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Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data [article]

Michael DeGiorgio, Raquel Assis
2020 bioRxiv   pre-print
Thus, here we develop CLOUD, a multi-layer neural network built upon a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary  ...  Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often  ...  Last, we apply CLOUD to empirical data from Drosophila Bachtrog, 2013, Assis, 2019] to classify retention mechanisms and predict underlying evolutionary parameters after gene duplication in this lineage  ... 
doi:10.1101/2020.06.19.162107 fatcat:a3uka274dnfqtk3mswkqqtaj24

A lightweight, high performance communication protocol for grid computing

Phillip M. Dickens
2009 Cluster Computing  
This can lead to very poor performance if the control mechanisms interpret such loss as congestion in the network and, in response to such perceived network congestion, trigger very aggressive congestion  ...  We discuss the integration of the classifier into the congestion control structures of an existing high-performance communication protocol, and provide empirical results showing that it correctly diagnosed  ...  incorrectly classified as network contention.  ... 
doi:10.1007/s10586-009-0107-x fatcat:dhjjvexcnzbbbdo5hvu2msmho4

Triple Memory Networks: a Brain-Inspired Method for Continual Learning [article]

Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, Yi Zhong
2020 arXiv   pre-print
TMNs model the interplay of hippocampus, prefrontal cortex and sensory cortex (a neocortex region) as a triple-network architecture of generative adversarial networks (GAN).  ...  The underlying neural mechanisms possibly attribute to the interplay of hippocampus-dependent memory system and neocortex-dependent memory system, mediated by prefrontal cortex.  ...  The loss function of the auxiliary classifier L D consists of a cross entropy term and an elastic weight consolidation (EWC) regularizer on empirical FIM F D .  ... 
arXiv:2003.03143v1 fatcat:eoiv3imfcrejrfvdbr3m5l4cxa

Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment

Ibrahim Masood, Nadia Zulikha Zainal Abidin, Nur Rashida Roshidi, Noor Azlina Rejab, Mohd Faizal Johari
2013 Applied Mechanics and Materials  
Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically.  ...  Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control.  ...  Based on this guideline, further experiments have been performed to evaluate the performance of an ANN model classifier when design with FFDOE compared to an empirical design.  ... 
doi:10.4028/www.scientific.net/amm.465-466.1149 fatcat:murczextl5fwlaqmuoeldwfcte

Coherency of circadian rhythms in the SCN is governed by the interplay of two coupling factors

Isao T. Tokuda, Daisuke Ono, Sato Honma, Ken-Ichi Honma, Hanspeter Herzel, Joseph Ayers
2018 PLoS Computational Biology  
In mammals, a network of coupled neurons in the suprachiasmatic nucleus (SCN) is entrained to environmental light-dark cycles and orchestrates the timing of peripheral organs.  ...  We use empirical orthogonal functions (EOFs) to characterize spatio-temporal patterns. Simulations of coupled stochastic single cell oscillators can reproduce the diversity of observed patterns.  ...  The detailed coupling mechanisms between SCN neurons are debated.  ... 
doi:10.1371/journal.pcbi.1006607 pmid:30532130 pmcid:PMC6301697 fatcat:de7cjgu6bfejbb7tnbllgafaje

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN [article]

Ke Sun, Zhanxing Zhu, Zhouchen Lin
2019 arXiv   pre-print
This new defense mechanism that uses boundary samples to enhance the robustness of networks opens up a new way to defense adversarial attacks consistently.  ...  In this work, we propose a novel defense mechanism called Boundary Conditional GAN to enhance the robustness of deep neural networks against adversarial examples.  ...  Discussions and Conclusion Through our empirical observation, we found that the diversity and accuracy of conditional GAN is of significant importance for our defense mechanism.  ... 
arXiv:1902.11029v1 fatcat:qv3kj47gtfcrvmnyhixrwgf3fe

Modern Neural Networks Generalize on Small Data Sets

Matthew Olson, Abraham J. Wyner, Richard Berk
2018 Neural Information Processing Systems  
In this paper, we use a linear program to empirically decompose fitted neural networks into ensembles of low-bias sub-networks.  ...  We show that these sub-networks are relatively uncorrelated which leads to an internal regularization process, very much like a random forest, which can explain why a neural network is surprisingly resistant  ...  Empirically, this illustrates that a neural network can be decomposed as a collection of diverse sub-networks.  ... 
dblp:conf/nips/OlsonWB18 fatcat:iyh3kygqzbfkhislyt65gqwvhu

