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Deep learning-based pupil model predicts time and spectral dependent light responses
2021
Scientific Reports
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Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. ...
State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. ...
Acknowledgements Calculations on the Lichtenberg high-performance computer of the Technical University of Darmstadt were conducted for this research. ...
doi:10.1038/s41598-020-79908-5
pmid:33436693
fatcat:r74efwdi4bcbrj5jbbtxqrgyou
Managing Distributed Adaptation of Mobile Applications
[chapter]
2007
Lecture Notes in Computer Science
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The network management facilities are provided by the the MobileIP network component tailored for mobile mobile in IP networks. ...
The context manager is responsible for the management of the pool of context listeners, including resource listeners, and for delegating these changes to the adaptation manager. Network management. ...
The adaptation reasoning relies on the use of utility functions allowing the computation of the utility for the user of an application variant given the current user needs and available computing and communication ...
doi:10.1007/978-3-540-72883-2_8
fatcat:h7kdvu2pjnabhidoriu3ozg7fm
Adaptive Dual Network Design for a Class of SIMO Systems with Nonlinear Time-variant Uncertainties
2010
Acta Automatica Sinica
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A novel adaptive dual network design consisting of a rough adjustment network (RAN) and a fine adjustment network (FAN) is proposed to eliminate the unknown time-variant uncertainties of servo system. ...
Simulation results and comparative study of this method with those of existing approaches demonstrate the effectiveness of the proposed adaptive dual network design for position tracking. ...
In this paper, we propose a dual network design approach including a Fourier series based sliding mode adaptive control (FSSMAC) network for rough adjustment and an adaptive prediction critic network for ...
doi:10.1016/s1874-1029(09)60023-9
fatcat:gq25scww2rg2haw7zk42dqvqre
Phenotype Heritability in holobionts: An Evolutionary Model
[article]
2020
bioRxiv
pre-print
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Here we test this hypothesis by studying an evolutionary model based on gene regulatory networks. ...
However, genetic studies have been unable to identify the genomic signatures responsible for such heritability, as identifying the genetic variants that make a population prone to a given disease is not ...
Fig. 4 4 (a) Plot of the normalized effect / 0 of the variants as a function of the variant index , with 1 ≤ ≤ 200, for a network with = 50 and connectivity = 2. ...
doi:10.1101/2020.05.05.079137
fatcat:cj4mai4bvbhqbmhysfylcb3usq
A Rendezvous of Content Adaptable Service and Product Line Modeling
[chapter]
2005
Lecture Notes in Computer Science
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In this paper, we propose a service decision modeling technique for content adaptable applications in ubiquitous environment. ...
Modeling with software features and product line concepts may support for making service decision strategy. ...
The Content Adaptable Service Decision Process This chapter suggests a process model for content adaptable service. ...
doi:10.1007/11497455_8
fatcat:lggve5lfvndqtn7pngpcczovma
Neural Networks for Modal and Virtual Learning
[chapter]
2009
IFIP Advances in Information and Communication Technology
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or guided by performance feedback (reinforcement) and an adaptive function Neural Network (ADFUNN) in which adaption applies simultaneously to both the weights and the individual neuron activation functions ...
from non-stationary input data, or time variant learning oblectives, where the optimal mode is a function of time. ...
or guided by performance feedback (reinforcement) and an adaptive function Neural Network (ADFUNN) in which adaption applies simultaneously to both the weights and the individual neuron activation functions ...
doi:10.1007/978-1-4419-0221-4_2
fatcat:g5ckb4jlurff7hgw7vfirq2itm
SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
[article]
2019
arXiv
pre-print
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However, existing frameworks of adaptive loss functions often suffer from slow convergence and poor choice of weights for the loss components. ...
Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning. ...
We would like to thank Fred Garber, Olga Mendoza-Shrock, Jamison Moody, Oliver Nina, Alexis Ronnebaum and Suzanne Sindi for their feedback and support. ...
arXiv:1912.12355v1
fatcat:nqbttgpb7zbedoicowr3hkol5e
Using a neural network for estimating plant gradients in real-time optimization with modifier adaptation
2019
IFAC-PapersOnLine
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This work proposes a method for estimating plant gradients based on neural networks (radial basis function network -RBFN). ...
This work proposes a method for estimating plant gradients based on neural networks (radial basis function network -RBFN). ...
