Mathematics and computing

  • Article
    | Open Access

    Microfluidic multipoles use arrays of sources and sinks to confine fluids and reagents without the use of physical channels. Here the authors use conformal mappings to predict both convective and diffusive transport in these flows and 3D print multipoles to automate surface-based immunoassays.

    • Pierre-Alexandre Goyette
    • , Étienne Boulais
    •  & Thomas Gervais
  • Article
    | Open Access

    Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics.

    • Tian Ge
    • , Chia-Yen Chen
    •  & Jordan W. Smoller
  • 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

    The impacts of technological development on social sphere lack strong empirical foundation. Here the authors presented quantitative analysis of the phenomenon of social acceleration across a range of digital datasets and found that interest appears in bursts that dissipate on decreasing timescales and occur with increasing frequency.

    • Philipp Lorenz-Spreen
    • , Bjarke Mørch Mønsted
    •  & Sune Lehmann
  • 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

    Convolutional Neural Networks (CNNs) have reached human-level benchmarks in classifying images, but they can be “fooled” by adversarial examples that elicit bizarre misclassifications from machines. Here, the authors show how humans can anticipate which objects CNNs will see in adversarial images.

    • Zhenglong Zhou
    •  & Chaz Firestone
  • Article
    | Open Access

    Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making.

    • Sebastian Lapuschkin
    • , Stephan Wäldchen
    •  & Klaus-Robert Müller
  • Article
    | Open Access

    Resource sharing over peer-to-peer technological networks is emerging as economically important, yet little is known about how people choose to share in this context. Here, the authors introduce a new game to model sharing, and test how players form sharing strategies depending on technological constraints.

    • Hirokazu Shirado
    • , George Iosifidis
    •  & Nicholas A. Christakis
  • Article
    | Open Access

    Universal cluster states for quantum computing can be assembled without feed-forward by fusing n-photon clusters with linear optics if the fusion success probability is above a threshold p. The authors bound p in terms of n and provide protocols for n = 3 clusters requiring lower fusion probability than before.

    • Mihir Pant
    • , Don Towsley
    •  & Saikat Guha
  • Article
    | Open Access

    Disordered hyperuniformity implies a hidden order on length scales that can be found in various amorphous materials. Klatt et al. analyse the evolution of random point patterns using Llyod’s algorithm and show that they converge to an effectively hyperuniform state regardless of the initial conditions.

    • Michael A. Klatt
    • , Jakov Lovrić
    •  & Salvatore Torquato
  • Article
    | Open Access

    The resolution limitations when using the ubiquitous algorithms that process images obtained using modern techniques are not straightforward to define. Here, the authors examine the performance of localization algorithms and use spatial statistics to provide a metric for assessing the resolution limit of such algorithms.

    • Edward A. K. Cohen
    • , Anish V. Abraham
    •  & Raimund J. Ober
  • Article
    | Open Access

    The incomplete nature and undefined structure of the existing catalysis research data has prevented comprehensive knowledge extraction. Here, the authors report a novel meta-analysis method that identifies correlations between a catalyst’s physico-chemical properties and its performance in a particular reaction.

    • Roman Schmack
    • , Alexandra Friedrich
    •  & Ralph Kraehnert
  • Article
    | Open Access

    Percolation is a tool used to investigate a network’s response as random links are removed. Here the author presents a generic analytic theory to describe how percolation properties are affected in coloured networks, where the colour can represent a network feature such as multiplexity or the belonging to a community.

    • Ivan Kryven
  • Article
    | Open Access

    Designing mechanical metamaterials is challenging because of the large number of non-periodic constituent elements. Here, the authors develop an approach to design arbitrarily shaped metamaterials that is more computationally efficient by six orders of magnitude compared to other approaches.

    • Lucas A. Shaw
    • , Frederick Sun
    •  & Jonathan B. Hopkins
  • Article
    | Open Access

    Similarly to entropy, majorization allows to quantify deviation from uniformity in a wide range of fields. In this paper, the authors use its generalization to the quantum realm to derive a complete set of necessary and sufficient conditions for thermal transformations of quantum states.

    • Gilad Gour
    • , David Jennings
    •  & Iman Marvian
  • Article
    | Open Access

    Predicting plastic deformation in crystals remains challenging owing to the nonlinear nature of stochastic avalanches involved, which resemble the critical phenomena. Salmenjoki et al. use machine learning to predict plastic deformation and show that it works better for those under large strains.

