Information technology articles within Nature Communications

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  • Article
    | Open Access

    Physical unclonable functions provide algorithm-independent cryptography based on non-distributable unique tokens. Here, the authors introduce unclonable functions based on random DNA pools, enabling secure decentralized authentication.

    • Anne M. Luescher
    • , Andreas L. Gimpel
    •  & Robert N. Grass

  • Article
    | Open Access

    In modern football games, data-driven analysis serves as a key driver in determining tactics. Wang, Veličković, Hennes et al. develop a geometric deep learning algorithm, named TacticAI, to solve high-dimensional learning tasks over corner kicks and suggest tactics favoured over existing ones 90% of the time.

    • Zhe Wang
    • , Petar Veličković
    •  & Karl Tuyls

  • Article
    | Open Access

    People will likely use ChatGPT to seek health advice. Here, the authors show promising performance of ChatGPT and open source models, but a lack of high accuracy considering medical question answering. Improvements are expected over time via domain-specific finetuning and integration of regulations.

    • Sarah Sandmann
    • , Sarah Riepenhausen
    •  & Julian Varghese

  • Article
    | Open Access

    Computing platforms based on chemical processes can be an alternative to digital computers in some scenarios but have limited programmability. Here the authors demonstrate a hybrid computing platform combining digital electronics and an oscillatory chemical reaction and demonstrate its computational capabilities.

    • Abhishek Sharma
    • , Marcus Tze-Kiat Ng
    •  & Leroy Cronin

  • 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

    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

    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

    Archiving data in synthetic DNA offers unprecedented storage density and longevity. To understand how experimental choices affect the integrity of digital data stored in DNA, the authors study the evolution of errors and bias and with a digital twin they supply tools for experimental planning and design of error-correcing codes.

    • Andreas L. Gimpel
    • , Wendelin J. Stark
    •  & Robert N. Grass

  • Article
    | Open Access

    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

  • Article
    | Open Access

    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

  • Article
    | Open Access

    One gap between the neuro-inspired computing and its applications lies in the intrinsic variability of the devices. Here, Payvand et al. suggest a technologically plausible co-design of the hardware architecture which takes into account and exploits the physics behind memristors.

    • Melika Payvand
    • , Filippo Moro
    •  & Giacomo Indiveri

  • Article
    | Open Access

    Continuous-variable QKD protocols are usually easier to implement than discrete-variables ones, but their security analyses are less developed. Here, the authors propose and demonstrate in the lab a CVQKD protocol that can generate composable keys secure against collective attacks.

    • Nitin Jain
    • , Hou-Man Chin
    •  & Ulrik L. Andersen

  • Article
    | Open Access

    Hybrid neural networks combine advantages of spiking and artificial neural networks in the context of computing and biological motivation. The authors propose a design framework with hybrid units for improved flexibility and efficiency of hybrid neural networks, and modulation of hybrid information flows.

    • Rong Zhao
    • , Zheyu Yang
    •  & Luping Shi

  • Article
    | Open Access

    Molecules offer enormous capacity for information storage. Here, the authors show that information can be encoded into molecules with sequences of paramagnetic lanthanide ions, and decoded using nuclear magnetic resonance spectroscopy.

    • Jan Kretschmer
    • , Tomáš David
    •  & Miloslav Polasek

  • Article
    | Open Access

    Several challenges still impede the deployment of optical switches in data centers. The authors report an optical switching and control system to synergistically overcome these challenges and provide enhanced performance for data center applications.

    • Xuwei Xue
    •  & Nicola Calabretta

  • Review Article
    | Open Access

    Synthetic DNA is the basis for promising technologies in data storage, barcoding, computing 62 and sercurity. In this review, the authors provide an overview of the field and its future.

    • Linda C. Meiser
    • , Bichlien H. Nguyen
    •  & Robert N. Grass

  • Article
    | Open Access

    Social media platforms moderating misinformation have been accused of political bias. Here, the authors use neutral social bots to show that, while there is no strong evidence for such a bias, the content to which Twitter users are exposed depends strongly on the political leaning of early Twitter connections.

    • Wen Chen
    • , Diogo Pacheco
    •  & Filippo Menczer

  • Article
    | Open Access

    Storage technology based on DNA is emerging as an information dense and durable medium. Here the authors use machine learning-based encoding and hybridization probes to execute similarity searches in a DNA database.

    • Callista Bee
    • , Yuan-Jyue Chen
    •  & Luis Ceze

  • 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

    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

    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

    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

    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

    Multiplayer games can be used as testbeds for the development of learning algorithms for artificial intelligence. Omidshafiei et al. show how to characterize and compare such games using a graph-based approach, generating new games that could potentially be interesting for training in a curriculum.

    • Shayegan Omidshafiei
    • , Karl Tuyls
    •  & Rémi Munos

  • Article
    | Open Access

    Designing efficient system for digital connectivity preserving information security remains a challenge. Here, the authors present hardware-intrinsic security solutions based on physical unclonable functions incorporating an inkjet-printed core circuit as an intrinsic source of entropy, integrated into a silicon-based CMOS system environment.

