Mathematics and computing articles within Nature Communications

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  • 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

  • Article
    | Open Access

    COVID-19-releated public health measures may have indirectly impacted mortality rates by causing or averting deaths. Here, the authors use data from Switzerland until April 2022 and estimate that, after accounting for deaths directly related to COVID-19, mortality was lower than expected, indicating some evidence of an overall positive impact of control measures.

    • Julien Riou
    • , Anthony Hauser
    •  & Garyfallos Konstantinoudis

  • Article
    | Open Access

    Convolutional operation is a very efficient way to handle tensor analytics, but it consumes a large quantity of additional memory. Here, the authors demonstrate an integrated photonic tensor processor which directly handles high-order tensors without tensor-matrix transformation.

    • Shaofu Xu
    • , Jing Wang
    •  & Weiwen Zou

  • Article
    | Open Access

    Traditional learning procedures for artificial intelligence rely on digital methods not suitable for physical hardware. Here, Nakajima et al. demonstrate gradient-free physical deep learning by augmenting a biologically inspired algorithm, accelerating the computation speed on optoelectronic hardware.

    • Mitsumasa Nakajima
    • , Katsuma Inoue
    •  & Kohei Nakajima

  • Article
    | Open Access

    This study presents the first rapid seismic multi-hazard and impact estimation system integrating advanced causal inference and remote sensing techniques, which jointly estimates regional-scale and high-resolution maps of seismic multi-hazards and building damage from InSAR imageries.

    • Susu Xu
    • , Joshua Dimasaka
    •  & Hae Young Noh

  • Article
    | Open Access

    Artificial neural networks are known to perform well on recently learned tasks, at the same time forgetting previously learned ones. The authors propose an unsupervised sleep replay algorithm to recover old tasks synaptic connectivity that may have been damaged after new task training.

    • Timothy Tadros
    • , Giri P. Krishnan
    •  & Maxim Bazhenov

  • Article
    | Open Access

    Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a set of utility and privacy metrics.

    • Chao Yan
    • , Yao Yan
    •  & Bradley A. Malin

  • Article
    | Open Access

    Artificial Intelligence has achieved success in a variety of single-player or competitive two-player games with no communication between players. Here, the authors propose an approach where Artificial Intelligence agents have ability to negotiate and form agreements, playing the board game Diplomacy.

    • János Kramár
    • , Tom Eccles
    •  & Yoram Bachrach

  • Article
    | Open Access

    Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

    • Sarthak Pati
    • , Ujjwal Baid
    •  & Spyridon Bakas

  • Article
    | Open Access

    Engineering quantum states requires precise manipulations at the atomic level. Here, the authors use deep reinforcement learning to manipulate Ag adatoms on Ag surfaces, which combined with path planning algorithms enables autonomous atomic assembly.

    • I-Ju Chen
    • , Markus Aapro
    •  & Adam S. Foster

  • Article
    | Open Access

    Here the authors introduce dual communities, characterized by strong connections at their boundaries, and show that they are formed as a trade-off between efficiency and resilience in supply networks.

    • Franz Kaiser
    • , Philipp C. Böttcher
    •  & Dirk Witthaut

  • Article
    | Open Access

    How the human visual system leverages the rich structure in object motion for perception remains unclear. Here, Bill et al. propose a theory of how the brain could infer motion relations in real time and offer a unifying explanation for various perceptual phenomena.

    • Johannes Bill
    • , Samuel J. Gershman
    •  & Jan Drugowitsch

  • Article
    | Open Access

    Inspired by the characteristics of textile-based flexible electronic sensors, the authors report a braided electronic cord with a low-cost, and automated fabrication to realize imperceptible, designable, and scalable user interfaces with the features of user-friendliness, excellent durability and rich interaction mode.

    • Min Chen
    • , Jingyu Ouyang
    •  & Guangming Tao

  • Article
    | Open Access

    Systemic risk and bank bailout approaches have been the source of discussions on scientific, financial and governmental forums. An artificial intelligence technique is proposed to inform equitable bailout decisions that minimise taxpayers’ losses.

    • Daniele Petrone
    • , Neofytos Rodosthenous
    •  & Vito Latora

  • Article
    | Open Access

    Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark.

    • Simon Ott
    • , Adriano Barbosa-Silva
    •  & Matthias Samwald

  • Perspective
    | Open Access

    Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.

