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

Webinar: So you want to be an Academic? Some Tips and Tricks

Professor Bob John of University of Nottingham, United Kingdom, will give a live webinar to our IEEE CIS members and friends. The information of the webinar is shown below: 

Webinar arrangement 

Topic: 

So you want to be an Academic? Some Tips and Tricks 

Date and time:      

2:00 PM, Oct 23, 2013, BST  (London Time)

9:00 AM, Oct 23, 2013, EDT  (New York Time)

9:00 PM, Oct 23, 2013, HKT  (Hong Kong Time) 

Webinar ID: 

112-059-947 

Registration

The webinar is free-of-charge. We only have limited seats. First come first served.

Please register for "So you want to be an Academic? Some Tips and Tricks" on Oct 23, 2013 2:00 PM BST at: 

https://attendee.gotowebinar.com/register/5187699622185856768 

Webinar information

Speaker:

Professor Bob John, Automated Scheduling, Optimisation and Planning Group (ASAP), University of Nottingham, United Kingdom 

Abstract:

This webinar will be given by Bob John who has led two highly successful computational intelligence research groups (www.cci.dmu.ac.uk and www.asap.ac.uk) for more than 12 years. He will give his personal views, based on his experiences, of what's needed to become an academic. Although based in the United Kingdom the points he makes will broadly translate to other countries. He will discuss your best strategies for producing publications, the role of networking, looking for funding and generally how to get on the academic career ladder. 

Speaker's Biography:

Bob has a BSc Mathematics, a MSc in Statistics and a PhD in Fuzzy Logic. He worked in industry for 10 years as a mathematician and knowledge engineer developing knowledge based systems for British Gas and the financial services industry. Bob spent 24 years at De Montfort University in various roles including Head of Department, Head of School and Deputy Dean. He led the Centre for Computational Intelligence research group from 2001 until 2012. He has over 150 research publications of which about 50 are in international journals. Bob joined the University of Nottingham this year where he heads up the Automated Scheduling, Optimisation and Planning (ASAP) research group in the School of Computer Science. The ASAP research group carries out multi-disciplinary research into mathematical models and algorithms for a variety of real-world optimisation problems. ASAP has 8 academic staff, 9 researchers and over 30 PhD students.

Only limited seats are available. Please register as soon as possible. 

After registering, you will receive a confirmation email containing information about joining the webinar.

Reminder: paper submission deadline for SysInt 2014

A reminder that the deadline for submitting abstracts to the 2nd International Conference on System-Integrated Intelligence (SysInt) 2014 is October 1, 2013. The deadline for submitting full papers is February 1, 2014. This conference will be held in Bremen, Germany, 2-4 July, 2014.

Reminder: paper submission deadline for ICC 2014

A reminder that the deadline for submitting papers to the International Conference on Intelligent Cloud Computing (ICC) 2014 is September 30, 2013. This conference will be held 24-26 February in Muscat, Oman.

On management 2

The Argentine guerrilla Che Guevara wrote in his book on guerrilla warfare:

"There is nothing more important than information. Moreover, it should be in perfect order, and done well by capable personnel".

I have found, as a manager, that this is very true. Management is about making decisions, and you cannot make good decisions if you do not have good information. This being the case, the most valuable staff you can have as a manager are the staff who will tell you what they think rather than telling you what you want to hear. Getting a forthright, unfiltered opinion is essential to any manager, and the staff who will give you this are the ones you must value the most.

Some managers find those sort of people hard to manage, but I never have. I think that's because, if someone is forthright in their opinion, then it is easier to make them happy. People who keep their thoughts to themselves are harder to manage because you don't always know how to make them happy, and if someone isn't happy in their work, they won't do their job well.

It is tempting to dismiss this as "touchy-feely stuff" that doesn't have anything to do with research, but that's not true. Managing research certainly requires a good knowledge of research, and a good research background - the best managers are leaders, and leaders should lead from the front. But managing research is not really about doing research, it's about managing people. And that is where so many academic labs fall down: they are headed by someone who is very good at research, but doesn't know how to deal with people. These labs are marked by dissatisfied staff and a high staff-turnover, as people arrive, get rapidly disillusioned, and leave. The lucky ones will find a better job somewhere else, while the unlucky ones end up with their careers in ruins. As a person of conscience, I do everything I can to avoid that happening to the people I manage - to the people for whom I am responsible.

