Results 111 to 120 of about 183,434 (340)

Malicious Code Detection Method Based on Hybrid Quantum Convolutional Neural Network [PDF]

Jisuanji kexue
Quantum computing is a new computing model based on quantum mechanics,with powerful parallel computing capabi-lity far beyond classical computing.Hybrid quantum convolutional neural network combines the dual advantages of quantum computing and classical ...
XIONG Qibing, MIAO Qiguang, YANG Tian, YUAN Benzheng, FEI Yangyang
doaj   +1 more source

Coordinated design of coding and modulation systems [PDF]

The joint optimization of the coding and modulation systems employed in telemetry systems was investigated. Emphasis was placed on formulating inner and outer coding standards used by the Goddard Spaceflight Center.
Ancheta, T., Johannesson, R., Lauer, G., Lee, L., Massey, J. L.   +4 more
core   +1 more source

There is no MacWilliams identity for convolutional codes [PDF]

, 1977
An example is provided of two convolutional codes that have the same transmission gain but whose dual codes do not. This shows that no analog of the MacWilliams identity for block codes can exist relating the transmission gains of a convolutional code ...
McEliece, Robert J., Shearer, James B.
core  

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Advanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu, Minhui Ouyang, Shufang Tan, Jianlin Guo, Ziqin Zhang, Xuan Liu, Risheng Liu, Hao Huang   +7 more
wiley   +1 more source

Determinate-state convolutional codes [PDF]

A determinate state convolutional code is formed from a conventional convolutional code by pruning away some of the possible state transitions in the decoding trellis. The type of staged power transfer used in determinate state convolutional codes proves
Collins, O., Hizlan, M.
core   +1 more source

DualPG‐DTA: A Large Language Model‐Powered Graph Neural Network Framework for Enhanced Drug‐Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting in Vivo Anti‐Leukemia Activity

Advanced Science, EarlyView.
This study introduces DualPG‐DTA, a framework integrating two pre‐trained models to generate molecular and protein representations. It constructs dual graphs processed by specialized neural networks with dynamic attention for feature fusion, achieving superior benchmark performance.
Yihao Chen, Jindi Huang, Cong Liu, Shipeng Zhang, Xinze Li, Zhang Zhang, Tie‐Gen Chen, Ling Wang   +7 more
wiley   +1 more source

HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning

Advanced Science, EarlyView.
HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li, Dong Zhang, Eryu Peng, Shijun Shen, Hamid Alinejad‐Rokny, Yao Liu, Junke Zheng, Cizhong Jiang, Youqiong Ye   +8 more
wiley   +1 more source

AMRConvNet: AMR-Coded Speech Enhancement Using Convolutional Neural\n Networks [PDF]

, 2020
Williard Joshua Jose
openalex   +1 more source

A long constraint length VLSI Viterbi decoder for the DSN [PDF]

A Viterbi decoder, capable of decoding convolutional codes with constraint lengths up to 15, is under development for the Deep Space Network (DSN). The objective is to complete a prototype of this decoder by late 1990, and demonstrate its performance ...
Collins, O., Pollara, F., Statman, J. I., Zimmerman, G.   +3 more
core   +1 more source

Pathomics Signature for Prognosis and CA19‐9 Interception in Pancreatic Ductal Adenocarcinoma: A Real‐Life, Multi‐Center Study

Advanced Science, EarlyView.
This study develops a deep learning‐based pathomics model to predict survival outcomes in pancreatic cancer patients. The CrossFormer architecture analyzes routine H&E‐stained tissue slides, identifying key prognostic features including stromal patterns, cellular characteristics, and immune infiltration.
Qiangda Chen, Zhihang Xu, Yiping Zou, Zhenlai Jiang, Yecheng Li, Taochen He, Hanlin Yin, Jiali Li, Yanfei An, Jiande Han, Yuqi Xie, Wei Gan, Yaolin Xu, Wenquan Wang, Junyi He, Haibo Wang, Wenchuan Wu, Zhenyu Ye, Wenhui Lou, Jihui Hao, Liang Liu, Jun Yu, Ning Pu   +22 more
wiley   +1 more source