Computer science articles within Nature Communications

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  • Article
    | Open Access

    All holographic displays and imaging techniques are fundamentally limited by the étendue supported by existing spatial light modulators. Here, the authors report on using artificial intelligence (AI) to learn an étendue expanding element that effectively increases étendue by two orders of magnitude.

    • Ethan Tseng
    • , Grace Kuo
    •  & Felix Heide

  • Article
    | Open Access

    Heterogeneous interactions between interactive entities are not well understood due to their complex configurations and many body interactions. Han et al. present a probabilistic-based machine learning method to discover the fundamental laws governing the interactions of heterogeneous systems.

    • Zhichao Han
    • , Olga Fink
    •  & David S. Kammer

  • Article
    | Open Access

    Solving combinatorial optimization problems using quantum or quantum-inspired machine learning models would benefit from strategies able to work with arbitrary objective functions. Here, the authors use the power of generative models to realise such a black-box solver, and show promising performances on some portfolio optimization examples.

    • Javier Alcazar
    • , Mohammad Ghazi Vakili
    •  & Alejandro Perdomo-Ortiz

  • Article
    | Open Access

    Mass spectrometry-based proteomics faces the challenge of processing vast data amounts. Here, the authors introduce AlphaPept, an open-source, Python-based framework that offers high speed analysis and easy integration for large-scale proteome analysis.

    • Maximilian T. Strauss
    • , Isabell Bludau
    •  & Matthias Mann

  • Article
    | Open Access

    Learning the dynamics governing a simulation or experiment usually requires coarse graining or projection, as the number of transition rates typically grows exponentially with system size. The authors show that transformers, neural networks introduced initially for natural language processing, can be used to parameterize the dynamics of large systems without coarse graining.

    • Corneel Casert
    • , Isaac Tamblyn
    •  & Stephen Whitelam

  • Article
    | Open Access

    A knowledge gap persists between machine learning developers and clinicians. Here, the authors show that the Advanced Data Analysis extension of ChatGPT could bridge this gap and simplify complex data analyses, making them more accessible to clinicians.

    • Soroosh Tayebi Arasteh
    • , Tianyu Han
    •  & Sven Nebelung

  • Article
    | Open Access

    Sensitivity-dependent data analysis methods disrupted the development of artificial olfactory technologies. Here, authors present a data-centric artificial olfactory system based on eigengraph that reflects the intrinsic electrochemical interaction.

    • Seung-Hyun Sung
    • , Jun Min Suh
    •  & Seong Chan Jun

  • Article
    | Open Access

    The Authors present a universal framework that utilizes photonic integrated circuits (PIC) to enhance the efficiency of reinforcement learning (RL). Leveraging the advantages of the hybrid architecture PIC (HyArch PIC), the PIC-RL experiment demonstrates a remarkable 56% improvement in efficiency for synthesizing perovskite materials.

    • Xuan-Kun Li
    • , Jian-Xu Ma
    •  & Xian-Min Jin

  • Article
    | Open Access

    Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements.

    • Laura Lewis
    • , Hsin-Yuan Huang
    •  & John Preskill

  • Article
    | Open Access

    Adaptive tactile interactions transfer across users, space, and time, via embroidered smart gloves is reported by the authors. The scalable fabrication and adaptive computation pipeline enable tactile occlusion alleviation, human skills transfer, and interactive teleoperation.

    • Yiyue Luo
    • , Chao Liu
    •  & Wojciech Matusik

  • Article
    | Open Access

    Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

    • Jun Ma
    • , Yuting He
    •  & Bo Wang

  • Article
    | Open Access

    It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.

    • Junyu Liu
    • , Minzhao Liu
    •  & Liang Jiang

  • Article
    | Open Access

    While federated learning is promising for efficient collaborative learning without revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high communication overhead, and struggles to adapt to heterogeneous models. Here, the authors show a federated distillation method to tackle these challenges, which leverages the strengths of knowledge distillation in a federated learning setting.

    • Jiawei Shao
    • , Fangzhao Wu
    •  & Jun Zhang

  • Article
    | Open Access

    Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

    • Zhijian Yang
    • , Junhao Wen
    •  & Christos Davatzikos

  • Article
    | Open Access

    Existing feature visualisation methods are not well-suited for regression tasks. Here, authors introduce a method to learn the manifold topology related to deep neural network output and target labels and provide insightful visualisations of the high-dimensional features while preserving the local geometry.

