Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware weights, to minimise inference accuracy degradation.

    • Charles Mackin
    • , Malte J. Rasch
    •  & Geoffrey W. Burr

  • Article
    | Open Access

    Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

    • Sergey P. Primakov
    • , Abdalla Ibrahim
    •  & Philippe Lambin

  • Article
    | Open Access

    Hybrid neural networks combine advantages of spiking and artificial neural networks in the context of computing and biological motivation. The authors propose a design framework with hybrid units for improved flexibility and efficiency of hybrid neural networks, and modulation of hybrid information flows.

    • Rong Zhao
    • , Zheyu Yang
    •  & Luping Shi

  • Article
    | Open Access

    Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled agents in the form of partial differential equations using emergent spatial embeddings.

    • Felix P. Kemeth
    • , Tom Bertalan
    •  & Ioannis G. Kevrekidis

  • Article
    | Open Access

    Molecules offer enormous capacity for information storage. Here, the authors show that information can be encoded into molecules with sequences of paramagnetic lanthanide ions, and decoded using nuclear magnetic resonance spectroscopy.

    • Jan Kretschmer
    • , Tomáš David
    •  & Miloslav Polasek

  • Article
    | Open Access

    Scattering of electrons from defects and boundaries in mesoscopic samples is encoded in quantum interference patterns of magneto-conductance, but these patterns are difficult to interpret. Here the authors use machine learning to reconstruct electron wavefunction intensities and sample geometry from magneto-conductance data.

    • Shunsuke Daimon
    • , Kakeru Tsunekawa
    •  & Eiji Saitoh

  • Article
    | Open Access

    Understanding the transport of the particles and fuel in the fusion plasma is fundamentally important. Here the authors report a cross-link interaction between electron- and ion-scale turbulences in plasma in terms of trapped electron mode and electron temperature gradient modes and their implication to fusion plasma.

    • Shinya Maeyama
    • , Tomo-Hiko Watanabe
    •  & Akihiro Ishizawa

  • Article
    | Open Access

    Non-pharmaceutical interventions (NPIs) and COVID-19 vaccination have been implemented concurrently, making their relative effects difficult to measure. Here, the authors show that effects of NPIs reduced as vaccine coverage increased, but that NPIs could still be important in the context of more transmissible variants.

    • Yong Ge
    • , Wen-Bin Zhang
    •  & Shengjie Lai

  • Article
    | Open Access

    Artificial intelligence approaches inspired by human cognitive function have usually single learned ability. The authors propose a multimodal foundation model that demonstrates the cross-domain learning and adaptation for broad range of downstream cognitive tasks.

    • Nanyi Fei
    • , Zhiwu Lu
    •  & Ji-Rong Wen

  • Article
    | Open Access

    Defining the dimension in bounded, inhomogeneous or discrete physical systems may be challenging. The authors introduce here a dynamics-based notion of dimension by analysing diffusive processes in space, relevant for non-ideal physical systems and networks.

    • Robert Peach
    • , Alexis Arnaudon
    •  & Mauricio Barahona

  • Article
    | Open Access

    Data-driven recovery of topology is challenging for networks beyond pairwise interactions. The authors propose a framework to reconstruct complex networks with higher-order interactions from time series, focusing on networks with 2-simplexes where social contagion and Ising dynamics generate binary data.

    • Huan Wang
    • , Chuang Ma
    •  & Hai-Feng Zhang

  • Article
    | Open Access

    Turing structures emerge in reaction-diffusion processes far from thermodynamic equilibrium involving chemicals with different diffusion coefficients in classic Turing systems. Here, authors show that a Turing structure with near zero strain semi-coherence interfaces can be constructed in homogeneous solutions.

    • Yuanming Zhang
    • , Ningsi Zhang
    •  & Zhigang Zou

  • Article
    | Open Access

    The atomic structure of heterogeneous catalysts is usually a blackbox. Here the authors demonstrate large-scale machine learning atomic simulations help to resolve the catalyst structure and reaction mechanism of encapsulated PtSnOx clusters in zeolite that feature a mortise-and-tenon joinery structure and the superior activity towards propane dehydrogenation.

    • Sicong Ma
    •  & Zhi-Pan Liu

  • Article
    | Open Access

    Double-gyroid networks assemble in diverse soft materials, yet the molecular packing that underlies their complex structure remains obscure. Here, authors advance a theory that resolves a long-standing puzzle about their formation in block copolymers.

    • Abhiram Reddy
    • , Michael S. Dimitriyev
    •  & Gregory M. Grason

  • Article
    | Open Access

    Several challenges still impede the deployment of optical switches in data centers. The authors report an optical switching and control system to synergistically overcome these challenges and provide enhanced performance for data center applications.

    • Xuwei Xue
    •  & Nicola Calabretta

  • Article
    | Open Access

    Brain-inspired neural generative models can be designed to learn complex probability distributions from data. Here the authors propose a neural generative computational framework, inspired by the theory of predictive processing in the brain, that facilitates parallel computing for complex tasks.

    • Alexander Ororbia
    •  & Daniel Kifer

  • 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

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