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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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Non-linear optics at twist interfaces in h-BN/SiC heterostructures

arXiv

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arXiv 2023年

作者： Biswas, Abhijit Xu, Rui Alvarez, Gustavo A. Zhang, Jin Christiansen-Salameh, Joyce Puthirath, Anand B. Burns, Kory Hachtel, Jordan A. Li, Tao Iyengar, Sathvik Ajay Gray, Tia Li, Chenxi Zhang, Xiang Kannan, Harikishan Elkins, Jacob Pieshkov, Tymofii S. Vajtai, Robert Birdwell, A. Glen Neupane, Mahesh R. Garratt, Elias J. Ivanov, Tony Pate, Bradford B. Zhao, Yuji Zhu, Hanyu Tian, Zhiting Rubio, Angel Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Max Planck Institute for the Structure and Dynamics of Matter Center for Free-Electron Laser Science Luruper Chaussee 149 22761 Germany Department of Materials Science & Engineering University of Virginia CharlottesvilleVA22904 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory RF Devices and Circuits AdelphiMD20783 United States Chemistry Division Naval Research Laboratory WashingtonDC20375 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Flatiron Institute New YorkNY10010 United States

Understanding the emergent electronic structure in twisted atomically thin layers has led to the exciting field of twistronics. However, practical applications of such systems are challenging since the specific angular correlations between the layers must be precisely controlled and the layers have to be single crystalline with uniform atomic ordering. Here, we suggest an alternative, simple and scalable approach where nanocrystalline two-dimensional (2D) film on three-dimensional (3D) substrates yield twisted-interface-dependent properties. Ultrawidebandgap hexagonal boron nitride (h-BN) thin films are directly grown on high in-plane lattice mismatched wide-bandgap silicon carbide (4H-SiC) substrates to explore the twist-dependent structure-property correlations. Concurrently, nanocrystalline h-BN thin film shows strong nonlinear second-harmonic generation and ultra-low cross-plane thermal conductivity at room temperature, which are attributed to the twisted domain edges between van der Waals stacked nanocrystals with random in-plane orientations. First-principles calculations based on time-dependent density functional theory manifest strong even-order optical nonlinearity in twisted h-BN layers. Our work unveils that directly deposited 2D nanocrystalline thin film on 3D substrates could provide easily accessible twist-interfaces, therefore enabling a simple and scalable approach to utilize the 2D-twistronics integrated in 3D material devices for next-generation nanotechnology. © 2023, CC BY.

关键词： Harmonic generation

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Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

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Nature communications 2025年第1期16卷 4900页

作者： Dhammika Leshan Wannigama Mohan Amarasiri Phatthranit Phattharapornjaroen Cameron Hurst Charin Modchang John Jefferson V Besa Kazuhiko Miyanaga Longzhu Cui Stefan Fernandez Angkana T Huang Puey Ounjai W K C P Werawatte Ali Hosseini Rad S M Porames Vatanaprasan Dylan John Jay Thammakorn Saethang Sirirat Luk-In Phitsanuruk Kanthawee Wanwara Thuptimdang Ratana Tacharoenmuang Bernadina Cynthia S P H Spencer Vitharana Natharin Ngamwongsatit Hitoshi Ishikawa Takashi Furukawa Yangzhong Wang Andrew C Singer Naveen Kumar Devanga Ragupathi Tanittha Chatsuwan Kazunari Sei Asuka Nanbo Asada Leelahavanichkul Talerngsak Kanjanabuch Hiroshi Hamamoto Paul G Higgins Daisuke Sano Anthony Kicic José O Valdebenito Jonas Bonnedahl Sam Trowsdale Parichart Hongsing Aisha Khatib Kenji Shibuya Shuichi Abe Department of Infectious Diseases Faculty of Medicine Yamagata University Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. Yamagata Prefectural University of Health Sciences Kamiyanagi Yamagata Japan. leshanwannigama@med.id.yamagata-u.ac.jp. The Lygodium Ceylon Health and Environmental Policy Research Center Colombo Sri Lanka. leshanwannigama@med.id.yamagata-u.ac.jp. Biofilms and Antimicrobial Resistance Consortium of ODA receiving countries The University of Sheffield Sheffield UK. leshanwannigama@med.id.yamagata-u.ac.jp. Pathogen Hunter's Research Collaborative Team Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Department of Civil and Environmental Engineering Graduate School of Engineering Tohoku University Sendai Miyagi Japan. Faculty of Health Science Technology Chulabhorn Royal Academy Bangkok Thailand. HRH Princess Chulabhorn Disaster and Emergency Medicine Center Chulabhorn Royal Academy Bangkok Thailand. Department of Clinical Epidemiology Faculty of Medicine Thammasat University Rangsit Thailand. Center of Excellence in Applied Epidemiology Thammasat University Rangsit Thailand. Mater Research Institute University of Queensland Brisbane QLD Australia. Biophysics Group Department of Physics Faculty of Science Mahidol University Bangkok Thailand. Centre of Excellence in Mathematics MHESI Bangkok Thailand. Thailand Center of Excellence in Physics Ministry of Higher Education Science Research and Innovation Bangkok Thailand. Department of Infectious Diseases and Infection Control Yamagata Prefectural Central Hospital Yamagata Japan. Co

