaran 1Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai Tamilnadu India 2School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India 3Department of Electrical and Electronics Engineering SSM College of Engineering Komarapalayam Namakkal Tamil Nadu India 4Professor and Dean - Academic Affairs Chandigarh University Mohali Punjab India 5Program Director - Telecommunications University of Technology and Applied Sciences Higher College of Technology Muscat Sultanate of Oman

Medical information System provides the best way to collect patient’s medical information. It helps the doctors to retrieve the patient’s previous medical history very faster and able to provide suitable treatment quickly. To keep medical information of a patient securely is a difficult task. Many MQTT algorithms have been developed to provide security for the patients information. The sensitive medical information of a patient can also be protected by using biometric technology. Internet of Thing (IoT) technology used to send the medical information to the doctor like blood group, BP value, cholesterol level and heart rate level. The proposed system helps to provide the comorbidity details of the Covid-19 infected patient’s to doctors mobile phone. It also takes the current health details of the patients like temperature, respiratory rate and heart beat rate. By considering the past and present medical data of the Covid-19 patients , this system recommends the type of treatment to be given for Covid-19 infected patients.

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Coupling rendering and generative adversarial networks for artificial SAS image generation

arXiv

引用

arXiv 2019年

作者： Reed, Albert Gerg, Isaac D. McKay, John D. Brown, Daniel C. Williams, David P. Jayasuriya, Suren School of Electrical Computer & Energy Engineering Arizona State University TempeAZ United States School of Arts Media & Engineering Arizona State University TempeAZ United States Applied Research Laboratory Pennsylvania State University State College PA United States Graduate Program in Acoustics Pennsylvania State University State College PA United States School of Electrical Engineering and Computer Science Pennsylvania State University State College PA United States NATO STO Centre for Maritime Research & Experimentation La Spezia Italy

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OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

arXiv

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arXiv 2022年

作者： Babier, Aaron Mahmood, Rafid Zhang, Binghao Alves, Victor G.L. Barragán-Montero, Ana Maria Beaudry, Joel Cardenas, Carlos E. Chang, Yankui Chen, Zijie Chun, Jaehee Diaz, Kelly Eraso, Harold David Faustmann, Erik Gaj, Sibaji Gay, Skylar Gronberg, Mary Guo, Bingqi He, Junjun Heilemann, Gerd Hira, Sanchit Huang, Yuliang Ji, Fuxin Jiang, Dashan Giraldo, Jean Carlo Jimenez Lee, Hoyeon Lian, Jun Liu, Shuolin Liu, Keng-Chi Marrugo, José Miki, Kentaro Nakamura, Kunio Netherton, Tucker Nguyen, Dan Nourzadeh, Hamidreza Osman, Alexander F.I. Peng, Zhao Muñoz, José Darío Quinto Ramsl, Christian Rhee, Dong Joo Rodriguez, Juan David Shan, Hongming Siebers, Jeffrey V. Soomro, Mumtaz H. Sun, Kay Hoyos, Andrés Usuga Valderrama, Carlos Verbeek, Rob Wang, Enpei Willems, Siri Wu, Qi Xu, Xuanang Yang, Sen Yuan, Lulin Zhu, Simeng Zimmermann, Lukas Moore, Kevin L. Purdie, Thomas G. McNiven, Andrea L. Chan, Timothy C.Y. Department Of Mechanical And Industrial Engineering University Of Toronto TorontoON Canada Vector Institute TorontoON Canada Department Of Radiation Oncology University Of Virginia Health System CharlottesvilleVA United States Department Of Molecular Imaging Radiation Oncology UCLouvain Louvain-la-Neuve Belgium Department Of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States Department Of Radiation Oncology The University Of Alabama At Birmingham BirminghamAL United States Department Of Engineering And Applied Physics University Of Science And Technology Of China Hefei China Shenying Medical Technology Co. Ltd. Guangdong Shenzhen China Department Of Radiation Oncology Yonsei University Seoul Korea Republic of Department Of Physics National University Of Colombia Medellín Colombia Atominstitut Vienna University Of Technology Vienna Austria Department Of Biomedical Engineering Cleveland Clinic ClevelandOH United States Department Of Radiation Physics The University Of Texas MD Anderson Cancer Center HoustonTX United States Department Of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department Of Radiation Oncology Medical University Of Vienna Vienna Austria Department Of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department Of Radiation Oncology Peking University Cancer Hospital And Institute Beijing China Department Of Electrical Engineering And Automation Anhui University Hefei China Department Of Radiation Oncology Massachusetts General Hospital BostonMA United States Department Of Radiation Oncology University Of North Carolina At Chapel Hill Chapel HillNC United States Department Of Medical Imaging Taiwan AI Labs Taipei Taiwan Department Of Biomedical And Health Sciences Hiroshima University Hiroshima Japan Laboratory Department Of Radiation Oncology The University Of Texas Southwestern Medical Center DallasTX United States Department Of Radiatio

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved;the deviation in dosevolume histogram (DVH) criterion;and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P © 2022, CC BY.

关键词： Forecasting

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Building a quantum engineering undergraduate program

arXiv

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arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： Engineering education

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Three dimensions, two microscopes, one code: Automatic differentiation for X-ray nanotomography beyond the depth of focus limit

arXiv

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arXiv 2019年

作者： Du, Ming Nashed, Youssef S. G. Kandel, Saugat Gursoy, Doga Jacobsen, Chris Department of Materials Science Northwestern University EvanstonIL60208 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Applied Physics Program Northwestern University EvanstonIL60208 United States Advanced Photon Source Argonne National Laboratory ArgonneIL60439 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Chemistry of Life Processes Institute Northwestern University EvanstonIL60208 United States

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in X-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of X-rays which enables thick specimens to be imaged, with improved spatial resolution which decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation, and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy, and for coherent scanning techniques like ptychography. Our implementation utilizes the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, which demonstrates a much straightforward way to solve optimization problems in computational imaging, and endows our program great flexibility and portability. Copyright © 2019, The Authors. All rights reserved.

关键词： Computational Imaging

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Weakly-Convex concave min-max optimization: Provable algorithms and applications in machine learning

arXiv

引用

arXiv 2018年

作者： Rafique, Hassan Liu, Mingrui Lin, Qihang Yang, Tianbao Program in Applied Mathematical and Computational Sciences The University of Iowa Iowa CityIA United States Department of Computer Science The University of Iowa United States Department of Business Analytics The University of Iowa United States

Min-max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex-concave min-max problem is an active topic of research with efficient algorithms and sound theoretical foundations developed. However, it remains a challenge to design provably efficient algorithms for non-convex min-max problems with or without smoothness. In this paper, we study a family of non-convex min-max problems, whose objective function is weakly convex in the variables of minimization and is concave in the variables of maximization. We propose a proximally guided stochastic subgradient method and a proximally guided stochastic variance-reduced method for the non-smooth and smooth instances, respectively, in this family of problems. We analyze the time complexities of the proposed methods for finding a nearly stationary point of the outer minimization problem corresponding to the min-max problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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