IEEE Transactions on Fuzzy Systems, Volume 21, Number 6, December 2013

1. An Improved Estimation Method for Unmodeled Dynamics Based on ANFIS and Its Application to Controller Design
Author(s): Zhang, Y. ; Chai, T. ; Wang, H. ; Chen, X. ; Su, C.-Y.
Pages: 989-1005

2. Linguistic Computational Model Based on 2-Tuples and Intervals
Author(s): Dong, Y. ; Zhang, G. ; Hong, W.-C. ; Yu, S.
Pages: 1006-1018

3. Dynamic Fuzzy Clustering and Its Application in Motion Segmentation
Author(s): Nguyen, T.M. ; Wu, Q.M.J.
Pages: 1019-1031

4. Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification
Author(s): Luo, M. ; Sun, F. ; Liu, H.
Pages: 1032-1043

5. Intelligent Hybrid Control System Design for Antilock Braking Systems Using Self-Organizing Function-Link Fuzzy Cerebellar Model Articulation Controller
Author(s): Lin, C.-M. ; Li, H.-Y.
Pages: 1044-1055

6. Simplified Interval Type-2 Fuzzy Logic Systems
Author(s): Mendel, J.M. ; Liu, X.
Pages: 1056-1069

7. Statistical Inference of Rough Set Dependence and Importance Analysis
Author(s): Hu, D. ; Yu, X.
Pages: 1070-1079

8. A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems
Author(s): Subramanian, K. ; Suresh, S. ; Sundararajan, N.
Pages: 1080-1095

9. Strongest Strong Cycles and $theta$-Fuzzy Graphs
Author(s): Mathew, S. ; Sunitha, M.S.
Pages: 1096-1104

10. Functional Machine With Takagi–Sugeno Inference to Coordinated Movement in Underwater Vehicle-Manipulator Systems
Author(s): dos Santos, C.H.F. ; De Pieri, E.R.
Pages: 1105-1114

11. Human Reliability Evaluation for Offshore Platform Musters Using Intuitionistic Fuzzy Sets
Author(s): Tyagi, S.K. ; Akram, M.
Pages: 1115-1122

12. Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence
Author(s): Pan, Y. ; Er, M.J.
Pages: 1123-1132

13. FINGRAMS: Visual Representations of Fuzzy Rule-Based Inference for Expert Analysis of Comprehensibility
Author(s): Pancho, D.P. ; Alonso, J.M. ; Cordon, O. ; Quirin, A. ; Magdalena, L.
Pages: 1133-1149

14. A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
Author(s): Bustince, H. ; Galar, M. ; Bedregal, B. ; Kolesarova, A. ; Mesiar, R.
Pages: 1150-1162

15. Defuzzification Functionals of Ordered Fuzzy Numbers
Author(s): Kosinski, W. ; Prokopowicz, P. ; Rosa, A.
Pages: 1163-1169

16. A Soft Modularity Function For Detecting Fuzzy Communities in Social Networks
Author(s): Havens, T.C. ; Bezdek, J.C. ; Leckie, C. ; Ramamohanarao, K. ; Palaniswami, M.
Pages: 1170-1175

IEEE Transactions on Evolutionary Computation, Volume 17, Number 6, December 2013

1. Striking a Mean- and Parent-Centric Balance in Real-Valued Crossover Operators
Author(s): Someya, H.
Pages: 737-754

2. Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots
Author(s): Lee, K.-B. ; Kim, J.-H.
Pages: 755-766

3. An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine
Author(s): Shim, V.A. ; Tan, K.C. ; Cheong, C.Y.
Pages: 767-785

4. An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization
Author(s): Liu, B. ; Zhang, Q. ; Fernandez, F.V. ; Gielen, G.G.E.
Pages: 786-796

5. Scaling Up Estimation of Distribution Algorithms for Continuous Optimization
Author(s): Dong, W. ; Chen, T. ; Tino, P. ; Yao, X.
Pages: 797-822

6. Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
Author(s): Garcia-Nieto, J. ; Olivera, A.C. ; Alba, E.
Pages: 823-839

7. Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems
Author(s): Sabar, N.R. ; Ayob, M. ; Kendall, G. ; Qu, R.
Pages: 840-861

8. Fitness Modeling With Markov Networks
Author(s): Brownlee, A.E.I. ; McCall, J.A.W. ; Zhang, Q.
Pages: 862-879

Neural Networks Volume 48 Pages 1-208

Neural Networks Letters

1. Is mutual information adequate for feature selection in regression?  
Pages: 1-7
Author(s): Benoît Frénay, Gauthier Doquire, Michel Verleysen
   
2. Neural architecture design based on extreme learning machine  
Pages: 19-24
Author(s): Andrés Bueno-Crespo, Pedro J. García-Laencina, José-Luis Sancho-Gómez
   
3. Comments on the “No-Prop” algorithm  
Pages: 59-60
Author(s): Meng-Hiot Lim

4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach  
Pages: 153-157
Author(s): He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian

Neuroscience

5. A model of task-specific focal dystonia  
Pages: 25-31
Author(s): Eckart Altenmüller, Dieter Müller

6. Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays  
Pages: 195-203
Author(s): Shiping Wen, Gang Bao, Zhigang Zeng, Yiran Chen, Tingwen Huang
   

Learning Systems

7. An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion 
Pages: 8-18
Author(s): Yuanyuan Liu, L.C. Jiao, Fanhua Shang, Fei Yin, F. Liu

8. Analysis of programming properties and the row–column generation method for 1-norm support vector machines 
Pages: 32-43
Author(s): Li Zhang, WeiDa Zhou

9. Fully corrective boosting with arbitrary loss and regularization  
Pages: 44-58
Author(s): Chunhua Shen, Hanxi Li, Anton van den Hengel
   
10. Fixed-final-time optimal control of nonlinear systems with terminal constraints  
Pages: 61-71
Author(s): Ali Heydari, S.N. Balakrishnan
   
11. Solving graph data issues using a layered architecture approach with applications to web spam detection
Pages: 78-90
Author(s): Franco Scarselli, Ah Chung Tsoi, Markus Hagenbuchner, Lucia Di Noi
   
12. Fuzzy rough sets, and a granular neural network for unsupervised feature selection 
Pages: 91-108
Author(s): Avatharam Ganivada, Shubhra Sankar Ray, Sankar K. Pal
   
13. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule  
Pages: 109-124
Author(s): Michael Beyeler, Nikil D. Dutt, Jeffrey L. Krichmar
   