    • Alexander Scholz
    • , Lukas Zimmermann
    •  & Jasmin Aghassi-Hagmann

  • Article
    | Open Access

    Designing efficient analog dynamical systems for solving hard optimization problems remains a challenge. Here, the authors demonstrate a dynamical system of thirty oscillators with reconfigurable coupling to compute optimal/near-optimal solutions to the hard Maximum Independent Set problem with over 90% accuracy.

    • Antik Mallick
    • , Mohammad Khairul Bashar
    •  & Nikhil Shukla

  • Article
    | Open Access

    Extracting central information from ever-growing data generated in our lives calls for new data mining methods. Ferreira et al. show a simple model, called chronnets, that can capture frequent patterns, spatial changes, outliers, and spatiotemporal clusters.

    • Leonardo N. Ferreira
    • , Didier A. Vega-Oliveros
    •  & Elbert E. N. Macau

  • Article
    | Open Access

    Multiple access channels model communication from multiple independent senders to a common receiver. By drawing a connection to the study of classical and quantum correlations using nonlocal games, Leditzky et al. reveal remarkably complex behaviour of the entanglement-assisted and unassisted information transmission capabilities of a multiple access channel.

    • Felix Leditzky
    • , Mohammad A. Alhejji
    •  & Graeme Smith

  • Article
    | Open Access

    Understanding the underlying mechanisms behind the successes of deep networks remains a challenge. Here, the author demonstrates an implicit regularization in training deep networks, showing that the control of complexity in the training is hidden within the optimization technique of gradient descent.

    • Tomaso Poggio
    • , Qianli Liao
    •  & Andrzej Banburski

  • Article
    | Open Access

    Small non-polymeric molecules have tremendous structural diversity that can be used to represent information. Here the authors encode data in synthesized libraries of Ugi products.

    • Christopher E. Arcadia
    • , Eamonn Kennedy
    •  & Jacob K. Rosenstein

  • Article
    | Open Access

    DNA strand displacement reactions can be difficult to scale up for computational tasks. Here the authors develop DNA switching circuits that achieve high-speed computing with fewer molecules.

    • Fei Wang
    • , Hui Lv
    •  & Chunhai Fan

  • Article
    | Open Access

    People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better courses of action in the future.

    • Chia-Chun Hung
    • , Timothy Lillicrap
    •  & Greg Wayne

  • Article
    | Open Access

    The extent to which brain structure and function are coupled remains a complex question. Here, the authors show that coupling strength between structural connectivity and functional activity can be quantified and reveals a cortical gradient spanning from lower-level sensory areas to high-level cognitive ones.

    • Maria Giulia Preti
    •  & Dimitri Van De Ville

  • Article
    | Open Access

    There is a lack of systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale. Here the authors use LinkedIn's employment history data to construct a global labor flow network from which they find that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance compared to existing aggregation units.

    • Jaehyuk Park
    • , Ian B. Wood
    •  & Yong-Yeol Ahn

  • Article
    | Open Access

    Recovering the properties of a network which has suffered adversarial intervention can find applications in uncovering targeted attacks on social networks. Here the authors propose a causal statistical inference framework for reconstructing a network which has suffered non-random, targeted attacks.

    • Yuankun Xue
    •  & Paul Bogdan

  • Article
    | Open Access

    DNA as a high density storage medium is receiving increasing attention, but long term physical storage is an unsolved problem. Here the authors show that up to 1 TB of data stored as dehydrated DNA spots on a glass cartridge can be retrieved in a spot of water using digital microfluidics with minimal data loss and contamination.

    • Sharon Newman
    • , Ashley P. Stephenson
    •  & Luis Ceze

  • Article
    | Open Access

    AI is used increasingly in medical diagnostics. Here, the authors present a deep learning model that masters medical knowledge, demonstrated by it having passed the written test of the 2017 National Medical Licensing Examination in China, and can provide help with clinical diagnosis based on electronic health care records.

    • Ji Wu
    • , Xien Liu
    •  & Ping Lv

  • Article
    | Open Access

    Designing molecular keys and combining advanced encryption standard cryptography with molecular steganography is a secure way for encoding messages. Here, the authors use the Ugi four-component reaction of perfluorinated acids to create a library of 500,000 molecular keys for encryption and decryption.

    • Andreas C. Boukis
    • , Kevin Reiter
    •  & Michael A. R. Meier

  • Article
    | Open Access

    Construction of a scalable quantum computer requires error-correcting codes to overcome the errors introduced by noise. Here, the authors develop a decoding algorithm for the gauge color code, and obtain its threshold values when physical errors and measurement faults are included.

    • Benjamin J. Brown
    • , Naomi H. Nickerson
    •  & Dan E. Browne

  • Article
    | Open Access

    Irreversible computation cannot be performed without a work cost, and energy dissipation imposes limitations on devices' performances. Here the authors show that the minimal work requirement of logical operations is given by the amount of discarded information, measured by entropy.

    • Philippe Faist
    • , Frédéric Dupuis
    •  & Renato Renner

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