Evolving Systems Vol 4, Issue 3, August 2013

1. Sliding mode control of fractional order nonlinear differential inclusion systems
Author(s): Saeed Balochian
Abstract    Full text HTML    Full text PDF

2. Solving the task assignment problem using Harmony Search algorithm
Author(s): Ayed Salman , Imtiaz Ahmad , Hanaa AL-Rushood & Suha Hamdan
Abstract    Full text HTML    Full text PDF

3. A fuzzy logic model based Markov random field for medical image segmentation
Author(s): Thanh Minh Nguyen & Q. M. Jonathan Wu
Abstract    Full text HTML    Full text PDF

4. The transformation method between tree and lattice for file management system
Author(s): Kazuhito Sawase & Hajime Nobuhara
Abstract    Full text HTML    Full text PDF

5. Application of neural network and fuzzy model to grinding process control
Author(s): A. O. Odior
Abstract    Full text HTML    Full text PDF

6. Iris data encryption based on Aztec Symbology
Author(s): Shrinivasrao B. Kulkarni , Ravindra S. Hegadi & Umakant P. Kulkarni
Abstract    Full text HTML    Full text PDF

Second Annual CIS IEEE Video Competition

Following on from the first video competition, which produced two outstanding winning videos about fuzzy  logic, we invite you to produce an introductory 3-minute video about one of the following Computational  Intelligence fields of interest:

● Neural Networks
● Evolutionary Computation
● Hybrid Intelligent Systems

The winners of the previous competition can be seen at:
● www.youtube.com/watch?v=J_Q5X0nTmrA
● www.youtube.com/watch?v=P8wY6mi1vV8

The aim of the video is to answer the question “what is...?”, for a Computational Intelligence field of interest, to an audience of high school students and the general public, with limited mathematical background. The video should also explain an example application of how the method can be used in everyday life. The video should be suitable for posting on YouTube and Facebook. The video competition is an exciting opportunity for all IEEE members to work together in conveying technical topics to nonexperts.

Prizes for entrants:

1st prize: 600USD + iPad* + CIS membership (1 year)
2nd prize: 500USD + CIS membership (1 year)
3rd prize: 400USD + CIS membership (1 year)

Prize for everyone: An iPad* will be raffled to anyone who “likes” a video on our www.facebook.com/IEEE.CIS page (excluding competition entrants and organisers).

Teams consist of 1 to 5 people. The team leader must be an IEEE member.

Registration deadline is 2nd September 2013.
Video submission deadline is 14th October 2013.
Winners will be notified on 1st December 2013.

Rules and submission details can be found at cis.ieee.org/videocomp. Please contact cis.ieee@gmail.com for specific queries.

*Disclaimer: The iPad prize does not apply to countries officially embargoed by the Office of Foreign Assets Control (OFAC) of the U.S. Department of the Treasury. In accordance with U.S. law, IEEE is unable to provide such goods to OFAC embargoed countries. In the case where the first place winner comes from an OFAC embargoed country, no iPad 2 will be awarded.

Reminder: paper submission deadline for PAKDD 2014

A reminder that the deadline for the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014 is 16 September 2013. This conference will be held in Tainan, Taiwan, 13-16 May 2014.

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.

IEEE Transactions on evolutionary Computation: Volume 17, Issue 4, August 2013

1. Multiobjective Metaheuristics for Traffic Grooming in Optical Networks
Author(s): Rubio-Largo, A. ; Vega-Rodriguez, M.A. ; Gomez-Pulido, J.A. ; Sanchez-Perez, J.M.

2. Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
Author(s): Wang, R. ; Purshouse, R.C. ; Fleming, P.J.

3. A Hybrid Framework for Evolutionary Multi-Objective Optimization
Author(s): Sindhya, K. ; Miettinen, K. ; Deb, K.

4. A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem
Author(s): Zhong, J.-H. ; Shen, M. ; Zhang, J. ; Chung, H.S.-H. ; Shi, Y.-H. ; Li, Y.

5. Evolutionary Foundation of Bounded Rationality in a Financial Market
Author(s): Kinoshita, K. ; Suzuki, K. ; Shimokawa, T.

6. Self-Adaptive Evolution Toward New Parameter Free Image Registration Methods
Author(s): Santamaria, J. ; Damas, S. ; Cordon, O. ; Escamez, A.

7. Clustered Memetic Algorithm With Local Heuristics for Ab Initio Protein Structure Prediction
Author(s): Islam, M.K. ; Chetty, M.

8. Reverse Engineering of Gene Regulatory Networks Using Dissipative Particle Swarm Optimization
Author(s): Palafox, L. ; Noman, N. ; Iba, H.

9. A Memetic Algorithm for Matching Spatial Configurations With the Histograms of Forces
Author(s): Buck, A.R. ; Keller, J.M. ; Skubic, M.