Mathematics and computing articles within Nature Communications

Featured

  • Article
    | Open Access

    Natural materials exhibit compelling functionalities owing to their irregular architectures, but the study on irregular architected materials is elusive. Here the authors report a generative computational framework to virtually grow irregular materials with optimized properties that match target stress distributions, facilitating tissue support for orthopedic femur restoration.

    • Yingqi Jia
    • , Ke Liu
    •  & Xiaojia Shelly Zhang
  • Article
    | Open Access

    Data-driven detection of governing rules and trends in dynamics of nonautonomous systems usually requires a significant amount of measured data. The authors propose an operator-theoretic technique for identifying trends and persistent cycles using data from a single measured trajectory, relevant for the analysis of climate dynamics.

    • Gary Froyland
    • , Dimitrios Giannakis
    •  & Joanna Slawinska
  • Article
    | Open Access

    Determining the different cell types that contribute to a mixture of DNA is key for research and diagnostic applications. Here, authors comprehensively benchmark DNA methylation-based deconvolution methods, evaluating their performance and robustness to technical bias.

    • Kobe De Ridder
    • , Huiwen Che
    •  & Bernard Thienpont
  • Article
    | Open Access

    Detecting tipping points and predicting extreme events from data remains a challenging problem in complex systems related to climate, ecology and finance. The authors propose a data-driven approach to estimate probabilities of rare events in complex systems, and detect tipping points/catastrophic shifts.

    • Gianluca Fabiani
    • , Nikolaos Evangelou
    •  & Ioannis G. Kevrekidis
  • Article
    | Open Access

    Visualising the structure of museum objects is a crucial step in understanding the origin, state, and composition of cultural heritage artifacts. Here the authors present an approach for creating computed tomography reconstructions using only standard 2D radiography equipment already available in most larger museums.

    • Francien G. Bossema
    • , Willem Jan Palenstijn
    •  & K. Joost Batenburg
  • Article
    | Open Access

    Understanding the role of coherent structures in the dynamics of turbulent flows is of high relevance for fluid dynamics, climate systems, and aerodynamics. The authors propose a deep learning approach to evaluate the importance of various types of coherent structure in the flow, to uncover main mechanisms of wall-bounded turbulence and develop techniques for its control.

    • Andrés Cremades
    • , Sergio Hoyas
    •  & Ricardo Vinuesa
  • Article
    | Open Access

    Current thyroid ultrasounds rely heavily on the experience and skills of the sonographer and of the radiologist, and the process is physically and cognitively exhausting. Here, the authors show a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nodules.

    • Kang Su
    • , Jingwei Liu
    •  & Peter Xiaoping Liu
  • Article
    | Open Access

    Creating accurate digital twins and controlling nonlinear systems displaying chaotic dynamics is challenging due to high system sensitivity to initial conditions and perturbations. The authors introduce a nonlinear controller for chaotic systems, based on next-generation reservoir computing, with improved accuracy, energy cost, and suitable for implementation with field-programmable gate arrays.

    • Robert M. Kent
    • , Wendson A. S. Barbosa
    •  & Daniel J. Gauthier
  • Article
    | Open Access

    Temperature and absolute humidity are associated with influenza activity, and recent data from Hong Kong have suggested ozone as an additional environmental driver. Here, the authors investigate the relationship between ozone and influenza transmission using surveillance data from the USA and find evidence for an inhibitory effect.

    • Fang Guo
    • , Pei Zhang
    •  & Linwei Tian
  • Article
    | Open Access

    The average Internet user spends over 40% of their waking hours online, yet the environmental footprint remains poorly understood. This study suggests that digital content consumption could exacerbate the pressure on the finite Earth’s carrying capacity.

    • Robert Istrate
    • , Victor Tulus
    •  & Gonzalo Guillén-Gosálbez
  • Article
    | Open Access

    Brains and neuromorphic systems learn with local learning rules in online-continual learning scenarios. Designing neural networks that learn effectively under these conditions is challenging. The authors introduce a neural network that implements an effective, principled approach to local, online-continual learning on associative memory tasks.

    • Nicholas Alonso
    •  & Jeffrey L. Krichmar
  • Article
    | Open Access

    Ising machines have been usually applied to predefined combinatorial problems due to their distinct physical properties. The authors introduce an approach that utilizes equilibrium propagation for the training of Ising machines and achieves high accuracy performance on classification tasks.