14. 1-norm support vector novelty detection and its sparseness  
Pages: 125-132
Author(s): Li Zhang, WeiDa Zhou
   
15. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning  
Pages: 173-179
Author(s): Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi
   

Mathematical and Computational Analysis

16. Multivariate neural network operators with sigmoidal activation functions 
Pages: 72-77
Author(s): Danilo Costarelli, Renato Spigler
   
17. Self-Organizing Hidden Markov Model Map (SOHMMM) 
Pages: 133-147
Author(s): Christos Ferles, Andreas Stafylopatis
   
18. The breaking of a delayed ring neural network contributes to stability: The rule and exceptions 
Pages: 148-152
Author(s): T.N. Khokhlova, M.M. Kipnis
   
19. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays  
Pages: 158-172
Author(s): Zhenyuan Guo, Jun Wang, Zheng Yan
   
20. Dynamical behaviors for discontinuous and delayed neural networks in the framework of Filippov differential inclusions  
Pages: 180-194
Author(s): Lihong Huang, Zuowei Cai, Lingling Zhang, Lian Duan
   

Letter to the Editor

21. Reply to the Comments on the “No-Prop” algorithm  
Pages: 204
Author(s): Bernard Widrow
   
22. A comment on “ image -stability and image -synchronization for fractional-order neural networks”  
Pages: 207-208
Author(s): Li Kexue, Peng Jigen, Gao Jinghuai
   

Erratum

23. Erratum to “Comments on the ‘No-Prop’ algorithm” [Neural Netw. 48 (2013) 59–60]  
Pages: 205
Author(s): Meng-Hiot Lim
   

Corrigendum

24. Corrigendum to “Noise enhanced clustering and competitive learning algorithms” [Neural Netw. 37 (2013) 132–140]  
Pages: 206
Author(s): Osonde Osoba, Bart Kosko

Neural Networks new articles 25 November - 1 December, 2013

1. Feature selection and multi-kernel learning for sparse representation on a manifold
Jim Jing-Yan Wang, Halima Bensmail, Xin Gao

2. Large-scale linear Nonparallel Support Vector Machine solver
Yingjie Tian, Yuan Ping

3. Neural network for solving convex quadratic bilevel programming problems
Xing He, Chuandong Li, Tingwen Huang, Chaojie Li

Neural Networks new articles 19-25 November, 2013

1. Compressed classification learning with Markov chain samples 
Feilong Cao, Tenghui Dai, Yongquan Zhang, Yuanpeng Tan
   
2. Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks  
Ancai Zhang, Jianlong Qiu, Jinhua She

3. Pointwise probability reinforcements for robust statistical inference 
Benoît Frénay, Michel Verleysen

4. Robust support vector machine-trained fuzzy system
Yahya Forghani, Hadi Sadoghi Yazdi

5. A linear recurrent kernel online learning algorithm with sparse updates 
Haijin Fan, Qing Song
   
6. A one-layer recurrent neural network for constrained nonsmooth invex optimization 
Guocheng Li, Zheng Yan, Jun Wang

7. Semi-supervised learning of class balance under class-prior change by distribution matching
Marthinus Christoffel du Plessis, Masashi Sugiyama
   
8. Batch gradient method with smoothing image regularization for training of feedforward neural networks
Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu

Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"

Aim

The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.

Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.

Scope

Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:

•    Species distribution and ecological niche modelling
•    Predicting species abundance
•    Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
•    Analysis of species assemblages
•    Issues in the preparation of ecological data for modelling
•    Modelling of pollutants in air, land or water
•    Modelling water quality
•    Predicting the effects of climate change
•    Predicting crop hazards, pests or diseases
•    Identifying landscape features
•    Modelling ecosystem biomass

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1)    Information on the format and templates for papers can be found here:
       http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)    Papers should be submitted via the IJCNN 2014 paper submission site:
       http://ieee-cis.org/conferences/ijcnn2014/upload.php3)
       Select the Special Session name in the Main Research topic dropdown list
4)    Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organisers

•    Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org
•    Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
•    Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 12, December 2013

1. Canonical Correlation Analysis on Data With Censoring and Error Information
Author(s): Sun, J. ; Keates, S.
Pages: 1909-1919

2. Highly Accurate Moving Object Detection in Variable Bit Rate Video-Based Traffic Monitoring Systems
Author(s): Huang, S.-C. ; Chen, B.-H.
Pages: 1920-1931

3. Recurrent Neural Collective Classification
Author(s): Monner, D.D. ; Reggia, J.A.
Pages: 1932-1943

4. Online Selective Kernel-Based Temporal Difference Learning
Author(s): Chen, X. ; Gao, Y. ; Wang, R.
Pages: 1944-1956

5. Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Author(s): Wu, L. ; Feng, Z. ; Lam, J.
Pages: 1957-1972

6. Artificial Endocrine Controller for Power Management in Robotic Systems
Author(s): Sauze, C. ; Neal, M.
Pages: 1973-1985

7. Operator Control of Interneural Computing Machines
Author(s): Shih, M.-H. ; Tsai, F.-S.
Pages: 1986-1998

8. Multiple Graph Label Propagation by Sparse Integration
Author(s): Karasuyama, M. ; Mamitsuka, H.
Pages: 1999-2012

9. Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning
Author(s): Gao, X. ; Gao, F. ; Tao, D. ; Li, X.
Pages: 2013-2026

10. H_{\infty } State Estimation for Complex Networks With Uncertain Inner Coupling and Incomplete Measurements
Author(s): Shen, B. ; Wang, Z. ; Ding, D. ; Shu, H.
Pages: 2027-2037

11. Goal Representation Heuristic Dynamic Programming on Maze Navigation
Author(s): Ni, Z. ; He, H. ; Wen, J. ; Xu, X.
Pages: 2038-2050

12. Accelerated Canonical Polyadic Decomposition Using Mode Reduction
Author(s): Zhou, G. ; Cichocki, A. ; Xie, S.
Pages: 2051-2062

13. Hardware Friendly Probabilistic Spiking Neural Network With Long-Term and Short-Term Plasticity
Author(s): Hsieh, H.-Y. ; Tang, K.-T.
Pages: 2063-2074

14. Neural Network Architecture for Cognitive Navigation in Dynamic Environments
Author(s): Villacorta-Atienza, J.A. ; Makarov, V.A.
Pages: 2075-2087

15. An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time
Author(s): Fairbank, M. ; Alonso, E. ; Prokhorov, D.
Pages: 2088-2100