    • Md Tauhidul Islam
    • , Zixia Zhou
    •  & Lei Xing

  • Article
    | Open Access

    Reconstructing transcriptome-wide spatially-resolved gene expressions requires modelling nonlinear patterns and spatial structures in RNA profiling data. Here, authors introduce a graph-guided neural hierarchical tensor decomposition model that incorporates spatial and functional relations for the task.

    • Tianci Song
    • , Charles Broadbent
    •  & Rui Kuang

  • Article
    | Open Access

    Using AI to predict disease can improve interventions slow down or prevent disease. Here, the authors show that generative AI models built on the framework of Transformer, the model that also empowers ChatGPT, can achieve state-of-the-art performance on disease predictions based on longitudinal electronic records.

    • Zhichao Yang
    • , Avijit Mitra
    •  & Hong Yu

  • Article
    | Open Access

    The modelling of human-like behaviours is one of the challenges in the field of Artificial Intelligence. Inspired by experimental studies of cultural evolution, the authors propose a reinforcement learning approach to generate agents capable of real-time  third-person imitation.

    • Avishkar Bhoopchand
    • , Bethanie Brownfield
    •  & Lei M. Zhang

  • Article
    | Open Access

    In order to be useful for future large-scale quantum computing, quantum error correction needs to allow for fast enough classical decoding time, while at the moment the slowdown is exponential in the size of the code. Here, the authors remove this roadblock, showing how to parallelize decoding and make the slowdown polynomial.

    • Luka Skoric
    • , Dan E. Browne
    •  & Earl T. Campbell

  • Article
    | Open Access

    Physical unclonable functions (PUFs) normally ensure authentication of small physical objects. Here, instead, the authors observe that also rooms and buildings can serve as PUFs. They apply this insight to monitor the integrity of enclosed environments, such as art galleries, bank vaults, or data centers.

    • Johannes Tobisch
    • , Sébastien Philippe
    •  & Ulrich Rührmair

  • Article
    | Open Access

    Combinatorial optimization problems can be solved on parallel hardware called Ising machines. Most studies have focused on the use of second-order Ising machines. Compared to second-order Ising machines, the authors show that higher-order Ising machines realized with coupled-oscillator networks can be more resource-efficient and provide superior solutions for constraint satisfaction problems.

    • Connor Bybee
    • , Denis Kleyko
    •  & Friedrich T. Sommer

  • Article
    | Open Access

    Our current understanding of the computational abilities of near-intermediate scale quantum (NISQ) computing devices is limited, in part due to the absence of a precise definition for this regime. Here, the authors formally define the NISQ realm and provide rigorous evidence that its capabilities are situated between the complexity classes BPP and BQP.

    • Sitan Chen
    • , Jordan Cotler
    •  & Jerry Li

  • Article
    | Open Access

    Visual oddity tasks delve into the visual analytic intelligence of humans, which remained challenging for artificial neural networks. The authors propose here a model with biologically inspired neural dynamics and synthetic saccadic eye movements with improved efficiency and accuracy in solving the visual oddity tasks.

    • Stanisław Woźniak
    • , Hlynur Jónsson
    •  & Evangelos Eleftheriou

  • Article
    | Open Access

    Accurate evaluation of Li-ion battery safety conditions can reduce unexpected cell failures. Here, authors present a large-scale electric vehicle charging dataset for benchmarking existing algorithms, and develop a deep learning algorithm for detecting Li-ion battery faults.

    • Jingzhao Zhang
    • , Yanan Wang
    •  & Minggao Ouyang

  • Article
    | Open Access

    Want to mute or focus on speech from a specific region in a crowded room? Here, the authors built an acoustic swarm that, along with neural networks, separates and localizes concurrent speakers in the 2D space with high precision.

    • Malek Itani
    • , Tuochao Chen
    •  & Shyamnath Gollakota

  • Article
    | Open Access

    Analysis of experimental data in condensed matter is often challenging due to system complexity and slow character of physical simulations. The authors propose a framework that combines machine learning with theoretical calculations to enable real-time analysis for electron, neutron, and x-ray spectroscopies.

