Results 131 to 140 of about 20,082 (264)

Initial State Privacy of Nonlinear Systems on Riemannian Manifolds

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley   +1 more source

Fusion or Confusion? Assessing the Impact of Visible‐Thermal Image Fusion for Automated Wildlife Detection

open access: yesRemote Sensing in Ecology and Conservation, EarlyView.
This study evaluates the performances of synchronous aerial visible (VIS) and thermal infrared (TIR) imagery for detecting great blue heron (Ardea herodias) nests and individuals using a YOLO11n model. VIS and TIR images were automatically aligned using deep learning, and both early and late fusion approaches were tested.
Camille Dionne‐Pierre   +6 more
wiley   +1 more source

Existence, multiplicity and asymptotic behavior of positive solutions to (p,q)-Laplacian elliptic systems

open access: yesElectronic Journal of Differential Equations
In this article, we study elliptic systems consisting of $p$-Laplacian equations and $q$-Laplacian equations (for short $(p,q)$-Laplacian elliptic system).
Xiankui Meng, Meiqiang Feng
doaj  

Rapid and Efficient Affinity Isolation of Engineered Extracellular Vesicles Encapsulating Functional Proteins via Internal Twin‐Strep Tetraspanin Tagging

open access: yesSmall, EarlyView.
Structure‐guided internal twin‐Strep tagging of the tetraspanins CD63 or CD9 enables rapid and efficient antibody‐free affinity isolation of engineered extracellular vesicles (EVs) using Strep technology while allowing luminal protein loading. The approach removes over 96% of contaminants, separates tagged EVs from coexisting viral particles, and ...
Minh‐Tu Pham   +14 more
wiley   +1 more source

Maximum and anti-maximum principles for the p-Laplacian with a nonlinear boundary condition

open access: yesElectronic Journal of Differential Equations, 2006
In this paper we study the maximum and the anti-maximum principles for the problem $Delta _{p}u=|u|^{p-2}u$ in the bounded smooth domain $Omega subset mathbb{R}^{N}$, with $|abla u|^{p-2}frac{partial u}{partial u }=lambda |u|^{p-2}u+h$ as a non linear ...
Aomar Anane, Omar Chakrone, Najat Moradi
doaj  

p-Laplacians for Manifold-Valued Hypergraphs

open access: yes
Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we define function spaces and corresponding symmetric products on the nodes and edges to derive hypergraph Laplacians.
Jo Andersson Stokke   +3 more
openaire   +3 more sources

Against All Odds?—A Surprisingly Not So Easy Dice Game

open access: yesTeaching Statistics, EarlyView.
ABSTRACT A game with 11 players, 2 dice, and simple rules. However, it turns out that the best strategy is not only absent from the literature, but that misconceptions about winning strategies also prevail among surveyed students. This makes it all the more surprising that it is not so easy to get to the bottom of the game mathematically.
Christoph Oberbucher   +1 more
wiley   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma   +4 more
wiley   +1 more source

Home - About - Disclaimer - Privacy