Mathematics and computing articles within Nature Communications

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  • 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

  • Article
    | Open Access

    The digital transformation and Industry 4.0 technologies are rapidly shaping the future of manufacturing. Here, authors use reliable big data to quantitatively evaluate lubricants performance and select desirable candidates for application in target manufacturing processes.

    • Xiao Yang
    • , Heli Liu
    •  & Liliang Wang

  • Article
    | Open Access

    SARS-CoV-2 variants of concern have been associated with reduced vaccine effectiveness, even after a booster dose. In this study, authors aim to estimate vaccine effectiveness against hospitalisation with the Omicron and Delta variants, using different definitions of hospitalisation in secondary care data.

    • Julia Stowe
    • , Nick Andrews
    •  & Jamie Lopez Bernal

  • Article
    | Open Access

    Artificial Intelligence can support diagnostic workflows in oncology, but they are vulnerable to adversarial attacks. Here, the authors show that convolutional neural networks are highly susceptible to white- and black-box adversarial attacks in clinically relevant classification tasks.

    • Narmin Ghaffari Laleh
    • , Daniel Truhn
    •  & Jakob Nikolas Kather

  • Article
    | Open Access

    Human-operated optimization of non-aqueous Li-ion battery liquid electrolytes is a time-consuming process. Here, the authors propose an automated workflow that couples robotic experiments with machine learning to optimize liquid electrolyte formulations without human intervention.

    • Adarsh Dave
    • , Jared Mitchell
    •  & Venkatasubramanian Viswanathan

  • Article
    | Open Access

    The challenge of high-speed and high-accuracy coherent photonic neurons for deep learning applications lies to solve noise related issues. Here, Mourgias-Alexandris et al. address this problem by introducing a noise-resilient hardware architectural and a deep learning training platform.

    • G. Mourgias-Alexandris
    • , M. Moralis-Pegios
    •  & N. Pleros

  • Article
    | Open Access

    Increasing the capacity of existing lines or adding new lines in power grids may, counterintuitively, reduce the system performance and promote blackouts. The authors propose an approach for prediction of edges that lower system performance and defining potential constrains for grid extensions.

    • Benjamin Schäfer
    • , Thiemo Pesch
    •  & Marc Timme

  • Article
    | Open Access

    Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics.

    • Lasse Espeholt
    • , Shreya Agrawal
    •  & Nal Kalchbrenner

  • Article
    | Open Access

    Humans can infer rules for building words in a new language from a handful of examples, and linguists also can infer language patterns across related languages. Here, the authors provide an algorithm which models these grammatical abilities by synthesizing human-understandable programs for building words.

    • Kevin Ellis
    • , Adam Albright
    •  & Timothy J. O’Donnell

  • Article
    | Open Access

    The ’Roadmap’ for relaxation of COVID-19 restrictions in England in 2021 was informed by mathematical modelling. Here, the authors perform a retrospective assessment of the accuracy of modelling predictions and identify the main sources of uncertainty that led to observed values deviating from projections.

    • Matt J. Keeling
    • , Louise Dyson
    •  & Samuel Moore

  • Article
    | Open Access

    The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.

    • Matthias C. Caro
    • , Hsin-Yuan Huang
    •  & Patrick J. Coles

  • Article
    | Open Access

    Accurate forecasts of lithium-ion battery performance will ease concerns about the reliability of electric vehicles. Here, the authors leverage electrochemical impedance spectroscopy and machine learning to show that future capacity can be predicted amid uneven use, with no historical data requirement.

    • Penelope K. Jones
    • , Ulrich Stimming
    •  & Alpha A. Lee

  • Article
    | Open Access

    Advanced computer vision technology can provide near real-time home monitoring to support "aging in place” by detecting falls and symptoms related to seizures and stroke. In this paper, the authors propose a strategy that uses homomorphic encryption, which guarantees information confidentiality while retaining action detection.

    • Miran Kim
    • , Xiaoqian Jiang
    •  & Shayan Shams

  • Article
    | Open Access

    3D printing is prone to errors and continuous monitoring and real-time correction during processing remains a significant challenge limiting its applied potential. Here, authors train a neural network to detect and correct diverse errors in real time across many geometries, materials and even printing setups.

