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| 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
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Article
| Open AccessA spectral method for assessing and combining multiple data visualizations
Dimension reduction is an indispensable part of modern data science, and many algorithms have been developed. Here, the authors develop a theoretically justified, simple to use and reliable spectral method to assess and combine multiple dimension reduction visualizations of a given dataset from diverse algorithms.
- Rong Ma
- , Eric D. Sun
- & James Zou
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Article
| Open AccessSearching for spin glass ground states through deep reinforcement learning
Finding the ground states of spin glasses relevant for disordered magnets and many other physical systems is computationally challenging. The authors propose here a deep reinforcement learning framework for calculating the ground states, which can be trained on small-scale spin glass instances and then applied to arbitrarily large ones.
- Changjun Fan
- , Mutian Shen
- & Yang-Yu Liu
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Article
| Open AccessCartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data
Existing genomic data analysis methods tend to not take full advantage of underlying biological characteristics. Here, the authors leverage the inherent interactions of scRNA-seq data and develop a cartography strategy to contrive the data into a spatially configured genomap for accurate deep pattern discovery.
- Md Tauhidul Islam
- & Lei Xing
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Article
| Open AccessQuantifying the direct and indirect protection provided by insecticide treated bed nets against malaria
Long lasting insecticide treated mosquito nets (LLINs) provide protection from malaria through both direct effects to the user and indirect community-level effects. Here, the authors use mathematical modelling to assess the relative contributions of these effects under different insecticide resistance and LLIN usage scenarios.
- H. Juliette T. Unwin
- , Ellie Sherrard-Smith
- & Azra C. Ghani
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Article
| Open AccessLearning local equivariant representations for large-scale atomistic dynamics
The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an E(3)- equivariant neural network architecture that combines the high accuracy of equivariant neural networks with the scalability of local methods.
- Albert Musaelian
- , Simon Batzner
- & Boris Kozinsky
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Article
| Open AccessUniversal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
Rigorous results about the real computational advantages of quantum machine learning are few. Here, the authors prove that a PROMISEBQP-complete problem can be expressed by variational quantum classifiers and quantum support vector machines, meaning that a quantum advantage can be achieved for all ML classification problems that cannot be classically solved in polynomial time.
- Jonas Jäger
- & Roman V. Krems
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Article
| Open AccessQuantum machine learning beyond kernel methods
Comparing the capabilities of different quantum machine learning protocols is difficult. Here, the authors show that different learning models based on parametrized quantum circuits can all be seen as quantum linear models, thus driving general conclusions on their resource requirements and capabilities.
- Sofiene Jerbi
- , Lukas J. Fiderer
- & Vedran Dunjko
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Article
| Open AccessDigital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling
The complementarity of acids and bases is a fundamental chemical concept. Here, the authors use simple acid-base chemistry to encode binary information and perform information processing including digital circuits and neural networks using robotic fluid handling.
- Ahmed A. Agiza
- , Kady Oakley
- & Sherief Reda
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Article
| Open AccessTopological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
A major challenge in analyzing scRNA-seq data arises from challenges related to dimensionality and the prevalence of dropout events. Here the authors develop a deep graph learning method called scMGCA based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments, outperforming other state-of-the-art models across multiple platforms.
- Zhuohan Yu
- , Yanchi Su
- & Xiangtao Li
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Article
| Open AccessscMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
Many methods for single cell data integration have been developed, though mosaic integration remains challenging. Here the authors present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches, that jointly learns cell representations and marker features across modalities for different cell clusters, to interpret the cell clusters from different modalities.
- Ziqi Zhang
- , Haoran Sun
- & Xiuwei Zhang
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Article
| Open AccessImmune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial
Authors have previously reported on the efficacy and safety of the recombinant spike protein nanoparticle vaccine, NVX-CoV2373, in healthy adults. In this work, they assess anti-spike binding IgG, anti-RBD binding IgG and neutralising antibody titer as correlates of risk and protection against COVID-19.
- Youyi Fong
- , Yunda Huang
- & Peter B. Gilbert
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Article
| Open AccessImpact of the Euro 2020 championship on the spread of COVID-19
In this Bayesian inference study, the authors aim to quantify the impact of the men’s 2020 UEFA Euro Football Championship on COVID-19 spread in twelve participating countries. They estimate that 0.84 million cases and 1,700 deaths were attributable to the championship, with most impacts in England and Scotland.
- Jonas Dehning
- , Sebastian B. Mohr
- & Viola Priesemann
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Article
| Open AccessControllable branching of robust response patterns in nonlinear mechanical resonators
Feedback control applied to mechanical resonators can lead to the formation of various complex dynamic behaviors. Here the authors demonstrate flexible and controllable switching between dynamical structures in the response of harmonically driven micro-mechanical resonators.
