Results 61 to 70 of about 152,215 (282)

A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data.

PLoS ONE
Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial.
Benoîte de Saporta, Aymar Thierry d'Argenlieu, Régis Sabbadin, Alice Cleynen   +3 more
doaj   +1 more source

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long‐Term Active Place Avoidance Memory and “Shadow” Proteins

Advanced Science, EarlyView.
Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han, Alejandro Grau‐Perales, Rayna M. Harris, Edith Lesburguères, Hsin‐Yi Kao, Asit Pal, Juan Marcos Alarcon, Todd C. Sacktor, Stefano Martiniani, Hans A. Hofmann, André A. Fenton   +10 more
wiley   +1 more source

Lightweight opportunistic routing forwarding strategy based on Markov chain

Tongxin xuebao, 2017
A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was
Feng LI, Ya-li SI, Zhen CHEN, Li-min SHEN   +3 more
doaj   +2 more sources

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

IEEE Access, 2020
Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication's reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment.
Quan Zhou, Yonggui Li, Yingtao Niu
doaj   +1 more source

Partially Observable Risk-Sensitive Markov Decision Processes

, 2016
We consider the problem of minimizing a certainty equivalent of the total or discounted cost over a finite and an infinite time horizon which is generated by a Partially Observable Markov Decision Process (POMDP).
Bäuerle, Nicole, Rieder, Ulrich
core   +1 more source

Bisimulations and Logical Characterizations on Continuous-Time Markov Decision Processes [PDF]

, 2014
The conference version of this paper was published at VMCAI ...
Song, Lei, Zhang, Lijun, Godskesen, Jens Christian   +2 more
openaire   +4 more sources

First passage risk probability optimality for continuous time Markov decision processes [PDF]

Kybernetika, 2019
Summary: In this paper, we study continuous time Markov decision processes (CTMDPs) with a denumerable state space, a Borel action space, unbounded transition rates and nonnegative reward function. The optimality criterion to be considered is the first passage risk probability criterion.
Huo, Haifeng, Wen, Xian
openaire   +2 more sources

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

Comparison of stochastic prediction models based on visual inspections of bridge decks

Journal of Civil Engineering and Management, 2017
Due to a considerable amount of information required to support the decision-making processes, an increasing number of infrastructure owners use computerized management systems.
Ivan Zambon, Anja Vidovic, Alfred Strauss, Jose Matos, Joao Amado   +4 more
doaj   +1 more source

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

Advanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong, Ji Ma, Yidan Lu, Jiahui Zhang, Wanyue Li, Yeke Chen, Teng Zhang, Xuechao Chen, Zhangguo Yu, Peng Lu   +9 more
wiley   +1 more source