Evidence map›Paper›PMID 42271058›Full record

ArticleNature2026

Efficient and accurate neural-field reconstruction using resistive memory.

Yifei Yu, Xinyuan Zhang, Shaocong Wang, Woyu Zhang, Xiuzhe Wu, Yangu He, Jichang Yang, Yue Zhang, Ning Lin, Bo Wang and 16 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Yifei YuDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0009-0003-9208-2903
Xinyuan ZhangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Shaocong WangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-7195-8676
Woyu ZhangState Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.
Xiuzhe WuDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Yangu HeDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Jichang YangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0003-3760-6762
Yue ZhangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Ning LinDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0002-6198-7043
Bo WangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Xi ChenDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Songqi WangDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0009-0001-3771-8150
Xiaoshan WuDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Shihao HanDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Yi LiDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Meng XuDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.ORCID http://orcid.org/0000-0003-1674-6037
Hegan ChenDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China.
Wenkui ZhangState Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
Jingyi ChenState Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Fudan University, Shanghai, China.
Xumeng ZhangState Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0002-3828-151X
Xiaojuan QiDepartment of Electrical and Computer Engineering, the University of Hong Kong, Hong Kong, China. xjqi@eee.hku.hk.ORCID http://orcid.org/0000-0002-4285-1626
Dashan ShangState Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China. shangdashan@ime.ac.cn.ORCID http://orcid.org/0000-0003-3573-8390
Qi LiuState Key Laboratory of Integrated Chips and Systems, College of Integrated Circuits and Micro-Nano Electronics, Frontier Institute of Chip and System, Fudan University, Shanghai, China. qi_liu@fudan.edu.cn.ORCID http://orcid.org/0000-0001-7062-831X
Zhongrui WangSchool of Microelectronics, Southern University of Science and Technology, Shenzhen, China. wangzr@sustech.edu.cn.ORCID http://orcid.org/0000-0003-2264-0677
Kwang-Ting ChengACCESS - AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China.ORCID http://orcid.org/0000-0002-3885-4912
Ming LiuState Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.ORCID http://orcid.org/0009-0002-2570-7793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Applications such as medical imaging, augmented and virtual reality, and embodied artificial intelligence (AI) depend on the ability to reconstruct complex signals from sparse observations. These applications are characterized by incomplete measurements and limited computational resources. Traditional approaches to digital hardware face the following challenges: explicit signal representations require heavy sampling and storage, data movement across the von Neumann bottleneck dominates energy and latency, and CMOS (complementary metal-oxide-semiconductor)-based circuits offer limited parallel efficiency. Here we present a software-hardware co-optimization framework for sparse-input signal reconstruction. At the software level, we use neural fields

Indexed as

Neural Networks, ComputerAnimalsBrainHumansIntelligent SystemsMultilayer PerceptronsNormal DistributionSoftware

Identifiers

PMID42271058

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.