Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Functional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.

    • Gabriele Lohmann
    • , Johannes Stelzer
    •  & Klaus Scheffler

  • Article
    | Open Access

    Visual search requires recognizing an object “invariantly”, despite changes in its appearance. Here, the authors show that humans can efficiently and invariantly search for objects in complex scenes and introduce a biologically-inspired zero-shot model that captures human eye movements during search.

    • Mengmi Zhang
    • , Jiashi Feng
    •  & Gabriel Kreiman

  • Article
    | Open Access

    Causality inference in time series analysis based on temporal precedence principle between cause and effect fails to detect mutual causal interactions. Here, Yang et al. introduce a causal decomposition approach based on the covariation principle of cause and effect that overcomes this limitation.

    • Albert C. Yang
    • , Chung-Kang Peng
    •  & Norden E. Huang

  • Article
    | Open Access

    Understanding the occurrence of sudden changes in plasma parameters is important for the operation of magnetically confined fusion devices. Here the authors use simulation to shed light on the formation of abrupt large-amplitude events and the associated redistribution of energetic ions in a tokamak.

    • Andreas Bierwage
    • , Kouji Shinohara
    •  & Masatoshi Yagi

  • Article
    | Open Access

    Genome-wide association studies (GWAS) of neuroimaging data pose a significant computational burden because of the need to correct for multiple testing in both the genetic and the imaging data. Here, Ganjgahi et al. develop WLS-REML which significantly reduces computation running times in brain imaging GWAS.

    • Habib Ganjgahi
    • , Anderson M. Winkler
    •  & Thomas E. Nichols

  • Article
    | Open Access

    Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.

    • Zhongrui Wang
    • , Mingyi Rao
    •  & J. Joshua Yang

  • Article
    | Open Access

    Anticrack propagation in snow results from the mixed-mode failure and collapse of a buried weak layer and can lead to slab avalanches. Here, authors reproduce the complex dynamics of anticrack propagation observed in field experiments using a Material Point Method with large strain elastoplasticity.

    • J. Gaume
    • , T. Gast
    •  & C. Jiang

  • Article
    | Open Access

    Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.

    • Angelo Ziletti
    • , Devinder Kumar
    •  & Luca M. Ghiringhelli

  • Article
    | Open Access

    Despite advances in ENSO modeling, super El Niño events remain largely unpredictable. Hameed et al. postulate that ENSO-IOD interaction is crucial for super El Niño development and identify a self-limiting factor that constrains ENSO dynamics from generating these extreme events on their own.

    • Saji N. Hameed
    • , Dachao Jin
    •  & Vishnu Thilakan

  • Article
    | Open Access

    Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

    • Irem Boybat
    • , Manuel Le Gallo
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    Mitochondrial populations in cells may consist of heteroplasmic mixtures of mtDNA types, and their evolution through development, aging and generations is central to genetic diseases. Here the authors dissect these population dynamics using a large mouse-based data set to characterise the dynamics of heteroplasmy mean and variance throughout life and across generations.

    • Joerg P. Burgstaller
    • , Thomas Kolbe
    •  & Iain G. Johnston

  • Article
    | Open Access

    Bottom-up fabrication via on-surface molecular self-assembly is a useful way to make nanomaterials, but finding appropriate precursor molecules for a given structure remains a challenge. Here the authors present an informatics technique linking self-assembled structures with precursor properties, helping identify molecules for target nanomaterials.

    • Daniel M. Packwood
    •  & Taro Hitosugi

  • Article
    | Open Access

    Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

    • Decebal Constantin Mocanu
    • , Elena Mocanu
    •  & Antonio Liotta

  • Article
    | Open Access

    Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.

    • Can Li
    • , Daniel Belkin
    •  & Qiangfei Xia

  • Article
    | Open Access

    From infectious diseases to brain activity, complex systems can be approximated using autoregressive models. Here, the authors show that incomplete sampling can bias estimates of the stability of such systems, and introduce a novel, unbiased metric for use in such situations.

    • Jens Wilting
    •  & Viola Priesemann

  • Article
    | Open Access

    How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

    • Ana P. Millán
    • , J. J. Torres
    •  & J Marro

  • Article
    | Open Access

    Dimensionality reduction and visualization methods lack a principled way of comparing multiple datasets. Here, Abid et al. introduce contrastive PCA, which identifies low-dimensional structures enriched in one dataset compared to another and enables visualization of dataset-specific patterns.

    • Abubakar Abid
    • , Martin J. Zhang
    •  & James Zou

  • Article
    | Open Access

    The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.

    • N. Gong
    • , T. Idé
    •  & T. Ando

  • Article
    | Open Access

    Systematic changes in stock market prices or in the migration behaviour of cancer cells may be hidden behind random fluctuations. Here, Mark et al. describe an empirical approach to identify when and how such real-world systems undergo systematic changes.

    • Christoph Mark
    • , Claus Metzner
    •  & Ben Fabry

  • Article
    | Open Access

    Wolbachia infection in mosquitoes reduces dengue virus spread under specific lab conditions, prompting its use in disease control. Here, King et al. show that Wolbachia increases mean and variance in mosquito susceptibility and explain how this affects Wolbachia invasion and dengue transmission.

    • Jessica G. King
    • , Caetano Souto-Maior
    •  & M. Gabriela M. Gomes

  • Article
    | Open Access

    Designing molecular keys and combining advanced encryption standard cryptography with molecular steganography is a secure way for encoding messages. Here, the authors use the Ugi four-component reaction of perfluorinated acids to create a library of 500,000 molecular keys for encryption and decryption.

