1. Click Start
2. Click Run
3. gpedit.msc <enter>
4. Drill down to Computer configuration
   1. Windows Settings
      1. Security Settings
         1. Software Restriction Policies
5. Right click Software Restriction Policies
6. Choose Create New Policies
7. On the right hand side, right click Enforcement
8. Choose Properties
9. Down below, choose All users except local administrators
10. Click OK
11. Close Group Policy
12. Open a Command Prompt
13. Run the command gpupdate /force <enter>
14. When local group policy is finished updating, re-run the VMware Server 2.0 setup

Servetto et al. formulate multi-path routing as packet-level random walks between a source and a sink. Local parameters of the random walks are derived analytically for regular grids, grids with random pertubations, and grids with dynamic pertubations. Resulting load distributions resemble gaussian copulae.

An interesting question (at least for me) is how to constrain random walks around a predefined trajectory, e.g. a Bézier curve, or an electrostatic field line. There might be a centralized approach with cubic complexity (Cholesky)…

Bayesian inference and Gaussian processes

Carl Edward Rasmussen

SIDnet-SWANS is a discrete event simulator for wireless sensor network applications. I’ll try to use it for some simulations in multipole routing – stay tuned for some hand-on reviews. Oh, you might be interested in cranes as well.

David MacKay – Gaussian Process Basics

Adaptive query processing: why, how, when, what next?

Optimizing Energy-Efficient Query Processing in Wireless Sensor Networks

Routing and processing multiple aggregate queries in sensor networks

I’m dabbling in CONET‘s research cluster “Scalable Data Processing“. Read more for some relevant papers (I really like Zotero)

Workload-Aware Query Routing Trees in Wireless Sensor Networks

A Scalable and Efficient Approach for Obtaining Measurements in CAN-Based Control Systems

Organic Computing aims at the utilization of concepts such as emergence and self-organization, and studies the mechanisms of self-organized emergence in technical systems. In order to measure, to control, and design emergence, a definition of emergence applicable to intelligent technical systems is needed [1].

Emergence in Philosophy of Mind
In philosophy of mind, the notion of emergence has been investigated as a a form of non-reductive physicalism involving a layered view of nature, with layers arranged in terms of increasing complexity, and higher-order properties supervene over lower levels without direct causal interaction.

1. Weak Emergence describes a type of emergence in which the emergent property is reducible to its individual constituents. Weak emergence is based on three theses:
   1. thesis of physical monism restricting the type of components of complex systems with emergent properties to physical entities, i.e. ruling out supernatural influences
   2. thesis of systemic properties characterizing the type of potential emergent properties as collective properties, i.e. the system as a whole has a property of type but single components do not possess properties of type
   3. thesis of synchronous determinism specifying the relation between the system’s micro-structure and its emergent properties, i.e. emergent properties of a system are reducible to structure and properties of its individual components.
2. Strong Emergence describes a type of emergence in which the emergent property is irreducible to the system’s components. Strong emergence is based on three theses:
   1. thesis of physical monism
   2. thesis of systemic properties
   3. thesis of irreducibility, i.e. systemic, emergent properties don’t arise from the behavior, properties, and structure of individual components

