Results 81 to 90 of about 22,800 (222)

Transient Antiskyrmion‐Mediated Topological Transitions in Isotropic Magnets

Advanced Science, EarlyView.
A transient antiskyrmion‐mediated pathway that drives repeated stripe‐to‐skyrmion transitions is revealed, producing a net increase in topological charge in isotropic Dzyaloshinskii–Moriya interaction films. Experiments and simulations identify the antiskyrmion as a metastable excitation, enabling stochastic bitstream generation for probabilistic ...
Bingqian Dai, Tianyi Wang, Albert Lee, Shijie Xu, Zhongjian Bian, Xinyue Zhu, Puyang Huang, Aadi Chaturvedi, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Hanshen Huang, Lixuan Tai, Kin Wong, Jinbo Yang, Anjan Soumyanarayanan, Zhaochu Luo, Kang L. Wang   +18 more
wiley   +1 more source

Convergent and Divergent Connectivity Patterns of the Arcuate Fasciculus in Macaques and Humans

Advanced Science, EarlyView.
This study employs viral‐based single‐neuron tracing and dMRI‐based whole‐brain tractography to investigate arcuate fasciculus (AF) trajectories in macaque monkeys, and compares with the human AF connectome using spectral embedding. Results demonstrate conserved AF topography spanning temporoparietal‐auditory‐frontal pathways across primates, with ...
Jiahao Huang, Ruifeng Li, Wenwen Yu, Anan Li, Xiangning Li, Mingchao Yan, Lei Xie, Qingrun Zeng, Xueyan Jia, Shuxin Wang, Ronghui Ju, Feng Chen, Qingming Luo, Hui Gong, Andrew Zalesky, Xiaoquan Yang, Yuanjing Feng, Zheng Wang   +17 more
wiley   +1 more source

Multi‐View Biomedical Foundation Models for Molecule‐Target and Property Prediction

Advanced Science, EarlyView.
Molecular foundation models can provide accurate predictions for a large set of downstream tasks. We develop MMELON, an approach that integrates pre‐trained graph, image, and text foundation models and validate our multi‐view model on over 120 tasks, including GPCR binding.
Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi, Diwakar Mahajan, Hongyang Li, Yuxin Yang, Elif Eyigoz, Aldo Guzmán‐Sáenz, Daniel E. Platt, Timothy H. Rumbell, Kenney Ng, Sanjoy Dey, Myson Burch, Bum Chul Kwon, Pablo Meyer, Feixiong Cheng, Jianying Hu, Joseph A. Morrone   +17 more
wiley   +1 more source

Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors

Advanced Science, EarlyView.
A multimodal cross‐attentive graph neural network integrates molecular graphs with androgen and estrogen adverse outcome pathway (AOP)–anchored in vitro assay signals to predict in vivo endocrine disruption. By fusing information on Tier‐1 AOP logits with chemical structures, the framework achieves high accuracy and provides assay‐traceable ...
Eder Soares de Almeida Santos, Gustavo Felizardo Santos Sandes, Artur Christian Garcia da Silva, Holli‐Joi Martin, Eugene N. Muratov, Rodolpho de Campos Braga, Bruno Junior Neves   +6 more
wiley   +1 more source

In Situ Quantization with Memory‐Transistor Transfer Unit Based on Electrochemical Random‐Access Memory for Edge Applications

Advanced Science, EarlyView.
By combining ionic nonvolatile memories and transistors, this work proposes a compact synaptic unit to enable low‐precision neural network training. The design supports in situ weight quantization without extra programming and achieves accuracy comparable to ideal methods. This work obtains energy consumption advantage of 25.51× (ECRAM) and 4.84× (RRAM)
Zhen Yang, Yuxiang Yang, Baiqian Wang, Yaoyu Tao, Zelun Pan, Lei Cai, Teng Zhang, Longhao Yan, Xianbin Li, Yuchao Yang   +9 more
wiley   +1 more source

Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks

Applied Sciences
Accidents caused by falls among the elderly have become a significant social issue, making fall detection systems increasingly needed. Fall detection systems such as internet of things (IoT) devices must be affordable and compact because they must be ...
Hyeongwon Cho, Soongyu Kang, Yunseong Sim, Seongjoo Lee, Yunho Jung   +4 more
doaj   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Combinatorial Attacks on Binarized Neural Networks

, 2018
Binarized Neural Networks (BNNs) have recently attracted significant interest due to their computational efficiency. Concurrently, it has been shown that neural networks may be overly sensitive to "attacks" - tiny adversarial changes in the input - which may be detrimental to their use in safety-critical domains.
Khalil, Elias B., Gupta, Amrita, Dilkina, Bistra   +2 more
openaire   +2 more sources

Scaling Binarized Neural Networks on Reconfigurable Logic [PDF]

Proceedings of the 8th Workshop and 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms, 2017
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, which contain an abundance of fine-grained compute resources and can result in smaller, lower power implementations, or conversely in ...
Fraser, Nicholas J., Umuroglu, Yaman, Gambardella, Giulio, Blott, Michaela, Leong, Philip W., Vissers, Kees, Jahre, Magnus   +6 more
openaire   +3 more sources

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source