Mathematics and computing

  • Article
    | Open Access

    In organic chemistry, synthetic routes for new molecules are often specified in terms of reacting molecules only. The current work reports an artificial intelligence model to predict the full sequence of experimental operations for an arbitrary chemical equation.

    • Alain C. Vaucher
    • , Philippe Schwaller
    •  & Teodoro Laino
  • Article
    | Open Access

    Deep neural networks usually rapidly forget the previously learned tasks while training new ones. Laborieux et al. propose a method for training binarized neural networks inspired by neuronal metaplasticity that allows to avoid catastrophic forgetting and is relevant for neuromorphic applications.

    • Axel Laborieux
    • , Maxence Ernoult
    •  & Damien Querlioz
  • Article
    | Open Access

    The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memory computing on high-dimensional vectors at accuracy matching 32-bit software equivalent.

    • Geethan Karunaratne
    • , Manuel Schmuck
    •  & Abbas Rahimi
  • Article
    | Open Access

    Transparent photodetectors based on graphene stacked vertically along the optical axis have shown promising potential for light field reconstruction. Here, the authors develop transparent photodetector arrays and implement a neural network for real-time 3D optical imaging and object tracking.

    • Dehui Zhang
    • , Zhen Xu
    •  & Theodore B. Norris
  • Article
    | Open Access

    The adult ocellated lizard skin colour pattern is effectively generated by a stochastic cellular automaton (CA) of skin scales. Here authors use reaction diffusion (RD) numerical simulations in 3D on realistic lizard skin geometries and demonstrate that skin thickness variation on its own is sufficient to cause scale-by-scale coloration and CA dynamics during RD patterning.

    • Anamarija Fofonjka
    •  & Michel C. Milinkovitch
  • Article
    | Open Access

    Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here, the authors model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical and applied urban settings.

    • Anjalika Nande
    • , Justin Sheen
    •  & Alison L. Hill
  • Article
    | Open Access

    The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution.

    • Entong Zhao
    • , Jeongwon Lee
    •  & Gyu-Boong Jo
  • Article
    | Open Access

    In genome-wide association meta-analysis, it is often difficult to find an independent dataset of sufficient size to replicate associations. Here, the authors have developed MAMBA to calculate the probability of replicability based on consistency between datasets within the meta-analysis.

    • Daniel McGuire
    • , Yu Jiang
    •  & Dajiang J. Liu
  • Article
    | Open Access

    Recent technological, social, and educational changes are profoundly impacting our work, but what makes labour markets resilient to those labour shocks? Here, the authors show that labour markets resemble ecological systems whose resilience depends critically on the network of skill similarities between different jobs.

    • Esteban Moro
    • , Morgan R. Frank
    •  & Iyad Rahwan
  • Article
    | Open Access

    Multiphoton microscopy requires precise increases in excitation power with imaging depth to generate contrast without damaging the sample. Here the authors show how an adaptive illumination function can be learned from the sample’s shape and used for in vivo imaging of whole lymph nodes.

    • Henry Pinkard
    • , Hratch Baghdassarian
    •  & Laura Waller
  • Article
    | Open Access

    Deep neural networks are widely considered as good models for biological vision. Here, we describe several qualitative similarities and differences in object representations between brains and deep networks that elucidate when deep networks can be considered good models for biological vision and how they can be improved.

    • Georgin Jacob
    • , R. T. Pramod
    •  & S. P. Arun
  • Article
    | Open Access

    During geomagnetic substorms, the energy accumulated from solar wind is abruptly transported to ionosphere. Here, the authors show application of community detection on the time-varying networks constructed from all magnetometers collaborating with the SuperMAG initiative.

    • L. Orr
    • , S. C. Chapman
    •  & W. Guo
  • Article
    | Open Access

    Parametrised quantum circuits are a promising hybrid classical-quantum approach, but rigorous results on their effective capabilities are rare. Here, the authors explore the feasibility of training depending on the type of cost functions, showing that local ones are less prone to the barren plateau problem.

    • M. Cerezo
    • , Akira Sone
    •  & Patrick J. Coles
  • Article
    | Open Access

    Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the model’s performance.

    • Dávid Péter Kovács
    • , William McCorkindale
    •  & Alpha A. Lee
  • Article
    | Open Access

    The process of thin sheet crumpling is characterized by high complexity due to an infinite number of possible configurations. Andrejevic et al. show that ordered behavior can emerge in crumpled sheets, and uncover the correspondence between crumpling and fragmentation processes.

    • Jovana Andrejevic
    • , Lisa M. Lee
    •  & Chris H. Rycroft
  • Article
    | Open Access

    Optical analog computing has so far been mostly limited to solving a single instance of a mathematical problem at a time. Here, the authors show that the linearity of the wave equation allows to solve several problems simultaneously, and demonstrate it using an MW transmissive cavity.