16. Semisupervised Multitask Learning With Gaussian Processes
Author(s): Skolidis, G. ; Sanguinetti, G.
Pages: 2101-2112

17. Nonlinear Projection Trick in Kernel Methods: An Alternative to the Kernel Trick
Author(s): Kwak, N.
Pages: 2113-2119

Evolving Systems Vol 4, Issue 4, November 2013

1. Editorial: Adaptive connectionist systems for engineering applications
Author(s): Chrisina Jayne
Abstract    Full text HTML    Full text PDF

2. Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data
Author(s): Antonios Papaleonidas & Lazaros Iliadis
Abstract    Full text HTML    Full text PDF

3. Information dynamics based self-adaptive reservoir for delay temporal memory tasks
Author(s): Sakyasingha Dasgupta , Florentin Wörgötter & Poramate Manoonpong
Abstract    Full text HTML    Full text PDF

4. EANN 2012: exploratory analysis of mobile phone traffic patterns using 1-dimensional SOM, clustering and anomaly detection
Author(s): Pekka Kumpulainen & Kimmo Hätönen
Abstract    Full text HTML    Full text PDF

5. Neural Adaptive Control in Application Service Management Environment
Author(s): Tomasz D. Sikora & George D. Magoulas
Abstract    Full text HTML    Full text PDF

Reminder: paper submission deadline for SCDM 2014

A reminder that the deadline for submitting papers to the First International Conference on Data Mining (SCDM) 2014 is 15 December 2014. This conference will be held in Kuala Lumpur, Malaysia, 16-18 June, 2014.

Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"

Aim

The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.

Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.

Scope

Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:

•    Species distribution and ecological niche modelling
•    Predicting species abundance
•    Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
•    Analysis of species assemblages
•    Issues in the preparation of ecological data for modelling
•    Modelling of pollutants in air, land or water
•    Modelling water quality
•    Predicting the effects of climate change
•    Predicting crop hazards, pests or diseases
•    Identifying landscape features
•    Modelling ecosystem biomass

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1)    Information on the format and templates for papers can be found here:
       http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)    Papers should be submitted via the IJCNN 2014 paper submission site:
       http://ieee-cis.org/conferences/ijcnn2014/upload.php3) 
       Select the Special Session name in the Main Research topic dropdown list
4)    Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organisers

•    Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org
•    Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
•    Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn

IEEE Transactions on Fuzzy Systems: Volume 21, Issue 5, October 2013

1. Fuzzy-Model-Based Fault-Tolerant Design for Nonlinear Stochastic Systems Against Simultaneous Sensor and Actuator Faults
Author(s): Ming Liu ; Xibin Cao ; Peng Shi
Page(s): 789-799

2. Stability Analysis of Polynomial-Fuzzy-Model-Based Control Systems Using Switching Polynomial Lyapunov Function
Author(s): Lam, H.K. ; Narimani, M. ; Hongyi Li ; Honghai Liu
Page(s): 800-813

3. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space
Author(s): Xu-Qing Tang ; Ping Zhu
Page(s): 814-824

4. RFRR: Robust Fuzzy Rough Reduction
Author(s): Suyun Zhao ; Hong Chen ; Cuiping Li ; Mengyao Zhai ; Xiaoyong Du
Page(s): 825-841

5. Model Checking of Linear-Time Properties Based on Possibility Measure
Author(s): Yongming Li ; Lijun Li
Page(s): 842-854

6. Clustering Spatiotemporal Data: An Augmented Fuzzy C-Means
Author(s): Izakian, H. ; Pedrycz, W. ; Jamal, I.
Page(s): 855-868

7. Conditional Density Estimation Using Probabilistic Fuzzy Systems
Author(s): van den Berg, J. ; Kaymak, U. ; Almeida, R.J.
Page(s): 869-882

8. Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach
Author(s): Lin Zhao ; Huijun Gao ; Karimi, H.R.
Page(s): 883-897

9. Multiary α-Resolution Principle for a Lattice-Valued Logic
Author(s): Yang Xu ; Jun Liu ; Xiaomei Zhong ; Shuwei Chen
Page(s): 898-912

10. Adaptive Fuzzy Decentralized Output Feedback Control for Nonlinear Large-Scale Systems With Unknown Dead-Zone Inputs
Author(s): Shaocheng Tong ; Yongming Li
Page(s): 913-925

11. Chaos-Based Fuzzy Regression Approach to Modeling Customer Satisfaction for Product Design
Author(s): Huimin Jiang ; Kwong, C.K. ; Ip, W.H. ; Zengqiang Chen
Page(s): 926-936

12. Induction of Shadowed Sets Based on the Gradual Grade of Fuzziness
Author(s): Tahayori, H. ; Sadeghian, A. ; Pedrycz, W.
Page(s): 937-949

13. A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Multiclassifiers
Author(s): Trawinski, K. ; Cordon, O. ; Sanchez, L. ; Quirin, A.
Page(s): 950-965

14. Network-Based Robust Passive Control for Fuzzy Systems With Randomly Occurring Uncertainties
Author(s): Zheng-Guang Wu ; Peng Shi ; Hongye Su ; Jian Chu
Page(s): 966-970

15. A Simple Fuzzy Method to Remove Mixed Gaussian-Impulsive Noise From Color Images
Author(s): Camarena, J.-G. ; Gregori, V. ; Morillas, S. ; Sapena, A.
Page(s): 971-977

16. Proximity-Based Clustering: A Search for Structural Consistency in Data With Semantic Blocks of Features
Author(s): Pedrycz, W.
Page(s): 978-982

17. A Note on Fuzzy Relational Equations With Min-Implication Composition
Author(s): Pingke Li
Page(s): 983-986

18. Comments on “Quantized Control Design for Impulsive Fuzzy Networked Systems”
Author(s): Guotao Hui ; Jun Yang ; Bonan Huang
Page(s): 987

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 11, November 2013

1. Error Surface of Recurrent Neural Networks
Author(s): Phan, M.C. ; Hagan, M.T.
Pages: 1709-1721
 
2. Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram
Author(s): Tengtrairat, N. ; Gao, B. ; Woo, W.L. ; Dlay, S.S.
Pages:  1722-1735

3. On the SVMpath Singularity
Author(s): Dai, J. ; Chang, C. ; Mai, F. ; Zhao, D. ; Xu, W.
Pages: 1736-1748
 
4. Multistability of Two Kinds of Recurrent Neural Networks With Activation Functions Symmetrical About the Origin on the Phase Plane
Author(s): Zeng, Z. ; Zheng, W.X.
Pages: 1749-1762
 
