Results 131 to 140 of about 140,910 (288)
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Do People Make Decisions Under Risk Based on Ignorance? An Empirical Test of the Priority Heuristic against Cumulative Prospect Theory [PDF]
Brandstätter, Gigerenzer and Hertwig (2006) put forward the priority heuristic (PH) as a fast and frugal heuristic for decisions under risk. According to the PH, individuals do not make trade-offs between gains and probabilities, as proposed by expected ...
Andreas Glöckner, Tilmann Betsch
core
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Context Awareness and Human–Robot Interaction Optimization for Museum Intelligent Guide Robot
This study presents a context‐aware human–robot interaction framework designed for intelligent museum guide robots. The system features a three‐layer architecture—perception, understanding, and behavior execution—that enables adaptive and meaningful interactions with museum visitors.
Anna Zou, Yue Meng, Shijing Tong
wiley +1 more source
High‐Speed Altitude Regulation With Neuromorphic Camera and Lightweight Embedded Computation
Neuromorphic cameras deliver rapid, high‐dynamic‐range sensing but overwhelm embedded processors at high speeds. This work presents a lightweight, optimized Lucas–Kanade optical flow method with parallelization, gyroscopic derotation, and adaptive event slicing.
Simon L. Jeger +3 more
wiley +1 more source
A machine learning‐driven digital twin simulates an aptamer‐functionalized BioFET measuring 17β‐estradiol. Real‐time Isd signals are processed, features are extracted, and trained models estimate hormone concentration. In parallel, a one‐step‐ahead forward model learns biosensor dynamics and generates realistic synthetic signals, enabling in silico ...
Anastasiia Gorelova +4 more
wiley +1 more source
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source

