Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Ecosystems must be able to bounce back from perturbations to persist without species extinctions. This study uses theoretical modelling to show the importance of reactivity—how species respond in the short term to perturbations—for assessing the health of complex ecosystems, revealing that it can be a better predictor of extinction risk than stability.

    • Yuguang Yang
    • , Katharine Z. Coyte
    •  & Aming Li

  • Article
    | Open Access

    Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones.

    • Xiaoyun Yuan
    • , Yong Wang
    •  & Lu Fang

  • Article
    | Open Access

    In order to be useful for future large-scale quantum computing, quantum error correction needs to allow for fast enough classical decoding time, while at the moment the slowdown is exponential in the size of the code. Here, the authors remove this roadblock, showing how to parallelize decoding and make the slowdown polynomial.

    • Luka Skoric
    • , Dan E. Browne
    •  & Earl T. Campbell

  • Article
    | Open Access

    Over their careers, medicinal chemists develop a gut feeling for what is a promising molecule. Here, the authors use machine learning models to learn this intuition and show that it can be successfully applied in several drug discovery scenarios.

    • Oh-Hyeon Choung
    • , Riccardo Vianello
    •  & José Jiménez-Luna

  • Article
    | Open Access

    Rapid adoption of zero-emission vehicles with a concurrent transition to clean electricity is essential to achieve U.S. transportation decarbonization goals. Managing travel demand can ease this transition by reducing the need for clean electricity supply. @cghoehne, @nrel, #NRELMobility

    • Christopher Hoehne
    • , Matteo Muratori
    •  & Ookie Ma

  • Article
    | Open Access

    Studies on mutant invasion typically assume populations in isolation, rather than part of an ecological community. Here, the authors use computational models to investigate how enemy-victim interactions influence properties of mutant invasion, showing that selection is substantially weakened.

    • Dominik Wodarz
    •  & Natalia L. Komarova

  • Article
    | Open Access

    Physical unclonable functions (PUFs) normally ensure authentication of small physical objects. Here, instead, the authors observe that also rooms and buildings can serve as PUFs. They apply this insight to monitor the integrity of enclosed environments, such as art galleries, bank vaults, or data centers.

    • Johannes Tobisch
    • , Sébastien Philippe
    •  & Ulrich Rührmair

  • Article
    | Open Access

    Critical transitions and qualitative changes of dynamics in cardiac, ecological, and economical systems, can be characterized by discrete-time bifurcations. The authors propose a deep learning framework that provides early warning signals for critical transitions in discrete-time experimental data.

    • Thomas M. Bury
    • , Daniel Dylewsky
    •  & Gil Bub

  • Article
    | Open Access

    Networks with higher-order interactions provide better description of social and biological systems, however tools to analyze their function still need to be developed. The authors introduce here a decomposition of network in hyper-cores, that gives better understanding of spreading processes and can be applied to fingerprint real-world datasets.

    • Marco Mancastroppa
    • , Iacopo Iacopini
    •  & Alain Barrat

  • Review Article
    | Open Access

    In this Review article, the authors discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

    • Clement A. Adebamowo
    • , Shawneequa Callier
    •  & Sally N. Adebamowo

  • Article
    | Open Access

    Combinatorial optimization problems can be solved on parallel hardware called Ising machines. Most studies have focused on the use of second-order Ising machines. Compared to second-order Ising machines, the authors show that higher-order Ising machines realized with coupled-oscillator networks can be more resource-efficient and provide superior solutions for constraint satisfaction problems.

    • Connor Bybee
    • , Denis Kleyko
    •  & Friedrich T. Sommer

  • Article
    | Open Access

    In 1952, Turing unlocked the reaction-diffusion basis of natural patterns, such as zebra stripes. The authors propose a reaction-diffusion model that recreates characteristics of the flagellar waveform for bull sperm and Chlamydomonas flagella.

    • James F. Cass
    •  & Hermes Bloomfield-Gadêlha

  • Article
    | Open Access

    Archiving data in synthetic DNA offers unprecedented storage density and longevity. To understand how experimental choices affect the integrity of digital data stored in DNA, the authors study the evolution of errors and bias and with a digital twin they supply tools for experimental planning and design of error-correcing codes.

    • Andreas L. Gimpel
    • , Wendelin J. Stark
    •  & Robert N. Grass

  • Article
    | Open Access

    Our current understanding of the computational abilities of near-intermediate scale quantum (NISQ) computing devices is limited, in part due to the absence of a precise definition for this regime. Here, the authors formally define the NISQ realm and provide rigorous evidence that its capabilities are situated between the complexity classes BPP and BQP.

    • Sitan Chen
    • , Jordan Cotler
    •  & Jerry Li

  • Article
    | Open Access

    Capillary breakup in multimaterial fibers is explored for the self-assembly of optoelectronic systems. However, its insights primarily stem from numerical simulations, qualitative at best. The authors formulate an analytical model of such breakup, obtaining a window in the governing parameters where the generally chaotic breakup becomes predictable and thus engineerable.

    • Camila Faccini de Lima
    • , Fan Wang
    •  & Alexander Gumennik

  • Article
    | Open Access

    Visual oddity tasks delve into the visual analytic intelligence of humans, which remained challenging for artificial neural networks. The authors propose here a model with biologically inspired neural dynamics and synthetic saccadic eye movements with improved efficiency and accuracy in solving the visual oddity tasks.

    • Stanisław Woźniak
    • , Hlynur Jónsson
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    Accurate evaluation of Li-ion battery safety conditions can reduce unexpected cell failures. Here, authors present a large-scale electric vehicle charging dataset for benchmarking existing algorithms, and develop a deep learning algorithm for detecting Li-ion battery faults.

