Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    How regional anatomy shapes function is not well understood. Here, the authors evaluate the performance of 40 communication models in predicting functional connectivity, and find regional heterogeneity in terms of fit and optimal model, and that regional coupling varies over the human lifespan.

    • Farnaz Zamani Esfahlani
    • , Joshua Faskowitz
    •  & Richard F. Betzel

  • Article
    | Open Access

    Rydberg atoms are sensitive to microwave signals and hence can be used to detect them. Here the authors demonstrate a Rydberg receiver enhanced by deep learning, Rydberg atoms acting as antennae, to receive, extract, and decode the multi-frequency microwave signal effectively.

    • Zong-Kai Liu
    • , Li-Hua Zhang
    •  & Bao-Sen Shi

  • Article
    | Open Access

    Here, the authors simulate COVID-19 outbreaks on an empirical contact network derived from digital contact data collected on cruise ships. They model impacts of different control measures and find that combinations of measures, particularly vaccination and rapid antigen testing, are important for mitigating outbreaks.

    • Rachael Pung
    • , Josh A. Firth
    •  & Adam J. Kucharski

  • Article
    | Open Access

    Tasks involving continual learning and adaptation to real-time scenarios remain challenging for artificial neural networks in contrast to real brain. The authors propose here a brain-inspired optimizer based on mechanisms of synaptic integration and strength regulation for improved performance of both artificial and spiking neural networks.

    • Giorgia Dellaferrera
    • , Stanisław Woźniak
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    This paper proposes HYPER, a method for screening more people using fewer tests by testing pools formed via hypergraph factorization. HYPER is not only efficient but is also simple to implement, flexible, and has maximally balanced pools.

    • David Hong
    • , Rounak Dey
    •  & Edgar Dobriban

  • Article
    | Open Access

    Integration of mathematical modeling, ecological analyses of patient biopsies, and neoantigen heterogeneity suggests recruitment of immunosuppressive cells is key to initializing transformation from adenoma to carcinoma in human colorectal cancer.

    • Chandler D. Gatenbee
    • , Ann-Marie Baker
    •  & Alexander R. A. Anderson

  • Article
    | Open Access

    The 2001–2019 web of international waste trade is investigated, allowing the identification of countries at threat of improper handling and disposal of waste. Chemical tracers are used to identify the environmental impact of waste in these countries.

    • Johann H. Martínez
    • , Sergi Romero
    •  & Ernesto Estrada

  • Article
    | Open Access

    Ranking lists are relevant to various areas of nature and society, however their evolution with the elements changing rank in time remained unexplored. The authors uncover a mechanism of ranking dynamics induced by the flux governing the arrival of new elements in the list, for improved predictability of ranking models.

    • Gerardo Iñiguez
    • , Carlos Pineda
    •  & Albert-László Barabási

  • Article
    | Open Access

    Reservoir computing has demonstrated high-level performance, however efficient hardware implementations demand an architecture with minimum system complexity. The authors propose a rotating neuron-based architecture for physically implementing all-analog resource efficient reservoir computing system.

    • Xiangpeng Liang
    • , Yanan Zhong
    •  & Huaqiang Wu

  • Article
    | Open Access

    Simulations of turbulent flows are relevant for aerodynamic and weather modeling, however challenging to capture flow dynamics in the near wall region. To solve this problem, the authors propose a multi-agent reinforcement learning approach to discover wall models for large-eddy simulations.

    • H. Jane Bae
    •  & Petros Koumoutsakos

  • Article
    | Open Access

    The authors use an agent-based model to investigate the potential of reactive vaccination strategies for COVID-19 outbreak mitigation. They find that distributing vaccines in schools and workplaces where cases are detected is more impactful than non-reactive strategies in a wide range of epidemic scenarios.

    • Benjamin Faucher
    • , Rania Assab
    •  & Chiara Poletto

  • Article
    | Open Access

    The SARS-CoV-2 virus has altered people’s lives around the world, not only through the disease it causes, but also through unprecedented restrictions. Here the authors document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes.

    • Kristina Gligorić
    • , Arnaud Chiolero
    •  & Robert West

  • Perspective
    | Open Access

    A grand challenge in robotics is realising intelligent agents capable of autonomous interaction with the environment. In this Perspective, the authors discuss the potential, challenges and future direction of research aimed at demonstrating embodied intelligent robotics via neuromorphic technology.

    • Chiara Bartolozzi
    • , Giacomo Indiveri
    •  & Elisa Donati

  • Article
    | Open Access

    The targeted discovery of molecules with specific structural and chemical properties is an open challenge in computational chemistry. Here, the authors propose a conditional generative neural network for the inverse design of 3d molecular structures.

    • Niklas W. A. Gebauer
    • , Michael Gastegger
    •  & Kristof T. Schütt

  • Article
    | Open Access

    Applying the language of computational complexity to study real-world experiments requires a rigorous framework. Here, the authors provide such a framework and establish that there can be an exponential savings in resources if an experimentalist can entangle apparatuses with experimental samples.

    • Dorit Aharonov
    • , Jordan Cotler
    •  & Xiao-Liang Qi

  • Article
    | Open Access

    Current data-driven modelling techniques perform reliably on linear systems or on those that can be linearized. Cenedese et al. develop a data-based reduced modeling method for non-linear, high-dimensional physical systems. Their models reconstruct and predict the dynamics of the full physical system.

    • Mattia Cenedese
    • , Joar Axås
    •  & George Haller

  • Perspective
    | Open Access

    Animal ecologists are increasingly limited by constraints in data processing. Here, Tuia and colleagues discuss how collaboration between ecologists and data scientists can harness machine learning to capitalize on the data generated from technological advances and lead to novel modeling approaches.

    • Devis Tuia
    • , Benjamin Kellenberger
    •  & Tanya Berger-Wolf

  • Article
    | Open Access

    Large amounts of interaction data are collected by messaging apps, mobile phone carriers, and social media. Creţu et al. propose a behavioral profiling attack model and show that the stability of people’s interaction networks over time can allow individuals to be re-identified in interaction datasets.

    • Ana-Maria Creţu
    • , Federico Monti
    •  & Yves-Alexandre de Montjoye

  • Article
    | Open Access

    The evolution of networks with structure changing in time is dependent on their past states and relevant to diffusion and spreading processes. The authors show that temporal network’s memory is described by multidimensional patterns at a microscopic scale, and cannot be reduced to a scalar quantity.

    • Oliver E. Williams
    • , Lucas Lacasa
    •  & Vito Latora

  • Article
    | Open Access

    Topology optimization, relevant for materials design and engineering, requires solving of challenging high-dimensional problems. The authors introduce a self-directed online learning approach, as embedding of deep learning in optimization methods, that accelerates the training and optimization processes.

    • Changyu Deng
    • , Yizhou Wang
    •  & Wei Lu

  • Article
    | Open Access

    It is an outstanding question in quantum gravity how to describe the emergence of classical spacetime geometry from a quantum state. Here, the authors propose a construction in the context of the gauge/gravity correspondence, producing the classical geometry from a quantum state at the boundary of spacetime.

    • Robert J. Berman
    • , Tristan C. Collins
    •  & Daniel Persson

  • Review Article
    | Open Access

    Synthetic DNA is the basis for promising technologies in data storage, barcoding, computing 62 and sercurity. In this review, the authors provide an overview of the field and its future.

    • Linda C. Meiser
    • , Bichlien H. Nguyen
    •  & Robert N. Grass

  • 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

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