    • Andreas C. Boukis
    • , Kevin Reiter
    •  & Michael A. R. Meier

  • Article
    | Open Access

    Droplet evaporation control has applications in inkjet printing and surface patterning. Here, the authors show that on slippery curved substrates droplets evaporate by slowly retracting and then suddenly snapping, which can be exploited to design surfaces that control an evaporation sequence.

    • Gary G. Wells
    • , Élfego Ruiz-Gutiérrez
    •  & Rodrigo Ledesma-Aguilar

  • Article
    | Open Access

    Sokolov et al. have previously shown how bacteria are expelled in response to a rotating microparticle. Here the authors find that when the microparticle is spun at much higher rotation rates bacteria are trapped around it and then are expelled radially upon rotation cessation in an explosion-like manner.

    • Andrey Sokolov
    • , Leonardo Dominguez Rubio
    •  & Igor S. Aranson

  • Article
    | Open Access

    Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.

    • Francisco Pereira
    • , Bin Lou
    •  & Evelina Fedorenko

  • Article
    | Open Access

    Algorithmic information theory measures the complexity of strings. Here the authors provide a practical bound on the probability that a randomly generated computer program produces a given output of a given complexity and apply this upper bound to RNA folding and financial trading algorithms.

    • Kamaludin Dingle
    • , Chico Q. Camargo
    •  & Ard A. Louis

  • Article
    | Open Access

    Simplified neuron models, such as generalized leaky integrate-and-fire (GLIF) models, are extensively used in network modeling. Here the authors systematically generate and compare GLIF models of varying complexity for their ability to classify cell types in the Allen Cell Types Database and faithfully reproduce spike trains.

    • Corinne Teeter
    • , Ramakrishnan Iyer
    •  & Stefan Mihalas

  • Article
    | Open Access

    Wave propagation is often nonlinear in character, yet the interplay between disorder and nonlinearity remains elusive. Kim et al. use experiments and corroborating numerical simulations to investigate this phenomenon and demonstrate superdiffusive energy transport in disordered granular chains.

    • Eunho Kim
    • , Alejandro J. Martínez
    •  & Jinkyu Yang

  • Article
    | Open Access

    Plants use multiple cues to monitor seasonal temperatures. Here, the authors show that Arabidopsis requires not only prolonged cold, but the absence of temperature spikes above 15 °C to epigenetically silence FLC during winter.

    • Jo Hepworth
    • , Rea L. Antoniou-Kourounioti
    •  & Caroline Dean

  • Article
    | Open Access

    Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.

    • Hua Li
    •  & Sharon Aviran

  • Article
    | Open Access

    Increase in high throughput sequencing (HTS) data warrants compression methods to facilitate better storage and communication. Here, Ginart et al. introduce Assembltrie, a reference-free compression tool which is guaranteed to achieve optimality for error-free reads.

    • Antonio A. Ginart
    • , Joseph Hui
    •  & David N. Tse

  • Article
    | Open Access

    Our understanding of material instabilities in soft solids remains elusive mainly due to the mathematical challenges in capturing localised phenomena within nonlinear elastic materials. Ciarletta develops an analytical theory to describe the nucleation threshold of creases in agreement with experiments.

    • P. Ciarletta

  • Article
    | Open Access

    The security of DIQKD is difficult to prove, as one needs to take into account every possible attack strategy. Here, the authors develop a method to determine the entropy of a system as the sum of the entropies of its parts. Applied to DIQKD, this implies that it suffices to consider i.i.d. attacks.

    • Rotem Arnon-Friedman
    • , Frédéric Dupuis
    •  & Thomas Vidick

  • Article
    | Open Access

    Meso-scale architecture of connectomes is usually modeled as segregated clusters and communities. Here the authors report that non-assortative communities are better able to capture the functional connectivity for some networks and offer measures of community diversity that predict cognitive performance.

    • Richard F. Betzel
    • , John D. Medaglia
    •  & Danielle S. Bassett

  • Article
    | Open Access

    Single-cell RNA sequencing (scRNA-seq) data provides information on transcriptomic heterogeneity within cell populations. Here, Risso et al develop ZINB-WaVE for low-dimensional representations of scRNA-seq data that account for zero inflation, over-dispersion, and the count nature of the data.

    • Davide Risso
    • , Fanny Perraudeau
    •  & Jean-Philippe Vert

  • Article
    | Open Access

    Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.

    • Jacob W. Crandall
    • , Mayada Oudah
    •  & Iyad Rahwan

  • Article
    | Open Access

    Most time series techniques tend to ignore data uncertainties, which results in inaccurate conclusions. Here, Goswami et al. represent time series as a sequence of probability density functions, and reliably detect abrupt transitions by identifying communities in probabilistic recurrence networks.

    • Bedartha Goswami
    • , Niklas Boers
    •  & Jürgen Kurths

  • Article
    | Open Access

    Certain physical problems such as the rupture of a thin sheet can be difficult to solve as computations breakdown at the point of rupture. Here the authors propose a regularization approach to overcome this breakdown which could help dealing with mathematical models that have finite time singularities.

    • Panayotis G. Kevrekidis
    • , Constantinos I. Siettos
    •  & Yannis G. Kevrekidis

  • Article
    | Open Access

    Collective self-organized behavior can be observed in a variety of systems such as colloids and microswimmers. Here O’Keeffe et al. propose a model of oscillators which move in space and tend to synchronize with neighboring oscillators and outline five types of collective self-organized states.

    • Kevin P. O’Keeffe
    • , Hyunsuk Hong
    •  & Steven H. Strogatz

  • Article
    | Open Access

    Cascade propagation models represent a range of processes on networks, such as power-grid blackouts and epidemic outbreaks. Here the authors investigate temporal profiles of avalanches and show how nonsymmetric average avalanche shapes can occur at criticality.

    • James P. Gleeson
    •  & Rick Durrett

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