5. Safety-Aware Semi-Supervised Classification
Author(s): Wang, Y. ; Chen, S.
Pages: 1763-1772

6. Neural Network Approaches for Noisy Language Modeling
Author(s): Li, J. ; Ouazzane, K. ; Kazemian, H.B. ; Afzal, M.S.
Pages: 1773-1784

7. A New Discrete-Continuous Algorithm for Radial Basis Function Networks Construction
Author(s): Zhang, L. ; Li, K. ; He, H. ; Irwin, G.W.
Pages: 1785-1798

8. Finding Potential Support Vectors in Separable Classification Problems
Author(s): Varagnolo, D. ; Del Favero, S. ; Dinuzzo, F. ; Schenato, L. ; Pillonetto, G.
Pages: 1799-1813

9. Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks
Author(s): Fu, Z.-J. ; Xie, W.-F. ; Han, X. ; Luo, W.-D.
Pages: 1814-1823

10. Hierarchical Similarity Transformations Between Gaussian Mixtures
Author(s): Rigas, G. ; Nikou, C. ; Goletsis, Y. ; Fotiadis, D.I.
Pages: 1824-1835

11. Negative Correlation Ensemble Learning for Ordinal Regression
Author(s): Fernandez-Navarro, F. ; Gutierrez, P.A. ; Hervas-Martinez, C. ; Yao, X.
Pages: 1836-1849

12. Online Learning of a Dirichlet Process Mixture of Beta-Liouville Distributions Via Variational Inference
Author(s): Fan, W. ; Bouguila, N.
Pages: 1850-1862

13. Transfer Ordinal Label Learning
Author(s): Seah, C.-W. ; Tsang, I.W. ; Ong, Y.-S.
Pages: 1863-1876

14. Pseudo-Orthogonalization of Memory Patterns for Associative Memory
Author(s): Oku, M. ; Makino, T. ; Aihara, K.
Pages: 1877-1887

15. Multilabel Classification Using Error-Correcting Codes of Hard or Soft Bits
Author(s): Ferng, C.-S. ; Lin, H.-T.
Pages: 1888-1900

16. Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation
Author(s): Gonzalez, P. ; Alvarez, E. ; Barranquero, J. ; Diez, J. ; Gonzalez-Quiros, R. ; Nogueira, E. ; Lopez-Urrutia, A. ; del Coz, J.J.
Pages: 1901-1905

17. Corrections to: “Estimator Design for Discrete-Time Switched Neural Networks With Asynchronous Switching and Time-Varying Delay”
Author(s): Zhang, D. ; Yu, L. ; Wang, Q.-G. ; Ong, C.
Pages:  1906

Prioritizing insect pests with Kohonen SOM

My research interests and activities are split between two fields: computational intelligence (obviously) and ecological modelling. Although I got into ecological modelling via computational intelligence, many of my recent publications in ecological modelling haven't had anything to do with computational intelligence. An exception to this is a recently published paper in the journal Neobiota that I am a coauthor of: "Prioritizing the risk of plant pests by clustering methods: self-organising maps, k-means and hierarchical clustering".

The problem is this: given the species that are known to exist in various geo-political regions of the world, what is the likelihood of one of those species establishing in a region where it is not already present? Species presences and absences are represented by binary vectors, where each region has a vector, a one represents a presence of a particular species in that region, and a zero represents an absence in that region. By clustering the assemblage vectors using a SOM, it is possible to infer which species pose the greatest threat to any particular region.

The rationale behind this approach is that regions that have similar species assemblages are likely to have similar environments. So if several assemblages end up in the same cluster, and a species is present in many of those regions but absent in others, then that species is likely to become established in the regions from which it is absent.

In this work the SOM were used as data clustering algorithms, with the vector quantisation abilities of the SOM being largely underutilized. My own contribution to the work was the realisation that the SOM were being used to cluster data, and hence to test the approach against the much-faster k-means clustering algorithm. I found that k-means is just as effective at producing good clusters as the SOM, and is much faster.

There are some problems with this work as well: it is virtually impossible to determine which approach is better without testing data. Which means that if you are clustering a set of species assemblages, you also need some more up-to-date data to validate the predictions. I do have some thoughts on getting around this, which I am currently investigating.

Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"

Aim

The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.

Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.

Scope

Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:

•    Species distribution and ecological niche modelling
•    Predicting species abundance
•    Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
•    Analysis of species assemblages
•    Issues in the preparation of ecological data for modelling
•    Modelling of pollutants in air, land or water
•    Modelling water quality
•    Predicting the effects of climate change
•    Predicting crop hazards, pests or diseases
•    Identifying landscape features
•    Modelling ecosystem biomass

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1)    Information on the format and templates for papers can be found here:
       http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)    Papers should be submitted via the IJCNN 2014 paper submission site:
       http://ieee-cis.org/conferences/ijcnn2014/upload.php3) 
       Select the Special Session name in the Main Research topic dropdown list
4)    Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organisers

•    Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org
•    Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
•    Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn

Conference paper deadline: KES 2014

The deadline for submitting papers to the 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2014 is 15 March, 2014. This conference will be held in Gdynia, Poland, 15-17 September, 2014.

Conference paper deadline: EAIS 2014

The deadline for submitting papers to the IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) 2014 is January 15, 2014. This conference will be held in Linz, Austria, June 2-4, 2014.

Reminder: paper submission deadline: ICAISC 2014

A reminder that the deadline for submitting papers to the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC) 2014 is November 20, 2013 (beware the front page linked to has embedded folk-music playing). This conference will be held in Zakopane, Poland, June 1-5 2014.

Video: How to publish your research in IEEE CIS publications

A panel discussion on how to publish in the IEEE Computational Intelligence Society's publications. This discussion wraps up the individual talks by Professors Xin Yao, Kay Chen Tan, Derong Liu, Garrison Greenwood, Chin-Teng Lin and Simon M. Lucas . This panel session was part of the CEC 2013 conference.

How to publish your research: Video of Professor Simon M. Lucas

Professor Simon M. Lucas talks about how to publish research in IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games. This talk was part of a panel session at CEC 2013 and mostly talks about what kind of papers are published in TCIAIG.

How to publish your research: Video of Professor Chin-Teng Lin

Professor Chin-Teng Lin, who is the editor-in-chief of IEEE Transactions on Fuzzy Systems, speaks about publishing in that journal. This talk was part of a panel discussion at the CEC 2013 conference. Some of the points he makes in this talk are applicable to publishing in most journals:

• Read existing papers, know the field
• Present an issue of significance
• Choose a journal that fits well with your research
• Use the correct format for that journal
• Have focus & vision, don't be too ambitious with your paper
• Write clearly
• Get pre-review, ask  your colleagues to check your paper before submission
• Proofread!
• Be patient, reviews take time

How to publish your research: Video of Professor Garrison Greenwood

Professor Garrison Greenwood talks about how to publish your research in the IEEE Transactions on Evolutionary Computation. This talk was part of a panel session at the CEC 2013 conference.

