Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    The dynamics of complex physical systems can be determined by the balance of a few dominant processes. Callaham et al. propose a machine learning approach for the identification of dominant regimes from experimental or numerical data with examples from turbulence, optics, neuroscience, and combustion.

    • Jared L. Callaham
    • , James V. Koch
    •  & Steven L. Brunton

  • Article
    | Open Access

    Most demonstrations of quantum advantages with optics rely on single photons, and are thus difficult to scale up. Here, the authors use coherent states to demonstrate a quantum advantage for the task of verifying the solution to a NP-complete problem when only partial information on the solution is available.

    • Federico Centrone
    • , Niraj Kumar
    •  & Iordanis Kerenidis

  • Article
    | Open Access

    The Tafel slope in electrochemical catalysis is usually determined from experimental data and remains error-prone. Here, the authors develop a Bayesian approach for Tafel slope quantification, and apply it to study the prevalence of certain "cardinal" Tafel slopes in the electrochemical CO2 reduction literature.

    • Aditya M. Limaye
    • , Joy S. Zeng
    •  & Karthish Manthiram

  • Article
    | Open Access

    Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.

    • Roman Zeleznik
    • , Borek Foldyna
    •  & Hugo J. W. L. Aerts

  • Article
    | Open Access

    While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. Here, the authors report the results of a 4-week population-based controlled experiment, where they assessed the impact of the Spanish DCT app.

    • Pablo Rodríguez
    • , Santiago Graña
    •  & Lucas Lacasa

  • Article
    | Open Access

    The advantages coming from involving quantum systems in machine learning are still not fully clear. Here, the authors propose a software/hardware co-design framework towards quantum-friendly neural networks showing quantum advantage, representing data as either random variables or numbers in unitary matrices.

    • Weiwen Jiang
    • , Jinjun Xiong
    •  & Yiyu Shi

  • Article
    | Open Access

    Simple lower bounds on the rates of device-independent quantum information protocols can often overestimate the power of the eavesdropping party. Here, the authors use new entropic quantities defined as semidefinite programs to improve bounds in several regimes without expensive computational resources

    • Peter Brown
    • , Hamza Fawzi
    •  & Omar Fawzi

  • Article
    | Open Access

    Phase diagrams describe how a system changes phenomenologically as an external parameter, such as a magnetic field strength, is varied. Here, the authors prove that in general such a phase diagram is uncomputable, by explicitly constructing a one-parameter Hamiltonian for which this is the case.

    • Johannes Bausch
    • , Toby S. Cubitt
    •  & James D. Watson

  • Article
    | Open Access

    Test, trace, and isolate programmes are central to COVID-19 control. Here, Viola Priesemann and colleagues evaluate how to allocate scarce resources to keep numbers low, and find that if case numbers exceed test, trace and isolate capacity, there will be a self-accelerating spread.

    • Sebastian Contreras
    • , Jonas Dehning
    •  & Viola Priesemann

  • Article
    | Open Access

    Mendelian randomization is a popular method to detect causal relationships between traits, but can be confounded by instances of horizontal pleiotropy. Here, the authors present a Mendelian randomization workflow which includes causal discovery analysis and filtering of genetic instruments based on their conditional independencies.

    • David Amar
    • , Nasa Sinnott-Armstrong
    •  & Manuel A. Rivas

  • Article
    | Open Access

    Aberrant synchronous oscillations have been associated with numerous brain disorders, including essential tremor. The authors show that synchronous cerebellar activity can casually affect essential tremor and that its underlying mechanism may be related to the temporal coherence of the tremulous movement.

    • Sebastian R. Schreglmann
    • , David Wang
    •  & Nir Grossman

  • Article
    | Open Access

    Metallization of pure hydrogen via overlapping of electronic bands requires high pressure above 3 Mbar. Here the authors study the Ba-H system and discover a unique superhydride BaH12 that contains molecular hydrogen, which demonstrates metallic properties and superconductivity below 1.5 Mbar.

    • Wuhao Chen
    • , Dmitrii V. Semenok
    •  & Tian Cui

  • Article
    | Open Access

    As spiteful behaviors harm both the actor and the target, it is challenging to understand how these behaviors could be adaptive. Here Fulker et al. show that spite can be favored by feedbacks with network structure that create correlated and anti-correlated behavioral interactions simultaneously.

    • Zachary Fulker
    • , Patrick Forber
    •  & Christoph Riedl

  • Article
    | Open Access

    Lack of a widespread surveillance network hampers accurate infectious disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory diseases for locations without surveillance is feasible.

    • Sen Pei
    • , Xian Teng
    •  & Jeffrey Shaman

  • Article
    | Open Access

    Digital trace data from search engines lacks information about the experiences of the individuals generating the data. Here the authors link search data and human computation to build a tracking model of influenza-like illness.

