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How does the primate brain combine generative and discriminative computations in vision?

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arXiv 2024年

作者： Peters, Benjamin DiCarlo, James J. Gureckis, Todd Haefner, Ralf Isik, Leyla Tenenbaum, Joshua Konkle, Talia Naselaris, Thomas Stachenfeld, Kimberly Tavares, Zenna Tsao, Doris Yildirim, Ilker Kriegeskorte, Nikolaus Zuckerman Mind Brain Behavior Institute Columbia University United States School of Psychology & Neuroscience University of Glasgow United Kingdom Department of Brain and Cognitive Sciences MIT United States McGovern Institute for Brain Research MIT United States NSF Center for Brains Minds and Machines MIT United States Quest for Intelligence Schwarzman College of Computing MIT United States Department of Psychology New York University United States Brain and Cognitive Sciences University of Rochester United States Center for Visual Science University of Rochester United States Department of Cognitive Science Johns Hopkins University United States Computer Science and Artificial Intelligence Laboratory MIT United States Department of Psychology Harvard University United States Center for Brain Science Harvard University United States Kempner Institute for Natural and Artificial Intelligence Harvard University United States Department of Neuroscience University of Minnesota United States DeepMind United Kingdom Data Science Institute Columbia University United States Dept of Molecular & Cell Biology University of California Berkeley United States Howard Hughes Medical Institute United States Department of Psychology Yale University United States Department of Statistics and Data Science Yale University United States Department of Psychology Columbia University United States Department of Neuroscience Columbia University United States Department of Electrical Engineering Columbia University United States

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision. © 2024, CC BY.

关键词： Feedforward neural networks

FAIR for AI: An interdisciplinary and international community building perspective

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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

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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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Correction to: Small-Scale Dynamic Aurora

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Space Science Reviews 2021年第4期217卷 1-2页

作者： Ryuho Kataoka Christopher C. Chaston David Knudsen Kristina A. Lynch Robert L. Lysak Yan Song Robert Rankin Kiyoka Murase Takeshi Sakanoi Joshua Semeter Tomo-Hiko Watanabe Daniel Whiter National Institute of Polar Research Tokyo Japan SOKENDAI Tokyo Japan Space Sciences Laboratory University of California Berkeley USA Dept of Physics and Astronomy University of Calgary Calgary Canada Dept of Physics and Astronomy Dartmouth College Hanover USA School of Physics and Astronomy University of Minnesota Minneapolis USA Physics & Astronomy University of Southampton Southampton UK Planetary Plasma and Atmospheric Research Center Aoba Sendai Japan Department of Electrical and Computer Engineering and Center for Space Physics Boston University Boston USA Department of Physics Nagoya University Nagoya Japan

A Correction to this paper has been published: https://***/10.1007/s11214-021-00821-y

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Observation of Three-Photon Spontaneous Parametric Down-Conversion in a Superconducting Parametric Cavity

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Physical Review X 2020年第1期10卷 011011-011011页

作者： C. W. Sandbo Chang Carlos Sabín P. Forn-Díaz Fernando Quijandría A. M. Vadiraj I. Nsanzineza G. Johansson C. M. Wilson Institute for Quantum Computing and Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada Instituto de Física Fundamental CSIC Serrano 113-bis 28006 Madrid Spain Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) Bellaterra (Barcelona) 08193 Spain Barcelona Supercomputing Center-CNS Barcelona 08034 Spain Microtechnology and Nanoscience MC2 Chalmers University of Technology SE-412 96 Göteborg Sweden