The main points of this talk are:

• Establish the context of your research in the Related Research section following the introduction (as a side-note, in other fields this is the introduction: why does computer science insist of separating them?

• Use enough citations, and no more

• Write enough detail for a competent researcher to replicate your work, don't try to write a tutorial

• Read the instructions for authors

• Use good grammar and spelling

How to publish your research: Video of Professor Derong Liu

Professor Derong Liu talks about how to publish your research in IEEE Transactions on Neural Networks and Learning Systems at a panel session at the CEC 2013 conference. This talk gives a lot of information about the editorial and review process, as well as how to increase your chances of having a paper accepted and even how to get on the editorial board of the journal.

Call for papers: Special Session for WCCI 2014 "Applications of Computational lntelligence in Ecological Informatics and Environmental Modelling"

Aim

The aim of this special session is to provide a forum for recent research in the application of computational intelligence in the areas of ecological informatics, ecological modelling and environmental modelling.

Ecological informatics and the related field of ecological modelling involve constructing computational models of ecological systems. These models include such things as the distribution or abundance of particular species, models of the interaction between multiple species, and models of the future development of populations. Environmental modelling is closely related and involves constructing models of the physical environment that biological eco-systems inhabit. These models cover such topics as the climate and climate change and the detection of landscape features from geographical data. Models have also been constructed of such environmental topics as waste management systems, water quality and drainage systems and air pollution. As these are highly-complex systems, algorithms from the field of computational intelligence have already been widely applied to modelling this data. Previous work has successfully applied artificial neural networks, fuzzy systems, evolutionary algorithms, support vector machines and combinations of these including neuro-fuzzy and neuro-evolutionary approaches. In each case, computational intelligence methods were shown to be more effective at solving the problem than the alternative methods.

Scope

Topics relevant to this special session include, but are not limited to, the following applications of computational intelligence, including Artificial Neural Networks, Fuzzy Systems, and Evolutionary Algorithms:

•    Species distribution and ecological niche modelling
•    Predicting species abundance
•    Remote sensing image analysis and content-based image retrieval for Ecological Informatics and Environmental Modelling
•    Analysis of species assemblages
•    Issues in the preparation of ecological data for modelling
•    Modelling of pollutants in air, land or water
•    Modelling water quality
•    Predicting the effects of climate change
•    Predicting crop hazards, pests or diseases
•    Identifying landscape features
•    Modelling ecosystem biomass

Deadline

The deadline for submissions to this special session is 20 December 2013.

Information for Authors

1)    Information on the format and templates for papers can be found here:
       http://www.ieee-wcci2014.org/Paper%20Submission.htm
2)    Papers should be submitted via the IJCNN 2014 paper submission site:
       http://ieee-cis.org/conferences/ijcnn2014/upload.php3) 
       Select the Special Session name in the Main Research topic dropdown list
4)    Fill out the input fields, upload the PDF file of your paper and finalize your submission by the deadline of December 20, 2013

Organisers

•    Dr Michael J Watts, AIS St Helens, Auckland, New Zealand. mjwatts@ieee.org
•    Associate Professor Russel Pears, Auckland University of Technology, Auckland, New Zealand, russel.pears@aut.ac.nz
•    Professor Jie Yang, Shanghai Jiao Tong University, Shanghai, China, jieyang@sjtu.edu.cn

How to publish your research: Video of Professor Kay Chen Tan

A video of a talk by Professor Kay Chen Tan as part of a panel session at the CEC 2013 conference. In this video he describes the IEEE CIS Computational Intelligence Magazine.

How to publish your research: Video of Professor Xin Yao

This is a video of a talk given by Professor Xin Yao as part of a panel session at the CEC 2013 conference. A couple of the most salient points that I noticed:

1) If you want to publish your research, you must first do good research
2) Contact the editors of the journals you want to publish in before submitting

IEEE Transactions on Computational Intelligence and AI in Games: Volume 5, Issue 3, September 2013

1. Monte Carlo Search Algorithm Discovery for Single-Player Games
Author(s): F. Maes, D. L. St-Pierre, and D. Ernst
Pages: 201-213

2. On Scalability, Generalization, and Hybridization of Coevolutionary Learning: A Case Study for Othello
Author(s): M. Szubert, W. Jaśkowski, and K. Krawiec
Pages: 214-226

3. Database-Driven Real-Time Heuristic Search in Video-Game Pathfinding
Author(s): R. Lawrence and V. Bulitko
Pages: 227-241

4. Backward Induction and Repeated Games With Strategy Constraints: An Inspiration From the Surprise Exam Paradox
Author(s): J. Li, G. Kendall, and A. V. Vasilakos
Pages: 242-250

5. An Efficient Approach to Solving Nonograms
Author(s): I.-C. Wu, D.-J. Sun, L.-P. Chen, K.-Y. Chen, C.-H. Kuo, H.-H. Kang, and H.-H. Lin
Pages: 251-264

6. Applicability of GPGPU Computing to Real-Time AI Solutions in Games
Author(s): W. Blewitt, G. Ushaw, and G. Morgan
Pages: 265-275

7. Crowdsourcing the Aesthetics of Platform Games
Author(s): N. Shaker, G. N. Yannakakis, and J. Togelius
Pages: 276-290

IEEE Transactions on Autonomous Mental Development: Volume 5, Issue 3, 2013

1. GUEST EDITORIAL: Microdynamics of Interaction: Capturing and Modeling Infants’ Social Learning
Author(s): K. J. Rohlfing and G. O. Deák
Pages: 189-191

2. Mothers’ infant-directed gaze during object demonstration highlights action boundaries and goals
Author(s): R. J. Brand, E. Hollenbeck, and J. F. Kominsky
Pages: 192-201

3. From Action to Interaction: Infant Object Exploration and Mothers’ Contingent Responsiveness
Author(s): C. S. Tamis-LeMonda, Y. Kuchirko, and L. Tafuro
Pages: 202-209

4. Young Children’s Dialogical Actions: The Beginnings of Purposeful Intersubjectivity
Author(s): J. Rączaszek-Leonardi, I. Nomikou, and K.J.Rohlfing
Pages: 210-221