    • Sathya R. Chitturi
    • , Zhurun Ji
    •  & Joshua J. Turner

  • Article
    | Open Access

    In this work, authors explore DC-DC converter monitoring and control and demonstrate a generalizable digital twin based buck converter system that enables dynamic synchronization even under reference value changes, physical system model variation, and physical controller failure.

    • Zhongcheng Lei
    • , Hong Zhou
    •  & Guo-Ping Liu

  • Article
    | Open Access

    Transfer learning can be applied in computer vision and natural language processing to utilize knowledge from a source task to improve performance on a target task. The authors propose a framework for transfer learning with kernel methods for improved image classification and virtual drug screening.

    • Adityanarayanan Radhakrishnan
    • , Max Ruiz Luyten
    •  & Caroline Uhler

  • Article
    | Open Access

    Fano varieties are mathematical shapes that are basic units in geometry, they are challenging to classify in high dimensions. The authors introduce a machine learning approach that picks out geometric structure from complex mathematical data where rigorous analytical methods are lacking.

    • Tom Coates
    • , Alexander M. Kasprzyk
    •  & Sara Veneziale

  • Article
    | Open Access

    Security proofs against general attacks are the ultimate goal of QKD. Here, the authors show how the Generalised Entropy Accumulation Theorem can be used, for some classes of QKD scenarios, to translate security proofs against collective attacks in the asymptotic regime into proofs against general attacks in the finite-size regime.

    • Tony Metger
    •  & Renato Renner

  • Article
    | Open Access

    Inspired by human analogical reasoning in cognitive science, the authors propose an approach combining deep learning systems with an analogical reasoning mechanism, to detect abstract similarity in real-world images without intensive training in reasoning tasks.

    • Taylor Webb
    • , Shuhao Fu
    •  & Hongjing Lu

  • Comment
    | Open Access

    The current gap between computing algorithms and neuromorphic hardware to emulate brains is an outstanding bottleneck in developing neural computing technologies. Aimone and Parekh discuss the possibility of bridging this gap using theoretical computing frameworks from a neuroscience perspective.

    • James B. Aimone
    •  & Ojas Parekh

  • Perspective
    | Open Access

    Learning from human brains to build powerful computers is attractive, yet extremely challenging due to the lack of a guiding computing theory. Jaeger et al. give a perspective on a bottom-up approach to engineer unconventional computing systems, which is fundamentally different to the classical theory based on Turing machines.

    • Herbert Jaeger
    • , Beatriz Noheda
    •  & Wilfred G. van der Wiel

  • Perspective
    | Open Access

    The design of polymers for regenerative medicine could be accelerated with the help of machine learning. Here the authors note that machine learning has been applied successfully in other areas of polymer chemistry, while highlighting that data limitations must be overcome to enable widespread adoption within polymeric biomaterials.

    • Samantha M. McDonald
    • , Emily K. Augustine
    •  & Matthew L. Becker

  • Article
    | Open Access

    Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems; however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

    • Peter Y. Lu
    • , Rumen Dangovski
    •  & Marin Soljačić

  • Article
    | Open Access

    Synchronization of e-wearables can be challenging due to device performance variations. Here, the authors develop a general neural network-based solution that analyses and correct disparities between multiple virtual clocks and demonstrate it for a Bluetooth synchronized motion capture system at high frequency.

    • Karthikeyan Kalyanasundaram Balasubramanian
    • , Andrea Merello
    •  & Marco Crepaldi

  • Article
    | Open Access

    Diagnosing shortcut learning in clinical models is difficult, as sensitive attributes may be causally linked with disease. Using multitask learning, the authors propose a method to directly test for the presence of shortcut learning in clinical ML systems.

    • Alexander Brown
    • , Nenad Tomasev
    •  & Jessica Schrouff

  • Article
    | Open Access

    In biology, individuals are known to achieve higher navigation accuracy when moving in a group compared to single animals. The authors show that simple self-propelled robotic modules that are incapable of accurate motion as individuals can achieve accurate group navigation once coupled via deformable elastic links.

    • Federico Pratissoli
    • , Andreagiovanni Reina
    •  & Roderich Groß

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