    • Jingzhao Zhang
    • , Yanan Wang
    •  & Minggao Ouyang

  • Article
    | Open Access

    Want to mute or focus on speech from a specific region in a crowded room? Here, the authors built an acoustic swarm that, along with neural networks, separates and localizes concurrent speakers in the 2D space with high precision.

    • Malek Itani
    • , Tuochao Chen
    •  & Shyamnath Gollakota

  • Article
    | Open Access

    Analysis of experimental data in condensed matter is often challenging due to system complexity and slow character of physical simulations. The authors propose a framework that combines machine learning with theoretical calculations to enable real-time analysis for electron, neutron, and x-ray spectroscopies.

    • Sathya R. Chitturi
    • , Zhurun Ji
    •  & Joshua J. Turner

  • Article
    | Open Access

    High computational cost severely limit the applications of biophysically detailed multi-compartment models. Here, the authors present DeepDendrite, a GPU-optimized tool that drastically accelerates detailed neuron simulations for neuroscience and AI, enabling exploration of intricate neuronal processes and dendritic learning mechanisms in these fields.

    • Yichen Zhang
    • , Gan He
    •  & Tiejun Huang

  • Article
    | Open Access

    In this work, authors explore DC-DC converter monitoring and control and demonstrate a generalizable digital twin based buck converter system that enables dynamic synchronization even under reference value changes, physical system model variation, and physical controller failure.

    • Zhongcheng Lei
    • , Hong Zhou
    •  & Guo-Ping Liu

  • Article
    | Open Access

    Transfer learning can be applied in computer vision and natural language processing to utilize knowledge from a source task to improve performance on a target task. The authors propose a framework for transfer learning with kernel methods for improved image classification and virtual drug screening.

    • Adityanarayanan Radhakrishnan
    • , Max Ruiz Luyten
    •  & Caroline Uhler

  • Article
    | Open Access

    Robust genome-wide association study (GWAS) methods that can utilise time-to-event information such as age-of-onset will help increase power in analyses for common health outcomes. Here, the authors propose a computationally efficient time-to-event model for GWAS.

    • Emil M. Pedersen
    • , Esben Agerbo
    •  & Bjarni J. Vilhjálmsson

  • Article
    | Open Access

    Fano varieties are mathematical shapes that are basic units in geometry, they are challenging to classify in high dimensions. The authors introduce a machine learning approach that picks out geometric structure from complex mathematical data where rigorous analytical methods are lacking.

    • Tom Coates
    • , Alexander M. Kasprzyk
    •  & Sara Veneziale

  • Article
    | Open Access

    Here, the reaction of the suicide inhibitor sulbactam with the M. tuberculosis β-lactamase (BlaC) is investigated with time-resolved crystallography. Singular Value Decomposition is implemented to extract kinetic information despite changes in unit cell parameters during the time-course of the reaction.

    • Tek Narsingh Malla
    • , Kara Zielinski
    •  & Marius Schmidt

  • Article
    | Open Access

    Modern microscopes can image a sample with sub-Angstrom and sub-picosecond resolutions, but this often requires analysis of tremendously large datasets. Here, the authors demonstrate that an autonomous experiment can yield over a 70% reduction in dataset size while still producing high-fidelity images of the sample.

    • Saugat Kandel
    • , Tao Zhou
    •  & Mathew J. Cherukara

  • Article
    | Open Access

    Image stitching in fluorescence microscopy can be a hindrance to image quality and to downstream quantitative analyses. Here, the authors propose a deep learning-based stripe self-correction method that corrects diverse stripes and artifacts for stitched microscopic images.

    • Shu Wang
    • , Xiaoxiang Liu
    •  & Jianxin Chen

  • Article
    | Open Access

    Security proofs against general attacks are the ultimate goal of QKD. Here, the authors show how the Generalised Entropy Accumulation Theorem can be used, for some classes of QKD scenarios, to translate security proofs against collective attacks in the asymptotic regime into proofs against general attacks in the finite-size regime.

    • Tony Metger
    •  & Renato Renner

  • Article
    | Open Access

    Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.

    • Gerardo Iñiguez
    • , Sara Heydari
    •  & Jari Saramäki

  • Article
    | Open Access

    Many expression deconvolution approaches have been developed to estimate % RNA contributions of diverse cell types to mixed RNA measurements. Here, the authors have developed a complementary approach called scProjection to recover cell type-specific expression profiles from mixed RNA measurements.

    • Nelson Johansen
    • , Hongru Hu
    •  & Gerald Quon

  • Article
    | Open Access

    Authors utilize a number of models (mathematical, in vitro and in vivo infection) to analyse pre-clinical and Phase I clinical trial data, in regard to potential risk of resistance associated with a Plasmodium falciparum inhibitor, cabamiquine.

    • Eva Stadler
    • , Mohamed Maiga
    •  & Thomas Spangenberg

  • Article
    | Open Access

    Here, Mei and Chen propose an in-memory mechanical computing architecture with simplified and reduced data exchange, where computing occurs within mechanical memory units, to facilitate the design of intelligent mechanical systems.

    • Tie Mei
    •  & Chang Qing Chen

  • Article
    | Open Access

    Inspired by human analogical reasoning in cognitive science, the authors propose an approach combining deep learning systems with an analogical reasoning mechanism, to detect abstract similarity in real-world images without intensive training in reasoning tasks.

    • Taylor Webb
    • , Shuhao Fu
    •  & Hongjing Lu

  • Comment
    | Open Access

    The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective.

    • James B. Aimone
    •  & Ojas Parekh

  • Perspective
    | Open Access

    Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

    • Herbert Jaeger
    • , Beatriz Noheda
    •  & Wilfred G. van der Wiel

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