Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    In stochastic games, there is a feedback loop between a group’s social behaviors and its environment. Kleshnina et al. show that groups are often more cooperative when they know the exact state of their environment, although there are also intriguing cases when ignorance is beneficial.

    • Maria Kleshnina
    • , Christian Hilbe
    •  & Martin A. Nowak

  • Article
    | Open Access

    Generalization - that is, the ability to extrapolate from training data to unseen data - is fundamental in machine learning, and thus also for quantum ML. Here, the authors show that QML algorithms are able to generalise the training they had on a specific distribution and learn over different distributions.

    • Matthias C. Caro
    • , Hsin-Yuan Huang
    •  & Zoë Holmes

  • Article
    | Open Access

    Accurate prediction of peptidic hydrogels could prove useful for diverse biomedical applications. Here, the authors develop a “human-in-the-loop” approach that integrates coarse-grained molecular dynamics, machine learning, and experimentation to design natural peptide hydrogels.

    • Tengyan Xu
    • , Jiaqi Wang
    •  & Huaimin Wang

  • Article
    | Open Access

    The authors show that the ramified ductal network of the mouse salivary gland develops from a set of simple probabilistic rules, where ductal elongation and branching are driven by the persistent expansion of the surrounding tissue.

    • Ignacio Bordeu
    • , Lemonia Chatzeli
    •  & Benjamin D. Simons

  • Article
    | Open Access

    The authors propose a confocal complemented signal-object collaborative regularization method for non-line-of-sight (NLOS) imaging without specific requirements on the spatial pattern of measurement points. The method extends the application range of NLOS imaging.

    • Xintong Liu
    • , Jianyu Wang
    •  & Lingyun Qiu

  • Article
    | Open Access

    The sparse, noisy, and distorted raw photon data captured by single-photon cameras make it difficult to estimate scene properties under challenging illumination conditions. Here, the authors present Collaborative photon processing for Active Single-Photon Imaging (CASPI), a technology-agnostic, application-agnostic, and training-free photon processing pipeline for high-resolution single-photon cameras.

    • Jongho Lee
    • , Atul Ingle
    •  & Mohit Gupta

  • Article
    | Open Access

    Li-ion batteries are used to store energy harvested from photovoltaics. However, battery use is sporadic and standard diagnostic methods cannot be applied. Here, the authors propose a methodology for diagnosing photovoltaics-connected Li-ion batteries that use trained machine learning algorithms.

    • Matthieu Dubarry
    • , Nahuel Costa
    •  & Dax Matthews

  • Article
    | Open Access

    Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.

    • Shilu Zhang
    • , Saptarshi Pyne
    •  & Sushmita Roy

  • Article
    | Open Access

    Federated learning enables multi-institutional collaborations on decentralized data with improved privacy protection. Here, authors propose a new scheme for decentralized federated learning with much less communication overhead and stronger privacy.

    • Shivam Kalra
    • , Junfeng Wen
    •  & H. R. Tizhoosh

  • Article
    | Open Access

    Recent experiments reveal undetermined crystalline phases near the melting minimum region in lithium. Here, the authors use a crystal structure search method combined with machine learning to explore the energy landscape of lithium and predict complex crystal structures.

    • Xiaoyang Wang
    • , Zhenyu Wang
    •  & Yanming Ma

  • Article
    | Open Access

    In quantum technologies, scalable ways to characterise errors in quantum hardware are highly needed. Here, the authors propose an approximate version of quantum process tomography based on tensor network representations of the processes and data-driven optimisation.

    • Giacomo Torlai
    • , Christopher J. Wood
    •  & Leandro Aolita

  • Article
    | Open Access

    Carbon (12C) nucleus has interesting characteristics including the existence of the Hoyle state. Here the authors discuss the structure of the nuclear states of 12C by using nuclear lattice effective field theory.

    • Shihang Shen
    • , Serdar Elhatisari
    •  & Ulf-G. Meißner

  • Article
    | Open Access

    Here the authors have realized a programmable incoherent optical neural network that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space.

    • Yuchi Huo
    • , Hujun Bao
    •  & Sung-Eui Yoon

  • Article
    | Open Access

    Multimodal biological data is challenging to analyze. Here, the authors develop UnitedNet, an explainable deep neural network for analyzing single-cell multimodal biological data and estimating relationships between gene expression and other modalities with cell-type specificity.

    • Xin Tang
    • , Jiawei Zhang
    •  & Jia Liu

  • Article
    | Open Access

    A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants.

    • Adityanarayanan Radhakrishnan
    • , Sam F. Friedman
    •  & Caroline Uhler

  • Article
    | Open Access

    The global risk of record-breaking heatwaves is assessed, with the most at-risk regions identified. It is shown that record-smashing events that currently appear implausible could happen anywhere as a result of climate change.

    • Vikki Thompson
    • , Dann Mitchell
    •  & Julia M. Slingo

  • Article
    | Open Access

    Evidence suggests that increased consumption of ultra-processed food has adverse health implications, however, it remains difficult to classify processed food. Here, the authors introduce FPro, a machine learning-based score predicting the degree of food processing.

    • Giulia Menichetti
    • , Babak Ravandi
    •  & Albert-László Barabási

  • Article
    | Open Access

    Neutron scattering experiments are important for studying materials properties. Here, the authors present a probabilistic active learning approach for neutron spectroscopy with three-axes spectrometers and demonstrate optimization of beam time use by favoring informative regions of signal.

