Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    The digital transformation and Industry 4.0 technologies are rapidly shaping the future of manufacturing. Here, authors use reliable big data to quantitatively evaluate lubricants performance and select desirable candidates for application in target manufacturing processes.

    • Xiao Yang
    • , Heli Liu
    •  & Liliang Wang

  • Article
    | Open Access

    SARS-CoV-2 variants of concern have been associated with reduced vaccine effectiveness, even after a booster dose. In this study, authors aim to estimate vaccine effectiveness against hospitalisation with the Omicron and Delta variants, using different definitions of hospitalisation in secondary care data.

    • Julia Stowe
    • , Nick Andrews
    •  & Jamie Lopez Bernal

  • Article
    | Open Access

    Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

    • Narmin Ghaffari Laleh
    • , Daniel Truhn
    •  & Jakob Nikolas Kather

  • Article
    | Open Access

    Human-operated optimization of non-aqueous Li-ion battery liquid electrolytes is a time-consuming process. Here, the authors propose an automated workflow that couples robotic experiments with machine learning to optimize liquid electrolyte formulations without human intervention.

    • Adarsh Dave
    • , Jared Mitchell
    •  & Venkatasubramanian Viswanathan

  • Article
    | Open Access

    The challenge of high-speed and high-accuracy coherent photonic neurons for deep learning applications lies to solve noise related issues. Here, Mourgias-Alexandris et al. address this problem by introducing a noise-resilient hardware architectural and a deep learning training platform.

    • G. Mourgias-Alexandris
    • , M. Moralis-Pegios
    •  & N. Pleros

  • Article
    | Open Access

    Increasing the capacity of existing lines or adding new lines in power grids may, counterintuitively, reduce the system performance and promote blackouts. The authors propose an approach for prediction of edges that lower system performance and defining potential constrains for grid extensions.

    • Benjamin Schäfer
    • , Thiemo Pesch
    •  & Marc Timme

  • Article
    | Open Access

    Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics.

    • Lasse Espeholt
    • , Shreya Agrawal
    •  & Nal Kalchbrenner

  • Article
    | Open Access

    Humans can infer rules for building words in a new language from a handful of examples, and linguists also can infer language patterns across related languages. Here, the authors provide an algorithm which models these grammatical abilities by synthesizing human-understandable programs for building words.

    • Kevin Ellis
    • , Adam Albright
    •  & Timothy J. O’Donnell

  • Article
    | Open Access

    The ’Roadmap’ for relaxation of COVID-19 restrictions in England in 2021 was informed by mathematical modelling. Here, the authors perform a retrospective assessment of the accuracy of modelling predictions and identify the main sources of uncertainty that led to observed values deviating from projections.

    • Matt J. Keeling
    • , Louise Dyson
    •  & Samuel Moore

  • Article
    | Open Access

    The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.

    • Matthias C. Caro
    • , Hsin-Yuan Huang
    •  & Patrick J. Coles

  • Article
    | Open Access

    Accurate forecasts of lithium-ion battery performance will ease concerns about the reliability of electric vehicles. Here, the authors leverage electrochemical impedance spectroscopy and machine learning to show that future capacity can be predicted amid uneven use, with no historical data requirement.

    • Penelope K. Jones
    • , Ulrich Stimming
    •  & Alpha A. Lee

  • Article
    | Open Access

    Advanced computer vision technology can provide near real-time home monitoring to support "aging in place” by detecting falls and symptoms related to seizures and stroke. In this paper, the authors propose a strategy that uses homomorphic encryption, which guarantees information confidentiality while retaining action detection.

    • Miran Kim
    • , Xiaoqian Jiang
    •  & Shayan Shams

  • Article
    | Open Access

    3D printing is prone to errors and continuous monitoring and real-time correction during processing remains a significant challenge limiting its applied potential. Here, authors train a neural network to detect and correct diverse errors in real time across many geometries, materials and even printing setups.

    • Douglas A. J. Brion
    •  & Sebastian W. Pattinson

  • Article
    | Open Access

    Dynamic remodeling of the actin cytoskeleton underlies cell movement, but is challenging to characterize at the molecular level. Here, the authors present a method to extract actin filament velocities in living cells, and compare their results to current models of cytoskeletal dynamics.

    • Cayla M. Miller
    • , Elgin Korkmazhan
    •  & Alexander R. Dunn

  • Article
    | Open Access

    Continuous-variable QKD protocols are usually easier to implement than discrete-variables ones, but their security analyses are less developed. Here, the authors propose and demonstrate in the lab a CVQKD protocol that can generate composable keys secure against collective attacks.

    • Nitin Jain
    • , Hou-Man Chin
    •  & Ulrik L. Andersen

  • Article
    | Open Access

    In network systems governed by oscillatory activity, such as brain networks or power grids, configurations of synchrony may define network functions. The authors introduce a control approach for the formation of desired synchrony patterns through optimal interventions on the network parameters.

    • Tommaso Menara
    • , Giacomo Baggio
    •  & Fabio Pasqualetti

  • Article
    | Open Access

    Series of machine learning models, relevant for tasks in biology, medicine, and finance, usually involve complex feature attribution techniques. The authors introduce a tractable method to compute local feature attributions for a series of machine learning models inspired by connections to the Shapley value.

    • Hugh Chen
    • , Scott M. Lundberg
    •  & Su-In Lee

  • Article
    | Open Access

    High-throughput electron tomography has been challenging due to time-consuming alignment and reconstruction. Here, the authors demonstrate real-time tomography with dynamic 3D tomographic visualization integrated in tomviz, an open-source 3D data analysis tool.

    • Jonathan Schwartz
    • , Chris Harris
    •  & Robert Hovden

  • Article
    | Open Access

    Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the authors apply some of the latest advances in natural language processing, generative Transformers, to train ProtGPT2, a language model that explores unseen regions of the protein space while designing proteins with nature-like properties.

    • Noelia Ferruz
    • , Steffen Schmidt
    •  & Birte Höcker

  • Article
    | Open Access

    Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.

    • Helin Yang
    • , Kwok-Yan Lam
    •  & H. Vincent Poor

  • Article
    | Open Access

    As mass quarantines, absences due to sickness, or other shocks thin out patient-physician networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, the authors propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model.

    • Michaela Kaleta
    • , Jana Lasser
    •  & Peter Klimek

  • Article
    | Open Access

    Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

    • Ahalya Prabhakar
    •  & Todd Murphey

  • Article
    | Open Access

    A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

    • Honglei Ren
    • , Benjamin L. Walker
    •  & Qing Nie

  • 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

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