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Marks, M., Jin, Q., Sturman, O. et al. Deep-learning-based identification, tracking, pose estimation and behaviour classification of interacting primates and mice in complex environments.

  • Markus Marks
  • Qiuhan Jin
  • Mehmet Fatih Yanik

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  • Swarms of microrobots could eventually be used to deliver drugs to specific targets in the body, but the coordination of these swarms in complex environments is challenging. Yang and colleagues present a real-time autonomous distribution planning method based on deep learning that controls both the shape and position of the swarm, as well as the imaging system used for swarm navigation to cover longer distances.

    • Lidong Yang
    • Jialin Jiang
    • Li Zhang
  • With the availability of a vast and growing number of digital publications, machine reading and other knowledge mining tools, computational methods can be applied at scale to extract insights from the scientific literature. Belikov et al. develop a Bayesian method to mine the biomedical literature that identifies robust scientific findings which could improve the planning of further experiments and scientific investigation.

    • Alexander V. Belikov
    • Andrey Rzhetsky
    • James Evans
  • The invariant causal prediction (ICP) framework tries to determine the causal variables given an outcome variable, but considerable effort is needed to adapt existing ICP methods to the clinical domain. The authors propose an automated causal inference method that could potentially address the challenges of applying the ICP framework to complex clinical datasets.

    • Ji Q. Wu
    • Nanda Horeweg
    • Viktor H. Koelzer
    Article Open Access
  • The use of deep neural networks for the automated analysis of behavioural videos has emerged as a tool in neuroscience, medicine and psychology. Marks and colleagues present a pipeline capable of tracking and identifying animals, as well as classifying individual and interacting animal behaviour in video recordings and even in complex environments.

    • Markus Marks
    • Qiuhan Jin
    • Mehmet Fatih Yanik
  • Combinatorial optimization, the search for the minimum of an objective function within a finite but very large set of candidate solutions, finds many important and challenging applications in science and industry. A new graph neural network deep learning approach that incorporates concepts from statistical physics is used to develop a robust solver that can tackle a large class of NP-hard combinatorial optimization problems.

    • Martin J. A. Schuetz
    • J. Kyle Brubaker
    • Helmut G. Katzgraber
Nature Machine Intelligence aims to bring different fields together in the study, engineering and application of intelligent machines. We publish research on a large variety of topics in machine learning, robotics, cognitive science and a range of AI approaches. We also provide a platform for comments and reviews to discuss emerging inter-disciplinary themes as well as the significant impact that machine intelligence has on other fields in science and on society.
Publishing online monthly from January 2019, Nature Machine Intelligence is interested in the best research from across the fields of artificial intelligence, machine learning and robotics. All editorial decisions are made by a team of full-time professional editors.
Nature Machine Intelligence is run by a team of full-time editors. The Chief Editor is Liesbeth Venema who was previously a physics editor at Nature. Trenton Jerde started in March 2018, Yann Sweeney joined in July and Jacob Huth joined in November 2018, completing the team.
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