    • Stefan Wojcik
    • , Avleen S. Bijral
    •  & David Lazer

  • Article
    | Open Access

    Influencer networks include a small set of highly-connected nodes and can reach synchrony only via strong node interaction. Tönjes et al. show that introducing an optimal amount of noise enhances synchronization of such networks, which may be relevant for neuroscience or opinion dynamics applications.

    • Ralf Tönjes
    • , Carlos E. Fiore
    •  & Tiago Pereira

  • Article
    | Open Access

    Standard benchmarking of single-molecule localization microscopy cannot quantify nanoscale accuracy of arbitrary datasets. Here, the authors present Wasserstein-induced flux, a method using a chosen perturbation and knowledge of the imaging system to measure confidence of individual localizations.

    • Hesam Mazidi
    • , Tianben Ding
    •  & Matthew D. Lew

  • Article
    | Open Access

    Current inequality and market consumption modelling appears to be subjective. Here the authors combined all three axes of poverty modelling - Engel-Krishnakumar’s microeconomics, Aoki-Chattopadhyay’s mathematical precept and found that multivariate construction is a key component of economic data analysis, implying all modes of income and expenditure need to be considered to arrive at a proper weighted prediction of poverty.

    • Amit K. Chattopadhyay
    • , T. Krishna Kumar
    •  & Iain Rice

  • Article
    | Open Access

    The authors present a passive meta-neural-network for real-time recognition of objects by analysis of acoustic scattering. It consists of unit cells termed meta-neurons, mimicking an analogous neural network for classical waves, and is shown to recognise handwritten digits and misaligned orbital-angular-momentum vortices.

    • Jingkai Weng
    • , Yujiang Ding
    •  & Jianchun Cheng

  • Article
    | Open Access

    Accurate cell detection in dense bacterial biofilms is challenging. Here, the authors report an image analysis pipeline that is able to accurately segment and classify single bacterial cells in 3D fluorescence images: Bacterial Cell Morphometry 3D (BCM3D).

    • Mingxing Zhang
    • , Ji Zhang
    •  & Andreas Gahlmann

  • Article
    | Open Access

    The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.

    • Qingyu Zhao
    • , Ehsan Adeli
    •  & Kilian M. Pohl

  • Article
    | Open Access

    Large volumes of true random numbers are needed for increasing requirements of secure data encryption. Here the authors use the stochastic nature of DNA synthesis to obtain millions of gigabytes of unbiased randomness.

    • Linda C. Meiser
    • , Julian Koch
    •  & Robert N. Grass

  • Article
    | Open Access

    Whether a turbulent flow would inevitably develop singular behavior at the smallest length scales is an ongoing intriguing debate. Using large-scale numerical simulations, Buaria et al. find an unexpected non-linear mechanism which counteracts local vorticity growth instead of enabling it.

    • Dhawal Buaria
    • , Alain Pumir
    •  & Eberhard Bodenschatz

  • Article
    | Open Access

    Supply networks with optimal structure do not contain loops but these can arise as a result of damages or fluctuations. Here Kaiser et al. uncover the mechanisms of loop formation, predict their location and draw analogies with loop formation in biological networks such as plants and animal vasculature.

    • Franz Kaiser
    • , Henrik Ronellenfitsch
    •  & Dirk Witthaut

  • Article
    | Open Access

    Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry.

    • Samuel Boobier
    • , David R. J. Hose
    •  & Bao N. Nguyen

  • Article
    | Open Access

    Artificial intelligence (AI) has demonstrated promise in predicting acutekidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability across sites. Here, the authors develop an AKI prediction model and a measure for model transportability across six independent health systems.

    • Xing Song
    • , Alan S. L. Yu
    •  & Mei Liu

  • Article
    | Open Access

    The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.

    • Jayaraman J. Thiagarajan
    • , Bindya Venkatesh
    •  & Brian Spears

  • Article
    | Open Access

    Organ segmentation of whole-body mouse images is essential for quantitative analysis, but is tedious and error-prone. Here the authors develop a deep learning pipeline to segment major organs and the skeleton in volumetric whole-body scans in less than a second, and present probability maps and uncertainty estimates.

    • Oliver Schoppe
    • , Chenchen Pan
    •  & Bjoern H. Menze

  • Article
    | Open Access

    Multiplayer games can be used as testbeds for the development of learning algorithms for artificial intelligence. Omidshafiei et al. show how to characterize and compare such games using a graph-based approach, generating new games that could potentially be interesting for training in a curriculum.

    • Shayegan Omidshafiei
    • , Karl Tuyls
    •  & Rémi Munos

  • Article
    | Open Access

    Designing efficient system for digital connectivity preserving information security remains a challenge. Here, the authors present hardware-intrinsic security solutions based on physical unclonable functions incorporating an inkjet-printed core circuit as an intrinsic source of entropy, integrated into a silicon-based CMOS system environment.

    • Alexander Scholz
    • , Lukas Zimmermann
    •  & Jasmin Aghassi-Hagmann

  • Article
    | Open Access

    Distributed health data networks (DHDNs) leverage data from multiple healthcare systems, but often face major analytical challenges in the presence of missing data. This paper develops distributed multiple imputation methods that do not require sharing subject-level data across health systems.