- Axel M. Eriksson
- , Oriel Shoshani
- & David A. Czaplewski
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Article
| Open AccessLeveraging molecular structure and bioactivity with chemical language models for de novo drug design
Generative Deep Learning holds promise for mining the unexplored “chemical universe” for new drugs. Here, the authors demonstrate the de novo design of phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors for the PI3K/Akt pathway in human tumor cells.
- Michael Moret
- , Irene Pachon Angona
- & Gisbert Schneider
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Article
| Open AccessDirect and indirect effects of the COVID-19 pandemic on mortality in Switzerland
COVID-19-releated public health measures may have indirectly impacted mortality rates by causing or averting deaths. Here, the authors use data from Switzerland until April 2022 and estimate that, after accounting for deaths directly related to COVID-19, mortality was lower than expected, indicating some evidence of an overall positive impact of control measures.
- Julien Riou
- , Anthony Hauser
- & Garyfallos Konstantinoudis
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Article
| Open AccessHigh-order tensor flow processing using integrated photonic circuits
Convolutional operation is a very efficient way to handle tensor analytics, but it consumes a large quantity of additional memory. Here, the authors demonstrate an integrated photonic tensor processor which directly handles high-order tensors without tensor-matrix transformation.
- Shaofu Xu
- , Jing Wang
- & Weiwen Zou
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Article
| Open AccessPhysical deep learning with biologically inspired training method: gradient-free approach for physical hardware
Traditional learning procedures for artificial intelligence rely on digital methods not suitable for physical hardware. Here, Nakajima et al. demonstrate gradient-free physical deep learning by augmenting a biologically inspired algorithm, accelerating the computation speed on optoelectronic hardware.
- Mitsumasa Nakajima
- , Katsuma Inoue
- & Kohei Nakajima
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Matters Arising
| Open AccessReply to: A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis
- Kip D. Zimmerman
- , Ciaran Evans
- & Carl D. Langefeld
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Matters Arising
| Open AccessA balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis
- Alan E. Murphy
- & Nathan G. Skene
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Article
| Open AccessSeismic multi-hazard and impact estimation via causal inference from satellite imagery
This study presents the first rapid seismic multi-hazard and impact estimation system integrating advanced causal inference and remote sensing techniques, which jointly estimates regional-scale and high-resolution maps of seismic multi-hazards and building damage from InSAR imageries.
- Susu Xu
- , Joshua Dimasaka
- & Hae Young Noh
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Article
| Open AccessSleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks
Artificial neural networks are known to perform well on recently learned tasks, at the same time forgetting previously learned ones. The authors propose an unsupervised sleep replay algorithm to recover old tasks synaptic connectivity that may have been damaged after new task training.
- Timothy Tadros
- , Giri P. Krishnan
- & Maxim Bazhenov
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| Open AccessA Multifaceted benchmarking of synthetic electronic health record generation models
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a set of utility and privacy metrics.
- Chao Yan
- , Yao Yan
- & Bradley A. Malin
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Article
| Open AccessNegotiation and honesty in artificial intelligence methods for the board game of Diplomacy
Artificial Intelligence has achieved success in a variety of single-player or competitive two-player games with no communication between players. Here, the authors propose an approach where Artificial Intelligence agents have ability to negotiate and form agreements, playing the board game Diplomacy.
- János Kramár
- , Tom Eccles
- & Yoram Bachrach
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Article
| Open AccessFederated learning enables big data for rare cancer boundary detection
Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.
- Sarthak Pati
- , Ujjwal Baid
- & Spyridon Bakas
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Article
| Open AccessPrecise atom manipulation through deep reinforcement learning
Engineering quantum states requires precise manipulations at the atomic level. Here, the authors use deep reinforcement learning to manipulate Ag adatoms on Ag surfaces, which combined with path planning algorithms enables autonomous atomic assembly.
- I-Ju Chen
- , Markus Aapro
- & Adam S. Foster
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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
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Article
| Open AccessDeciphering clinical abbreviations with a privacy protecting machine learning system
Patient notes contain shorthand and abbreviations that may be jargon or clinical vernacular. Here the authors train large machine learning models on public web data to decode such text by replacing abbreviations with their meanings.
- Alvin Rajkomar
- , Eric Loreaux
- & Juraj Gottweis
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Article
| Open AccessVisual motion perception as online hierarchical inference
How the human visual system leverages the rich structure in object motion for perception remains unclear. Here, Bill et al. propose a theory of how the brain could infer motion relations in real time and offer a unifying explanation for various perceptual phenomena.
- Johannes Bill
- , Samuel J. Gershman
- & Jan Drugowitsch
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Article
| Open AccessGhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies
The authors present GhostKnockoff, a method for genome-wide association studies which can be applied to enhance existing and future studies to identify functional variants with weaker statistical effects that might be missed by conventional association tests.