Guest editorial: special issue on predictive models for software quality

Leandro L. Minku, Ayşe B. Bener, Burak Turhan
2018 Software quality journal  
The results also suggest that the typical majority vote mechanism used to combine classifiers in ensembles may not be ideal for software defect prediction.  ...  Therefore, other combination mechanisms should be investigated.  ... 
doi:10.1007/s11219-018-9409-7 fatcat:253ddzlnl5dttdnprkkw743yci

Learning based access control in online social networks

Mohamed Shehab, Gorrell Cheek, Hakim Touati, Anna C. Squicciarini, Pau-Chen Cheng
2010 Proceedings of the 19th international conference on World wide web - WWW '10  
Furthermore, we provide mechanisms to enable users to fuse policy decisions that are provided by their friends or others in the social network.  ...  Our approach is based on a supervised learning mechanism that leverages user provided example policy settings as training sets to build classifiers that are the basis for auto-generated policies.  ...  A data set was collected by crawling the Last.FM social network. This data set was used for empirical testing of the our learning based approach for access control in social networks.  ... 
doi:10.1145/1772690.1772863 dblp:conf/www/ShehabCTSC10 fatcat:ktdtg32bvnbdxpbmoizmxsxrya

Bridging Machine Learning and Cryptography in Defence Against Adversarial Attacks [chapter]

Olga Taran, Shideh Rezaeifar, Slava Voloshynovskiy
2019 Landolt-Börnstein - Group III Condensed Matter  
This questions the security of deep neural networks (DNN) for many security-and trust-sensitive domains.  ...  We show empirically that our system is efficient against most famous state-of-the-art attacks in black-box and gray-box scenarios.  ...  -We present a new defence mechanism for DNN classifiers based on cryptographic principles.  ... 
doi:10.1007/978-3-030-11012-3_23 fatcat:tele2oo3s5ddphvfrcuczfw26u

Bridging machine learning and cryptography in defence against adversarial attacks [article]

Olga Taran, Shideh Rezaeifar, Slava Voloshynovskiy
2018 arXiv   pre-print
This questions the security of deep neural networks (DNN) for many security- and trust-sensitive domains.  ...  We show empirically that our system is efficient against most famous state-of-the-art attacks in black-box and gray-box scenarios.  ...  -We present a new defence mechanism for DNN classifiers based on cryptographic principles.  ... 
arXiv:1809.01715v1 fatcat:dvkxukxqtfdhjasnjjrzrhxjzy

Selective Classification for Deep Neural Networks [article]

Yonatan Geifman, Ran El-Yaniv
2017 arXiv   pre-print
In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level.  ...  At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability).  ...  While the rejection mechanisms we considered were extremely effective, it might be possible to identify superior mechanisms for a given classifier f .  ... 
arXiv:1705.08500v2 fatcat:p2udmyibhzaotjqm6ngij5ryem

Common Organizing Mechanisms in Ecological and Socio-economic Networks [article]

Serguei Saavedra, Felix Reed-Tsochas, Brian Uzzi
2011 arXiv   pre-print
However, the specific mechanism that may underlie similarities in nature and human systems has not been analyzed.  ...  - for both ecological networks and socio-economic networks.  ...  In Section 8 we use the BC model to study the effects of different organizing mechanisms and interaction constraints on the hierarchical arrangement of empirical networks.  ... 
arXiv:1110.0376v1 fatcat:tmyywtjlirfxla367y7hjiq6he

Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease [article]

Junren Pan, Shuqiang Wang
2022 arXiv   pre-print
In this work, a novel cross-modal transformer generative adversarial network(CT-GAN) is proposed to fuse functional information contained in resting-state functional magnetic resonance imaging (rs-fMRI  ...  The developed bi-attention mechanism can match functional information to structural information efficiently and maximize the capability of extracting complementary information from rs-fMRI and DTI.  ...  We can now introduce the bi-attention mechanism of transformers.  ... 
arXiv:2206.13393v2 fatcat:wiyuxxup7nbkhoeanwgbpzpcj4

Symmetric core-cohesive blockmodel in preschool children's interaction networks

Marjan Cugmas, Dawn DeLay, Aleš Žiberna, Anuška Ferligoj, Enrique Hernandez-Lemus
2020 PLoS ONE  
Yet, they did not provide any evidence that such a global network structure actually appears in any empirical data.  ...  Researchers have extensively studied the social mechanisms that drive the formation of networks observed among preschool children.  ...  The main focus of earlier studies was on social mechanisms in the context of empirical networks while less attention was paid to the social mechanisms in the context of specific global network structures  ... 
doi:10.1371/journal.pone.0226801 pmid:31940323 pmcid:PMC6961920 fatcat:irplnima6fbjtf2f5yh3q4aa34
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