5. From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
Author(s): T. Cederborg and P.-Y. Oudeyer
Pages: 222-239

6. Supporting Early Vocabulary Development: What Sort of Responsiveness Matters?
Author(s): M. L. McGillion, J. S. Herbert, J. M. Pine, T. Keren-Portnoy, M. M. Vihman, and D. E. Matthews
Pages: 240-248

7. SEED Framework of Early Language Development: The Dynamic Coupling of Infant–Caregiver Perceiving and Acting Forms a Continuous Loop during Interaction
Author(s): P. Zukow-Goldring and N. d. V. Rader
Pages: 249-257

8. Methodological Considerations For Investigating the Microdynamics of Social Interaction Development
Author(s): K. de Barbaro, C. M. Johnson, D. Forster, and G. O. Deák
Pages: 258

IEEE Transactions on Evolutionary Computation: Volume 17, Issue 5, October 2013

1. On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization
Author(s): A. Y. S. Lam, V. O. K. Li, and J. Xu
Pages: 605-620

2. A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem
Author(s): S. Nguyen, M. Zhang, M. Johnston, and K. C. Tan
Pages: 621-639

3. An Evolutionary Negative-Correlation Framework for Robust Analog-Circuit Design Under Uncertain Faults
Author(s): M. Liu and J. He
Pages: 640-665

4. Multimodal Optimization Using a Biobjective Differential Evolution Algorithm Enhanced With Mean Distance-Based Selection
Author(s): A. Basak, S. Das, and K. C. Tan
Pages: 666-685

5. Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization Based Classifiers
Author(s): A. R. Baig, W. Shahzad, and S. Khan
Pages: 686-704

6. An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods
Author(s): M. Hu, T. Wu, and J. D. Weir
Pages: 705-720

7. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): S. Yang, M. Li, X. Liu, and J. Zheng
Pages: 721

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 10, October 2013

1. Adaptive Optimal Control of Unknown Constrained-Input Systems Using Policy Iteration and Neural Networks
Author(s): H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani
Pages: 1513-1525

2. Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition
Author(s): V. G. Kaburlasos, S. E. Papadakis, and G. A. Papakostas
Pages: 1526-1538

3. Rapid Feedforward Computation by Temporal Encoding and Learning With Spiking Neurons
Author(s): Q. Yu, H. Tang, K. C. Tan, and H. Li
Pages: 1539-1552

4. Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data
Author(s): R. Jenssen
Pages: 1553-1564

5. RBF-Based Technique for Statistical Demodulation of Pathological Tremor
Author(s): F. Gianfelici
Pages: 1565-1574

6. Automated Induction of Heterogeneous Proximity Measures for Supervised Spectral Embedding
Author(s): E. Rodriguez-Martinez, T. Mu, J. Jiang, and J. Y. Goulermas
Pages: 1575-1587

7. Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies
Author(s): J. Qin and C. Yu
Pages: 1588-1597

8. Learning Capability of Relaxed Greedy Algorithms
Author(s): S. Lin, Y. Rong, X. Sun, and Z. Xu
Pages: 1598-1608

9. Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection
Author(s): M. Tan, I. W. Tsang, and L. Wang
Pages: 1609-1622

10. Ensemble Learning in Fixed Expansion Layer Networks for Mitigating Catastrophic Forgetting
Author(s): R. Coop, A. Mishtal, and I. Arel
Pages: 1623-1634

11. SVR Learning-Based Spatiotemporal Fuzzy Logic Controller for Nonlinear Spatially Distributed Dynamic Systems
Author(s): X.-X. Zhang, Y. Jiang, H.-X. Li, and S.-Y. Li
Pages: 1635-1647

12. Single Image Super-Resolution With Multiscale Similarity Learning
Author(s): K. Zhang, X. Gao, D. Tao, and X. Li
Pages: 1648-1659

13. Tracking Algorithms for Multiagent Systems
Author(s): D. Meng, Y. Jia, J. Du, and F. Yu
Pages: 1660-1676

14. A Robust Elicitation Algorithm for Discovering DNA Motifs Using Fuzzy Self-Organizing Maps
Author(s): D. Wang and S. Tapan
Pages: 1677-1688

15. EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
Author(s): C.-T. Lin, S.-F. Tsai, and L.-W. Ko
Pages: 1689-1700

16. New Algebraic Criteria for Synchronization Stability of Chaotic Memristive Neural Networks With Time-Varying Delays
Author(s): G. Zhang and Y. Shen
Pages: 1701

Reminder: paper submission deadline for IEEE CIFEr 2014

A reminder that the deadline for submitting papers to the IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr) 2014 is November 1, 2013. This conference will be held in London, UK, 27-28 March, 2014.

On the importance of a good supervisor

One day, a fox was walking through the forest when he met a rabbit sitting outside a rabbit hole reading a pile of papers. "What are you doing?" the fox asked the rabbit. The rabbit looked up at the fox and replied "I'm doing the literature review for my thesis. It's on the superiority of rabbits over foxes. Would you like to come inside and discuss it?". The fox hungrily licked his lips, followed the rabbit into the rabbit hole, and was never seen again. 

Some time later, a wolf was walking through the forest and saw the rabbit sitting outside of his rabbit hole making notes on a thick pile of paper with a big, red, pen. "What are you doing?" the wolf asked the rabbit. The rabbit looked up and replied "I'm revising my thesis". The wolf asked the rabbit "What's your thesis about?" and the rabbit said "It's on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The wolf hungrily licked his lips, followed the rabbit down the rabbit hole, and was never seen again.

Some time later, a hare was walking through the forest when he saw the rabbit sitting in the sun with a big, satisfied grin on this face. "Why are you looking so happy?" the hare asked the rabbit. The rabbit looked at the hare and said "I've just been awarded my PhD. My thesis was on the superiority of rabbits over foxes and wolves. Would you like to come inside and discuss it?". The hare, curious about such a topic, followed the rabbit down the rabbit hole into the warren. In one corner of the rabbit's room was a pile of fox bones. In another corner was a pile of wolf bones. Sitting between the two piles of bones was a lion.

So you see, it doesn't matter what your thesis is on, as long as your supervisor is a lion.

A newly published article (discussed in more detail by one of the authors here) has examined the influence of several factors that may determine how successful a scientist is in their career, where success is measured by the number of publications the scientist (biologists in this case) has. While factors such as gender and language had some slight effect, the factor that was most influential was the number of publications a scientist had before completing their PhD.