    • Changgee Chang
    • , Yi Deng
    •  & Qi Long

  • Article
    | Open Access

    Nested and modular patterns are vastly observed in mutualistic networks across genres and geographic conditions. Here, the authors show a unified mechanism that underlies the assembly and evolution of such networks, based on adaptive niche interactions of the participants.

    • Weiran Cai
    • , Jordan Snyder
    •  & Raissa M. D’Souza

  • Perspective
    | Open Access

    The accurate representation of data is essential in science communication, however, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour vision deficiency remain prevalent. Here, the authors present a simple guide for the scientific use of colour and highlight ways for the scientific community to identify and prevent the misuse of colour in science.

    • Fabio Crameri
    • , Grace E. Shephard
    •  & Philip J. Heron

  • Article
    | Open Access

    Theories of human categorization have traditionally been evaluated in the context of simple, low-dimensional stimuli. In this work, the authors use a large dataset of human behavior over 10,000 natural images to re-evaluate these theories, revealing interesting differences from previous results.

    • Ruairidh M. Battleday
    • , Joshua C. Peterson
    •  & Thomas L. Griffiths

  • Article
    | Open Access

    Time-dependent errors are one of the main obstacles to fully-fledged quantum information processing. Here, the authors develop a general methodology to monitor time-dependent errors, which could be used to make other characterisation protocols time-resolved, and demonstrate it on a trapped-ion qubit.

    • Timothy Proctor
    • , Melissa Revelle
    •  & Kevin Young

  • Article
    | Open Access

    High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities.

    • Mihail Bogojeski
    • , Leslie Vogt-Maranto
    •  & Kieron Burke

  • Article
    | Open Access

    Beam shaping methods can generate optical fields with nontrivial topologies, which are invariant against perturbations and thus interesting for information encoding. Here, the authors introduce the realization of framed optical knots to encode programs with the conjoined use of prime factorization.

    • Hugo Larocque
    • , Alessio D’Errico
    •  & Ebrahim Karimi

  • Article
    | Open Access

    An ongoing global debate concerns effective and sustainable lockdown release strategies in the current pandemic. Here, the authors implement a network model at healthcare-relevant spatial scale to show that coordinated local strategies can be effective in containing further resurgence of the disease.

    • Fabio Della Rossa
    • , Davide Salzano
    •  & Mario di Bernardo

  • Article
    | Open Access

    RNA can be used as a programmable tool for detection of biological analytes. Here the authors use deep neural networks to predict toehold switch functionality in synthetic biology applications.

    • Nicolaas M. Angenent-Mari
    • , Alexander S. Garruss
    •  & James J. Collins

  • Article
    | Open Access

    The performance of a trained neural network may be biased even by generic features of its architecture. Yu et al. ask for the disordered lattice of atoms producing a certain wave localization and the network prefers to answer with power-law distributed displacements.

    • Sunkyu Yu
    • , Xianji Piao
    •  & Namkyoo Park

  • Article
    | Open Access

    The intermittency of solar resources is one of the primary challenges for the large-scale integration of the renewable energy. Here Yin et al. used satellite data and climate model outputs to evaluate the geographic patterns of future solar power reliability, highlighting the tradeoff between the maximum potential power and the power reliability.

    • Jun Yin
    • , Annalisa Molini
    •  & Amilcare Porporato

  • Article
    | Open Access

    Heterogenous ice nucleation is a ubiquitous phenomenon, but predicting the ice nucleation ability of a substrate is challenging. Here the authors develop a machine-learning data-driven approach to predict the ice nucleation ability of substrates, which is based on four descriptors related to physical properties of the interface.

    • Martin Fitzner
    • , Philipp Pedevilla
    •  & Angelos Michaelides

  • Article
    | Open Access

    Designing efficient analog dynamical systems for solving hard optimization problems remains a challenge. Here, the authors demonstrate a dynamical system of thirty oscillators with reconfigurable coupling to compute optimal/near-optimal solutions to the hard Maximum Independent Set problem with over 90% accuracy.

    • Antik Mallick
    • , Mohammad Khairul Bashar
    •  & Nikhil Shukla

  • Article
    | Open Access

    Principal component analysis is often used in studies of ancient DNA, but does not account for the age of the samples. Here, the authors present a factor analysis (FA) which corrects for this by including the effect of allele frequency drift over time.

    • Olivier François
    •  & Flora Jay

  • Article
    | Open Access

    The pyruvate dehydrogenase complex (PDC) is a multienzyme complex connecting glycolysis to mitochondrial oxidation of pyruvate. Cryo-EM analysis of PDC from Neurospora crassa reveals localization of fungi-specific protein X (PX) and confirms that it functions like the mammalian E3BP, recruiting the E3 component of PDC.

    • B. O. Forsberg
    • , S. Aibara
    •  & E. Lindahl

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