    • Henri Salmenjoki
    • , Mikko J. Alava
    •  & Lasse Laurson
  • Article
    | Open Access

    The software Optimer has aided the programmable one-pot oligosaccharide synthesis with a library of 50 Building BLocks (BBLs). Here, the authors expanded Optimer's validated and virtual libraries of BBLs and developed Auto-CHO, a software which allows the one-pot programmable synthesis of more complex glycans.

    • Cheng-Wei Cheng
    • , Yixuan Zhou
    •  & Chi-Huey Wong
  • Article
    | Open Access

    Diversity is believed to raise effectiveness and performance but it contains many aspects. Here the authors studied the relationship between research impact and five classes of diversity and found that ethnic diversity had the strongest correlation with scientific impact.

    • Bedoor K. AlShebli
    • , Talal Rahwan
    •  & Wei Lee Woon
  • Article
    | Open Access

    Modern microscopes can generate high volumes of 3D images, driving difficulties in data handling and processing. Here, the authors present a content-adaptive image representation as an alternative to standard pixels that goes beyond data compression to overcome storage, memory, and processing bottlenecks.

    • Bevan L. Cheeseman
    • , Ulrik Günther
    •  & Ivo F. Sbalzarini
  • Article
    | Open Access

    It is now possible to predict what a chemical smells like based on its chemical structure, however to date, this has only been done for a small number of odor descriptors. Here, using natural-language semantic representations, the authors demonstrate prediction of a much wider range of descriptors.

    • E. Darío Gutiérrez
    • , Amit Dhurandhar
    •  & Guillermo A. Cecchi
  • Article
    | Open Access

    It is often advantageous to transform a strongly nonlinear system into a linear one in order to simplify its analysis for prediction and control. Here the authors combine dynamical systems with deep learning to identify these hard-to-find transformations.

    • Bethany Lusch
    • , J. Nathan Kutz
    •  & Steven L. Brunton
  • Article
    | Open Access

    Continuous-time computation paradigm could represent a viable alternative to the standard digital one when dealing with certain classes of problems. Here, the authors propose a generalised version of a continuous-time solver and simulate its performances in solving MaxSAT and two-colour Ramsey problems.

    • Botond Molnár
    • , Ferenc Molnár
    •  & Mária Ercsey-Ravasz
  • Article
    | Open Access

    HIV infected cells persist for decades in patients under ART, but the mechanisms responsible remain unclear. Here, Reeves et al. use modeling approaches adapted from ecology to show that cellular proliferation, rather than viral replication, generates a majority of infected cells during ART.

    • Daniel B. Reeves
    • , Elizabeth R. Duke
    •  & Joshua T. Schiffer
  • Article
    | Open Access

    Accurate and actionable biomarkers that integrate diverse molecular, functional and clinical information hold great promise in precision medicine. Here, the authors develop SIMMS, a method for pathway-based cross-disease biomarker discovery.

    • Syed Haider
    • , Cindy Q. Yao
    •  & Paul C. Boutros
  • Article
    | Open Access

    Complex networks can be a useful tool to investigate problems in social science. Here the authors use game theory to establish a network model and then use a machine learning approach to characterize the role of nodes within a social network.

    • Yuan Yuan
    • , Ahmad Alabdulkareem
    •  & Alex ‘Sandy’ Pentland
  • Article
    | Open Access

    Nanoparticle applications are limited by insufficient understanding of physiochemical properties on in vivo disposition. Here, the authors explore the influence of size, surface chemistry and administration on the biodisposition of mesoporous silica nanoparticles using image-based pharmacokinetics.

    • Prashant Dogra
    • , Natalie L. Adolphi
    •  & C. Jeffrey Brinker
  • Article
    | Open Access

    Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.

    • Federico M. Paruzzo
    • , Albert Hofstetter
    •  & Lyndon Emsley
  • 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

    Dynamics in cold atomic ensembles involve complex many-body interactions that are hard to treat analytically. Here, the authors use machine learning to optimise the cooling and trapping of neutral atoms, showing an improvement in the resulting resonant optical depth compared to more traditional solutions.

    • A. D. Tranter
    • , H. J. Slatyer
    •  & G. T. Campbell
  • Article
    | Open Access

    Self-folding origami have applications for mechanical metamaterials but one of their pitfalls is that many undesirable folding modes exist. Here the authors propose an algorithm to determine which folding joints to make stiffer in order to ensure that the sheet is folded into the chosen state.