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Sensing the local magnetic environment through optically active defects in a layered magnetic semiconductor

arXiv

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arXiv 2022年

作者： Klein, Julian Song, Zhigang Pingault, Benjamin Dirnberger, Florian Chi, Hang Curtis, Jonathan B. Dana, Rami Bushati, Rezlind Quan, Jiamin Dekanovsky, Lukas Sofer, Zdenek Alù, Andrea Menon, Vinod M. Moodera, Jagadeesh S. Lončar, Marko Narang, Prineha Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States College of Letters and Sciences UCLA Los AngelesCA90095 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Physics City College of New York New YorkNY10031 United States Francis Bitter Magnet Laboratory Plasma Science and Fusion Center Massachusetts Institute of Technology CambridgeMA02139 United States U.S. Army CCDC Army Research Laboratory AdelphiMD20783 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Atomic-level defects in van der Waals (vdW) materials are essential building blocks for quantum technologies and quantum sensing applications. The layered magnetic semiconductor CrSBr is an outstanding candidate for exploring optically active defects owing to a direct gap in addition to a rich magnetic phase diagram including a recently hypothesized defect-induced magnetic order at low temperature. Here, we show optically active defects in CrSBr that are probes of the local magnetic environment. We observe spectrally narrow (1 meV) defect emission in CrSBr that is correlated with both the bulk magnetic order and an additional low temperature defect-induced magnetic order. We elucidate the origin of this magnetic order in the context of local and non-local exchange coupling effects. Our work establishes vdW magnets like CrSBr as an exceptional platform to optically study defects that are correlated with the magnetic lattice. We anticipate that controlled defect creation allows for tailor-made complex magnetic textures and phases with the unique ingredient of direct optical access. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic semiconductors

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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

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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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Zeeman polaritons as a platform for probing Dicke physics in condensed matter

arXiv

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arXiv 2024年

作者： Kritzell, T. Elijah Doumani, Jacques Asano, Tobias Yamada, Sota Tay, Fuyang Xu, Hongjing Yan, Han Katayama, Ikufumi Takeda, Jun Nevidomskyy, Andriy Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Physics Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Miyagi Sendai980-8577 Japan Rice Advanced Materials Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd3Ga5O12. The electron paramagnetic resonance of Gd3+ ions is tuned by a magnetic field into resonance with a Fabry–Pérot cavity mode, resulting in the formation of spin–photon hybrid states, or Zeeman polaritons. We observe that the light–matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin–boson system is more compatible with the Dicke model and has advantages over boson–boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light–matter hybrids. Copyright © 2024, The Authors. All rights reserved.

关键词： Phonons

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Two-Step Electronic Response to Magnetic Ordering in a van der Waals Ferromagnet

arXiv

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arXiv 2023年

作者： Wu, Han Zhu, Jian-Xin Chen, Lebing Butcher, Matthew W. Yue, Ziqin Yuan, Dongsheng He, Yu Oh, Ji Seop Huang, Jianwei Wu, Shan Gong, Cheng Guo, Yucheng Mo, Sung-Kwan Denlinger, Jonathan D. Lu, Donghui Hashimoto, Makoto Stone, Matthew B. Kolesnikov, Alexander I. Chi, Songxue Kono, Junichiro Nevidomskyy, Andriy H. Birgeneau, Robert J. Dai, Pengcheng Yi, Ming Department of Physics and Astronomy Rice Center for Quantum Materials Rice University HoustonTX77005 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Integrated Nanotechnologies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States 1-1 Namiki Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Yale University New HavenCT06511 United States Department of Electrical and Computer Engineering Quantum Technology Center University of Maryland College ParkMD20742 United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States Department of Materials Science and Engineering University of California Berkeley United States

The two-dimensional (2D) material Cr2Ge2Te6 is a member of the class of insulating van der Waals magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets. © 2023, CC BY.

关键词： Electronic structure

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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