Results 71 to 80 of about 12,894,975 (292)

Communicating via Markov Decision Processes

, 2021
ICML ...
Sokota, S, de Witt, CS, Igl, M, Zintgraf, L, Torr, P, Strohmeier, M, Kolter, JZ, Whiteson, S, Foerster, J   +8 more
openaire   +4 more sources

Bounded parameter Markov decision processes with average reward criterion

, 2007
. Bounded parameter Markov Decision Processes (BMDPs) address the issue of dealing with uncertainty in the parameters of a Markov Decision Process (MDP).
Tewari, Ambuj, Ambuj Tewari, Bartlett, Peter L., Peter L. Bartlett   +3 more
core   +1 more source

Charting Endocrine Progenitors Across Species and Organs

Advanced Science, EarlyView.
Endocrine progenitors give rise to the hormone‐producing cells of the pancreas and intestine. Using single‐cell multiomics and proteomics, this study compares these progenitors across species, systems, and organs, mapping the conserved and species‐specific gene regulatory networks that guide their formation.
Changying Jing, Michael Sterr, Özüm Sehnaz Caliskan, Lama Saber, Nicole Katarina Rogers, Samuel Ogden, Kei Kozawa, Christos Karampelias, Jessica Jaki, Aimée Bastidas‐Ponce, Falk Janos Farkas, Oliver Czarnecki, Osvaldo Rodrigues Pereira, Perla Cota, Valerie Vandenbempt, Diego Balboa, Anika Böttcher, Natalie Krahmer, Mohammad Lotfollahi, Julie B. Sneddon, Heiko Lickert, Mostafa Bakhti   +21 more
wiley   +1 more source

Markov Decision Processing Networks

CoRR
We introduce Markov Decision Processing Networks (MDPNs) as a multiclass queueing network model where service is a controlled, finite-state Markov process. The model exhibits a decision-dependent service process where actions taken influence future service availability.
Sanidhay Bhambay, Thirupathaiah Vasantam, Neil Walton   +2 more
openaire   +2 more sources

Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

, 2011
Due to the limitation of current condition monitoring technologies, the estimates of asset health states may contain some uncertainties. A maintenance strategy ignoring this uncertainty of asset health state can cause additional costs or downtime.
Wolff, Rodney C., Yifan Zhou, Mathew, Joseph, Rodney Wolff, Sun, Yong, Yong Sun, Ma, Lin, Lin Ma, Joseph Mathew, Zhou, Yifan   +9 more
core   +1 more source

Markov chain Monte Carlo methods for state-space models with point process observations [PDF]

, 2012
This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan, Girolami, Mark, Yuan, Ke   +2 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Absorbing Markov decision processes

ESAIM: Control, Optimisation and Calculus of Variations
In this paper, we study discrete-time absorbing Markov Decision Processes (MDP) with measurable state space and Borel action space with a given initial distribution. For such models, solutions to the characteristic equation that are not occupation measures may exist.
Dufour, François, Prieto-Rumeau, Tomás
openaire   +3 more sources

Learning Moisture‐Induced Damage From Vision: Diffusion Models for Real‐Time Monitoring of Additive Manufacturing Processes

Advanced Science, EarlyView.
We introduce a vision‐based real‐time monitoring system for additive manufacturing that detects subtle moisture‐induced degradation via a diffusion model‐based framework. The approach enables nondestructive assessment of moisture‐induced damage level and mechanical performance and establishes a practical route toward more intelligent, reliable, and ...
Jiyoung Jung, Yuna Yoo, Dharneedar Ravichandran, Dahyun Daniel Lim, Grace X. Gu   +4 more
wiley   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source