    • Douglas A. J. Brion
    •  & Sebastian W. Pattinson

  • Article
    | Open Access

    Dynamic remodeling of the actin cytoskeleton underlies cell movement, but is challenging to characterize at the molecular level. Here, the authors present a method to extract actin filament velocities in living cells, and compare their results to current models of cytoskeletal dynamics.

    • Cayla M. Miller
    • , Elgin Korkmazhan
    •  & Alexander R. Dunn

  • Article
    | Open Access

    Continuous-variable QKD protocols are usually easier to implement than discrete-variables ones, but their security analyses are less developed. Here, the authors propose and demonstrate in the lab a CVQKD protocol that can generate composable keys secure against collective attacks.

    • Nitin Jain
    • , Hou-Man Chin
    •  & Ulrik L. Andersen

  • Article
    | Open Access

    In network systems governed by oscillatory activity, such as brain networks or power grids, configurations of synchrony may define network functions. The authors introduce a control approach for the formation of desired synchrony patterns through optimal interventions on the network parameters.

    • Tommaso Menara
    • , Giacomo Baggio
    •  & Fabio Pasqualetti

  • Article
    | Open Access

    Series of machine learning models, relevant for tasks in biology, medicine, and finance, usually involve complex feature attribution techniques. The authors introduce a tractable method to compute local feature attributions for a series of machine learning models inspired by connections to the Shapley value.

    • Hugh Chen
    • , Scott M. Lundberg
    •  & Su-In Lee

  • Article
    | Open Access

    High-throughput electron tomography has been challenging due to time-consuming alignment and reconstruction. Here, the authors demonstrate real-time tomography with dynamic 3D tomographic visualization integrated in tomviz, an open-source 3D data analysis tool.

    • Jonathan Schwartz
    • , Chris Harris
    •  & Robert Hovden

  • Article
    | Open Access

    Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Here the authors apply some of the latest advances in natural language processing, generative Transformers, to train ProtGPT2, a language model that explores unseen regions of the protein space while designing proteins with nature-like properties.

    • Noelia Ferruz
    • , Steffen Schmidt
    •  & Birte Höcker

  • Article
    | Open Access

    Designing energy-efficient computing solution for the implementation of AI algorithms in edge devices remains a challenge. Yang et al. proposes a decentralized brain-inspired computing method enabling multiple edge devices to collaboratively train a global model without a fixed central coordinator.

    • Helin Yang
    • , Kwok-Yan Lam
    •  & H. Vincent Poor

  • Article
    | Open Access

    As mass quarantines, absences due to sickness, or other shocks thin out patient-physician networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, the authors propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model.

    • Michaela Kaleta
    • , Jana Lasser
    •  & Peter Klimek

  • Article
    | Open Access

    Information-based search strategies are relevant for the learning of interacting agents dynamics and usually need predefined data. The authors propose a method to collect data for learning a predictive sensor model, without requiring domain knowledge, human input, or previously existing data.

    • Ahalya Prabhakar
    •  & Todd Murphey

  • Article
    | Open Access

    A critical task in spatial transcriptomics analysis is to understand inherently spatial relationships among cells. Here, the authors present a deep learning framework to integrate spatial and transcriptional information, spatially extending pseudotime and revealing spatiotemporal organization of cells.

    • Honglei Ren
    • , Benjamin L. Walker
    •  & Qing Nie

  • Article
    | Open Access

    Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware weights, to minimise inference accuracy degradation.

    • Charles Mackin
    • , Malte J. Rasch
    •  & Geoffrey W. Burr

  • Article
    | Open Access

    Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

    • Sergey P. Primakov
    • , Abdalla Ibrahim
    •  & Philippe Lambin

  • Article
    | Open Access

    Hybrid neural networks combine advantages of spiking and artificial neural networks in the context of computing and biological motivation. The authors propose a design framework with hybrid units for improved flexibility and efficiency of hybrid neural networks, and modulation of hybrid information flows.

    • Rong Zhao
    • , Zheyu Yang
    •  & Luping Shi

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