Mathematics and computing articles within Nature Communications

Featured

  • 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

  • Article
    | Open Access

    Bubbles at an air-liquid interface will rupture when their spherical cap becomes sufficiently drained. It is now shown that the film thickness of large bare viscous bubbles is highly non-uniformly distributed, and that a bubble’s thickness profile relates to its drainage velocity.

    • Casey Bartlett
    • , Alexandros T. Oratis
    •  & James C. Bird

  • Article
    | Open Access

    Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. Here, the authors develop a theoretically justified, simple to use and reliable spectral method to assess and combine multiple dimension reduction visualizations of a given dataset from diverse algorithms.

    • Rong Ma
    • , Eric D. Sun
    •  & James Zou

  • Article
    | Open Access

    Finding the ground states of spin glasses relevant for disordered magnets and many other physical systems is computationally challenging. The authors propose here a deep reinforcement learning framework for calculating the ground states, which can be trained on small-scale spin glass instances and then applied to arbitrarily large ones.

    • Changjun Fan
    • , Mutian Shen
    •  & Yang-Yu Liu

  • Article
    | Open Access

    Existing genomic data analysis methods tend to not take full advantage of underlying biological characteristics. Here, the authors leverage the inherent interactions of scRNA-seq data and develop a cartography strategy to contrive the data into a spatially configured genomap for accurate deep pattern discovery.

    • Md Tauhidul Islam
    •  & Lei Xing

  • Article
    | Open Access

    Long lasting insecticide treated mosquito nets (LLINs) provide protection from malaria through both direct effects to the user and indirect community-level effects. Here, the authors use mathematical modelling to assess the relative contributions of these effects under different insecticide resistance and LLIN usage scenarios.

    • H. Juliette T. Unwin
    • , Ellie Sherrard-Smith
    •  & Azra C. Ghani

  • Article
    | Open Access

    The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.

    • Albert Musaelian
    • , Simon Batzner
    •  & Boris Kozinsky

  • Article
    | Open Access

    Rigorous results about the real computational advantages of quantum machine learning are few. Here, the authors prove that a PROMISEBQP-complete problem can be expressed by variational quantum classifiers and quantum support vector machines, meaning that a quantum advantage can be achieved for all ML classification problems that cannot be classically solved in polynomial time.

    • Jonas Jäger
    •  & Roman V. Krems

  • Article
    | Open Access

    Comparing the capabilities of different quantum machine learning protocols is difficult. Here, the authors show that different learning models based on parametrized quantum circuits can all be seen as quantum linear models, thus driving general conclusions on their resource requirements and capabilities.

    • Sofiene Jerbi
    • , Lukas J. Fiderer
    •  & Vedran Dunjko

  • Article
    | Open Access

    A major challenge in analyzing scRNA-seq data arises from challenges related to dimensionality and the prevalence of dropout events. Here the authors develop a deep graph learning method called scMGCA based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments, outperforming other state-of-the-art models across multiple platforms.

    • Zhuohan Yu
    • , Yanchi Su
    •  & Xiangtao Li

  • Article
    | Open Access

    Many methods for single cell data integration have been developed, though mosaic integration remains challenging. Here the authors present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches, that jointly learns cell representations and marker features across modalities for different cell clusters, to interpret the cell clusters from different modalities.

    • Ziqi Zhang
    • , Haoran Sun
    •  & Xiuwei Zhang

  • Article
    | Open Access

    Authors have previously reported on the efficacy and safety of the recombinant spike protein nanoparticle vaccine, NVX-CoV2373, in healthy adults. In this work, they assess anti-spike binding IgG, anti-RBD binding IgG and neutralising antibody titer as correlates of risk and protection against COVID-19.

    • Youyi Fong
    • , Yunda Huang
    •  & Peter B. Gilbert

  • Article
    | Open Access

    In this Bayesian inference study, the authors aim to quantify the impact of the men’s 2020 UEFA Euro Football Championship on COVID-19 spread in twelve participating countries. They estimate that 0.84 million cases and 1,700 deaths were attributable to the championship, with most impacts in England and Scotland.

    • Jonas Dehning
    • , Sebastian B. Mohr
    •  & Viola Priesemann

  • Article
    | Open Access

    Feedback control applied to mechanical resonators can lead to the formation of various complex dynamic behaviors. Here the authors demonstrate flexible and controllable switching between dynamical structures in the response of harmonically driven micro-mechanical resonators.

    • Axel M. Eriksson
    • , Oriel Shoshani
    •  & David A. Czaplewski

Browse broader subjects

Browse narrower subjects

Browse Mathematics and computing across other nature.com journals