In other words, someone who has learned to produce papers before they finish their PhD is more likely to be able to continue producing papers after they have finished their PhD. To me this seems analagous to saying that someone who has learned how to drive can drive. Apparently, stating the blindingly obvious is original research as long as it uses statistics.

Who does a pre-PhD learn this paper-production skill from? Most of the time, from their supervisor. A supervisor who produces a lot of papers, and includes their students in the process of doing so, will produce PhD graduates who have the skills to produce papers post-PhD. If the supervisor doesn't teach the student how to publish, where else will they get this skill?

The most disturbing implication of this is that if a student chooses the wrong supervisor, they will have little chance of a successful career. The article linked to above states that the institution that the PhD graduates from has no influence on success and the influence of other factors is weak. As an aside, this reinforces something I've been saying for a while - that the reputation of an institution is good for marketing, but says little about the quality of the staff there.

The sentiment behind the story at the top of this post, is that as long as your supervisor is a good supervisor, you will be successful. This makes choosing the right supervisor probably the most critical decision an aspiring academic can ever make, yet they must make it when they have little knowledge and no experience on which to draw to make that decision. This is a huge problem - how many perfectly capable researchers have had their careers destroyed, before they have even begun, by a bad choice of supervisor? More importantly, how do those of us who are post-PhD stop it from happening in the future?

I really wish I had an answer to that question.

Neural Networks Volume 47 Pages 1-150

1. Computation in the Cerebellum  
Author(s): Dieter Jaeger, Henrik Jorntell, Mitsuo Kawato
Pages: 1-2

2. The importance of stochastic signaling processes in the induction of long-term synaptic plasticity  
Author(s): Erik De Schutter
Pages: 3-10

3. Dendritic calcium signaling in cerebellar Purkinje cell  
Author(s): Kazuo Kitamura, Masanobu Kano
Pages: 11-17

4. Bistability in Purkinje neurons: Ups and downs in cerebellar research  
Author(s): Jordan D.T. Engbers, Fernando R. Fernandez, Ray W. Turner
Pages: 18-31

5. Mechanisms producing time course of cerebellar long-term depression  
Author(s): Taegon Kim, Keiko Tanaka-Yamamoto
Pages: 32-35

6. Cerebellar LTD vs. motor learning—Lessons learned from studying GluD2  
Author(s): Michisuke Yuzaki
Pages: 36-41

7. Adaptive coupling of inferior olive neurons in cerebellar learning  
Author(s): Isao T. Tokuda, Huu Hoang, Nicolas Schweighofer, Mitsuo Kawato
Pages: 42-50

8. Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation  
Author(s): Miho Onizuka, Huu Hoang, Mitsuo Kawato, Isao T. Tokuda, Nicolas Schweighofer, Yuichi Katori, Kazuyuki Aihara, Eric J. Lang, Keisuke Toyama
Pages: 51-63

9. Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control  
Author(s): Ivan Herreros, Paul F.M.J. Verschure
Pages: 64-71

10. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: A model study  
Author(s): Soichi Nagao, Takeru Honda, Tadashi Yamazaki
Pages: 72-80
   
11. Classical conditioning of motor responses: What is the learning mechanism?  
Author(s): Germund Hesslow, Dan-Anders Jirenhed, Anders Rasmussen, Fredrik Johansson
Pages: 81-87

12. Cross-correlations between pairs of neurons in cerebellar cortex in vivo  
Author(s): Fredrik Bengtsson, Pontus Geborek, Henrik Jörntell
Pages: 88-94

13. Using a million cell simulation of the cerebellum: Network scaling and task generality  
Author(s): Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, Michael D. Mauk
Pages: 95-102
   
14. Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit  
Author(s): Tadashi Yamazaki, Jun Igarashi
Pages: 103-111

15. Modeling the generation of output by the cerebellar nuclei  
Author(s): Volker Steuber, Dieter Jaeger
Pages: 112-119

16. Modeling cancelation of periodic inputs with burst-STDP and feedback  
Author(s): K. Bol, G. Marsat, J.F. Mejias, L. Maler, A. Longtin
Pages: 120-133

17. Adaptive filters and internal models: Multilevel description of cerebellar function  
Author(s): John Porrill, Paul Dean, Sean R. Anderson
Pages: 134-149

Neural Networks: 3-9 September 2013

1. Dynamical behaviors for discontinuous and delayed neural networks in the framework of Filippov differential inclusions
Authors: Lihong Huang, Zuowei Cai, Lingling Zhang, Lian Duan
  
2. Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays
Authors: Zhenyuan Guo, Jun Wang, Zheng Yan
 
3. On the construction of the relevance vector machine based on Bayesian Ying-Yang harmony learning
Dansong Cheng, Minh Nhut Nguyen, Junbin Gao, Daming Shi
   
4. Exponential stabilization of delayed recurrent neural networks: A state estimation based approach
Authors: He Huang, Tingwen Huang, Xiaoping Chen, Chunjiang Qian

Reminder: conference submission deadline for SIAM SDM 2014

A reminder that the deadline for submitting abstracts to the 2014 SIAM International Conference on Data Mining (SIAM SDM) is 6 October 2013. The deadline for submitting full papers is 13 October 2013. This conference will be held in Philadelphia, USA, 24-26 April, 2014.

Rules for success

Adam Savage of Mythbusters fame has come up with a list of ten rules for success. Surprisingly enough, I think that most of them apply to success in academia as well as life in general, or at least life as a maker who blows things up on television. The rules (shamelessly copy-and-pasted from Boing Boing) are:

1. Get good at something.
Really good. Get good at as many things as you can. Being good at one thing makes it easier to get good at other things. 

2. Getting good at stuff takes practice.
Lots and lots of practice.

3. Get OBSESSED.
Everyone at the top of their field is obsessed with what they're doing.

4. Doing something well and thoroughly is its OWN reward.

5. Show and Tell.
If you do something well and you're happy with it, for FSM's sake, tell EVERYONE.

6. If you want something, ASK.
If something piques your interest, tell someone. If you want to learn something, ask someone, like your BOSS. As an employer, I can tell you, people who want to learn new skills are people I want to keep employed.

7. Have GOALS.
Make up goals. Set goals. Regularly assess where you are and where you want to be in terms of them. This is a kind of prayer that works, and works well. Allow for the fact that things will NEVER turn out like you think they will, and you must be prepared to end up miles from where you intended.

8. Be nice. To EVERYONE.
Life is way too short to be an asshole. If you are an asshole, apologize.

9. FAIL.
You will fail. It's one of our jobs in life. Keep failing. When you fail, admit it. When you don't, don't get cocky. 'Cause you're just about to fail again.

