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Atacama Cosmology Telescope: Constraints on cosmic birefringence

Physical Review D 2020年第8期101卷 083527-083527页

作者： Toshiya Namikawa Yilun Guan Omar Darwish Blake D. Sherwin Simone Aiola Nicholas Battaglia James A. Beall Daniel T. Becker J. Richard Bond Erminia Calabrese Grace E. Chesmore Steve K. Choi Mark J. Devlin Joanna Dunkley Rolando Dünner Anna E. Fox Patricio A. Gallardo Vera Gluscevic Dongwon Han Matthew Hasselfield Gene C. Hilton Adam D. Hincks Renée Hložek Johannes Hubmayr Kevin Huffenberger John P. Hughes Brian J. Koopman Arthur Kosowsky Thibaut Louis Marius Lungu Amanda MacInnis Mathew S. Madhavacheril Maya Mallaby-Kay Loïc Maurin Jeffrey McMahon Kavilan Moodley Sigurd Naess Federico Nati Laura B. Newburgh John P. Nibarger Michael D. Niemack Lyman A. Page Frank J. Qu Naomi Robertson Alessandro Schillaci Neelima Sehgal Cristóbal Sifón Sara M. Simon David N. Spergel Suzanne T. Staggs Emilie R. Storer Alexander van Engelen Jeff van Lanen Edward J. Wollack Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road Cambridge CB3 0WA United Kingdom Department of Physics and Astronomy University of Pittsburgh Pittsburgh Pennsylvania 15260 USA Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New York New York 10010 USA Department of Astronomy Cornell University Ithaca New York 14853 USA National Institute of Standards and Technology 325 Broadway Boulder Colorado 80305 USA Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street Toronto Ontario M5S 3H8 Canada School of Physics and Astronomy Cardiff University Cardiff CF10 3AT United Kingdom Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street Philadelphia Pennsylvania 19104 USA Department of Astrophysical Sciences Princeton University 4 Ivy Lane Princeton New Jersey 08544 USA Joseph Henry Laboratories of Physics Jadwin Hall Princeton University Princeton New Jersey 08544 USA Instituto de Astrofísica and Centro de Astro-Ingeniería Facultad de Física Pontificia Universidad Católica de Chile Avenida Vicuña Mackenna 4860 7820436 Macul Santiago Chile Department of Physics Cornell University Ithaca New York 14853 USA Department of Physics and Astronomy University of Southern California Los Angeles California 90089-0484 USA Physics and Astronomy Department Stony Brook University Stony Brook New York 11794 USA Dunlap Institute for Astronomy and Astrophysics University of Toronto 50 St. George Street Toronto Ontario M5S 3H4 Canada David A. Dunlap Department of Astronomy and Astrophysics University of Toronto 50 St. George Street Toronto Ontario M5S 3H4 Canada Quantum Sensors Group NIST Boulder 325 Broadway Boulder Colorado 80305 USA Department of Physics Florida State University 609 Keen Physics Building Tallahassee Florida 32306 USA Departmen

We present new constraints on anisotropic birefringence of the cosmic microwave background polarization using two seasons of data from the Atacama Cosmology Telescope covering 456 square degrees of sky. The birefringence power spectrum, measured using a curved-sky quadratic estimator, is consistent with zero. Our results provide the tightest current constraint on birefringence over a range of angular scales between 5 arc minutes and 9°. We improve previous upper limits on the amplitude of a scale-invariant birefringence power spectrum by a factor of between 2 and 3. Assuming a nearly massless axion field during inflation, our result is equivalent to a 2σ upper limit on the Chern-Simons coupling constant between axions and photons of gαγ<4.0×10−2/HI, where HI is the inflationary Hubble scale.

