Results 41 to 50 of about 6,939 (213)

Snowmelt predicts earlier breeding across the latitudinal range of an Arctic nesting seabird, the Little Auk (Alle alle)

Journal of Animal Ecology, Volume 95, Issue 7, Page 1248-1259, July 2026.
Multi‐year monitoring of the crevice‐nesting High Arctic seabird, the Little Auk (Alle alle), across four colonies spanning distinct climatic regimes revealed that snowmelt timing is a key and consistent driver of breeding phenology. Earlier snowmelt advances access to nesting habitat, enabling birds to initiate reproduction sooner. These findings show
Martyna Syposz, Øystein Varpe, Sébastien Descamps, Jérôme Fort, David Grémillet, Ann Harding, Dariusz Jakubas, Dorota Kidawa, Nomikos Skyllas, Hallvard Strøm, Tom S. L. Versluijs, Katarzyna Wojczulanis‐Jakubas   +11 more
wiley   +1 more source

A seismic random noise suppression method based on CNN-Mamba

Meitian dizhi yu kantan
BackgroundSeismic random noise suppression is recognized as a key step to improve the quality of seismic data. Data-driven deep learning provides an intelligent solution for the noise suppression.
Xiujuan WEI, Xingye LIU, Huailai ZHOU
doaj   +1 more source

FMDNet: Spatial‐frequency feature routing for low‐dose CT denoising

Journal of Applied Clinical Medical Physics, Volume 27, Issue 6, June 2026.
Abstract Background Low‐dose computed tomography (LDCT) is widely used to reduce radiation exposure, but the reduced photon budget amplifies quantum noise and can introduce structured artifacts that obscure subtle boundaries and textures. Many deep learning denoisers process features in a single stream, which may encourage either over‐smoothing of weak
Yujie Yao, Yunsen Liang, Wen Xiao, Zibo Zhou, Yaoxue Xu, Yonghao Pan, Xiao Xia   +6 more
wiley   +1 more source

Bitemporal Remote Sensing Change Detection With State-Space Models

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Change detection in very-high-resolution remote sensing images has gained significant attention, particularly with the rise of deep learning techniques such as convolutional neural networks and Transformers.
Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir   +6 more
doaj   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

Dynamic Vision Mamba

CoRR
Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. However, spatial redundancy still exists in these models, represented by token and block redundancy. For token redundancy, we analytically find that early token pruning methods will result in inconsistency between ...
Mengxuan Wu, Zekai Li, Zhiyuan Liang, Moyang Li, Xuanlei Zhao, Samir Khaki, Zheng Zhu, Xiaojiang Peng, Konstantinos N. Plataniotis, Kai Wang 0036, Wangbo Zhao, Yang You 0001   +11 more
openaire   +2 more sources

DB-YOLO: A Dual-Branch Parallel Industrial Defect Detection Network

Sensors
Insulator defect detection in power inspection tasks faces significant challenges due to the large variations in defect sizes and complex backgrounds, which hinder the accurate identification of both small and large defects.
Ziling Fan, Yan Zhao, Chaofu Liu, Jinliang Qiu   +3 more
doaj   +1 more source

AI‐Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention‐Based Modeling

Laryngoscope Investigative Otolaryngology, Volume 11, Issue 3, June 2026.
We developed SUM‐MucusNet, an artificial intelligence system for reliable mucus segmentation in nasal endoscopy, designed to overcome challenges from poor image quality and illumination artifacts. The model represents a modest but statistically supported improvement over the next‐best model, achieving a Dice score of 71%. SUM‐MucusNet enables real‐time
Dipesh Gyawali, Jonathan Bidwell, Elena Karras, Thomas J. Mundy, Kayla E. Baker, Elena D. Bartolone, Dhara Patel, Akio Fujiwara, Edward D. McCoul   +8 more
wiley   +1 more source

H Mamba : Hyperbolic Mamba for Sequential Recommendation

ACM Transactions on Information Systems
Sequential recommendation systems require both temporal efficiency to handle long interaction histories and hierarchical representation to model complex user-item relationships.
Qianru Zhang, Honggang Wen, Wei Yuan, Crystal Chen, Menglin Yang, Siu-Ming Yiu, Hongzhi Yin   +6 more
openaire   +3 more sources

GeoMamba: Toward Efficient Geography-Aware Sequential POI Recommendation

IEEE Access
“Where to go next” is the fundamental problem in sequential point-of-interest (POI) recommendation, which takes as input the individual check-in history, mines the dynamic preference and suggests the expected POI for the next step behavior.
Jiubing Chen, Haoyu Wang, Jianxin Shang
doaj   +1 more source