Results 51 to 60 of about 20,283,454 (331)

Nonlinear Markov Processes in Big Networks

, 2016
Big networks express various large-scale networks in many practical areas such as computer networks, internet of things, cloud computation, manufacturing systems, transportation networks, and healthcare systems. This paper analyzes such big networks, and
Li, Quan-Lin
core   +2 more sources

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

A pure jump Markov process with a random singularity spectrum [PDF]

, 2009
We construct a non-decreasing pure jump Markov process, whose jump measure heavily depends on the values taken by the process. We determine the singularity spectrum of this process, which turns out to be random and to depend locally on the values taken ...
J. Barral, N. Fournier, S. Jaffard, Stéphane Seuret   +3 more
semanticscholar   +1 more source

Lipid‐Facilitated Opening of the ADAM10 Sheddase Revealed by Enhanced Sampling Simulations

Advanced Science, EarlyView.
Phosphatidylserine acts as a lipid trigger to enhance activation of the sheddase ADAM10. By integrating fluorescence spectroscopy assays with enhanced sampling molecular dynamics simulations, this study shows that phosphatidylserine promotes ADAM10 catalytic activity along with expansion of its extracellular domains, enhancing accessibility to scaffold
Adrien Schahl, Nandan Haloi, Marta Carroni, Shengpan Zhang, Quentin James Sattentau, Erdinc Sezgin, Lucie Delemotte, Rebecca J. Howard   +7 more
wiley   +1 more source

Controllable Summarization with Constrained Markov Decision Process

Transactions of the Association for Computational Linguistics, 2021
We study controllable text summarization, which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a novel training framework based on Constrained Markov Decision Process (CMDP)
Hou Pong Chan, Lu Wang, Irwin King
doaj   +1 more source

Diversity Patterns of Domestic Herbivore Viruses in China Reveal Transmission Dynamics with Disease Management Implications

Advanced Science, EarlyView.
This study performs pan‐viromic profiling of 14,529 samples from 5,710 domestic herbivores across five Chinese provinces, establishing the DhCN‐Virome (1,085,360 viral metagenomes). It reveals species/sample‐specific viromic signatures and cross‐species transmission dynamics, aiding unified disease control.
Yue Sun, Yong Li, Bagen Temur, Yuanqing Lin, Yuhang Liu, Le Yi, Zheng Sun, Gang Zhang, Jun Li, Yu Guo, Linchuan Li, Jinshan Cai, Wenliang Tian, Gen Meng, Lingling Jiang, Min Fang, Fuying Ding, Xuezhang Zhou, Changchun Tu, Biao He   +19 more
wiley   +1 more source

Levy Approximation of Impulsive Recurrent Process with Semi-Markov Switching [PDF]

, 2009
In this paper, the weak convergence of impulsive recurrent process with semi-Markov switching in the scheme of Levy approximation is proved. Singular perturbation problem for the compensating operator of the extended Markov renewal process is used to ...
Koroliuk, V. S., Limnios, N., Samoilenko, I. V.   +2 more
core  

Markov vs. nonMarkovian processes A comment on the paper Stochastic feedback, nonlinear families of Markov processes, and nonlinear Fokker-Planck equations by T.D. Frank

, 2007
The purpose of this comment is to correct mistaken assumptions and claims made in the paper Stochastic feedback, nonlinear families of Markov processes, and nonlinear Fokker-Planck equations by T. D. Frank.
Arnold, Bassler, Borland, Courbage, Durrett, Feller, Frank, Friedman, Gallucio, Hänggi, Hänggi, Joseph L. McCauley, Wang   +12 more
core   +1 more source

NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction

Advanced Science, EarlyView.
Chromatin loops are central to gene regulation and 3D genome organization. Leveraging Nanopore sequencing's ability to jointly capture DNA sequence and methylation, we present NanoLoop, the first framework for genome‐wide chromatin loop prediction using Nanopore data.
Wenjie Huang, Li Tang, Matthew C. Hill, Yonghao Zhang, Jun Xie, Min Li   +5 more
wiley   +1 more source

Features of using Markov decision-making processes when modeling attacks on artificial intelligence systems

Вестник Самарского университета: Естественнонаучная серия
In this paper, we study the features of modeling attacks on artificial intelligence systems. Markov decision-making processes are used in the construction of the model.
Igor A. Vetrov, Vladislav V. Podtopelny
doaj   +1 more source