关键词： Cosmic microwave background Cosmology Axions

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The atacama cosmology telescope: Summary of dr4 and dr5 data products and data access

arXiv

引用

arXiv 2021年

作者： Mallaby-Kay, Maya Atkins, Zachary Aiola, Simone Amodeo, Stefania Austermann, Jason E. Beall, James A. Becker, Daniel T. Bond, J. Richard Calabrese, Erminia Chesmore, Grace E. Choi, Steve K. Crowley, Kevin T. Darwish, Omar Denison, Edward V. Devlin, Mark J. Duff, Shannon M. Duivenvoorden, Adriaan J. Dunkley, Jo Ferraro, Simone Fichman, Kyra Gallardo, Patricio A. Golec, Joseph E. Guan, Yilun Han, Dongwon Hasselfield, Matthew Hill, J. Colin Hilton, Gene C. Hilton, Matt Hložek, Renée Hubmayr, Johannes Huffenberger, Kevin M. Hughes, John P. Koopman, Brian J. Louis, Thibaut MacInnis, Amanda Madhavacheril, Mathew S. McMahon, Jeff Moodley, Kavilan Naess, Sigurd Namikawa, Toshiya Nati, Federico Newburgh, Laura B. Nibarger, John P. Niemack, Michael D. Page, Lyman A. Salatino, Maria Schaan, Emmanuel Schillaci, Alessandro Sehgal, Neelima Sherwin, Blake D. Sifón, Cristóbal Simon, Sara Staggs, Suzanne T. Storer, Emilie R. Ullom, Joel N. Van Engelen, Alexander Van Lanen, Jeff Vale, Leila R. Wollack, Edward J. Xu, Zhilei Department of Astronomy University of Chicago ChicagoIL United States Joseph Henry Laboratories of Physics Jadwin Hall Princeton University PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Astronomy Cornell University IthacaNY14853 United States NIST Quantum Devices Group 325 Broadway Mailcode 817.03 BoulderCO80305 United States Canadian Institute for Theoretical Astrophysics University of Toronto 60 St. George Street TorontoONM5S 3H8 Canada School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Cornell University IthacaNY14853 United States Department of Physics University of California Berkeley LeConte Hall BerkeleyCA94720 United States Department of Applied Mathematics and Theoretical Physics University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Department of Physics and Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States Department of Astrophysical Sciences Peyton Hall Princeton University PrincetonNJ08544 United States Lawrence Berkeley National Laboratory One Cyclotron Road BerkeleyCA94720 United States Department of Physics University of Michigan Ann ArborUSA48103 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Physics and Astronomy Department Stony Brook University Stony BrookNY11794 United States Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10003 United States Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal We

Two recent large data releases for the Atacama Cosmology Telescope (ACT), called DR4 and DR5, are available for public access. These data include temperature and polarization maps that cover nearly half the sky at arcminute resolution in three frequency bands;lensing maps and componentseparated maps covering ∼ 2,100 deg2 of sky;derived power spectra and cosmological likelihoods;a catalog of over 4,000 galaxy clusters;and supporting ancillary products including beam functions and masks. The data and products are described in a suite of ACT papers;here we provide a summary. In order to facilitate ease of access to these data we present a set of Jupyter IPython notebooks developed to introduce users to DR4, DR5, and the tools needed to analyze these data. The data products (excluding simulations) and the set of notebooks are publicly available on the NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA);simulation products are available on the National Energy Research Scientific Computing Center (NERSC). © 2021, CC BY.

关键词： NASA

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Corrigendum to “Nano-crystals of cerium-hafnium binary oxide: Their size-dependent structure” [J. Alloys Compd. 644 (2015) 996–1002]

引用

Journal of Alloys and Compounds 2017年 725卷 1330-1330页

作者： Joan M. Raitano Syed Khalid Nebojsa Marinkovic Siu-Wai Chan Department of Applied Physics and Applied Mathematics Materials Science and Engineering Program Columbia University New York NY 10027 USA National Synchrotron Light Source Brookhaven National Laboratory Upton NY 11973 USA Chemical Engineering Department Columbia University 500 W 120th St Mudd 801 New York NY 10027 USA

