Mathematics and computing articles within Nature Communications

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  • 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
  • Article
    | Open Access

    Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

    • Jun Ma
    • , Yuting He
    •  & Bo Wang
  • Article
    | Open Access

    In this work, the authors report the FreeDTS software to simulate biomembranes at the mesoscale. The software provides various membrane simulations, focusing on protein organization and shape remodeling. A versatile tool propelling realistic membrane studies and diverse applications.

    • Weria Pezeshkian
    •  & John H. Ipsen
  • Article
    | Open Access

    Cryo-EM is the go-to method for visualizing large, flexible biomolecules. Here, authors introduce a new Gaussian mixture modelling method for cryo-EM modelling tasks, including refinement, composite map generation and ensemble representation.

    • Joseph G. Beton
    • , Thomas Mulvaney
    •  & Maya Topf
  • Article
    | Open Access

    It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

    • Junyu Liu
    • , Minzhao Liu
    •  & Liang Jiang
  • Article
    | Open Access

    While federated learning is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Here, the authors show a federated distillation method to tackle these challenges, which leverages the strengths of knowledge distillation in a federated learning setting.

    • Jiawei Shao
    • , Fangzhao Wu
    •  & Jun Zhang
  • Article
    | Open Access

    Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

    • Zhijian Yang
    • , Junhao Wen
    •  & Christos Davatzikos
  • Article
    | Open Access

    Network structures can be examined at different scales, and subnetworks in the form of motifs can provide insights into global network properties. The authors propose an approach to decompose a network into a set of latent motifs, which can be used for network comparison, network denoising, and edge inference.

    • Hanbaek Lyu
    • , Yacoub H. Kureh
    •  & Mason A. Porter
  • Article
    | Open Access

    Near-eye displays are pivotal for building augmented and virtual reality platforms, but hurdles remain in achieving comfort and realistic visual experiences. Here, authors demonstrate compact 3D holographic glasses with focus cues by combining merits of waveguide displays and holographic displays.

    • Changwon Jang
    • , Kiseung Bang
    •  & Douglas Lanman
  • Article
    | Open Access

    Generating microfluidic droplets with application-specific desired characteristics is hard. Here the authors report fluid-agnostic machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable single and double emulsions.

    • Ali Lashkaripour
    • , David P. McIntyre
    •  & Polly M. Fordyce
  • Article
    | Open Access

    Neural wavefunctions have become a highly accurate approach to solve the Schrödinger equation. Here, the authors propose an approach to optimize for a generalized wavefunction across compounds, which can help developing a foundation wavefunction model.

    • Michael Scherbela
    • , Leon Gerard
    •  & Philipp Grohs
  • Article
    | Open Access

    Designing efficient artificial neural network circuit architectures for optimal information routing remains a challenge. Here, the authors propose “Mosaic", the first demonstration of on-chip in-memory spike routing using memristors, optimized for small-world graphs prevalent in mammalian brains, offering orders of magnitude reduction in routing events compared to current approaches.

    • Thomas Dalgaty
    • , Filippo Moro
    •  & Melika Payvand
  • Article
    | Open Access

    Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.

    • Md Tauhidul Islam
    • , Zixia Zhou
    •  & Lei Xing
  • Article
    | Open Access

    Antibody Mediated Prevention (AMP) trials showed that the broadly neutralizing antibody VRC01 could prevent some HIV-1 acquisitions. Here the authors use VRC01 levels and the sensitivity of each acquired HIV virus to predict viral loads in the AMP studies and show that VRC01 influenced viral loads, though potency was lower in vivo than expected.

    • Daniel B. Reeves
    • , Bryan T. Mayer
    •  & Srilatha Edupuganti
  • Article
    | Open Access

    Developing efficient reservoir computing hardware that combines optically excited acoustic and spin waves with high spatial density remains a challenge. In this work, the authors propose a design capable of recognizing visual shapes drawn by a laser within remarkably confined spaces, down to 10 square microns.

    • Dmytro D. Yaremkevich
    • , Alexey V. Scherbakov
    •  & Manfred Bayer
  • Article
    | Open Access

    Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Here, the authors propose a bio-inspired artificial neural network hardware able to self-adapt to solve new complex tasks, by autonomously connecting nodes using electropolymerization.

    • Kamila Janzakova
    • , Ismael Balafrej
    •  & Fabien Alibart
  • Article
    | Open Access

    Abrupt regime shifts could in theory be predicted from early warning signals. Here, the authors show that true critical transitions are challenging to classify in lake planktonic systems, due to mismatches between trophic levels, and reveal uneven performance of early warning signal detection methods.

    • Duncan A. O’Brien
    • , Smita Deb
    •  & Christopher F. Clements
  • Article
    | Open Access

    Using AI to predict disease can improve interventions slow down or prevent disease. Here, the authors show that generative AI models built on the framework of Transformer, the model that also empowers ChatGPT, can achieve state-of-the-art performance on disease predictions based on longitudinal electronic records.

    • Zhichao Yang
    • , Avijit Mitra
    •  & Hong Yu
  • Article
    | Open Access

    Scintillators are widely used for radiation detection and require proper calibration in such applications. Here the authors discuss a Bayesian inference and machine learning method in combination with the Compton-edge probing that can describe the non-proportional scintillation response of inorganic scintillators.

    • David Breitenmoser
    • , Francesco Cerutti
    •  & Sabine Mayer
  • Article
    | Open Access

    Acute GVHD severity grading is based on target organ assessments. Here, the authors show that data-driven grading can identify 12 distinct grades with specific aGVHD phenotypes, which are associated with clinical outcomes, and that their method outperformed conventional gradings.

    • Evren Bayraktar
    • , Theresa Graf
    •  & Amin T. Turki
  • Article
    | Open Access

    The modelling of human-like behaviours is one of the challenges in the field of Artificial Intelligence. Inspired by experimental studies of cultural evolution, the authors propose a reinforcement learning approach to generate agents capable of real-time  third-person imitation.

    • Avishkar Bhoopchand
    • , Bethanie Brownfield
    •  & Lei M. Zhang
  • Article
    | Open Access

    The usual treatment of wave scattering theory relies on a formalism that does not easily allow for probing optimal spectral response. Here, the authors show how an alternative formalism, encoding fundamental principles of causality and passivity, can be used to make sense of complex scattered fields’ structures.

    • Lang Zhang
    • , Francesco Monticone
    •  & Owen D. Miller
  • Article
    | Open Access

    Embedding of complex networks in the latent geometry allows for a better understanding of their features. The authors propose a framework for mapping complex networks into high-dimensional hyperbolic space to capture their intrinsic dimensionality, navigability and community structure.

    • Robert Jankowski
    • , Antoine Allard
    •  & M. Ángeles Serrano