Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Optimal control of complex dynamical systems can be challenging due to cost constraints and analytical intractability. The authors propose a machine-learning-based control framework able to learn control signals and force complex high-dimensional dynamical systems towards a desired target state.

    • Lucas Böttcher
    • , Nino Antulov-Fantulin
    •  & Thomas Asikis

  • Comment
    | Open Access

    Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.

    • Lina Jaurigue
    •  & Kathy Lüdge

  • Article
    | Open Access

    Global and local learning represent two distinct approaches to artificial intelligence. In this manuscript, Wu et al present a hybrid learning strategy, drawing from elements of both approaches, and implement it on a co-designed neuromorphic platform.

    • Yujie Wu
    • , Rong Zhao
    •  & Luping Shi

  • Article
    | Open Access

    The pedestrian-induced oscillation of the London Millennium Bridge is considered as an example of emerging synchronisation. Belykh et al. provide an alternative mechanism for emergence of coherent oscillatory bridge dynamics where synchrony is a consequence, not the cause, of the instability.

    • Igor Belykh
    • , Mateusz Bocian
    •  & Allan McRobie

  • Article
    | Open Access

    Navigation and trajectory planning in environments with background flow, relevant for robotics, are challenging provided information only on local surrounding. The authors propose a reinforcement learning approach for time-efficient navigation of a swimmer through unsteady two-dimensional flows.

    • Peter Gunnarson
    • , Ioannis Mandralis
    •  & John O. Dabiri

  • Article
    | Open Access

    Twisted flux tubes are prominent candidates for the progenitors of solar active regions. Here, the authors show a clear signature of the emergence of pre-twisted magnetic flux tubes using magnetic winding, which detects the emerging magnetic topology despite the deformation experienced by the emerging magnetic field.

    • D. MacTaggart
    • , C. Prior
    •  & S. L. Guglielmino

  • Article
    | Open Access

    The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. Here, the authors use deep neural networks to discover non-linear relationships between geographical variables and mobility flows.

    • Filippo Simini
    • , Gianni Barlacchi
    •  & Luca Pappalardo

  • Article
    | Open Access

    The Earth’s climate system is highly complex, however it exhibits certain persistent cyclic patterns like the El Niño Southern Oscillation. The authors apply the spectral theory of dynamical systems and data science techniques to extract such coherent modes of climate variability from high-dimensional observational data.

    • Gary Froyland
    • , Dimitrios Giannakis
    •  & Joanna Slawinska

  • Article
    | Open Access

    A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously recognized.

    • Bertrand Rouet-Leduc
    • , Romain Jolivet
    •  & Claudia Hulbert

  • Article
    | Open Access

    Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predictive features.

    • Behzad Vahedi
    • , Morteza Karimzadeh
    •  & Hamidreza Zoraghein

  • Article
    | Open Access

    Though skin-conformable electro-physiological sensors are attractive for epidermal electronics, their optimal design remains a challenge. Here, the authors report a computational design approach for realizing multi-modal electro-physiological sensors that optimizes electrode layout design.

    • Aditya Shekhar Nittala
    • , Andreas Karrenbauer
    •  & Jürgen Steimle

  • Article
    | Open Access

    The ejection sites of the martian meteorites are still unknown. Here, the authors build a database of 90 million craters and show that Tharsis region is the most likely source of depleted shergottites ejected 1.1 Ma ago, thus confirming that some portions of the mantle were recently anomalously hot.

    • A. Lagain
    • , G. K. Benedix
    •  & K. Miljković

  • Article
    | Open Access

    Gradient-based and non-gradient-based methods for training neural networks are usually considered to be fundamentally different. The authors derive, and illustrate numerically, an analytic equivalence between the dynamics of neural network training under conditioned stochastic mutations, and under gradient descent.

    • Stephen Whitelam
    • , Viktor Selin
    •  & Isaac Tamblyn

  • Article
    | Open Access

    The state of Victoria, Australia experienced a substantial second wave of COVID-19 but brought it under control with strict non-pharmaceutical interventions. Here, the authors model the second wave in Victoria to estimate the impacts of the different interventions.