Novel Definitions of Emergence

1. A. Stephan suggests [2] alternative definitions of emergence that add stronger requirements to the definition of weak emergence:
   1. Emergence as a consequence of collective self-organization: interesting systemic properties are realized by interactions of identical or similar system components, e.g. ant colonies.
   2. Emergence as a consequence of non-programmed functionality: systems, which interact with their environment, show a certain goal-oriented, adaptive behavior that is not a result of dedicated control or explicit programming.
   3. Emergence as a consequence of interactive complexity: systemic properties are the result of complex interactions of components.
   4. Emergence as incompressible development: a system is called emergent if a systemic macro-state can be derived from the system’s micro-dynamics and the external conditions only by simulation.
   5. Emergence as structure-unpredictability: formation of new properties, patterns, or structures follows the laws of deterministic chaos and is unpredictable in this sense.
2. J. Fromm provides [3] a comprehensive classification and universal taxonomy of the key types and forms of emergence in multi-agent systems. Fromm starts with a clear definition of emergent properties, i.e. a systemic property is emergent if it is not a property of any fundamental component, and emergence is the appearance of the emergent properties and structures on a higher level of organization or complexity. Fromm’s taxonomy is based on different feedback types and the structure of causality or cause-and-effect relationships. The different types of emergence are roughly classified through four types:
   1. Nominal emergence without top-down feedback
      1. Simple Intentional Emergence, e.g. planned function of a machine as an emergent property of its components
      2. Simple Unintentional Emergence, e.g. statistical quantities and properties of identical particles (pressure, volume, temperature, time)
   2. Weak emergence including top-down feedback (two basic forms of interaction: direct interaction, e.g. flocking trick, and indirect interaction through environment, e.g. pheromone trick)
      1. Weak Emergence (stable), usually negative feedback that imposes constraints on possible actions of components, balance between bottom-up influences and top-down feedback from the group or environment. Typical examples are foraging behavior of ants, flocking behavior of fish and birds, optimal pricing of goods in an economy
      2. Weak Emergence (in-stable), usually positive feedback leading to, e.g. cluster formation in economic and social migrations, drug addiction, economic inflation, abrupt collective opinion shifts, celebrity effect
   3. Multiple emergence with many feedbacks
      1. Emergence with multiple feedback, many emergent properties or processes are combinations of long-range inhibition (negative feedback) and short-range activation (positive feedback). These systems are known as activator-inhibitor systems and belong to reaction-diffusion systems. Typical examples are stripes and spots in animals coat patterns, chaotic and unpredictable behavior of real financial or stock markets.
      2. Adaptive Emergence with multiple feedback, different levels of complexity and organization in life forms are associated with evolutionary transitions that characterize the crossing of fitness gaps and fitness barriers. Abrupt, unsteady changes and jumps in complexity are the consequence of unsteady fitness landscapes and barriers. Catastrophes catalyze the transition to higher forms of complexity through a sudden, dramatic increase of challenges in the environment. Tunneling through fitness barriers is possible through the borrowing of complexity. Unpredictable catastrophic events or fluctuations can enhance evolution and accelerate adaption. Therefore this form of emergence is named adaptive emergence and it is associated to the appearance of new or the dramatic change of existing ecological niches. The same argument applies to scientific revolutions that overcome a mental barrier with a new scientific paradigm.
   4. Strong emergence is often related to very large jumps in complexity and evolutionary transitions, e.g. genetic and memetic evolution. In a strongly emergent system, it proves completely impracticable to account for the macroscopic behavior by applying the laws at the level of its constituents. Due to the problem of combinatoric explosion with respect to the Landauer-Wheeler-Lloyd limit, any deterministic algorithm, rule or law renders infeasible.
3. Tom De Wolf’s Relationship of Emergence and Self-Organization [4]
   
4. Russ Abbott’s Explanation of Emergence [5]
   
5. Christian Mueller-Schloer’s Quantitative View of Emergence [6]
   

References

1. C. Müller-Schloer and B. Sick, “Emergence in Organic Computing Systems: Discussion of a Controversial Concept”, Autonomic and Trusted Computing, 2006, pp. 1-16; http://dx.doi.org/10.1007/11839569_1.
2. A. Stephan, “Zur Rolle des Emergenzbegriffes in der Philosophie des Geistes und in der Kognitionswissenschaft”, Philosophie und Neurowissenschaften, 1770, Taschenbuch Wissenschaft, Suhrkamp, 2006
3. J. Fromm, “Types and Forms of Emergence,” nlin/0506028, Jun. 2005; http://arxiv.org/abs/nlin/0506028
4. T. De Wolf and T. Holvoet, “Emergence Versus Self-Organisation: Different Concepts but Promising When Combined,” Engineering Self-Organising Systems, 2005, pp. 1-15; http://dx.doi.org/10.1007/11494676_1
5. R. Abbott, “Emergence explained: Abstractions: Getting epiphenomena to do real work,” Complexity,  vol. 12, 2006, pp. 13-26.
6. M. Mnif and C. Muller-Schloer, “Quantitative Emergence,” Adaptive and Learning Systems, 2006 IEEE Mountain Workshop on, 2006, pp. 78-84