Mathematics and computing

  • Article
    | Open Access

    Designing reliable and energy-efficient memristor-based artificial neural networks remains a challenge. Here, the authors demonstrate a technology-agnostic approach, committee machines, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.

    • D. Joksas
    • , P. Freitas
    •  & A. Mehonic
  • Article
    | Open Access

    Although power laws are observed during nanoindentation and the power-law exponents are estimated to be approximately 1.5-1.6 for face-centered cubic metals, the origin of the exponent remains unclear. In this paper, we show the power-law statistics in pop-in magnitudes and unveil the nature of the exponent.

    • Yuji Sato
    • , Shuhei Shinzato
    •  & Shigenobu Ogata
  • Article
    | Open Access

    One challenge that faces artificial intelligence is the inability of deep neural networks to continuously learn new information without catastrophically forgetting what has been learnt before. To solve this problem, here the authors propose a replay-based algorithm for deep learning without the need to store data.

    • Gido M. van de Ven
    • , Hava T. Siegelmann
    •  & Andreas S. Tolias
  • 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

    In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

    • Jonathan G. Richens
    • , Ciarán M. Lee
    •  & Saurabh Johri
  • Article
    | Open Access

    Both the mathematics and outcomes of the Method of Reflections (MR) and Fitness and Complexity algorithm (FC) approaches differ largely. Here the authors recast both methods in a mathematical and multidimensional framework to reconcile both and show that the conflicts between the two methodologies to measure economic complexity can be resolved by a neat mathematical method based on linear-algebra tools within a bipartite-networks framework.

    • Carla Sciarra
    • , Guido Chiarotti
    •  & Francesco Laio
  • Article
    | Open Access

    It is not clear which designs, other than completely randomized ones, are valid for scRNA-seq experiments so that batch effects can be adjusted. Here the authors show that under flexible reference panel and chain-type designs, biological variability can also be separated from batch effects, at least by BUSseq.

    • Fangda Song
    • , Ga Ming Angus Chan
    •  & Yingying Wei
  • Article
    | Open Access

    The choice of molecular representations can severely impact the performances of machine-learning methods. Here the authors demonstrate a persistence homology based molecular representation through an active-learning approach for predicting CO2/N2 interaction energies at the density functional theory (DFT) level.

    • Jacob Townsend
    • , Cassie Putman Micucci
    •  & Konstantinos D. Vogiatzis
  • Article
    | Open Access

    Complex systems in the real world are often characterized by connected patterns interacting between each other in multiple ways. Here, Della Rossa et al. describe a general method to determine symmetries in multilayer networks and then relate them to different synchronization modes that the networks can exhibit.

    • Fabio Della Rossa
    • , Louis Pecora
    •  & Francesco Sorrentino
  • Article
    | Open Access

    The physical architectures of information storage dictate how data is encoded, organised and accessed. Here the authors use DNA with a single-strand overhang as a physical address to access specific data and do in-storage file operations in a scalable and reusuable manner.

    • Kevin N. Lin
    • , Kevin Volkel
    •  & Albert J. Keung
  • Article
    | Open Access

    Designing efficient artificial networks able to quickly converge to optimal performance for a given task remains a challenge. Here, the authors demonstrate a relation between criticality, task-performance and information theoretic fingerprint in a spiking neuromorphic network with synaptic plasticity.

    • Benjamin Cramer
    • , David Stöckel
    •  & Viola Priesemann
  • Article
    | Open Access

    Every year, hundreds of people die at sea because of vessel accidents, and a key challenge in reducing these fatalities is to make Search and Rescue (SAR) planning more efficient. Here, the authors uncover hidden flow features that attract floating objects, providing specific information for optimal SAR planning.

    • Mattia Serra
    • , Pratik Sathe
    •  & George Haller
  • Article
    | Open Access

    It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data.

    • Siyang Leng
    • , Huanfei Ma
    •  & Luonan Chen
  • Article
    | Open Access

    It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and satellite imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution imagery with errors comparable to that of existing ground data.

    • Christopher Yeh
    • , Anthony Perez
    •  & Marshall Burke
  • Article
    | Open Access

    Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.

    • Erol S. Kavvas
    • , Laurence Yang
    •  & Bernhard O. Palsson
  • Article
    | Open Access

    Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.

    • Yue Li
    • , Pratheeksha Nair
    •  & Manolis Kellis
  • Article
    | Open Access

    Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory.

    • Vinay Joshi
    • , Manuel Le Gallo
    •  & Evangelos Eleftheriou
  • Article
    | Open Access

    Population structure enables emergence of cooperation among individuals, but the impact of the dynamic nature of real interaction networks is not understood. Here, the authors study the evolution of cooperation on temporal networks and find that temporality enhances the evolution of cooperation.

    • Aming Li
    • , Lei Zhou
    •  & Simon A. Levin
  • Article
    | Open Access

    To advance the design of soft robots, novel computational frameworks that accurately model the dynamics of soft material systems are required. Here, the authors report a numerical framework for studying locomotion in limbed soft robots that is based on the discrete elastic rods algorithm.