    • Leto Peel
    • , Tiago P. Peixoto
    •  & Manlio De Domenico

  • Article
    | Open Access

    Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for data drifts. Here, the authors develop a framework for continuously monitoring and updating prognostic models and applied it to predict 28-day survival in COVID-19 patients.

    • Todd J. Levy
    • , Kevin Coppa
    •  & Theodoros P. Zanos

  • Article
    | Open Access

    Traditional bulk sequencing data lack information about cell-type-specific gene expression. Here, the authors develop a Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq, and apply it to analyze the cell type fractions and cell-type-specific gene expression in clinical data.

    • Yanshuo Chen
    • , Yixuan Wang
    •  & Yu Li

  • Article
    | Open Access

    Diffusive motions in complex environments such as living biological cells or soft matter systems can be analyzed with single-particle-tracking approaches, where accuracy of output may vary. The authors involve a machine-learning technique for decoding anomalous-diffusion data and provide an uncertainty estimate together with predicted output.

    • Henrik Seckler
    •  & Ralf Metzler

  • Article
    | Open Access

    Safe clinical deployment of deep learning models for digital pathology requires reliable estimates of predictive uncertainty. Here the authors describe an algorithm for quantifying whole-slide image uncertainty, demonstrating their approach with models trained to distinguish lung cancer subtypes.

    • James M. Dolezal
    • , Andrew Srisuwananukorn
    •  & Alexander T. Pearson

  • Article
    | Open Access

    Additively manufactured materials contain different types of volumetric defects. Here, the authors utilize the most distinguishing morphological features among different defect types to propose a defect classification methodology.

    • Arun Poudel
    • , Mohammad Salman Yasin
    •  & Nima Shamsaei

  • Article
    | Open Access

    Topological quantum error correction is a promising approach towards fault-tolerant quantum computing, but suffers from large time overhead. Here, the authors generalise the stabiliser toric code to a single-shot 3D subsystem toric code, featuring good performance and resilience to measurement errors.

    • Aleksander Kubica
    •  & Michael Vasmer

  • Article
    | Open Access

    The development of machine learning systems has to ensure their robustness and reliability. The authors introduce a framework that defines a principled process of machine learning system formation, from research to production, for various domains and data scenarios.

    • Alexander Lavin
    • , Ciarán M. Gilligan-Lee
    •  & Yarin Gal

  • Article
    | Open Access

    The use of machine learning to characterise quantum states has been demonstrated, but usually training the algorithm using data from the same state one wants to characterise. Here, the authors show an algorithm that can learn all states that share structural similarities with the ones used for the training.

    • Yan Zhu
    • , Ya-Dong Wu
    •  & Giulio Chiribella

  • Article
    | Open Access

    Reducing of dimension is often necessary to detect and analyze patterns in large datasets and complex networks. Here, the authors propose a method for detection of the intrinsic dimensionality of high-dimensional networks to reproduce their complex structure using a reduced tractable geometric representation.

    • Pedro Almagro
    • , Marián Boguñá
    •  & M. Ángeles Serrano

  • Article
    | Open Access

    Improving the image depth perception of holograms while maintaining high image quality is a current challenge. Here the authors propose an efficient solution relying on a multi-plane hologram technique that reconstruct different blurred images and sharply focused images depending on a propagation distance.

    • Daeho Yang
    • , Wontaek Seo
    •  & Hong-Seok Lee

  • Article
    | Open Access

    It is unclear how the brain keeps track of the number of times different events are experienced. Here, a neural circuit is proposed for this problem inspired by a classic solution in computer science, and evidence of this circuit is shown in the fruit fly brain.

    • Sanjoy Dasgupta
    • , Daisuke Hattori
    •  & Saket Navlakha

  • Article
    | Open Access

    Socioeconomic segregation is one of the main factors behind large-scale inequalities in urban areas and its characterisation remains challenging. The authors propose a family of non-parametric measures to quantify spatial heterogeneity through diffusion, and show how this relates to segregation and deprivation

    • Sandro Sousa
    •  & Vincenzo Nicosia

  • Article
    | Open Access

    One gap between the neuro-inspired computing and its applications lies in the intrinsic variability of the devices. Here, Payvand et al. suggest a technologically plausible co-design of the hardware architecture which takes into account and exploits the physics behind memristors.

    • Melika Payvand
    • , Filippo Moro
    •  & Giacomo Indiveri

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