    • Jérémie Laydevant
    • , Danijela Marković
    •  & Julie Grollier
  • Article
    | Open Access

    Normal mode analysis is a crucial step in structural biology, but is based on an expensive diagonalisation of the system’s Hessian. Here the authors present INCHING, a GPU-based approach to accelerate this task up to >250 times over current methods for macromolecular assemblies.

    • Jordy Homing Lam
    • , Aiichiro Nakano
    •  & Vsevolod Katritch
  • Article
    | Open Access

    All holographic displays and imaging techniques are fundamentally limited by the étendue supported by existing spatial light modulators. Here, the authors report on using artificial intelligence (AI) to learn an étendue expanding element that effectively increases étendue by two orders of magnitude.

    • Ethan Tseng
    • , Grace Kuo
    •  & Felix Heide
  • Article
    | Open Access

    Oscillating neural networks promise ultralow power consumption and rapid computation for tackling complex optimization problems. Here, the authors demonstrate VO2 oscillators to solve NP-complete problems with projected power consumption of 13 µW/oscillator.

    • Olivier Maher
    • , Manuel Jiménez
    •  & Siegfried Karg
  • Article
    | Open Access

    The problem of reversibility within general quantum resource theories is still an open one. Here, the authors prove that a reversible entanglement manipulation framework (and, consequently, the concept of entanglement entropy) can be formally established by adjusting the setting to allow for probabilistic operations

    • Bartosz Regula
    •  & Ludovico Lami
  • Article
    | Open Access

    Heterogeneous interactions between interactive entities are not well understood due to their complex configurations and many body interactions. Han et al. present a probabilistic-based machine learning method to discover the fundamental laws governing the interactions of heterogeneous systems.

    • Zhichao Han
    • , Olga Fink
    •  & David S. Kammer
  • Article
    | Open Access

    Detection of radiation is important for environmental health and safety. Here the authors demonstrate a method for radiation detection and mapping in 2D using minimum number of detectors and inter-pixel padding to increase the contrast between pixels.

    • Ryotaro Okabe
    • , Shangjie Xue
    •  & Mingda Li
  • Article
    | Open Access

    Successful memorization could be decoded from brain activity. Here the authors decode human memory success from EEG recordings, suggesting memory is linked to context.

    • Yuxuan Li
    • , Jesse K. Pazdera
    •  & Michael J. Kahana
  • Article
    | Open Access

    Physical unclonable functions provide algorithm-independent cryptography based on non-distributable unique tokens. Here, the authors introduce unclonable functions based on random DNA pools, enabling secure decentralized authentication.

    • Anne M. Luescher
    • , Andreas L. Gimpel
    •  & Robert N. Grass
  • Article
    | Open Access

    Solving combinatorial optimization problems using quantum or quantum-inspired machine learning models would benefit from strategies able to work with arbitrary objective functions. Here, the authors use the power of generative models to realise such a black-box solver, and show promising performances on some portfolio optimization examples.

    • Javier Alcazar
    • , Mohammad Ghazi Vakili
    •  & Alejandro Perdomo-Ortiz
  • Article
    | Open Access

    For reservoir computing, improving prediction accuracy while maintaining low computing complexity remains a challenge. Inspired by the Granger causality, Li et al. design a data-driven and model-free framework by integrating the inference process and the inferred results on high-order structures.

    • Xin Li
    • , Qunxi Zhu
    •  & Wei Lin
  • Article
    | Open Access

    In modern football games, data-driven analysis serves as a key driver in determining tactics. Wang, Veličković, Hennes et al. develop a geometric deep learning algorithm, named TacticAI, to solve high-dimensional learning tasks over corner kicks and suggest tactics favoured over existing ones 90% of the time.

    • Zhe Wang
    • , Petar Veličković
    •  & Karl Tuyls
  • Article
    | Open Access

    Here, the authors develop a hybrid agent-based model to quantify the contributions of intrinsic cellular mechanisms and bone ecosystem factors to therapy resistance in multiple myeloma. They show that intrinsic mechanisms are essential for resistance, and that the bone microenvironment provides a protective niche that increases the likelihood.

    • Ryan T. Bishop
    • , Anna K. Miller
    •  & David Basanta
  • Article
    | Open Access

    Understanding machine learning models’ ability to extrapolate from training data to unseen data - known as generalisation - has recently undergone a paradigm shift, while a similar understanding for their quantum counterparts is still missing. Here, the authors show that uniform generalization bounds pessimistically estimate the performance of quantum machine learning models.