    • Weicheng Huang
    • , Xiaonan Huang
    •  & M. Khalid Jawed
  • Article
    | Open Access

    The demands on transportation systems continue to grow while the methods for analyzing and forecasting traffic conditions remain limited. Here the authors show a parameter-independent approach for an accurate description, identification and forecasting of spatio-temporal traffic patterns directly from data.

    • A. M. Avila
    •  & I. Mezić
  • Article
    | Open Access

    The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.

    • Antônio H. Ribeiro
    • , Manoel Horta Ribeiro
    •  & Antonio Luiz P. Ribeiro
  • Article
    | Open Access

    Phenomena like imitation, herding and positive feedbacks in the complex financial markets characterize the emergence of endogenous instabilities, which however is still understudied. Here the authors show that the graph-based approach is helpful to timely recognize phases of increasing instability that can drive the system to a new market configuration.

    • Alessandro Spelta
    • , Andrea Flori
    •  & Fabio Pammolli
  • Article
    | Open Access

    Natural hazards can have huge impacts on individuals and societies, however, monitoring the economic recovery in the aftermath of extreme events remains a challenge. Here, the authors find that Facebook posting activity of small businesses can be used to monitor post-disaster economic recovery, and can allow local governments to better target distribution of resources.

    • Robert Eyre
    • , Flavia De Luca
    •  & Filippo Simini
  • Article
    | Open Access

    The authors propose a learning rule for a neuron model with dendrite. In their model, somatodendritic interaction implements self-supervised learning applicable to a wide range of sequence learning tasks, including spike pattern detection, chunking temporal input and blind source separation.

    • Toshitake Asabuki
    •  & Tomoki Fukai
  • 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

    Polymer crosslinking in desalination membranes adds stability on the cost of molecular transportation rates through the membrane. Here the authors tailor crosslinking of desalination membranes to overcome the stability and transport trade-off, and demonstrate a pervaporation desalination thin-film composite membrane with high water flux.

    • Yun Long Xue
    • , Jin Huang
    •  & Pei Li
  • Article
    | Open Access

    Unconventional computing architectures might outperform current ones, but their realization has been limited to solving simple specific problems. Here, a network of interconnected Belousov-Zhabotinski reactions, operated by independent magnetic stirrers, performs encoding/decoding operations and data storage.

    • Juan Manuel Parrilla-Gutierrez
    • , Abhishek Sharma
    •  & Leroy Cronin
  • 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

    It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers.

    • Kerstin Beer
    • , Dmytro Bondarenko
    •  & Ramona Wolf
  • 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

    The physical limits and reliability of PCR-based random access of DNA encoded data is unknown. Here the authors demonstrate reliable file recovery from as few as ten copies per sequence, providing a data density limit of 17 exabytes per gram.

    • Lee Organick
    • , Yuan-Jyue Chen
    •  & Luis Ceze
  • Article
    | Open Access

    Chronic lymphocytic leukemia is an indolent disease, and many patients succumb to infection rather than the direct effects of the disease. Here, the authors use medical records and machine learning to predict the patients that may be at risk of infection, which may enable a change in the course of their treatment.

    • Rudi Agius
    • , Christian Brieghel
    •  & Carsten U. Niemann
  • 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

    The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

    • Xavier Domingo-Almenara
    • , Carlos Guijas
    •  & Gary Siuzdak
  • Article
    | Open Access

    Aggregation of matter, common in stratified fluid systems, is essential to the carbon cycle and ocean ecology. Although the current understanding of aggregation involves only collision and adhesion, here Camassa et al. reveal a self-assembly phenomenon arising solely from diffusion-induced flows.

    • Roberto Camassa
    • , Daniel M. Harris
    •  & Richard M. McLaughlin
  • Article
    | Open Access

    Technologies for acquiring explainable features from medical images need further development. Here, the authors report a deep learning based automated acquisition of explainable features from pathology images, and show a higher accuracy of their method as compared to pathologist based diagnosis of prostate cancer recurrence.

    • Yoichiro Yamamoto
    • , Toyonori Tsuzuki
    •  & Go Kimura
  • Article
    | Open Access

    Relapse, reinfection and recrudescence can all cause recurrent infection after treatment of Plasmodium vivax malaria in endemic areas, but are difficult to distinguish. Here the authors show that they can be differentiated probabilistically and thereby demonstrate the high efficacy of primaquine treatment in preventing relapse.

    • Aimee R. Taylor
    • , James A. Watson
    •  & Nicholas J. White
  • Perspective
    | Open Access

    Recent research in motor neuroscience has focused on optimal feedback control of single, simple tasks while robotics and AI are making progress towards flexible movement control in complex environments employing hierarchical control strategies. Here, the authors argue for a return to hierarchical models of motor control in neuroscience.

    • Josh Merel
    • , Matthew Botvinick
    •  & Greg Wayne
  • Article
    | Open Access

    t-SNE is widely used for dimensionality reduction and visualization of high-dimensional single-cell data. Here, the authors introduce a protocol to help avoid common shortcomings of t-SNE, for example, enabling preservation of the global structure of the data.

    • Dmitry Kobak
    •  & Philipp Berens
  • Article
    | Open Access

    Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements.

    • Philip Pearce
    • , Francis G. Woodhouse
    •  & Jörn Dunkel