Spontaneous parametric down-conversion (SPDC) has been a key enabling technology in exploring quantum phenomena and their applications for decades. For instance, traditional SPDC, which splits a high-energy pump photon into two lower-energy photons, is a common way to produce entangled photon pairs. Since the early realizations of SPDC, researchers have thought to generalize it to higher order, e.g., to produce entangled photon triplets. However, directly generating photon triplets through a single SPDC process has remained elusive. Here, using a flux-pumped superconducting parametric cavity, we demonstrate direct three-photon SPDC, with photon triplets generated in a single cavity mode or split between multiple modes. With strong pumping, the states can be quite bright, with flux densities exceeding 60 photons per second per hertz. The observed states are strongly non-Gaussian, which has important implications for potential applications. In the single-mode case, we observe a triangular star-shaped distribution of quadrature voltages, indicative of the long-predicted “star state.” The observed state shows strong third-order correlations, as expected for a state generated by a cubic Hamiltonian. By pumping at the sum frequency of multiple modes, we observe strong three-body correlations between multiple modes, strikingly, in the absence of second-order correlations. We further analyze the third-order correlations under mode transformations by the symplectic symmetry group, showing that the observed transformation properties serve to “fingerprint” the specific cubic Hamiltonian that generates them. The observed non-Gaussian, third-order correlations represent an important step forward in quantum optics and may have a strong impact on quantum communication with microwave fields as well as continuous-variable quantum computation.

关键词： Quantum communication Quantum computation Quantum optics

Double resonant tunable second harmonic generation in two-dimensional layered materials through band nesting

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arXiv 2021年

作者： Biswas, Sudipta Romen Yu, Jin Wang, Zhenwei da Costa, Diego Rabelo Zhao, Chujun Yuan, Shengjun Low, Tony Department of Electrical & Computer Engineering University of Minnesota MinneapolisMN55455 United States Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering Science Shanghai University Shanghai200444 China Institute for Molecules and Materials Radboud University Heijendaalseweg 135 Nijmegen6525AJ Netherlands Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and Technology Wuhan University Wuhan430072 China Departamento de Física Universidade Federal do Ceará Campus do Pici CE Fortaleza60455-900 Brazil Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and Electronics Hunan University Changsha410082 China School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States

We proposed a mechanism to generate giant anisotropic second harmonic nonlinear response via double resonance effect, achieved through band nesting via electronic bandstructure engineering. The ideal band setup would be a triplet of nested bands separated by the fundamental resonance energy, ~ω. We demonstrate theoretically that the proposed phenomenon can be realized in bilayer SnS by band tuning with perpendicular electrical bias, which maximizes the second harmonic susceptibility by several orders of magnitude. Moreover, the tunability of the polarization anisotropy can be useful for realizing novel polarization-sensitive devices. © 2021, CC BY.

关键词： Harmonic generation

Report on Reproducibility in Condensed Matter Physics

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arXiv 2025年

作者： Akrap, A. Bordelon, D. Chatterjee, S. Dahlberg, E.D. Devaty, R.P. Frolov, S.M. Gould, C. Greene, L.H. Guchhait, S. Hamlin, J.J. Hunt, B.M. Jardine, M.J.A. Kayyalha, M. Kurchin, R.C. Kozii, V. Legg, H.F. Mazin, I.I. Mourik, V. Özgüler, A.B. Peñuela-Parra, J. Seradjeh, B. Skinner, B. Quader, K.F. Zwolak, J.P. Department of Physics Faculty of Science University of Zagreb ZagrebHR-10000 Croatia Pittsburgh Supercomputing Center PittsburghPA15213 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States School of Physics and Astronomy The University of Minnesota MinneapolisMN55455 United States Department of Physics and Astronomy University of Pittsburgh PittsburghPA15260 United States Universitat Würzburg Am Hubland Würzburg Germany Institute for Topological Insulators Am Hubland Würzburg Germany National High Magnetic Field Laboratory Florida State University FL32310 United States Department of Physics Florida State University FL32306 United States Department of Physics and Astronomy Howard University WashingtonDC20059 United States Department of Physics University of Florida GainesvilleFL32611 United States Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical Engineering The Pennsylvania State University University ParkPA16802 United States Materials Research Institute The Pennsylvania State University University ParkPA16802 United States Department of Physics University of Basel Basel4056 Switzerland SUPA School of Physics and Astronomy University of St. Andrews St AndrewsKY16 9SS United Kingdom Department of Physics and Astronomy George Mason University FairfaxVA22030 United States Center for Quantum Science and Engineering George Mason University FairfaxVA22030 United States Forschungszentrum Jülich GmbH Aachen52074 Germany Department of Mechanical and Materials Science University of Pittsburgh PittsburghPA15261 United States Haas School of Business University of California BerkeleyCA94720 United States Department of Physics Indiana University BloomingtonIN47405 United States Indiana University Center for Spacetime Symmetries BloomingtonIN47405 United States Quantum Science and Engineering Center