    • Elies Gil-Fuster
    • , Jens Eisert
    •  & Carlos Bravo-Prieto
  • Article
    | Open Access

    When stem cells develop into tissues intracellular signalling is rewired, errors in this process lead to cancer. Here, authors applied tools from differential geometry made by Albert Einstein’s General Relativity to understand and predict biological network rewiring in health and disease.

    • Anthony Baptista
    • , Ben D. MacArthur
    •  & Christopher R. S. Banerji
  • Article
    | Open Access

    Forecasting the future behaviors based on observed data remains a challenging task especially for large nonlinear systems. The authors propose a data-driven approach combining manifold learning and delay embeddings for prediction of dynamics for all components in high-dimensional systems.

    • Tao Wu
    • , Xiangyun Gao
    •  & Jürgen Kurths
  • Article
    | Open Access

    SARS-CoV-2 variants with mutations in spike have emerged during the pandemic. Magaret et al. show that in Latin America, efficacy of the Ad26.COV2.S vaccine against moderate to severe–critical COVID-19 varied by sequence features, antibody escape scores, and neutralization impacting features of the SARS-CoV-2 variant.

    • Craig A. Magaret
    • , Li Li
    •  & Peter B. Gilbert
  • Article
    | Open Access

    The SARS-CoV-2 Alpha variant of concern emerged in the UK in late 2020 but spread internationally before it was detected. Here, the authors reconstruct the dynamics of dissemination of this variant out of the UK by combining extent of genomic sequencing, travel volume, and local epidemic dynamics in a Bayesian model.

    • Benjamin Faucher
    • , Chiara E. Sabbatini
    •  & Chiara Poletto
  • Perspective
    | Open Access

    Reservoir Computing has shown advantageous performance in signal processing and learning tasks due to compact design and ability for fast training. Here, the authors discuss the parallel progress of mathematical theory, algorithm design and experimental realizations of Reservoir Computers, and identify emerging opportunities as well as existing challenges for their large-scale industrial adoption.

    • Min Yan
    • , Can Huang
    •  & Jie Sun
  • Article
    | Open Access

    People will likely use ChatGPT to seek health advice. Here, the authors show promising performance of ChatGPT and open source models, but a lack of high accuracy considering medical question answering. Improvements are expected over time via domain-specific finetuning and integration of regulations.

    • Sarah Sandmann
    • , Sarah Riepenhausen
    •  & Julian Varghese
  • Article
    | Open Access

    Computing platforms based on chemical processes can be an alternative to digital computers in some scenarios but have limited programmability. Here the authors demonstrate a hybrid computing platform combining digital electronics and an oscillatory chemical reaction and demonstrate its computational capabilities.

    • Abhishek Sharma
    • , Marcus Tze-Kiat Ng
    •  & Leroy Cronin
  • Article
    | Open Access

    Discovery of 2D materials with useful electronic properties is challenging. Here, the authors use DFT to design a stable semiconducting 2D carbon allotrope for optoelectronic applications that has light charge carriers and unusual secondary bandgap.

    • Zhenzhe Zhang
    • , Hanh D. M. Pham
    •  & Rustam Z. Khaliullin
  • Article
    | Open Access

    Data drift is the systematic change in the underlying distribution of input features in prediction models, and can cause deterioration in model performance. Here, the authors highlight the importance of detecting data drift in clinical settings and evaluate methods for detecting drift in medical image data.

    • Ali Kore
    • , Elyar Abbasi Bavil
    •  & Mohamed Abdalla
  • Article
    | Open Access

    Learning the dynamics governing a simulation or experiment usually requires coarse graining or projection, as the number of transition rates typically grows exponentially with system size. The authors show that transformers, neural networks introduced initially for natural language processing, can be used to parameterize the dynamics of large systems without coarse graining.

    • Corneel Casert
    • , Isaac Tamblyn
    •  & Stephen Whitelam
  • Article
    | Open Access

    Predicting the evolution of dynamical systems remains challenging, requiring high computational effort or effective reduction of the system into a low-dimensional space. Here, the authors present a data-driven approach for predicting the evolution of systems exhibiting spatiotemporal dynamics in response to external input signals.

    • Francesco Regazzoni
    • , Stefano Pagani
    •  & Alfio Quarteroni