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Breaking Mechanical Holography in Combinatorial Metamaterials

arXiv

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arXiv 2024年

作者： Sirote-Katz, Chaviva Palti, Ofri Spiro, Naomi Kálmán, Tamás Shokef, Yair Department of Biomedical Engineering Tel Aviv University Tel Aviv69978 Israel The Future Scientists Center Alpha Program Tel Aviv Youth University Tel Aviv69978 Israel University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mathematics Institute of Science Tokyo H-214 2-12-1 Ookayama Tokyo Meguro-ku152-8551 Japan Hiroshima University Higashi-Hiroshima Hiroshima739-8526 Japan School of Mechanical Engineering Tel Aviv University Tel Aviv69978 Israel Center for Computational Molecular and Materials Science Tel Aviv University Tel Aviv69978 Israel Center for Physics and Chemistry of Living Systems Tel Aviv University Tel Aviv69978 Israel

Combinatorial mechanical metamaterials are made of anisotropic, flexible blocks, such that multiple metamaterials may be constructed using a single block type, and the system’s response strongly depends on the mutual orientations of the blocks within the lattice. We study a family of possible block types for the square, honeycomb, and cubic lattices. Blocks that are centrally symmetric induce holographic order, such that mechanical compatibility (meaning that blocks do not impede each other’s motion) implies bulk-boundary coupling. With them, one can design a compatible metamaterial that will deform in any desired texture only on part of its boundary. With blocks that break holographic order, we demonstrate how to design the deformation texture on the entire boundary. Correspondingly, the number of compatible holographic metamaterials scales exponentially with the boundary, while in non-holographic cases we show that it scales exponentially with the bulk. © 2024, CC BY.

关键词： Holography

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Thermal generation, manipulation and detection of skyrmions

arXiv

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arXiv 2020年

作者： Wang, Zidong Guo, Minghua Zhou, Heng-An Zhao, Le Xu, Teng Tomasello, Riccardo Bai, Hao Dong, Yiqing Je, Soong Geun Chao, Weilun Han, Hee-Sung Lee, Suseok Lee, Ki-Suk Yao, Yunyan Han, Wei Song, Cheng Wu, Huaqiang Carpentieri, Mario Finocchio, Giovanni Im, Mi-Young Lin, Shi-Zeng Jiang, Wanjun State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Tsinghua University Beijing100084 China Institute of Microelectronics Tsinghua University Beijing100084 China Institute of Applied and Computational Mathematics FORTH Heraklion-CreteGR-70013 Greece Center for X-ray Optics Advanced Light Source Lawrence Berkeley National Laboratory Cyclotron Road BerkeleyCA94720 United States Department of Physics Chonnam National University Gwangju61186 Korea Republic of School of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Daegu Gyeongbuk Institute of Science and Technology Daegu711-873 Korea Republic of International Center for Quantum Materials School of Physics Peking University Beijing100871 China School of Materials Science and Engineering Tsinghua University Beijing100084 China Department of Electrical and Information Engineering Polytechnic of Bari Bari70125 Italy Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Theoretical Division T-4 and CNLS Los Alamos National Laboratory Los AlamosNM87545 United States

Recent years have witnessed significant progresses in realizing skyrmions in chiral magnets1-4 and asymmetric magnetic multilayers5-13, as well as their electrical manipulation2,7,8,10. Equally important, thermal generation, manipulation and detection of skyrmions can be exploited for prototypical new architecture with integrated computation14 and energy harvesting15. It has yet to verify if skyrmions can be purely generated by heating16,17, and if their resultant direction of motion driven by temperature gradients follows the diffusion or, oppositely, the magnonic spin torque17-21. Here, we address these important issues in microstructured devices made of multilayers - [Ta/CoFeB/MgO]15, [Pt/CoFeB/MgO/Ta]15 and [Pt/Co/Ta]15 integrated with on-chip heaters, by using a full-field soft X-ray microscopy. The thermal generation of densely packed skyrmions is attributed to the low energy barrier at the device edge, together with the thermally induced morphological transition from stripe domains to skyrmions. The unidirectional diffusion of skyrmions from the hot region towards the cold region is experimentally observed. It can be theoretically explained by the combined contribution from repulsive forces between skyrmions, and thermal spin-orbit torques in competing with magnonic spin torques17,18,20,21 and entropic forces22. These thermally generated skyrmions can be further electrically detected by measuring the accompanied anomalous Nernst voltages23. The on-chip thermoelectric generation, manipulation and detection of skyrmions could open another exciting avenue for enabling skyrmionics, and promote interdisciplinary studies among spin caloritronics15, magnonics24 and skyrmionics3,4,12. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

