Mathematics and computing articles within Nature Communications

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  • 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

  • Perspective
    | Open Access

    The design of polymers for regenerative medicine could be accelerated with the help of machine learning. Here the authors note that machine learning has been applied successfully in other areas of polymer chemistry, while highlighting that data limitations must be overcome to enable widespread adoption within polymeric biomaterials.

    • Samantha M. McDonald
    • , Emily K. Augustine
    •  & Matthew L. Becker

  • Article
    | Open Access

    The serial interval (time between symptom onset in an infector and infectee) is usually estimated from contact tracing data, but this is not always available. Here, the authors develop a method for estimation of serial intervals using whole genome sequencing data and apply it data from clusters of SARS-CoV-2 in Victoria, Australia.

    • Jessica E. Stockdale
    • , Kurnia Susvitasari
    •  & Caroline Colijn

  • Article
    | Open Access

    Inferring co-expressions with scRNA-seq data is challenging, and existing methods suffer from inflated false positives and biases. Here, the authors proposed CS-CORE, which yields unbiased estimates and identifies co-expressions that are more reproducible and biologically relevant for scRNA-seq data.

    • Chang Su
    • , Zichun Xu
    •  & Jingfei Zhang

  • Article
    | Open Access

    Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

    • Peter Y. Lu
    • , Rumen Dangovski
    •  & Marin Soljačić

  • Article
    | Open Access

    Synchronization of e-wearables can be challenging due to device performance variations. Here, the authors develop a general neural network-based solution that analyses and correct disparities between multiple virtual clocks and demonstrate it for a Bluetooth synchronized motion capture system at high frequency.

    • Karthikeyan Kalyanasundaram Balasubramanian
    • , Andrea Merello
    •  & Marco Crepaldi

  • Article
    | Open Access

    While experts analyze cytomorphology to diagnose myelodysplastic syndromes, definitive diagnosis requires complementary information such as karyotype and molecular genetics testing. Here, the authors present a computational method that automatically detects, characterizes and helps identify blood cell characteristics associated with this group of diseases.

    • José Guilherme de Almeida
    • , Emma Gudgin
    •  & Moritz Gerstung

  • Article
    | Open Access

    Diagnosing shortcut learning in clinical models is difficult, as sensitive attributes may be causally linked with disease. Using multitask learning, the authors propose a method to directly test for the presence of shortcut learning in clinical ML systems.

    • Alexander Brown
    • , Nenad Tomasev
    •  & Jessica Schrouff

  • Article
    | Open Access

    In biology, individuals are known to achieve higher navigation accuracy when moving in a group compared to single animals. The authors show that simple self-propelled robotic modules that are incapable of accurate motion as individuals can achieve accurate group navigation once coupled via deformable elastic links.

    • Federico Pratissoli
    • , Andreagiovanni Reina
    •  & Roderich Groß

  • Article
    | Open Access

    Recent advances in multi-condition single-cell multi-cohort studies enable exploration of diverse cell states. Here, authors present scMerge2, an algorithm that allows integration of a large COVID-19 data collection with over five million cells to uncover distinct signatures of disease progression.

    • Yingxin Lin
    • , Yue Cao
    •  & Jean Y. H. Yang

  • Article
    | Open Access

    Rare quantum tunneling two-level systems are known to govern the glass physics at low temperatures, but it remains challenging to detect them in simulations. Ciarella et al. show a machine learning approach to efficiently identify the structural defects, allowing to predict the quantum splitting.

    • Simone Ciarella
    • , Dmytro Khomenko
    •  & Francesco Zamponi

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