We present recommendations for how to improve reproducibility in the field of condensed matter physics. This area of physics has consistently produced both fundamental insights into the functioning of matter as well as transformative inventions. Our recommendations result from a collaboration that includes researchers in academia and government laboratories, scientific journalists, legal professionals, representatives of publishers, professional societies, and other experts. The group met in person in May 2024 at a conference at the University of Pittsburgh to discuss the growing challenges related to research reproducibility in condensed matter physics. We discuss best practices and policies at all stages of the scientific process to safeguard the value condensed matter research brings to society. We look forward to comments and suggestions, especially regarding subfield-specific recommendations, and will incorporate them into the next version of the report. © 2025, CC BY.

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Classical and quantum time crystals in a levitated nanoparticle without drive

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Physical Review A 2020年第2期102卷 023113-023113页

作者： Yi Huang Qihao Guo Anda Xiong Tongcang Li Zhang-qi Yin School of Physics and Astronomy University of Minnesota Minneapolis Minnesota 55455 USA Department of Applied Physics Xi'an Jiaotong University Xi'an Shaanxi 710049 China School of Physics and Astronomy University of Birmingham Birmingham England United Kingdom Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center Purdue University West Lafayette Indiana 47907 USA Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Center of Quantum Technology Research School of Physics Beijing Institute of Technology Beijing 100081 China

A time crystal is defined as a phase of matter spontaneously exhibiting a periodicity in time. Previous studies focused on discrete quantum time crystals under periodic drive. Here, we propose a time crystal model based on a levitated charged nanoparticle in a static magnetic field without drive. Both the classical time crystal in thermal equilibrium and the quantum time crystal in the ground state can emerge in the spin rotational mode, under a strong magnetic field or large charge-to-mass ratio limit. Additionally, the time polycrystal is defined and naturally appears in this model. Our model paves a way for realizing time crystals in thermal equilibrium.

关键词： Tests of fundamental symmetries Nanoparticles Optical tweezers

AGNet: Weighing Black Holes with Deep Learning

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arXiv 2021年

作者： Lin, Joshua Yao-Yu Pandya, Sneh Pratap, Devanshi Liu, Xin Kind, Matias Carrasco Kindratenko, Volodymyr Department of Physics University of Illinois at Urbana-Champaign 1110 West Green Street UrbanaIL61801 United States National Center for Supercomputing Applications 605 East Springfield Avenue ChampaignIL61820 United States Center for Computational Astrophysics Flatiron Institute Simons Foundation 162 5th Ave New YorkNY10010 United States Department of Computer Science University of Illinois at Urbana-Champaign 201 North Goodwin Avenue UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 306 North Wright Street UrbanaIL61801 United States

Supermassive black holes (SMBHs) are commonly found at the centers of most massive galaxies. Measuring SMBH mass is crucial for understanding the origin and evolution of SMBHs. Traditional approaches, on the other hand, necessitate the collection of spectroscopic data, which is costly. We present an algorithm that weighs SMBHs using quasar light time series information, including colors, multiband magnitudes, and the variability of the light curves, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a sample of 38, 939 spectroscopically confirmed quasars to map out the nonlinear encoding between SMBH mass and multi-band optical light curves. We find a 1σ scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code, AGNet, is publicly available at https://***/snehjp2/AGNet. © 2021, CC BY.

关键词： Galaxies