Mathematics and computing articles within Nature Communications

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  • Article
    | Open Access

    Wavefront shaping is used to overcome scattering in biological tissues during imaging, but determining the compensation is slow. Here, the authors use holographic phase stepping interferometry, where new phase information is updated after each measurement, enabling fast improvement of the wavefront correction.

    • Molly A. May
    • , Nicolas Barré
    •  & Alexander Jesacher

  • Article
    | Open Access

    In many machine learning applications, one uses pre-trained neural networks, having limited access to training and test data. Martin et al. show how to predict trends in the quality of such neural networks without access to this information, relevant for reproducibility, diagnostics, and validation.

    • Charles H. Martin
    • , Tongsu (Serena) Peng
    •  & Michael W. Mahoney

  • Article
    | Open Access

    Mechanisms of cluster formation in networks with directed links differ from those in undirected networks. Lodi et al. propose a method to compute interdependencies among clusters of nodes in directed networks. They show that clusters can be one-way dependent, as found in social and neural networks.

    • Matteo Lodi
    • , Francesco Sorrentino
    •  & Marco Storace

  • Article
    | Open Access

    Population structure can influence the probability of and time to fixation of new mutants. Here, Tkadlec et al. demonstrate mathematically that structures that increase fixation probability necessarily slow fixation, but also identify amplifying structures with minimal reductions in fixation time.

    • Josef Tkadlec
    • , Andreas Pavlogiannis
    •  & Martin A. Nowak

  • Article
    | Open Access

    The authors present a microwave imaging system that can operate in continuous transmit-receive mode. Using an array of transmitters, a single receiver and a reconstruction matrix that correlate random time patterns with the captured signal, they demonstrate real-time imaging and tracking through a wall.

    • Fabio C. S. da Silva
    • , Anthony B. Kos
    •  & Archita Hati

  • Article
    | Open Access

    Physical principles underlying machine learning analysis of quantum gas microscopy data are not well understood. Here the authors develop a neural network based approach to classify image data in terms of multi-site correlation functions and reveal the role of fourth-order correlations in the Fermi-Hubbard model.

    • Cole Miles
    • , Annabelle Bohrdt
    •  & Eun-Ah Kim

  • Article
    | Open Access

    Networks describe the intricate patterns of interaction occurring within ecological systems, but they are unfortunately difficult to construct from data. Here, the authors show how Bayesian statistical techniques can separate structure from noise in networks gathered in observational studies of plant-pollinator systems.

    • Jean-Gabriel Young
    • , Fernanda S. Valdovinos
    •  & M. E. J. Newman

  • Article
    | Open Access

    In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower costs. Here, the authors present the flow diverter performance assessment in-silico trial, which models the treatment of intracranial aneurysms with a flow-diverting stent.

    • Ali Sarrami-Foroushani
    • , Toni Lassila
    •  & Alejandro F. Frangi

  • Article
    | Open Access

    Network embedding is a machine learning technique for construction of low-dimensional representations of large networks. Gu et al. propose a method for the identification of an optimal embedding dimension for the encoding of network structural information inspired by natural language processing.

    • Weiwei Gu
    • , Aditya Tandon
    •  & Filippo Radicchi

  • Article
    | Open Access

    Rapid, accurate and specific point-of-care diagnostics can help manage and contain fast-spreading infections. Here, the authors present a nanopore-based system that uses artificial intelligence to discriminate between four coronaviruses in saliva, with little need for sample pre-processing.

    • Masateru Taniguchi
    • , Shohei Minami
    •  & Kazunori Tomono

  • Article
    | Open Access

    Gravity waves are observed in Venus atmosphere, but their characteristics are not well-known. Here, the authors show spontaneous generation of gravity waves from the thermal tides in the Venus atmosphere as small-scale gravity waves are resolved in high-resolution general circulation model.