    • James M. Trauer
    • , Michael J. Lydeamore
    •  & Romain Ragonnet

  • Article
    | Open Access

    Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics but often difficult to characterize. Here the authors compare approaches for single trajectory analysis through an open competition, showing that machine learning methods outperform classical approaches.

    • Gorka Muñoz-Gil
    • , Giovanni Volpe
    •  & Carlo Manzo

  • Article
    | Open Access

    Recovery of underlying governing laws or equations describing the evolution of complex systems from data can be challenging if dataset is damaged or incomplete. The authors propose a learning approach which allows to discover governing partial differential equations from scarce and noisy data.

    • Zhao Chen
    • , Yang Liu
    •  & Hao Sun

  • Article
    | Open Access

    Establishing the time since death (TSD) is vital in many forensic investigations. By combining thermometry, photogrammetry and numerical thermodynamic modelling, the TSD can be determined non-invasively for bodies of arbitrary shape and posture with an unprecedented accuracy of 0.26 h ± 1.38 h.

    • Leah S. Wilk
    • , Gerda J. Edelman
    •  & Maurice C. G. Aalders

  • Article
    | Open Access

    The effectiveness of digital contact tracing for COVID-19 control remains uncertain. Here, the authors use data from the Smittestopp app, used in Norway in spring 2020, and estimate that 80% of nearby devices were detected by the app, and at least 11% of close contacts were not visible to manual contact tracing.

    • Ahmed Elmokashfi
    • , Joakim Sundnes
    •  & Olav Lysne

  • Article
    | Open Access

    The authors propose a new framework, deep evolutionary reinforcement learning, evolves agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.

    • Agrim Gupta
    • , Silvio Savarese
    •  & Li Fei-Fei

  • Article
    | Open Access

    Household air pollution derived from cooking fuels is a major source of health and environmental problems. Here, the authors provide detailed global, regional and country estimates of cooking fuel usage from 1990 to 2030 and project that 31% of people will still be mainly using polluting fuels in 2030.

    • Oliver Stoner
    • , Jessica Lewis
    •  & Heather Adair-Rohani

  • Article
    | Open Access

    Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries.

    • Jordan W. Squair
    • , Matthieu Gautier
    •  & Grégoire Courtine

  • Article
    | Open Access

    Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound imaging.

    • Yiqiu Shen
    • , Farah E. Shamout
    •  & Krzysztof J. Geras

  • Article
    | Open Access

    Some regions on the Moon are permanently covered in shadow and are therefore extremely difficult to see into. We develop a deep learning driven algorithm which enhances images of these regions, allowing us to see inside them with high resolution for the first time.

    • V. T. Bickel
    • , B. Moseley
    •  & M. Shirley

  • Article
    | Open Access

    Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a complex, constrained measurement as a function of multiple free parameters.

    • Ryan Roussel
    • , Juan Pablo Gonzalez-Aguilera
    •  & Auralee Edelen

  • Article
    | Open Access

    Rift Valley fever is a zoonotic haemorrhagic fever with complex transmission dynamics influenced by environmental variables and animal movements. Here, the authors develop a metapopulation model incorporating these factors and use it to identify the main drivers of transmission in the Comoros archipelago.

    • Warren S. D. Tennant
    • , Eric Cardinale
    •  & Raphaëlle Métras

  • Article
    | Open Access

    Social media platforms moderating misinformation have been accused of political bias. Here, the authors use neutral social bots to show that, while there is no strong evidence for such a bias, the content to which Twitter users are exposed depends strongly on the political leaning of early Twitter connections.

    • Wen Chen
    • , Diogo Pacheco
    •  & Filippo Menczer

  • Article
    | Open Access

    Reservoir computers are artificial neural networks that can be trained on small data sets, but require large random matrices and numerous metaparameters. The authors propose an improved reservoir computer that overcomes these limitations and shows advantageous performance for complex forecasting tasks

    • Daniel J. Gauthier
    • , Erik Bollt
    •  & Wendson A. S. Barbosa

  • Article
    | Open Access

    Image-based simulation for obtaining physical quantities is limited by the uncertainty in the underlying image segmentation. Here, the authors introduce a workflow for efficiently quantifying segmentation uncertainty and creating uncertainty distributions of the resulting physics quantities.