    • Mario Teixeira Parente
    • , Georg Brandl
    •  & Astrid Schneidewind

  • Article
    | Open Access

    Neuroscience has long inspired AI, however the neuroevolutionary search that produces sophisticated behaviors has not been systematized. This paper defines neurodevelopmental ML as a discovery process for structures that promote complex computations.

    • Dániel L. Barabási
    • , Taliesin Beynon
    •  & Nicolas Perez-Nieves

  • Article
    | Open Access

    In order to be used on a large scale, unclonable tags for anti-counterfeiting should allow mass production at low cost, as well as fast and easy authentication. Here, the authors show how to use one-step annealing of gold films to quickly realize robust tags with high capacity, allowing fast deep-learning based authentication via smartphone readout.

    • Ningfei Sun
    • , Ziyu Chen
    •  & Qian Liu

  • Article
    | Open Access

    Automatic extraction of consistent governing laws from data is a challenging problem. The authors propose a method that takes as input experimental data and background theory and combines symbolic regression with logical reasoning to obtain scientifically meaningful symbolic formulas.

    • Cristina Cornelio
    • , Sanjeeb Dash
    •  & Lior Horesh

  • Article
    | Open Access

    Accurately annotating cell types is a fundamental step in single-cell omics data analysis. Here, the authors develop a computational method called Cellcano based on a two-round supervised learning algorithm to identify cell types for scATAC-seq data and perform benchmarking to demonstrate its accuracy, robustness and computational efficiency.

    • Wenjing Ma
    • , Jiaying Lu
    •  & Hao Wu

  • Article
    | Open Access

    The biological plausibility of backpropagation and its relationship with synaptic plasticity remain open questions. The authors propose a meta-learning approach to discover interpretable plasticity rules to train neural networks under biological constraints. The meta-learned rules boost the learning efficiency via bio-inspired synaptic plasticity.

    • Navid Shervani-Tabar
    •  & Robert Rosenbaum

  • Perspective
    | Open Access

    One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.

    • Anthony Zador
    • , Sean Escola
    •  & Doris Tsao

  • Article
    | Open Access

    Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. Here the authors benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches and suggest several high-performance methods under different conditions based on simulation and real data analyses.

    • Hai C. T. Nguyen
    • , Bukyung Baik
    •  & Dougu Nam

  • Article
    | Open Access

    Visualization of large complex networks is challenging with limitations for the network size and depicting specific structures. The authors propose a Graph Neural Network based algorithm with improved speed and the quality of graph layouts, which allows for fast and informative visualization of large networks.

    • Csaba Both
    • , Nima Dehmamy
    •  & Albert-László Barabási

  • Article
    | Open Access

    Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals.

    • Mattia Mazzoli
    • , Riccardo Gallotti
    •  & José J. Ramasco

  • Article
    | Open Access

    Triadic interactions are higher-order interactions relevant to many real complex systems. The authors develop a percolation theory for networks with triadic interactions and identify basic mechanisms for observing dynamical changes of the giant component such as the ones occurring in neuronal and climate networks.

    • Hanlin Sun
    • , Filippo Radicchi
    •  & Ginestra Bianconi

  • Article
    | Open Access

    The inference of clonal architectures in cancer using single-cell RNA-seq data remains challenging. Here, the authors develop SCEVAN, a variational algorithm for copy number-based clonal structure inference in single-cell RNA-seq data that can characterise evolution and heterogeneity in the tumour and the microenvironment.

    • Antonio De Falco
    • , Francesca Caruso
    •  & Michele Ceccarelli

  • Article
    | Open Access

    As lamellar materials, smectics exhibit both liquid and solid characteristics, making them difficult to model at the mesoscale. Paget et al. propose a complex tensor order parameter that reflects the smectic symmetries, capable of describing complex defects including dislocations and disclinations.

    • Jack Paget
    • , Marco G. Mazza
    •  & Tyler N. Shendruk

  • Article
    | Open Access

    Learning analytical models from noisy data remains challenging and depends essentially on the noise level. The authors analyze the transition of the model-learning problem from a low-noise phase to a phase where noise is too high for the underlying model to be learned by any method, and estimate upper bounds for the transition noise.

    • Oscar Fajardo-Fontiveros
    • , Ignasi Reichardt
    •  & Roger Guimerà

  • Article
    | Open Access

    The full potential of single-cell RNA-sequencing applied to precision medicine has yet to be reached. Here, we propose a drug recommendation system ASGARD, which predicts drugs by considering cell clusters to address the intercellular heterogeneity within each patient.

    • Bing He
    • , Yao Xiao
    •  & Lana X. Garmire

  • Article
    | Open Access

    The increasing scale of single-cell RNA-seq studies presents new challenge for integrating datasets from different batches. Here, the authors develop scDML, a tool that simultaneously removes batch effects, improves clustering performance, recovers true cell types, and scales well to large datasets.

    • Xiaokang Yu
    • , Xinyi Xu
    •  & Xiangjie Li

  • Article
    | Open Access

    Populations of swarming coupled oscillators with inhomogeneous natural frequencies and chirality are relevant for active matter systems and micro-robotics. The authors model and analyze a variety of their self-organized behaviors that mimic natural and artificial micro-scale collective systems.

    • Steven Ceron
    • , Kevin O’Keeffe
    •  & Kirstin Petersen

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