    • Miguel Camacho
    • , Brian Edwards
    •  & Nader Engheta
  • Article
    | Open Access

    Controlling the behavior of a complex network usually requires a knowledge of the network dynamics. Baggio et al. propose a data-driven framework to control a complex dynamical network, effective for non-complete or random datasets, which is of relevance for power grids and neural networks.

    • Giacomo Baggio
    • , Danielle S. Bassett
    •  & Fabio Pasqualetti
  • Article
    | Open Access

    Theoretical aspects of automated learning from data involving deep neural networks have open questions. Here Giambagli et al. show that training the neural networks in the spectral domain of the network coupling matrices can reduce the amount of learning parameters and improve the pre-training process.

    • Lorenzo Giambagli
    • , Lorenzo Buffoni
    •  & Duccio Fanelli
  • Article
    | Open Access

    The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.

    • Justin S. Smith
    • , Benjamin Nebgen
    •  & Kipton Barros
  • Article
    | Open Access

    The role of children in the spread of COVID-19 is not fully understood, and the circumstances under which schools should be opened are therefore debated. Here, the authors demonstrate protocols by which schools in France can be safely opened without overwhelming the healthcare system.

    • Laura Di Domenico
    • , Giulia Pullano
    •  & Vittoria Colizza
  • Article
    | Open Access

    The dynamics of complex physical systems can be determined by the balance of a few dominant processes. Callaham et al. propose a machine learning approach for the identification of dominant regimes from experimental or numerical data with examples from turbulence, optics, neuroscience, and combustion.

    • Jared L. Callaham
    • , James V. Koch
    •  & Steven L. Brunton
  • Article
    | Open Access

    Most demonstrations of quantum advantages with optics rely on single photons, and are thus difficult to scale up. Here, the authors use coherent states to demonstrate a quantum advantage for the task of verifying the solution to a NP-complete problem when only partial information on the solution is available.

    • Federico Centrone
    • , Niraj Kumar
    •  & Iordanis Kerenidis
  • Article
    | Open Access

    The Tafel slope in electrochemical catalysis is usually determined from experimental data and remains error-prone. Here, the authors develop a Bayesian approach for Tafel slope quantification, and apply it to study the prevalence of certain "cardinal" Tafel slopes in the electrochemical CO2 reduction literature.

    • Aditya M. Limaye
    • , Joy S. Zeng
    •  & Karthish Manthiram
  • Article
    | Open Access

    Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.

    • Roman Zeleznik
    • , Borek Foldyna
    •  & Hugo J. W. L. Aerts
  • Article
    | Open Access

    While Digital contact tracing (DCT) has been argued to be a valuable complement to manual tracing in the containment of COVID-19, no empirical evidence of its effectiveness is available to date. Here, the authors report the results of a 4-week population-based controlled experiment, where they assessed the impact of the Spanish DCT app.

    • Pablo Rodríguez
    • , Santiago Graña
    •  & Lucas Lacasa
  • Article
    | Open Access

    The advantages coming from involving quantum systems in machine learning are still not fully clear. Here, the authors propose a software/hardware co-design framework towards quantum-friendly neural networks showing quantum advantage, representing data as either random variables or numbers in unitary matrices.

    • Weiwen Jiang
    • , Jinjun Xiong
    •  & Yiyu Shi
  • Article
    | Open Access

    Simple lower bounds on the rates of device-independent quantum information protocols can often overestimate the power of the eavesdropping party. Here, the authors use new entropic quantities defined as semidefinite programs to improve bounds in several regimes without expensive computational resources

    • Peter Brown
    • , Hamza Fawzi
    •  & Omar Fawzi
  • Article
    | Open Access

    Phase diagrams describe how a system changes phenomenologically as an external parameter, such as a magnetic field strength, is varied. Here, the authors prove that in general such a phase diagram is uncomputable, by explicitly constructing a one-parameter Hamiltonian for which this is the case.

    • Johannes Bausch
    • , Toby S. Cubitt
    •  & James D. Watson
  • Article
    | Open Access

    Test, trace, and isolate programmes are central to COVID-19 control. Here, Viola Priesemann and colleagues evaluate how to allocate scarce resources to keep numbers low, and find that if case numbers exceed test, trace and isolate capacity, there will be a self-accelerating spread.

    • Sebastian Contreras
    • , Jonas Dehning
    •  & Viola Priesemann
  • Article
    | Open Access

    Mendelian randomization is a popular method to detect causal relationships between traits, but can be confounded by instances of horizontal pleiotropy. Here, the authors present a Mendelian randomization workflow which includes causal discovery analysis and filtering of genetic instruments based on their conditional independencies.