    • Michael C. Krygier
    • , Tyler LaBonte
    •  & Scott A. Roberts

  • Article
    | Open Access

    Finding a biologically-relevant inductive bias for training DNNs on large fitness landscapes is challenging. Here, the authors propose a method called Epistatic Net that improves DNN prediction accuracy and interpretation speed by integrating the knowledge that higher-order epistatic interactions are usually sparse.

    • Amirali Aghazadeh
    • , Hunter Nisonoff
    •  & Kannan Ramchandran

  • Article
    | Open Access

    Network dismantling allows to find minimum set of units attacking which leads to system’s break down. Grassia et al. propose a deep-learning framework for dismantling of large networks which can be used to quantify the vulnerability of networks and detect early-warning signals of their collapse.

    • Marco Grassia
    • , Manlio De Domenico
    •  & Giuseppe Mangioni

  • Article
    | Open Access

    Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.

    • J. Bracher
    • , D. Wolffram
    •  & Frost Tianjian Xu

  • Article
    | Open Access

    Accurate seasonal forecasts of sea ice are highly valuable, particularly in the context of sea ice loss due to global warming. A new machine learning tool for sea ice forecasting offers a substantial increase in accuracy over current physics-based dynamical model predictions.

    • Tom R. Andersson
    • , J. Scott Hosking
    •  & Emily Shuckburgh

  • Article
    | Open Access

    Neural Networks are known to perform poorly outside of their training domain. Here the authors propose an inverse sampling strategy to train neural network potentials enabling to drive atomistic systems towards high-likelihood and high-uncertainty configurations without the need for molecular dynamics simulations.

    • Daniel Schwalbe-Koda
    • , Aik Rui Tan
    •  & Rafael Gómez-Bombarelli

  • Article
    | Open Access

    Lineage tracing and snapshots of transcriptional state at the single-cell level are powerful, complementary tools for studying development. Here, the authors propose a mathematical method combining lineage tracing with trajectory inference to improve our understanding of development.

    • Aden Forrow
    •  & Geoffrey Schiebinger

  • Review Article
    | Open Access

    Seed banks are generated when individuals enter a dormant state, a phenomenon that has evolved among diverse taxa, but that is also found in stem cells, brains, and tumors. Here, Lennon et al. synthesize the fundamentals of seed-bank theory and the emergence of complex patterns and dynamics in mathematics and the life sciences.

    • Jay T. Lennon
    • , Frank den Hollander
    •  & Jochen Blath

  • Article
    | Open Access

    Storage technology based on DNA is emerging as an information dense and durable medium. Here the authors use machine learning-based encoding and hybridization probes to execute similarity searches in a DNA database.

    • Callista Bee
    • , Yuan-Jyue Chen
    •  & Luis Ceze

  • Article
    | Open Access

    Prediction of contagion dynamics is of relevance for epidemic and social complex networks. Murphy et al. propose a data-driven approach based on deep learning which allows to learn mechanisms governing network dynamics and make predictions beyond the training data for arbitrary network structures.

    • Charles Murphy
    • , Edward Laurence
    •  & Antoine Allard

  • Article
    | Open Access

    The analysis of networks and network processes can require low-dimensional representations, possible for specific structures only. The authors propose a geometric formalism which allows to unfold the mechanisms of dynamical processes propagation in various networks, relevant for control and community detection.

    • Adam Gosztolai
    •  & Alexis Arnaudon

  • Article
    | Open Access

    Quantitative methods to assess the quality of hPSC-derived organoids have not been developed. Here they present a prediction algorithm to assess the transcriptomic similarity between hPSC-derived organoids and the corresponding human target organs and perform validation on lung bud organoids, antral gastric organoids, and cardiomyocytes.

    • Mi-Ok Lee
    • , Su-gi Lee
    •  & Hyun-Soo Cho

  • Article
    | Open Access

    Wavefront shaping is used to overcome scattering in biological tissues during imaging, but determining the compensation is slow. Here, the authors use holographic phase stepping interferometry, where new phase information is updated after each measurement, enabling fast improvement of the wavefront correction.

    • Molly A. May
    • , Nicolas Barré
    •  & Alexander Jesacher

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