Results 151 to 160 of about 45,458 (300)

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System

International Journal of Prognostics and Health Management
Autonomous Centerline Tracking (ACT) enables an uninhabited aircraft system (UAS) to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. Guidelines by the European Union Aviation Safety Agency (
Yuning He, Johann Schumann
doaj   +1 more source

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

Co-Optimization of Capacity and Operation for Battery-Hydrogen Hybrid Energy Storage Systems Based on Deep Reinforcement Learning and Mixed Integer Programming

Energies
The hybrid energy storage system (HESS) that combines battery with hydrogen storage exploits complementary power/energy characteristics, but most studies optimize capacity and operation separately, leading to suboptimal overall performance.
Tiantian Qian, Kaifeng Zhang, Difen Shi, Lei Zhang   +3 more
doaj   +1 more source

Modeling Features at Runtime

, 2010
A feature represents a functional requirement fulfilled by a system. Since many maintenance tasks are expressed in terms of features, it is important to establish the correspondence between a feature and its implementation in source code.
Marcus Denker, Jorge Ressia, Denker, Marcus, Nierstrasz, Oscar, Greevy, Orla, Orla Greevy, Oscar Nierstrasz, Ressia, Jorge   +7 more
core   +1 more source

Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining

Advanced Intelligent Systems, EarlyView.
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson, Nebojša Maleševic, Anders Björkman, Christian Antfolk   +3 more
wiley   +1 more source

Human‐in‐the‐Loop Object Segmentation for 3D Gaussian Splatting via Finger‐based VR Interface

Advanced Intelligent Systems, EarlyView.
This study introduces a human‐in‐the‐loop segmentation framework for 3D Gaussian Splatting that integrates real‐time optimization with intuitive VR‐based finger prompting. Compared with existing automatic, learning‐based methods, it achieves significantly higher accuracy and reduced segmentation time.
Yongseok Lee, Hyunreal Park, Hyunsu Kim, Harim Ji, Dongho Yee, Dongjun Lee   +5 more
wiley   +1 more source

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

Advanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi   +2 more
wiley   +1 more source

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen, John Lim, Timothy H. Wong, Ismail M. Khater, Ivan Robert Nabi, Ghassan Hamarneh   +8 more
wiley   +1 more source

“It Is Much Safer to Be Sparse than Connected”: Safe Control of Robotic Swarm Density Dynamics with PDE Optimization with State Constraints

Advanced Intelligent Systems, EarlyView.
This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley   +1 more source