Featured
-
-
Article
| Open AccessCharting cellular differentiation trajectories with Ricci flow
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 AccessPredicting multiple observations in complex systems through low-dimensional embeddings
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 AccessUnderstanding the infection severity and epidemiological characteristics of mpox in the UK
Mpox cases without known travel links to endemic countries began to be detected in the UK in mid-2022. In this study, the authors characterise the severity of mpox cases in the UK and estimate the overall infection hospitalisation risk at ~4%.
- Thomas Ward
- , Christopher E. Overton
- & Martyn Fyles
-
Perspective
| Open AccessEmerging opportunities and challenges for the future of reservoir computing
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 AccessLearning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks
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 AccessSequential stacking link prediction algorithms for temporal networks
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 AccessAnticipating regime shifts by mixing early warning signals from different nodes
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 AccessLearning low-rank latent mesoscale structures in networks
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 AccessHigh monoclonal neutralization titers reduced breakthrough HIV-1 viral loads in the Antibody Mediated Prevention trials
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 AccessEarly warning signals have limited applicability to empirical lake data
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 AccessAll electromagnetic scattering bodies are matrix-valued oscillators
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 AccessThe D-Mercator method for the multidimensional hyperbolic embedding of real networks
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
-
Article
| Open AccessImitation dynamics on networks with incomplete information
Studies of the evolution of cooperation often assume information use that is inconsistent with empirical observations. Here, the authors’ research on general imitation dynamics reveals that cooperation is fostered by individuals using less personal information and more social information.
- Xiaochen Wang
- , Lei Zhou
- & Aming Li
-
Article
| Open AccessImpact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia
In this study, the authors develop a mathematical modelling framework to estimate the impacts of non-pharmaceutical interventions and vaccination on COVID-19 incidence. The model accounts for changes in SARS-CoV-2 variant and population immunity, and here they use it to investigate epidemic dynamics in French Polynesia.
- Lloyd A. C. Chapman
- , Maite Aubry
- & Adam J. Kucharski
-
Article
| Open AccessConstructing temporal networks with bursty activity patterns
Many real-world systems are characterized by bursty dynamics with interchanging periods of intense activity and quiescence. The authors propose a method to construct temporal networks that match a given activity pattern, and apply it to empirical bursty patterns.
- Anzhi Sheng
- , Qi Su
- & Joshua B. Plotkin
-
Article
| Open AccessReactivity of complex communities can be more important than stability
Ecosystems must be able to bounce back from perturbations to persist without species extinctions. This study uses theoretical modelling to show the importance of reactivity—how species respond in the short term to perturbations—for assessing the health of complex ecosystems, revealing that it can be a better predictor of extinction risk than stability.
- Yuguang Yang
- , Katharine Z. Coyte
- & Aming Li
-
Article
| Open AccessMutant fixation in the presence of a natural enemy
Studies on mutant invasion typically assume populations in isolation, rather than part of an ecological community. Here, the authors use computational models to investigate how enemy-victim interactions influence properties of mutant invasion, showing that selection is substantially weakened.
- Dominik Wodarz
- & Natalia L. Komarova
-
Article
| Open AccessPredicting discrete-time bifurcations with deep learning
Critical transitions and qualitative changes of dynamics in cardiac, ecological, and economical systems, can be characterized by discrete-time bifurcations. The authors propose a deep learning framework that provides early warning signals for critical transitions in discrete-time experimental data.
- Thomas M. Bury
- , Daniel Dylewsky
- & Gil Bub
-
Article
| Open AccessThe reaction-diffusion basis of animated patterns in eukaryotic flagella
In 1952, Turing unlocked the reaction-diffusion basis of natural patterns, such as zebra stripes. The authors propose a reaction-diffusion model that recreates characteristics of the flagellar waveform for bull sperm and Chlamydomonas flagella.
- James F. Cass
- & Hermes Bloomfield-Gadêlha
-
Article
| Open AccessMultimaterial fiber as a physical simulator of a capillary instability
Capillary breakup in multimaterial fibers is explored for the self-assembly of optoelectronic systems. However, its insights primarily stem from numerical simulations, qualitative at best. The authors formulate an analytical model of such breakup, obtaining a window in the governing parameters where the generally chaotic breakup becomes predictable and thus engineerable.
- Camila Faccini de Lima
- , Fan Wang
- & Alexander Gumennik
-
Article
| Open AccessHardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing promises efficient DNN inference acceleration but suffers from nonidealities. Here, hardware-aware training methods are improved so that various larger DNNs of diverse topologies nevertheless achieve iso-accuracy.
- Malte J. Rasch
- , Charles Mackin
- & Vijay Narayanan
-
Article
| Open AccessUniversal patterns in egocentric communication networks
Personal communication networks through mobile phones and online platforms can be characterized by patterns of tie strengths. The authors propose a model to explain driving mechanisms of emerging tie strength heterogeneity in social networks, observing similarity of patterns across various datasets.
- Gerardo Iñiguez
- , Sara Heydari
- & Jari Saramäki
-
Article
| Open AccessSequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule
Prediction of future inputs is a key computational task for the brain. Here, the authors proposed a predictive learning rule in neurons that leads to anticipation and recall of inputs, and that reproduces experimentally observed STDP phenomena.
