Matched: common in homoepitaxy, sometimes in heteroepitaxy

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• Matched common in homoepitaxy, sometimes in
  heteroepitaxy

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micelle
cross section
reverse micelle
cross section
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Systems Self-Organization in Natural

• What are the mechanisms for integrating subunits
  activity into a coherently structured entity?
• From simple neurons to the thinking brain
• From individuals to the society
• From molecule to pattern

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Self-Organization in Natural Systems

• What are the mechanisms for integrating subunits
  activity into a coherently structured entity?
• From simple neurons to the thinking brain
• From individuals to the society
• From molecule to pattern

C3H4O4 NaBr NaBrO3 HSO3 C12H8N2SO2Fe
Malonic acid Sodium bromide Sodium
bromate Sulfuric acid 1,10 Phenanthroline ferrous
sulfate
11
Definitions

• What is Chaos ? Poincarré Lorenz Prigogine
• disorder, confusion, is opposed to order and
  method
• Chaos define a particular state of a system
  that is characterized by the following behaviors
• Do not repeat
• Sensible to initial conditions sharp differences
  can produce wide divergent results
• Moreover, ordered and characterized by an
  unpredictable determinism
• When moving away from equillibrium state gt high
  organization
• Non equillibrium phasis bifurcations
• Amplification gt Symetry break

12
Definitions

• What is Self-organization in natural systems?
• Self-organization is a process in which pattern
  at the global level of a system emerges solely
  from numerous interactions among the lower level
  components of the system. Deneubourg 1977
• Moreover, the rules specifying interactions
  among the systems components are executed using
  only local information, without reference to the
  global pattern
• In other words, the pattern is an emergent
  property of the system, rather than a property
  imposed on the system by an external influence

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Definitions

• What is an emergent property ?
• Many Agents
• Simple rules
• Many interactions
• Decentralization
• Emergent properties
• Unreductibility
• Macro-level (odre magnitude difference)
• Feed-back effect on the micro-level

Conditions
Observations
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Self-Organization in Natural Systems
CONDENSED MATTER
COMPLEX SYSTEMS

• A physical point of view

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Non-living pattern formation

• Based on physical and chemical properties
• Belousov-Zhabotinsky reaction
• Bénard convection cells
• Sand dune ripples
• Glass cracks
• Mud cracks

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Non-living pattern formation

• Based on physical and chemical properties
• Belousov-Zhabotinsky reaction
• Bénard convection cells
• Sand dune ripples
• Glass cracks
• Mud cracks

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Non-living pattern formation

• Based on physical and chemical properties
• Belousov-Zhabotinsky reaction
• Bénard convection cells
• Sand dune ripples
• Glass cracks
• Mud cracks

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Non-living pattern formation

• Based on physical and chemical properties
• Belousov-Zhabotinsky reaction
• Bénard convection cells
• Sand dune ripples
• Glass cracks
• Mud cracks

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Non-living pattern formation

• Based on physical and chemical properties
• Belousov-Zhabotinsky reaction
• Bénard convection cells
• Sand dune ripples
• Glass cracks
• Mud cracks

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Pattern formation in biological systems

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerization
• Occular dominance stripes

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Pattern formation in biological systems

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerization
• Occular dominance stripes

23
Pattern formation in biological systems

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerization
• Occular dominance stripes

24
Pattern formation in biological systems

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerization
• Occular dominance stripes

25
Pattern formation in biological systems

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerisation
• Occular dominance stripes

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Pattern formation in biological systems

• Most of those patterns are in fact fixed states
  of reactions that have occurred long time ago

• Patterns characterizing individuals
• Giraffe coat
• Zebra
• Leopard
• Vermiculated rabbitfish
• Cone shells
• Finger prints
• Morel
• Metamerisation
• Occular dominance stripes

or process is still running.
Mechanisms ?
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Pattern formation in biological systems

• Patterns occurring during collective movement
• Microorganisms
• Insects and Crustaceans
• Social insects
• Fishes
• Birds
• Mammalians

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Pattern formation in biological systems

• Patterns occurring during collective movement
• Microorganisms
• Insects and Crustaceans
• Social insects
• Fishes
• Birds
• Mammalians

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Pattern formation in biological systems

• Patterns occurring during collective movement
• Microorganisms
• Insects and Crustaceans
• Social insects
• Fishes
• Birds
• Mammalians

• Those patterns results from a permanent
  reorganization

mechanisms ?