10. WORK YOUR ASS OFF.
Work like your life depends on it...

In my opinion, these rules apply to success in research and academia as well. Looking at them one-by-one:

1. Get good at something

While there is something to be said about the value of a generalist, everyone in research is a specialist at something. This is what the process of getting a PhD is about: becoming an expert, or specialist, in one particular topic. Of course, it's better to be good at several things, which is why I've been able to publish about computational intelligence and ecology as well as developing software. But I was only able to get into ecology because I'm good at computational intelligence, especially neural networks, and I was only able to get into neural networks because I'm a good programmer. So, being good at one thing can lead to being good at another thing.

2. Getting good at stuff takes practice

When I was an undergrad I was always programming - it was what I did to relax. But I got really good at it, which led me to neural networks and research. I've also written a lot of papers: the early ones were pretty bad, but after enough practice I got to be good at that as well. Even my experimental design has improved through practice. It's been said that mastering any skill takes 10,000 hours of practice, which doesn't seem too far off the mark to me.

3. Get OBSESSED

Obsession can be dangerous, it can keep you from your family, ruin your health and drive away your friends. But obsession also drives you to find the last bug in your code, to run just one more experiment, to refine your writing just that little bit more. Obsession leads to great results and great research.

4. Doing something well and thoroughly is its OWN reward

This is really close to the heart of research. Academics don't get paid for the journal articles they publish (despite the huge profits the journal publishers make, the content is provided for free). For an academic, doing your work well enough to get published is its own reward, and only research done well and thoroughly gets published.

5. Show and Tell

If you're an academic or a scientist, you should have something to say to the world about your work. That is why we publish our research, which is just the grown-up, scientist way of showing and telling the world about you've done.

6. If you want something, ASK

Some bosses, the good ones, will want to develop their staff. Development means pursuing something that you are interested in, something that you can do well and something that will help you do your job better. Even if it's only peripherally related to your job, it's still worth asking for support.

7. Have GOALS

Everyone in academia should have goals. Everyone in academia with goals should know that you're probably going to end up with something that is completely different to your goal, but just as good. Two years ago my goal was to get a permanent lecturer / senior lecturer position at a university. Now I'm the head of department at a private college. A different role to what I was aiming for, but just as good, if not better.

8. Be nice. To EVERYONE

My best friend likes to say that good things happen to good people, and it's true. Not because of any mystical, karmic nonsense, but because people who are nice to others make more friends and are the kind of people that others like to help out. Treating people badly might achieve short term goals, but long term, it's a self-defeating strategy.

9. FAIL

You learn more from your failures than you do from your successes. There are certainly people who don't fail early in their careers, and become professors in their early thirties, but they also unfortunately tend to be insufferably arrogant people. Failure teaches you humility, and it teaches you persistence. If my nine-year-old daughter is trying to learn how to do something, and is doing it wrongly, I don't stop her because she needs to learn through failure, and she needs to learn persistence. An academic is the same: you need to fail to learn what doesn't work.

10. WORK YOUR ASS OFF

The people who are most successful are the ones who work the hardest. Which is why I'm sitting at my dining table typing on a laptop at 11:15pm instead of dozing happily next to my wife.

IEEE Transactions on Neural Networks and Learning Systems: Volume 24, Issue 9, September 2013

1. Study of the Convergence Behavior of the Complex Kernel Least Mean Square Algorithm
Author(s): Paul, T.K. ; Ogunfunmi, T.
Pages: 1349-1363

2. Transductive Face Sketch-Photo Synthesis
Author(s): Wang, N. ; Tao, D. ; Gao, X. ; Li, X. ; Li, J.
Pages: 1364-1376

3. Learning Sparse Kernel Classifiers for Multi-Instance Classification
Author(s): Fu, Z. ; Lu, G. ; Ting, K.M. ; Zhang, D.
Pages: 1377-1389

4. FPGA-Based Distributed Computing Microarchitecture for Complex Physical Dynamics Investigation
Author(s): Borgese, G. ; Pace, C. ; Pantano, P. ; Bilotta, E.
Pages: 1390-1399

5. Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties
Author(s): Li, Z. ; Su, C.-Y.
Pages: 1400-1413

6. Cube Kohonen Self-Organizing Map (CKSOM) Model With New Equations in Organizing Unstructured Data
Author(s): Lim, S.P. ; Haron, H.
Pages: 1414-1424

7. Real-Time Model Predictive Control Using a Self-Organizing Neural Network
Author(s): Han, H.-G. ; Wu, X.-L. ; Qiao, J.-F.
Pages: 1425-1436

8. Memory Models of Adaptive Behavior
Author(s): Traversa, F.L. ; Pershin, Y.V. ; Di Ventra, M.
Pages: 1437-1448

9. Model of an Excitatory Synapse Based on Stochastic Processes
Author(s): L'Esperance, P.-Y. ; Labib, R.
Pages: 1449-1458

10. Combined Convex Technique on Delay-Dependent Stability for Delayed Neural Networks
Author(s): Li, T. ; Wang, T. ; Song, A. ; Fei, S.
Pages: 1459-1465

11. Low-Temperature Fabrication of Spiking Soma Circuits Using Nanocrystalline-Silicon TFTs
Author(s): Subramaniam, A. ; Cantley, K.D. ; Stiegler, H.J. ; Chapman, R.A. ; Vogel, E.M.
Pages: 1466-1471

12. Effect of Input Noise and Output Node Stochastic on Wang's kWTA
Author(s): Sum, J. ; Leung, C.-S. ; Ho, K.
Pages: 1472-1477

13. Controllability and Observability of Boolean Control Networks With Time-Variant Delays in States
Author(s): Zhang, L. ; Zhang, K.
Pages: 1478-1483

14. Quantized Kernel Recursive Least Squares Algorithm
Author(s): Chen, B. ; Zhao, S. ; Zhu, P. ; Principe, J.C.
Pages: 1484-1490

15. On the Optimal Class Representation in Linear Discriminant Analysis
Author(s): Iosifidis, A. ; Tefas, A. ; Pitas, I.
Pages: 1491-1496

16. L\infty Analysis and State-Feedback Control of Hopfield Networks
Author(s): Stoica, A.-M. ; Yaesh, I.
Pages: 1497-1502

17. Sequential Blind Identification of Underdetermined Mixtures Using a Novel Deflation Scheme
Author(s): Zhang, M. ; Yu, S. ; Wei, G.
Pages: 1503-1509