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  • Camera trapping is a widely adopted method for monitoring terrestrial mammals. However, a drawback is the amount of human annotation needed to keep pace with continuous data collection. The authors developed a hybrid system of machine learning and humans in the loop, which minimizes annotation load and improves efficiency.

    • Zhongqi Miao
    • Ziwei Liu
    • Wayne M. Getz
  • Combining generative models and reinforcement learning has become a promising direction for computational drug design, but it is challenging to train an efficient model that produces candidate molecules with high diversity. Jike Wang and colleagues present a method, using knowledge distillation, to condense a conditional transformer model to make it usable in reinforcement learning while still generating diverse molecules that optimize multiple molecular properties.

    • Jike Wang
    • Chang-Yu Hsieh
    • Tingjun Hou
  • A growing number of researchers are developing approaches to improve fairness in machine learning applications in areas such as healthcare, employment and social services, to avoid propagating and amplifying racial and other inequities. An empirical study explores the trade-off between increasing fairness and model accuracy across several social policy areas and finds that this trade-off is negligible in practice.

    • Kit T. Rodolfa
    • Hemank Lamba
    • Rayid Ghani
  • Turbulent optical distortions in the atmosphere limit the ability of optical technologies such as laser communication and long-distance environmental monitoring. A new method using adversarial networks learns to counter the physical processes underlying the turbulence so that complex optical scenes can be reconstructed.

    • Darui Jin
    • Ying Chen
    • Xiangzhi Bai
  • The use of sparse signals in spiking neural networks, modelled on biological neurons, offers in principle a highly efficient approach for artificial neural networks when implemented on neuromorphic hardware, but new training approaches are needed to improve performance. Using a new type of activity-regularizing surrogate gradient for backpropagation combined with recurrent networks of tunable and adaptive spiking neurons, state-of-the-art performance for spiking neural networks is demonstrated on benchmarks in the time domain.

    • Bojian Yin
    • Federico Corradi
    • Sander M. Bohté
  • T-cell immunity is driven by the interaction between peptides presented by major histocompatibility complexes (pMHCs) and T-cell receptors (TCRs). Only a small proportion of neoantigens elicit T-cell responses, and it is not clear which neoantigens are recognized by which TCRs. The authors develop a transfer learning model to predict TCR binding specificity to class-I pMHCs.

    • Tianshi Lu
    • Ze Zhang
    • Tao Wang
  • Spiking neural networks promise fast and energy-efficient information processing. The ‘time-to-first-spike’ coding scheme, where the time elapsed before a neuron’s first spike is utilized as the main variable, is a particularly efficient approach and Göltz and Kriener et al. demonstrate that error backpropagation, an essential ingredient for learning in neural networks, can be implemented in this scheme.

    • J. Göltz
    • L. Kriener
    • M. A. Petrovici
  • Incorporating prior knowledge in deep learning models can overcome the difficulties of supervised learning, including the need for large amounts of annotated data. An approach in this area called deep reasoning networks is applied to the complex task of mapping crystal structures from X-ray diffraction data for multi-element oxide structures, and identified 13 phases from 307 X-ray diffraction patterns in the previously unsolved Bi-Cu-V oxide system.

    • Di Chen
    • Yiwei Bai
    • Carla P. Gomes
  • The relationship between brain organization, connectivity and computation is not well understood. The authors construct neuromorphic artificial neural networks endowed with biological connection patterns derived from diffusion-weighted imaging. The neuromorphic networks are trained to perform a memory task, revealing an interaction between network structure and dynamics.

    • Laura E. Suárez
    • Blake A. Richards
    • Bratislav Misic
  • Radiomics has been used to discover imaging signatures that predict therapy response and outcomes, but clinical translation has been slow. Using machine learning methods, the authors report tumour subtypes that are applicable across major imaging modalities and three cancer types. The tumour subtypes have distinct radiological and molecular features, as well as survival outcomes after conventional therapies.

    • Jia Wu
    • Chao Li
    • Ruijiang Li
  • Auction games present an interesting challenge for multi-agent learning. Finding the Bayes Nash equilibria for optimum bidding strategies is intractable for numerical approaches. In a new, deep learning approach, strategies are represented as neural networks, and policy iteration based on gradient dynamics in self-play enables learning of local equilibria.

    • Martin Bichler
    • Maximilian Fichtl
    • Paul Sutterer
  • Molecular simulations informed by experimental data can provide detailed knowledge of complex biomolecular structure. However, it is a challenging task to weight experimental information with respect to the underlying model. A self-adapting type of dynamic particle swarm optimization can tackle the parameter selection problem, which is demonstrated on small-angle X-ray scattering-guided protein simulations.

    • Marie Weiel
    • Markus Götz
    • Alexander Schug
    Article Open Access
  • Particle image velocimetry is an imaging technique to determine the velocity components of flow fields, of use in a range of complex engineering problems including in environmental, aerospace and biomedical engineering. A recurrent neural network-based approach for learning displacement fields in an end-to-end manner is applied to this technique and achieves state-of-the-art accuracy and, moreover, allows generalization to new data, eliminating the need for traditional handcrafted models.

    • Christian Lagemann
    • Kai Lagemann
    • Wolfgang Schröder
  • Deep learning-based methods to generate new molecules can require huge amounts of data to train. Skinnider et al. show that models developed for natural language processing work well for generating molecules from small amounts of training data, and identify robust metrics to evaluate the quality of generated molecules.

    • Michael A. Skinnider
    • R. Greg Stacey
    • Leonard J. Foster
  • Methods are available to support clinical decisions regarding adjuvant therapies in breast cancer, but they have limitations in accuracy, generalizability and interpretability. Alaa et al. present an automated machine learning model of breast cancer that predicts patient survival and adjuvant treatment benefit to guide personalized therapeutic decisions.

    • Ahmed M. Alaa
    • Deepti Gurdasani
    • Mihaela van der Schaar
  • With edge computing on custom hardware, real-time inference with deep neural networks can reach the nanosecond timescale. An important application in this regime is event processing at particle collision detectors like those at the Large Hadron Collider (LHC). To ensure high performance as well as reduced resource consumption, a method is developed, and made available as an extension of the Keras library, to automatically design optimal quantization of the different layers in a deep neural network.

    • Claudionor N. Coelho Jr
    • Aki Kuusela
    • Sioni Summers