    • Menachem Stern
    • , Viraaj Jayaram
    •  & Arvind Murugan
  • Article
    | Open Access

    Progressive diseases tend to be heterogeneous in their underlying aetiology mechanism, disease manifestation, and disease time course. Here, Young and colleagues devise a computational method to account for both phenotypic heterogeneity and temporal heterogeneity, and demonstrate it using two neurodegenerative disease cohorts.

    • Alexandra L Young
    • , Razvan V Marinescu
    •  & Ansgar J Furst
  • Article
    | Open Access

    With the rapid development of quantum computers, quantum machine learning approaches are emerging as powerful tools to perform electronic structure calculations. Here, the authors develop a quantum machine learning algorithm, which demonstrates significant improvements in solving quantum many-body problems.

    • Rongxin Xia
    •  & Sabre Kais
  • Article
    | Open Access

    Materials databases currently neglect the temperature effect on compound thermodynamics. Here the authors introduce a Gibbs energy descriptor enabling the high-throughput prediction of temperature-dependent thermodynamics across a wide range of compositions and temperatures for inorganic solids.

    • Christopher J. Bartel
    • , Samantha L. Millican
    •  & Aaron M. Holder
  • Article
    | Open Access

    Functional magnetic resonance imaging (fMRI) is a powerful technique for measuring human brain activity, but the statistical analysis of fMRI data can be difficult. Here, the authors introduce a new fMRI analysis tool, LISA, which provides increased statistical power compared to existing techniques.

    • Gabriele Lohmann
    • , Johannes Stelzer
    •  & Klaus Scheffler
  • Article
    | Open Access

    Visual search requires recognizing an object “invariantly”, despite changes in its appearance. Here, the authors show that humans can efficiently and invariantly search for objects in complex scenes and introduce a biologically-inspired zero-shot model that captures human eye movements during search.

    • Mengmi Zhang
    • , Jiashi Feng
    •  & Gabriel Kreiman
  • Article
    | Open Access

    Causality inference in time series analysis based on temporal precedence principle between cause and effect fails to detect mutual causal interactions. Here, Yang et al. introduce a causal decomposition approach based on the covariation principle of cause and effect that overcomes this limitation.

    • Albert C. Yang
    • , Chung-Kang Peng
    •  & Norden E. Huang
  • Article
    | Open Access

    Understanding the occurrence of sudden changes in plasma parameters is important for the operation of magnetically confined fusion devices. Here the authors use simulation to shed light on the formation of abrupt large-amplitude events and the associated redistribution of energetic ions in a tokamak.

    • Andreas Bierwage
    • , Kouji Shinohara
    •  & Masatoshi Yagi
  • Article
    | Open Access

    Genome-wide association studies (GWAS) of neuroimaging data pose a significant computational burden because of the need to correct for multiple testing in both the genetic and the imaging data. Here, Ganjgahi et al. develop WLS-REML which significantly reduces computation running times in brain imaging GWAS.

    • Habib Ganjgahi
    • , Anderson M. Winkler
    •  & Thomas E. Nichols
  • Article
    | Open Access

    Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.

    • Zhongrui Wang
    • , Mingyi Rao
    •  & J. Joshua Yang
  • Article
    | Open Access

    Anticrack propagation in snow results from the mixed-mode failure and collapse of a buried weak layer and can lead to slab avalanches. Here, authors reproduce the complex dynamics of anticrack propagation observed in field experiments using a Material Point Method with large strain elastoplasticity.

    • J. Gaume
    • , T. Gast
    •  & C. Jiang
  • Article
    | Open Access

    Classifying crystal structures using their space group is important to understand material properties, but the process currently requires user input. Here, the authors use machine learning to automatically classify more than 100,000 simulated perfect and defective crystal structures.

    • Angelo Ziletti
    • , Devinder Kumar
    •  & Luca M. Ghiringhelli
  • Article
    | Open Access

    Despite advances in ENSO modeling, super El Niño events remain largely unpredictable. Hameed et al. postulate that ENSO-IOD interaction is crucial for super El Niño development and identify a self-limiting factor that constrains ENSO dynamics from generating these extreme events on their own.

    • Saji N. Hameed
    • , Dachao Jin
    •  & Vishnu Thilakan
  • Article
    | Open Access

    Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

    • Irem Boybat
    • , Manuel Le Gallo
    •  & Evangelos Eleftheriou