scholarly journals The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning

10.5772/5290 ◽  
2008 ◽  
Author(s):  
Wets Janssens
Keyword(s):  
Reinforcement Learning ◽  
Sequence Data ◽  
Location Information ◽  
Allocation Of Time

Morphology and phylogenetic analyses reveal Montagnula puerensis sp. nov. (Didymosphaeriaceae, Pleosporales) from southwest China

Phytotaxa ◽  
2021 ◽  
Vol 514 (1) ◽  
pp. 1-25
Author(s):  
TIANYE DU ◽  
KEVIN D. HYDE ◽  
AUSANA MAPOOK ◽  
PETER E. MORTIMER ◽  
JIANCHU XU ◽  
...  
Keyword(s):  
Yunnan Province ◽  
Southwest China ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Morphological Characteristics ◽  
Location Information ◽  
Fruiting Bodies ◽  
Host Genus ◽  
Extant Species ◽  
Culture Characteristics

A dead woody sample of Acer sp. with fungal fruiting bodies was collected in Pu’er City of Yunnan Province. Multigene phylogenetic analyses of LSU, ITS, SSU, and tef1-α sequence data showed that our collection belongs to Montagnula and is well separated from all other extant species. Montagnula puerensis is compared with all extant species by morphological characteristics, culture characteristics, host, and location information and is the first report of Montagnula from the host genus Acer.

scholarly journals Allocating time and location information to activity–travel patterns through reinforcement learning

2007 ◽  
Vol 20 (5) ◽  
pp. 466-477 ◽  
Author(s):  
Davy Janssens ◽  
Yu Lan ◽  
Geert Wets ◽  
Guoqing Chen
Keyword(s):  
Reinforcement Learning ◽  
Location Information ◽  
Travel Patterns

scholarly journals Constructing Unrooted Phylogenetic Trees with Reinforcement Learning

2021 ◽  
Vol 66 (1) ◽  
pp. 37
Author(s):  
P. Liptak ◽  
A. Kiss
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Phylogenetic Trees ◽  
New Technologies ◽  
Sequence Data ◽  
Evolutionary Relationship ◽  
Distance Matrix ◽  
Sequencing Technologies ◽  
Reinforced Learning ◽  
The Relationship

With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

RLC: A Reinforcement Learning-Based Charging Algorithm for Mobile Devices

2021 ◽  
Vol 17 (4) ◽  
pp. 1-23
Author(s):  
Tang Liu ◽  
Baijun Wu ◽  
Wenzheng Xu ◽  
Xianbo Cao ◽  
Jian Peng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Devices ◽  
Mobile Agents ◽  
Mobile Application ◽  
Maximization Problem ◽  
Location Information ◽  
Residual Lifetime ◽  
Precise Location ◽  
Mobile Charger ◽  
Design Options

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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