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Ten quick tips for deep learning in biology

arXiv

引用

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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Successes and failures of simple statistical physics models for a network of real neurons

arXiv

引用

arXiv 2021年

作者： Meshulam, Leenoy Gauthier, Jeffrey L. Brody, Carlos D. Tank, David W. Bialek, William Center for Computational Neuroscience University of Washington SeattleWA98195 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States Princeton Neuroscience Institute Princeton University PrincetonNJ08544 United States Joseph Henry Laboratories of Physics Princeton University PrincetonNJ08544 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Department of Molecular Biology Princeton University PrincetonNJ08544 United States Howard Hughes Medical Institute Princeton University PrincetonNJ08544 United States Department of Biology Swarthmore College SwarthmorePA19081 United States Initiative for the Theoretical Sciences The Graduate Center City University of New York 365 Fifth Ave. New YorkNY10016 United States

Biological networks exhibit complex, coordinated patterns of activity. Can these patterns be captured precisely in simple models? Here we use measurements of simultaneous activity in 1000+ neurons in the mouse brain to test the validity of models grounded in statistical physics. When cells are dense samples from a small region, we find extremely detailed quantitative agreement between theory and experiment;sparse samples from larger regions lead to model failures. These results show we can aspire to more than qualitative agreement between simplifying theoretical ideas and the detailed behavior of a complex biological system. Copyright © 2021, The Authors. All rights reserved.

关键词： Statistical physics

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Electron correlation dynamics of strong-field double ionization of atoms below recollision threshold

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Journal of physics: Conference Series 2011年第1期276卷

作者： Yunquan Liu Difa Ye Jie Liu A Rudenko S Tschuch M Dürr M Siegel U Morgner Qihuang Gong R Moshammer J Ullrich Department of Physics and State Key Laboratory for Mesoscopic Physics Peking University Beijing 100871 People's Republic of China Max-Planck-Institut für Kernphysik D-69117 Heidelberg Germany Center for Applied Physics and Technology Peking University 100084 Beijing People's Republic of China Institute of Applied Physics and Computational Mathematics 100088 Beijing People's Republic of China Max-Planck Advanced Study Group at CFEL 22607 Hamburg Germany Leibniz Universität Hannover Welfengarten 1 D-30167 Hannover Germany

In recent combined experimental and theoretical study we have explored nonsequential double ionization of neon and argon atoms in the infrared light field (800nm) below the recollision threshold. We find that the two-electron correlation dynamics depends on atomic structure- 'side-by-side emission' (correlation) for Ne and 'back-to-back emission' (anticorrelation) for argon atoms. This can be explained theoretically within our three dimensional classical model calculation including tunnelling effect. The multiple recollisions as well as recollision-induced-excitation-tunnelling (RIET) effect dominate the anticorrelation of argon, whereas the laser-assisted instantaneous recollision dominates the correlation of neon.

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Criterion and principles of easily-modified program

引用

IOP Conference Series: Materials Science and Engineering 2021年第1期1027卷

作者： A Prutzkow Department of Computational and Applied Mathematics Ryazan State Radio Engineering University named after V. F. Utkin 59/1 Gagarina str. Ryazan 390005 Russian Federation Department of Mathematics Physics and Medical Computer Sciences Ryazan State Medical University named after I. P. Pavlov 9 Vysokovoltnaya str. Ryazan 390026 Russian Federation

Ease of modifying is one of the characteristics of the program. The existing approaches do not clarify this concept. These approaches are clean code and maintainability. We formulate simple and explicit criterion and principles of easily-modified programs. The criterion is an E.W. Dijkstra's criterion. The principles are (1) the names of the elements should reflect their purpose in the program; (2) use indentation for nested statements and blank lines; (3) one program element must have one assignment and one assignment must correspond to only one element; (4) optimize dependencies between program elements. We introduce a metric for easily-modified programs as well. It's a number of commands in methods and static blocks in a class.

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