- Matteo Saponati
- & Martin Vinck
-
Article
| Open AccessCOMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data
Understanding the evolution of a tumor is important for predicting its resistance to treatment. This paper presents a new computational method, COMPASS, for inferring the joint phylogeny of single nucleotide variants and copy number alterations from targeted scDNAseq data.
- Etienne Sollier
- , Jack Kuipers
- & Katharina Jahn
-
Article
| Open AccessA general model-based causal inference method overcomes the curse of synchrony and indirect effect
Traditional causal inference methods struggle to distinguish direct causation from synchrony and indirect effects. Here, authors present GOBI that overcomes this by testing a general model’s ability to reproduce data, providing accurate and broadly applicable causality inference for complex systems.
- Se Ho Park
- , Seokmin Ha
- & Jae Kyoung Kim
-
Article
| Open AccessThe effect of environmental information on evolution of cooperation in stochastic games
In stochastic games, there is a feedback loop between a group’s social behaviors and its environment. Kleshnina et al. show that groups are often more cooperative when they know the exact state of their environment, although there are also intriguing cases when ignorance is beneficial.
- Maria Kleshnina
- , Christian Hilbe
- & Martin A. Nowak
-
Article
| Open AccessWaves traveling over a map of visual space can ignite short-term predictions of sensory input
Waves of neural activity travel across single regions in the visual cortex, but their computational role is unclear. Here, the authors present a neural network model demonstrating that waves traveling over retinotopic maps can enable short-term predictions of future inputs.
- Gabriel B. Benigno
- , Roberto C. Budzinski
- & Lyle Muller
-
Article
| Open AccessInflationary theory of branching morphogenesis in the mouse salivary gland
The authors show that the ramified ductal network of the mouse salivary gland develops from a set of simple probabilistic rules, where ductal elongation and branching are driven by the persistent expansion of the surrounding tissue.
- Ignacio Bordeu
- , Lemonia Chatzeli
- & Benjamin D. Simons
-
Article
| Open AccessNon-line-of-sight imaging with arbitrary illumination and detection pattern
The authors propose a confocal complemented signal-object collaborative regularization method for non-line-of-sight (NLOS) imaging without specific requirements on the spatial pattern of measurement points. The method extends the application range of NLOS imaging.
- Xintong Liu
- , Jianyu Wang
- & Lingyun Qiu
-
Article
| Open AccessBifurcation behaviors shape how continuous physical dynamics solves discrete Ising optimization
Physical and physics-inspired computation is emerging as a new paradigm for tackling hard optimization problems. In this work, the authors establish rigorous mathematical conditions together with new design principles for physical as well as simulated dynamical systems to solve general Ising models.
- Juntao Wang
- , Daniel Ebler
- & Jie Sun
-
Article
| Open AccessSpatial immunization to abate disease spreading in transportation hubs
Efficient spatial targeting of interventions could reduce the spread of infections in transportation hubs. Here, the authors assess the optimal locations to target in Heathrow airport using disease transmission models informed by a contact network based on anonymised location data from 200,000 individuals.
- Mattia Mazzoli
- , Riccardo Gallotti
- & José J. Ramasco
-
Article
| Open AccessMultistability, intermittency, and hybrid transitions in social contagion models on hypergraphs
Social interactions often occur in groups of individuals, which can be mathematically represented as hypergraphs. In this study, the authors analyze the appearance of multistability, intermittency, and hybrid phase transitions in social contagion models on hypergraphs.
- Guilherme Ferraz de Arruda
- , Giovanni Petri
- & Yamir Moreno
-
Article
| Open AccessThe dynamic nature of percolation on networks with triadic interactions
Triadic interactions are higher-order interactions relevant to many real complex systems. The authors develop a percolation theory for networks with triadic interactions and identify basic mechanisms for observing dynamical changes of the giant component such as the ones occurring in neuronal and climate networks.
- Hanlin Sun
- , Filippo Radicchi
- & Ginestra Bianconi
-
Article
| Open AccessComplex-tensor theory of simple smectics
As lamellar materials, smectics exhibit both liquid and solid characteristics, making them difficult to model at the mesoscale. Paget et al. propose a complex tensor order parameter that reflects the smectic symmetries, capable of describing complex defects including dislocations and disclinations.
- Jack Paget
- , Marco G. Mazza
- & Tyler N. Shendruk
-
Article
| Open AccessDiverse behaviors in non-uniform chiral and non-chiral swarmalators
Populations of swarming coupled oscillators with inhomogeneous natural frequencies and chirality are relevant for active matter systems and micro-robotics. The authors model and analyze a variety of their self-organized behaviors that mimic natural and artificial micro-scale collective systems.
- Steven Ceron
- , Kevin O’Keeffe
- & Kirstin Petersen
-
Article
| Open AccessUniversal non-monotonic drainage in large bare viscous bubbles
Bubbles at an air-liquid interface will rupture when their spherical cap becomes sufficiently drained. It is now shown that the film thickness of large bare viscous bubbles is highly non-uniformly distributed, and that a bubble’s thickness profile relates to its drainage velocity.