- Zihuai He
- , Linxi Liu
- & Iuliana Ionita-Laza
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Article
| Open AccessImperceptible, designable, and scalable braided electronic cord
Inspired by the characteristics of textile-based flexible electronic sensors, the authors report a braided electronic cord with a low-cost, and automated fabrication to realize imperceptible, designable, and scalable user interfaces with the features of user-friendliness, excellent durability and rich interaction mode.
- Min Chen
- , Jingyu Ouyang
- & Guangming Tao
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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
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Article
| Open AccessMapping global dynamics of benchmark creation and saturation in artificial intelligence
Recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, the authors introduce methodologies for creating condensed maps of the global dynamics of benchmark.
- Simon Ott
- , Adriano Barbosa-Silva
- & Matthias Samwald
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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
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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
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Article
| Open AccessDeep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
Traditional bulk sequencing data lack information about cell-type-specific gene expression. Here, the authors develop a Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq, and apply it to analyze the cell type fractions and cell-type-specific gene expression in clinical data.
- Yanshuo Chen
- , Yixuan Wang
- & Yu Li
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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
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Article
| Open AccessUncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
Safe clinical deployment of deep learning models for digital pathology requires reliable estimates of predictive uncertainty. Here the authors describe an algorithm for quantifying whole-slide image uncertainty, demonstrating their approach with models trained to distinguish lung cancer subtypes.
- James M. Dolezal
- , Andrew Srisuwananukorn
- & Alexander T. Pearson
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Article
| Open AccessFeature-based volumetric defect classification in metal additive manufacturing
Additively manufactured materials contain different types of volumetric defects. Here, the authors utilize the most distinguishing morphological features among different defect types to propose a defect classification methodology.
- Arun Poudel
- , Mohammad Salman Yasin
- & Nima Shamsaei
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Article
| Open AccessSingle-shot quantum error correction with the three-dimensional subsystem toric code
Topological quantum error correction is a promising approach towards fault-tolerant quantum computing, but suffers from large time overhead. Here, the authors generalise the stabiliser toric code to a single-shot 3D subsystem toric code, featuring good performance and resilience to measurement errors.
- Aleksander Kubica
- & Michael Vasmer
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Article
| Open AccessTechnology readiness levels for machine learning systems
The development of machine learning systems has to ensure their robustness and reliability. The authors introduce a framework that defines a principled process of machine learning system formation, from research to production, for various domains and data scenarios.
- Alexander Lavin
- , Ciarán M. Gilligan-Lee
- & Yarin Gal
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Article
| Open AccessFlexible learning of quantum states with generative query neural networks
The use of machine learning to characterise quantum states has been demonstrated, but usually training the algorithm using data from the same state one wants to characterise. Here, the authors show an algorithm that can learn all states that share structural similarities with the ones used for the training.
- Yan Zhu
- , Ya-Dong Wu
- & Giulio Chiribella
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Article
| Open AccessGenetic model of the El Laco magnetite-apatite deposits by extrusion of iron-rich melt
Can volcanoes erupt ore deposits? This study combines observations, experiments, and simulations to show that iron ore deposits on El Laco volcano formed by eruption of melt sourced from separation of Fe-rich melt from a silicate magma body beneath.
- Tobias Keller
- , Fernando Tornos
- & Jenny Suckale
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Article
| Open AccessDetecting the ultra low dimensionality of real networks
Reducing of dimension is often necessary to detect and analyze patterns in large datasets and complex networks. Here, the authors propose a method for detection of the intrinsic dimensionality of high-dimensional networks to reproduce their complex structure using a reduced tractable geometric representation.
- Pedro Almagro
- , Marián Boguñá
- & M. Ángeles Serrano
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Article
| Open AccessDiffraction-engineered holography: Beyond the depth representation limit of holographic displays
Improving the image depth perception of holograms while maintaining high image quality is a current challenge. Here the authors propose an efficient solution relying on a multi-plane hologram technique that reconstruct different blurred images and sharply focused images depending on a propagation distance.
- Daeho Yang
- , Wontaek Seo
- & Hong-Seok Lee
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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
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Article
| Open AccessA neural theory for counting memories
It is unclear how the brain keeps track of the number of times different events are experienced. Here, a neural circuit is proposed for this problem inspired by a classic solution in computer science, and evidence of this circuit is shown in the fruit fly brain.
- Sanjoy Dasgupta
- , Daisuke Hattori
- & Saket Navlakha
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Article
| Open AccessNoise-injected analog Ising machines enable ultrafast statistical sampling and machine learning
Ising machines are accelerators for computing difficult optimization problems. In this work, Böhm et al. demonstrate a method that extends their use to perform statistical sampling and machine learning orders-of-magnitudes faster than digital computers.
- Fabian Böhm
- , Diego Alonso-Urquijo
- & Guy Van der Sande
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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