Mathematics and computing articles within Nature Communications

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  • 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

  • Article
    | Open Access

    The effects of data leakage on predictive models in neuroimaging studies are not well understood. Here, the authors show that data leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have more minor effects.

    • Matthew Rosenblatt
    • , Link Tejavibulya
    •  & Dustin Scheinost

  • Article
    | Open Access

    A knowledge gap persists between machine learning developers and clinicians. Here, the authors show that the Advanced Data Analysis extension of ChatGPT could bridge this gap and simplify complex data analyses, making them more accessible to clinicians.

    • Soroosh Tayebi Arasteh
    • , Tianyu Han
    •  & Sven Nebelung

  • Perspective
    | Open Access

    Optical computing via free-space-based structured optical materials allows to access optical information without the need for preprocessing or optoelectronic conversion. In this Perspective, the authors describe opportunities and challenges in their use for optical computing, information processing, computational imaging and sensing.

    • Jingtian Hu
    • , Deniz Mengu
    •  & Aydogan Ozcan

  • Comment
    | Open Access

    Selecting omic biomarkers using both their effect size and their differential status significance (i.e., selecting the “volcano-plot outer spray”) has long been equally biologically relevant and statistically troublesome. However, recent proposals are paving the way to resolving this dilemma.

    • Thomas Burger

  • Article
    | Open Access

    Extracting scientific data from published research is a complex task required specialised tools. Here the authors present a scheme based on large language models to automatise the retrieval of information from text in a flexible and accessible manner.

    • John Dagdelen
    • , Alexander Dunn
    •  & Anubhav Jain

  • Article
    | Open Access

    Link prediction in temporal networks is relevant for many real-world systems, however, current approaches are usually characterized by high computational costs. The authors propose a temporal link prediction framework based on the sequential stacking of static network features, for improved computational speed, appropriate for temporal networks with completely unobserved or partially observed target layers.

    • Xie He
    • , Amir Ghasemian
    •  & Peter J. Mucha

  • Article
    | Open Access

    Reduced-order models provide better understanding for complex spatio-temporal dynamics of fluid flows with high numbers of degrees of freedom and non-linear interactions. The authors propose a variational autoencoder and transformer framework for learning the temporal dynamics of the nonlinear reduced-order models relevant for fluid dynamics, weather forecasting, and biomedical engineering.

    • Alberto Solera-Rico
    • , Carlos Sanmiguel Vila
    •  & Ricardo Vinuesa

  • Article
    | Open Access

    Detecting hydrogen gas in humid air is an unresolved challenge of significant importance for the safe implementation of hydrogen (energy) technologies. Here, authors demonstrate how the use of neural networks enables the sensing of hydrogen in highly humid air with a detection limit of 100 ppm.

    • David Tomeček
    • , Henrik Klein Moberg
    •  & Christoph Langhammer

  • Article
    | Open Access

    Sensitivity-dependent data analysis methods disrupted the development of artificial olfactory technologies. Here, authors present a data-centric artificial olfactory system based on eigengraph that reflects the intrinsic electrochemical interaction.

    • Seung-Hyun Sung
    • , Jun Min Suh
    •  & Seong Chan Jun

  • Article
    | Open Access

    Analysis of capacitive behavior of electrode materials used in batteries and pseudocapacitors is challenging. Here, authors report an electrochemical signal analysis method available as an online tool to classify the charge storage behavior of a material as battery-like or a pseudocapacitor-like.

    • Siraprapha Deebansok
    • , Jie Deng
    •  & Olivier Fontaine

  • Article
    | Open Access

    The Authors present a universal framework that utilizes photonic integrated circuits (PIC) to enhance the efficiency of reinforcement learning (RL). Leveraging the advantages of the hybrid architecture PIC (HyArch PIC), the PIC-RL experiment demonstrates a remarkable 56% improvement in efficiency for synthesizing perovskite materials.

    • Xuan-Kun Li
    • , Jian-Xu Ma
    •  & Xian-Min Jin

  • Article
    | Open Access

    Early warning signals for rapid regime shifts in complex networks are of importance for ecology, climate and epidemics, where heterogeneities in network nodes and connectivity make construction of early warning signals challenging. The authors propose a method for selecting an optimal set of nodes from which a reliable early warning signal can be obtained.

    • Naoki Masuda
    • , Kazuyuki Aihara
    •  & Neil G. MacLaren

  • Article
    | Open Access

    Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements.

    • Laura Lewis
    • , Hsin-Yuan Huang
    •  & John Preskill

  • Article
    | Open Access

    Adaptive tactile interactions transfer across users, space, and time, via embroidered smart gloves is reported by the authors. The scalable fabrication and adaptive computation pipeline enable tactile occlusion alleviation, human skills transfer, and interactive teleoperation.

    • Yiyue Luo
    • , Chao Liu
    •  & Wojciech Matusik

  • Article
    | Open Access

    Encoding and downsampling images is key for visual prostheses. Here, the authors show that an actor-model framework using the inherent computation of the retinal network yields better performance in downsampling images compared to learning-free methods.

    • Franklin Leong
    • , Babak Rahmani
    •  & Diego Ghezzi

  • Article
    | Open Access

    Global challenges demand global solutions. Here, the authors show a distributed self-driving lab architecture in The World Avatar, linking robots in Cambridge and Singapore for asynchronous multi-objective reaction optimisation.

    • Jiaru Bai
    • , Sebastian Mosbach
    •  & Markus Kraft

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