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Learning from Evolution for Evolution [chapter]

Stefan Kögel, Matthias Tichy, Abhishek Chakraborty, Alexander Fay, Birgit Vogel-Heuser, Christopher Haubeck, Gabriele Taentzer, Timo Kehrer, Jan Ladiges, Lars Grunske, Mattias Ulbrich, Safa Bougouffa (+7 others)
2019 Managed Software Evolution  
Hence, they enable learning from evolutions for evolutions. evolutions presented in Sect. 10.1.  ...  Learning from Evolution for Evolution Learning from Evolution for Evolution 1b is a modified version of the Pick-and-Place Unit (PPU) Scenario 1, which only uses metallic workpieces.  ... 
doi:10.1007/978-3-030-13499-0_10 fatcat:ucb3p4d2orgxjmptomc5tw4dzi

Bootstrapping $Q$ -Learning for Robotics From Neuro-Evolution Results

Matthieu Zimmer, Stephane Doncieux
2018 IEEE Transactions on Cognitive and Developmental Systems  
It is proposed to define a process to make a robot build its own representation for a reinforcement learning algorithm.  ...  The principle is to first use a direct policy search in the sensori-motor space, i.e. with no predefined discrete sets of states nor actions, and then extract from the corresponding learning traces discrete  ...  Bootstrapping Q-Learning for Robotics from Neuro-Evolution Results Matthieu Zimmer, Stephane Doncieux, Member, IEEE Abstract-Reinforcement learning problems are hard to solve in a robotics context as  ... 
doi:10.1109/tcds.2016.2628817 fatcat:afwp2m22wvagngylysgivbumci

Leap-frog neural network for learning the symplectic evolution from partitioned data [article]

Xin Li, Jian Li, Zhihong Jeff Xia
2022 arXiv   pre-print
For the Hamiltonian system, this work considers the learning and prediction of the position (q) and momentum (p) variables generated by a symplectic evolution map.  ...  For predicting the system evolution in a short timescale, the LFNN could effectively avoid the issue of accumulative error.  ...  We then are thinking about that whether we could develop the idea "generating function" for learning the symplectic evolution from the position and momentum variables.  ... 
arXiv:2208.14148v1 fatcat:movwcdxa6rcc3pcn6bmqnjic6a

Design-based learning for a sustainable future: student outcomes resulting from a biomimicry curriculum in an evolution course

Erin Fried, Andrew Martin, Alexa Esler, Antoine Tran, Lisa Corwin
2020 Evolution: Education and Outreach  
Research from socio-scientific, design-based, and problem-based learning demonstrates that contextualized, real-world tasks can improve students' ability to apply their scientific knowledge in practical  ...  To assess the targeted outcomes, we analyzed students' responses from a pre-post assessment.  ...  Amanda Carrico (University of Colorado-ENVS); we are also grateful to The Ecology and Evolutionary Biology Department (department housing this class) and the Center for STEM Learning at the University  ... 
doi:10.1186/s12052-020-00136-6 fatcat:3nw7nuhmwva4bczrgauw2dw33u

Lessons Learned from Authoring for Inquiry Learning: A Tale of Authoring Tool Evolution [chapter]

Tom Murray, Beverly Woolf, David Marshall
2004 Lecture Notes in Computer Science  
We present an argument for ongoing and deep participation by subject matter experts (SMEs, i.e. teachers and domain experts) in advanced learning environment (LE) development, and thus for the need for  ...  We summarize lessons learned along they way about authoring tool usability. 1  ...  In addition, each successive version added new functionality as the need for it was realized. Lessons Learned from Three Authoring Tools A Network-based representation.  ... 
doi:10.1007/978-3-540-30139-4_19 fatcat:plz5v3z6ajczrcwlze5xgcepqu

Deep Neighbor Information Learning from Evolution Trees for Phylogenetic Likelihood Estimates

Cheng Ling, Wenhao Cheng, Haoyu Zhang, Hanhao Zhu, Hua Zhang
2020 IEEE Access  
The basic idea of machine learning is to learn theory from massive data automatically by reasoning, pattern matching or sample learning [33, 35] .  ...  The approach initially learns the deep neighbor information between nodes from the topology representations of evolutionary trees, integrates likelihood information from these trees, and trains a nonlinear  ... 
doi:10.1109/access.2020.3043150 fatcat:ho3lie4zr5djrmtfxyrtcpfbs4

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction [article]

Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie-Yan Liu, Wei-Mou Zheng, Dongbo Bu
2020 bioRxiv   pre-print
Here, we report a deep neural network framework (called CopulaNet) to learn residue co-evolution directly from MSA without any handcrafted features.  ...  Residue co-evolution has become the primary principle for estimating inter-residue distances since the residues in close spatial proximity tend to co-evolve.  ...  financial supports for this study and publication charges.  ... 
doi:10.1101/2020.10.06.327585 fatcat:ibaqr2pwivhvjfsrcpqctuocoi