- Casey Bartlett
- , Alexandros T. Oratis
- & James C. Bird
-
Article
| Open AccessDual communities in spatial networks
Here the authors introduce dual communities, characterized by strong connections at their boundaries, and show that they are formed as a trade-off between efficiency and resilience in supply networks.
- Franz Kaiser
- , Philipp C. Böttcher
- & Dirk Witthaut
-
Article
| Open AccessAn AI approach for managing financial systemic risk via bank bailouts by taxpayers
Systemic risk and bank bailout approaches have been the source of discussions on scientific, financial and governmental forums. An artificial intelligence technique is proposed to inform equitable bailout decisions that minimise taxpayers’ losses.
- Daniele Petrone
- , Neofytos Rodosthenous
- & Vito Latora
-
Perspective
| Open AccessStatistical inference links data and theory in network science
Theoretical models and structures recovered from measured data serve for analysis of complex networks. The authors discuss here existing gaps between theoretical methods and real-world applied networks, and potential ways to improve the interplay between theory and applications.
- Leto Peel
- , Tiago P. Peixoto
- & Manlio De Domenico
-
Article
| Open AccessDevelopment and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients
Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for data drifts. Here, the authors develop a framework for continuously monitoring and updating prognostic models and applied it to predict 28-day survival in COVID-19 patients.
- Todd J. Levy
- , Kevin Coppa
- & Theodoros P. Zanos
-
Article
| Open AccessBayesian deep learning for error estimation in the analysis of anomalous diffusion
Diffusive motions in complex environments such as living biological cells or soft matter systems can be analyzed with single-particle-tracking approaches, where accuracy of output may vary. The authors involve a machine-learning technique for decoding anomalous-diffusion data and provide an uncertainty estimate together with predicted output.
- Henrik Seckler
- & Ralf Metzler
-
Article
| Open AccessHelheim Glacier ice velocity variability responds to runoff and terminus position change at different timescales
Factors driving ice flow variability in Greenland vary by timescale. At seasonal scale, Helheim Glacier ice velocity responds most strongly to meltwater runoff. Glacier terminus position drives velocity variability at longer time scales.
- Lizz Ultee
- , Denis Felikson
- & Bryan Riel
-
Article
| Open AccessQuantifying ethnic segregation in cities through random walks
Socioeconomic segregation is one of the main factors behind large-scale inequalities in urban areas and its characterisation remains challenging. The authors propose a family of non-parametric measures to quantify spatial heterogeneity through diffusion, and show how this relates to segregation and deprivation
- Sandro Sousa
- & Vincenzo Nicosia
-
Article
| Open AccessComparison of the 2021 COVID-19 roadmap projections against public health data in England
The ’Roadmap’ for relaxation of COVID-19 restrictions in England in 2021 was informed by mathematical modelling. Here, the authors perform a retrospective assessment of the accuracy of modelling predictions and identify the main sources of uncertainty that led to observed values deviating from projections.
- Matt J. Keeling
- , Louise Dyson
- & Samuel Moore
-
Article
| Open AccessImpedance-based forecasting of lithium-ion battery performance amid uneven usage
Accurate forecasts of lithium-ion battery performance will ease concerns about the reliability of electric vehicles. Here, the authors leverage electrochemical impedance spectroscopy and machine learning to show that future capacity can be predicted amid uneven use, with no historical data requirement.
- Penelope K. Jones
- , Ulrich Stimming
- & Alpha A. Lee
-
Article
| Open AccessFunctional control of oscillator networks
In network systems governed by oscillatory activity, such as brain networks or power grids, configurations of synchrony may define network functions. The authors introduce a control approach for the formation of desired synchrony patterns through optimal interventions on the network parameters.
- Tommaso Menara
- , Giacomo Baggio
- & Fabio Pasqualetti
-
Article
| Open AccessNuclear speed and cycle length co-vary with local density during syncytial blastoderm formation in a cricket
Early in insect embryo development, many nuclei share one large cell, travel varied paths and self-organize into a single layer. Donoughe et al. illuminate this process with live-imaging, modeling, and experimental changes to the embryo’s shape.
- Seth Donoughe
- , Jordan Hoffmann
- & Cassandra G. Extavour
-
Article
| Open AccessOptimised weight programming for analogue memory-based deep neural networks
Device-level complexity represents a big shortcoming for the hardware realization of analogue memory-based deep neural networks. Mackin et al. report a generalized computational framework, translating software-trained weights into analogue hardware weights, to minimise inference accuracy degradation.
- Charles Mackin
- , Malte J. Rasch
- & Geoffrey W. Burr
-
Article
| Open AccessLearning emergent partial differential equations in a learned emergent space
Machine learning tools allow to extract dynamical systems from data, however this problem remains challenging for networks and systems of interacting agents. The authors introduce an approach to learn a predictive model for the dynamics of coupled agents in the form of partial differential equations using emergent spatial embeddings.
- Felix P. Kemeth
- , Tom Bertalan
- & Ioannis G. Kevrekidis