    • Norihiko Sugimoto
    • , Yukiko Fujisawa
    •  & Yoshi-Yuki Hayashi

  • Article
    | Open Access

    Osimertinib and dacomitinib are approved as first-line treatment of EGFR-mutant NSCLC but resistance can arise. Here, the authors use a computational model to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy that was confirmed tolerable and effective in an ongoing phase I clinical trial.

    • Kamrine E. Poels
    • , Adam J. Schoenfeld
    •  & Franziska Michor

  • Article
    | Open Access

    Mobility restrictions implemented to reduce the spread of COVID-19 have significantly impacted walking behavior. In this study, the authors integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 US metropolitan areas.

    • Ruth F. Hunter
    • , Leandro Garcia
    •  & Esteban Moro

  • Article
    | Open Access

    Despite the consensus that mass vaccination against SARS-CoV-2 will ultimately end the pandemic, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. Here, the authors investigate relaxation scenarios using an age-structured transmission model that has been fitted to data for Portugal.

    • João Viana
    • , Christiaan H. van Dorp
    •  & Ganna Rozhnova

  • Article
    | Open Access

    Fluctuations in ecosystems and other large dynamical systems are driven by intrinsic and extrinsic noise and contain hidden information which is difficult to extract. Here, the authors derive analytical characterizations of fluctuations in random interacting systems, allowing inference of network properties from time series data.

    • Yvonne Krumbeck
    • , Qian Yang
    •  & Tim Rogers

  • Article
    | Open Access

    Social interaction outcomes can depend on the type of information individuals possess and how it is used in decision-making. Here, Zhou et al. find that self-evaluation based decision-making rules lead to evolutionary outcomes that are robust to different population structures and ways of self-evaluation.

    • Lei Zhou
    • , Bin Wu
    •  & Long Wang

  • Article
    | Open Access

    Reinbold et al. propose a physics-informed data-driven approach that successfully discovers a dynamical model using high-dimensional, noisy and incomplete experimental data describing a weakly turbulent fluid flow. This approach is relevant to other non-equilibrium spatially-extended systems.

    • Patrick A. K. Reinbold
    • , Logan M. Kageorge
    •  & Roman O. Grigoriev

  • Article
    | Open Access

    Generating new sensible molecular structures is a key problem in computer aided drug discovery. Here the authors propose a graph-based molecular generative model that outperforms previously proposed graph-based generative models of molecules and performs comparably to several SMILES-based models.

    • Omar Mahmood
    • , Elman Mansimov
    •  & Kyunghyun Cho

  • Article
    | Open Access

    A single damage can lead to a complete collapse of supply networks due to a cascading failure mechanism. Kaiser et al. show that by adding new connections network isolators can be created, that can inhibit failure spreading relevant for power grids and water transmission systems.

    • Franz Kaiser
    • , Vito Latora
    •  & Dirk Witthaut

  • Article
    | Open Access

    Influenza forecasting in the United States is challenging and consequential, with the ability to improve the public health response. Here the authors show the performance of the multiscale flu forecasting model, Dante, that won the CDC’s 2018/19 national, regional and state flu forecasting challenges.

    • Dave Osthus
    •  & Kelly R. Moran

  • Article
    | Open Access

    Gene regulatory networks are a useful means of inferring functional interactions from large-scale genomic data. Here, the authors develop a Bayesian framework integrating GWAS summary statistics with gene regulatory networks to identify genetic enrichments and associations simultaneously.

    • Xiang Zhu
    • , Zhana Duren
    •  & Wing Hung Wong

  • Article
    | Open Access

    Several prognostic indices are available to predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers. Here, the authors introduce the epidemicity index, a complementary index to evaluate the potential for transient increases of SARS-Cov-2 epidemics.

    • Lorenzo Mari
    • , Renato Casagrandi
    •  & Marino Gatto

  • Article
    | Open Access

    Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.

    • Hsin-Yuan Huang
    • , Michael Broughton
    •  & Jarrod R. McClean

  • 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

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