Dinesh Krishnamoorthy for providing the process models used in this work. The authors acknowledge Financial Support from the Norwegian research council /Intpart, SUBPRO ...
doi:10.1016/j.ifacol.2019.06.161
fatcat:mvyyb766fndgzi7fptrsiidmpm
A Spiking Neuron and Population Model based on the Growth Transform Dynamical System
[article]
2019
bioRxiv
pre-print
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One such variant described in this paper is a network that adapts itself according to the global dynamics to encode the steady-state solution with a reduced number of spikes. ...
The model allows integration and control of two different types of dynamics: (a) sub-threshold dynamics that determines the fixed-point solution of an underlying network objective or energy functional; ...
We then introduce a form of the energy function for a non-spiking variant of a neuron model based on this dynamical system, and extend it to a spiking variant that stochastically minimizes the network ...
doi:10.1101/523944
fatcat:kxqrmxchezgebmchquecggjjgm
yMap: An automated method to map yeast variants to protein modifications and functional regions
2016
Bioinformatics
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However, for model organisms such as yeast such tools are lacking, specifically to predict the effect of protein sequence altering variants on the protein level. ...
For the human genome, several tools are available to predict the impact of these variants on gene and protein functions. ...
Together, this suggests adaption may be achieved through rewiring of the signaling network. ...
doi:10.1093/bioinformatics/btw658
pmid:27797766
pmcid:PMC5408805
fatcat:dyttnruvajg7zakydwrs7qxufa
Hybridised Loss Functions for Improved Neural Network Generalisation
[chapter]
2022
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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2022 pre-print arXiv:2204.12244v1 fatcat:y7chqus7i5b4jh4kdj3md2alba
Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. ...
not be significantly different than the best loss function tested for all problems considered. ...
Introduction Loss functions in artificial neural networks (ANNs) are used to quantify the error produced by the model on a given dataset. ...
doi:10.1007/978-3-030-93314-2_11
fatcat:iyyuuxvyoff4pdxhatlhfyutvi
Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks
[article]
2022
arXiv
pre-print
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A Deep neural network is utilized and a deep learning algorithm is developed to learn the time-varying heterogeneous transmission rates for each variant. ...
Short-term forecasting of daily cases is demonstrated using long short term memory neural network and an adaptive neuro-fuzzy inference system. ...
Neural Network (EINN) and we forecast using an adaptive neuro-fuzzy system (ANFIS), which we called the EINN-ANFIS model. ...
arXiv:2205.06834v1
fatcat:fhwchw55e5c47ig4phwmck24na
Putting together QoS and security in autonomic pervasive systems
2010
Proceedings of the 6th ACM workshop on QoS and security for wireless and mobile networks - Q2SWinet '10
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This paper presents an adaptation model based on selection of component compositions enabling to capture dynamic and fine-grained trade-offs between both QoS and security in those systems. ...
We illustrate through a case study of a typical Beyond 3G adaptive multimedia streaming service how the model may be applied to find the right balance between different QoS and security dimensions. ...
QoS adaptation planning on 1000 variants yields adaptation times around 1 s which may be acceptable for an end user. However, this may not be the case when adapting security mechanisms. ...
doi:10.1145/1868630.1868634
dblp:conf/mswim/AliaLHE10
fatcat:uagzadrh4bgkxalnxpuos2hsja
DANE: Domain Adaptive Network Embedding
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1906.00684v1 fatcat:6qi7mtbinbdttn63gbwrrvvcom
Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. ...
However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. ...
Domain adaptation on networks aims to train a machine learning model M for a downstream task by minimizing its loss function on G src and ensure that M can also have good performance when we transfer it ...
arXiv:1906.00684v2
fatcat:5tmdx55hvjg2to7s6ie44ef454
Hosting and Using Services with QoS Guarantee in Self-adaptive Service Systems
[chapter]
2010
Lecture Notes in Computer Science
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In this paper we propose a solution based on the integration of an SLA mechanism into a compositional adaptation planning framework and describe a simple yet powerful implementation targeted for resource ...
To this end, service level negotiation and agreements (SLA) are important to ensure coordinated end to end adaptation. ...
Both information can be included in the utility function for adaptation reasoning. ...
doi:10.1007/978-3-642-13645-0_2
fatcat:fij3eyt5zbgu3mouroytgk2gwu
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