    • David Amar
    • , Nasa Sinnott-Armstrong
    •  & Manuel A. Rivas
  • Article
    | Open Access

    Aberrant synchronous oscillations have been associated with numerous brain disorders, including essential tremor. The authors show that synchronous cerebellar activity can casually affect essential tremor and that its underlying mechanism may be related to the temporal coherence of the tremulous movement.

    • Sebastian R. Schreglmann
    • , David Wang
    •  & Nir Grossman
  • Article
    | Open Access

    Metallization of pure hydrogen via overlapping of electronic bands requires high pressure above 3 Mbar. Here the authors study the Ba-H system and discover a unique superhydride BaH12 that contains molecular hydrogen, which demonstrates metallic properties and superconductivity below 1.5 Mbar.

    • Wuhao Chen
    • , Dmitrii V. Semenok
    •  & Tian Cui
  • Article
    | Open Access

    As spiteful behaviors harm both the actor and the target, it is challenging to understand how these behaviors could be adaptive. Here Fulker et al. show that spite can be favored by feedbacks with network structure that create correlated and anti-correlated behavioral interactions simultaneously.

    • Zachary Fulker
    • , Patrick Forber
    •  & Christoph Riedl
  • Article
    | Open Access

    Lack of a widespread surveillance network hampers accurate infectious disease forecasting. Here the authors provide a framework to optimize the selection of surveillance site locations and show that accurate forecasting of respiratory diseases for locations without surveillance is feasible.

    • Sen Pei
    • , Xian Teng
    •  & Jeffrey Shaman
  • Article
    | Open Access

    Digital trace data from search engines lacks information about the experiences of the individuals generating the data. Here the authors link search data and human computation to build a tracking model of influenza-like illness.

    • Stefan Wojcik
    • , Avleen S. Bijral
    •  & David Lazer
  • Article
    | Open Access

    Influencer networks include a small set of highly-connected nodes and can reach synchrony only via strong node interaction. Tönjes et al. show that introducing an optimal amount of noise enhances synchronization of such networks, which may be relevant for neuroscience or opinion dynamics applications.

    • Ralf Tönjes
    • , Carlos E. Fiore
    •  & Tiago Pereira
  • Article
    | Open Access

    Standard benchmarking of single-molecule localization microscopy cannot quantify nanoscale accuracy of arbitrary datasets. Here, the authors present Wasserstein-induced flux, a method using a chosen perturbation and knowledge of the imaging system to measure confidence of individual localizations.

    • Hesam Mazidi
    • , Tianben Ding
    •  & Matthew D. Lew
  • Article
    | Open Access

    Current inequality and market consumption modelling appears to be subjective. Here the authors combined all three axes of poverty modelling - Engel-Krishnakumar’s microeconomics, Aoki-Chattopadhyay’s mathematical precept and found that multivariate construction is a key component of economic data analysis, implying all modes of income and expenditure need to be considered to arrive at a proper weighted prediction of poverty.

    • Amit K. Chattopadhyay
    • , T. Krishna Kumar
    •  & Iain Rice
  • Article
    | Open Access

    The authors present a passive meta-neural-network for real-time recognition of objects by analysis of acoustic scattering. It consists of unit cells termed meta-neurons, mimicking an analogous neural network for classical waves, and is shown to recognise handwritten digits and misaligned orbital-angular-momentum vortices.

    • Jingkai Weng
    • , Yujiang Ding
    •  & Jianchun Cheng
  • Article
    | Open Access

    Accurate cell detection in dense bacterial biofilms is challenging. Here, the authors report an image analysis pipeline that is able to accurately segment and classify single bacterial cells in 3D fluorescence images: Bacterial Cell Morphometry 3D (BCM3D).

    • Mingxing Zhang
    • , Ji Zhang
    •  & Andreas Gahlmann
  • Article
    | Open Access

    The presence of confounding effects is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Here, the authors introduce an end-to-end approach for deriving features invariant to confounding factors as inputs to prediction models.

    • Qingyu Zhao
    • , Ehsan Adeli
    •  & Kilian M. Pohl
  • Article
    | Open Access

    Large volumes of true random numbers are needed for increasing requirements of secure data encryption. Here the authors use the stochastic nature of DNA synthesis to obtain millions of gigabytes of unbiased randomness.

    • Linda C. Meiser
    • , Julian Koch
    •  & Robert N. Grass
  • Article
    | Open Access

    Whether a turbulent flow would inevitably develop singular behavior at the smallest length scales is an ongoing intriguing debate. Using large-scale numerical simulations, Buaria et al. find an unexpected non-linear mechanism which counteracts local vorticity growth instead of enabling it.

    • Dhawal Buaria
    • , Alain Pumir
    •  & Eberhard Bodenschatz