Results 111 to 120 of about 20,283,454 (331)
A Semi-Markov Leaky Integrate-and-Fire Model
In this paper, a Leaky Integrate-and-Fire (LIF) model for the membrane potential of a neuron is considered, in case the potential process is a semi-Markov process.
Giacomo Ascione, Bruno Toaldo
doaj +1 more source
Feature Disentangling and Combination Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions
Spin–orbit torque magnetic tunnel junctions (SOT‐MTJs) enable efficient feature disentangling and integration in image data. A proposed algorithm leverages SOT‐MTJs as true random number generators to disentangle and recombine features in real time, with experimental validation on emoji and facial datasets.
Xiaohan Li +15 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
Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza +2 more
wiley +1 more source
A likelihood ratio test for stationarity of rating transitions [PDF]
For a time-continuous discrete-state Markov process as model for rating transitions, we study the time-stationarity by means of a likelihood ratio test. For multiple Markov process data from a multiplicative intensity model, maximum likelihood parameter ...
Walter, Ronja, Weißbach, Rafael
core
Enabling Stochastic Dynamic Games for Robotic Swarms
This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Background. Increasing the adequacy of mathematical models of the impact of malicious software (MS), used to substantiate the requirements for the characteristics and directions for improving the anti-virus mechanisms of computer systems (CS), can be ...
R.A. Khvorov +5 more
doaj +1 more source
Stochastic Process Mining, in particular, Markov processes, is used to represent uncertainty and variability in Activities of Daily Living (ADLs). However, the Markov models inherently assume that the time spent in each state must follow an exponential ...
Eman Shaikh +4 more
doaj +1 more source
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source

