Results 101 to 110 of about 20,022 (282)

Room‐Temperature Skyrmionic Synapse in 2D Ferromagnet Fe3GaTe2 Operating via Collective Spin Texture Transformation

Advanced Materials, EarlyView.
We demonstrate a neuromorphic synapse in 2D Fe3GaTe2 flakes. The device operates via a current‐driven transformation from a skyrmion‐lattice to a stripe‐domain state, yielding a linear anomalous Hall resistance response with a tunable slope to enable multiply‐accumulate operations. Simulations confirm its viability in artificial neural networks.
Jixiang Huang, Tongji Zhu, PeiYu Cai, Xiaoming Ma, Zhen Wang, Ruifu Zhang, Yi Hao, Jingdi Lu, Liubing He, Jun Xu, Yuejie Zhang, Wanjun Jiang, Jing Tao, Shiming Lei, Pavel Parchinskiy, Jing Teng, Lingfei Wang, Wei Niu, Elton J.G. Santos, Xiaoqian Zhang, Peng Li   +20 more
wiley   +1 more source

Viral Infection‐Inspired Autonomous Detection of Fusion‐Competent Viruses for Screening and Environmental Surveillance

Advanced Materials, EarlyView.
Inspired by viral entry mechanisms, the FUSION assay enables autonomous detection of respiratory viruses via membrane fusion–triggered CRISPR‐Cas13a activation. VEACON selectively fuses with fusion‐competent viruses, triggering fluorescence within confined vesicles.
Jae Chul Park, Yidam Song, Hyun‐Woo Choi, Jaeuk Sung, Harin Jin, WonSeok Gwak, Kirim Yoo, Soohwang Lee, Jongeon Park, Jeongmin Kim, Hye‐Jun Jo, Jahyun Koo, Youngdo Jeong, Kwan Hyi Lee, Seung‐Jung Kee, Hojun Kim   +15 more
wiley   +1 more source

Inverse Design of Amorphous Materials With Targeted Properties

Advanced Materials, EarlyView.
AMDEN is a diffusion model framework for the inverse design of amorphous materials with targeted properties. By incorporating Hamiltonian Monte Carlo refinement into the denoising process, the framework overcomes the challenge of generating thermally relaxed disordered structures.
Jonas A. Finkler, Yan Lin, Tao Du, Jilin Hu, Morten M. Smedskjaer   +4 more
wiley   +1 more source

Toward Variational Structural Learning of Bayesian Networks

IEEE Access
This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables.
Andres R. Masegosa, Manuel Gomez-Olmedo
doaj   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Investigation of connection between deep learning and probabilistic graphical models

, 2018
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author.
Hager, Paul Andrew
core  

Probabilistic graphical models for human interaction analysis

, 2006
The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is ...
Zhang, Dong
core   +1 more source

P244: Developing Bayesian graphical models to provide continuous, probabilistic variant interpretation

Genetics in Medicine Open, 2023
Toby Manders, Yuya Kobayashi, John Nicoludis, Arun Numpally, Flavia Facio, Britt Johnson, Keith Nykamp, Robert Nussbaum, Alex Colavin   +8 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

A Methodology for Acquiring Qualitative Knowledge for Probabilistic Graphical Models

, 2008
We present a practical and general methodology that simplifies the task of acquiring and formulating qualitative knowledge for constructing probabilistic graphical models (PGMs). The methodology efficiently captures and communicates expert knowledge, and

core