The Evolution of "Enhanced" Cognitive Behavior Therapy for Eating Disorders: Learning From Treatment Nonresponse

Zafra Cooper, Christopher G. Fairburn
2011 Cognitive and Behavioral Practice  
In this paper we discuss how the development of this broader theory and treatment arose from focusing on those patients who did not respond well to earlier versions of the treatment.  ...  In recent years there has been widespread acceptance that cognitive behavior therapy (CBT) is the treatment of choice for bulimia nervosa.  ...  SELF-EVALUATION L I F E as it is easier to learn and implement, with the new, more complex, form being reserved for patients of the type that previously benefited least from treatment.  ... 
doi:10.1016/j.cbpra.2010.07.007 pmid:23814455 pmcid:PMC3695554 fatcat:534bvdslljbkfdxn37eu7ajwem

Mathematical derivations and supplemental figures from The importance of life history and population regulation for the evolution of social learning

Dominik Deffner, Richard McElreath
2020 Figshare  
We discuss why life history and age structure are important for social learning and present an exemplary model of the evolution of social learning in which demographic properties of the population arise  ...  To explain the empirical association between social learning and long life spans and to appreciate the implications for human evolution, we need further modelling frameworks allowing strategic learning  ...  S5 Results for simulations with low cost of individual learning (c = 0.01).  ... 
doi:10.6084/m9.figshare.12279650.v1 fatcat:j2np4e3irbbfteitedy7grrnwm

Slow-Fast Cognitive Phenotypes and Their Significance for Social Behavior: What Can We Learn From Honeybees?

Dhruba Naug, Catherine Tait
2021 Frontiers in Ecology and Evolution  
Using Tinbergen's explanatory framework for different levels of analyses and drawing from the large body of knowledge about honeybee behavior, we discuss the observed interindividual variation in cognitive  ...  of how slow-fast cognitive differences, by influencing collective behavior, impact social evolution.  ...  ACKNOWLEDGMENTS We would like to thank the funding from National Science Foundation, United States and Foundation for Food and Agricultural Research, United States for supporting the research and the effort  ... 
doi:10.3389/fevo.2021.766414 fatcat:hczdndkd6zaqhmr7hwzziw4tum

Digital board game design for an English vocabulary learning tool while learning from home [chapter]

D.K. Aditya, I.N. Kusmayanti, R. Hendryanti, P.F. Alam
2021 Dynamics of Industrial Revolution 4.0: Digital Technology Transformation and Cultural Evolution  
This current research explores the design phase from the game designers' perspectives.  ...  , a digital board game for English vocabulary learning focusing on the design phase involving language experts, game designers, and software developers.  ...  ACKNOWLEDGEMENTS The research team would like to thank Telkom University for funding the development of Square Talks!  ... 
doi:10.1201/9781003193241-31 fatcat:6vq6qr4fdvg53mn2ec6pyvlyjm

A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint [article]

Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik
2021 arXiv   pre-print
A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality.  ...  Here, we unprecedentedly explore the question: Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities?  ...  Another work also proposed a infinite sample selection strategy for brain network evolution prediction from baseline [9] .  ... 
arXiv:2110.03535v1 fatcat:le52memzfvd3vmxu4g56xxzjpu

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie-Yan Liu, Wei-Mou Zheng, Dongbo Bu
2021 Nature Communications  
Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA.  ...  Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure.  ...  providing financial supports for this study and publication charges.  ... 
doi:10.1038/s41467-021-22869-8 pmid:33953201 fatcat:6qnsdwl5zbeujbrjfrakq6spna

A comparison between data requirements and availability for calibrating predictive ecological models for lowland UK woodlands: learning new tricks from old trees

Matthew R. Evans, Aristides Moustakas
2016 Ecology and Evolution  
The mensuration of trees has changed little in the last 400 years (focussing almost exclusively on DBH) despite major changes in the nature of the source of value obtained from trees over this time.  ...  This results in there being insufficient data to parameterize process-based models in order to meet the societal demand for ecological prediction.  ...  Ecology and Evolution published by John Wiley & Sons Ltd.  ... 
doi:10.1002/ece3.2217 pmid:27547315 pmcid:PMC4979709 fatcat:n5km7l7kfreh7feccxzh2y7asy

Lessons learned from the evolution of terrestrial animal health surveillance in Canada and options for creating a new collaborative national structure

V Wayne Lees, Cameron Prince
2017 Canadian veterinary journal  
Use of this article is limited to a single copy for personal study.  ...  Anyone interested in obtaining reprints should contact the CVMA office (hbroughton@cvma-acmv.org) for additional copies or permission to use this material elsewhere.  ...  We wish to thank James Dunlop of TDV Global and Harry Gardiner of the CFIA for their helpful suggestions. CVJ  ... 
pmid:28487589 pmcid:PMC5394601 fatcat:ywf4sbdrvrdgrafaiclwdn4xvq
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