Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Developing efficient reservoir computing hardware that combines optically excited acoustic and spin waves with high spatial density remains a challenge. In this work, the authors propose a design capable of recognizing visual shapes drawn by a laser within remarkably confined spaces, down to 10 square microns.

    • Dmytro D. Yaremkevich
    • , Alexey V. Scherbakov
    •  & Manfred Bayer

  • Article
    | Open Access

    Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Here, the authors propose a bio-inspired artificial neural network hardware able to self-adapt to solve new complex tasks, by autonomously connecting nodes using electropolymerization.

    • Kamila Janzakova
    • , Ismael Balafrej
    •  & Fabien Alibart

  • Article
    | Open Access

    Abrupt regime shifts could in theory be predicted from early warning signals. Here, the authors show that true critical transitions are challenging to classify in lake planktonic systems, due to mismatches between trophic levels, and reveal uneven performance of early warning signal detection methods.

    • Duncan A. O’Brien
    • , Smita Deb
    •  & Christopher F. Clements

  • Article
    | Open Access

    Using AI to predict disease can improve interventions slow down or prevent disease. Here, the authors show that generative AI models built on the framework of Transformer, the model that also empowers ChatGPT, can achieve state-of-the-art performance on disease predictions based on longitudinal electronic records.

    • Zhichao Yang
    • , Avijit Mitra
    •  & Hong Yu

  • Article
    | Open Access

    Scintillators are widely used for radiation detection and require proper calibration in such applications. Here the authors discuss a Bayesian inference and machine learning method in combination with the Compton-edge probing that can describe the non-proportional scintillation response of inorganic scintillators.

    • David Breitenmoser
    • , Francesco Cerutti
    •  & Sabine Mayer

  • Article
    | Open Access

    Acute GVHD severity grading is based on target organ assessments. Here, the authors show that data-driven grading can identify 12 distinct grades with specific aGVHD phenotypes, which are associated with clinical outcomes, and that their method outperformed conventional gradings.

    • Evren Bayraktar
    • , Theresa Graf
    •  & Amin T. Turki

  • Article
    | Open Access

    The modelling of human-like behaviours is one of the challenges in the field of Artificial Intelligence. Inspired by experimental studies of cultural evolution, the authors propose a reinforcement learning approach to generate agents capable of real-time  third-person imitation.

    • Avishkar Bhoopchand
    • , Bethanie Brownfield
    •  & Lei M. Zhang

  • Article
    | Open Access

    The usual treatment of wave scattering theory relies on a formalism that does not easily allow for probing optimal spectral response. Here, the authors show how an alternative formalism, encoding fundamental principles of causality and passivity, can be used to make sense of complex scattered fields’ structures.

    • Lang Zhang
    • , Francesco Monticone
    •  & Owen D. Miller

  • Article
    | Open Access

    Embedding of complex networks in the latent geometry allows for a better understanding of their features. The authors propose a framework for mapping complex networks into high-dimensional hyperbolic space to capture their intrinsic dimensionality, navigability and community structure.

    • Robert Jankowski
    • , Antoine Allard
    •  & M. Ángeles Serrano

  • Article
    | Open Access

    Novel indicators of infectious disease prevalence could improve real-time surveillance and support healthcare planning. Here, the authors show that sales data for non-prescription medications from a UK high street retailer can improve the accuracy of models forecasting mortality from respiratory infections.

    • Elizabeth Dolan
    • , James Goulding
    •  & Laila J. Tata

  • Article
    | Open Access

    There is a need for dataset-dependent MS2 acquisition in trapped ion mobility spectrometry imaging. Here the authors report spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) which enables on-tissue metabolite and lipid annotation in mass spectrometry bioimaging studies, and use this to visualise the chemical space in rat brains.

    • Steffen Heuckeroth
    • , Arne Behrens
    •  & Robin Schmid

  • Article
    | Open Access

    Studies of the evolution of cooperation often assume information use that is inconsistent with empirical observations. Here, the authors’ research on general imitation dynamics reveals that cooperation is fostered by individuals using less personal information and more social information.

    • Xiaochen Wang
    • , Lei Zhou
    •  & Aming Li

  • Article
    | Open Access

    In this study, the authors develop a mathematical modelling framework to estimate the impacts of non-pharmaceutical interventions and vaccination on COVID-19 incidence. The model accounts for changes in SARS-CoV-2 variant and population immunity, and here they use it to investigate epidemic dynamics in French Polynesia.

    • Lloyd A. C. Chapman
    • , Maite Aubry
    •  & Adam J. Kucharski

  • Article
    | Open Access

    Many real-world systems are characterized by bursty dynamics with interchanging periods of intense activity and quiescence. The authors propose a method to construct temporal networks that match a given activity pattern, and apply it to empirical bursty patterns.

    • Anzhi Sheng
    • , Qi Su
    •  & Joshua B. Plotkin

  • Article
    | Open Access

    Pseudotime analysis is prevalent in single-cell RNA-seq, but it remains challenging to perform it across multiple samples and experimental conditions. Here, the authors develop Lamian, a computational framework for multi-sample pseudotime analysis that adjusts for biological and technical variation to detect gene program changes along cell trajectories and across conditions.

    • Wenpin Hou
    • , Zhicheng Ji
    •  & Hongkai Ji

  • 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

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