Results 31 to 40 of about 68,852 (252)
Alexandria Engineering JournalInfrastructure assets, such as pavements, naturally deteriorate over time due to traffic loads, environmental conditions, and other external factors. Traditionally, deterministic models have been employed to predict performance, aiding in work planning ...
Che Shobry Shahid +5 more
doaj +1 more source
Lazy Probabilistic Model Checking without Determinisation [PDF]
, 2015 The bottleneck in the quantitative analysis of Markov chains and Markov decision processes against specifications given in LTL or as some form of nondeterministic B\"uchi automata is the inclusion of a determinisation step of the automaton under ...
Hahn, Ernst Moritz +4 more
core +2 more sources
Advanced Science, EarlyView.Integrated omics analysis of matched primary and liver metastatic NPC tumors reveals a unique NOTCH1+ CSC subpopulation exhibiting enhanced stemness properties and tumorigenic capacity. With in vitro and in vivo assays, exosomal transfer of tumor‐derived FTO from NOTCH1+ cells to the endothelium promotes vascular permeability and metastatic potential ...
Chun Wu +23 more
wiley +1 more source
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Advanced Science, EarlyView.Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Asymptotic Expansions for Stationary Distributions of Perturbed Semi-Markov Processes
, 2016 New algorithms for computing of asymptotic expansions for stationary distributions of nonlinearly perturbed semi-Markov processes are presented. The algorithms are based on special techniques of sequential phase space reduction, which can be applied to ...
A. Hanen +84 more
core +1 more source
Advanced Intelligent Systems, EarlyView.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
, 2017 We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex ...
Esfahani, Peyman Mohajerin +3 more
core +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Verification of Uncertain POMDPs Using Barrier Certificates
, 2018 We consider a class of partially observable Markov decision processes (POMDPs) with uncertain transition and/or observation probabilities. The uncertainty takes the form of probability intervals.
Ahmadi, Mohamadreza +3 more
core +1 more source