Results 121 to 130 of about 147,704 (285)
Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu +5 more
wiley +1 more source
The Faraday Scalpel: Electrochemical Nerve Lesioning Mechanisms Studied in Invertebrate Models
Direct‐current produces nerve lesioning through discrete electrochemical reactions. Using hypoxia‐sensitive locust nerves and hypoxia‐tolerant leech nerves, we map three injury pathways: cathodic oxygen reduction, cathodic alkalization, and anodic chloride oxidation. These findings establish electrochemical lesioning—the “Faraday Scalpel”—as a precise,
Petra Ondráčková +5 more
wiley +1 more source
In view of the low segmentation accuracy for small-scale object and insufficient segmentation of local boundary for semantic segmentation methods based on Deep Learning, this paper proposes an image semantic segmentation approach based on attention ...
Zuoqiang Du, Yuan Liang
doaj +1 more source
Nighttime Semantic Segmentation with Attention and Low-Light Enhancement [PDF]
With the development of deep learning technology and the improvements in computing power, semantic segmentation of natural scene images captured during the day shows promising results.
Ci XIAO, Yang XU, Yongdan ZHANG, Mingwen FENG, Yiqian HUANG
doaj +1 more source
Semantic image segmentation using morphological tools [PDF]
In this work, we study the extraction of semantic objects using morphological tools. We decompose the image into its level sets and level lines (the borders of the level sets). Specifically, from all the level lines we extract the ones that contain T-junctions, have compact form, and are well-contrasted to obtain the semantic objects in the scene.
openaire +2 more sources
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv +11 more
wiley +1 more source
Research Advances in Deep Learning for Image Semantic Segmentation Techniques
Image semantic segmentation represents a significant area of research within the field of computer vision. With the advent of deep learning, image semantic segmentation techniques that integrate deep learning have demonstrated superior accuracy compared ...
Zhiguo Xiao +6 more
doaj +1 more source
Stable Diffusion Models Reveal a Persisting Human–AI Gap in Visual Creativity
This study examines visual creativity in humans and generative AI using the TCIA framework. Human artists outperform AI overall, yet structured human guidance substantially improves AI outputs and evaluations. Findings reveal that alignment with human creativity depends critically on contextual framing, highlighting both the promise and current ...
Silvia Rondini +8 more
wiley +1 more source
Dataset of bird's eye chilies farm for stereo image semantic segmentation. [PDF]
Saipullah KM +5 more
europepmc +1 more source
Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen +10 more
wiley +1 more source

