Nature Machine Intelligence
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#1Rohan Shad (SU: Stanford University)H-Index: 3
#2John P. Cunningham (CU: Columbia University)H-Index: 38
Last. William Hiesinger (SU: Stanford University)H-Index: 18
view all 5 authors...
The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization and translatable clinical decision support systems. Among the key issues raised in the report, data availability, the need for novel computing architectures and explainable artificial intelligence algorithms are still relevant, despite the tremendous progress made over...
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#1Dongmyung Shin (SNU: Seoul National University)H-Index: 14
#2Younghoon Kim (SNU: Seoul National University)H-Index: 65
Last. Jongho Lee (SNU: Seoul National University)H-Index: 23
view all 7 authors...
Carefully engineered radiofrequency (RF) pulses play a key role in a number of systems such as mobile phone, radar and magnetic resonance imaging. The design of an RF waveform, however, is often posed as an inverse problem with no general solution. As a result, various design methods, each with a specific purpose, have been developed on the basis of the intuition of human experts. In this work we propose an artificial intelligence (AI)-powered RF pulse design framework, DeepRF, which utilizes th...
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#1Ricardo Vinuesa (KTH: Royal Institute of Technology)H-Index: 26
#2Beril Sirmacek (Saxion University of Applied Sciences)H-Index: 14
We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for instance via symbolic models obtained through inductive biases, to ensure a sustainable development of AI.
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#1Jing Gong (THU: Tsinghua University)H-Index: 5
Last. Qiangfeng Cliff Zhang (THU: Tsinghua University)H-Index: 27
view all 5 authors...
Sequencing-based RNA structure probing can generate transcriptome-wide profiles of RNA secondary structures. Sufficient structural coverage is needed to obtain unbiased insights about RNA structures and functions, yet probing methods often yield uneven coverage, with missing structural scores across many transcripts. To overcome this barrier, we developed StructureImpute, a deep learning framework inspired by depth completion from computer vision that integrates an RNA sequence with available RN...
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#1Hyungjin Chung (KAIST: Korea Advanced Institute of Science and Technology)H-Index: 5
#2Jong Chul Ye (KAIST: Korea Advanced Institute of Science and Technology)H-Index: 53
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#1Jike Wang (ZJU: Zhejiang University)H-Index: 2
#2Chang-Yu Hsieh (Tencent)H-Index: 18
Last. Qiaojun He (ZJU: Zhejiang University)H-Index: 43
view all 13 authors...
Machine learning-based generative models can generate novel molecules with desirable physiochemical and pharmacological properties from scratch. Many excellent generative models have been proposed, but multi-objective optimizations in molecular generative tasks are still quite challenging for most existing models. Here we proposed the multi-constraint molecular generation (MCMG) approach that can satisfy multiple constraints by combining conditional transformer and reinforcement learning algorit...
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#1Giulia Dominijanni (Sant'Anna School of Advanced Studies)H-Index: 1
#2Solaiman Shokur (Sant'Anna School of Advanced Studies)H-Index: 10
Last. Domenico Prattichizzo (Italian Institute of Technology)H-Index: 58
view all 11 authors...
The emergence of robotic body augmentation provides exciting innovations that will revolutionize the fields of robotics, human–machine interaction and wearable electronics. Although augmentative devices such as extra robotic arms and fingers are informed by restorative technologies in many ways, they also introduce unique challenges for bidirectional human–machine collaboration. Can humans adapt and learn to operate a new robotic limb collaboratively with their biological limbs, without restrict...
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#1Nicholas A. Lesica (UCL: University College London)H-Index: 19
#2Nishchay Mehta (UCL: University College London)H-Index: 7
Last. Fan-Gang Zeng (UCI: University of California, Irvine)H-Index: 59
view all 6 authors...
The advances in artificial intelligence that are transforming many fields have yet to make an impact in hearing. Hearing healthcare continues to rely on a labour-intensive service model that fails to provide access to the majority of those in need, while hearing research suffers from a lack of computational tools with the capacity to match the complexities of auditory processing. This Perspective is a call for the artificial intelligence and hearing communities to come together to bring about a ...
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#1Tianshi Lu (The University of Texas Southwestern Medical Center)H-Index: 7
#2Ze Zhang (The University of Texas Southwestern Medical Center)H-Index: 5
Last. Jun Wang (NYU: New York University)H-Index: 14
view all 13 authors...
Neoantigens play a key role in the recognition of tumour cells by T cells; however, only a small proportion of neoantigens truly elicit T-cell responses, and few clues exist as to which neoantigens are recognized by which T-cell receptors (TCRs). We built a transfer learning-based model named the pMHC–TCR binding prediction network (pMTnet) to predict TCR binding specificities of the neoantigens—and T cell antigens in general—presented by class I major histocompatibility complexes. pMTnet was co...
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#1C. De Wagter (Delft University of Technology)H-Index: 15
#2Federico Paredes-Valles (Delft University of Technology)H-Index: 3
Last. G. C. H. E. de Croon (Delft University of Technology)H-Index: 19
view all 4 authors...
In the AlphaPilot Challenge, teams compete to fly autonomous drones through an obstacle course as fast as possible. The 2019 winning team MAVLab reflects on the challenge of beating human pilots.
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