• No leader
• No preexisting tracks
• High sensitivity to heterogeneities
• Based on the nearest neighbor perception

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Activation-inhibition mechanism
autocatalyzis
Inspired by equations of reaction-diffusion
Turing 1949
inhibition
The activator autocatalyzes its own production,
and also activates the inhibitor. The inhibitor
disrupts the autocatalytic process. Meanwhile,
the two substances diffuse through the system at
different rates, with the inhibitor migrating
faster. The result local activation and
long-range inhibition
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Activation-inhibition mechanism

• Activation-inhibition and self-organization share
  a common mechanism
• Starting point a homogeneous substrate
• (lacking pattern)
• Positive feedback
• (short-range activation, autocatalyzes)
• Negative feedback
• (long-range inhibition)

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Pattern formation in colonies activity

• Patterns resulting from the activity of a
  society of
• social insects
• Ants
• Bees
• Wasps
• Termites
• Mammalians
• African Mole-rats
• Humans

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Pattern formation in colonies activity

• Patterns resulting from the activity of a
  society of
• social insects
• Ant
• Bees
• Wasps
• Termites
• Mammalians
• African Mole-rats
• Humans

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Pattern formation in colonies activity

• Patterns resulting from the activity of a
  society of
• social insects
• Ant
• Bees
• Wasps
• Termites
• Mammalians
• African Mole-rats
• Humans

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Attraction-repulsion mechanisms

• Relations between Activation-inhibition
  mechanisms and attraction-repulsion mechanisms
• They share a common mechanism
• Starting point a homogeneous substrate (lacking
  or different pattern)
• Positive feedback (local activation or attraction
  rate to aggregates size)
• Negative feedback (long-range inhibition,
  depletion in individuals)

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Self-Organization in Natural Systems

• Definitions
• Pattern formation
• In living and non-living systems
• Social systems
• Sociality and gregarism
• Cellular systems
• Cells build animals
• Properties of self-organized systems

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How cells build the animal ?

• From one cell to the next generation
• From one cell to the thinking brain
• Planed mechanisms
• Expression of the genetic program
• Scale changes
• And long range communication
• Self-organizing mechanisms
• Reaction-diffusion (activation-inhibition)
• Cells migrations (Aggregation-repulsion)

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How cells build the animal ?

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

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How cells build the animal ?
Strict genetic program Complex triggering

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

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How cells build the animal ?
Amplification of a behaviour (metabolism)t
rigger cell environment

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

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How cells build the animal ?

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

Contact Mechanical
Direct
Indirect
Secretion diffusion At different range and time
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How cells build the animal ?
Nucleus (DNA)

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

• Cytoplasm
• RNA
• Proteins
• toxins

Controled exchanges
Internal state, memoryof previous events
(environments)
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How cells build the animal ?

• Accidental changes in cell environment
• Backward differentiation
• Not all animals
• Global communication (blood circulationand
  nervous system)
• Not all cells
• Wounds should respect
• Gradients
• Periods of sensibility

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

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How cells build the animal ?

• Low dynamic STRUCTURES
• High dynamic FUNCTIONING
• Neural activity
• Immune system answer

• Cell proliferation
• Cell differentiation
• Cell communication
• Cell memory
• Regenerative potential

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Ants

• Organizing highways to and from their foraging
  sites by leaving pheromone trails
• Form chains from their own bodies to create a
  bridge to pull and hold leafs together with silk
• Division of labour between major and minor ants

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Social Insects

• Problem solving benefits include
• Flexible
• Robust
• Decentralized
• Self-Organized

49
Interrupt The Flow
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The Path Thickens!
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The New Shortest Path
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Adapting to Environment Changes
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Four Ingredients of Self Organization

• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations - randomness
• Reliance on multiple interactions

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WEB CLUSTERING

• Why?
• The size of the internet has doubling its size
  every year. Estimated 2.1 billion as of July 2001
• Organizing and categorizing document is not
  scalable to the growth of internet.
• Document clustering?
• Is the operation of grouping similar document
  to classes that can be used to obtain an analysis
  of the content.
• Ant clustering algorithm categorize web document
  to different interest domain.

55
Ant Colony Models for Data Clustering

• Data clustering?
• is the task that seek to identify groups of
  similar objects based on the value of their
  attributes.
• Messor sancta ants collect and pile dead corpses
  to form cemeteries (Deneubourg et al. )

f fraction of items in the neighborhood of the
agent k1, k2 threshold constants
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Ant Colony Models for Data Clustering

• The model later extend by Lume Faieta to
  include distance function d, between data objects
  .
• c is a cell, N(c) is the number of adjacent cells
  of c, alpha is constant

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Experimental Results

• t 0

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Experimental Results

• t 50,000

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Experimental Results

• t 200,000

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Experimental Results

• t 300,000